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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 17 Nov 2010 09:20:01 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/17/t1289985955fvo4s598b8e1dgh.htm/, Retrieved Sat, 13 Aug 2022 21:49:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=96550, Retrieved Sat, 13 Aug 2022 21:49:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact557
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2010-11-17 09:20:01] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
-   PD    [Multiple Regression] [multicollineariteit] [2010-11-19 13:07:10] [8a9a6f7c332640af31ddca253a8ded58]
- R PD    [Multiple Regression] [Workshop 7 - Corr...] [2010-11-19 13:21:15] [8b017ffbf7b0eded54d8efebfb3e4cfa]
-           [Multiple Regression] [workshop 7 - tuto...] [2010-11-19 16:22:39] [956e8df26b41c50d9c6c2ec1b6a122a8]
-    D        [Multiple Regression] [WS7 comp 5] [2010-11-23 09:29:52] [dc30d19c3bc2be07fe595ad36c2cf923]
- R             [Multiple Regression] [ws 7] [2010-11-23 18:48:40] [4f85667043e8913570b3eb8f368f82b2]
-               [Multiple Regression] [] [2010-12-02 15:19:40] [2e1e44f0ae3cb9513dc28781dfdb387b]
- R  D            [Multiple Regression] [WS 7 multicolline...] [2011-11-24 17:16:22] [65d03b877b3f337979d6af245efa927d]
-               [Multiple Regression] [] [2010-12-03 17:47:47] [b07cd1964830aab808142229b1166ece]
-           [Multiple Regression] [workshop 7 multip...] [2010-11-23 14:11:54] [af8eb90b4bf1bcfcc4325c143dbee260]
- RM        [Multiple Regression] [nbnbnb] [2011-11-20 14:00:07] [a9671b130b33f9fcb98554992ce4582f]
-    D    [Multiple Regression] [Workshop 7 - Multi] [2010-11-19 13:35:10] [6f0e7a2d1a07390e3505a2db8288f975]
-   PD    [Multiple Regression] [] [2010-11-19 14:23:14] [8a9a6f7c332640af31ddca253a8ded58]
-   PD    [Multiple Regression] [] [2010-11-23 16:07:34] [d7b28a0391ab3b2ddc9f9fba95a43f33]
-   PD    [Multiple Regression] [Workshop 7 Determ...] [2010-11-23 17:48:05] [74be16979710d4c4e7c6647856088456]
-   PD    [Multiple Regression] [] [2010-11-23 18:21:32] [a9671b130b33f9fcb98554992ce4582f]
-    D      [Multiple Regression] [] [2010-11-23 18:49:43] [a9671b130b33f9fcb98554992ce4582f]
-   PD    [Multiple Regression] [] [2010-11-23 18:20:51] [f3d662049ef6875ba0c96bb458434b66]
-   PD    [Multiple Regression] [] [2010-11-23 18:52:21] [f3d662049ef6875ba0c96bb458434b66]
-    D    [Multiple Regression] [Multiple regression] [2010-11-23 19:49:51] [3fb95cad3bbcce10c72dbbcc5bec5662]
-   PD    [Multiple Regression] [ws 7] [2010-11-23 19:56:37] [bd591a1ebb67d263a02e7adae3fa1a4d]
-   PD      [Multiple Regression] [WS 7 - minitutorial] [2010-11-24 18:11:33] [bd591a1ebb67d263a02e7adae3fa1a4d]
-   PD        [Multiple Regression] [meervoudige regre...] [2010-12-17 13:03:18] [bd591a1ebb67d263a02e7adae3fa1a4d]
-    D        [Multiple Regression] [multiple regressi...] [2010-12-17 16:58:50] [bd591a1ebb67d263a02e7adae3fa1a4d]
-   PD    [Multiple Regression] [ws 7] [2010-11-23 20:07:25] [bd591a1ebb67d263a02e7adae3fa1a4d]
-    D    [Multiple Regression] [mini tutorial wor...] [2010-11-30 17:13:46] [9f313cc7203314d73bf17d2b325aee79]
-   PD    [Multiple Regression] [traders and order...] [2010-12-02 15:04:44] [75b8170d590d2aca2c97c1862bb2167f]
-   PD      [Multiple Regression] [] [2010-12-03 12:34:34] [916599f00c9c716123aa8433d9efa14f]
-             [Multiple Regression] [Juiste met dummie...] [2010-12-03 19:17:06] [75b8170d590d2aca2c97c1862bb2167f]
-   PD      [Multiple Regression] [] [2010-12-03 12:40:48] [916599f00c9c716123aa8433d9efa14f]
-             [Multiple Regression] [] [2010-12-03 19:14:57] [c895532cb7349383dee5125244983cc8]
-             [Multiple Regression] [Juiste zonder dum...] [2010-12-03 19:15:31] [75b8170d590d2aca2c97c1862bb2167f]
-   PD        [Multiple Regression] [] [2010-12-18 14:34:57] [75b8170d590d2aca2c97c1862bb2167f]
-   P           [Multiple Regression] [] [2010-12-18 15:22:17] [75b8170d590d2aca2c97c1862bb2167f]
-                 [Multiple Regression] [Multiple Regressi...] [2010-12-28 08:19:07] [75b8170d590d2aca2c97c1862bb2167f]
-                   [Multiple Regression] [Multiple Regressi...] [2010-12-29 16:14:24] [c895532cb7349383dee5125244983cc8]
-                   [Multiple Regression] [Multiple Regressi...] [2010-12-29 16:17:04] [c895532cb7349383dee5125244983cc8]
-                 [Multiple Regression] [berekening 4] [2010-12-28 13:28:29] [916599f00c9c716123aa8433d9efa14f]
-                 [Multiple Regression] [berekening 5] [2010-12-28 13:41:37] [916599f00c9c716123aa8433d9efa14f]
-               [Multiple Regression] [Multiple Regressi...] [2010-12-28 08:08:02] [75b8170d590d2aca2c97c1862bb2167f]
-                 [Multiple Regression] [Multiple Regressi...] [2010-12-29 16:12:31] [c895532cb7349383dee5125244983cc8]
-               [Multiple Regression] [berekening 3] [2010-12-28 13:21:22] [916599f00c9c716123aa8433d9efa14f]
-   PD      [Multiple Regression] [] [2010-12-03 12:49:57] [916599f00c9c716123aa8433d9efa14f]
-   PD        [Multiple Regression] [zonder orders, zo...] [2010-12-03 13:02:03] [916599f00c9c716123aa8433d9efa14f]
-               [Multiple Regression] [Zonder lineair, m...] [2010-12-03 19:20:38] [75b8170d590d2aca2c97c1862bb2167f]
-             [Multiple Regression] [Juiste met dummie...] [2010-12-03 19:18:29] [75b8170d590d2aca2c97c1862bb2167f]
-   P       [Multiple Regression] [] [2010-12-03 19:13:39] [c895532cb7349383dee5125244983cc8]
-   P       [Multiple Regression] [traders and order...] [2010-12-03 19:13:39] [75b8170d590d2aca2c97c1862bb2167f]
-         [Multiple Regression] [] [2010-12-02 22:53:36] [f9d37301ea08122b4d103fe011f2b292]

[Truncated]
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Dataseries X:
41	38	13	12	14	12	53
39	32	16	11	18	11	83
30	35	19	15	11	14	66
31	33	15	6	12	12	67
34	37	14	13	16	21	76
35	29	13	10	18	12	78
39	31	19	12	14	22	53
34	36	15	14	14	11	80
36	35	14	12	15	10	74
37	38	15	9	15	13	76
38	31	16	10	17	10	79
36	34	16	12	19	8	54
38	35	16	12	10	15	67
39	38	16	11	16	14	54
33	37	17	15	18	10	87
32	33	15	12	14	14	58
36	32	15	10	14	14	75
38	38	20	12	17	11	88
39	38	18	11	14	10	64
32	32	16	12	16	13	57
32	33	16	11	18	9.5	66
31	31	16	12	11	14	68
39	38	19	13	14	12	54
37	39	16	11	12	14	56
39	32	17	12	17	11	86
41	32	17	13	9	9	80
36	35	16	10	16	11	76
33	37	15	14	14	15	69
33	33	16	12	15	14	78
34	33	14	10	11	13	67
31	31	15	12	16	9	80
27	32	12	8	13	15	54
37	31	14	10	17	10	71
34	37	16	12	15	11	84
34	30	14	12	14	13	74
32	33	10	7	16	8	71
29	31	10	9	9	20	63
36	33	14	12	15	12	71
29	31	16	10	17	10	76
35	33	16	10	13	10	69
37	32	16	10	15	9	74
34	33	14	12	16	14	75
38	32	20	15	16	8	54
35	33	14	10	12	14	52
38	28	14	10	15	11	69
37	35	11	12	11	13	68
38	39	14	13	15	9	65
33	34	15	11	15	11	75
36	38	16	11	17	15	74
38	32	14	12	13	11	75
32	38	16	14	16	10	72
32	30	14	10	14	14	67
32	33	12	12	11	18	63
34	38	16	13	12	14	62
32	32	9	5	12	11	63
37	35	14	6	15	14.5	76
39	34	16	12	16	13	74
29	34	16	12	15	9	67
37	36	15	11	12	10	73
35	34	16	10	12	15	70
30	28	12	7	8	20	53
38	34	16	12	13	12	77
34	35	16	14	11	12	80
31	35	14	11	14	14	52
34	31	16	12	15	13	54
35	37	17	13	10	11	80
36	35	18	14	11	17	66
30	27	18	11	12	12	73
39	40	12	12	15	13	63
35	37	16	12	15	14	69
38	36	10	8	14	13	67
31	38	14	11	16	15	54
34	39	18	14	15	13	81
38	41	18	14	15	10	69
34	27	16	12	13	11	84
39	30	17	9	12	19	80
37	37	16	13	17	13	70
34	31	16	11	13	17	69
28	31	13	12	15	13	77
37	27	16	12	13	9	54
33	36	16	12	15	11	79
35	37	16	12	15	9	71
37	33	15	12	16	12	73
32	34	15	11	15	12	72
33	31	16	10	14	13	77
38	39	14	9	15	13	75
33	34	16	12	14	12	69
29	32	16	12	13	15	54
33	33	15	12	7	22	70
31	36	12	9	17	13	73
36	32	17	15	13	15	54
35	41	16	12	15	13	77
32	28	15	12	14	15	82
29	30	13	12	13	12.5	80
39	36	16	10	16	11	80
37	35	16	13	12	16	69
35	31	16	9	14	11	78
37	34	16	12	17	11	81
32	36	14	10	15	10	76
38	36	16	14	17	10	76
37	35	16	11	12	16	73
36	37	20	15	16	12	85
32	28	15	11	11	11	66
33	39	16	11	15	16	79
40	32	13	12	9	19	68
38	35	17	12	16	11	76
41	39	16	12	15	16	71
36	35	16	11	10	15	54
43	42	12	7	10	24	46
30	34	16	12	15	14	85
31	33	16	14	11	15	74
32	41	17	11	13	11	88
32	33	13	11	14	15	38
37	34	12	10	18	12	76
37	32	18	13	16	10	86
33	40	14	13	14	14	54
34	40	14	8	14	13	67
33	35	13	11	14	9	69
38	36	16	12	14	15	90
33	37	13	11	12	15	54
31	27	16	13	14	14	76
38	39	13	12	15	11	89
37	38	16	14	15	8	76
36	31	15	13	15	11	73
31	33	16	15	13	11	79
39	32	15	10	17	8	90
44	39	17	11	17	10	74
33	36	15	9	19	11	81
35	33	12	11	15	13	72
32	33	16	10	13	11	71
28	32	10	11	9	20	66
40	37	16	8	15	10	77
27	30	12	11	15	15	65
37	38	14	12	15	12	74
32	29	15	12	16	14	85
28	22	13	9	11	23	54
34	35	15	11	14	14	63
30	35	11	10	11	16	54
35	34	12	8	15	11	64
31	35	11	9	13	12	69
32	34	16	8	15	10	54
30	37	15	9	16	14	84
30	35	17	15	14	12	86
31	23	16	11	15	12	77
40	31	10	8	16	11	89
32	27	18	13	16	12	76
36	36	13	12	11	13	60
32	31	16	12	12	11	75
35	32	13	9	9	19	73
38	39	10	7	16	12	85
42	37	15	13	13	17	79
34	38	16	9	16	9	71
35	39	16	6	12	12	72
38	34	14	8	9	19	69
33	31	10	8	13	18	78
36	32	17	15	13	15	54
32	37	13	6	14	14	69
33	36	15	9	19	11	81
34	32	16	11	13	9	84
32	38	12	8	12	18	84
34	36	13	8	13	16	69
27	26	13	10	10	24	66
31	26	12	8	14	14	81
38	33	17	14	16	20	82
34	39	15	10	10	18	72
24	30	10	8	11	23	54
30	33	14	11	14	12	78
26	25	11	12	12	14	74
34	38	13	12	9	16	82
27	37	16	12	9	18	73
37	31	12	5	11	20	55
36	37	16	12	16	12	72
41	35	12	10	9	12	78
29	25	9	7	13	17	59
36	28	12	12	16	13	72
32	35	15	11	13	9	78
37	33	12	8	9	16	68
30	30	12	9	12	18	69
31	31	14	10	16	10	67
38	37	12	9	11	14	74
36	36	16	12	14	11	54
35	30	11	6	13	9	67
31	36	19	15	15	11	70
38	32	15	12	14	10	80
22	28	8	12	16	11	89
32	36	16	12	13	19	76
36	34	17	11	14	14	74
39	31	12	7	15	12	87
28	28	11	7	13	14	54
32	36	11	5	11	21	61
32	36	14	12	11	13	38
38	40	16	12	14	10	75
32	33	12	3	15	15	69
35	37	16	11	11	16	62
32	32	13	10	15	14	72
37	38	15	12	12	12	70
34	31	16	9	14	19	79
33	37	16	12	14	15	87
33	33	14	9	8	19	62
26	32	16	12	13	13	77
30	30	16	12	9	17	69
24	30	14	10	15	12	69
34	31	11	9	17	11	75
34	32	12	12	13	14	54
33	34	15	8	15	11	72
34	36	15	11	15	13	74
35	37	16	11	14	12	85
35	36	16	12	16	15	52
36	33	11	10	13	14	70
34	33	15	10	16	12	84
34	33	12	12	9	17	64
41	44	12	12	16	11	84
32	39	15	11	11	18	87
30	32	15	8	10	13	79
35	35	16	12	11	17	67
28	25	14	10	15	13	65
33	35	17	11	17	11	85
39	34	14	10	14	12	83
36	35	13	8	8	22	61
36	39	15	12	15	14	82
35	33	13	12	11	12	76
38	36	14	10	16	12	58
33	32	15	12	10	17	72
31	32	12	9	15	9	72
34	36	13	9	9	21	38
32	36	8	6	16	10	78
31	32	14	10	19	11	54
33	34	14	9	12	12	63
34	33	11	9	8	23	66
34	35	12	9	11	13	70
34	30	13	6	14	12	71
33	38	10	10	9	16	67
32	34	16	6	15	9	58
41	33	18	14	13	17	72
34	32	13	10	16	9	72
36	31	11	10	11	14	70
37	30	4	6	12	17	76
36	27	13	12	13	13	50
29	31	16	12	10	11	72
37	30	10	7	11	12	72
27	32	12	8	12	10	88
35	35	12	11	8	19	53
28	28	10	3	12	16	58
35	33	13	6	12	16	66
37	31	15	10	15	14	82
29	35	12	8	11	20	69
32	35	14	9	13	15	68
36	32	10	9	14	23	44
19	21	12	8	10	20	56
21	20	12	9	12	16	53
31	34	11	7	15	14	70
33	32	10	7	13	17	78
36	34	12	6	13	11	71
33	32	16	9	13	13	72
37	33	12	10	12	17	68
34	33	14	11	12	15	67
35	37	16	12	9	21	75
31	32	14	8	9	18	62
37	34	13	11	15	15	67
35	30	4	3	10	8	83
27	30	15	11	14	12	64
34	38	11	12	15	12	68
40	36	11	7	7	22	62
29	32	14	9	14	12	72




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time15 seconds
R Server'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 15 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96550&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]15 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96550&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96550&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time15 seconds
R Server'George Udny Yule' @ 72.249.76.132







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 5.45153304933195 + 0.0321668940555692Connected[t] + 0.0427653888947307Separate[t] + 0.558489671132888Software[t] + 0.0702334998707389Happiness[t] -0.0311264244363035Depression[t] + 0.00747850801958737Belonging[t] -0.00491058171792585t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  5.45153304933195 +  0.0321668940555692Connected[t] +  0.0427653888947307Separate[t] +  0.558489671132888Software[t] +  0.0702334998707389Happiness[t] -0.0311264244363035Depression[t] +  0.00747850801958737Belonging[t] -0.00491058171792585t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96550&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  5.45153304933195 +  0.0321668940555692Connected[t] +  0.0427653888947307Separate[t] +  0.558489671132888Software[t] +  0.0702334998707389Happiness[t] -0.0311264244363035Depression[t] +  0.00747850801958737Belonging[t] -0.00491058171792585t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96550&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96550&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 5.45153304933195 + 0.0321668940555692Connected[t] + 0.0427653888947307Separate[t] + 0.558489671132888Software[t] + 0.0702334998707389Happiness[t] -0.0311264244363035Depression[t] + 0.00747850801958737Belonging[t] -0.00491058171792585t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.451533049331951.9427942.8060.0054010.0027
Connected0.03216689405556920.0344240.93440.3509670.175483
Separate0.04276538889473070.0350761.21920.2238860.111943
Software0.5584896711328880.05372610.395200
Happiness0.07023349987073890.0578091.21490.2255180.112759
Depression-0.03112642443630350.041759-0.74540.4567250.228362
Belonging0.007478508019587370.0118690.63010.5291910.264595
t-0.004910581717925850.00168-2.92270.003780.00189

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 5.45153304933195 & 1.942794 & 2.806 & 0.005401 & 0.0027 \tabularnewline
Connected & 0.0321668940555692 & 0.034424 & 0.9344 & 0.350967 & 0.175483 \tabularnewline
Separate & 0.0427653888947307 & 0.035076 & 1.2192 & 0.223886 & 0.111943 \tabularnewline
Software & 0.558489671132888 & 0.053726 & 10.3952 & 0 & 0 \tabularnewline
Happiness & 0.0702334998707389 & 0.057809 & 1.2149 & 0.225518 & 0.112759 \tabularnewline
Depression & -0.0311264244363035 & 0.041759 & -0.7454 & 0.456725 & 0.228362 \tabularnewline
Belonging & 0.00747850801958737 & 0.011869 & 0.6301 & 0.529191 & 0.264595 \tabularnewline
t & -0.00491058171792585 & 0.00168 & -2.9227 & 0.00378 & 0.00189 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96550&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]5.45153304933195[/C][C]1.942794[/C][C]2.806[/C][C]0.005401[/C][C]0.0027[/C][/ROW]
[ROW][C]Connected[/C][C]0.0321668940555692[/C][C]0.034424[/C][C]0.9344[/C][C]0.350967[/C][C]0.175483[/C][/ROW]
[ROW][C]Separate[/C][C]0.0427653888947307[/C][C]0.035076[/C][C]1.2192[/C][C]0.223886[/C][C]0.111943[/C][/ROW]
[ROW][C]Software[/C][C]0.558489671132888[/C][C]0.053726[/C][C]10.3952[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0702334998707389[/C][C]0.057809[/C][C]1.2149[/C][C]0.225518[/C][C]0.112759[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0311264244363035[/C][C]0.041759[/C][C]-0.7454[/C][C]0.456725[/C][C]0.228362[/C][/ROW]
[ROW][C]Belonging[/C][C]0.00747850801958737[/C][C]0.011869[/C][C]0.6301[/C][C]0.529191[/C][C]0.264595[/C][/ROW]
[ROW][C]t[/C][C]-0.00491058171792585[/C][C]0.00168[/C][C]-2.9227[/C][C]0.00378[/C][C]0.00189[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96550&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96550&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.451533049331951.9427942.8060.0054010.0027
Connected0.03216689405556920.0344240.93440.3509670.175483
Separate0.04276538889473070.0350761.21920.2238860.111943
Software0.5584896711328880.05372610.395200
Happiness0.07023349987073890.0578091.21490.2255180.112759
Depression-0.03112642443630350.041759-0.74540.4567250.228362
Belonging0.007478508019587370.0118690.63010.5291910.264595
t-0.004910581717925850.00168-2.92270.003780.00189







Multiple Linear Regression - Regression Statistics
Multiple R0.667212929789438
R-squared0.445173093678205
Adjusted R-squared0.430002045458468
F-TEST (value)29.3435949336093
F-TEST (DF numerator)7
F-TEST (DF denominator)256
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85421524491434
Sum Squared Residuals880.157228665026

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.667212929789438 \tabularnewline
R-squared & 0.445173093678205 \tabularnewline
Adjusted R-squared & 0.430002045458468 \tabularnewline
F-TEST (value) & 29.3435949336093 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 256 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.85421524491434 \tabularnewline
Sum Squared Residuals & 880.157228665026 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96550&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.667212929789438[/C][/ROW]
[ROW][C]R-squared[/C][C]0.445173093678205[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.430002045458468[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]29.3435949336093[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]256[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.85421524491434[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]880.157228665026[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96550&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96550&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.667212929789438
R-squared0.445173093678205
Adjusted R-squared0.430002045458468
F-TEST (value)29.3435949336093
F-TEST (DF numerator)7
F-TEST (DF denominator)256
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85421524491434
Sum Squared Residuals880.157228665026







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.0985387854790-3.09853878547897
21615.75062807565610.249371924343889
31917.10632188991671.89367811008325
41512.16160524103192.83839475896812
51416.4017873467223-2.40178734672229
61314.8470133702108-1.84701337021082
71915.39411954043473.60588045956527
81517.1034911605066-2.10349116050658
91416.0596585119288-2.05965851192881
101514.46131972028220.538680279717755
111615.00398977859880.996010221401189
121616.1957780658441-0.195778065844116
131615.54520079549590.45479920450408
141615.49757042279100.502429577209031
151718.0026152345095-1.00261523450952
161515.4966907599622-0.496690759962239
171514.58783765963910.412162340360932
182016.42213291884223.5778670811578
191815.53184129240092.46815870759905
201615.5983979603540.401602039646002
211615.39447915384270.605520846157257
221615.21361417839330.78638582160669
231916.49214037872652.50785962127346
241615.16091922295150.839080777048478
251716.14837639146470.851623608535269
261716.221803070780.778196929219997
271614.90835279021401.09164720978602
281516.8091087350266-1.80910873502655
291615.68482375194730.315176248052743
301414.2630295587570-0.263029558757013
311515.7659606607022-0.765960660702237
321212.8492889523859-0.849288952385907
331414.8039620225922-0.803962022592174
341616.0017496144186-0.00174961441855540
351415.4902098814983-1.49020988149829
361013.0304769205532-3.03047692055322
371013.0355345646574-3.03553456465739
381415.7470324913881-1.74703249138813
391614.5545559199381.44544408006200
401614.49489392472291.50510607527711
411614.72053770649711.27946229350291
421415.7009510594818-1.70095105948177
432017.48712155669652.51287844330346
441414.1533777639022-0.153377763902245
451414.4623553291315-0.462355329131485
461115.4909495615117-4.49094956151173
471416.6307612737306-2.63076127373059
481515.1467421663187-0.146742166318659
491615.41787661632300.58212338367696
501415.6302473667626-1.6302473667626
511617.0252984963352-1.02529849633518
521414.1419408813432-0.141940881343227
531215.0171855791395-3.01718557913949
541616.0361860907356-0.0361860907356081
55911.3432897998036-2.34328979980356
561412.38497814452031.61502185547966
571615.85454010930220.145459890697828
581615.52988322876590.47011677123408
591515.1123930302181-0.112393030218124
601614.22106056522641.77893943477357
611211.55955340846620.440446591533846
621615.64068175553980.359318244460177
631616.5488168530774-0.548816853077411
641414.7109860019853-0.710986001985279
651615.30632115833420.693678841665758
661716.05418603320130.945813966798659
671816.33317707986111.66682292013889
681814.39588818744263.60411181255738
691215.8997083739692-3.89970837396925
701615.65157867302610.348421326973927
711013.4123806085750-3.41238060857496
721414.9242951062704-0.924295106270414
731816.92805867454331.07194132545669
741817.1409836239110.85901637608901
751615.23229487529480.76770512470519
761713.49188698970073.50811301029926
771616.419099992442-0.419099992441987
781614.53119884767541.46880115232456
791315.2165773344004-2.21657733440037
801615.14214025715690.857859742843126
811615.65862745062780.341372549372196
821615.76324083063170.236759169368344
831515.6434137240469-0.643413724046948
841514.88423238192270.115767618077307
851614.16073547223411.83926452776585
861414.1555692846506-0.155569284650594
871615.36748817802790.632511821972129
881614.87258884882471.12741115117525
891514.51948139025870.480518609741331
901213.9079725164080-1.90797251640802
911716.75849437545860.241505624541383
921615.80556191940360.194438080596370
931515.0531067912421-0.0531067912420874
941315.0298518503278-2.02985185032776
951614.74361333653481.25638666346519
961615.78824288132970.211757118670259
971613.67738396548952.32261603451051
981615.77370837563650.226291624363468
991414.4297816437613-0.429781643761336
1001616.9922981106499-0.992298110649853
1011614.67662466255271.32337533744731
1022017.45421844256342.54578155743658
1031514.23966037274950.760339627250459
1041614.95985844448531.04014155551470
1051314.8422042092773-1.84220420927732
1061715.70172996487471.29827003512525
1071615.70112345875230.298876541247739
1081614.35865146879431.64134853120570
1091212.3043422991135-0.304342299113495
1101615.28568089566040.714319104339601
1111615.99282714923440.00717285076563326
1121715.05640936909211.94359063090789
1131314.2811780773625-1.28117807736249
1141214.5798742612204-2.57987426122044
1151816.16147284443871.83852715556128
1161415.8657328435429-1.86573284354288
1171413.22888782890700.771112171092978
1181314.7929151358429-1.79291513584292
1191615.52038420622400.479615793776025
1201314.4292215835434-1.42922158354343
1211615.38542326764150.614576732358469
1221315.8212093173508-2.82120931735076
1231616.8545044640026-0.85450446400258
1241515.8437647974654-0.84376479746541
1251616.7849339139009-0.784933913900921
1261514.65872160107570.341278398924294
1271715.49058390584561.50941609415438
1281514.04825211200880.951747887991243
1291214.6858650734517-2.68586507345170
1301613.94027147954572.05972852045428
1311013.7239532443361-3.72395324433606
1321613.45833215416292.54166784583712
1331214.1659890224415-2.16598902244154
1341415.5440460090552-1.54404600905523
1351515.0836566962205-0.0836566962204762
1361312.11211273473090.887887265269139
1371514.53127780695890.46872219304114
1381113.4989500572247-2.49895005722466
1391213.0064804164844-1.00648041648442
1401113.339956434492-2.33995643449199
1411612.85649991512233.14350008487771
1421513.64412442582351.35587557417655
1431716.84136395828370.158636041716305
1441614.12440384704751.87559615295253
1451013.2667514301309-3.26675143013094
1461815.49754546736302.50245453263696
1471314.9457512386837-1.94575123868368
1481614.84301010530701.15698989469302
1491312.827227670110.172772329889987
1501012.9004577169405-2.90045771694048
1511515.8784182905414-0.878418290541436
1521613.82486309168812.17513690831192
1531611.85258101474954.14741898525048
1541412.39630251826531.60369748173468
1551012.4816282956809-2.4816282956809
1561716.43930656379340.560693436206563
1571311.70668585473181.29331414526825
1581513.90093466047101.09906533952902
1591614.53739613320241.46260386679759
1601212.6989037635456-0.6989037635456
1611312.69310492059890.306895079401114
1621312.67020411464900.329795885350963
1631212.3813576310274-0.381357631027413
1641716.21309801790220.786901982097844
1651513.66822027825111.33177972174891
1661011.6197611469958-1.61976114699577
1671414.3441924705758-0.344192470575764
1681114.1943469919182-3.19434699191817
1691314.7895963339482-1.78959633394817
1701614.38719268389761.61280731610236
1711210.48153201795311.51846798204690
1721615.33778810465530.662211895344686
1731213.8444384221823-1.84443842218235
174911.3336124343809-2.3336124343809
1751214.9070414350127-2.90704143501266
1761514.47300757445320.526992425546797
1771212.2943276210920-0.294327621092045
1781212.6503684441930-0.650368444193027
1791413.79386819549250.206131804507502
1801213.2889048934373-1.28890489343726
1811615.00687376064150.993126239358496
1821111.4515058779588-0.451505877958803
1831916.70157676851062.29842323148939
1841515.1109818809655-0.110981880965516
185814.5969865862610-6.59698658626102
1861614.69893555689941.30106444310065
1871714.38958070849442.61041929150556
1881212.3486229107255-0.348622910725456
1891111.4120697144516-0.412069714451611
1901110.45496806318950.545031936810458
1911414.4364908904417-0.436490890441748
1921615.37642779828200.62357220171798
193129.722481419343272.27751858065673
1941614.08864046437771.91135953562229
1951313.6328845134380-0.632884513437968
1961514.99897541085330.00102458914673653
1971612.91262601217053.08737398782951
1981614.99194364506601.00805635493404
1991412.40763309691111.59236690308889
2001614.46036154757351.53963845242654
2011614.03332000290351.96667999709652
2021413.29546183599230.704538164007682
2031113.3129603848872-2.31296038488723
2041214.4949222642595-2.4949222642595
2051512.67787629914692.32212370085312
2061514.41883656983920.581163430160789
2071614.53201478385261.46798521614739
2081614.84312544615901.15687455384097
2091113.5801453187234-2.58014531872336
2101513.88855340965331.11144659034666
2111214.2037853885328-2.20378538853276
2121215.7224255491506-3.72242554915059
2131514.10907935897690.890920641023053
2141512.09057881164022.90942118835982
2151614.46474325730631.53525674269368
2161413.08051386717530.919486132824675
2171714.57487132482132.42512867517873
2181413.90492310732140.0950768926785521
2191311.83210547004741.16789452995262
2201515.1299096914369-0.129909691436863
2211314.5726876835671-1.57268768356711
2221413.89214896343540.107851036564569
2231514.19998768899480.800012311005216
2241213.0554532006112-1.05545320061118
2251312.26891749151280.731082508487161
226811.6573695969631-3.65736959696312
2271413.68327913284810.316720867151934
2281412.81428909454271.18571090545734
2291112.1978908737620-1.19789087376204
2301212.8303898458872-0.83038984588718
2311311.18548873836501.81451126163496
2321013.2189058291037-3.21890582910369
2331611.3487875113224.65121248867801
2341815.77375167331492.22624832668513
2351313.7266606548843-0.72666065488433
2361113.2215618348084-2.22156183480842
237410.9938193483991-6.99381934839914
2381314.1796817218455-1.17968172184546
2391614.13674396300881.86325603699122
2401011.5930618646107-1.59306186461068
2411212.1626452683162-0.162645268316154
2421213.3960154190304-1.39601541903039
243108.810367300487071.18963269951293
2441310.97974899918712.02025100081286
2451513.58020958912071.41979041087934
2461211.80713291791590.192867082084079
2471412.7458333034011.25416669659900
2481012.3830320431314-2.38303204313137
2491210.70456268355481.29543731644515
2501211.52224734561410.477752654385854
2511111.7208297915302-0.720829791530176
2521011.5207040112402-1.52070401124024
2531211.27374420882630.726255791173698
2541612.70749683969783.29250316030215
2551213.2078556645355-1.20785566453552
2561413.71970841263680.280291587363209
2571614.13888496961291.86111503038709
2581411.55367985172182.44632014827823
2591314.0549432381567-1.05494323815667
26049.3330935436994-5.33309354369941
2611513.55310182796561.44689817203439
2621114.7741198188765-3.77411981887649
2631111.1662281765916-0.166228176591611
2641412.63108337060341.36891662939664

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.0985387854790 & -3.09853878547897 \tabularnewline
2 & 16 & 15.7506280756561 & 0.249371924343889 \tabularnewline
3 & 19 & 17.1063218899167 & 1.89367811008325 \tabularnewline
4 & 15 & 12.1616052410319 & 2.83839475896812 \tabularnewline
5 & 14 & 16.4017873467223 & -2.40178734672229 \tabularnewline
6 & 13 & 14.8470133702108 & -1.84701337021082 \tabularnewline
7 & 19 & 15.3941195404347 & 3.60588045956527 \tabularnewline
8 & 15 & 17.1034911605066 & -2.10349116050658 \tabularnewline
9 & 14 & 16.0596585119288 & -2.05965851192881 \tabularnewline
10 & 15 & 14.4613197202822 & 0.538680279717755 \tabularnewline
11 & 16 & 15.0039897785988 & 0.996010221401189 \tabularnewline
12 & 16 & 16.1957780658441 & -0.195778065844116 \tabularnewline
13 & 16 & 15.5452007954959 & 0.45479920450408 \tabularnewline
14 & 16 & 15.4975704227910 & 0.502429577209031 \tabularnewline
15 & 17 & 18.0026152345095 & -1.00261523450952 \tabularnewline
16 & 15 & 15.4966907599622 & -0.496690759962239 \tabularnewline
17 & 15 & 14.5878376596391 & 0.412162340360932 \tabularnewline
18 & 20 & 16.4221329188422 & 3.5778670811578 \tabularnewline
19 & 18 & 15.5318412924009 & 2.46815870759905 \tabularnewline
20 & 16 & 15.598397960354 & 0.401602039646002 \tabularnewline
21 & 16 & 15.3944791538427 & 0.605520846157257 \tabularnewline
22 & 16 & 15.2136141783933 & 0.78638582160669 \tabularnewline
23 & 19 & 16.4921403787265 & 2.50785962127346 \tabularnewline
24 & 16 & 15.1609192229515 & 0.839080777048478 \tabularnewline
25 & 17 & 16.1483763914647 & 0.851623608535269 \tabularnewline
26 & 17 & 16.22180307078 & 0.778196929219997 \tabularnewline
27 & 16 & 14.9083527902140 & 1.09164720978602 \tabularnewline
28 & 15 & 16.8091087350266 & -1.80910873502655 \tabularnewline
29 & 16 & 15.6848237519473 & 0.315176248052743 \tabularnewline
30 & 14 & 14.2630295587570 & -0.263029558757013 \tabularnewline
31 & 15 & 15.7659606607022 & -0.765960660702237 \tabularnewline
32 & 12 & 12.8492889523859 & -0.849288952385907 \tabularnewline
33 & 14 & 14.8039620225922 & -0.803962022592174 \tabularnewline
34 & 16 & 16.0017496144186 & -0.00174961441855540 \tabularnewline
35 & 14 & 15.4902098814983 & -1.49020988149829 \tabularnewline
36 & 10 & 13.0304769205532 & -3.03047692055322 \tabularnewline
37 & 10 & 13.0355345646574 & -3.03553456465739 \tabularnewline
38 & 14 & 15.7470324913881 & -1.74703249138813 \tabularnewline
39 & 16 & 14.554555919938 & 1.44544408006200 \tabularnewline
40 & 16 & 14.4948939247229 & 1.50510607527711 \tabularnewline
41 & 16 & 14.7205377064971 & 1.27946229350291 \tabularnewline
42 & 14 & 15.7009510594818 & -1.70095105948177 \tabularnewline
43 & 20 & 17.4871215566965 & 2.51287844330346 \tabularnewline
44 & 14 & 14.1533777639022 & -0.153377763902245 \tabularnewline
45 & 14 & 14.4623553291315 & -0.462355329131485 \tabularnewline
46 & 11 & 15.4909495615117 & -4.49094956151173 \tabularnewline
47 & 14 & 16.6307612737306 & -2.63076127373059 \tabularnewline
48 & 15 & 15.1467421663187 & -0.146742166318659 \tabularnewline
49 & 16 & 15.4178766163230 & 0.58212338367696 \tabularnewline
50 & 14 & 15.6302473667626 & -1.6302473667626 \tabularnewline
51 & 16 & 17.0252984963352 & -1.02529849633518 \tabularnewline
52 & 14 & 14.1419408813432 & -0.141940881343227 \tabularnewline
53 & 12 & 15.0171855791395 & -3.01718557913949 \tabularnewline
54 & 16 & 16.0361860907356 & -0.0361860907356081 \tabularnewline
55 & 9 & 11.3432897998036 & -2.34328979980356 \tabularnewline
56 & 14 & 12.3849781445203 & 1.61502185547966 \tabularnewline
57 & 16 & 15.8545401093022 & 0.145459890697828 \tabularnewline
58 & 16 & 15.5298832287659 & 0.47011677123408 \tabularnewline
59 & 15 & 15.1123930302181 & -0.112393030218124 \tabularnewline
60 & 16 & 14.2210605652264 & 1.77893943477357 \tabularnewline
61 & 12 & 11.5595534084662 & 0.440446591533846 \tabularnewline
62 & 16 & 15.6406817555398 & 0.359318244460177 \tabularnewline
63 & 16 & 16.5488168530774 & -0.548816853077411 \tabularnewline
64 & 14 & 14.7109860019853 & -0.710986001985279 \tabularnewline
65 & 16 & 15.3063211583342 & 0.693678841665758 \tabularnewline
66 & 17 & 16.0541860332013 & 0.945813966798659 \tabularnewline
67 & 18 & 16.3331770798611 & 1.66682292013889 \tabularnewline
68 & 18 & 14.3958881874426 & 3.60411181255738 \tabularnewline
69 & 12 & 15.8997083739692 & -3.89970837396925 \tabularnewline
70 & 16 & 15.6515786730261 & 0.348421326973927 \tabularnewline
71 & 10 & 13.4123806085750 & -3.41238060857496 \tabularnewline
72 & 14 & 14.9242951062704 & -0.924295106270414 \tabularnewline
73 & 18 & 16.9280586745433 & 1.07194132545669 \tabularnewline
74 & 18 & 17.140983623911 & 0.85901637608901 \tabularnewline
75 & 16 & 15.2322948752948 & 0.76770512470519 \tabularnewline
76 & 17 & 13.4918869897007 & 3.50811301029926 \tabularnewline
77 & 16 & 16.419099992442 & -0.419099992441987 \tabularnewline
78 & 16 & 14.5311988476754 & 1.46880115232456 \tabularnewline
79 & 13 & 15.2165773344004 & -2.21657733440037 \tabularnewline
80 & 16 & 15.1421402571569 & 0.857859742843126 \tabularnewline
81 & 16 & 15.6586274506278 & 0.341372549372196 \tabularnewline
82 & 16 & 15.7632408306317 & 0.236759169368344 \tabularnewline
83 & 15 & 15.6434137240469 & -0.643413724046948 \tabularnewline
84 & 15 & 14.8842323819227 & 0.115767618077307 \tabularnewline
85 & 16 & 14.1607354722341 & 1.83926452776585 \tabularnewline
86 & 14 & 14.1555692846506 & -0.155569284650594 \tabularnewline
87 & 16 & 15.3674881780279 & 0.632511821972129 \tabularnewline
88 & 16 & 14.8725888488247 & 1.12741115117525 \tabularnewline
89 & 15 & 14.5194813902587 & 0.480518609741331 \tabularnewline
90 & 12 & 13.9079725164080 & -1.90797251640802 \tabularnewline
91 & 17 & 16.7584943754586 & 0.241505624541383 \tabularnewline
92 & 16 & 15.8055619194036 & 0.194438080596370 \tabularnewline
93 & 15 & 15.0531067912421 & -0.0531067912420874 \tabularnewline
94 & 13 & 15.0298518503278 & -2.02985185032776 \tabularnewline
95 & 16 & 14.7436133365348 & 1.25638666346519 \tabularnewline
96 & 16 & 15.7882428813297 & 0.211757118670259 \tabularnewline
97 & 16 & 13.6773839654895 & 2.32261603451051 \tabularnewline
98 & 16 & 15.7737083756365 & 0.226291624363468 \tabularnewline
99 & 14 & 14.4297816437613 & -0.429781643761336 \tabularnewline
100 & 16 & 16.9922981106499 & -0.992298110649853 \tabularnewline
101 & 16 & 14.6766246625527 & 1.32337533744731 \tabularnewline
102 & 20 & 17.4542184425634 & 2.54578155743658 \tabularnewline
103 & 15 & 14.2396603727495 & 0.760339627250459 \tabularnewline
104 & 16 & 14.9598584444853 & 1.04014155551470 \tabularnewline
105 & 13 & 14.8422042092773 & -1.84220420927732 \tabularnewline
106 & 17 & 15.7017299648747 & 1.29827003512525 \tabularnewline
107 & 16 & 15.7011234587523 & 0.298876541247739 \tabularnewline
108 & 16 & 14.3586514687943 & 1.64134853120570 \tabularnewline
109 & 12 & 12.3043422991135 & -0.304342299113495 \tabularnewline
110 & 16 & 15.2856808956604 & 0.714319104339601 \tabularnewline
111 & 16 & 15.9928271492344 & 0.00717285076563326 \tabularnewline
112 & 17 & 15.0564093690921 & 1.94359063090789 \tabularnewline
113 & 13 & 14.2811780773625 & -1.28117807736249 \tabularnewline
114 & 12 & 14.5798742612204 & -2.57987426122044 \tabularnewline
115 & 18 & 16.1614728444387 & 1.83852715556128 \tabularnewline
116 & 14 & 15.8657328435429 & -1.86573284354288 \tabularnewline
117 & 14 & 13.2288878289070 & 0.771112171092978 \tabularnewline
118 & 13 & 14.7929151358429 & -1.79291513584292 \tabularnewline
119 & 16 & 15.5203842062240 & 0.479615793776025 \tabularnewline
120 & 13 & 14.4292215835434 & -1.42922158354343 \tabularnewline
121 & 16 & 15.3854232676415 & 0.614576732358469 \tabularnewline
122 & 13 & 15.8212093173508 & -2.82120931735076 \tabularnewline
123 & 16 & 16.8545044640026 & -0.85450446400258 \tabularnewline
124 & 15 & 15.8437647974654 & -0.84376479746541 \tabularnewline
125 & 16 & 16.7849339139009 & -0.784933913900921 \tabularnewline
126 & 15 & 14.6587216010757 & 0.341278398924294 \tabularnewline
127 & 17 & 15.4905839058456 & 1.50941609415438 \tabularnewline
128 & 15 & 14.0482521120088 & 0.951747887991243 \tabularnewline
129 & 12 & 14.6858650734517 & -2.68586507345170 \tabularnewline
130 & 16 & 13.9402714795457 & 2.05972852045428 \tabularnewline
131 & 10 & 13.7239532443361 & -3.72395324433606 \tabularnewline
132 & 16 & 13.4583321541629 & 2.54166784583712 \tabularnewline
133 & 12 & 14.1659890224415 & -2.16598902244154 \tabularnewline
134 & 14 & 15.5440460090552 & -1.54404600905523 \tabularnewline
135 & 15 & 15.0836566962205 & -0.0836566962204762 \tabularnewline
136 & 13 & 12.1121127347309 & 0.887887265269139 \tabularnewline
137 & 15 & 14.5312778069589 & 0.46872219304114 \tabularnewline
138 & 11 & 13.4989500572247 & -2.49895005722466 \tabularnewline
139 & 12 & 13.0064804164844 & -1.00648041648442 \tabularnewline
140 & 11 & 13.339956434492 & -2.33995643449199 \tabularnewline
141 & 16 & 12.8564999151223 & 3.14350008487771 \tabularnewline
142 & 15 & 13.6441244258235 & 1.35587557417655 \tabularnewline
143 & 17 & 16.8413639582837 & 0.158636041716305 \tabularnewline
144 & 16 & 14.1244038470475 & 1.87559615295253 \tabularnewline
145 & 10 & 13.2667514301309 & -3.26675143013094 \tabularnewline
146 & 18 & 15.4975454673630 & 2.50245453263696 \tabularnewline
147 & 13 & 14.9457512386837 & -1.94575123868368 \tabularnewline
148 & 16 & 14.8430101053070 & 1.15698989469302 \tabularnewline
149 & 13 & 12.82722767011 & 0.172772329889987 \tabularnewline
150 & 10 & 12.9004577169405 & -2.90045771694048 \tabularnewline
151 & 15 & 15.8784182905414 & -0.878418290541436 \tabularnewline
152 & 16 & 13.8248630916881 & 2.17513690831192 \tabularnewline
153 & 16 & 11.8525810147495 & 4.14741898525048 \tabularnewline
154 & 14 & 12.3963025182653 & 1.60369748173468 \tabularnewline
155 & 10 & 12.4816282956809 & -2.4816282956809 \tabularnewline
156 & 17 & 16.4393065637934 & 0.560693436206563 \tabularnewline
157 & 13 & 11.7066858547318 & 1.29331414526825 \tabularnewline
158 & 15 & 13.9009346604710 & 1.09906533952902 \tabularnewline
159 & 16 & 14.5373961332024 & 1.46260386679759 \tabularnewline
160 & 12 & 12.6989037635456 & -0.6989037635456 \tabularnewline
161 & 13 & 12.6931049205989 & 0.306895079401114 \tabularnewline
162 & 13 & 12.6702041146490 & 0.329795885350963 \tabularnewline
163 & 12 & 12.3813576310274 & -0.381357631027413 \tabularnewline
164 & 17 & 16.2130980179022 & 0.786901982097844 \tabularnewline
165 & 15 & 13.6682202782511 & 1.33177972174891 \tabularnewline
166 & 10 & 11.6197611469958 & -1.61976114699577 \tabularnewline
167 & 14 & 14.3441924705758 & -0.344192470575764 \tabularnewline
168 & 11 & 14.1943469919182 & -3.19434699191817 \tabularnewline
169 & 13 & 14.7895963339482 & -1.78959633394817 \tabularnewline
170 & 16 & 14.3871926838976 & 1.61280731610236 \tabularnewline
171 & 12 & 10.4815320179531 & 1.51846798204690 \tabularnewline
172 & 16 & 15.3377881046553 & 0.662211895344686 \tabularnewline
173 & 12 & 13.8444384221823 & -1.84443842218235 \tabularnewline
174 & 9 & 11.3336124343809 & -2.3336124343809 \tabularnewline
175 & 12 & 14.9070414350127 & -2.90704143501266 \tabularnewline
176 & 15 & 14.4730075744532 & 0.526992425546797 \tabularnewline
177 & 12 & 12.2943276210920 & -0.294327621092045 \tabularnewline
178 & 12 & 12.6503684441930 & -0.650368444193027 \tabularnewline
179 & 14 & 13.7938681954925 & 0.206131804507502 \tabularnewline
180 & 12 & 13.2889048934373 & -1.28890489343726 \tabularnewline
181 & 16 & 15.0068737606415 & 0.993126239358496 \tabularnewline
182 & 11 & 11.4515058779588 & -0.451505877958803 \tabularnewline
183 & 19 & 16.7015767685106 & 2.29842323148939 \tabularnewline
184 & 15 & 15.1109818809655 & -0.110981880965516 \tabularnewline
185 & 8 & 14.5969865862610 & -6.59698658626102 \tabularnewline
186 & 16 & 14.6989355568994 & 1.30106444310065 \tabularnewline
187 & 17 & 14.3895807084944 & 2.61041929150556 \tabularnewline
188 & 12 & 12.3486229107255 & -0.348622910725456 \tabularnewline
189 & 11 & 11.4120697144516 & -0.412069714451611 \tabularnewline
190 & 11 & 10.4549680631895 & 0.545031936810458 \tabularnewline
191 & 14 & 14.4364908904417 & -0.436490890441748 \tabularnewline
192 & 16 & 15.3764277982820 & 0.62357220171798 \tabularnewline
193 & 12 & 9.72248141934327 & 2.27751858065673 \tabularnewline
194 & 16 & 14.0886404643777 & 1.91135953562229 \tabularnewline
195 & 13 & 13.6328845134380 & -0.632884513437968 \tabularnewline
196 & 15 & 14.9989754108533 & 0.00102458914673653 \tabularnewline
197 & 16 & 12.9126260121705 & 3.08737398782951 \tabularnewline
198 & 16 & 14.9919436450660 & 1.00805635493404 \tabularnewline
199 & 14 & 12.4076330969111 & 1.59236690308889 \tabularnewline
200 & 16 & 14.4603615475735 & 1.53963845242654 \tabularnewline
201 & 16 & 14.0333200029035 & 1.96667999709652 \tabularnewline
202 & 14 & 13.2954618359923 & 0.704538164007682 \tabularnewline
203 & 11 & 13.3129603848872 & -2.31296038488723 \tabularnewline
204 & 12 & 14.4949222642595 & -2.4949222642595 \tabularnewline
205 & 15 & 12.6778762991469 & 2.32212370085312 \tabularnewline
206 & 15 & 14.4188365698392 & 0.581163430160789 \tabularnewline
207 & 16 & 14.5320147838526 & 1.46798521614739 \tabularnewline
208 & 16 & 14.8431254461590 & 1.15687455384097 \tabularnewline
209 & 11 & 13.5801453187234 & -2.58014531872336 \tabularnewline
210 & 15 & 13.8885534096533 & 1.11144659034666 \tabularnewline
211 & 12 & 14.2037853885328 & -2.20378538853276 \tabularnewline
212 & 12 & 15.7224255491506 & -3.72242554915059 \tabularnewline
213 & 15 & 14.1090793589769 & 0.890920641023053 \tabularnewline
214 & 15 & 12.0905788116402 & 2.90942118835982 \tabularnewline
215 & 16 & 14.4647432573063 & 1.53525674269368 \tabularnewline
216 & 14 & 13.0805138671753 & 0.919486132824675 \tabularnewline
217 & 17 & 14.5748713248213 & 2.42512867517873 \tabularnewline
218 & 14 & 13.9049231073214 & 0.0950768926785521 \tabularnewline
219 & 13 & 11.8321054700474 & 1.16789452995262 \tabularnewline
220 & 15 & 15.1299096914369 & -0.129909691436863 \tabularnewline
221 & 13 & 14.5726876835671 & -1.57268768356711 \tabularnewline
222 & 14 & 13.8921489634354 & 0.107851036564569 \tabularnewline
223 & 15 & 14.1999876889948 & 0.800012311005216 \tabularnewline
224 & 12 & 13.0554532006112 & -1.05545320061118 \tabularnewline
225 & 13 & 12.2689174915128 & 0.731082508487161 \tabularnewline
226 & 8 & 11.6573695969631 & -3.65736959696312 \tabularnewline
227 & 14 & 13.6832791328481 & 0.316720867151934 \tabularnewline
228 & 14 & 12.8142890945427 & 1.18571090545734 \tabularnewline
229 & 11 & 12.1978908737620 & -1.19789087376204 \tabularnewline
230 & 12 & 12.8303898458872 & -0.83038984588718 \tabularnewline
231 & 13 & 11.1854887383650 & 1.81451126163496 \tabularnewline
232 & 10 & 13.2189058291037 & -3.21890582910369 \tabularnewline
233 & 16 & 11.348787511322 & 4.65121248867801 \tabularnewline
234 & 18 & 15.7737516733149 & 2.22624832668513 \tabularnewline
235 & 13 & 13.7266606548843 & -0.72666065488433 \tabularnewline
236 & 11 & 13.2215618348084 & -2.22156183480842 \tabularnewline
237 & 4 & 10.9938193483991 & -6.99381934839914 \tabularnewline
238 & 13 & 14.1796817218455 & -1.17968172184546 \tabularnewline
239 & 16 & 14.1367439630088 & 1.86325603699122 \tabularnewline
240 & 10 & 11.5930618646107 & -1.59306186461068 \tabularnewline
241 & 12 & 12.1626452683162 & -0.162645268316154 \tabularnewline
242 & 12 & 13.3960154190304 & -1.39601541903039 \tabularnewline
243 & 10 & 8.81036730048707 & 1.18963269951293 \tabularnewline
244 & 13 & 10.9797489991871 & 2.02025100081286 \tabularnewline
245 & 15 & 13.5802095891207 & 1.41979041087934 \tabularnewline
246 & 12 & 11.8071329179159 & 0.192867082084079 \tabularnewline
247 & 14 & 12.745833303401 & 1.25416669659900 \tabularnewline
248 & 10 & 12.3830320431314 & -2.38303204313137 \tabularnewline
249 & 12 & 10.7045626835548 & 1.29543731644515 \tabularnewline
250 & 12 & 11.5222473456141 & 0.477752654385854 \tabularnewline
251 & 11 & 11.7208297915302 & -0.720829791530176 \tabularnewline
252 & 10 & 11.5207040112402 & -1.52070401124024 \tabularnewline
253 & 12 & 11.2737442088263 & 0.726255791173698 \tabularnewline
254 & 16 & 12.7074968396978 & 3.29250316030215 \tabularnewline
255 & 12 & 13.2078556645355 & -1.20785566453552 \tabularnewline
256 & 14 & 13.7197084126368 & 0.280291587363209 \tabularnewline
257 & 16 & 14.1388849696129 & 1.86111503038709 \tabularnewline
258 & 14 & 11.5536798517218 & 2.44632014827823 \tabularnewline
259 & 13 & 14.0549432381567 & -1.05494323815667 \tabularnewline
260 & 4 & 9.3330935436994 & -5.33309354369941 \tabularnewline
261 & 15 & 13.5531018279656 & 1.44689817203439 \tabularnewline
262 & 11 & 14.7741198188765 & -3.77411981887649 \tabularnewline
263 & 11 & 11.1662281765916 & -0.166228176591611 \tabularnewline
264 & 14 & 12.6310833706034 & 1.36891662939664 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96550&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]16.0985387854790[/C][C]-3.09853878547897[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.7506280756561[/C][C]0.249371924343889[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]17.1063218899167[/C][C]1.89367811008325[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.1616052410319[/C][C]2.83839475896812[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]16.4017873467223[/C][C]-2.40178734672229[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.8470133702108[/C][C]-1.84701337021082[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.3941195404347[/C][C]3.60588045956527[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]17.1034911605066[/C][C]-2.10349116050658[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]16.0596585119288[/C][C]-2.05965851192881[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.4613197202822[/C][C]0.538680279717755[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.0039897785988[/C][C]0.996010221401189[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.1957780658441[/C][C]-0.195778065844116[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.5452007954959[/C][C]0.45479920450408[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.4975704227910[/C][C]0.502429577209031[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]18.0026152345095[/C][C]-1.00261523450952[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.4966907599622[/C][C]-0.496690759962239[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.5878376596391[/C][C]0.412162340360932[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.4221329188422[/C][C]3.5778670811578[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.5318412924009[/C][C]2.46815870759905[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.598397960354[/C][C]0.401602039646002[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.3944791538427[/C][C]0.605520846157257[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]15.2136141783933[/C][C]0.78638582160669[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.4921403787265[/C][C]2.50785962127346[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]15.1609192229515[/C][C]0.839080777048478[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]16.1483763914647[/C][C]0.851623608535269[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.22180307078[/C][C]0.778196929219997[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.9083527902140[/C][C]1.09164720978602[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.8091087350266[/C][C]-1.80910873502655[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.6848237519473[/C][C]0.315176248052743[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.2630295587570[/C][C]-0.263029558757013[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.7659606607022[/C][C]-0.765960660702237[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.8492889523859[/C][C]-0.849288952385907[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.8039620225922[/C][C]-0.803962022592174[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]16.0017496144186[/C][C]-0.00174961441855540[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.4902098814983[/C][C]-1.49020988149829[/C][/ROW]
[ROW][C]36[/C][C]10[/C][C]13.0304769205532[/C][C]-3.03047692055322[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]13.0355345646574[/C][C]-3.03553456465739[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.7470324913881[/C][C]-1.74703249138813[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.554555919938[/C][C]1.44544408006200[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.4948939247229[/C][C]1.50510607527711[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]14.7205377064971[/C][C]1.27946229350291[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.7009510594818[/C][C]-1.70095105948177[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.4871215566965[/C][C]2.51287844330346[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.1533777639022[/C][C]-0.153377763902245[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.4623553291315[/C][C]-0.462355329131485[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.4909495615117[/C][C]-4.49094956151173[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.6307612737306[/C][C]-2.63076127373059[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.1467421663187[/C][C]-0.146742166318659[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.4178766163230[/C][C]0.58212338367696[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.6302473667626[/C][C]-1.6302473667626[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]17.0252984963352[/C][C]-1.02529849633518[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.1419408813432[/C][C]-0.141940881343227[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]15.0171855791395[/C][C]-3.01718557913949[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]16.0361860907356[/C][C]-0.0361860907356081[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.3432897998036[/C][C]-2.34328979980356[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.3849781445203[/C][C]1.61502185547966[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.8545401093022[/C][C]0.145459890697828[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.5298832287659[/C][C]0.47011677123408[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.1123930302181[/C][C]-0.112393030218124[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.2210605652264[/C][C]1.77893943477357[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.5595534084662[/C][C]0.440446591533846[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.6406817555398[/C][C]0.359318244460177[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.5488168530774[/C][C]-0.548816853077411[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.7109860019853[/C][C]-0.710986001985279[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.3063211583342[/C][C]0.693678841665758[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]16.0541860332013[/C][C]0.945813966798659[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.3331770798611[/C][C]1.66682292013889[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.3958881874426[/C][C]3.60411181255738[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.8997083739692[/C][C]-3.89970837396925[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.6515786730261[/C][C]0.348421326973927[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.4123806085750[/C][C]-3.41238060857496[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.9242951062704[/C][C]-0.924295106270414[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.9280586745433[/C][C]1.07194132545669[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.140983623911[/C][C]0.85901637608901[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.2322948752948[/C][C]0.76770512470519[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.4918869897007[/C][C]3.50811301029926[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.419099992442[/C][C]-0.419099992441987[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.5311988476754[/C][C]1.46880115232456[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]15.2165773344004[/C][C]-2.21657733440037[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.1421402571569[/C][C]0.857859742843126[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.6586274506278[/C][C]0.341372549372196[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]15.7632408306317[/C][C]0.236759169368344[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.6434137240469[/C][C]-0.643413724046948[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]14.8842323819227[/C][C]0.115767618077307[/C][/ROW]
[ROW][C]85[/C][C]16[/C][C]14.1607354722341[/C][C]1.83926452776585[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.1555692846506[/C][C]-0.155569284650594[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]15.3674881780279[/C][C]0.632511821972129[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]14.8725888488247[/C][C]1.12741115117525[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.5194813902587[/C][C]0.480518609741331[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]13.9079725164080[/C][C]-1.90797251640802[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]16.7584943754586[/C][C]0.241505624541383[/C][/ROW]
[ROW][C]92[/C][C]16[/C][C]15.8055619194036[/C][C]0.194438080596370[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]15.0531067912421[/C][C]-0.0531067912420874[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]15.0298518503278[/C][C]-2.02985185032776[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]14.7436133365348[/C][C]1.25638666346519[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.7882428813297[/C][C]0.211757118670259[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]13.6773839654895[/C][C]2.32261603451051[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]15.7737083756365[/C][C]0.226291624363468[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]14.4297816437613[/C][C]-0.429781643761336[/C][/ROW]
[ROW][C]100[/C][C]16[/C][C]16.9922981106499[/C][C]-0.992298110649853[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]14.6766246625527[/C][C]1.32337533744731[/C][/ROW]
[ROW][C]102[/C][C]20[/C][C]17.4542184425634[/C][C]2.54578155743658[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]14.2396603727495[/C][C]0.760339627250459[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]14.9598584444853[/C][C]1.04014155551470[/C][/ROW]
[ROW][C]105[/C][C]13[/C][C]14.8422042092773[/C][C]-1.84220420927732[/C][/ROW]
[ROW][C]106[/C][C]17[/C][C]15.7017299648747[/C][C]1.29827003512525[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.7011234587523[/C][C]0.298876541247739[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]14.3586514687943[/C][C]1.64134853120570[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]12.3043422991135[/C][C]-0.304342299113495[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]15.2856808956604[/C][C]0.714319104339601[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.9928271492344[/C][C]0.00717285076563326[/C][/ROW]
[ROW][C]112[/C][C]17[/C][C]15.0564093690921[/C][C]1.94359063090789[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]14.2811780773625[/C][C]-1.28117807736249[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]14.5798742612204[/C][C]-2.57987426122044[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]16.1614728444387[/C][C]1.83852715556128[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]15.8657328435429[/C][C]-1.86573284354288[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.2288878289070[/C][C]0.771112171092978[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]14.7929151358429[/C][C]-1.79291513584292[/C][/ROW]
[ROW][C]119[/C][C]16[/C][C]15.5203842062240[/C][C]0.479615793776025[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]14.4292215835434[/C][C]-1.42922158354343[/C][/ROW]
[ROW][C]121[/C][C]16[/C][C]15.3854232676415[/C][C]0.614576732358469[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]15.8212093173508[/C][C]-2.82120931735076[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]16.8545044640026[/C][C]-0.85450446400258[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]15.8437647974654[/C][C]-0.84376479746541[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]16.7849339139009[/C][C]-0.784933913900921[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]14.6587216010757[/C][C]0.341278398924294[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]15.4905839058456[/C][C]1.50941609415438[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]14.0482521120088[/C][C]0.951747887991243[/C][/ROW]
[ROW][C]129[/C][C]12[/C][C]14.6858650734517[/C][C]-2.68586507345170[/C][/ROW]
[ROW][C]130[/C][C]16[/C][C]13.9402714795457[/C][C]2.05972852045428[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]13.7239532443361[/C][C]-3.72395324433606[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]13.4583321541629[/C][C]2.54166784583712[/C][/ROW]
[ROW][C]133[/C][C]12[/C][C]14.1659890224415[/C][C]-2.16598902244154[/C][/ROW]
[ROW][C]134[/C][C]14[/C][C]15.5440460090552[/C][C]-1.54404600905523[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]15.0836566962205[/C][C]-0.0836566962204762[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]12.1121127347309[/C][C]0.887887265269139[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]14.5312778069589[/C][C]0.46872219304114[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]13.4989500572247[/C][C]-2.49895005722466[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]13.0064804164844[/C][C]-1.00648041648442[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]13.339956434492[/C][C]-2.33995643449199[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]12.8564999151223[/C][C]3.14350008487771[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]13.6441244258235[/C][C]1.35587557417655[/C][/ROW]
[ROW][C]143[/C][C]17[/C][C]16.8413639582837[/C][C]0.158636041716305[/C][/ROW]
[ROW][C]144[/C][C]16[/C][C]14.1244038470475[/C][C]1.87559615295253[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]13.2667514301309[/C][C]-3.26675143013094[/C][/ROW]
[ROW][C]146[/C][C]18[/C][C]15.4975454673630[/C][C]2.50245453263696[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]14.9457512386837[/C][C]-1.94575123868368[/C][/ROW]
[ROW][C]148[/C][C]16[/C][C]14.8430101053070[/C][C]1.15698989469302[/C][/ROW]
[ROW][C]149[/C][C]13[/C][C]12.82722767011[/C][C]0.172772329889987[/C][/ROW]
[ROW][C]150[/C][C]10[/C][C]12.9004577169405[/C][C]-2.90045771694048[/C][/ROW]
[ROW][C]151[/C][C]15[/C][C]15.8784182905414[/C][C]-0.878418290541436[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]13.8248630916881[/C][C]2.17513690831192[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]11.8525810147495[/C][C]4.14741898525048[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]12.3963025182653[/C][C]1.60369748173468[/C][/ROW]
[ROW][C]155[/C][C]10[/C][C]12.4816282956809[/C][C]-2.4816282956809[/C][/ROW]
[ROW][C]156[/C][C]17[/C][C]16.4393065637934[/C][C]0.560693436206563[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]11.7066858547318[/C][C]1.29331414526825[/C][/ROW]
[ROW][C]158[/C][C]15[/C][C]13.9009346604710[/C][C]1.09906533952902[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]14.5373961332024[/C][C]1.46260386679759[/C][/ROW]
[ROW][C]160[/C][C]12[/C][C]12.6989037635456[/C][C]-0.6989037635456[/C][/ROW]
[ROW][C]161[/C][C]13[/C][C]12.6931049205989[/C][C]0.306895079401114[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.6702041146490[/C][C]0.329795885350963[/C][/ROW]
[ROW][C]163[/C][C]12[/C][C]12.3813576310274[/C][C]-0.381357631027413[/C][/ROW]
[ROW][C]164[/C][C]17[/C][C]16.2130980179022[/C][C]0.786901982097844[/C][/ROW]
[ROW][C]165[/C][C]15[/C][C]13.6682202782511[/C][C]1.33177972174891[/C][/ROW]
[ROW][C]166[/C][C]10[/C][C]11.6197611469958[/C][C]-1.61976114699577[/C][/ROW]
[ROW][C]167[/C][C]14[/C][C]14.3441924705758[/C][C]-0.344192470575764[/C][/ROW]
[ROW][C]168[/C][C]11[/C][C]14.1943469919182[/C][C]-3.19434699191817[/C][/ROW]
[ROW][C]169[/C][C]13[/C][C]14.7895963339482[/C][C]-1.78959633394817[/C][/ROW]
[ROW][C]170[/C][C]16[/C][C]14.3871926838976[/C][C]1.61280731610236[/C][/ROW]
[ROW][C]171[/C][C]12[/C][C]10.4815320179531[/C][C]1.51846798204690[/C][/ROW]
[ROW][C]172[/C][C]16[/C][C]15.3377881046553[/C][C]0.662211895344686[/C][/ROW]
[ROW][C]173[/C][C]12[/C][C]13.8444384221823[/C][C]-1.84443842218235[/C][/ROW]
[ROW][C]174[/C][C]9[/C][C]11.3336124343809[/C][C]-2.3336124343809[/C][/ROW]
[ROW][C]175[/C][C]12[/C][C]14.9070414350127[/C][C]-2.90704143501266[/C][/ROW]
[ROW][C]176[/C][C]15[/C][C]14.4730075744532[/C][C]0.526992425546797[/C][/ROW]
[ROW][C]177[/C][C]12[/C][C]12.2943276210920[/C][C]-0.294327621092045[/C][/ROW]
[ROW][C]178[/C][C]12[/C][C]12.6503684441930[/C][C]-0.650368444193027[/C][/ROW]
[ROW][C]179[/C][C]14[/C][C]13.7938681954925[/C][C]0.206131804507502[/C][/ROW]
[ROW][C]180[/C][C]12[/C][C]13.2889048934373[/C][C]-1.28890489343726[/C][/ROW]
[ROW][C]181[/C][C]16[/C][C]15.0068737606415[/C][C]0.993126239358496[/C][/ROW]
[ROW][C]182[/C][C]11[/C][C]11.4515058779588[/C][C]-0.451505877958803[/C][/ROW]
[ROW][C]183[/C][C]19[/C][C]16.7015767685106[/C][C]2.29842323148939[/C][/ROW]
[ROW][C]184[/C][C]15[/C][C]15.1109818809655[/C][C]-0.110981880965516[/C][/ROW]
[ROW][C]185[/C][C]8[/C][C]14.5969865862610[/C][C]-6.59698658626102[/C][/ROW]
[ROW][C]186[/C][C]16[/C][C]14.6989355568994[/C][C]1.30106444310065[/C][/ROW]
[ROW][C]187[/C][C]17[/C][C]14.3895807084944[/C][C]2.61041929150556[/C][/ROW]
[ROW][C]188[/C][C]12[/C][C]12.3486229107255[/C][C]-0.348622910725456[/C][/ROW]
[ROW][C]189[/C][C]11[/C][C]11.4120697144516[/C][C]-0.412069714451611[/C][/ROW]
[ROW][C]190[/C][C]11[/C][C]10.4549680631895[/C][C]0.545031936810458[/C][/ROW]
[ROW][C]191[/C][C]14[/C][C]14.4364908904417[/C][C]-0.436490890441748[/C][/ROW]
[ROW][C]192[/C][C]16[/C][C]15.3764277982820[/C][C]0.62357220171798[/C][/ROW]
[ROW][C]193[/C][C]12[/C][C]9.72248141934327[/C][C]2.27751858065673[/C][/ROW]
[ROW][C]194[/C][C]16[/C][C]14.0886404643777[/C][C]1.91135953562229[/C][/ROW]
[ROW][C]195[/C][C]13[/C][C]13.6328845134380[/C][C]-0.632884513437968[/C][/ROW]
[ROW][C]196[/C][C]15[/C][C]14.9989754108533[/C][C]0.00102458914673653[/C][/ROW]
[ROW][C]197[/C][C]16[/C][C]12.9126260121705[/C][C]3.08737398782951[/C][/ROW]
[ROW][C]198[/C][C]16[/C][C]14.9919436450660[/C][C]1.00805635493404[/C][/ROW]
[ROW][C]199[/C][C]14[/C][C]12.4076330969111[/C][C]1.59236690308889[/C][/ROW]
[ROW][C]200[/C][C]16[/C][C]14.4603615475735[/C][C]1.53963845242654[/C][/ROW]
[ROW][C]201[/C][C]16[/C][C]14.0333200029035[/C][C]1.96667999709652[/C][/ROW]
[ROW][C]202[/C][C]14[/C][C]13.2954618359923[/C][C]0.704538164007682[/C][/ROW]
[ROW][C]203[/C][C]11[/C][C]13.3129603848872[/C][C]-2.31296038488723[/C][/ROW]
[ROW][C]204[/C][C]12[/C][C]14.4949222642595[/C][C]-2.4949222642595[/C][/ROW]
[ROW][C]205[/C][C]15[/C][C]12.6778762991469[/C][C]2.32212370085312[/C][/ROW]
[ROW][C]206[/C][C]15[/C][C]14.4188365698392[/C][C]0.581163430160789[/C][/ROW]
[ROW][C]207[/C][C]16[/C][C]14.5320147838526[/C][C]1.46798521614739[/C][/ROW]
[ROW][C]208[/C][C]16[/C][C]14.8431254461590[/C][C]1.15687455384097[/C][/ROW]
[ROW][C]209[/C][C]11[/C][C]13.5801453187234[/C][C]-2.58014531872336[/C][/ROW]
[ROW][C]210[/C][C]15[/C][C]13.8885534096533[/C][C]1.11144659034666[/C][/ROW]
[ROW][C]211[/C][C]12[/C][C]14.2037853885328[/C][C]-2.20378538853276[/C][/ROW]
[ROW][C]212[/C][C]12[/C][C]15.7224255491506[/C][C]-3.72242554915059[/C][/ROW]
[ROW][C]213[/C][C]15[/C][C]14.1090793589769[/C][C]0.890920641023053[/C][/ROW]
[ROW][C]214[/C][C]15[/C][C]12.0905788116402[/C][C]2.90942118835982[/C][/ROW]
[ROW][C]215[/C][C]16[/C][C]14.4647432573063[/C][C]1.53525674269368[/C][/ROW]
[ROW][C]216[/C][C]14[/C][C]13.0805138671753[/C][C]0.919486132824675[/C][/ROW]
[ROW][C]217[/C][C]17[/C][C]14.5748713248213[/C][C]2.42512867517873[/C][/ROW]
[ROW][C]218[/C][C]14[/C][C]13.9049231073214[/C][C]0.0950768926785521[/C][/ROW]
[ROW][C]219[/C][C]13[/C][C]11.8321054700474[/C][C]1.16789452995262[/C][/ROW]
[ROW][C]220[/C][C]15[/C][C]15.1299096914369[/C][C]-0.129909691436863[/C][/ROW]
[ROW][C]221[/C][C]13[/C][C]14.5726876835671[/C][C]-1.57268768356711[/C][/ROW]
[ROW][C]222[/C][C]14[/C][C]13.8921489634354[/C][C]0.107851036564569[/C][/ROW]
[ROW][C]223[/C][C]15[/C][C]14.1999876889948[/C][C]0.800012311005216[/C][/ROW]
[ROW][C]224[/C][C]12[/C][C]13.0554532006112[/C][C]-1.05545320061118[/C][/ROW]
[ROW][C]225[/C][C]13[/C][C]12.2689174915128[/C][C]0.731082508487161[/C][/ROW]
[ROW][C]226[/C][C]8[/C][C]11.6573695969631[/C][C]-3.65736959696312[/C][/ROW]
[ROW][C]227[/C][C]14[/C][C]13.6832791328481[/C][C]0.316720867151934[/C][/ROW]
[ROW][C]228[/C][C]14[/C][C]12.8142890945427[/C][C]1.18571090545734[/C][/ROW]
[ROW][C]229[/C][C]11[/C][C]12.1978908737620[/C][C]-1.19789087376204[/C][/ROW]
[ROW][C]230[/C][C]12[/C][C]12.8303898458872[/C][C]-0.83038984588718[/C][/ROW]
[ROW][C]231[/C][C]13[/C][C]11.1854887383650[/C][C]1.81451126163496[/C][/ROW]
[ROW][C]232[/C][C]10[/C][C]13.2189058291037[/C][C]-3.21890582910369[/C][/ROW]
[ROW][C]233[/C][C]16[/C][C]11.348787511322[/C][C]4.65121248867801[/C][/ROW]
[ROW][C]234[/C][C]18[/C][C]15.7737516733149[/C][C]2.22624832668513[/C][/ROW]
[ROW][C]235[/C][C]13[/C][C]13.7266606548843[/C][C]-0.72666065488433[/C][/ROW]
[ROW][C]236[/C][C]11[/C][C]13.2215618348084[/C][C]-2.22156183480842[/C][/ROW]
[ROW][C]237[/C][C]4[/C][C]10.9938193483991[/C][C]-6.99381934839914[/C][/ROW]
[ROW][C]238[/C][C]13[/C][C]14.1796817218455[/C][C]-1.17968172184546[/C][/ROW]
[ROW][C]239[/C][C]16[/C][C]14.1367439630088[/C][C]1.86325603699122[/C][/ROW]
[ROW][C]240[/C][C]10[/C][C]11.5930618646107[/C][C]-1.59306186461068[/C][/ROW]
[ROW][C]241[/C][C]12[/C][C]12.1626452683162[/C][C]-0.162645268316154[/C][/ROW]
[ROW][C]242[/C][C]12[/C][C]13.3960154190304[/C][C]-1.39601541903039[/C][/ROW]
[ROW][C]243[/C][C]10[/C][C]8.81036730048707[/C][C]1.18963269951293[/C][/ROW]
[ROW][C]244[/C][C]13[/C][C]10.9797489991871[/C][C]2.02025100081286[/C][/ROW]
[ROW][C]245[/C][C]15[/C][C]13.5802095891207[/C][C]1.41979041087934[/C][/ROW]
[ROW][C]246[/C][C]12[/C][C]11.8071329179159[/C][C]0.192867082084079[/C][/ROW]
[ROW][C]247[/C][C]14[/C][C]12.745833303401[/C][C]1.25416669659900[/C][/ROW]
[ROW][C]248[/C][C]10[/C][C]12.3830320431314[/C][C]-2.38303204313137[/C][/ROW]
[ROW][C]249[/C][C]12[/C][C]10.7045626835548[/C][C]1.29543731644515[/C][/ROW]
[ROW][C]250[/C][C]12[/C][C]11.5222473456141[/C][C]0.477752654385854[/C][/ROW]
[ROW][C]251[/C][C]11[/C][C]11.7208297915302[/C][C]-0.720829791530176[/C][/ROW]
[ROW][C]252[/C][C]10[/C][C]11.5207040112402[/C][C]-1.52070401124024[/C][/ROW]
[ROW][C]253[/C][C]12[/C][C]11.2737442088263[/C][C]0.726255791173698[/C][/ROW]
[ROW][C]254[/C][C]16[/C][C]12.7074968396978[/C][C]3.29250316030215[/C][/ROW]
[ROW][C]255[/C][C]12[/C][C]13.2078556645355[/C][C]-1.20785566453552[/C][/ROW]
[ROW][C]256[/C][C]14[/C][C]13.7197084126368[/C][C]0.280291587363209[/C][/ROW]
[ROW][C]257[/C][C]16[/C][C]14.1388849696129[/C][C]1.86111503038709[/C][/ROW]
[ROW][C]258[/C][C]14[/C][C]11.5536798517218[/C][C]2.44632014827823[/C][/ROW]
[ROW][C]259[/C][C]13[/C][C]14.0549432381567[/C][C]-1.05494323815667[/C][/ROW]
[ROW][C]260[/C][C]4[/C][C]9.3330935436994[/C][C]-5.33309354369941[/C][/ROW]
[ROW][C]261[/C][C]15[/C][C]13.5531018279656[/C][C]1.44689817203439[/C][/ROW]
[ROW][C]262[/C][C]11[/C][C]14.7741198188765[/C][C]-3.77411981887649[/C][/ROW]
[ROW][C]263[/C][C]11[/C][C]11.1662281765916[/C][C]-0.166228176591611[/C][/ROW]
[ROW][C]264[/C][C]14[/C][C]12.6310833706034[/C][C]1.36891662939664[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96550&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96550&T=4

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.0985387854790-3.09853878547897
21615.75062807565610.249371924343889
31917.10632188991671.89367811008325
41512.16160524103192.83839475896812
51416.4017873467223-2.40178734672229
61314.8470133702108-1.84701337021082
71915.39411954043473.60588045956527
81517.1034911605066-2.10349116050658
91416.0596585119288-2.05965851192881
101514.46131972028220.538680279717755
111615.00398977859880.996010221401189
121616.1957780658441-0.195778065844116
131615.54520079549590.45479920450408
141615.49757042279100.502429577209031
151718.0026152345095-1.00261523450952
161515.4966907599622-0.496690759962239
171514.58783765963910.412162340360932
182016.42213291884223.5778670811578
191815.53184129240092.46815870759905
201615.5983979603540.401602039646002
211615.39447915384270.605520846157257
221615.21361417839330.78638582160669
231916.49214037872652.50785962127346
241615.16091922295150.839080777048478
251716.14837639146470.851623608535269
261716.221803070780.778196929219997
271614.90835279021401.09164720978602
281516.8091087350266-1.80910873502655
291615.68482375194730.315176248052743
301414.2630295587570-0.263029558757013
311515.7659606607022-0.765960660702237
321212.8492889523859-0.849288952385907
331414.8039620225922-0.803962022592174
341616.0017496144186-0.00174961441855540
351415.4902098814983-1.49020988149829
361013.0304769205532-3.03047692055322
371013.0355345646574-3.03553456465739
381415.7470324913881-1.74703249138813
391614.5545559199381.44544408006200
401614.49489392472291.50510607527711
411614.72053770649711.27946229350291
421415.7009510594818-1.70095105948177
432017.48712155669652.51287844330346
441414.1533777639022-0.153377763902245
451414.4623553291315-0.462355329131485
461115.4909495615117-4.49094956151173
471416.6307612737306-2.63076127373059
481515.1467421663187-0.146742166318659
491615.41787661632300.58212338367696
501415.6302473667626-1.6302473667626
511617.0252984963352-1.02529849633518
521414.1419408813432-0.141940881343227
531215.0171855791395-3.01718557913949
541616.0361860907356-0.0361860907356081
55911.3432897998036-2.34328979980356
561412.38497814452031.61502185547966
571615.85454010930220.145459890697828
581615.52988322876590.47011677123408
591515.1123930302181-0.112393030218124
601614.22106056522641.77893943477357
611211.55955340846620.440446591533846
621615.64068175553980.359318244460177
631616.5488168530774-0.548816853077411
641414.7109860019853-0.710986001985279
651615.30632115833420.693678841665758
661716.05418603320130.945813966798659
671816.33317707986111.66682292013889
681814.39588818744263.60411181255738
691215.8997083739692-3.89970837396925
701615.65157867302610.348421326973927
711013.4123806085750-3.41238060857496
721414.9242951062704-0.924295106270414
731816.92805867454331.07194132545669
741817.1409836239110.85901637608901
751615.23229487529480.76770512470519
761713.49188698970073.50811301029926
771616.419099992442-0.419099992441987
781614.53119884767541.46880115232456
791315.2165773344004-2.21657733440037
801615.14214025715690.857859742843126
811615.65862745062780.341372549372196
821615.76324083063170.236759169368344
831515.6434137240469-0.643413724046948
841514.88423238192270.115767618077307
851614.16073547223411.83926452776585
861414.1555692846506-0.155569284650594
871615.36748817802790.632511821972129
881614.87258884882471.12741115117525
891514.51948139025870.480518609741331
901213.9079725164080-1.90797251640802
911716.75849437545860.241505624541383
921615.80556191940360.194438080596370
931515.0531067912421-0.0531067912420874
941315.0298518503278-2.02985185032776
951614.74361333653481.25638666346519
961615.78824288132970.211757118670259
971613.67738396548952.32261603451051
981615.77370837563650.226291624363468
991414.4297816437613-0.429781643761336
1001616.9922981106499-0.992298110649853
1011614.67662466255271.32337533744731
1022017.45421844256342.54578155743658
1031514.23966037274950.760339627250459
1041614.95985844448531.04014155551470
1051314.8422042092773-1.84220420927732
1061715.70172996487471.29827003512525
1071615.70112345875230.298876541247739
1081614.35865146879431.64134853120570
1091212.3043422991135-0.304342299113495
1101615.28568089566040.714319104339601
1111615.99282714923440.00717285076563326
1121715.05640936909211.94359063090789
1131314.2811780773625-1.28117807736249
1141214.5798742612204-2.57987426122044
1151816.16147284443871.83852715556128
1161415.8657328435429-1.86573284354288
1171413.22888782890700.771112171092978
1181314.7929151358429-1.79291513584292
1191615.52038420622400.479615793776025
1201314.4292215835434-1.42922158354343
1211615.38542326764150.614576732358469
1221315.8212093173508-2.82120931735076
1231616.8545044640026-0.85450446400258
1241515.8437647974654-0.84376479746541
1251616.7849339139009-0.784933913900921
1261514.65872160107570.341278398924294
1271715.49058390584561.50941609415438
1281514.04825211200880.951747887991243
1291214.6858650734517-2.68586507345170
1301613.94027147954572.05972852045428
1311013.7239532443361-3.72395324433606
1321613.45833215416292.54166784583712
1331214.1659890224415-2.16598902244154
1341415.5440460090552-1.54404600905523
1351515.0836566962205-0.0836566962204762
1361312.11211273473090.887887265269139
1371514.53127780695890.46872219304114
1381113.4989500572247-2.49895005722466
1391213.0064804164844-1.00648041648442
1401113.339956434492-2.33995643449199
1411612.85649991512233.14350008487771
1421513.64412442582351.35587557417655
1431716.84136395828370.158636041716305
1441614.12440384704751.87559615295253
1451013.2667514301309-3.26675143013094
1461815.49754546736302.50245453263696
1471314.9457512386837-1.94575123868368
1481614.84301010530701.15698989469302
1491312.827227670110.172772329889987
1501012.9004577169405-2.90045771694048
1511515.8784182905414-0.878418290541436
1521613.82486309168812.17513690831192
1531611.85258101474954.14741898525048
1541412.39630251826531.60369748173468
1551012.4816282956809-2.4816282956809
1561716.43930656379340.560693436206563
1571311.70668585473181.29331414526825
1581513.90093466047101.09906533952902
1591614.53739613320241.46260386679759
1601212.6989037635456-0.6989037635456
1611312.69310492059890.306895079401114
1621312.67020411464900.329795885350963
1631212.3813576310274-0.381357631027413
1641716.21309801790220.786901982097844
1651513.66822027825111.33177972174891
1661011.6197611469958-1.61976114699577
1671414.3441924705758-0.344192470575764
1681114.1943469919182-3.19434699191817
1691314.7895963339482-1.78959633394817
1701614.38719268389761.61280731610236
1711210.48153201795311.51846798204690
1721615.33778810465530.662211895344686
1731213.8444384221823-1.84443842218235
174911.3336124343809-2.3336124343809
1751214.9070414350127-2.90704143501266
1761514.47300757445320.526992425546797
1771212.2943276210920-0.294327621092045
1781212.6503684441930-0.650368444193027
1791413.79386819549250.206131804507502
1801213.2889048934373-1.28890489343726
1811615.00687376064150.993126239358496
1821111.4515058779588-0.451505877958803
1831916.70157676851062.29842323148939
1841515.1109818809655-0.110981880965516
185814.5969865862610-6.59698658626102
1861614.69893555689941.30106444310065
1871714.38958070849442.61041929150556
1881212.3486229107255-0.348622910725456
1891111.4120697144516-0.412069714451611
1901110.45496806318950.545031936810458
1911414.4364908904417-0.436490890441748
1921615.37642779828200.62357220171798
193129.722481419343272.27751858065673
1941614.08864046437771.91135953562229
1951313.6328845134380-0.632884513437968
1961514.99897541085330.00102458914673653
1971612.91262601217053.08737398782951
1981614.99194364506601.00805635493404
1991412.40763309691111.59236690308889
2001614.46036154757351.53963845242654
2011614.03332000290351.96667999709652
2021413.29546183599230.704538164007682
2031113.3129603848872-2.31296038488723
2041214.4949222642595-2.4949222642595
2051512.67787629914692.32212370085312
2061514.41883656983920.581163430160789
2071614.53201478385261.46798521614739
2081614.84312544615901.15687455384097
2091113.5801453187234-2.58014531872336
2101513.88855340965331.11144659034666
2111214.2037853885328-2.20378538853276
2121215.7224255491506-3.72242554915059
2131514.10907935897690.890920641023053
2141512.09057881164022.90942118835982
2151614.46474325730631.53525674269368
2161413.08051386717530.919486132824675
2171714.57487132482132.42512867517873
2181413.90492310732140.0950768926785521
2191311.83210547004741.16789452995262
2201515.1299096914369-0.129909691436863
2211314.5726876835671-1.57268768356711
2221413.89214896343540.107851036564569
2231514.19998768899480.800012311005216
2241213.0554532006112-1.05545320061118
2251312.26891749151280.731082508487161
226811.6573695969631-3.65736959696312
2271413.68327913284810.316720867151934
2281412.81428909454271.18571090545734
2291112.1978908737620-1.19789087376204
2301212.8303898458872-0.83038984588718
2311311.18548873836501.81451126163496
2321013.2189058291037-3.21890582910369
2331611.3487875113224.65121248867801
2341815.77375167331492.22624832668513
2351313.7266606548843-0.72666065488433
2361113.2215618348084-2.22156183480842
237410.9938193483991-6.99381934839914
2381314.1796817218455-1.17968172184546
2391614.13674396300881.86325603699122
2401011.5930618646107-1.59306186461068
2411212.1626452683162-0.162645268316154
2421213.3960154190304-1.39601541903039
243108.810367300487071.18963269951293
2441310.97974899918712.02025100081286
2451513.58020958912071.41979041087934
2461211.80713291791590.192867082084079
2471412.7458333034011.25416669659900
2481012.3830320431314-2.38303204313137
2491210.70456268355481.29543731644515
2501211.52224734561410.477752654385854
2511111.7208297915302-0.720829791530176
2521011.5207040112402-1.52070401124024
2531211.27374420882630.726255791173698
2541612.70749683969783.29250316030215
2551213.2078556645355-1.20785566453552
2561413.71970841263680.280291587363209
2571614.13888496961291.86111503038709
2581411.55367985172182.44632014827823
2591314.0549432381567-1.05494323815667
26049.3330935436994-5.33309354369941
2611513.55310182796561.44689817203439
2621114.7741198188765-3.77411981887649
2631111.1662281765916-0.166228176591611
2641412.63108337060341.36891662939664







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.4024752294716530.8049504589433070.597524770528347
120.7998130195522830.4003739608954340.200186980447717
130.7608266649872490.4783466700255020.239173335012751
140.6735526211782490.6528947576435020.326447378821751
150.6181146999759330.7637706000481350.381885300024067
160.6808332009748060.6383335980503880.319166799025194
170.6081426280273660.7837147439452690.391857371972634
180.8576943804218130.2846112391563730.142305619578187
190.8309802892137440.3380394215725120.169019710786256
200.7804673886153920.4390652227692160.219532611384608
210.7164350906766010.5671298186467980.283564909323399
220.6779248597001360.6441502805997280.322075140299864
230.640080623968090.719838752063820.35991937603191
240.6080028194115910.7839943611768170.391997180588409
250.5446025202534490.9107949594931030.455397479746551
260.4951188739165560.9902377478331110.504881126083444
270.4356950665268120.8713901330536240.564304933473188
280.4894287276521380.9788574553042760.510571272347862
290.4295311932516870.8590623865033740.570468806748313
300.439110691341420.878221382682840.56088930865858
310.3907776313005630.7815552626011260.609222368699437
320.3865362509458790.7730725018917580.613463749054121
330.3675737435383950.7351474870767890.632426256461605
340.3119461115168870.6238922230337730.688053888483113
350.3007900756320030.6015801512640060.699209924367997
360.3994469562214770.7988939124429550.600553043778523
370.4802336405472530.9604672810945070.519766359452747
380.4564144938699520.9128289877399040.543585506130048
390.5040367816736190.9919264366527620.495963218326381
400.4846625643473580.9693251286947160.515337435652642
410.4489815388141270.8979630776282530.551018461185873
420.4193309291449730.8386618582899460.580669070855027
430.4354887871740670.8709775743481330.564511212825933
440.3883570187665280.7767140375330560.611642981233472
450.3497844417106850.6995688834213690.650215558289315
460.5744902589496470.8510194821007070.425509741050353
470.5925137823736660.8149724352526690.407486217626334
480.5540651435835890.8918697128328230.445934856416411
490.5413440298923260.9173119402153480.458655970107674
500.5141860993611120.9716278012777750.485813900638888
510.4699707815049480.9399415630098950.530029218495052
520.4284275994191450.856855198838290.571572400580855
530.4356612904445400.8713225808890790.56433870955546
540.4060088780298080.8120177560596160.593991121970192
550.4042776889552190.8085553779104380.595722311044781
560.4207346972468530.8414693944937070.579265302753147
570.3801415205875370.7602830411750740.619858479412463
580.3656289476085470.7312578952170940.634371052391453
590.3265080055678500.6530160111356990.67349199443215
600.3533720372327260.7067440744654520.646627962767274
610.3272204306739440.6544408613478880.672779569326056
620.2931319780199750.586263956039950.706868021980025
630.2591175374011090.5182350748022190.74088246259889
640.2265144769622930.4530289539245870.773485523037706
650.202909488587840.405818977175680.79709051141216
660.1931317947470190.3862635894940380.806868205252981
670.195831245199530.391662490399060.80416875480047
680.3093029909179460.6186059818358920.690697009082054
690.4222184651265270.8444369302530530.577781534873473
700.3901976574227450.780395314845490.609802342577255
710.4626709970512310.9253419941024620.537329002948769
720.4277994404902010.8555988809804030.572200559509799
730.4210243957462380.8420487914924760.578975604253762
740.3996317935083070.7992635870166140.600368206491693
750.3635917953612980.7271835907225960.636408204638702
760.4415616385406130.8831232770812260.558438361459387
770.4034167204255120.8068334408510250.596583279574488
780.3826230797418080.7652461594836160.617376920258192
790.3920519671908380.7841039343816760.607948032809162
800.3577742307192150.7155484614384290.642225769280785
810.3260492214260770.6520984428521540.673950778573923
820.2940411602945140.5880823205890280.705958839705486
830.2670596465294380.5341192930588760.732940353470562
840.237094035748180.474188071496360.76290596425182
850.2342196076945640.4684392153891280.765780392305436
860.2054573319940030.4109146639880060.794542668005997
870.1823600852479810.3647201704959630.817639914752019
880.1686672469805200.3373344939610390.83133275301948
890.1460354047632340.2920708095264670.853964595236766
900.1414013986751040.2828027973502070.858598601324896
910.1214534031524880.2429068063049770.878546596847512
920.1055383353625660.2110766707251320.894461664637434
930.090473150312270.180946300624540.90952684968773
940.09445170453054460.1889034090610890.905548295469455
950.08514528825709690.1702905765141940.914854711742903
960.07134375971094190.1426875194218840.928656240289058
970.07653904197525280.1530780839505060.923460958024747
980.06391729571907280.1278345914381460.936082704280927
990.05325605400948820.1065121080189760.946743945990512
1000.0471964643477650.094392928695530.952803535652235
1010.04182377086681770.08364754173363540.958176229133182
1020.04956466407379060.09912932814758130.95043533592621
1030.04181593253059850.0836318650611970.958184067469402
1040.03715462474442480.07430924948884950.962845375255575
1050.04437996451128160.08875992902256310.955620035488718
1060.03924672407584480.07849344815168960.960753275924155
1070.03210154074753880.06420308149507760.967898459252461
1080.03063896195038330.06127792390076670.969361038049617
1090.02497665092764700.04995330185529390.975023349072353
1100.02068028807217340.04136057614434690.979319711927827
1110.01653670186114390.03307340372228770.983463298138856
1120.01746109681400980.03492219362801970.98253890318599
1130.01518680487295510.03037360974591020.984813195127045
1140.02034278401881460.04068556803762920.979657215981185
1150.01955457891592410.03910915783184810.980445421084076
1160.01915658281760530.03831316563521070.980843417182395
1170.01618954154275540.03237908308551090.983810458457245
1180.01614287982130710.03228575964261430.983857120178693
1190.01311159662557510.02622319325115020.986888403374425
1200.01185128625019710.02370257250039410.988148713749803
1210.009611389131385030.01922277826277010.990388610868615
1220.01429761320826020.02859522641652050.98570238679174
1230.011918248598680.023836497197360.98808175140132
1240.01004172312432280.02008344624864560.989958276875677
1250.008203863469080540.01640772693816110.99179613653092
1260.006524021410003180.01304804282000640.993475978589997
1270.005924994717088630.01184998943417730.994075005282911
1280.004989580314375740.009979160628751470.995010419685624
1290.006796767787734380.01359353557546880.993203232212266
1300.007378964708857870.01475792941771570.992621035291142
1310.01503800357014710.03007600714029420.984961996429853
1320.01790200110102910.03580400220205810.982097998898971
1330.01900142777862680.03800285555725360.980998572221373
1340.01793995121491930.03587990242983860.98206004878508
1350.01432808310788720.02865616621577430.985671916892113
1360.01223094262633360.02446188525266720.987769057373666
1370.009906487418237380.01981297483647480.990093512581763
1380.01253842108576510.02507684217153020.987461578914235
1390.01076182162125940.02152364324251870.98923817837874
1400.01290529551809070.02581059103618130.98709470448191
1410.02015601666196070.04031203332392130.97984398333804
1420.01852428145511750.03704856291023490.981475718544883
1430.01502053120934940.03004106241869880.98497946879065
1440.01572360125878710.03144720251757420.984276398741213
1450.02669745511910170.05339491023820340.973302544880898
1460.03289928393127840.06579856786255690.967100716068721
1470.03471066253461680.06942132506923350.965289337465383
1480.03082681579115800.06165363158231590.969173184208842
1490.02516944911617980.05033889823235960.97483055088382
1500.03474316691801310.06948633383602620.965256833081987
1510.02956496453309810.05912992906619610.970435035466902
1520.03121189996585670.06242379993171350.968788100034143
1530.06149216472039820.1229843294407960.938507835279602
1540.05926934843115930.1185386968623190.94073065156884
1550.0668514672949830.1337029345899660.933148532705017
1560.05688283720364140.1137656744072830.943117162796359
1570.05072635077972720.1014527015594540.949273649220273
1580.04430694577376070.08861389154752130.95569305422624
1590.04289637055972630.08579274111945250.957103629440274
1600.03682892885311240.07365785770622480.963171071146888
1610.03014940476039840.06029880952079670.969850595239602
1620.02468825111722200.04937650223444410.975311748882778
1630.02045569225456280.04091138450912550.979544307745437
1640.01746633422788430.03493266845576860.982533665772116
1650.01528997122958700.03057994245917390.984710028770413
1660.01621585711291290.03243171422582590.983784142887087
1670.01295191907958660.02590383815917320.987048080920413
1680.01900979434554720.03801958869109440.980990205654453
1690.01931450605355420.03862901210710840.980685493946446
1700.01780193125318510.03560386250637020.982198068746815
1710.01632721355884300.03265442711768590.983672786441157
1720.01317765009859540.02635530019719080.986822349901405
1730.01281280658023840.02562561316047670.987187193419762
1740.01468595121699860.02937190243399710.985314048783001
1750.01889299089026870.03778598178053740.981107009109731
1760.01514540637781220.03029081275562440.984854593622188
1770.01198087198819830.02396174397639660.988019128011802
1780.01007815072384060.02015630144768120.98992184927616
1790.0078379874092930.0156759748185860.992162012590707
1800.006892283844467870.01378456768893570.993107716155532
1810.005579292334205590.01115858466841120.994420707665794
1820.004301702273412350.00860340454682470.995698297726588
1830.004542724867746860.009085449735493730.995457275132253
1840.003463407804700040.006926815609400070.9965365921953
1850.09454855289349250.1890971057869850.905451447106507
1860.08376732863415820.1675346572683160.916232671365842
1870.09373198735774270.1874639747154850.906268012642257
1880.07914224297580070.1582844859516010.9208577570242
1890.07054186547488560.1410837309497710.929458134525114
1900.05967056132143190.1193411226428640.940329438678568
1910.05168880038133330.1033776007626670.948311199618667
1920.04301097668946430.08602195337892860.956989023310536
1930.04331207625446960.08662415250893930.95668792374553
1940.04106966838062530.08213933676125070.958930331619375
1950.03570277389819500.07140554779639010.964297226101805
1960.02845501402156690.05691002804313380.971544985978433
1970.03732224023959890.07464448047919780.962677759760401
1980.03051949953524970.06103899907049940.96948050046475
1990.02740578193646920.05481156387293850.97259421806353
2000.02327653896243990.04655307792487970.97672346103756
2010.02131135558663870.04262271117327730.978688644413361
2020.0178165544415180.0356331088830360.982183445558482
2030.01984691091191020.03969382182382050.98015308908809
2040.02636750807308010.05273501614616020.97363249192692
2050.02782370670694420.05564741341388840.972176293293056
2060.02175032821015790.04350065642031580.978249671789842
2070.01938692708129760.03877385416259530.980613072918702
2080.01549037224012280.03098074448024570.984509627759877
2090.01807789947052180.03615579894104350.981922100529478
2100.01496369412228920.02992738824457840.98503630587771
2110.01839583923516730.03679167847033450.981604160764833
2120.03247176111908810.06494352223817630.967528238880912
2130.02537109455514480.05074218911028950.974628905444855
2140.03147911690339770.06295823380679550.968520883096602
2150.02676125047967190.05352250095934380.973238749520328
2160.02072936713290750.04145873426581490.979270632867093
2170.02313706630027160.04627413260054330.976862933699728
2180.01960675534811660.03921351069623320.980393244651883
2190.0182943903919270.0365887807838540.981705609608073
2200.01374190380798180.02748380761596360.986258096192018
2210.01140251372411610.02280502744823220.988597486275884
2220.008311548018902740.01662309603780550.991688451981097
2230.006416313818356980.01283262763671400.993583686181643
2240.004916911599013790.009833823198027590.995083088400986
2250.003439601113773090.006879202227546180.996560398886227
2260.007101991232983530.01420398246596710.992898008767017
2270.005651567358694770.01130313471738950.994348432641305
2280.004122617759569380.008245235519138750.99587738224043
2290.002931619834977220.005863239669954450.997068380165023
2300.002075990459030080.004151980918060160.99792400954097
2310.002268509892050110.004537019784100220.99773149010795
2320.00779851916707010.01559703833414020.99220148083293
2330.02936984049576300.05873968099152590.970630159504237
2340.04495074724397150.0899014944879430.955049252756029
2350.03309141058089400.06618282116178790.966908589419106
2360.02710115142265250.0542023028453050.972898848577348
2370.2389590159114610.4779180318229220.761040984088539
2380.192949272019250.38589854403850.80705072798075
2390.1632899251623000.3265798503246010.8367100748377
2400.1304476206621910.2608952413243810.86955237933781
2410.1168174708181940.2336349416363870.883182529181806
2420.1779079840648250.355815968129650.822092015935175
2430.1504539502042450.3009079004084900.849546049795755
2440.1514528225349910.3029056450699820.84854717746501
2450.1328522494438520.2657044988877050.867147750556148
2460.1164758208434470.2329516416868950.883524179156553
2470.07925629730887160.1585125946177430.920743702691128
2480.07042908849462260.1408581769892450.929570911505377
2490.05349779011521540.1069955802304310.946502209884785
2500.1307798132060180.2615596264120360.869220186793982
2510.1074728615960920.2149457231921840.892527138403908
2520.543978389503580.912043220992840.45602161049642
2530.3822887274810960.7645774549621920.617711272518904

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.402475229471653 & 0.804950458943307 & 0.597524770528347 \tabularnewline
12 & 0.799813019552283 & 0.400373960895434 & 0.200186980447717 \tabularnewline
13 & 0.760826664987249 & 0.478346670025502 & 0.239173335012751 \tabularnewline
14 & 0.673552621178249 & 0.652894757643502 & 0.326447378821751 \tabularnewline
15 & 0.618114699975933 & 0.763770600048135 & 0.381885300024067 \tabularnewline
16 & 0.680833200974806 & 0.638333598050388 & 0.319166799025194 \tabularnewline
17 & 0.608142628027366 & 0.783714743945269 & 0.391857371972634 \tabularnewline
18 & 0.857694380421813 & 0.284611239156373 & 0.142305619578187 \tabularnewline
19 & 0.830980289213744 & 0.338039421572512 & 0.169019710786256 \tabularnewline
20 & 0.780467388615392 & 0.439065222769216 & 0.219532611384608 \tabularnewline
21 & 0.716435090676601 & 0.567129818646798 & 0.283564909323399 \tabularnewline
22 & 0.677924859700136 & 0.644150280599728 & 0.322075140299864 \tabularnewline
23 & 0.64008062396809 & 0.71983875206382 & 0.35991937603191 \tabularnewline
24 & 0.608002819411591 & 0.783994361176817 & 0.391997180588409 \tabularnewline
25 & 0.544602520253449 & 0.910794959493103 & 0.455397479746551 \tabularnewline
26 & 0.495118873916556 & 0.990237747833111 & 0.504881126083444 \tabularnewline
27 & 0.435695066526812 & 0.871390133053624 & 0.564304933473188 \tabularnewline
28 & 0.489428727652138 & 0.978857455304276 & 0.510571272347862 \tabularnewline
29 & 0.429531193251687 & 0.859062386503374 & 0.570468806748313 \tabularnewline
30 & 0.43911069134142 & 0.87822138268284 & 0.56088930865858 \tabularnewline
31 & 0.390777631300563 & 0.781555262601126 & 0.609222368699437 \tabularnewline
32 & 0.386536250945879 & 0.773072501891758 & 0.613463749054121 \tabularnewline
33 & 0.367573743538395 & 0.735147487076789 & 0.632426256461605 \tabularnewline
34 & 0.311946111516887 & 0.623892223033773 & 0.688053888483113 \tabularnewline
35 & 0.300790075632003 & 0.601580151264006 & 0.699209924367997 \tabularnewline
36 & 0.399446956221477 & 0.798893912442955 & 0.600553043778523 \tabularnewline
37 & 0.480233640547253 & 0.960467281094507 & 0.519766359452747 \tabularnewline
38 & 0.456414493869952 & 0.912828987739904 & 0.543585506130048 \tabularnewline
39 & 0.504036781673619 & 0.991926436652762 & 0.495963218326381 \tabularnewline
40 & 0.484662564347358 & 0.969325128694716 & 0.515337435652642 \tabularnewline
41 & 0.448981538814127 & 0.897963077628253 & 0.551018461185873 \tabularnewline
42 & 0.419330929144973 & 0.838661858289946 & 0.580669070855027 \tabularnewline
43 & 0.435488787174067 & 0.870977574348133 & 0.564511212825933 \tabularnewline
44 & 0.388357018766528 & 0.776714037533056 & 0.611642981233472 \tabularnewline
45 & 0.349784441710685 & 0.699568883421369 & 0.650215558289315 \tabularnewline
46 & 0.574490258949647 & 0.851019482100707 & 0.425509741050353 \tabularnewline
47 & 0.592513782373666 & 0.814972435252669 & 0.407486217626334 \tabularnewline
48 & 0.554065143583589 & 0.891869712832823 & 0.445934856416411 \tabularnewline
49 & 0.541344029892326 & 0.917311940215348 & 0.458655970107674 \tabularnewline
50 & 0.514186099361112 & 0.971627801277775 & 0.485813900638888 \tabularnewline
51 & 0.469970781504948 & 0.939941563009895 & 0.530029218495052 \tabularnewline
52 & 0.428427599419145 & 0.85685519883829 & 0.571572400580855 \tabularnewline
53 & 0.435661290444540 & 0.871322580889079 & 0.56433870955546 \tabularnewline
54 & 0.406008878029808 & 0.812017756059616 & 0.593991121970192 \tabularnewline
55 & 0.404277688955219 & 0.808555377910438 & 0.595722311044781 \tabularnewline
56 & 0.420734697246853 & 0.841469394493707 & 0.579265302753147 \tabularnewline
57 & 0.380141520587537 & 0.760283041175074 & 0.619858479412463 \tabularnewline
58 & 0.365628947608547 & 0.731257895217094 & 0.634371052391453 \tabularnewline
59 & 0.326508005567850 & 0.653016011135699 & 0.67349199443215 \tabularnewline
60 & 0.353372037232726 & 0.706744074465452 & 0.646627962767274 \tabularnewline
61 & 0.327220430673944 & 0.654440861347888 & 0.672779569326056 \tabularnewline
62 & 0.293131978019975 & 0.58626395603995 & 0.706868021980025 \tabularnewline
63 & 0.259117537401109 & 0.518235074802219 & 0.74088246259889 \tabularnewline
64 & 0.226514476962293 & 0.453028953924587 & 0.773485523037706 \tabularnewline
65 & 0.20290948858784 & 0.40581897717568 & 0.79709051141216 \tabularnewline
66 & 0.193131794747019 & 0.386263589494038 & 0.806868205252981 \tabularnewline
67 & 0.19583124519953 & 0.39166249039906 & 0.80416875480047 \tabularnewline
68 & 0.309302990917946 & 0.618605981835892 & 0.690697009082054 \tabularnewline
69 & 0.422218465126527 & 0.844436930253053 & 0.577781534873473 \tabularnewline
70 & 0.390197657422745 & 0.78039531484549 & 0.609802342577255 \tabularnewline
71 & 0.462670997051231 & 0.925341994102462 & 0.537329002948769 \tabularnewline
72 & 0.427799440490201 & 0.855598880980403 & 0.572200559509799 \tabularnewline
73 & 0.421024395746238 & 0.842048791492476 & 0.578975604253762 \tabularnewline
74 & 0.399631793508307 & 0.799263587016614 & 0.600368206491693 \tabularnewline
75 & 0.363591795361298 & 0.727183590722596 & 0.636408204638702 \tabularnewline
76 & 0.441561638540613 & 0.883123277081226 & 0.558438361459387 \tabularnewline
77 & 0.403416720425512 & 0.806833440851025 & 0.596583279574488 \tabularnewline
78 & 0.382623079741808 & 0.765246159483616 & 0.617376920258192 \tabularnewline
79 & 0.392051967190838 & 0.784103934381676 & 0.607948032809162 \tabularnewline
80 & 0.357774230719215 & 0.715548461438429 & 0.642225769280785 \tabularnewline
81 & 0.326049221426077 & 0.652098442852154 & 0.673950778573923 \tabularnewline
82 & 0.294041160294514 & 0.588082320589028 & 0.705958839705486 \tabularnewline
83 & 0.267059646529438 & 0.534119293058876 & 0.732940353470562 \tabularnewline
84 & 0.23709403574818 & 0.47418807149636 & 0.76290596425182 \tabularnewline
85 & 0.234219607694564 & 0.468439215389128 & 0.765780392305436 \tabularnewline
86 & 0.205457331994003 & 0.410914663988006 & 0.794542668005997 \tabularnewline
87 & 0.182360085247981 & 0.364720170495963 & 0.817639914752019 \tabularnewline
88 & 0.168667246980520 & 0.337334493961039 & 0.83133275301948 \tabularnewline
89 & 0.146035404763234 & 0.292070809526467 & 0.853964595236766 \tabularnewline
90 & 0.141401398675104 & 0.282802797350207 & 0.858598601324896 \tabularnewline
91 & 0.121453403152488 & 0.242906806304977 & 0.878546596847512 \tabularnewline
92 & 0.105538335362566 & 0.211076670725132 & 0.894461664637434 \tabularnewline
93 & 0.09047315031227 & 0.18094630062454 & 0.90952684968773 \tabularnewline
94 & 0.0944517045305446 & 0.188903409061089 & 0.905548295469455 \tabularnewline
95 & 0.0851452882570969 & 0.170290576514194 & 0.914854711742903 \tabularnewline
96 & 0.0713437597109419 & 0.142687519421884 & 0.928656240289058 \tabularnewline
97 & 0.0765390419752528 & 0.153078083950506 & 0.923460958024747 \tabularnewline
98 & 0.0639172957190728 & 0.127834591438146 & 0.936082704280927 \tabularnewline
99 & 0.0532560540094882 & 0.106512108018976 & 0.946743945990512 \tabularnewline
100 & 0.047196464347765 & 0.09439292869553 & 0.952803535652235 \tabularnewline
101 & 0.0418237708668177 & 0.0836475417336354 & 0.958176229133182 \tabularnewline
102 & 0.0495646640737906 & 0.0991293281475813 & 0.95043533592621 \tabularnewline
103 & 0.0418159325305985 & 0.083631865061197 & 0.958184067469402 \tabularnewline
104 & 0.0371546247444248 & 0.0743092494888495 & 0.962845375255575 \tabularnewline
105 & 0.0443799645112816 & 0.0887599290225631 & 0.955620035488718 \tabularnewline
106 & 0.0392467240758448 & 0.0784934481516896 & 0.960753275924155 \tabularnewline
107 & 0.0321015407475388 & 0.0642030814950776 & 0.967898459252461 \tabularnewline
108 & 0.0306389619503833 & 0.0612779239007667 & 0.969361038049617 \tabularnewline
109 & 0.0249766509276470 & 0.0499533018552939 & 0.975023349072353 \tabularnewline
110 & 0.0206802880721734 & 0.0413605761443469 & 0.979319711927827 \tabularnewline
111 & 0.0165367018611439 & 0.0330734037222877 & 0.983463298138856 \tabularnewline
112 & 0.0174610968140098 & 0.0349221936280197 & 0.98253890318599 \tabularnewline
113 & 0.0151868048729551 & 0.0303736097459102 & 0.984813195127045 \tabularnewline
114 & 0.0203427840188146 & 0.0406855680376292 & 0.979657215981185 \tabularnewline
115 & 0.0195545789159241 & 0.0391091578318481 & 0.980445421084076 \tabularnewline
116 & 0.0191565828176053 & 0.0383131656352107 & 0.980843417182395 \tabularnewline
117 & 0.0161895415427554 & 0.0323790830855109 & 0.983810458457245 \tabularnewline
118 & 0.0161428798213071 & 0.0322857596426143 & 0.983857120178693 \tabularnewline
119 & 0.0131115966255751 & 0.0262231932511502 & 0.986888403374425 \tabularnewline
120 & 0.0118512862501971 & 0.0237025725003941 & 0.988148713749803 \tabularnewline
121 & 0.00961138913138503 & 0.0192227782627701 & 0.990388610868615 \tabularnewline
122 & 0.0142976132082602 & 0.0285952264165205 & 0.98570238679174 \tabularnewline
123 & 0.01191824859868 & 0.02383649719736 & 0.98808175140132 \tabularnewline
124 & 0.0100417231243228 & 0.0200834462486456 & 0.989958276875677 \tabularnewline
125 & 0.00820386346908054 & 0.0164077269381611 & 0.99179613653092 \tabularnewline
126 & 0.00652402141000318 & 0.0130480428200064 & 0.993475978589997 \tabularnewline
127 & 0.00592499471708863 & 0.0118499894341773 & 0.994075005282911 \tabularnewline
128 & 0.00498958031437574 & 0.00997916062875147 & 0.995010419685624 \tabularnewline
129 & 0.00679676778773438 & 0.0135935355754688 & 0.993203232212266 \tabularnewline
130 & 0.00737896470885787 & 0.0147579294177157 & 0.992621035291142 \tabularnewline
131 & 0.0150380035701471 & 0.0300760071402942 & 0.984961996429853 \tabularnewline
132 & 0.0179020011010291 & 0.0358040022020581 & 0.982097998898971 \tabularnewline
133 & 0.0190014277786268 & 0.0380028555572536 & 0.980998572221373 \tabularnewline
134 & 0.0179399512149193 & 0.0358799024298386 & 0.98206004878508 \tabularnewline
135 & 0.0143280831078872 & 0.0286561662157743 & 0.985671916892113 \tabularnewline
136 & 0.0122309426263336 & 0.0244618852526672 & 0.987769057373666 \tabularnewline
137 & 0.00990648741823738 & 0.0198129748364748 & 0.990093512581763 \tabularnewline
138 & 0.0125384210857651 & 0.0250768421715302 & 0.987461578914235 \tabularnewline
139 & 0.0107618216212594 & 0.0215236432425187 & 0.98923817837874 \tabularnewline
140 & 0.0129052955180907 & 0.0258105910361813 & 0.98709470448191 \tabularnewline
141 & 0.0201560166619607 & 0.0403120333239213 & 0.97984398333804 \tabularnewline
142 & 0.0185242814551175 & 0.0370485629102349 & 0.981475718544883 \tabularnewline
143 & 0.0150205312093494 & 0.0300410624186988 & 0.98497946879065 \tabularnewline
144 & 0.0157236012587871 & 0.0314472025175742 & 0.984276398741213 \tabularnewline
145 & 0.0266974551191017 & 0.0533949102382034 & 0.973302544880898 \tabularnewline
146 & 0.0328992839312784 & 0.0657985678625569 & 0.967100716068721 \tabularnewline
147 & 0.0347106625346168 & 0.0694213250692335 & 0.965289337465383 \tabularnewline
148 & 0.0308268157911580 & 0.0616536315823159 & 0.969173184208842 \tabularnewline
149 & 0.0251694491161798 & 0.0503388982323596 & 0.97483055088382 \tabularnewline
150 & 0.0347431669180131 & 0.0694863338360262 & 0.965256833081987 \tabularnewline
151 & 0.0295649645330981 & 0.0591299290661961 & 0.970435035466902 \tabularnewline
152 & 0.0312118999658567 & 0.0624237999317135 & 0.968788100034143 \tabularnewline
153 & 0.0614921647203982 & 0.122984329440796 & 0.938507835279602 \tabularnewline
154 & 0.0592693484311593 & 0.118538696862319 & 0.94073065156884 \tabularnewline
155 & 0.066851467294983 & 0.133702934589966 & 0.933148532705017 \tabularnewline
156 & 0.0568828372036414 & 0.113765674407283 & 0.943117162796359 \tabularnewline
157 & 0.0507263507797272 & 0.101452701559454 & 0.949273649220273 \tabularnewline
158 & 0.0443069457737607 & 0.0886138915475213 & 0.95569305422624 \tabularnewline
159 & 0.0428963705597263 & 0.0857927411194525 & 0.957103629440274 \tabularnewline
160 & 0.0368289288531124 & 0.0736578577062248 & 0.963171071146888 \tabularnewline
161 & 0.0301494047603984 & 0.0602988095207967 & 0.969850595239602 \tabularnewline
162 & 0.0246882511172220 & 0.0493765022344441 & 0.975311748882778 \tabularnewline
163 & 0.0204556922545628 & 0.0409113845091255 & 0.979544307745437 \tabularnewline
164 & 0.0174663342278843 & 0.0349326684557686 & 0.982533665772116 \tabularnewline
165 & 0.0152899712295870 & 0.0305799424591739 & 0.984710028770413 \tabularnewline
166 & 0.0162158571129129 & 0.0324317142258259 & 0.983784142887087 \tabularnewline
167 & 0.0129519190795866 & 0.0259038381591732 & 0.987048080920413 \tabularnewline
168 & 0.0190097943455472 & 0.0380195886910944 & 0.980990205654453 \tabularnewline
169 & 0.0193145060535542 & 0.0386290121071084 & 0.980685493946446 \tabularnewline
170 & 0.0178019312531851 & 0.0356038625063702 & 0.982198068746815 \tabularnewline
171 & 0.0163272135588430 & 0.0326544271176859 & 0.983672786441157 \tabularnewline
172 & 0.0131776500985954 & 0.0263553001971908 & 0.986822349901405 \tabularnewline
173 & 0.0128128065802384 & 0.0256256131604767 & 0.987187193419762 \tabularnewline
174 & 0.0146859512169986 & 0.0293719024339971 & 0.985314048783001 \tabularnewline
175 & 0.0188929908902687 & 0.0377859817805374 & 0.981107009109731 \tabularnewline
176 & 0.0151454063778122 & 0.0302908127556244 & 0.984854593622188 \tabularnewline
177 & 0.0119808719881983 & 0.0239617439763966 & 0.988019128011802 \tabularnewline
178 & 0.0100781507238406 & 0.0201563014476812 & 0.98992184927616 \tabularnewline
179 & 0.007837987409293 & 0.015675974818586 & 0.992162012590707 \tabularnewline
180 & 0.00689228384446787 & 0.0137845676889357 & 0.993107716155532 \tabularnewline
181 & 0.00557929233420559 & 0.0111585846684112 & 0.994420707665794 \tabularnewline
182 & 0.00430170227341235 & 0.0086034045468247 & 0.995698297726588 \tabularnewline
183 & 0.00454272486774686 & 0.00908544973549373 & 0.995457275132253 \tabularnewline
184 & 0.00346340780470004 & 0.00692681560940007 & 0.9965365921953 \tabularnewline
185 & 0.0945485528934925 & 0.189097105786985 & 0.905451447106507 \tabularnewline
186 & 0.0837673286341582 & 0.167534657268316 & 0.916232671365842 \tabularnewline
187 & 0.0937319873577427 & 0.187463974715485 & 0.906268012642257 \tabularnewline
188 & 0.0791422429758007 & 0.158284485951601 & 0.9208577570242 \tabularnewline
189 & 0.0705418654748856 & 0.141083730949771 & 0.929458134525114 \tabularnewline
190 & 0.0596705613214319 & 0.119341122642864 & 0.940329438678568 \tabularnewline
191 & 0.0516888003813333 & 0.103377600762667 & 0.948311199618667 \tabularnewline
192 & 0.0430109766894643 & 0.0860219533789286 & 0.956989023310536 \tabularnewline
193 & 0.0433120762544696 & 0.0866241525089393 & 0.95668792374553 \tabularnewline
194 & 0.0410696683806253 & 0.0821393367612507 & 0.958930331619375 \tabularnewline
195 & 0.0357027738981950 & 0.0714055477963901 & 0.964297226101805 \tabularnewline
196 & 0.0284550140215669 & 0.0569100280431338 & 0.971544985978433 \tabularnewline
197 & 0.0373222402395989 & 0.0746444804791978 & 0.962677759760401 \tabularnewline
198 & 0.0305194995352497 & 0.0610389990704994 & 0.96948050046475 \tabularnewline
199 & 0.0274057819364692 & 0.0548115638729385 & 0.97259421806353 \tabularnewline
200 & 0.0232765389624399 & 0.0465530779248797 & 0.97672346103756 \tabularnewline
201 & 0.0213113555866387 & 0.0426227111732773 & 0.978688644413361 \tabularnewline
202 & 0.017816554441518 & 0.035633108883036 & 0.982183445558482 \tabularnewline
203 & 0.0198469109119102 & 0.0396938218238205 & 0.98015308908809 \tabularnewline
204 & 0.0263675080730801 & 0.0527350161461602 & 0.97363249192692 \tabularnewline
205 & 0.0278237067069442 & 0.0556474134138884 & 0.972176293293056 \tabularnewline
206 & 0.0217503282101579 & 0.0435006564203158 & 0.978249671789842 \tabularnewline
207 & 0.0193869270812976 & 0.0387738541625953 & 0.980613072918702 \tabularnewline
208 & 0.0154903722401228 & 0.0309807444802457 & 0.984509627759877 \tabularnewline
209 & 0.0180778994705218 & 0.0361557989410435 & 0.981922100529478 \tabularnewline
210 & 0.0149636941222892 & 0.0299273882445784 & 0.98503630587771 \tabularnewline
211 & 0.0183958392351673 & 0.0367916784703345 & 0.981604160764833 \tabularnewline
212 & 0.0324717611190881 & 0.0649435222381763 & 0.967528238880912 \tabularnewline
213 & 0.0253710945551448 & 0.0507421891102895 & 0.974628905444855 \tabularnewline
214 & 0.0314791169033977 & 0.0629582338067955 & 0.968520883096602 \tabularnewline
215 & 0.0267612504796719 & 0.0535225009593438 & 0.973238749520328 \tabularnewline
216 & 0.0207293671329075 & 0.0414587342658149 & 0.979270632867093 \tabularnewline
217 & 0.0231370663002716 & 0.0462741326005433 & 0.976862933699728 \tabularnewline
218 & 0.0196067553481166 & 0.0392135106962332 & 0.980393244651883 \tabularnewline
219 & 0.018294390391927 & 0.036588780783854 & 0.981705609608073 \tabularnewline
220 & 0.0137419038079818 & 0.0274838076159636 & 0.986258096192018 \tabularnewline
221 & 0.0114025137241161 & 0.0228050274482322 & 0.988597486275884 \tabularnewline
222 & 0.00831154801890274 & 0.0166230960378055 & 0.991688451981097 \tabularnewline
223 & 0.00641631381835698 & 0.0128326276367140 & 0.993583686181643 \tabularnewline
224 & 0.00491691159901379 & 0.00983382319802759 & 0.995083088400986 \tabularnewline
225 & 0.00343960111377309 & 0.00687920222754618 & 0.996560398886227 \tabularnewline
226 & 0.00710199123298353 & 0.0142039824659671 & 0.992898008767017 \tabularnewline
227 & 0.00565156735869477 & 0.0113031347173895 & 0.994348432641305 \tabularnewline
228 & 0.00412261775956938 & 0.00824523551913875 & 0.99587738224043 \tabularnewline
229 & 0.00293161983497722 & 0.00586323966995445 & 0.997068380165023 \tabularnewline
230 & 0.00207599045903008 & 0.00415198091806016 & 0.99792400954097 \tabularnewline
231 & 0.00226850989205011 & 0.00453701978410022 & 0.99773149010795 \tabularnewline
232 & 0.0077985191670701 & 0.0155970383341402 & 0.99220148083293 \tabularnewline
233 & 0.0293698404957630 & 0.0587396809915259 & 0.970630159504237 \tabularnewline
234 & 0.0449507472439715 & 0.089901494487943 & 0.955049252756029 \tabularnewline
235 & 0.0330914105808940 & 0.0661828211617879 & 0.966908589419106 \tabularnewline
236 & 0.0271011514226525 & 0.054202302845305 & 0.972898848577348 \tabularnewline
237 & 0.238959015911461 & 0.477918031822922 & 0.761040984088539 \tabularnewline
238 & 0.19294927201925 & 0.3858985440385 & 0.80705072798075 \tabularnewline
239 & 0.163289925162300 & 0.326579850324601 & 0.8367100748377 \tabularnewline
240 & 0.130447620662191 & 0.260895241324381 & 0.86955237933781 \tabularnewline
241 & 0.116817470818194 & 0.233634941636387 & 0.883182529181806 \tabularnewline
242 & 0.177907984064825 & 0.35581596812965 & 0.822092015935175 \tabularnewline
243 & 0.150453950204245 & 0.300907900408490 & 0.849546049795755 \tabularnewline
244 & 0.151452822534991 & 0.302905645069982 & 0.84854717746501 \tabularnewline
245 & 0.132852249443852 & 0.265704498887705 & 0.867147750556148 \tabularnewline
246 & 0.116475820843447 & 0.232951641686895 & 0.883524179156553 \tabularnewline
247 & 0.0792562973088716 & 0.158512594617743 & 0.920743702691128 \tabularnewline
248 & 0.0704290884946226 & 0.140858176989245 & 0.929570911505377 \tabularnewline
249 & 0.0534977901152154 & 0.106995580230431 & 0.946502209884785 \tabularnewline
250 & 0.130779813206018 & 0.261559626412036 & 0.869220186793982 \tabularnewline
251 & 0.107472861596092 & 0.214945723192184 & 0.892527138403908 \tabularnewline
252 & 0.54397838950358 & 0.91204322099284 & 0.45602161049642 \tabularnewline
253 & 0.382288727481096 & 0.764577454962192 & 0.617711272518904 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96550&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.402475229471653[/C][C]0.804950458943307[/C][C]0.597524770528347[/C][/ROW]
[ROW][C]12[/C][C]0.799813019552283[/C][C]0.400373960895434[/C][C]0.200186980447717[/C][/ROW]
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[ROW][C]15[/C][C]0.618114699975933[/C][C]0.763770600048135[/C][C]0.381885300024067[/C][/ROW]
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[ROW][C]18[/C][C]0.857694380421813[/C][C]0.284611239156373[/C][C]0.142305619578187[/C][/ROW]
[ROW][C]19[/C][C]0.830980289213744[/C][C]0.338039421572512[/C][C]0.169019710786256[/C][/ROW]
[ROW][C]20[/C][C]0.780467388615392[/C][C]0.439065222769216[/C][C]0.219532611384608[/C][/ROW]
[ROW][C]21[/C][C]0.716435090676601[/C][C]0.567129818646798[/C][C]0.283564909323399[/C][/ROW]
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[ROW][C]47[/C][C]0.592513782373666[/C][C]0.814972435252669[/C][C]0.407486217626334[/C][/ROW]
[ROW][C]48[/C][C]0.554065143583589[/C][C]0.891869712832823[/C][C]0.445934856416411[/C][/ROW]
[ROW][C]49[/C][C]0.541344029892326[/C][C]0.917311940215348[/C][C]0.458655970107674[/C][/ROW]
[ROW][C]50[/C][C]0.514186099361112[/C][C]0.971627801277775[/C][C]0.485813900638888[/C][/ROW]
[ROW][C]51[/C][C]0.469970781504948[/C][C]0.939941563009895[/C][C]0.530029218495052[/C][/ROW]
[ROW][C]52[/C][C]0.428427599419145[/C][C]0.85685519883829[/C][C]0.571572400580855[/C][/ROW]
[ROW][C]53[/C][C]0.435661290444540[/C][C]0.871322580889079[/C][C]0.56433870955546[/C][/ROW]
[ROW][C]54[/C][C]0.406008878029808[/C][C]0.812017756059616[/C][C]0.593991121970192[/C][/ROW]
[ROW][C]55[/C][C]0.404277688955219[/C][C]0.808555377910438[/C][C]0.595722311044781[/C][/ROW]
[ROW][C]56[/C][C]0.420734697246853[/C][C]0.841469394493707[/C][C]0.579265302753147[/C][/ROW]
[ROW][C]57[/C][C]0.380141520587537[/C][C]0.760283041175074[/C][C]0.619858479412463[/C][/ROW]
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[ROW][C]67[/C][C]0.19583124519953[/C][C]0.39166249039906[/C][C]0.80416875480047[/C][/ROW]
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[ROW][C]88[/C][C]0.168667246980520[/C][C]0.337334493961039[/C][C]0.83133275301948[/C][/ROW]
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[ROW][C]90[/C][C]0.141401398675104[/C][C]0.282802797350207[/C][C]0.858598601324896[/C][/ROW]
[ROW][C]91[/C][C]0.121453403152488[/C][C]0.242906806304977[/C][C]0.878546596847512[/C][/ROW]
[ROW][C]92[/C][C]0.105538335362566[/C][C]0.211076670725132[/C][C]0.894461664637434[/C][/ROW]
[ROW][C]93[/C][C]0.09047315031227[/C][C]0.18094630062454[/C][C]0.90952684968773[/C][/ROW]
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[ROW][C]97[/C][C]0.0765390419752528[/C][C]0.153078083950506[/C][C]0.923460958024747[/C][/ROW]
[ROW][C]98[/C][C]0.0639172957190728[/C][C]0.127834591438146[/C][C]0.936082704280927[/C][/ROW]
[ROW][C]99[/C][C]0.0532560540094882[/C][C]0.106512108018976[/C][C]0.946743945990512[/C][/ROW]
[ROW][C]100[/C][C]0.047196464347765[/C][C]0.09439292869553[/C][C]0.952803535652235[/C][/ROW]
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[ROW][C]103[/C][C]0.0418159325305985[/C][C]0.083631865061197[/C][C]0.958184067469402[/C][/ROW]
[ROW][C]104[/C][C]0.0371546247444248[/C][C]0.0743092494888495[/C][C]0.962845375255575[/C][/ROW]
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[ROW][C]106[/C][C]0.0392467240758448[/C][C]0.0784934481516896[/C][C]0.960753275924155[/C][/ROW]
[ROW][C]107[/C][C]0.0321015407475388[/C][C]0.0642030814950776[/C][C]0.967898459252461[/C][/ROW]
[ROW][C]108[/C][C]0.0306389619503833[/C][C]0.0612779239007667[/C][C]0.969361038049617[/C][/ROW]
[ROW][C]109[/C][C]0.0249766509276470[/C][C]0.0499533018552939[/C][C]0.975023349072353[/C][/ROW]
[ROW][C]110[/C][C]0.0206802880721734[/C][C]0.0413605761443469[/C][C]0.979319711927827[/C][/ROW]
[ROW][C]111[/C][C]0.0165367018611439[/C][C]0.0330734037222877[/C][C]0.983463298138856[/C][/ROW]
[ROW][C]112[/C][C]0.0174610968140098[/C][C]0.0349221936280197[/C][C]0.98253890318599[/C][/ROW]
[ROW][C]113[/C][C]0.0151868048729551[/C][C]0.0303736097459102[/C][C]0.984813195127045[/C][/ROW]
[ROW][C]114[/C][C]0.0203427840188146[/C][C]0.0406855680376292[/C][C]0.979657215981185[/C][/ROW]
[ROW][C]115[/C][C]0.0195545789159241[/C][C]0.0391091578318481[/C][C]0.980445421084076[/C][/ROW]
[ROW][C]116[/C][C]0.0191565828176053[/C][C]0.0383131656352107[/C][C]0.980843417182395[/C][/ROW]
[ROW][C]117[/C][C]0.0161895415427554[/C][C]0.0323790830855109[/C][C]0.983810458457245[/C][/ROW]
[ROW][C]118[/C][C]0.0161428798213071[/C][C]0.0322857596426143[/C][C]0.983857120178693[/C][/ROW]
[ROW][C]119[/C][C]0.0131115966255751[/C][C]0.0262231932511502[/C][C]0.986888403374425[/C][/ROW]
[ROW][C]120[/C][C]0.0118512862501971[/C][C]0.0237025725003941[/C][C]0.988148713749803[/C][/ROW]
[ROW][C]121[/C][C]0.00961138913138503[/C][C]0.0192227782627701[/C][C]0.990388610868615[/C][/ROW]
[ROW][C]122[/C][C]0.0142976132082602[/C][C]0.0285952264165205[/C][C]0.98570238679174[/C][/ROW]
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[ROW][C]125[/C][C]0.00820386346908054[/C][C]0.0164077269381611[/C][C]0.99179613653092[/C][/ROW]
[ROW][C]126[/C][C]0.00652402141000318[/C][C]0.0130480428200064[/C][C]0.993475978589997[/C][/ROW]
[ROW][C]127[/C][C]0.00592499471708863[/C][C]0.0118499894341773[/C][C]0.994075005282911[/C][/ROW]
[ROW][C]128[/C][C]0.00498958031437574[/C][C]0.00997916062875147[/C][C]0.995010419685624[/C][/ROW]
[ROW][C]129[/C][C]0.00679676778773438[/C][C]0.0135935355754688[/C][C]0.993203232212266[/C][/ROW]
[ROW][C]130[/C][C]0.00737896470885787[/C][C]0.0147579294177157[/C][C]0.992621035291142[/C][/ROW]
[ROW][C]131[/C][C]0.0150380035701471[/C][C]0.0300760071402942[/C][C]0.984961996429853[/C][/ROW]
[ROW][C]132[/C][C]0.0179020011010291[/C][C]0.0358040022020581[/C][C]0.982097998898971[/C][/ROW]
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[ROW][C]134[/C][C]0.0179399512149193[/C][C]0.0358799024298386[/C][C]0.98206004878508[/C][/ROW]
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[ROW][C]136[/C][C]0.0122309426263336[/C][C]0.0244618852526672[/C][C]0.987769057373666[/C][/ROW]
[ROW][C]137[/C][C]0.00990648741823738[/C][C]0.0198129748364748[/C][C]0.990093512581763[/C][/ROW]
[ROW][C]138[/C][C]0.0125384210857651[/C][C]0.0250768421715302[/C][C]0.987461578914235[/C][/ROW]
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[ROW][C]148[/C][C]0.0308268157911580[/C][C]0.0616536315823159[/C][C]0.969173184208842[/C][/ROW]
[ROW][C]149[/C][C]0.0251694491161798[/C][C]0.0503388982323596[/C][C]0.97483055088382[/C][/ROW]
[ROW][C]150[/C][C]0.0347431669180131[/C][C]0.0694863338360262[/C][C]0.965256833081987[/C][/ROW]
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[ROW][C]153[/C][C]0.0614921647203982[/C][C]0.122984329440796[/C][C]0.938507835279602[/C][/ROW]
[ROW][C]154[/C][C]0.0592693484311593[/C][C]0.118538696862319[/C][C]0.94073065156884[/C][/ROW]
[ROW][C]155[/C][C]0.066851467294983[/C][C]0.133702934589966[/C][C]0.933148532705017[/C][/ROW]
[ROW][C]156[/C][C]0.0568828372036414[/C][C]0.113765674407283[/C][C]0.943117162796359[/C][/ROW]
[ROW][C]157[/C][C]0.0507263507797272[/C][C]0.101452701559454[/C][C]0.949273649220273[/C][/ROW]
[ROW][C]158[/C][C]0.0443069457737607[/C][C]0.0886138915475213[/C][C]0.95569305422624[/C][/ROW]
[ROW][C]159[/C][C]0.0428963705597263[/C][C]0.0857927411194525[/C][C]0.957103629440274[/C][/ROW]
[ROW][C]160[/C][C]0.0368289288531124[/C][C]0.0736578577062248[/C][C]0.963171071146888[/C][/ROW]
[ROW][C]161[/C][C]0.0301494047603984[/C][C]0.0602988095207967[/C][C]0.969850595239602[/C][/ROW]
[ROW][C]162[/C][C]0.0246882511172220[/C][C]0.0493765022344441[/C][C]0.975311748882778[/C][/ROW]
[ROW][C]163[/C][C]0.0204556922545628[/C][C]0.0409113845091255[/C][C]0.979544307745437[/C][/ROW]
[ROW][C]164[/C][C]0.0174663342278843[/C][C]0.0349326684557686[/C][C]0.982533665772116[/C][/ROW]
[ROW][C]165[/C][C]0.0152899712295870[/C][C]0.0305799424591739[/C][C]0.984710028770413[/C][/ROW]
[ROW][C]166[/C][C]0.0162158571129129[/C][C]0.0324317142258259[/C][C]0.983784142887087[/C][/ROW]
[ROW][C]167[/C][C]0.0129519190795866[/C][C]0.0259038381591732[/C][C]0.987048080920413[/C][/ROW]
[ROW][C]168[/C][C]0.0190097943455472[/C][C]0.0380195886910944[/C][C]0.980990205654453[/C][/ROW]
[ROW][C]169[/C][C]0.0193145060535542[/C][C]0.0386290121071084[/C][C]0.980685493946446[/C][/ROW]
[ROW][C]170[/C][C]0.0178019312531851[/C][C]0.0356038625063702[/C][C]0.982198068746815[/C][/ROW]
[ROW][C]171[/C][C]0.0163272135588430[/C][C]0.0326544271176859[/C][C]0.983672786441157[/C][/ROW]
[ROW][C]172[/C][C]0.0131776500985954[/C][C]0.0263553001971908[/C][C]0.986822349901405[/C][/ROW]
[ROW][C]173[/C][C]0.0128128065802384[/C][C]0.0256256131604767[/C][C]0.987187193419762[/C][/ROW]
[ROW][C]174[/C][C]0.0146859512169986[/C][C]0.0293719024339971[/C][C]0.985314048783001[/C][/ROW]
[ROW][C]175[/C][C]0.0188929908902687[/C][C]0.0377859817805374[/C][C]0.981107009109731[/C][/ROW]
[ROW][C]176[/C][C]0.0151454063778122[/C][C]0.0302908127556244[/C][C]0.984854593622188[/C][/ROW]
[ROW][C]177[/C][C]0.0119808719881983[/C][C]0.0239617439763966[/C][C]0.988019128011802[/C][/ROW]
[ROW][C]178[/C][C]0.0100781507238406[/C][C]0.0201563014476812[/C][C]0.98992184927616[/C][/ROW]
[ROW][C]179[/C][C]0.007837987409293[/C][C]0.015675974818586[/C][C]0.992162012590707[/C][/ROW]
[ROW][C]180[/C][C]0.00689228384446787[/C][C]0.0137845676889357[/C][C]0.993107716155532[/C][/ROW]
[ROW][C]181[/C][C]0.00557929233420559[/C][C]0.0111585846684112[/C][C]0.994420707665794[/C][/ROW]
[ROW][C]182[/C][C]0.00430170227341235[/C][C]0.0086034045468247[/C][C]0.995698297726588[/C][/ROW]
[ROW][C]183[/C][C]0.00454272486774686[/C][C]0.00908544973549373[/C][C]0.995457275132253[/C][/ROW]
[ROW][C]184[/C][C]0.00346340780470004[/C][C]0.00692681560940007[/C][C]0.9965365921953[/C][/ROW]
[ROW][C]185[/C][C]0.0945485528934925[/C][C]0.189097105786985[/C][C]0.905451447106507[/C][/ROW]
[ROW][C]186[/C][C]0.0837673286341582[/C][C]0.167534657268316[/C][C]0.916232671365842[/C][/ROW]
[ROW][C]187[/C][C]0.0937319873577427[/C][C]0.187463974715485[/C][C]0.906268012642257[/C][/ROW]
[ROW][C]188[/C][C]0.0791422429758007[/C][C]0.158284485951601[/C][C]0.9208577570242[/C][/ROW]
[ROW][C]189[/C][C]0.0705418654748856[/C][C]0.141083730949771[/C][C]0.929458134525114[/C][/ROW]
[ROW][C]190[/C][C]0.0596705613214319[/C][C]0.119341122642864[/C][C]0.940329438678568[/C][/ROW]
[ROW][C]191[/C][C]0.0516888003813333[/C][C]0.103377600762667[/C][C]0.948311199618667[/C][/ROW]
[ROW][C]192[/C][C]0.0430109766894643[/C][C]0.0860219533789286[/C][C]0.956989023310536[/C][/ROW]
[ROW][C]193[/C][C]0.0433120762544696[/C][C]0.0866241525089393[/C][C]0.95668792374553[/C][/ROW]
[ROW][C]194[/C][C]0.0410696683806253[/C][C]0.0821393367612507[/C][C]0.958930331619375[/C][/ROW]
[ROW][C]195[/C][C]0.0357027738981950[/C][C]0.0714055477963901[/C][C]0.964297226101805[/C][/ROW]
[ROW][C]196[/C][C]0.0284550140215669[/C][C]0.0569100280431338[/C][C]0.971544985978433[/C][/ROW]
[ROW][C]197[/C][C]0.0373222402395989[/C][C]0.0746444804791978[/C][C]0.962677759760401[/C][/ROW]
[ROW][C]198[/C][C]0.0305194995352497[/C][C]0.0610389990704994[/C][C]0.96948050046475[/C][/ROW]
[ROW][C]199[/C][C]0.0274057819364692[/C][C]0.0548115638729385[/C][C]0.97259421806353[/C][/ROW]
[ROW][C]200[/C][C]0.0232765389624399[/C][C]0.0465530779248797[/C][C]0.97672346103756[/C][/ROW]
[ROW][C]201[/C][C]0.0213113555866387[/C][C]0.0426227111732773[/C][C]0.978688644413361[/C][/ROW]
[ROW][C]202[/C][C]0.017816554441518[/C][C]0.035633108883036[/C][C]0.982183445558482[/C][/ROW]
[ROW][C]203[/C][C]0.0198469109119102[/C][C]0.0396938218238205[/C][C]0.98015308908809[/C][/ROW]
[ROW][C]204[/C][C]0.0263675080730801[/C][C]0.0527350161461602[/C][C]0.97363249192692[/C][/ROW]
[ROW][C]205[/C][C]0.0278237067069442[/C][C]0.0556474134138884[/C][C]0.972176293293056[/C][/ROW]
[ROW][C]206[/C][C]0.0217503282101579[/C][C]0.0435006564203158[/C][C]0.978249671789842[/C][/ROW]
[ROW][C]207[/C][C]0.0193869270812976[/C][C]0.0387738541625953[/C][C]0.980613072918702[/C][/ROW]
[ROW][C]208[/C][C]0.0154903722401228[/C][C]0.0309807444802457[/C][C]0.984509627759877[/C][/ROW]
[ROW][C]209[/C][C]0.0180778994705218[/C][C]0.0361557989410435[/C][C]0.981922100529478[/C][/ROW]
[ROW][C]210[/C][C]0.0149636941222892[/C][C]0.0299273882445784[/C][C]0.98503630587771[/C][/ROW]
[ROW][C]211[/C][C]0.0183958392351673[/C][C]0.0367916784703345[/C][C]0.981604160764833[/C][/ROW]
[ROW][C]212[/C][C]0.0324717611190881[/C][C]0.0649435222381763[/C][C]0.967528238880912[/C][/ROW]
[ROW][C]213[/C][C]0.0253710945551448[/C][C]0.0507421891102895[/C][C]0.974628905444855[/C][/ROW]
[ROW][C]214[/C][C]0.0314791169033977[/C][C]0.0629582338067955[/C][C]0.968520883096602[/C][/ROW]
[ROW][C]215[/C][C]0.0267612504796719[/C][C]0.0535225009593438[/C][C]0.973238749520328[/C][/ROW]
[ROW][C]216[/C][C]0.0207293671329075[/C][C]0.0414587342658149[/C][C]0.979270632867093[/C][/ROW]
[ROW][C]217[/C][C]0.0231370663002716[/C][C]0.0462741326005433[/C][C]0.976862933699728[/C][/ROW]
[ROW][C]218[/C][C]0.0196067553481166[/C][C]0.0392135106962332[/C][C]0.980393244651883[/C][/ROW]
[ROW][C]219[/C][C]0.018294390391927[/C][C]0.036588780783854[/C][C]0.981705609608073[/C][/ROW]
[ROW][C]220[/C][C]0.0137419038079818[/C][C]0.0274838076159636[/C][C]0.986258096192018[/C][/ROW]
[ROW][C]221[/C][C]0.0114025137241161[/C][C]0.0228050274482322[/C][C]0.988597486275884[/C][/ROW]
[ROW][C]222[/C][C]0.00831154801890274[/C][C]0.0166230960378055[/C][C]0.991688451981097[/C][/ROW]
[ROW][C]223[/C][C]0.00641631381835698[/C][C]0.0128326276367140[/C][C]0.993583686181643[/C][/ROW]
[ROW][C]224[/C][C]0.00491691159901379[/C][C]0.00983382319802759[/C][C]0.995083088400986[/C][/ROW]
[ROW][C]225[/C][C]0.00343960111377309[/C][C]0.00687920222754618[/C][C]0.996560398886227[/C][/ROW]
[ROW][C]226[/C][C]0.00710199123298353[/C][C]0.0142039824659671[/C][C]0.992898008767017[/C][/ROW]
[ROW][C]227[/C][C]0.00565156735869477[/C][C]0.0113031347173895[/C][C]0.994348432641305[/C][/ROW]
[ROW][C]228[/C][C]0.00412261775956938[/C][C]0.00824523551913875[/C][C]0.99587738224043[/C][/ROW]
[ROW][C]229[/C][C]0.00293161983497722[/C][C]0.00586323966995445[/C][C]0.997068380165023[/C][/ROW]
[ROW][C]230[/C][C]0.00207599045903008[/C][C]0.00415198091806016[/C][C]0.99792400954097[/C][/ROW]
[ROW][C]231[/C][C]0.00226850989205011[/C][C]0.00453701978410022[/C][C]0.99773149010795[/C][/ROW]
[ROW][C]232[/C][C]0.0077985191670701[/C][C]0.0155970383341402[/C][C]0.99220148083293[/C][/ROW]
[ROW][C]233[/C][C]0.0293698404957630[/C][C]0.0587396809915259[/C][C]0.970630159504237[/C][/ROW]
[ROW][C]234[/C][C]0.0449507472439715[/C][C]0.089901494487943[/C][C]0.955049252756029[/C][/ROW]
[ROW][C]235[/C][C]0.0330914105808940[/C][C]0.0661828211617879[/C][C]0.966908589419106[/C][/ROW]
[ROW][C]236[/C][C]0.0271011514226525[/C][C]0.054202302845305[/C][C]0.972898848577348[/C][/ROW]
[ROW][C]237[/C][C]0.238959015911461[/C][C]0.477918031822922[/C][C]0.761040984088539[/C][/ROW]
[ROW][C]238[/C][C]0.19294927201925[/C][C]0.3858985440385[/C][C]0.80705072798075[/C][/ROW]
[ROW][C]239[/C][C]0.163289925162300[/C][C]0.326579850324601[/C][C]0.8367100748377[/C][/ROW]
[ROW][C]240[/C][C]0.130447620662191[/C][C]0.260895241324381[/C][C]0.86955237933781[/C][/ROW]
[ROW][C]241[/C][C]0.116817470818194[/C][C]0.233634941636387[/C][C]0.883182529181806[/C][/ROW]
[ROW][C]242[/C][C]0.177907984064825[/C][C]0.35581596812965[/C][C]0.822092015935175[/C][/ROW]
[ROW][C]243[/C][C]0.150453950204245[/C][C]0.300907900408490[/C][C]0.849546049795755[/C][/ROW]
[ROW][C]244[/C][C]0.151452822534991[/C][C]0.302905645069982[/C][C]0.84854717746501[/C][/ROW]
[ROW][C]245[/C][C]0.132852249443852[/C][C]0.265704498887705[/C][C]0.867147750556148[/C][/ROW]
[ROW][C]246[/C][C]0.116475820843447[/C][C]0.232951641686895[/C][C]0.883524179156553[/C][/ROW]
[ROW][C]247[/C][C]0.0792562973088716[/C][C]0.158512594617743[/C][C]0.920743702691128[/C][/ROW]
[ROW][C]248[/C][C]0.0704290884946226[/C][C]0.140858176989245[/C][C]0.929570911505377[/C][/ROW]
[ROW][C]249[/C][C]0.0534977901152154[/C][C]0.106995580230431[/C][C]0.946502209884785[/C][/ROW]
[ROW][C]250[/C][C]0.130779813206018[/C][C]0.261559626412036[/C][C]0.869220186793982[/C][/ROW]
[ROW][C]251[/C][C]0.107472861596092[/C][C]0.214945723192184[/C][C]0.892527138403908[/C][/ROW]
[ROW][C]252[/C][C]0.54397838950358[/C][C]0.91204322099284[/C][C]0.45602161049642[/C][/ROW]
[ROW][C]253[/C][C]0.382288727481096[/C][C]0.764577454962192[/C][C]0.617711272518904[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96550&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96550&T=5

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.4024752294716530.8049504589433070.597524770528347
120.7998130195522830.4003739608954340.200186980447717
130.7608266649872490.4783466700255020.239173335012751
140.6735526211782490.6528947576435020.326447378821751
150.6181146999759330.7637706000481350.381885300024067
160.6808332009748060.6383335980503880.319166799025194
170.6081426280273660.7837147439452690.391857371972634
180.8576943804218130.2846112391563730.142305619578187
190.8309802892137440.3380394215725120.169019710786256
200.7804673886153920.4390652227692160.219532611384608
210.7164350906766010.5671298186467980.283564909323399
220.6779248597001360.6441502805997280.322075140299864
230.640080623968090.719838752063820.35991937603191
240.6080028194115910.7839943611768170.391997180588409
250.5446025202534490.9107949594931030.455397479746551
260.4951188739165560.9902377478331110.504881126083444
270.4356950665268120.8713901330536240.564304933473188
280.4894287276521380.9788574553042760.510571272347862
290.4295311932516870.8590623865033740.570468806748313
300.439110691341420.878221382682840.56088930865858
310.3907776313005630.7815552626011260.609222368699437
320.3865362509458790.7730725018917580.613463749054121
330.3675737435383950.7351474870767890.632426256461605
340.3119461115168870.6238922230337730.688053888483113
350.3007900756320030.6015801512640060.699209924367997
360.3994469562214770.7988939124429550.600553043778523
370.4802336405472530.9604672810945070.519766359452747
380.4564144938699520.9128289877399040.543585506130048
390.5040367816736190.9919264366527620.495963218326381
400.4846625643473580.9693251286947160.515337435652642
410.4489815388141270.8979630776282530.551018461185873
420.4193309291449730.8386618582899460.580669070855027
430.4354887871740670.8709775743481330.564511212825933
440.3883570187665280.7767140375330560.611642981233472
450.3497844417106850.6995688834213690.650215558289315
460.5744902589496470.8510194821007070.425509741050353
470.5925137823736660.8149724352526690.407486217626334
480.5540651435835890.8918697128328230.445934856416411
490.5413440298923260.9173119402153480.458655970107674
500.5141860993611120.9716278012777750.485813900638888
510.4699707815049480.9399415630098950.530029218495052
520.4284275994191450.856855198838290.571572400580855
530.4356612904445400.8713225808890790.56433870955546
540.4060088780298080.8120177560596160.593991121970192
550.4042776889552190.8085553779104380.595722311044781
560.4207346972468530.8414693944937070.579265302753147
570.3801415205875370.7602830411750740.619858479412463
580.3656289476085470.7312578952170940.634371052391453
590.3265080055678500.6530160111356990.67349199443215
600.3533720372327260.7067440744654520.646627962767274
610.3272204306739440.6544408613478880.672779569326056
620.2931319780199750.586263956039950.706868021980025
630.2591175374011090.5182350748022190.74088246259889
640.2265144769622930.4530289539245870.773485523037706
650.202909488587840.405818977175680.79709051141216
660.1931317947470190.3862635894940380.806868205252981
670.195831245199530.391662490399060.80416875480047
680.3093029909179460.6186059818358920.690697009082054
690.4222184651265270.8444369302530530.577781534873473
700.3901976574227450.780395314845490.609802342577255
710.4626709970512310.9253419941024620.537329002948769
720.4277994404902010.8555988809804030.572200559509799
730.4210243957462380.8420487914924760.578975604253762
740.3996317935083070.7992635870166140.600368206491693
750.3635917953612980.7271835907225960.636408204638702
760.4415616385406130.8831232770812260.558438361459387
770.4034167204255120.8068334408510250.596583279574488
780.3826230797418080.7652461594836160.617376920258192
790.3920519671908380.7841039343816760.607948032809162
800.3577742307192150.7155484614384290.642225769280785
810.3260492214260770.6520984428521540.673950778573923
820.2940411602945140.5880823205890280.705958839705486
830.2670596465294380.5341192930588760.732940353470562
840.237094035748180.474188071496360.76290596425182
850.2342196076945640.4684392153891280.765780392305436
860.2054573319940030.4109146639880060.794542668005997
870.1823600852479810.3647201704959630.817639914752019
880.1686672469805200.3373344939610390.83133275301948
890.1460354047632340.2920708095264670.853964595236766
900.1414013986751040.2828027973502070.858598601324896
910.1214534031524880.2429068063049770.878546596847512
920.1055383353625660.2110766707251320.894461664637434
930.090473150312270.180946300624540.90952684968773
940.09445170453054460.1889034090610890.905548295469455
950.08514528825709690.1702905765141940.914854711742903
960.07134375971094190.1426875194218840.928656240289058
970.07653904197525280.1530780839505060.923460958024747
980.06391729571907280.1278345914381460.936082704280927
990.05325605400948820.1065121080189760.946743945990512
1000.0471964643477650.094392928695530.952803535652235
1010.04182377086681770.08364754173363540.958176229133182
1020.04956466407379060.09912932814758130.95043533592621
1030.04181593253059850.0836318650611970.958184067469402
1040.03715462474442480.07430924948884950.962845375255575
1050.04437996451128160.08875992902256310.955620035488718
1060.03924672407584480.07849344815168960.960753275924155
1070.03210154074753880.06420308149507760.967898459252461
1080.03063896195038330.06127792390076670.969361038049617
1090.02497665092764700.04995330185529390.975023349072353
1100.02068028807217340.04136057614434690.979319711927827
1110.01653670186114390.03307340372228770.983463298138856
1120.01746109681400980.03492219362801970.98253890318599
1130.01518680487295510.03037360974591020.984813195127045
1140.02034278401881460.04068556803762920.979657215981185
1150.01955457891592410.03910915783184810.980445421084076
1160.01915658281760530.03831316563521070.980843417182395
1170.01618954154275540.03237908308551090.983810458457245
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1900.05967056132143190.1193411226428640.940329438678568
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1950.03570277389819500.07140554779639010.964297226101805
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1990.02740578193646920.05481156387293850.97259421806353
2000.02327653896243990.04655307792487970.97672346103756
2010.02131135558663870.04262271117327730.978688644413361
2020.0178165544415180.0356331088830360.982183445558482
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2530.3822887274810960.7645774549621920.617711272518904







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level100.0411522633744856NOK
5% type I error level860.353909465020576NOK
10% type I error level1250.51440329218107NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 10 & 0.0411522633744856 & NOK \tabularnewline
5% type I error level & 86 & 0.353909465020576 & NOK \tabularnewline
10% type I error level & 125 & 0.51440329218107 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96550&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]10[/C][C]0.0411522633744856[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]86[/C][C]0.353909465020576[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]125[/C][C]0.51440329218107[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96550&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96550&T=6

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level100.0411522633744856NOK
5% type I error level860.353909465020576NOK
10% type I error level1250.51440329218107NOK



Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}