## Free Statistics

of Irreproducible Research!

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 07:43:53 +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/t128998342128i6qsjxv676vga.htm/, Retrieved Mon, 26 Feb 2024 05:13:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=96530, Retrieved Mon, 26 Feb 2024 05:13:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact841
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
- R PD    [Multiple Regression] [WS7 Tutorial] [2010-11-18 16:04:53] [afe9379cca749d06b3d6872e02cc47ed]
-    D      [Multiple Regression] [WS7 Tutorial Popu...] [2010-11-22 10:41:15] [afe9379cca749d06b3d6872e02cc47ed]
- R  D        [Multiple Regression] [] [2010-12-02 15:03:12] [11b6443b23f19c2dbda3ee0ee9d024b2]
- RMPD        [Univariate Explorative Data Analysis] [] [2010-12-02 15:42:43] [11b6443b23f19c2dbda3ee0ee9d024b2]
- R  D          [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2010-12-02 19:58:40] [69c775ce4d55db2aa75a88e773e8d700]
- R PD        [Multiple Regression] [] [2010-12-02 16:12:01] [11b6443b23f19c2dbda3ee0ee9d024b2]
-             [Multiple Regression] [WS 4: Personality...] [2010-12-02 16:43:36] [4f1a20f787b3465111b61213cdeef1a9]
- RMPD        [] [Multiple regressi...] [-0001-11-30 00:00:00] [74be16979710d4c4e7c6647856088456]
- RMPD        [] [Multiple regressi...] [-0001-11-30 00:00:00] [74be16979710d4c4e7c6647856088456]
- R PD        [Multiple Regression] [] [2010-12-02 17:36:13] [11b6443b23f19c2dbda3ee0ee9d024b2]
- RMPD        [] [Multiple regressi...] [-0001-11-30 00:00:00] [74be16979710d4c4e7c6647856088456]
- RMPD        [] [Multiple regressi...] [-0001-11-30 00:00:00] [74be16979710d4c4e7c6647856088456]
- R PD        [Multiple Regression] [] [2010-12-02 18:02:49] [94f4aa1c01e87d8321fffb341ed4df07]
- R             [Multiple Regression] [] [2011-11-25 00:47:59] [74be16979710d4c4e7c6647856088456]
- R P             [Multiple Regression] [] [2011-11-27 17:03:35] [3931071255a6f7f4a767409781cc5f7d]
- RMPD        [Univariate Explorative Data Analysis] [EDA - assignment ...] [2010-12-02 18:07:45] [74be16979710d4c4e7c6647856088456]
-             [Multiple Regression] [] [2010-12-02 18:37:01] [f47feae0308dca73181bb669fbad1c56]
- RM            [Multiple Regression] [] [2012-11-20 16:54:06] [74be16979710d4c4e7c6647856088456]
- R             [Multiple Regression] [W7 ] [2012-11-20 19:29:08] [783d8509970888a6ec44a5a7a0d2a339]
- RM            [Multiple Regression] [Workshop 7] [2012-11-20 19:35:01] [74be16979710d4c4e7c6647856088456]
- R PD        [Multiple Regression] [] [2010-12-02 23:03:05] [d67ce207bd02ca41b9162077ae11c874]
-   PD        [Multiple Regression] [Tutorial1] [2010-12-03 19:28:18] [a7c91bc614e4e21e8b9c8593f39a36f1]
- R  D        [Multiple Regression] [WS 7 Mini-tutorial] [2011-11-20 14:29:34] [f5fdea4413921432bb019d1f20c4f2ec]
- R  D          [Multiple Regression] [WS 7 Mini-tutorial] [2011-11-20 14:47:41] [f5fdea4413921432bb019d1f20c4f2ec]
-    D            [Multiple Regression] [Ws 7 Mini-tutoria...] [2011-11-20 15:44:07] [f5fdea4413921432bb019d1f20c4f2ec]
-   P               [Multiple Regression] [Ws 7 Mini-tutoria...] [2011-11-20 15:51:02] [f5fdea4413921432bb019d1f20c4f2ec]
- RMP             [Kendall tau Correlation Matrix] [workshop 10 a] [2012-12-07 13:55:33] [dbae308bdff61c0f4902cc85498d0d35]
- R P               [Kendall tau Correlation Matrix] [workshop 10 b] [2012-12-07 14:31:15] [dbae308bdff61c0f4902cc85498d0d35]
- RMP               [Multiple Regression] [workshop 10 c] [2012-12-07 14:38:23] [dbae308bdff61c0f4902cc85498d0d35]
- RMP               [Recursive Partitioning (Regression Trees)] [workshop 10 d] [2012-12-07 14:46:54] [dbae308bdff61c0f4902cc85498d0d35]
-   P                 [Recursive Partitioning (Regression Trees)] [WS 10 recursive p...] [2012-12-10 17:38:57] [8c30f4dd45e15fd207e4faf2fdf6253e]
-                   [Kendall tau Correlation Matrix] [WS 10 Pearson cor...] [2012-12-10 15:35:41] [8c30f4dd45e15fd207e4faf2fdf6253e]
- R P               [Kendall tau Correlation Matrix] [WS 10 Kendall] [2012-12-10 15:49:57] [8c30f4dd45e15fd207e4faf2fdf6253e]
- RMP               [Multiple Regression] [WS 10 multiple re...] [2012-12-10 15:57:07] [8c30f4dd45e15fd207e4faf2fdf6253e]
- R PD        [Multiple Regression] [] [2011-11-21 16:29:10] [bdca8f3e7c3554be8c1291e54f61d441]
- R PD        [Multiple Regression] [] [2011-11-21 21:29:21] [bdca8f3e7c3554be8c1291e54f61d441]
- R PD        [Multiple Regression] [] [2011-11-21 21:29:21] [bdca8f3e7c3554be8c1291e54f61d441]
- R PD        [Multiple Regression] [] [2011-11-21 21:29:21] [bdca8f3e7c3554be8c1291e54f61d441]
- R PD        [Multiple Regression] [] [2011-11-22 00:46:31] [bdca8f3e7c3554be8c1291e54f61d441]
- R             [Multiple Regression] [] [2011-12-19 20:06:55] [74be16979710d4c4e7c6647856088456]
- RM            [Multiple Regression] [WS7] [2012-11-20 16:38:51] [bdca8f3e7c3554be8c1291e54f61d441]
- RM            [Multiple Regression] [] [2012-11-20 18:17:58] [10b150b957285d7c50cf113330698f19]
- RM            [Multiple Regression] [] [2012-11-20 18:19:47] [10b150b957285d7c50cf113330698f19]
- RM            [Multiple Regression] [] [2012-11-20 18:21:00] [10b150b957285d7c50cf113330698f19]
- RM            [Multiple Regression] [] [2012-12-21 15:52:30] [74be16979710d4c4e7c6647856088456]
- R  D        [Multiple Regression] [ws7-1] [2011-11-22 10:24:02] [f7a862281046b7153543b12c78921b36]
-    D          [Multiple Regression] [ws7-1] [2011-11-22 10:38:43] [f7a862281046b7153543b12c78921b36]
- R  D            [Multiple Regression] [ws7-3] [2011-11-22 17:14:48] [f7a862281046b7153543b12c78921b36]

[Truncated]
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Dataseries X:
41	38	13	12	14	12	53	32
39	32	16	11	18	11	83	51
30	35	19	15	11	14	66	42
31	33	15	6	12	12	67	41
34	37	14	13	16	21	76	46
35	29	13	10	18	12	78	47
39	31	19	12	14	22	53	37
34	36	15	14	14	11	80	49
36	35	14	12	15	10	74	45
37	38	15	9	15	13	76	47
38	31	16	10	17	10	79	49
36	34	16	12	19	8	54	33
38	35	16	12	10	15	67	42
39	38	16	11	16	14	54	33
33	37	17	15	18	10	87	53
32	33	15	12	14	14	58	36
36	32	15	10	14	14	75	45
38	38	20	12	17	11	88	54
39	38	18	11	14	10	64	41
32	32	16	12	16	13	57	36
32	33	16	11	18	9.5	66	41
31	31	16	12	11	14	68	44
39	38	19	13	14	12	54	33
37	39	16	11	12	14	56	37
39	32	17	12	17	11	86	52
41	32	17	13	9	9	80	47
36	35	16	10	16	11	76	43
33	37	15	14	14	15	69	44
33	33	16	12	15	14	78	45
34	33	14	10	11	13	67	44
31	31	15	12	16	9	80	49
27	32	12	8	13	15	54	33
37	31	14	10	17	10	71	43
34	37	16	12	15	11	84	54
34	30	14	12	14	13	74	42
32	33	10	7	16	8	71	44
29	31	10	9	9	20	63	37
36	33	14	12	15	12	71	43
29	31	16	10	17	10	76	46
35	33	16	10	13	10	69	42
37	32	16	10	15	9	74	45
34	33	14	12	16	14	75	44
38	32	20	15	16	8	54	33
35	33	14	10	12	14	52	31
38	28	14	10	15	11	69	42
37	35	11	12	11	13	68	40
38	39	14	13	15	9	65	43
33	34	15	11	15	11	75	46
36	38	16	11	17	15	74	42
38	32	14	12	13	11	75	45
32	38	16	14	16	10	72	44
32	30	14	10	14	14	67	40
32	33	12	12	11	18	63	37
34	38	16	13	12	14	62	46
32	32	9	5	12	11	63	36
37	35	14	6	15	14.5	76	47
39	34	16	12	16	13	74	45
29	34	16	12	15	9	67	42
37	36	15	11	12	10	73	43
35	34	16	10	12	15	70	43
30	28	12	7	8	20	53	32
38	34	16	12	13	12	77	45
34	35	16	14	11	12	80	48
31	35	14	11	14	14	52	31
34	31	16	12	15	13	54	33
35	37	17	13	10	11	80	49
36	35	18	14	11	17	66	42
30	27	18	11	12	12	73	41
39	40	12	12	15	13	63	38
35	37	16	12	15	14	69	42
38	36	10	8	14	13	67	44
31	38	14	11	16	15	54	33
34	39	18	14	15	13	81	48
38	41	18	14	15	10	69	40
34	27	16	12	13	11	84	50
39	30	17	9	12	19	80	49
37	37	16	13	17	13	70	43
34	31	16	11	13	17	69	44
28	31	13	12	15	13	77	47
37	27	16	12	13	9	54	33
33	36	16	12	15	11	79	46
35	37	16	12	15	9	71	45
37	33	15	12	16	12	73	43
32	34	15	11	15	12	72	44
33	31	16	10	14	13	77	47
38	39	14	9	15	13	75	45
33	34	16	12	14	12	69	42
29	32	16	12	13	15	54	33
33	33	15	12	7	22	70	43
31	36	12	9	17	13	73	46
36	32	17	15	13	15	54	33
35	41	16	12	15	13	77	46
32	28	15	12	14	15	82	48
29	30	13	12	13	12.5	80	47
39	36	16	10	16	11	80	47
37	35	16	13	12	16	69	43
35	31	16	9	14	11	78	46
37	34	16	12	17	11	81	48
32	36	14	10	15	10	76	46
38	36	16	14	17	10	76	45
37	35	16	11	12	16	73	45
36	37	20	15	16	12	85	52
32	28	15	11	11	11	66	42
33	39	16	11	15	16	79	47
40	32	13	12	9	19	68	41
38	35	17	12	16	11	76	47
41	39	16	12	15	16	71	43
36	35	16	11	10	15	54	33
43	42	12	7	10	24	46	30
30	34	16	12	15	14	85	52
31	33	16	14	11	15	74	44
32	41	17	11	13	11	88	55
32	33	13	11	14	15	38	11
37	34	12	10	18	12	76	47
37	32	18	13	16	10	86	53
33	40	14	13	14	14	54	33
34	40	14	8	14	13	67	44
33	35	13	11	14	9	69	42
38	36	16	12	14	15	90	55
33	37	13	11	12	15	54	33
31	27	16	13	14	14	76	46
38	39	13	12	15	11	89	54
37	38	16	14	15	8	76	47
36	31	15	13	15	11	73	45
31	33	16	15	13	11	79	47
39	32	15	10	17	8	90	55
44	39	17	11	17	10	74	44
33	36	15	9	19	11	81	53
35	33	12	11	15	13	72	44
32	33	16	10	13	11	71	42
28	32	10	11	9	20	66	40
40	37	16	8	15	10	77	46
27	30	12	11	15	15	65	40
37	38	14	12	15	12	74	46
32	29	15	12	16	14	85	53
28	22	13	9	11	23	54	33
34	35	15	11	14	14	63	42
30	35	11	10	11	16	54	35
35	34	12	8	15	11	64	40
31	35	11	9	13	12	69	41
32	34	16	8	15	10	54	33
30	37	15	9	16	14	84	51
30	35	17	15	14	12	86	53
31	23	16	11	15	12	77	46
40	31	10	8	16	11	89	55
32	27	18	13	16	12	76	47
36	36	13	12	11	13	60	38
32	31	16	12	12	11	75	46
35	32	13	9	9	19	73	46
38	39	10	7	16	12	85	53
42	37	15	13	13	17	79	47
34	38	16	9	16	9	71	41
35	39	16	6	12	12	72	44
38	34	14	8	9	19	69	43
33	31	10	8	13	18	78	51
36	32	17	15	13	15	54	33
32	37	13	6	14	14	69	43
33	36	15	9	19	11	81	53
34	32	16	11	13	9	84	51
32	38	12	8	12	18	84	50
34	36	13	8	13	16	69	46
27	26	13	10	10	24	66	43
31	26	12	8	14	14	81	47
38	33	17	14	16	20	82	50
34	39	15	10	10	18	72	43
24	30	10	8	11	23	54	33
30	33	14	11	14	12	78	48
26	25	11	12	12	14	74	44
34	38	13	12	9	16	82	50
27	37	16	12	9	18	73	41
37	31	12	5	11	20	55	34
36	37	16	12	16	12	72	44
41	35	12	10	9	12	78	47
29	25	9	7	13	17	59	35
36	28	12	12	16	13	72	44
32	35	15	11	13	9	78	44
37	33	12	8	9	16	68	43
30	30	12	9	12	18	69	41
31	31	14	10	16	10	67	41
38	37	12	9	11	14	74	42
36	36	16	12	14	11	54	33
35	30	11	6	13	9	67	41
31	36	19	15	15	11	70	44
38	32	15	12	14	10	80	48
22	28	8	12	16	11	89	55
32	36	16	12	13	19	76	44
36	34	17	11	14	14	74	43
39	31	12	7	15	12	87	52
28	28	11	7	13	14	54	30
32	36	11	5	11	21	61	39
32	36	14	12	11	13	38	11
38	40	16	12	14	10	75	44
32	33	12	3	15	15	69	42
35	37	16	11	11	16	62	41
32	32	13	10	15	14	72	44
37	38	15	12	12	12	70	44
34	31	16	9	14	19	79	48
33	37	16	12	14	15	87	53
33	33	14	9	8	19	62	37
26	32	16	12	13	13	77	44
30	30	16	12	9	17	69	44
24	30	14	10	15	12	69	40
34	31	11	9	17	11	75	42
34	32	12	12	13	14	54	35
33	34	15	8	15	11	72	43
34	36	15	11	15	13	74	45
35	37	16	11	14	12	85	55
35	36	16	12	16	15	52	31
36	33	11	10	13	14	70	44
34	33	15	10	16	12	84	50
34	33	12	12	9	17	64	40
41	44	12	12	16	11	84	53
32	39	15	11	11	18	87	54
30	32	15	8	10	13	79	49
35	35	16	12	11	17	67	40
28	25	14	10	15	13	65	41
33	35	17	11	17	11	85	52
39	34	14	10	14	12	83	52
36	35	13	8	8	22	61	36
36	39	15	12	15	14	82	52
35	33	13	12	11	12	76	46
38	36	14	10	16	12	58	31
33	32	15	12	10	17	72	44
31	32	12	9	15	9	72	44
34	36	13	9	9	21	38	11
32	36	8	6	16	10	78	46
31	32	14	10	19	11	54	33
33	34	14	9	12	12	63	34
34	33	11	9	8	23	66	42
34	35	12	9	11	13	70	43
34	30	13	6	14	12	71	43
33	38	10	10	9	16	67	44
32	34	16	6	15	9	58	36
41	33	18	14	13	17	72	46
34	32	13	10	16	9	72	44
36	31	11	10	11	14	70	43
37	30	4	6	12	17	76	50
36	27	13	12	13	13	50	33
29	31	16	12	10	11	72	43
37	30	10	7	11	12	72	44
27	32	12	8	12	10	88	53
35	35	12	11	8	19	53	34
28	28	10	3	12	16	58	35
35	33	13	6	12	16	66	40
37	31	15	10	15	14	82	53
29	35	12	8	11	20	69	42
32	35	14	9	13	15	68	43
36	32	10	9	14	23	44	29
19	21	12	8	10	20	56	36
21	20	12	9	12	16	53	30
31	34	11	7	15	14	70	42
33	32	10	7	13	17	78	47
36	34	12	6	13	11	71	44
33	32	16	9	13	13	72	45
37	33	12	10	12	17	68	44
34	33	14	11	12	15	67	43
35	37	16	12	9	21	75	43
31	32	14	8	9	18	62	40
37	34	13	11	15	15	67	41
35	30	4	3	10	8	83	52
27	30	15	11	14	12	64	38
34	38	11	12	15	12	68	41
40	36	11	7	7	22	62	39
29	32	14	9	14	12	72	43

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 16 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 & 16 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96530&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]16 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=96530&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96530&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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 16 seconds R Server 'George Udny Yule' @ 72.249.76.132

 Multiple Linear Regression - Estimated Regression Equation Learning[t] = + 3.77956658273111 + 0.0469437389403786Connected[t] + 0.0419978310517143Separate[t] + 0.607405158110716Software[t] + 0.098631079258643Happiness[t] -0.0393791266630193Depression[t] + 0.0158660592613680Belonging[t] -0.0194154193631033Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  3.77956658273111 +  0.0469437389403786Connected[t] +  0.0419978310517143Separate[t] +  0.607405158110716Software[t] +  0.098631079258643Happiness[t] -0.0393791266630193Depression[t] +  0.0158660592613680Belonging[t] -0.0194154193631033Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96530&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  3.77956658273111 +  0.0469437389403786Connected[t] +  0.0419978310517143Separate[t] +  0.607405158110716Software[t] +  0.098631079258643Happiness[t] -0.0393791266630193Depression[t] +  0.0158660592613680Belonging[t] -0.0194154193631033Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96530&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96530&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] = + 3.77956658273111 + 0.0469437389403786Connected[t] + 0.0419978310517143Separate[t] + 0.607405158110716Software[t] + 0.098631079258643Happiness[t] -0.0393791266630193Depression[t] + 0.0158660592613680Belonging[t] -0.0194154193631033Belonging_Final[t] + e[t]

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 3.77956658273111 1.917652 1.9709 0.049809 0.024904 Connected 0.0469437389403786 0.034787 1.3495 0.178384 0.089192 Separate 0.0419978310517143 0.035648 1.1781 0.239847 0.119923 Software 0.607405158110716 0.051845 11.7158 0 0 Happiness 0.098631079258643 0.057963 1.7016 0.09004 0.04502 Depression -0.0393791266630193 0.042557 -0.9253 0.35567 0.177835 Belonging 0.0158660592613680 0.037843 0.4193 0.675376 0.337688 Belonging_Final -0.0194154193631033 0.056444 -0.344 0.731147 0.365573

\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) & 3.77956658273111 & 1.917652 & 1.9709 & 0.049809 & 0.024904 \tabularnewline
Connected & 0.0469437389403786 & 0.034787 & 1.3495 & 0.178384 & 0.089192 \tabularnewline
Separate & 0.0419978310517143 & 0.035648 & 1.1781 & 0.239847 & 0.119923 \tabularnewline
Software & 0.607405158110716 & 0.051845 & 11.7158 & 0 & 0 \tabularnewline
Happiness & 0.098631079258643 & 0.057963 & 1.7016 & 0.09004 & 0.04502 \tabularnewline
Depression & -0.0393791266630193 & 0.042557 & -0.9253 & 0.35567 & 0.177835 \tabularnewline
Belonging & 0.0158660592613680 & 0.037843 & 0.4193 & 0.675376 & 0.337688 \tabularnewline
Belonging_Final & -0.0194154193631033 & 0.056444 & -0.344 & 0.731147 & 0.365573 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96530&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]3.77956658273111[/C][C]1.917652[/C][C]1.9709[/C][C]0.049809[/C][C]0.024904[/C][/ROW]
[ROW][C]Connected[/C][C]0.0469437389403786[/C][C]0.034787[/C][C]1.3495[/C][C]0.178384[/C][C]0.089192[/C][/ROW]
[ROW][C]Separate[/C][C]0.0419978310517143[/C][C]0.035648[/C][C]1.1781[/C][C]0.239847[/C][C]0.119923[/C][/ROW]
[ROW][C]Software[/C][C]0.607405158110716[/C][C]0.051845[/C][C]11.7158[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.098631079258643[/C][C]0.057963[/C][C]1.7016[/C][C]0.09004[/C][C]0.04502[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0393791266630193[/C][C]0.042557[/C][C]-0.9253[/C][C]0.35567[/C][C]0.177835[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0158660592613680[/C][C]0.037843[/C][C]0.4193[/C][C]0.675376[/C][C]0.337688[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.0194154193631033[/C][C]0.056444[/C][C]-0.344[/C][C]0.731147[/C][C]0.365573[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96530&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96530&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 Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 3.77956658273111 1.917652 1.9709 0.049809 0.024904 Connected 0.0469437389403786 0.034787 1.3495 0.178384 0.089192 Separate 0.0419978310517143 0.035648 1.1781 0.239847 0.119923 Software 0.607405158110716 0.051845 11.7158 0 0 Happiness 0.098631079258643 0.057963 1.7016 0.09004 0.04502 Depression -0.0393791266630193 0.042557 -0.9253 0.35567 0.177835 Belonging 0.0158660592613680 0.037843 0.4193 0.675376 0.337688 Belonging_Final -0.0194154193631033 0.056444 -0.344 0.731147 0.365573

 Multiple Linear Regression - Regression Statistics Multiple R 0.653394258005351 R-squared 0.426924056394364 Adjusted R-squared 0.411254011061397 F-TEST (value) 27.2445961273770 F-TEST (DF numerator) 7 F-TEST (DF denominator) 256 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.88446241278468 Sum Squared Residuals 909.106837810756

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.653394258005351 \tabularnewline
R-squared & 0.426924056394364 \tabularnewline
Adjusted R-squared & 0.411254011061397 \tabularnewline
F-TEST (value) & 27.2445961273770 \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 & 1.88446241278468 \tabularnewline
Sum Squared Residuals & 909.106837810756 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96530&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.653394258005351[/C][/ROW]
[ROW][C]R-squared[/C][C]0.426924056394364[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]27.2445961273770[/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]1.88446241278468[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]909.106837810756[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96530&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96530&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 R 0.653394258005351 R-squared 0.426924056394364 Adjusted R-squared 0.411254011061397 F-TEST (value) 27.2445961273770 F-TEST (DF numerator) 7 F-TEST (DF denominator) 256 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.88446241278468 Sum Squared Residuals 909.106837810756

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 13 15.7169326674778 -2.71693266747778 2 16 15.3046452988162 0.695354701183782 3 19 16.5342266059759 2.46577339402406 4 15 11.2431990710256 3.7568009289744 5 14 15.8896873324328 -1.88968733243279 6 13 14.3424239462714 -1.34242394627140 7 19 14.8381920087898 4.16180799121016 8 15 16.6568417465609 -1.65684174656087 9 14 15.6143966049743 -1.61439660497434 10 15 13.8398822625452 1.16011773745481 11 16 14.5244132197985 1.47558678020148 12 16 15.9613451914131 0.0386548085868819 13 16 14.9654168965065 1.03458310349348 14 16 15.1305945765763 0.86940542342365 15 17 17.7266051778577 -0.726605177857671 16 15 15.0073602272847 -0.00736022728472072 17 15 14.0333112689484 0.966688731051586 18 20 16.0395466632557 3.96045333674427 19 18 15.09418616242 2.90581383758001 20 16 15.1861376221519 0.813862377848057 21 16 15.0015368334676 0.99846316653241 22 16 14.5838648261738 1.41613517382616 23 19 16.2269009876065 2.77309901239347 24 16 14.6382510537831 1.36174894621694 25 17 15.8416021360893 1.15839786391071 26 17 15.8344851325850 1.16551486741503 27 16 14.5294011985975 1.47059880140252 28 15 16.4169297769606 -1.41692977696059 29 16 15.2955174564432 0.704482543556828 30 14 13.6173944562786 0.382605543721379 31 15 15.3672314701030 -0.367231470103049 32 12 12.1577948842104 -0.157794884210425 33 14 14.4670335229458 -0.467033522945819 34 16 15.5490474808797 0.450952519120254 35 14 15.1519977706766 -1.15199777067662 36 10 12.4548067707195 -2.4548067707195 37 10 12.2908025947004 -2.29080259470041 38 14 15.4428753504870 -1.44287535048698 39 16 14.1125676496403 1.88743235035968 40 16 14.0503006909743 1.94969930902571 41 16 14.3599156612012 1.64008433879884 42 14 15.4129095162212 -1.41290951622119 43 20 17.4975592437467 2.50244075625334 44 14 13.7379997106144 0.262000289385554 45 14 14.1390257843911 -0.139025784391116 46 11 15.1505593881384 -4.1505593881384 47 14 16.4190959972096 -2.41909599720959 48 15 14.7812339122260 0.218766087773978 49 16 15.1915977233103 0.80840227668973 50 14 15.3615153637810 -1.36151536378102 51 16 16.8537398386884 -0.853739838688414 52 14 13.7316892738080 0.268310726191956 53 12 14.6138653598004 -2.61386535980045 54 16 15.5906899034319 0.409310096568081 55 9 10.7137318072366 -1.71373180723661 56 14 11.8326046050634 2.16739539493663 57 16 15.6937236900133 0.306276309986652 58 16 15.2303555712627 0.769644428737268 59 15 14.8230045585446 0.176995441455372 60 16 13.7932224493505 2.20677755064947 61 12 10.8367279492074 1.16327205079259 62 16 15.4378640177442 0.562135982255836 63 16 16.2989869704333 -0.298986970433305 64 14 14.4388877335844 -0.438887733584363 65 16 15.1500442700275 0.849955729972452 66 17 15.7438538414077 1.25614615859233 67 18 16.0903465015184 1.90965349848157 68 18 14.0764904918967 3.92350950810328 69 12 15.8084608807317 -3.80846088073167 70 16 15.4728479832678 0.527152016732215 71 10 13.0122458267498 -3.01224582674977 72 14 14.7156655383903 -0.715665538390303 73 18 16.8379895442731 1.16201045572692 74 18 17.1928281858955 0.807171814104506 75 16 15.0094686892977 0.990531310702272 76 17 13.0902524925774 3.90974750742255 77 16 16.4072325443378 -0.407232544337829 78 16 14.2122817226739 1.78771827732615 79 13 14.9614853283133 -1.96148532831330 80 16 15.0831385107766 0.916861489223383 81 16 15.5360989694856 0.463901030514361 82 16 15.6432294770163 0.356770522983692 83 15 15.6201822872087 -0.620182287208735 84 15 14.6861437075647 0.313856292435273 85 16 13.8827626275351 2.11723737246489 86 14 13.9517886120021 0.0482113879978810 87 16 15.2330941862993 0.76690581370072 88 16 14.6813029945340 1.31869700546595 89 15 14.1033381737051 0.896661826294864 90 12 13.6433035668958 -1.64330356689577 91 17 16.8321246414488 0.167875358551157 92 16 15.7294852307762 0.270514769223806 93 15 14.9057923352787 0.0942076647212783 94 13 14.8364568188003 -1.83645681880029 95 16 14.6980328060634 1.30196719393662 96 16 15.6960780466908 0.303921953309242 97 16 13.4832846792557 2.51671532074434 98 16 15.8300417014575 0.169958298542461 99 14 14.2661258632027 -0.266125863202743 100 16 17.1940865071683 -1.19408650716827 101 16 14.5059011287886 1.49409887121141 102 20 17.5790992836758 2.42090071632415 103 15 14.0226460140409 0.977353985959115 104 16 14.8383762518519 1.16162374814813 105 13 14.7124447739459 -1.71244477394589 106 17 15.7604373152473 1.23956268475275 107 16 15.7720645248471 0.227935475152921 108 16 14.2326042643852 1.76739573561481 109 12 12.0024802659182 -0.00248026591817174 110 16 15.1718385499616 0.828161450038395 111 16 15.9384880334039 0.0615119665961127 112 17 14.8625328282602 2.13746717173979 113 13 14.5286402413612 -1.52864024136121 114 12 14.614568460888 -2.61456846088799 115 18 16.2764524443605 1.72354755563947 116 14 15.9504759627417 -1.95047596274165 117 14 12.9924621951951 1.00753780480488 118 13 14.7858242392293 -1.78582423922934 119 16 15.5144579558839 0.48554204411607 120 13 14.3730308681848 -1.37303086818477 121 16 15.4072695332184 0.592730466781637 122 13 15.9001483950515 -2.90014839505153 123 16 17.0738036864139 -1.07380368641386 124 15 15.9985652529538 -0.998565252953813 125 16 16.9217558949015 -0.921755894901497 126 15 14.7501471788131 0.249852821186918 127 17 15.7672102604739 1.23278973952610 128 15 14.0042319951691 0.995768004830924 129 12 14.7455979666711 -2.74559796667113 130 16 13.9018224660129 2.09817753398713 131 10 13.4900189227280 -3.49001892272798 132 16 13.4847293488174 2.51527065118258 133 12 14.1318955712898 -2.13189557128976 134 14 15.6891601643807 -1.68916016438072 135 15 15.1349525324793 -0.134952532479329 136 13 11.8798702989229 1.12012970107707 137 15 14.5406759892864 0.459324010713589 138 11 13.3639577865016 -2.36395778650162 139 12 12.9948717800782 -0.994871780078202 140 11 13.2797734052426 -2.27977340524255 141 16 12.8706670328481 3.12933296715187 142 15 13.5777970081450 1.42220299185502 143 17 17.0126296693111 -0.0126296693111276 144 16 14.2177232846361 1.78227671536388 145 10 13.3076482519712 -3.30764825197124 146 18 15.7108182646390 2.28918173536104 147 13 15.057515844885 -2.05751584488500 148 16 14.9198086004653 1.08019139953471 149 13 12.6377638044032 0.362236195596831 150 10 12.8783257394112 -2.8783257394112 151 15 16.1550432712530 -1.15504327125297 152 16 13.9923608515066 2.00763914849345 153 16 11.7040450513149 4.29595494868507 154 14 12.2499675462609 1.75003245373915 155 10 12.3106299805489 -2.31062998054890 156 17 16.8321246414488 0.167875358551157 157 13 11.5695393191606 1.43046068083939 158 15 14.0042319951691 0.995768004830924 159 16 14.6713955204085 1.32860447959148 160 12 12.5736517546432 -0.573651754643187 161 13 12.6006036915371 0.39939630846291 162 13 12.4665513538839 0.53344864611615 163 12 12.3881607885568 -0.388160788556804 164 17 16.5737899268770 0.42621007312302 165 15 13.6726004456851 1.32739955431489 166 10 11.4206728334644 -1.42067283346442 167 14 14.4691619974894 -0.469161997489407 168 11 14.2909865799885 -3.29098657998851 169 13 14.8482927619942 -1.84829276199418 170 16 14.4308747459494 1.56912525405060 171 12 10.3653118162962 1.63468818370377 172 16 15.7059483938507 0.294051606149257 173 12 13.9883936528962 -1.98839365289617 174 9 11.3120335908731 -2.31203359087306 175 12 15.2885887877223 -3.28858878772229 176 15 14.7442131156564 0.255786884343579 177 12 12.2632972969965 -0.263297296996452 178 12 12.6879346718068 -0.68793467180684 179 14 14.0621066117256 -0.0621066117256398 180 12 13.4762697050290 -1.47626970502904 181 16 15.4340480772343 0.565951922765726 182 11 11.5217489928797 -0.521748992879665 183 19 17.1604632713109 1.83953672868908 184 15 15.5206096079202 -0.520609607920211 185 8 14.7662880903322 -6.76628809033216 186 16 14.9680927196659 1.03190728033408 187 17 14.7476768686274 2.2523231313726 188 12 12.5418032885651 -0.541803288565062 189 11 11.5269675255856 -0.526967525585558 190 11 10.2993224089426 0.700677591057419 191 14 15.0449039081772 -1.04490390817718 192 16 15.8549236374795 0.145076362520500 193 12 9.65799989258033 2.34200010741967 194 16 14.3005132593300 1.69948674067001 195 13 13.9159846340245 -0.915984634024543 196 15 15.3686335282855 -0.368633528285528 197 16 13.0983431475463 2.90165685245371 198 16 15.3129697531759 0.687030246824146 199 14 12.4874552007084 1.51254479929164 200 16 14.7705797810563 1.22942021894372 201 16 14.1953897769368 1.80461022306323 202 14 13.5652608133924 0.434739186607567 203 11 13.7622976777595 -2.76229767775952 204 12 14.9165699771728 -2.91656997717275 205 15 12.9696665181991 2.03033348180092 206 15 14.8369644200455 0.163035579954474 207 16 14.847026495686 1.15297350431399 208 16 15.4339487103626 0.566051289637426 209 11 13.9167631437977 -2.91676314379775 210 15 14.3031594704995 0.696840529500508 211 12 14.507489606999 -2.507489606999 212 12 16.2896849694461 -4.28968496944614 213 15 14.3091704811001 0.690829518899902 214 15 12.1674958883062 2.83250411169378 215 16 14.8832893443442 1.11671065565580 216 14 13.4207878308238 0.57921216917622 217 17 15.0626619782301 1.93733802176992 218 14 14.3279169397482 -0.327916939748238 219 13 11.9902889016349 1.00971109836511 220 15 15.6158919610783 -0.615891961078344 221 13 15.0224913327296 -2.02249133272956 222 14 14.5733033465195 -0.573303346519544 223 15 14.5664459129041 0.43355408709592 224 12 13.4585313702885 -1.45853137028855 225 13 12.8042807399043 0.195719260095656 226 8 11.9668684285413 -3.96686842854126 227 14 14.3096831383973 -0.309683138397306 228 14 13.2737435527865 0.726256447213536 229 11 12.3432695732266 -1.34326957322662 230 12 13.1609985574185 -1.16099855741854 231 13 11.4799323515281 1.52006764847186 232 10 13.4650403340905 -3.46504033409047 233 16 11.7004538222459 4.29954617775412 234 18 16.4558663707500 1.54413362925002 235 13 14.3053988244790 -1.30539882447904 236 11 13.6549207425401 -2.65492074254014 237 4 11.1700281372820 -7.17002813728201 238 13 14.8152140281387 -1.81521402813869 239 16 14.5923633054321 1.40763669456793 240 10 11.9287261285823 -1.92872612858232 241 12 12.4071970658913 -0.407197065891325 242 12 13.7956003816510 -1.79560038165098 243 10 8.88634470078797 1.11365529921203 244 13 11.2770068802368 1.72299311976323 245 15 14.0926273160205 0.907372683979529 246 12 12.0467701780665 -0.0467701780665329 247 14 13.1538828662063 0.846117133793704 248 10 12.8902928435778 -2.89029284357777 249 12 10.8009658204609 1.19903417953906 250 12 11.8839336289646 0.116066371035425 251 11 12.1379198030589 -1.13791980305892 252 10 11.8622634576053 -1.86226345760533 253 12 11.6631437816570 0.336856218342975 254 16 13.1782247636368 2.82177523636317 255 12 13.7152063049677 -1.71520630496769 256 14 14.2640878596850 -0.264087859685045 257 16 14.6811885572799 1.31881144272010 258 14 11.8239286814975 2.17607131850247 259 13 14.7816409840600 -1.78164098406003 260 4 9.48330674262236 -5.48330674262236 261 15 14.174366651485 0.825633348514991 262 11 15.5502096888069 -4.55020968880690 263 11 11.4716452522508 -0.471645252250818 264 14 13.1672908525232 0.83270914747681

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 15.7169326674778 & -2.71693266747778 \tabularnewline
2 & 16 & 15.3046452988162 & 0.695354701183782 \tabularnewline
3 & 19 & 16.5342266059759 & 2.46577339402406 \tabularnewline
4 & 15 & 11.2431990710256 & 3.7568009289744 \tabularnewline
5 & 14 & 15.8896873324328 & -1.88968733243279 \tabularnewline
6 & 13 & 14.3424239462714 & -1.34242394627140 \tabularnewline
7 & 19 & 14.8381920087898 & 4.16180799121016 \tabularnewline
8 & 15 & 16.6568417465609 & -1.65684174656087 \tabularnewline
9 & 14 & 15.6143966049743 & -1.61439660497434 \tabularnewline
10 & 15 & 13.8398822625452 & 1.16011773745481 \tabularnewline
11 & 16 & 14.5244132197985 & 1.47558678020148 \tabularnewline
12 & 16 & 15.9613451914131 & 0.0386548085868819 \tabularnewline
13 & 16 & 14.9654168965065 & 1.03458310349348 \tabularnewline
14 & 16 & 15.1305945765763 & 0.86940542342365 \tabularnewline
15 & 17 & 17.7266051778577 & -0.726605177857671 \tabularnewline
16 & 15 & 15.0073602272847 & -0.00736022728472072 \tabularnewline
17 & 15 & 14.0333112689484 & 0.966688731051586 \tabularnewline
18 & 20 & 16.0395466632557 & 3.96045333674427 \tabularnewline
19 & 18 & 15.09418616242 & 2.90581383758001 \tabularnewline
20 & 16 & 15.1861376221519 & 0.813862377848057 \tabularnewline
21 & 16 & 15.0015368334676 & 0.99846316653241 \tabularnewline
22 & 16 & 14.5838648261738 & 1.41613517382616 \tabularnewline
23 & 19 & 16.2269009876065 & 2.77309901239347 \tabularnewline
24 & 16 & 14.6382510537831 & 1.36174894621694 \tabularnewline
25 & 17 & 15.8416021360893 & 1.15839786391071 \tabularnewline
26 & 17 & 15.8344851325850 & 1.16551486741503 \tabularnewline
27 & 16 & 14.5294011985975 & 1.47059880140252 \tabularnewline
28 & 15 & 16.4169297769606 & -1.41692977696059 \tabularnewline
29 & 16 & 15.2955174564432 & 0.704482543556828 \tabularnewline
30 & 14 & 13.6173944562786 & 0.382605543721379 \tabularnewline
31 & 15 & 15.3672314701030 & -0.367231470103049 \tabularnewline
32 & 12 & 12.1577948842104 & -0.157794884210425 \tabularnewline
33 & 14 & 14.4670335229458 & -0.467033522945819 \tabularnewline
34 & 16 & 15.5490474808797 & 0.450952519120254 \tabularnewline
35 & 14 & 15.1519977706766 & -1.15199777067662 \tabularnewline
36 & 10 & 12.4548067707195 & -2.4548067707195 \tabularnewline
37 & 10 & 12.2908025947004 & -2.29080259470041 \tabularnewline
38 & 14 & 15.4428753504870 & -1.44287535048698 \tabularnewline
39 & 16 & 14.1125676496403 & 1.88743235035968 \tabularnewline
40 & 16 & 14.0503006909743 & 1.94969930902571 \tabularnewline
41 & 16 & 14.3599156612012 & 1.64008433879884 \tabularnewline
42 & 14 & 15.4129095162212 & -1.41290951622119 \tabularnewline
43 & 20 & 17.4975592437467 & 2.50244075625334 \tabularnewline
44 & 14 & 13.7379997106144 & 0.262000289385554 \tabularnewline
45 & 14 & 14.1390257843911 & -0.139025784391116 \tabularnewline
46 & 11 & 15.1505593881384 & -4.1505593881384 \tabularnewline
47 & 14 & 16.4190959972096 & -2.41909599720959 \tabularnewline
48 & 15 & 14.7812339122260 & 0.218766087773978 \tabularnewline
49 & 16 & 15.1915977233103 & 0.80840227668973 \tabularnewline
50 & 14 & 15.3615153637810 & -1.36151536378102 \tabularnewline
51 & 16 & 16.8537398386884 & -0.853739838688414 \tabularnewline
52 & 14 & 13.7316892738080 & 0.268310726191956 \tabularnewline
53 & 12 & 14.6138653598004 & -2.61386535980045 \tabularnewline
54 & 16 & 15.5906899034319 & 0.409310096568081 \tabularnewline
55 & 9 & 10.7137318072366 & -1.71373180723661 \tabularnewline
56 & 14 & 11.8326046050634 & 2.16739539493663 \tabularnewline
57 & 16 & 15.6937236900133 & 0.306276309986652 \tabularnewline
58 & 16 & 15.2303555712627 & 0.769644428737268 \tabularnewline
59 & 15 & 14.8230045585446 & 0.176995441455372 \tabularnewline
60 & 16 & 13.7932224493505 & 2.20677755064947 \tabularnewline
61 & 12 & 10.8367279492074 & 1.16327205079259 \tabularnewline
62 & 16 & 15.4378640177442 & 0.562135982255836 \tabularnewline
63 & 16 & 16.2989869704333 & -0.298986970433305 \tabularnewline
64 & 14 & 14.4388877335844 & -0.438887733584363 \tabularnewline
65 & 16 & 15.1500442700275 & 0.849955729972452 \tabularnewline
66 & 17 & 15.7438538414077 & 1.25614615859233 \tabularnewline
67 & 18 & 16.0903465015184 & 1.90965349848157 \tabularnewline
68 & 18 & 14.0764904918967 & 3.92350950810328 \tabularnewline
69 & 12 & 15.8084608807317 & -3.80846088073167 \tabularnewline
70 & 16 & 15.4728479832678 & 0.527152016732215 \tabularnewline
71 & 10 & 13.0122458267498 & -3.01224582674977 \tabularnewline
72 & 14 & 14.7156655383903 & -0.715665538390303 \tabularnewline
73 & 18 & 16.8379895442731 & 1.16201045572692 \tabularnewline
74 & 18 & 17.1928281858955 & 0.807171814104506 \tabularnewline
75 & 16 & 15.0094686892977 & 0.990531310702272 \tabularnewline
76 & 17 & 13.0902524925774 & 3.90974750742255 \tabularnewline
77 & 16 & 16.4072325443378 & -0.407232544337829 \tabularnewline
78 & 16 & 14.2122817226739 & 1.78771827732615 \tabularnewline
79 & 13 & 14.9614853283133 & -1.96148532831330 \tabularnewline
80 & 16 & 15.0831385107766 & 0.916861489223383 \tabularnewline
81 & 16 & 15.5360989694856 & 0.463901030514361 \tabularnewline
82 & 16 & 15.6432294770163 & 0.356770522983692 \tabularnewline
83 & 15 & 15.6201822872087 & -0.620182287208735 \tabularnewline
84 & 15 & 14.6861437075647 & 0.313856292435273 \tabularnewline
85 & 16 & 13.8827626275351 & 2.11723737246489 \tabularnewline
86 & 14 & 13.9517886120021 & 0.0482113879978810 \tabularnewline
87 & 16 & 15.2330941862993 & 0.76690581370072 \tabularnewline
88 & 16 & 14.6813029945340 & 1.31869700546595 \tabularnewline
89 & 15 & 14.1033381737051 & 0.896661826294864 \tabularnewline
90 & 12 & 13.6433035668958 & -1.64330356689577 \tabularnewline
91 & 17 & 16.8321246414488 & 0.167875358551157 \tabularnewline
92 & 16 & 15.7294852307762 & 0.270514769223806 \tabularnewline
93 & 15 & 14.9057923352787 & 0.0942076647212783 \tabularnewline
94 & 13 & 14.8364568188003 & -1.83645681880029 \tabularnewline
95 & 16 & 14.6980328060634 & 1.30196719393662 \tabularnewline
96 & 16 & 15.6960780466908 & 0.303921953309242 \tabularnewline
97 & 16 & 13.4832846792557 & 2.51671532074434 \tabularnewline
98 & 16 & 15.8300417014575 & 0.169958298542461 \tabularnewline
99 & 14 & 14.2661258632027 & -0.266125863202743 \tabularnewline
100 & 16 & 17.1940865071683 & -1.19408650716827 \tabularnewline
101 & 16 & 14.5059011287886 & 1.49409887121141 \tabularnewline
102 & 20 & 17.5790992836758 & 2.42090071632415 \tabularnewline
103 & 15 & 14.0226460140409 & 0.977353985959115 \tabularnewline
104 & 16 & 14.8383762518519 & 1.16162374814813 \tabularnewline
105 & 13 & 14.7124447739459 & -1.71244477394589 \tabularnewline
106 & 17 & 15.7604373152473 & 1.23956268475275 \tabularnewline
107 & 16 & 15.7720645248471 & 0.227935475152921 \tabularnewline
108 & 16 & 14.2326042643852 & 1.76739573561481 \tabularnewline
109 & 12 & 12.0024802659182 & -0.00248026591817174 \tabularnewline
110 & 16 & 15.1718385499616 & 0.828161450038395 \tabularnewline
111 & 16 & 15.9384880334039 & 0.0615119665961127 \tabularnewline
112 & 17 & 14.8625328282602 & 2.13746717173979 \tabularnewline
113 & 13 & 14.5286402413612 & -1.52864024136121 \tabularnewline
114 & 12 & 14.614568460888 & -2.61456846088799 \tabularnewline
115 & 18 & 16.2764524443605 & 1.72354755563947 \tabularnewline
116 & 14 & 15.9504759627417 & -1.95047596274165 \tabularnewline
117 & 14 & 12.9924621951951 & 1.00753780480488 \tabularnewline
118 & 13 & 14.7858242392293 & -1.78582423922934 \tabularnewline
119 & 16 & 15.5144579558839 & 0.48554204411607 \tabularnewline
120 & 13 & 14.3730308681848 & -1.37303086818477 \tabularnewline
121 & 16 & 15.4072695332184 & 0.592730466781637 \tabularnewline
122 & 13 & 15.9001483950515 & -2.90014839505153 \tabularnewline
123 & 16 & 17.0738036864139 & -1.07380368641386 \tabularnewline
124 & 15 & 15.9985652529538 & -0.998565252953813 \tabularnewline
125 & 16 & 16.9217558949015 & -0.921755894901497 \tabularnewline
126 & 15 & 14.7501471788131 & 0.249852821186918 \tabularnewline
127 & 17 & 15.7672102604739 & 1.23278973952610 \tabularnewline
128 & 15 & 14.0042319951691 & 0.995768004830924 \tabularnewline
129 & 12 & 14.7455979666711 & -2.74559796667113 \tabularnewline
130 & 16 & 13.9018224660129 & 2.09817753398713 \tabularnewline
131 & 10 & 13.4900189227280 & -3.49001892272798 \tabularnewline
132 & 16 & 13.4847293488174 & 2.51527065118258 \tabularnewline
133 & 12 & 14.1318955712898 & -2.13189557128976 \tabularnewline
134 & 14 & 15.6891601643807 & -1.68916016438072 \tabularnewline
135 & 15 & 15.1349525324793 & -0.134952532479329 \tabularnewline
136 & 13 & 11.8798702989229 & 1.12012970107707 \tabularnewline
137 & 15 & 14.5406759892864 & 0.459324010713589 \tabularnewline
138 & 11 & 13.3639577865016 & -2.36395778650162 \tabularnewline
139 & 12 & 12.9948717800782 & -0.994871780078202 \tabularnewline
140 & 11 & 13.2797734052426 & -2.27977340524255 \tabularnewline
141 & 16 & 12.8706670328481 & 3.12933296715187 \tabularnewline
142 & 15 & 13.5777970081450 & 1.42220299185502 \tabularnewline
143 & 17 & 17.0126296693111 & -0.0126296693111276 \tabularnewline
144 & 16 & 14.2177232846361 & 1.78227671536388 \tabularnewline
145 & 10 & 13.3076482519712 & -3.30764825197124 \tabularnewline
146 & 18 & 15.7108182646390 & 2.28918173536104 \tabularnewline
147 & 13 & 15.057515844885 & -2.05751584488500 \tabularnewline
148 & 16 & 14.9198086004653 & 1.08019139953471 \tabularnewline
149 & 13 & 12.6377638044032 & 0.362236195596831 \tabularnewline
150 & 10 & 12.8783257394112 & -2.8783257394112 \tabularnewline
151 & 15 & 16.1550432712530 & -1.15504327125297 \tabularnewline
152 & 16 & 13.9923608515066 & 2.00763914849345 \tabularnewline
153 & 16 & 11.7040450513149 & 4.29595494868507 \tabularnewline
154 & 14 & 12.2499675462609 & 1.75003245373915 \tabularnewline
155 & 10 & 12.3106299805489 & -2.31062998054890 \tabularnewline
156 & 17 & 16.8321246414488 & 0.167875358551157 \tabularnewline
157 & 13 & 11.5695393191606 & 1.43046068083939 \tabularnewline
158 & 15 & 14.0042319951691 & 0.995768004830924 \tabularnewline
159 & 16 & 14.6713955204085 & 1.32860447959148 \tabularnewline
160 & 12 & 12.5736517546432 & -0.573651754643187 \tabularnewline
161 & 13 & 12.6006036915371 & 0.39939630846291 \tabularnewline
162 & 13 & 12.4665513538839 & 0.53344864611615 \tabularnewline
163 & 12 & 12.3881607885568 & -0.388160788556804 \tabularnewline
164 & 17 & 16.5737899268770 & 0.42621007312302 \tabularnewline
165 & 15 & 13.6726004456851 & 1.32739955431489 \tabularnewline
166 & 10 & 11.4206728334644 & -1.42067283346442 \tabularnewline
167 & 14 & 14.4691619974894 & -0.469161997489407 \tabularnewline
168 & 11 & 14.2909865799885 & -3.29098657998851 \tabularnewline
169 & 13 & 14.8482927619942 & -1.84829276199418 \tabularnewline
170 & 16 & 14.4308747459494 & 1.56912525405060 \tabularnewline
171 & 12 & 10.3653118162962 & 1.63468818370377 \tabularnewline
172 & 16 & 15.7059483938507 & 0.294051606149257 \tabularnewline
173 & 12 & 13.9883936528962 & -1.98839365289617 \tabularnewline
174 & 9 & 11.3120335908731 & -2.31203359087306 \tabularnewline
175 & 12 & 15.2885887877223 & -3.28858878772229 \tabularnewline
176 & 15 & 14.7442131156564 & 0.255786884343579 \tabularnewline
177 & 12 & 12.2632972969965 & -0.263297296996452 \tabularnewline
178 & 12 & 12.6879346718068 & -0.68793467180684 \tabularnewline
179 & 14 & 14.0621066117256 & -0.0621066117256398 \tabularnewline
180 & 12 & 13.4762697050290 & -1.47626970502904 \tabularnewline
181 & 16 & 15.4340480772343 & 0.565951922765726 \tabularnewline
182 & 11 & 11.5217489928797 & -0.521748992879665 \tabularnewline
183 & 19 & 17.1604632713109 & 1.83953672868908 \tabularnewline
184 & 15 & 15.5206096079202 & -0.520609607920211 \tabularnewline
185 & 8 & 14.7662880903322 & -6.76628809033216 \tabularnewline
186 & 16 & 14.9680927196659 & 1.03190728033408 \tabularnewline
187 & 17 & 14.7476768686274 & 2.2523231313726 \tabularnewline
188 & 12 & 12.5418032885651 & -0.541803288565062 \tabularnewline
189 & 11 & 11.5269675255856 & -0.526967525585558 \tabularnewline
190 & 11 & 10.2993224089426 & 0.700677591057419 \tabularnewline
191 & 14 & 15.0449039081772 & -1.04490390817718 \tabularnewline
192 & 16 & 15.8549236374795 & 0.145076362520500 \tabularnewline
193 & 12 & 9.65799989258033 & 2.34200010741967 \tabularnewline
194 & 16 & 14.3005132593300 & 1.69948674067001 \tabularnewline
195 & 13 & 13.9159846340245 & -0.915984634024543 \tabularnewline
196 & 15 & 15.3686335282855 & -0.368633528285528 \tabularnewline
197 & 16 & 13.0983431475463 & 2.90165685245371 \tabularnewline
198 & 16 & 15.3129697531759 & 0.687030246824146 \tabularnewline
199 & 14 & 12.4874552007084 & 1.51254479929164 \tabularnewline
200 & 16 & 14.7705797810563 & 1.22942021894372 \tabularnewline
201 & 16 & 14.1953897769368 & 1.80461022306323 \tabularnewline
202 & 14 & 13.5652608133924 & 0.434739186607567 \tabularnewline
203 & 11 & 13.7622976777595 & -2.76229767775952 \tabularnewline
204 & 12 & 14.9165699771728 & -2.91656997717275 \tabularnewline
205 & 15 & 12.9696665181991 & 2.03033348180092 \tabularnewline
206 & 15 & 14.8369644200455 & 0.163035579954474 \tabularnewline
207 & 16 & 14.847026495686 & 1.15297350431399 \tabularnewline
208 & 16 & 15.4339487103626 & 0.566051289637426 \tabularnewline
209 & 11 & 13.9167631437977 & -2.91676314379775 \tabularnewline
210 & 15 & 14.3031594704995 & 0.696840529500508 \tabularnewline
211 & 12 & 14.507489606999 & -2.507489606999 \tabularnewline
212 & 12 & 16.2896849694461 & -4.28968496944614 \tabularnewline
213 & 15 & 14.3091704811001 & 0.690829518899902 \tabularnewline
214 & 15 & 12.1674958883062 & 2.83250411169378 \tabularnewline
215 & 16 & 14.8832893443442 & 1.11671065565580 \tabularnewline
216 & 14 & 13.4207878308238 & 0.57921216917622 \tabularnewline
217 & 17 & 15.0626619782301 & 1.93733802176992 \tabularnewline
218 & 14 & 14.3279169397482 & -0.327916939748238 \tabularnewline
219 & 13 & 11.9902889016349 & 1.00971109836511 \tabularnewline
220 & 15 & 15.6158919610783 & -0.615891961078344 \tabularnewline
221 & 13 & 15.0224913327296 & -2.02249133272956 \tabularnewline
222 & 14 & 14.5733033465195 & -0.573303346519544 \tabularnewline
223 & 15 & 14.5664459129041 & 0.43355408709592 \tabularnewline
224 & 12 & 13.4585313702885 & -1.45853137028855 \tabularnewline
225 & 13 & 12.8042807399043 & 0.195719260095656 \tabularnewline
226 & 8 & 11.9668684285413 & -3.96686842854126 \tabularnewline
227 & 14 & 14.3096831383973 & -0.309683138397306 \tabularnewline
228 & 14 & 13.2737435527865 & 0.726256447213536 \tabularnewline
229 & 11 & 12.3432695732266 & -1.34326957322662 \tabularnewline
230 & 12 & 13.1609985574185 & -1.16099855741854 \tabularnewline
231 & 13 & 11.4799323515281 & 1.52006764847186 \tabularnewline
232 & 10 & 13.4650403340905 & -3.46504033409047 \tabularnewline
233 & 16 & 11.7004538222459 & 4.29954617775412 \tabularnewline
234 & 18 & 16.4558663707500 & 1.54413362925002 \tabularnewline
235 & 13 & 14.3053988244790 & -1.30539882447904 \tabularnewline
236 & 11 & 13.6549207425401 & -2.65492074254014 \tabularnewline
237 & 4 & 11.1700281372820 & -7.17002813728201 \tabularnewline
238 & 13 & 14.8152140281387 & -1.81521402813869 \tabularnewline
239 & 16 & 14.5923633054321 & 1.40763669456793 \tabularnewline
240 & 10 & 11.9287261285823 & -1.92872612858232 \tabularnewline
241 & 12 & 12.4071970658913 & -0.407197065891325 \tabularnewline
242 & 12 & 13.7956003816510 & -1.79560038165098 \tabularnewline
243 & 10 & 8.88634470078797 & 1.11365529921203 \tabularnewline
244 & 13 & 11.2770068802368 & 1.72299311976323 \tabularnewline
245 & 15 & 14.0926273160205 & 0.907372683979529 \tabularnewline
246 & 12 & 12.0467701780665 & -0.0467701780665329 \tabularnewline
247 & 14 & 13.1538828662063 & 0.846117133793704 \tabularnewline
248 & 10 & 12.8902928435778 & -2.89029284357777 \tabularnewline
249 & 12 & 10.8009658204609 & 1.19903417953906 \tabularnewline
250 & 12 & 11.8839336289646 & 0.116066371035425 \tabularnewline
251 & 11 & 12.1379198030589 & -1.13791980305892 \tabularnewline
252 & 10 & 11.8622634576053 & -1.86226345760533 \tabularnewline
253 & 12 & 11.6631437816570 & 0.336856218342975 \tabularnewline
254 & 16 & 13.1782247636368 & 2.82177523636317 \tabularnewline
255 & 12 & 13.7152063049677 & -1.71520630496769 \tabularnewline
256 & 14 & 14.2640878596850 & -0.264087859685045 \tabularnewline
257 & 16 & 14.6811885572799 & 1.31881144272010 \tabularnewline
258 & 14 & 11.8239286814975 & 2.17607131850247 \tabularnewline
259 & 13 & 14.7816409840600 & -1.78164098406003 \tabularnewline
260 & 4 & 9.48330674262236 & -5.48330674262236 \tabularnewline
261 & 15 & 14.174366651485 & 0.825633348514991 \tabularnewline
262 & 11 & 15.5502096888069 & -4.55020968880690 \tabularnewline
263 & 11 & 11.4716452522508 & -0.471645252250818 \tabularnewline
264 & 14 & 13.1672908525232 & 0.83270914747681 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96530&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]15.7169326674778[/C][C]-2.71693266747778[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.3046452988162[/C][C]0.695354701183782[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.5342266059759[/C][C]2.46577339402406[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]11.2431990710256[/C][C]3.7568009289744[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.8896873324328[/C][C]-1.88968733243279[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.3424239462714[/C][C]-1.34242394627140[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]14.8381920087898[/C][C]4.16180799121016[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.6568417465609[/C][C]-1.65684174656087[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.6143966049743[/C][C]-1.61439660497434[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]13.8398822625452[/C][C]1.16011773745481[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]14.5244132197985[/C][C]1.47558678020148[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]15.9613451914131[/C][C]0.0386548085868819[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]14.9654168965065[/C][C]1.03458310349348[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.1305945765763[/C][C]0.86940542342365[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.7266051778577[/C][C]-0.726605177857671[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.0073602272847[/C][C]-0.00736022728472072[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.0333112689484[/C][C]0.966688731051586[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.0395466632557[/C][C]3.96045333674427[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.09418616242[/C][C]2.90581383758001[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.1861376221519[/C][C]0.813862377848057[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.0015368334676[/C][C]0.99846316653241[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.5838648261738[/C][C]1.41613517382616[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.2269009876065[/C][C]2.77309901239347[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.6382510537831[/C][C]1.36174894621694[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.8416021360893[/C][C]1.15839786391071[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]15.8344851325850[/C][C]1.16551486741503[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.5294011985975[/C][C]1.47059880140252[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.4169297769606[/C][C]-1.41692977696059[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.2955174564432[/C][C]0.704482543556828[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]13.6173944562786[/C][C]0.382605543721379[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.3672314701030[/C][C]-0.367231470103049[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.1577948842104[/C][C]-0.157794884210425[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.4670335229458[/C][C]-0.467033522945819[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.5490474808797[/C][C]0.450952519120254[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.1519977706766[/C][C]-1.15199777067662[/C][/ROW]
[ROW][C]36[/C][C]10[/C][C]12.4548067707195[/C][C]-2.4548067707195[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]12.2908025947004[/C][C]-2.29080259470041[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.4428753504870[/C][C]-1.44287535048698[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.1125676496403[/C][C]1.88743235035968[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.0503006909743[/C][C]1.94969930902571[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]14.3599156612012[/C][C]1.64008433879884[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.4129095162212[/C][C]-1.41290951622119[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.4975592437467[/C][C]2.50244075625334[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]13.7379997106144[/C][C]0.262000289385554[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.1390257843911[/C][C]-0.139025784391116[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.1505593881384[/C][C]-4.1505593881384[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.4190959972096[/C][C]-2.41909599720959[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.7812339122260[/C][C]0.218766087773978[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.1915977233103[/C][C]0.80840227668973[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.3615153637810[/C][C]-1.36151536378102[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.8537398386884[/C][C]-0.853739838688414[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.7316892738080[/C][C]0.268310726191956[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.6138653598004[/C][C]-2.61386535980045[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.5906899034319[/C][C]0.409310096568081[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]10.7137318072366[/C][C]-1.71373180723661[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]11.8326046050634[/C][C]2.16739539493663[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.6937236900133[/C][C]0.306276309986652[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.2303555712627[/C][C]0.769644428737268[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]14.8230045585446[/C][C]0.176995441455372[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]13.7932224493505[/C][C]2.20677755064947[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]10.8367279492074[/C][C]1.16327205079259[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.4378640177442[/C][C]0.562135982255836[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.2989869704333[/C][C]-0.298986970433305[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.4388877335844[/C][C]-0.438887733584363[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.1500442700275[/C][C]0.849955729972452[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.7438538414077[/C][C]1.25614615859233[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.0903465015184[/C][C]1.90965349848157[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.0764904918967[/C][C]3.92350950810328[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.8084608807317[/C][C]-3.80846088073167[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.4728479832678[/C][C]0.527152016732215[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.0122458267498[/C][C]-3.01224582674977[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.7156655383903[/C][C]-0.715665538390303[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.8379895442731[/C][C]1.16201045572692[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.1928281858955[/C][C]0.807171814104506[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.0094686892977[/C][C]0.990531310702272[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.0902524925774[/C][C]3.90974750742255[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.4072325443378[/C][C]-0.407232544337829[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.2122817226739[/C][C]1.78771827732615[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.9614853283133[/C][C]-1.96148532831330[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.0831385107766[/C][C]0.916861489223383[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.5360989694856[/C][C]0.463901030514361[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]15.6432294770163[/C][C]0.356770522983692[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.6201822872087[/C][C]-0.620182287208735[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]14.6861437075647[/C][C]0.313856292435273[/C][/ROW]
[ROW][C]85[/C][C]16[/C][C]13.8827626275351[/C][C]2.11723737246489[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]13.9517886120021[/C][C]0.0482113879978810[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]15.2330941862993[/C][C]0.76690581370072[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]14.6813029945340[/C][C]1.31869700546595[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.1033381737051[/C][C]0.896661826294864[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]13.6433035668958[/C][C]-1.64330356689577[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]16.8321246414488[/C][C]0.167875358551157[/C][/ROW]
[ROW][C]92[/C][C]16[/C][C]15.7294852307762[/C][C]0.270514769223806[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]14.9057923352787[/C][C]0.0942076647212783[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]14.8364568188003[/C][C]-1.83645681880029[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]14.6980328060634[/C][C]1.30196719393662[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.6960780466908[/C][C]0.303921953309242[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]13.4832846792557[/C][C]2.51671532074434[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]15.8300417014575[/C][C]0.169958298542461[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]14.2661258632027[/C][C]-0.266125863202743[/C][/ROW]
[ROW][C]100[/C][C]16[/C][C]17.1940865071683[/C][C]-1.19408650716827[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]14.5059011287886[/C][C]1.49409887121141[/C][/ROW]
[ROW][C]102[/C][C]20[/C][C]17.5790992836758[/C][C]2.42090071632415[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]14.0226460140409[/C][C]0.977353985959115[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]14.8383762518519[/C][C]1.16162374814813[/C][/ROW]
[ROW][C]105[/C][C]13[/C][C]14.7124447739459[/C][C]-1.71244477394589[/C][/ROW]
[ROW][C]106[/C][C]17[/C][C]15.7604373152473[/C][C]1.23956268475275[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.7720645248471[/C][C]0.227935475152921[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]14.2326042643852[/C][C]1.76739573561481[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]12.0024802659182[/C][C]-0.00248026591817174[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]15.1718385499616[/C][C]0.828161450038395[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.9384880334039[/C][C]0.0615119665961127[/C][/ROW]
[ROW][C]112[/C][C]17[/C][C]14.8625328282602[/C][C]2.13746717173979[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]14.5286402413612[/C][C]-1.52864024136121[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]14.614568460888[/C][C]-2.61456846088799[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]16.2764524443605[/C][C]1.72354755563947[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]15.9504759627417[/C][C]-1.95047596274165[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]12.9924621951951[/C][C]1.00753780480488[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]14.7858242392293[/C][C]-1.78582423922934[/C][/ROW]
[ROW][C]119[/C][C]16[/C][C]15.5144579558839[/C][C]0.48554204411607[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]14.3730308681848[/C][C]-1.37303086818477[/C][/ROW]
[ROW][C]121[/C][C]16[/C][C]15.4072695332184[/C][C]0.592730466781637[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]15.9001483950515[/C][C]-2.90014839505153[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]17.0738036864139[/C][C]-1.07380368641386[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]15.9985652529538[/C][C]-0.998565252953813[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]16.9217558949015[/C][C]-0.921755894901497[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]14.7501471788131[/C][C]0.249852821186918[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]15.7672102604739[/C][C]1.23278973952610[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]14.0042319951691[/C][C]0.995768004830924[/C][/ROW]
[ROW][C]129[/C][C]12[/C][C]14.7455979666711[/C][C]-2.74559796667113[/C][/ROW]
[ROW][C]130[/C][C]16[/C][C]13.9018224660129[/C][C]2.09817753398713[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]13.4900189227280[/C][C]-3.49001892272798[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]13.4847293488174[/C][C]2.51527065118258[/C][/ROW]
[ROW][C]133[/C][C]12[/C][C]14.1318955712898[/C][C]-2.13189557128976[/C][/ROW]
[ROW][C]134[/C][C]14[/C][C]15.6891601643807[/C][C]-1.68916016438072[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]15.1349525324793[/C][C]-0.134952532479329[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]11.8798702989229[/C][C]1.12012970107707[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]14.5406759892864[/C][C]0.459324010713589[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]13.3639577865016[/C][C]-2.36395778650162[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]12.9948717800782[/C][C]-0.994871780078202[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]13.2797734052426[/C][C]-2.27977340524255[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]12.8706670328481[/C][C]3.12933296715187[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]13.5777970081450[/C][C]1.42220299185502[/C][/ROW]
[ROW][C]143[/C][C]17[/C][C]17.0126296693111[/C][C]-0.0126296693111276[/C][/ROW]
[ROW][C]144[/C][C]16[/C][C]14.2177232846361[/C][C]1.78227671536388[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]13.3076482519712[/C][C]-3.30764825197124[/C][/ROW]
[ROW][C]146[/C][C]18[/C][C]15.7108182646390[/C][C]2.28918173536104[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]15.057515844885[/C][C]-2.05751584488500[/C][/ROW]
[ROW][C]148[/C][C]16[/C][C]14.9198086004653[/C][C]1.08019139953471[/C][/ROW]
[ROW][C]149[/C][C]13[/C][C]12.6377638044032[/C][C]0.362236195596831[/C][/ROW]
[ROW][C]150[/C][C]10[/C][C]12.8783257394112[/C][C]-2.8783257394112[/C][/ROW]
[ROW][C]151[/C][C]15[/C][C]16.1550432712530[/C][C]-1.15504327125297[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]13.9923608515066[/C][C]2.00763914849345[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]11.7040450513149[/C][C]4.29595494868507[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]12.2499675462609[/C][C]1.75003245373915[/C][/ROW]
[ROW][C]155[/C][C]10[/C][C]12.3106299805489[/C][C]-2.31062998054890[/C][/ROW]
[ROW][C]156[/C][C]17[/C][C]16.8321246414488[/C][C]0.167875358551157[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]11.5695393191606[/C][C]1.43046068083939[/C][/ROW]
[ROW][C]158[/C][C]15[/C][C]14.0042319951691[/C][C]0.995768004830924[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]14.6713955204085[/C][C]1.32860447959148[/C][/ROW]
[ROW][C]160[/C][C]12[/C][C]12.5736517546432[/C][C]-0.573651754643187[/C][/ROW]
[ROW][C]161[/C][C]13[/C][C]12.6006036915371[/C][C]0.39939630846291[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.4665513538839[/C][C]0.53344864611615[/C][/ROW]
[ROW][C]163[/C][C]12[/C][C]12.3881607885568[/C][C]-0.388160788556804[/C][/ROW]
[ROW][C]164[/C][C]17[/C][C]16.5737899268770[/C][C]0.42621007312302[/C][/ROW]
[ROW][C]165[/C][C]15[/C][C]13.6726004456851[/C][C]1.32739955431489[/C][/ROW]
[ROW][C]166[/C][C]10[/C][C]11.4206728334644[/C][C]-1.42067283346442[/C][/ROW]
[ROW][C]167[/C][C]14[/C][C]14.4691619974894[/C][C]-0.469161997489407[/C][/ROW]
[ROW][C]168[/C][C]11[/C][C]14.2909865799885[/C][C]-3.29098657998851[/C][/ROW]
[ROW][C]169[/C][C]13[/C][C]14.8482927619942[/C][C]-1.84829276199418[/C][/ROW]
[ROW][C]170[/C][C]16[/C][C]14.4308747459494[/C][C]1.56912525405060[/C][/ROW]
[ROW][C]171[/C][C]12[/C][C]10.3653118162962[/C][C]1.63468818370377[/C][/ROW]
[ROW][C]172[/C][C]16[/C][C]15.7059483938507[/C][C]0.294051606149257[/C][/ROW]
[ROW][C]173[/C][C]12[/C][C]13.9883936528962[/C][C]-1.98839365289617[/C][/ROW]
[ROW][C]174[/C][C]9[/C][C]11.3120335908731[/C][C]-2.31203359087306[/C][/ROW]
[ROW][C]175[/C][C]12[/C][C]15.2885887877223[/C][C]-3.28858878772229[/C][/ROW]
[ROW][C]176[/C][C]15[/C][C]14.7442131156564[/C][C]0.255786884343579[/C][/ROW]
[ROW][C]177[/C][C]12[/C][C]12.2632972969965[/C][C]-0.263297296996452[/C][/ROW]
[ROW][C]178[/C][C]12[/C][C]12.6879346718068[/C][C]-0.68793467180684[/C][/ROW]
[ROW][C]179[/C][C]14[/C][C]14.0621066117256[/C][C]-0.0621066117256398[/C][/ROW]
[ROW][C]180[/C][C]12[/C][C]13.4762697050290[/C][C]-1.47626970502904[/C][/ROW]
[ROW][C]181[/C][C]16[/C][C]15.4340480772343[/C][C]0.565951922765726[/C][/ROW]
[ROW][C]182[/C][C]11[/C][C]11.5217489928797[/C][C]-0.521748992879665[/C][/ROW]
[ROW][C]183[/C][C]19[/C][C]17.1604632713109[/C][C]1.83953672868908[/C][/ROW]
[ROW][C]184[/C][C]15[/C][C]15.5206096079202[/C][C]-0.520609607920211[/C][/ROW]
[ROW][C]185[/C][C]8[/C][C]14.7662880903322[/C][C]-6.76628809033216[/C][/ROW]
[ROW][C]186[/C][C]16[/C][C]14.9680927196659[/C][C]1.03190728033408[/C][/ROW]
[ROW][C]187[/C][C]17[/C][C]14.7476768686274[/C][C]2.2523231313726[/C][/ROW]
[ROW][C]188[/C][C]12[/C][C]12.5418032885651[/C][C]-0.541803288565062[/C][/ROW]
[ROW][C]189[/C][C]11[/C][C]11.5269675255856[/C][C]-0.526967525585558[/C][/ROW]
[ROW][C]190[/C][C]11[/C][C]10.2993224089426[/C][C]0.700677591057419[/C][/ROW]
[ROW][C]191[/C][C]14[/C][C]15.0449039081772[/C][C]-1.04490390817718[/C][/ROW]
[ROW][C]192[/C][C]16[/C][C]15.8549236374795[/C][C]0.145076362520500[/C][/ROW]
[ROW][C]193[/C][C]12[/C][C]9.65799989258033[/C][C]2.34200010741967[/C][/ROW]
[ROW][C]194[/C][C]16[/C][C]14.3005132593300[/C][C]1.69948674067001[/C][/ROW]
[ROW][C]195[/C][C]13[/C][C]13.9159846340245[/C][C]-0.915984634024543[/C][/ROW]
[ROW][C]196[/C][C]15[/C][C]15.3686335282855[/C][C]-0.368633528285528[/C][/ROW]
[ROW][C]197[/C][C]16[/C][C]13.0983431475463[/C][C]2.90165685245371[/C][/ROW]
[ROW][C]198[/C][C]16[/C][C]15.3129697531759[/C][C]0.687030246824146[/C][/ROW]
[ROW][C]199[/C][C]14[/C][C]12.4874552007084[/C][C]1.51254479929164[/C][/ROW]
[ROW][C]200[/C][C]16[/C][C]14.7705797810563[/C][C]1.22942021894372[/C][/ROW]
[ROW][C]201[/C][C]16[/C][C]14.1953897769368[/C][C]1.80461022306323[/C][/ROW]
[ROW][C]202[/C][C]14[/C][C]13.5652608133924[/C][C]0.434739186607567[/C][/ROW]
[ROW][C]203[/C][C]11[/C][C]13.7622976777595[/C][C]-2.76229767775952[/C][/ROW]
[ROW][C]204[/C][C]12[/C][C]14.9165699771728[/C][C]-2.91656997717275[/C][/ROW]
[ROW][C]205[/C][C]15[/C][C]12.9696665181991[/C][C]2.03033348180092[/C][/ROW]
[ROW][C]206[/C][C]15[/C][C]14.8369644200455[/C][C]0.163035579954474[/C][/ROW]
[ROW][C]207[/C][C]16[/C][C]14.847026495686[/C][C]1.15297350431399[/C][/ROW]
[ROW][C]208[/C][C]16[/C][C]15.4339487103626[/C][C]0.566051289637426[/C][/ROW]
[ROW][C]209[/C][C]11[/C][C]13.9167631437977[/C][C]-2.91676314379775[/C][/ROW]
[ROW][C]210[/C][C]15[/C][C]14.3031594704995[/C][C]0.696840529500508[/C][/ROW]
[ROW][C]211[/C][C]12[/C][C]14.507489606999[/C][C]-2.507489606999[/C][/ROW]
[ROW][C]212[/C][C]12[/C][C]16.2896849694461[/C][C]-4.28968496944614[/C][/ROW]
[ROW][C]213[/C][C]15[/C][C]14.3091704811001[/C][C]0.690829518899902[/C][/ROW]
[ROW][C]214[/C][C]15[/C][C]12.1674958883062[/C][C]2.83250411169378[/C][/ROW]
[ROW][C]215[/C][C]16[/C][C]14.8832893443442[/C][C]1.11671065565580[/C][/ROW]
[ROW][C]216[/C][C]14[/C][C]13.4207878308238[/C][C]0.57921216917622[/C][/ROW]
[ROW][C]217[/C][C]17[/C][C]15.0626619782301[/C][C]1.93733802176992[/C][/ROW]
[ROW][C]218[/C][C]14[/C][C]14.3279169397482[/C][C]-0.327916939748238[/C][/ROW]
[ROW][C]219[/C][C]13[/C][C]11.9902889016349[/C][C]1.00971109836511[/C][/ROW]
[ROW][C]220[/C][C]15[/C][C]15.6158919610783[/C][C]-0.615891961078344[/C][/ROW]
[ROW][C]221[/C][C]13[/C][C]15.0224913327296[/C][C]-2.02249133272956[/C][/ROW]
[ROW][C]222[/C][C]14[/C][C]14.5733033465195[/C][C]-0.573303346519544[/C][/ROW]
[ROW][C]223[/C][C]15[/C][C]14.5664459129041[/C][C]0.43355408709592[/C][/ROW]
[ROW][C]224[/C][C]12[/C][C]13.4585313702885[/C][C]-1.45853137028855[/C][/ROW]
[ROW][C]225[/C][C]13[/C][C]12.8042807399043[/C][C]0.195719260095656[/C][/ROW]
[ROW][C]226[/C][C]8[/C][C]11.9668684285413[/C][C]-3.96686842854126[/C][/ROW]
[ROW][C]227[/C][C]14[/C][C]14.3096831383973[/C][C]-0.309683138397306[/C][/ROW]
[ROW][C]228[/C][C]14[/C][C]13.2737435527865[/C][C]0.726256447213536[/C][/ROW]
[ROW][C]229[/C][C]11[/C][C]12.3432695732266[/C][C]-1.34326957322662[/C][/ROW]
[ROW][C]230[/C][C]12[/C][C]13.1609985574185[/C][C]-1.16099855741854[/C][/ROW]
[ROW][C]231[/C][C]13[/C][C]11.4799323515281[/C][C]1.52006764847186[/C][/ROW]
[ROW][C]232[/C][C]10[/C][C]13.4650403340905[/C][C]-3.46504033409047[/C][/ROW]
[ROW][C]233[/C][C]16[/C][C]11.7004538222459[/C][C]4.29954617775412[/C][/ROW]
[ROW][C]234[/C][C]18[/C][C]16.4558663707500[/C][C]1.54413362925002[/C][/ROW]
[ROW][C]235[/C][C]13[/C][C]14.3053988244790[/C][C]-1.30539882447904[/C][/ROW]
[ROW][C]236[/C][C]11[/C][C]13.6549207425401[/C][C]-2.65492074254014[/C][/ROW]
[ROW][C]237[/C][C]4[/C][C]11.1700281372820[/C][C]-7.17002813728201[/C][/ROW]
[ROW][C]238[/C][C]13[/C][C]14.8152140281387[/C][C]-1.81521402813869[/C][/ROW]
[ROW][C]239[/C][C]16[/C][C]14.5923633054321[/C][C]1.40763669456793[/C][/ROW]
[ROW][C]240[/C][C]10[/C][C]11.9287261285823[/C][C]-1.92872612858232[/C][/ROW]
[ROW][C]241[/C][C]12[/C][C]12.4071970658913[/C][C]-0.407197065891325[/C][/ROW]
[ROW][C]242[/C][C]12[/C][C]13.7956003816510[/C][C]-1.79560038165098[/C][/ROW]
[ROW][C]243[/C][C]10[/C][C]8.88634470078797[/C][C]1.11365529921203[/C][/ROW]
[ROW][C]244[/C][C]13[/C][C]11.2770068802368[/C][C]1.72299311976323[/C][/ROW]
[ROW][C]245[/C][C]15[/C][C]14.0926273160205[/C][C]0.907372683979529[/C][/ROW]
[ROW][C]246[/C][C]12[/C][C]12.0467701780665[/C][C]-0.0467701780665329[/C][/ROW]
[ROW][C]247[/C][C]14[/C][C]13.1538828662063[/C][C]0.846117133793704[/C][/ROW]
[ROW][C]248[/C][C]10[/C][C]12.8902928435778[/C][C]-2.89029284357777[/C][/ROW]
[ROW][C]249[/C][C]12[/C][C]10.8009658204609[/C][C]1.19903417953906[/C][/ROW]
[ROW][C]250[/C][C]12[/C][C]11.8839336289646[/C][C]0.116066371035425[/C][/ROW]
[ROW][C]251[/C][C]11[/C][C]12.1379198030589[/C][C]-1.13791980305892[/C][/ROW]
[ROW][C]252[/C][C]10[/C][C]11.8622634576053[/C][C]-1.86226345760533[/C][/ROW]
[ROW][C]253[/C][C]12[/C][C]11.6631437816570[/C][C]0.336856218342975[/C][/ROW]
[ROW][C]254[/C][C]16[/C][C]13.1782247636368[/C][C]2.82177523636317[/C][/ROW]
[ROW][C]255[/C][C]12[/C][C]13.7152063049677[/C][C]-1.71520630496769[/C][/ROW]
[ROW][C]256[/C][C]14[/C][C]14.2640878596850[/C][C]-0.264087859685045[/C][/ROW]
[ROW][C]257[/C][C]16[/C][C]14.6811885572799[/C][C]1.31881144272010[/C][/ROW]
[ROW][C]258[/C][C]14[/C][C]11.8239286814975[/C][C]2.17607131850247[/C][/ROW]
[ROW][C]259[/C][C]13[/C][C]14.7816409840600[/C][C]-1.78164098406003[/C][/ROW]
[ROW][C]260[/C][C]4[/C][C]9.48330674262236[/C][C]-5.48330674262236[/C][/ROW]
[ROW][C]261[/C][C]15[/C][C]14.174366651485[/C][C]0.825633348514991[/C][/ROW]
[ROW][C]262[/C][C]11[/C][C]15.5502096888069[/C][C]-4.55020968880690[/C][/ROW]
[ROW][C]263[/C][C]11[/C][C]11.4716452522508[/C][C]-0.471645252250818[/C][/ROW]
[ROW][C]264[/C][C]14[/C][C]13.1672908525232[/C][C]0.83270914747681[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96530&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96530&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 Index Actuals InterpolationForecast ResidualsPrediction Error 1 13 15.7169326674778 -2.71693266747778 2 16 15.3046452988162 0.695354701183782 3 19 16.5342266059759 2.46577339402406 4 15 11.2431990710256 3.7568009289744 5 14 15.8896873324328 -1.88968733243279 6 13 14.3424239462714 -1.34242394627140 7 19 14.8381920087898 4.16180799121016 8 15 16.6568417465609 -1.65684174656087 9 14 15.6143966049743 -1.61439660497434 10 15 13.8398822625452 1.16011773745481 11 16 14.5244132197985 1.47558678020148 12 16 15.9613451914131 0.0386548085868819 13 16 14.9654168965065 1.03458310349348 14 16 15.1305945765763 0.86940542342365 15 17 17.7266051778577 -0.726605177857671 16 15 15.0073602272847 -0.00736022728472072 17 15 14.0333112689484 0.966688731051586 18 20 16.0395466632557 3.96045333674427 19 18 15.09418616242 2.90581383758001 20 16 15.1861376221519 0.813862377848057 21 16 15.0015368334676 0.99846316653241 22 16 14.5838648261738 1.41613517382616 23 19 16.2269009876065 2.77309901239347 24 16 14.6382510537831 1.36174894621694 25 17 15.8416021360893 1.15839786391071 26 17 15.8344851325850 1.16551486741503 27 16 14.5294011985975 1.47059880140252 28 15 16.4169297769606 -1.41692977696059 29 16 15.2955174564432 0.704482543556828 30 14 13.6173944562786 0.382605543721379 31 15 15.3672314701030 -0.367231470103049 32 12 12.1577948842104 -0.157794884210425 33 14 14.4670335229458 -0.467033522945819 34 16 15.5490474808797 0.450952519120254 35 14 15.1519977706766 -1.15199777067662 36 10 12.4548067707195 -2.4548067707195 37 10 12.2908025947004 -2.29080259470041 38 14 15.4428753504870 -1.44287535048698 39 16 14.1125676496403 1.88743235035968 40 16 14.0503006909743 1.94969930902571 41 16 14.3599156612012 1.64008433879884 42 14 15.4129095162212 -1.41290951622119 43 20 17.4975592437467 2.50244075625334 44 14 13.7379997106144 0.262000289385554 45 14 14.1390257843911 -0.139025784391116 46 11 15.1505593881384 -4.1505593881384 47 14 16.4190959972096 -2.41909599720959 48 15 14.7812339122260 0.218766087773978 49 16 15.1915977233103 0.80840227668973 50 14 15.3615153637810 -1.36151536378102 51 16 16.8537398386884 -0.853739838688414 52 14 13.7316892738080 0.268310726191956 53 12 14.6138653598004 -2.61386535980045 54 16 15.5906899034319 0.409310096568081 55 9 10.7137318072366 -1.71373180723661 56 14 11.8326046050634 2.16739539493663 57 16 15.6937236900133 0.306276309986652 58 16 15.2303555712627 0.769644428737268 59 15 14.8230045585446 0.176995441455372 60 16 13.7932224493505 2.20677755064947 61 12 10.8367279492074 1.16327205079259 62 16 15.4378640177442 0.562135982255836 63 16 16.2989869704333 -0.298986970433305 64 14 14.4388877335844 -0.438887733584363 65 16 15.1500442700275 0.849955729972452 66 17 15.7438538414077 1.25614615859233 67 18 16.0903465015184 1.90965349848157 68 18 14.0764904918967 3.92350950810328 69 12 15.8084608807317 -3.80846088073167 70 16 15.4728479832678 0.527152016732215 71 10 13.0122458267498 -3.01224582674977 72 14 14.7156655383903 -0.715665538390303 73 18 16.8379895442731 1.16201045572692 74 18 17.1928281858955 0.807171814104506 75 16 15.0094686892977 0.990531310702272 76 17 13.0902524925774 3.90974750742255 77 16 16.4072325443378 -0.407232544337829 78 16 14.2122817226739 1.78771827732615 79 13 14.9614853283133 -1.96148532831330 80 16 15.0831385107766 0.916861489223383 81 16 15.5360989694856 0.463901030514361 82 16 15.6432294770163 0.356770522983692 83 15 15.6201822872087 -0.620182287208735 84 15 14.6861437075647 0.313856292435273 85 16 13.8827626275351 2.11723737246489 86 14 13.9517886120021 0.0482113879978810 87 16 15.2330941862993 0.76690581370072 88 16 14.6813029945340 1.31869700546595 89 15 14.1033381737051 0.896661826294864 90 12 13.6433035668958 -1.64330356689577 91 17 16.8321246414488 0.167875358551157 92 16 15.7294852307762 0.270514769223806 93 15 14.9057923352787 0.0942076647212783 94 13 14.8364568188003 -1.83645681880029 95 16 14.6980328060634 1.30196719393662 96 16 15.6960780466908 0.303921953309242 97 16 13.4832846792557 2.51671532074434 98 16 15.8300417014575 0.169958298542461 99 14 14.2661258632027 -0.266125863202743 100 16 17.1940865071683 -1.19408650716827 101 16 14.5059011287886 1.49409887121141 102 20 17.5790992836758 2.42090071632415 103 15 14.0226460140409 0.977353985959115 104 16 14.8383762518519 1.16162374814813 105 13 14.7124447739459 -1.71244477394589 106 17 15.7604373152473 1.23956268475275 107 16 15.7720645248471 0.227935475152921 108 16 14.2326042643852 1.76739573561481 109 12 12.0024802659182 -0.00248026591817174 110 16 15.1718385499616 0.828161450038395 111 16 15.9384880334039 0.0615119665961127 112 17 14.8625328282602 2.13746717173979 113 13 14.5286402413612 -1.52864024136121 114 12 14.614568460888 -2.61456846088799 115 18 16.2764524443605 1.72354755563947 116 14 15.9504759627417 -1.95047596274165 117 14 12.9924621951951 1.00753780480488 118 13 14.7858242392293 -1.78582423922934 119 16 15.5144579558839 0.48554204411607 120 13 14.3730308681848 -1.37303086818477 121 16 15.4072695332184 0.592730466781637 122 13 15.9001483950515 -2.90014839505153 123 16 17.0738036864139 -1.07380368641386 124 15 15.9985652529538 -0.998565252953813 125 16 16.9217558949015 -0.921755894901497 126 15 14.7501471788131 0.249852821186918 127 17 15.7672102604739 1.23278973952610 128 15 14.0042319951691 0.995768004830924 129 12 14.7455979666711 -2.74559796667113 130 16 13.9018224660129 2.09817753398713 131 10 13.4900189227280 -3.49001892272798 132 16 13.4847293488174 2.51527065118258 133 12 14.1318955712898 -2.13189557128976 134 14 15.6891601643807 -1.68916016438072 135 15 15.1349525324793 -0.134952532479329 136 13 11.8798702989229 1.12012970107707 137 15 14.5406759892864 0.459324010713589 138 11 13.3639577865016 -2.36395778650162 139 12 12.9948717800782 -0.994871780078202 140 11 13.2797734052426 -2.27977340524255 141 16 12.8706670328481 3.12933296715187 142 15 13.5777970081450 1.42220299185502 143 17 17.0126296693111 -0.0126296693111276 144 16 14.2177232846361 1.78227671536388 145 10 13.3076482519712 -3.30764825197124 146 18 15.7108182646390 2.28918173536104 147 13 15.057515844885 -2.05751584488500 148 16 14.9198086004653 1.08019139953471 149 13 12.6377638044032 0.362236195596831 150 10 12.8783257394112 -2.8783257394112 151 15 16.1550432712530 -1.15504327125297 152 16 13.9923608515066 2.00763914849345 153 16 11.7040450513149 4.29595494868507 154 14 12.2499675462609 1.75003245373915 155 10 12.3106299805489 -2.31062998054890 156 17 16.8321246414488 0.167875358551157 157 13 11.5695393191606 1.43046068083939 158 15 14.0042319951691 0.995768004830924 159 16 14.6713955204085 1.32860447959148 160 12 12.5736517546432 -0.573651754643187 161 13 12.6006036915371 0.39939630846291 162 13 12.4665513538839 0.53344864611615 163 12 12.3881607885568 -0.388160788556804 164 17 16.5737899268770 0.42621007312302 165 15 13.6726004456851 1.32739955431489 166 10 11.4206728334644 -1.42067283346442 167 14 14.4691619974894 -0.469161997489407 168 11 14.2909865799885 -3.29098657998851 169 13 14.8482927619942 -1.84829276199418 170 16 14.4308747459494 1.56912525405060 171 12 10.3653118162962 1.63468818370377 172 16 15.7059483938507 0.294051606149257 173 12 13.9883936528962 -1.98839365289617 174 9 11.3120335908731 -2.31203359087306 175 12 15.2885887877223 -3.28858878772229 176 15 14.7442131156564 0.255786884343579 177 12 12.2632972969965 -0.263297296996452 178 12 12.6879346718068 -0.68793467180684 179 14 14.0621066117256 -0.0621066117256398 180 12 13.4762697050290 -1.47626970502904 181 16 15.4340480772343 0.565951922765726 182 11 11.5217489928797 -0.521748992879665 183 19 17.1604632713109 1.83953672868908 184 15 15.5206096079202 -0.520609607920211 185 8 14.7662880903322 -6.76628809033216 186 16 14.9680927196659 1.03190728033408 187 17 14.7476768686274 2.2523231313726 188 12 12.5418032885651 -0.541803288565062 189 11 11.5269675255856 -0.526967525585558 190 11 10.2993224089426 0.700677591057419 191 14 15.0449039081772 -1.04490390817718 192 16 15.8549236374795 0.145076362520500 193 12 9.65799989258033 2.34200010741967 194 16 14.3005132593300 1.69948674067001 195 13 13.9159846340245 -0.915984634024543 196 15 15.3686335282855 -0.368633528285528 197 16 13.0983431475463 2.90165685245371 198 16 15.3129697531759 0.687030246824146 199 14 12.4874552007084 1.51254479929164 200 16 14.7705797810563 1.22942021894372 201 16 14.1953897769368 1.80461022306323 202 14 13.5652608133924 0.434739186607567 203 11 13.7622976777595 -2.76229767775952 204 12 14.9165699771728 -2.91656997717275 205 15 12.9696665181991 2.03033348180092 206 15 14.8369644200455 0.163035579954474 207 16 14.847026495686 1.15297350431399 208 16 15.4339487103626 0.566051289637426 209 11 13.9167631437977 -2.91676314379775 210 15 14.3031594704995 0.696840529500508 211 12 14.507489606999 -2.507489606999 212 12 16.2896849694461 -4.28968496944614 213 15 14.3091704811001 0.690829518899902 214 15 12.1674958883062 2.83250411169378 215 16 14.8832893443442 1.11671065565580 216 14 13.4207878308238 0.57921216917622 217 17 15.0626619782301 1.93733802176992 218 14 14.3279169397482 -0.327916939748238 219 13 11.9902889016349 1.00971109836511 220 15 15.6158919610783 -0.615891961078344 221 13 15.0224913327296 -2.02249133272956 222 14 14.5733033465195 -0.573303346519544 223 15 14.5664459129041 0.43355408709592 224 12 13.4585313702885 -1.45853137028855 225 13 12.8042807399043 0.195719260095656 226 8 11.9668684285413 -3.96686842854126 227 14 14.3096831383973 -0.309683138397306 228 14 13.2737435527865 0.726256447213536 229 11 12.3432695732266 -1.34326957322662 230 12 13.1609985574185 -1.16099855741854 231 13 11.4799323515281 1.52006764847186 232 10 13.4650403340905 -3.46504033409047 233 16 11.7004538222459 4.29954617775412 234 18 16.4558663707500 1.54413362925002 235 13 14.3053988244790 -1.30539882447904 236 11 13.6549207425401 -2.65492074254014 237 4 11.1700281372820 -7.17002813728201 238 13 14.8152140281387 -1.81521402813869 239 16 14.5923633054321 1.40763669456793 240 10 11.9287261285823 -1.92872612858232 241 12 12.4071970658913 -0.407197065891325 242 12 13.7956003816510 -1.79560038165098 243 10 8.88634470078797 1.11365529921203 244 13 11.2770068802368 1.72299311976323 245 15 14.0926273160205 0.907372683979529 246 12 12.0467701780665 -0.0467701780665329 247 14 13.1538828662063 0.846117133793704 248 10 12.8902928435778 -2.89029284357777 249 12 10.8009658204609 1.19903417953906 250 12 11.8839336289646 0.116066371035425 251 11 12.1379198030589 -1.13791980305892 252 10 11.8622634576053 -1.86226345760533 253 12 11.6631437816570 0.336856218342975 254 16 13.1782247636368 2.82177523636317 255 12 13.7152063049677 -1.71520630496769 256 14 14.2640878596850 -0.264087859685045 257 16 14.6811885572799 1.31881144272010 258 14 11.8239286814975 2.17607131850247 259 13 14.7816409840600 -1.78164098406003 260 4 9.48330674262236 -5.48330674262236 261 15 14.174366651485 0.825633348514991 262 11 15.5502096888069 -4.55020968880690 263 11 11.4716452522508 -0.471645252250818 264 14 13.1672908525232 0.83270914747681

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.234449524103323 0.468899048206647 0.765550475896677 12 0.120293517840557 0.240587035681113 0.879706482159443 13 0.0815541573457802 0.163108314691560 0.91844584265422 14 0.102874776497918 0.205749552995836 0.897125223502082 15 0.0587519756515502 0.117503951303100 0.94124802434845 16 0.0391686675931634 0.0783373351863268 0.960831332406837 17 0.0630191184441749 0.126038236888350 0.936980881555825 18 0.25586432793531 0.51172865587062 0.74413567206469 19 0.194216632900151 0.388433265800301 0.80578336709985 20 0.137764883334639 0.275529766669278 0.862235116665361 21 0.0986062615513702 0.197212523102740 0.90139373844863 22 0.0968533990427727 0.193706798085545 0.903146600957227 23 0.257054377911139 0.514108755822278 0.742945622088861 24 0.321495418423947 0.642990836847893 0.678504581576054 25 0.294869538595019 0.589739077190038 0.705130461404981 26 0.277447857711977 0.554895715423954 0.722552142288023 27 0.352098888872558 0.704197777745116 0.647901111127442 28 0.363849648859342 0.727699297718684 0.636150351140658 29 0.346785592147590 0.693571184295181 0.65321440785241 30 0.380898386272267 0.761796772544533 0.619101613727733 31 0.328675803111161 0.657351606222323 0.671324196888839 32 0.289492867969932 0.578985735939864 0.710507132030068 33 0.262490777406898 0.524981554813796 0.737509222593102 34 0.226304417033047 0.452608834066093 0.773695582966953 35 0.187880541814586 0.375761083629173 0.812119458185414 36 0.304929814888216 0.609859629776433 0.695070185111784 37 0.334971074151198 0.669942148302397 0.665028925848802 38 0.325963622584243 0.651927245168487 0.674036377415757 39 0.35602273985543 0.71204547971086 0.64397726014457 40 0.334209449134650 0.668418898269301 0.66579055086535 41 0.298001365293739 0.596002730587478 0.701998634706261 42 0.263835728828234 0.527671457656468 0.736164271171766 43 0.278274265211429 0.556548530422857 0.721725734788571 44 0.236536521323935 0.47307304264787 0.763463478676065 45 0.214442280198149 0.428884560396298 0.78555771980185 46 0.392689717772571 0.785379435545142 0.607310282227429 47 0.545519033485654 0.908961933028692 0.454480966514346 48 0.496548776402329 0.993097552804658 0.503451223597671 49 0.483216160724748 0.966432321449497 0.516783839275252 50 0.468390230644651 0.936780461289303 0.531609769355349 51 0.42367172034991 0.84734344069982 0.57632827965009 52 0.378169197572531 0.756338395145061 0.621830802427469 53 0.386603609576097 0.773207219152193 0.613396390423903 54 0.369709551282599 0.739419102565198 0.630290448717401 55 0.360937557172319 0.721875114344638 0.639062442827681 56 0.339385238523737 0.678770477047474 0.660614761476263 57 0.299777754262648 0.599555508525295 0.700222245737352 58 0.271157720239429 0.542315440478858 0.728842279760571 59 0.23669426979653 0.47338853959306 0.76330573020347 60 0.242458614750553 0.484917229501107 0.757541385249447 61 0.218650159104958 0.437300318209917 0.781349840895042 62 0.191735670804926 0.383471341609851 0.808264329195074 63 0.163732057843763 0.327464115687526 0.836267942156237 64 0.13797888889491 0.27595777778982 0.86202111110509 65 0.119073866261549 0.238147732523097 0.880926133738452 66 0.110228851421388 0.220457702842776 0.889771148578612 67 0.105986150241298 0.211972300482595 0.894013849758702 68 0.223058679356041 0.446117358712083 0.776941320643959 69 0.313038852663478 0.626077705326957 0.686961147336522 70 0.282142509826931 0.564285019653863 0.717857490173069 71 0.397902377321231 0.795804754642462 0.602097622678769 72 0.359897835237819 0.719795670475638 0.640102164762181 73 0.351564646488890 0.703129292977779 0.64843535351111 74 0.336197027115224 0.672394054230448 0.663802972884776 75 0.304024154874081 0.608048309748162 0.695975845125919 76 0.369544725558949 0.739089451117898 0.630455274441051 77 0.333817781175649 0.667635562351299 0.66618221882435 78 0.31297705265364 0.62595410530728 0.68702294734636 79 0.326056568919713 0.652113137839427 0.673943431080287 80 0.298478347311551 0.596956694623103 0.701521652688449 81 0.270341906890842 0.540683813781685 0.729658093109158 82 0.239446042587499 0.478892085174997 0.760553957412501 83 0.215139662822704 0.430279325645408 0.784860337177296 84 0.187913722738409 0.375827445476819 0.81208627726159 85 0.184607191674081 0.369214383348162 0.815392808325919 86 0.1595725120416 0.3191450240832 0.8404274879584 87 0.140322537361321 0.280645074722642 0.859677462638679 88 0.130738801764796 0.261477603529592 0.869261198235204 89 0.112976437875891 0.225952875751782 0.887023562124109 90 0.110203114219269 0.220406228438538 0.889796885780731 91 0.0942919369448664 0.188583873889733 0.905708063055134 92 0.0814240906086628 0.162848181217326 0.918575909391337 93 0.0691622046865873 0.138324409373175 0.930837795313413 94 0.0710505619641158 0.142101123928232 0.928949438035884 95 0.0642047750978116 0.128409550195623 0.935795224902188 96 0.0537975865035424 0.107595173007085 0.946202413496458 97 0.0594315146399681 0.118863029279936 0.940568485360032 98 0.0492270511304384 0.0984541022608768 0.950772948869562 99 0.040363797123943 0.080727594247886 0.959636202876057 100 0.0353149770686564 0.0706299541373128 0.964685022931344 101 0.0313300139468171 0.0626600278936343 0.968669986053183 102 0.0368820710846457 0.0737641421692915 0.963117928915354 103 0.0316513902506383 0.0633027805012766 0.968348609749362 104 0.0285859141191522 0.0571718282383044 0.971414085880848 105 0.033817135813769 0.067634271627538 0.966182864186231 106 0.0297170878687317 0.0594341757374634 0.970282912131268 107 0.0241330786284951 0.0482661572569901 0.975866921371505 108 0.0240079113500846 0.0480158227001693 0.975992088649915 109 0.0194515049063904 0.0389030098127808 0.98054849509361 110 0.0159136661421487 0.0318273322842973 0.984086333857851 111 0.0126146522719215 0.0252293045438429 0.987385347728079 112 0.0130381277586815 0.0260762555173629 0.986961872241318 113 0.0116452146478809 0.0232904292957617 0.98835478535212 114 0.0167206560872473 0.0334413121744946 0.983279343912753 115 0.0159710234451857 0.0319420468903713 0.984028976554814 116 0.0152811978016854 0.0305623956033709 0.984718802198315 117 0.0126390299453936 0.0252780598907872 0.987360970054606 118 0.0126219562493753 0.0252439124987506 0.987378043750625 119 0.0102612016271452 0.0205224032542904 0.989738798372855 120 0.00903802567892176 0.0180760513578435 0.990961974321078 121 0.00735356780301546 0.0147071356060309 0.992646432196985 122 0.0113195512171051 0.0226391024342101 0.988680448782895 123 0.00953849599480395 0.0190769919896079 0.990461504005196 124 0.00847311639172432 0.0169462327834486 0.991526883608276 125 0.00693555972924156 0.0138711194584831 0.993064440270758 126 0.00565042103716476 0.0113008420743295 0.994349578962835 127 0.00508716401279403 0.0101743280255881 0.994912835987206 128 0.00409879352027439 0.00819758704054879 0.995901206479726 129 0.0060488698275874 0.0120977396551748 0.993951130172413 130 0.0065532380288123 0.0131064760576246 0.993446761971188 131 0.0135402943899644 0.0270805887799288 0.986459705610036 132 0.0162987275042281 0.0325974550084562 0.983701272495772 133 0.0175823649816711 0.0351647299633423 0.982417635018329 134 0.01698334499195 0.0339666899839 0.98301665500805 135 0.0138803744734776 0.0277607489469552 0.986119625526522 136 0.0117123386997019 0.0234246773994038 0.988287661300298 137 0.00937464333820999 0.0187492866764200 0.99062535666179 138 0.0114169021588460 0.0228338043176921 0.988583097841154 139 0.00993713987951886 0.0198742797590377 0.990062860120481 140 0.0112928154997642 0.0225856309995285 0.988707184500236 141 0.0175165648200129 0.0350331296400258 0.982483435179987 142 0.0159289579009637 0.0318579158019274 0.984071042099036 143 0.0126831635932337 0.0253663271864674 0.987316836406766 144 0.0134458333059829 0.0268916666119659 0.986554166694017 145 0.0256403134895243 0.0512806269790486 0.974359686510476 146 0.0314184109533007 0.0628368219066014 0.9685815890467 147 0.0329795780363363 0.0659591560726726 0.967020421963664 148 0.0297734227101538 0.0595468454203076 0.970226577289846 149 0.0248127287409383 0.0496254574818765 0.975187271259062 150 0.0344362320643697 0.0688724641287393 0.96556376793563 151 0.0297124189393623 0.0594248378787246 0.970287581060638 152 0.0312806581648996 0.0625613163297992 0.9687193418351 153 0.063880432978279 0.127760865956558 0.936119567021721 154 0.0627614357271275 0.125522871454255 0.937238564272872 155 0.0721747771912978 0.144349554382596 0.927825222808702 156 0.061747857433907 0.123495714867814 0.938252142566093 157 0.0551628794160264 0.110325758832053 0.944837120583974 158 0.0482704122823367 0.0965408245646734 0.951729587717663 159 0.0472261086820218 0.0944522173640437 0.952773891317978 160 0.0404930281933112 0.0809860563866224 0.959506971806689 161 0.0334124305697177 0.0668248611394353 0.966587569430282 162 0.0274667952760031 0.0549335905520062 0.972533204723997 163 0.0228756533010375 0.0457513066020749 0.977124346698963 164 0.0192964132818165 0.0385928265636331 0.980703586718183 165 0.0169353563376050 0.0338707126752100 0.983064643662395 166 0.0171843050634623 0.0343686101269246 0.982815694936538 167 0.0138076243872405 0.027615248774481 0.98619237561276 168 0.0203735861261563 0.0407471722523127 0.979626413873844 169 0.0201928859457739 0.0403857718915477 0.979807114054226 170 0.0188580171726754 0.0377160343453509 0.981141982827325 171 0.0181349653406303 0.0362699306812606 0.98186503465937 172 0.0146941965866968 0.0293883931733937 0.985305803413303 173 0.0147422907486943 0.0294845814973886 0.985257709251306 174 0.0164226161640721 0.0328452323281442 0.983577383835928 175 0.0219494596798690 0.0438989193597379 0.978050540320131 176 0.0176531523212624 0.0353063046425249 0.982346847678738 177 0.0143826922510692 0.0287653845021384 0.98561730774893 178 0.0117427428384760 0.0234854856769521 0.988257257161524 179 0.00919215979401736 0.0183843195880347 0.990807840205983 180 0.00800536172863925 0.0160107234572785 0.99199463827136 181 0.00660360418668579 0.0132072083733716 0.993396395813314 182 0.00534521714955178 0.0106904342991036 0.994654782850448 183 0.00560936622575513 0.0112187324515103 0.994390633774245 184 0.00443647295033514 0.00887294590067028 0.995563527049665 185 0.094086539900981 0.188173079801962 0.905913460099019 186 0.0816845811721888 0.163369162344378 0.918315418827811 187 0.0928439802674408 0.185687960534882 0.90715601973256 188 0.0802127135646717 0.160425427129343 0.919787286435328 189 0.0679596339456148 0.135919267891230 0.932040366054385 190 0.0563561435032724 0.112712287006545 0.943643856496728 191 0.0497111618841623 0.0994223237683247 0.950288838115838 192 0.0419611933152232 0.0839223866304465 0.958038806684777 193 0.0487155279839566 0.0974310559679132 0.951284472016043 194 0.0522890666310367 0.104578133262073 0.947710933368963 195 0.0443811738269538 0.0887623476539076 0.955618826173046 196 0.0370263282172378 0.0740526564344756 0.962973671782762 197 0.0483157061172391 0.0966314122344782 0.95168429388276 198 0.0395862946218533 0.0791725892437066 0.960413705378147 199 0.0367011562951482 0.0734023125902964 0.963298843704852 200 0.0310398676134195 0.062079735226839 0.96896013238658 201 0.0292219724944844 0.0584439449889688 0.970778027505516 202 0.0243805707067269 0.0487611414134538 0.975619429293273 203 0.0313053691066294 0.0626107382132588 0.96869463089337 204 0.0356803177739386 0.0713606355478772 0.964319682226061 205 0.0389063363767209 0.0778126727534419 0.96109366362328 206 0.0308791545420775 0.0617583090841551 0.969120845457922 207 0.0303422228248083 0.0606844456496166 0.969657777175192 208 0.024949872731147 0.049899745462294 0.975050127268853 209 0.0278681412054717 0.0557362824109434 0.972131858794528 210 0.0225417426210896 0.0450834852421793 0.97745825737891 211 0.0249775332931389 0.0499550665862777 0.975022466706861 212 0.0399735087422991 0.0799470174845981 0.960026491257701 213 0.0315871062441668 0.0631742124883335 0.968412893755833 214 0.0441589425989401 0.0883178851978801 0.95584105740106 215 0.0380593092869368 0.0761186185738735 0.961940690713063 216 0.0297508259210554 0.0595016518421109 0.970249174078945 217 0.0321955312729058 0.0643910625458115 0.967804468727094 218 0.0275294909822642 0.0550589819645284 0.972470509017736 219 0.0247418070044547 0.0494836140089093 0.975258192995545 220 0.0189633773705708 0.0379267547411415 0.98103662262943 221 0.0168947339209663 0.0337894678419326 0.983105266079034 222 0.0124206485849155 0.0248412971698309 0.987579351415085 223 0.0093117418620164 0.0186234837240328 0.990688258137984 224 0.00750211854397697 0.0150042370879539 0.992497881456023 225 0.00654705616129064 0.0130941123225813 0.99345294383871 226 0.0149951903144659 0.0299903806289319 0.985004809685534 227 0.0115416184372463 0.0230832368744927 0.988458381562754 228 0.00835031892340789 0.0167006378468158 0.991649681076592 229 0.0060968342432109 0.0121936684864218 0.993903165756789 230 0.00441036251732997 0.00882072503465995 0.99558963748267 231 0.00439461457994107 0.00878922915988214 0.99560538542006 232 0.00814497957146643 0.0162899591429329 0.991855020428534 233 0.0377565442118784 0.0755130884237568 0.962243455788122 234 0.0542942200569106 0.108588440113821 0.94570577994309 235 0.0405065857547010 0.0810131715094021 0.959493414245299 236 0.0361101125951766 0.0722202251903533 0.963889887404823 237 0.246505700570198 0.493011401140395 0.753494299429802 238 0.200782130963978 0.401564261927957 0.799217869036022 239 0.180044405468033 0.360088810936067 0.819955594531967 240 0.142128535333137 0.284257070666275 0.857871464666863 241 0.113707343103352 0.227414686206705 0.886292656896648 242 0.0945301032407307 0.189060206481461 0.90546989675927 243 0.0879023187197933 0.175804637439587 0.912097681280207 244 0.153550434222244 0.307100868444488 0.846449565777756 245 0.137630339717254 0.275260679434508 0.862369660282746 246 0.110109330538686 0.220218661077373 0.889890669461314 247 0.0777706008222238 0.155541201644448 0.922229399177776 248 0.0666948470256238 0.133389694051248 0.933305152974376 249 0.0624888270323013 0.124977654064603 0.937511172967699 250 0.0773809930539605 0.154761986107921 0.92261900694604 251 0.0460549764082260 0.0921099528164521 0.953945023591774 252 0.112285664162278 0.224571328324556 0.887714335837722 253 0.234998511095394 0.469997022190787 0.765001488904606

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.234449524103323 & 0.468899048206647 & 0.765550475896677 \tabularnewline
12 & 0.120293517840557 & 0.240587035681113 & 0.879706482159443 \tabularnewline
13 & 0.0815541573457802 & 0.163108314691560 & 0.91844584265422 \tabularnewline
14 & 0.102874776497918 & 0.205749552995836 & 0.897125223502082 \tabularnewline
15 & 0.0587519756515502 & 0.117503951303100 & 0.94124802434845 \tabularnewline
16 & 0.0391686675931634 & 0.0783373351863268 & 0.960831332406837 \tabularnewline
17 & 0.0630191184441749 & 0.126038236888350 & 0.936980881555825 \tabularnewline
18 & 0.25586432793531 & 0.51172865587062 & 0.74413567206469 \tabularnewline
19 & 0.194216632900151 & 0.388433265800301 & 0.80578336709985 \tabularnewline
20 & 0.137764883334639 & 0.275529766669278 & 0.862235116665361 \tabularnewline
21 & 0.0986062615513702 & 0.197212523102740 & 0.90139373844863 \tabularnewline
22 & 0.0968533990427727 & 0.193706798085545 & 0.903146600957227 \tabularnewline
23 & 0.257054377911139 & 0.514108755822278 & 0.742945622088861 \tabularnewline
24 & 0.321495418423947 & 0.642990836847893 & 0.678504581576054 \tabularnewline
25 & 0.294869538595019 & 0.589739077190038 & 0.705130461404981 \tabularnewline
26 & 0.277447857711977 & 0.554895715423954 & 0.722552142288023 \tabularnewline
27 & 0.352098888872558 & 0.704197777745116 & 0.647901111127442 \tabularnewline
28 & 0.363849648859342 & 0.727699297718684 & 0.636150351140658 \tabularnewline
29 & 0.346785592147590 & 0.693571184295181 & 0.65321440785241 \tabularnewline
30 & 0.380898386272267 & 0.761796772544533 & 0.619101613727733 \tabularnewline
31 & 0.328675803111161 & 0.657351606222323 & 0.671324196888839 \tabularnewline
32 & 0.289492867969932 & 0.578985735939864 & 0.710507132030068 \tabularnewline
33 & 0.262490777406898 & 0.524981554813796 & 0.737509222593102 \tabularnewline
34 & 0.226304417033047 & 0.452608834066093 & 0.773695582966953 \tabularnewline
35 & 0.187880541814586 & 0.375761083629173 & 0.812119458185414 \tabularnewline
36 & 0.304929814888216 & 0.609859629776433 & 0.695070185111784 \tabularnewline
37 & 0.334971074151198 & 0.669942148302397 & 0.665028925848802 \tabularnewline
38 & 0.325963622584243 & 0.651927245168487 & 0.674036377415757 \tabularnewline
39 & 0.35602273985543 & 0.71204547971086 & 0.64397726014457 \tabularnewline
40 & 0.334209449134650 & 0.668418898269301 & 0.66579055086535 \tabularnewline
41 & 0.298001365293739 & 0.596002730587478 & 0.701998634706261 \tabularnewline
42 & 0.263835728828234 & 0.527671457656468 & 0.736164271171766 \tabularnewline
43 & 0.278274265211429 & 0.556548530422857 & 0.721725734788571 \tabularnewline
44 & 0.236536521323935 & 0.47307304264787 & 0.763463478676065 \tabularnewline
45 & 0.214442280198149 & 0.428884560396298 & 0.78555771980185 \tabularnewline
46 & 0.392689717772571 & 0.785379435545142 & 0.607310282227429 \tabularnewline
47 & 0.545519033485654 & 0.908961933028692 & 0.454480966514346 \tabularnewline
48 & 0.496548776402329 & 0.993097552804658 & 0.503451223597671 \tabularnewline
49 & 0.483216160724748 & 0.966432321449497 & 0.516783839275252 \tabularnewline
50 & 0.468390230644651 & 0.936780461289303 & 0.531609769355349 \tabularnewline
51 & 0.42367172034991 & 0.84734344069982 & 0.57632827965009 \tabularnewline
52 & 0.378169197572531 & 0.756338395145061 & 0.621830802427469 \tabularnewline
53 & 0.386603609576097 & 0.773207219152193 & 0.613396390423903 \tabularnewline
54 & 0.369709551282599 & 0.739419102565198 & 0.630290448717401 \tabularnewline
55 & 0.360937557172319 & 0.721875114344638 & 0.639062442827681 \tabularnewline
56 & 0.339385238523737 & 0.678770477047474 & 0.660614761476263 \tabularnewline
57 & 0.299777754262648 & 0.599555508525295 & 0.700222245737352 \tabularnewline
58 & 0.271157720239429 & 0.542315440478858 & 0.728842279760571 \tabularnewline
59 & 0.23669426979653 & 0.47338853959306 & 0.76330573020347 \tabularnewline
60 & 0.242458614750553 & 0.484917229501107 & 0.757541385249447 \tabularnewline
61 & 0.218650159104958 & 0.437300318209917 & 0.781349840895042 \tabularnewline
62 & 0.191735670804926 & 0.383471341609851 & 0.808264329195074 \tabularnewline
63 & 0.163732057843763 & 0.327464115687526 & 0.836267942156237 \tabularnewline
64 & 0.13797888889491 & 0.27595777778982 & 0.86202111110509 \tabularnewline
65 & 0.119073866261549 & 0.238147732523097 & 0.880926133738452 \tabularnewline
66 & 0.110228851421388 & 0.220457702842776 & 0.889771148578612 \tabularnewline
67 & 0.105986150241298 & 0.211972300482595 & 0.894013849758702 \tabularnewline
68 & 0.223058679356041 & 0.446117358712083 & 0.776941320643959 \tabularnewline
69 & 0.313038852663478 & 0.626077705326957 & 0.686961147336522 \tabularnewline
70 & 0.282142509826931 & 0.564285019653863 & 0.717857490173069 \tabularnewline
71 & 0.397902377321231 & 0.795804754642462 & 0.602097622678769 \tabularnewline
72 & 0.359897835237819 & 0.719795670475638 & 0.640102164762181 \tabularnewline
73 & 0.351564646488890 & 0.703129292977779 & 0.64843535351111 \tabularnewline
74 & 0.336197027115224 & 0.672394054230448 & 0.663802972884776 \tabularnewline
75 & 0.304024154874081 & 0.608048309748162 & 0.695975845125919 \tabularnewline
76 & 0.369544725558949 & 0.739089451117898 & 0.630455274441051 \tabularnewline
77 & 0.333817781175649 & 0.667635562351299 & 0.66618221882435 \tabularnewline
78 & 0.31297705265364 & 0.62595410530728 & 0.68702294734636 \tabularnewline
79 & 0.326056568919713 & 0.652113137839427 & 0.673943431080287 \tabularnewline
80 & 0.298478347311551 & 0.596956694623103 & 0.701521652688449 \tabularnewline
81 & 0.270341906890842 & 0.540683813781685 & 0.729658093109158 \tabularnewline
82 & 0.239446042587499 & 0.478892085174997 & 0.760553957412501 \tabularnewline
83 & 0.215139662822704 & 0.430279325645408 & 0.784860337177296 \tabularnewline
84 & 0.187913722738409 & 0.375827445476819 & 0.81208627726159 \tabularnewline
85 & 0.184607191674081 & 0.369214383348162 & 0.815392808325919 \tabularnewline
86 & 0.1595725120416 & 0.3191450240832 & 0.8404274879584 \tabularnewline
87 & 0.140322537361321 & 0.280645074722642 & 0.859677462638679 \tabularnewline
88 & 0.130738801764796 & 0.261477603529592 & 0.869261198235204 \tabularnewline
89 & 0.112976437875891 & 0.225952875751782 & 0.887023562124109 \tabularnewline
90 & 0.110203114219269 & 0.220406228438538 & 0.889796885780731 \tabularnewline
91 & 0.0942919369448664 & 0.188583873889733 & 0.905708063055134 \tabularnewline
92 & 0.0814240906086628 & 0.162848181217326 & 0.918575909391337 \tabularnewline
93 & 0.0691622046865873 & 0.138324409373175 & 0.930837795313413 \tabularnewline
94 & 0.0710505619641158 & 0.142101123928232 & 0.928949438035884 \tabularnewline
95 & 0.0642047750978116 & 0.128409550195623 & 0.935795224902188 \tabularnewline
96 & 0.0537975865035424 & 0.107595173007085 & 0.946202413496458 \tabularnewline
97 & 0.0594315146399681 & 0.118863029279936 & 0.940568485360032 \tabularnewline
98 & 0.0492270511304384 & 0.0984541022608768 & 0.950772948869562 \tabularnewline
99 & 0.040363797123943 & 0.080727594247886 & 0.959636202876057 \tabularnewline
100 & 0.0353149770686564 & 0.0706299541373128 & 0.964685022931344 \tabularnewline
101 & 0.0313300139468171 & 0.0626600278936343 & 0.968669986053183 \tabularnewline
102 & 0.0368820710846457 & 0.0737641421692915 & 0.963117928915354 \tabularnewline
103 & 0.0316513902506383 & 0.0633027805012766 & 0.968348609749362 \tabularnewline
104 & 0.0285859141191522 & 0.0571718282383044 & 0.971414085880848 \tabularnewline
105 & 0.033817135813769 & 0.067634271627538 & 0.966182864186231 \tabularnewline
106 & 0.0297170878687317 & 0.0594341757374634 & 0.970282912131268 \tabularnewline
107 & 0.0241330786284951 & 0.0482661572569901 & 0.975866921371505 \tabularnewline
108 & 0.0240079113500846 & 0.0480158227001693 & 0.975992088649915 \tabularnewline
109 & 0.0194515049063904 & 0.0389030098127808 & 0.98054849509361 \tabularnewline
110 & 0.0159136661421487 & 0.0318273322842973 & 0.984086333857851 \tabularnewline
111 & 0.0126146522719215 & 0.0252293045438429 & 0.987385347728079 \tabularnewline
112 & 0.0130381277586815 & 0.0260762555173629 & 0.986961872241318 \tabularnewline
113 & 0.0116452146478809 & 0.0232904292957617 & 0.98835478535212 \tabularnewline
114 & 0.0167206560872473 & 0.0334413121744946 & 0.983279343912753 \tabularnewline
115 & 0.0159710234451857 & 0.0319420468903713 & 0.984028976554814 \tabularnewline
116 & 0.0152811978016854 & 0.0305623956033709 & 0.984718802198315 \tabularnewline
117 & 0.0126390299453936 & 0.0252780598907872 & 0.987360970054606 \tabularnewline
118 & 0.0126219562493753 & 0.0252439124987506 & 0.987378043750625 \tabularnewline
119 & 0.0102612016271452 & 0.0205224032542904 & 0.989738798372855 \tabularnewline
120 & 0.00903802567892176 & 0.0180760513578435 & 0.990961974321078 \tabularnewline
121 & 0.00735356780301546 & 0.0147071356060309 & 0.992646432196985 \tabularnewline
122 & 0.0113195512171051 & 0.0226391024342101 & 0.988680448782895 \tabularnewline
123 & 0.00953849599480395 & 0.0190769919896079 & 0.990461504005196 \tabularnewline
124 & 0.00847311639172432 & 0.0169462327834486 & 0.991526883608276 \tabularnewline
125 & 0.00693555972924156 & 0.0138711194584831 & 0.993064440270758 \tabularnewline
126 & 0.00565042103716476 & 0.0113008420743295 & 0.994349578962835 \tabularnewline
127 & 0.00508716401279403 & 0.0101743280255881 & 0.994912835987206 \tabularnewline
128 & 0.00409879352027439 & 0.00819758704054879 & 0.995901206479726 \tabularnewline
129 & 0.0060488698275874 & 0.0120977396551748 & 0.993951130172413 \tabularnewline
130 & 0.0065532380288123 & 0.0131064760576246 & 0.993446761971188 \tabularnewline
131 & 0.0135402943899644 & 0.0270805887799288 & 0.986459705610036 \tabularnewline
132 & 0.0162987275042281 & 0.0325974550084562 & 0.983701272495772 \tabularnewline
133 & 0.0175823649816711 & 0.0351647299633423 & 0.982417635018329 \tabularnewline
134 & 0.01698334499195 & 0.0339666899839 & 0.98301665500805 \tabularnewline
135 & 0.0138803744734776 & 0.0277607489469552 & 0.986119625526522 \tabularnewline
136 & 0.0117123386997019 & 0.0234246773994038 & 0.988287661300298 \tabularnewline
137 & 0.00937464333820999 & 0.0187492866764200 & 0.99062535666179 \tabularnewline
138 & 0.0114169021588460 & 0.0228338043176921 & 0.988583097841154 \tabularnewline
139 & 0.00993713987951886 & 0.0198742797590377 & 0.990062860120481 \tabularnewline
140 & 0.0112928154997642 & 0.0225856309995285 & 0.988707184500236 \tabularnewline
141 & 0.0175165648200129 & 0.0350331296400258 & 0.982483435179987 \tabularnewline
142 & 0.0159289579009637 & 0.0318579158019274 & 0.984071042099036 \tabularnewline
143 & 0.0126831635932337 & 0.0253663271864674 & 0.987316836406766 \tabularnewline
144 & 0.0134458333059829 & 0.0268916666119659 & 0.986554166694017 \tabularnewline
145 & 0.0256403134895243 & 0.0512806269790486 & 0.974359686510476 \tabularnewline
146 & 0.0314184109533007 & 0.0628368219066014 & 0.9685815890467 \tabularnewline
147 & 0.0329795780363363 & 0.0659591560726726 & 0.967020421963664 \tabularnewline
148 & 0.0297734227101538 & 0.0595468454203076 & 0.970226577289846 \tabularnewline
149 & 0.0248127287409383 & 0.0496254574818765 & 0.975187271259062 \tabularnewline
150 & 0.0344362320643697 & 0.0688724641287393 & 0.96556376793563 \tabularnewline
151 & 0.0297124189393623 & 0.0594248378787246 & 0.970287581060638 \tabularnewline
152 & 0.0312806581648996 & 0.0625613163297992 & 0.9687193418351 \tabularnewline
153 & 0.063880432978279 & 0.127760865956558 & 0.936119567021721 \tabularnewline
154 & 0.0627614357271275 & 0.125522871454255 & 0.937238564272872 \tabularnewline
155 & 0.0721747771912978 & 0.144349554382596 & 0.927825222808702 \tabularnewline
156 & 0.061747857433907 & 0.123495714867814 & 0.938252142566093 \tabularnewline
157 & 0.0551628794160264 & 0.110325758832053 & 0.944837120583974 \tabularnewline
158 & 0.0482704122823367 & 0.0965408245646734 & 0.951729587717663 \tabularnewline
159 & 0.0472261086820218 & 0.0944522173640437 & 0.952773891317978 \tabularnewline
160 & 0.0404930281933112 & 0.0809860563866224 & 0.959506971806689 \tabularnewline
161 & 0.0334124305697177 & 0.0668248611394353 & 0.966587569430282 \tabularnewline
162 & 0.0274667952760031 & 0.0549335905520062 & 0.972533204723997 \tabularnewline
163 & 0.0228756533010375 & 0.0457513066020749 & 0.977124346698963 \tabularnewline
164 & 0.0192964132818165 & 0.0385928265636331 & 0.980703586718183 \tabularnewline
165 & 0.0169353563376050 & 0.0338707126752100 & 0.983064643662395 \tabularnewline
166 & 0.0171843050634623 & 0.0343686101269246 & 0.982815694936538 \tabularnewline
167 & 0.0138076243872405 & 0.027615248774481 & 0.98619237561276 \tabularnewline
168 & 0.0203735861261563 & 0.0407471722523127 & 0.979626413873844 \tabularnewline
169 & 0.0201928859457739 & 0.0403857718915477 & 0.979807114054226 \tabularnewline
170 & 0.0188580171726754 & 0.0377160343453509 & 0.981141982827325 \tabularnewline
171 & 0.0181349653406303 & 0.0362699306812606 & 0.98186503465937 \tabularnewline
172 & 0.0146941965866968 & 0.0293883931733937 & 0.985305803413303 \tabularnewline
173 & 0.0147422907486943 & 0.0294845814973886 & 0.985257709251306 \tabularnewline
174 & 0.0164226161640721 & 0.0328452323281442 & 0.983577383835928 \tabularnewline
175 & 0.0219494596798690 & 0.0438989193597379 & 0.978050540320131 \tabularnewline
176 & 0.0176531523212624 & 0.0353063046425249 & 0.982346847678738 \tabularnewline
177 & 0.0143826922510692 & 0.0287653845021384 & 0.98561730774893 \tabularnewline
178 & 0.0117427428384760 & 0.0234854856769521 & 0.988257257161524 \tabularnewline
179 & 0.00919215979401736 & 0.0183843195880347 & 0.990807840205983 \tabularnewline
180 & 0.00800536172863925 & 0.0160107234572785 & 0.99199463827136 \tabularnewline
181 & 0.00660360418668579 & 0.0132072083733716 & 0.993396395813314 \tabularnewline
182 & 0.00534521714955178 & 0.0106904342991036 & 0.994654782850448 \tabularnewline
183 & 0.00560936622575513 & 0.0112187324515103 & 0.994390633774245 \tabularnewline
184 & 0.00443647295033514 & 0.00887294590067028 & 0.995563527049665 \tabularnewline
185 & 0.094086539900981 & 0.188173079801962 & 0.905913460099019 \tabularnewline
186 & 0.0816845811721888 & 0.163369162344378 & 0.918315418827811 \tabularnewline
187 & 0.0928439802674408 & 0.185687960534882 & 0.90715601973256 \tabularnewline
188 & 0.0802127135646717 & 0.160425427129343 & 0.919787286435328 \tabularnewline
189 & 0.0679596339456148 & 0.135919267891230 & 0.932040366054385 \tabularnewline
190 & 0.0563561435032724 & 0.112712287006545 & 0.943643856496728 \tabularnewline
191 & 0.0497111618841623 & 0.0994223237683247 & 0.950288838115838 \tabularnewline
192 & 0.0419611933152232 & 0.0839223866304465 & 0.958038806684777 \tabularnewline
193 & 0.0487155279839566 & 0.0974310559679132 & 0.951284472016043 \tabularnewline
194 & 0.0522890666310367 & 0.104578133262073 & 0.947710933368963 \tabularnewline
195 & 0.0443811738269538 & 0.0887623476539076 & 0.955618826173046 \tabularnewline
196 & 0.0370263282172378 & 0.0740526564344756 & 0.962973671782762 \tabularnewline
197 & 0.0483157061172391 & 0.0966314122344782 & 0.95168429388276 \tabularnewline
198 & 0.0395862946218533 & 0.0791725892437066 & 0.960413705378147 \tabularnewline
199 & 0.0367011562951482 & 0.0734023125902964 & 0.963298843704852 \tabularnewline
200 & 0.0310398676134195 & 0.062079735226839 & 0.96896013238658 \tabularnewline
201 & 0.0292219724944844 & 0.0584439449889688 & 0.970778027505516 \tabularnewline
202 & 0.0243805707067269 & 0.0487611414134538 & 0.975619429293273 \tabularnewline
203 & 0.0313053691066294 & 0.0626107382132588 & 0.96869463089337 \tabularnewline
204 & 0.0356803177739386 & 0.0713606355478772 & 0.964319682226061 \tabularnewline
205 & 0.0389063363767209 & 0.0778126727534419 & 0.96109366362328 \tabularnewline
206 & 0.0308791545420775 & 0.0617583090841551 & 0.969120845457922 \tabularnewline
207 & 0.0303422228248083 & 0.0606844456496166 & 0.969657777175192 \tabularnewline
208 & 0.024949872731147 & 0.049899745462294 & 0.975050127268853 \tabularnewline
209 & 0.0278681412054717 & 0.0557362824109434 & 0.972131858794528 \tabularnewline
210 & 0.0225417426210896 & 0.0450834852421793 & 0.97745825737891 \tabularnewline
211 & 0.0249775332931389 & 0.0499550665862777 & 0.975022466706861 \tabularnewline
212 & 0.0399735087422991 & 0.0799470174845981 & 0.960026491257701 \tabularnewline
213 & 0.0315871062441668 & 0.0631742124883335 & 0.968412893755833 \tabularnewline
214 & 0.0441589425989401 & 0.0883178851978801 & 0.95584105740106 \tabularnewline
215 & 0.0380593092869368 & 0.0761186185738735 & 0.961940690713063 \tabularnewline
216 & 0.0297508259210554 & 0.0595016518421109 & 0.970249174078945 \tabularnewline
217 & 0.0321955312729058 & 0.0643910625458115 & 0.967804468727094 \tabularnewline
218 & 0.0275294909822642 & 0.0550589819645284 & 0.972470509017736 \tabularnewline
219 & 0.0247418070044547 & 0.0494836140089093 & 0.975258192995545 \tabularnewline
220 & 0.0189633773705708 & 0.0379267547411415 & 0.98103662262943 \tabularnewline
221 & 0.0168947339209663 & 0.0337894678419326 & 0.983105266079034 \tabularnewline
222 & 0.0124206485849155 & 0.0248412971698309 & 0.987579351415085 \tabularnewline
223 & 0.0093117418620164 & 0.0186234837240328 & 0.990688258137984 \tabularnewline
224 & 0.00750211854397697 & 0.0150042370879539 & 0.992497881456023 \tabularnewline
225 & 0.00654705616129064 & 0.0130941123225813 & 0.99345294383871 \tabularnewline
226 & 0.0149951903144659 & 0.0299903806289319 & 0.985004809685534 \tabularnewline
227 & 0.0115416184372463 & 0.0230832368744927 & 0.988458381562754 \tabularnewline
228 & 0.00835031892340789 & 0.0167006378468158 & 0.991649681076592 \tabularnewline
229 & 0.0060968342432109 & 0.0121936684864218 & 0.993903165756789 \tabularnewline
230 & 0.00441036251732997 & 0.00882072503465995 & 0.99558963748267 \tabularnewline
231 & 0.00439461457994107 & 0.00878922915988214 & 0.99560538542006 \tabularnewline
232 & 0.00814497957146643 & 0.0162899591429329 & 0.991855020428534 \tabularnewline
233 & 0.0377565442118784 & 0.0755130884237568 & 0.962243455788122 \tabularnewline
234 & 0.0542942200569106 & 0.108588440113821 & 0.94570577994309 \tabularnewline
235 & 0.0405065857547010 & 0.0810131715094021 & 0.959493414245299 \tabularnewline
236 & 0.0361101125951766 & 0.0722202251903533 & 0.963889887404823 \tabularnewline
237 & 0.246505700570198 & 0.493011401140395 & 0.753494299429802 \tabularnewline
238 & 0.200782130963978 & 0.401564261927957 & 0.799217869036022 \tabularnewline
239 & 0.180044405468033 & 0.360088810936067 & 0.819955594531967 \tabularnewline
240 & 0.142128535333137 & 0.284257070666275 & 0.857871464666863 \tabularnewline
241 & 0.113707343103352 & 0.227414686206705 & 0.886292656896648 \tabularnewline
242 & 0.0945301032407307 & 0.189060206481461 & 0.90546989675927 \tabularnewline
243 & 0.0879023187197933 & 0.175804637439587 & 0.912097681280207 \tabularnewline
244 & 0.153550434222244 & 0.307100868444488 & 0.846449565777756 \tabularnewline
245 & 0.137630339717254 & 0.275260679434508 & 0.862369660282746 \tabularnewline
246 & 0.110109330538686 & 0.220218661077373 & 0.889890669461314 \tabularnewline
247 & 0.0777706008222238 & 0.155541201644448 & 0.922229399177776 \tabularnewline
248 & 0.0666948470256238 & 0.133389694051248 & 0.933305152974376 \tabularnewline
249 & 0.0624888270323013 & 0.124977654064603 & 0.937511172967699 \tabularnewline
250 & 0.0773809930539605 & 0.154761986107921 & 0.92261900694604 \tabularnewline
251 & 0.0460549764082260 & 0.0921099528164521 & 0.953945023591774 \tabularnewline
252 & 0.112285664162278 & 0.224571328324556 & 0.887714335837722 \tabularnewline
253 & 0.234998511095394 & 0.469997022190787 & 0.765001488904606 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96530&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]0.234449524103323[/C][C]0.468899048206647[/C][C]0.765550475896677[/C][/ROW]
[ROW][C]12[/C][C]0.120293517840557[/C][C]0.240587035681113[/C][C]0.879706482159443[/C][/ROW]
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[ROW][C]159[/C][C]0.0472261086820218[/C][C]0.0944522173640437[/C][C]0.952773891317978[/C][/ROW]
[ROW][C]160[/C][C]0.0404930281933112[/C][C]0.0809860563866224[/C][C]0.959506971806689[/C][/ROW]
[ROW][C]161[/C][C]0.0334124305697177[/C][C]0.0668248611394353[/C][C]0.966587569430282[/C][/ROW]
[ROW][C]162[/C][C]0.0274667952760031[/C][C]0.0549335905520062[/C][C]0.972533204723997[/C][/ROW]
[ROW][C]163[/C][C]0.0228756533010375[/C][C]0.0457513066020749[/C][C]0.977124346698963[/C][/ROW]
[ROW][C]164[/C][C]0.0192964132818165[/C][C]0.0385928265636331[/C][C]0.980703586718183[/C][/ROW]
[ROW][C]165[/C][C]0.0169353563376050[/C][C]0.0338707126752100[/C][C]0.983064643662395[/C][/ROW]
[ROW][C]166[/C][C]0.0171843050634623[/C][C]0.0343686101269246[/C][C]0.982815694936538[/C][/ROW]
[ROW][C]167[/C][C]0.0138076243872405[/C][C]0.027615248774481[/C][C]0.98619237561276[/C][/ROW]
[ROW][C]168[/C][C]0.0203735861261563[/C][C]0.0407471722523127[/C][C]0.979626413873844[/C][/ROW]
[ROW][C]169[/C][C]0.0201928859457739[/C][C]0.0403857718915477[/C][C]0.979807114054226[/C][/ROW]
[ROW][C]170[/C][C]0.0188580171726754[/C][C]0.0377160343453509[/C][C]0.981141982827325[/C][/ROW]
[ROW][C]171[/C][C]0.0181349653406303[/C][C]0.0362699306812606[/C][C]0.98186503465937[/C][/ROW]
[ROW][C]172[/C][C]0.0146941965866968[/C][C]0.0293883931733937[/C][C]0.985305803413303[/C][/ROW]
[ROW][C]173[/C][C]0.0147422907486943[/C][C]0.0294845814973886[/C][C]0.985257709251306[/C][/ROW]
[ROW][C]174[/C][C]0.0164226161640721[/C][C]0.0328452323281442[/C][C]0.983577383835928[/C][/ROW]
[ROW][C]175[/C][C]0.0219494596798690[/C][C]0.0438989193597379[/C][C]0.978050540320131[/C][/ROW]
[ROW][C]176[/C][C]0.0176531523212624[/C][C]0.0353063046425249[/C][C]0.982346847678738[/C][/ROW]
[ROW][C]177[/C][C]0.0143826922510692[/C][C]0.0287653845021384[/C][C]0.98561730774893[/C][/ROW]
[ROW][C]178[/C][C]0.0117427428384760[/C][C]0.0234854856769521[/C][C]0.988257257161524[/C][/ROW]
[ROW][C]179[/C][C]0.00919215979401736[/C][C]0.0183843195880347[/C][C]0.990807840205983[/C][/ROW]
[ROW][C]180[/C][C]0.00800536172863925[/C][C]0.0160107234572785[/C][C]0.99199463827136[/C][/ROW]
[ROW][C]181[/C][C]0.00660360418668579[/C][C]0.0132072083733716[/C][C]0.993396395813314[/C][/ROW]
[ROW][C]182[/C][C]0.00534521714955178[/C][C]0.0106904342991036[/C][C]0.994654782850448[/C][/ROW]
[ROW][C]183[/C][C]0.00560936622575513[/C][C]0.0112187324515103[/C][C]0.994390633774245[/C][/ROW]
[ROW][C]184[/C][C]0.00443647295033514[/C][C]0.00887294590067028[/C][C]0.995563527049665[/C][/ROW]
[ROW][C]185[/C][C]0.094086539900981[/C][C]0.188173079801962[/C][C]0.905913460099019[/C][/ROW]
[ROW][C]186[/C][C]0.0816845811721888[/C][C]0.163369162344378[/C][C]0.918315418827811[/C][/ROW]
[ROW][C]187[/C][C]0.0928439802674408[/C][C]0.185687960534882[/C][C]0.90715601973256[/C][/ROW]
[ROW][C]188[/C][C]0.0802127135646717[/C][C]0.160425427129343[/C][C]0.919787286435328[/C][/ROW]
[ROW][C]189[/C][C]0.0679596339456148[/C][C]0.135919267891230[/C][C]0.932040366054385[/C][/ROW]
[ROW][C]190[/C][C]0.0563561435032724[/C][C]0.112712287006545[/C][C]0.943643856496728[/C][/ROW]
[ROW][C]191[/C][C]0.0497111618841623[/C][C]0.0994223237683247[/C][C]0.950288838115838[/C][/ROW]
[ROW][C]192[/C][C]0.0419611933152232[/C][C]0.0839223866304465[/C][C]0.958038806684777[/C][/ROW]
[ROW][C]193[/C][C]0.0487155279839566[/C][C]0.0974310559679132[/C][C]0.951284472016043[/C][/ROW]
[ROW][C]194[/C][C]0.0522890666310367[/C][C]0.104578133262073[/C][C]0.947710933368963[/C][/ROW]
[ROW][C]195[/C][C]0.0443811738269538[/C][C]0.0887623476539076[/C][C]0.955618826173046[/C][/ROW]
[ROW][C]196[/C][C]0.0370263282172378[/C][C]0.0740526564344756[/C][C]0.962973671782762[/C][/ROW]
[ROW][C]197[/C][C]0.0483157061172391[/C][C]0.0966314122344782[/C][C]0.95168429388276[/C][/ROW]
[ROW][C]198[/C][C]0.0395862946218533[/C][C]0.0791725892437066[/C][C]0.960413705378147[/C][/ROW]
[ROW][C]199[/C][C]0.0367011562951482[/C][C]0.0734023125902964[/C][C]0.963298843704852[/C][/ROW]
[ROW][C]200[/C][C]0.0310398676134195[/C][C]0.062079735226839[/C][C]0.96896013238658[/C][/ROW]
[ROW][C]201[/C][C]0.0292219724944844[/C][C]0.0584439449889688[/C][C]0.970778027505516[/C][/ROW]
[ROW][C]202[/C][C]0.0243805707067269[/C][C]0.0487611414134538[/C][C]0.975619429293273[/C][/ROW]
[ROW][C]203[/C][C]0.0313053691066294[/C][C]0.0626107382132588[/C][C]0.96869463089337[/C][/ROW]
[ROW][C]204[/C][C]0.0356803177739386[/C][C]0.0713606355478772[/C][C]0.964319682226061[/C][/ROW]
[ROW][C]205[/C][C]0.0389063363767209[/C][C]0.0778126727534419[/C][C]0.96109366362328[/C][/ROW]
[ROW][C]206[/C][C]0.0308791545420775[/C][C]0.0617583090841551[/C][C]0.969120845457922[/C][/ROW]
[ROW][C]207[/C][C]0.0303422228248083[/C][C]0.0606844456496166[/C][C]0.969657777175192[/C][/ROW]
[ROW][C]208[/C][C]0.024949872731147[/C][C]0.049899745462294[/C][C]0.975050127268853[/C][/ROW]
[ROW][C]209[/C][C]0.0278681412054717[/C][C]0.0557362824109434[/C][C]0.972131858794528[/C][/ROW]
[ROW][C]210[/C][C]0.0225417426210896[/C][C]0.0450834852421793[/C][C]0.97745825737891[/C][/ROW]
[ROW][C]211[/C][C]0.0249775332931389[/C][C]0.0499550665862777[/C][C]0.975022466706861[/C][/ROW]
[ROW][C]212[/C][C]0.0399735087422991[/C][C]0.0799470174845981[/C][C]0.960026491257701[/C][/ROW]
[ROW][C]213[/C][C]0.0315871062441668[/C][C]0.0631742124883335[/C][C]0.968412893755833[/C][/ROW]
[ROW][C]214[/C][C]0.0441589425989401[/C][C]0.0883178851978801[/C][C]0.95584105740106[/C][/ROW]
[ROW][C]215[/C][C]0.0380593092869368[/C][C]0.0761186185738735[/C][C]0.961940690713063[/C][/ROW]
[ROW][C]216[/C][C]0.0297508259210554[/C][C]0.0595016518421109[/C][C]0.970249174078945[/C][/ROW]
[ROW][C]217[/C][C]0.0321955312729058[/C][C]0.0643910625458115[/C][C]0.967804468727094[/C][/ROW]
[ROW][C]218[/C][C]0.0275294909822642[/C][C]0.0550589819645284[/C][C]0.972470509017736[/C][/ROW]
[ROW][C]219[/C][C]0.0247418070044547[/C][C]0.0494836140089093[/C][C]0.975258192995545[/C][/ROW]
[ROW][C]220[/C][C]0.0189633773705708[/C][C]0.0379267547411415[/C][C]0.98103662262943[/C][/ROW]
[ROW][C]221[/C][C]0.0168947339209663[/C][C]0.0337894678419326[/C][C]0.983105266079034[/C][/ROW]
[ROW][C]222[/C][C]0.0124206485849155[/C][C]0.0248412971698309[/C][C]0.987579351415085[/C][/ROW]
[ROW][C]223[/C][C]0.0093117418620164[/C][C]0.0186234837240328[/C][C]0.990688258137984[/C][/ROW]
[ROW][C]224[/C][C]0.00750211854397697[/C][C]0.0150042370879539[/C][C]0.992497881456023[/C][/ROW]
[ROW][C]225[/C][C]0.00654705616129064[/C][C]0.0130941123225813[/C][C]0.99345294383871[/C][/ROW]
[ROW][C]226[/C][C]0.0149951903144659[/C][C]0.0299903806289319[/C][C]0.985004809685534[/C][/ROW]
[ROW][C]227[/C][C]0.0115416184372463[/C][C]0.0230832368744927[/C][C]0.988458381562754[/C][/ROW]
[ROW][C]228[/C][C]0.00835031892340789[/C][C]0.0167006378468158[/C][C]0.991649681076592[/C][/ROW]
[ROW][C]229[/C][C]0.0060968342432109[/C][C]0.0121936684864218[/C][C]0.993903165756789[/C][/ROW]
[ROW][C]230[/C][C]0.00441036251732997[/C][C]0.00882072503465995[/C][C]0.99558963748267[/C][/ROW]
[ROW][C]231[/C][C]0.00439461457994107[/C][C]0.00878922915988214[/C][C]0.99560538542006[/C][/ROW]
[ROW][C]232[/C][C]0.00814497957146643[/C][C]0.0162899591429329[/C][C]0.991855020428534[/C][/ROW]
[ROW][C]233[/C][C]0.0377565442118784[/C][C]0.0755130884237568[/C][C]0.962243455788122[/C][/ROW]
[ROW][C]234[/C][C]0.0542942200569106[/C][C]0.108588440113821[/C][C]0.94570577994309[/C][/ROW]
[ROW][C]235[/C][C]0.0405065857547010[/C][C]0.0810131715094021[/C][C]0.959493414245299[/C][/ROW]
[ROW][C]236[/C][C]0.0361101125951766[/C][C]0.0722202251903533[/C][C]0.963889887404823[/C][/ROW]
[ROW][C]237[/C][C]0.246505700570198[/C][C]0.493011401140395[/C][C]0.753494299429802[/C][/ROW]
[ROW][C]238[/C][C]0.200782130963978[/C][C]0.401564261927957[/C][C]0.799217869036022[/C][/ROW]
[ROW][C]239[/C][C]0.180044405468033[/C][C]0.360088810936067[/C][C]0.819955594531967[/C][/ROW]
[ROW][C]240[/C][C]0.142128535333137[/C][C]0.284257070666275[/C][C]0.857871464666863[/C][/ROW]
[ROW][C]241[/C][C]0.113707343103352[/C][C]0.227414686206705[/C][C]0.886292656896648[/C][/ROW]
[ROW][C]242[/C][C]0.0945301032407307[/C][C]0.189060206481461[/C][C]0.90546989675927[/C][/ROW]
[ROW][C]243[/C][C]0.0879023187197933[/C][C]0.175804637439587[/C][C]0.912097681280207[/C][/ROW]
[ROW][C]244[/C][C]0.153550434222244[/C][C]0.307100868444488[/C][C]0.846449565777756[/C][/ROW]
[ROW][C]245[/C][C]0.137630339717254[/C][C]0.275260679434508[/C][C]0.862369660282746[/C][/ROW]
[ROW][C]246[/C][C]0.110109330538686[/C][C]0.220218661077373[/C][C]0.889890669461314[/C][/ROW]
[ROW][C]247[/C][C]0.0777706008222238[/C][C]0.155541201644448[/C][C]0.922229399177776[/C][/ROW]
[ROW][C]248[/C][C]0.0666948470256238[/C][C]0.133389694051248[/C][C]0.933305152974376[/C][/ROW]
[ROW][C]249[/C][C]0.0624888270323013[/C][C]0.124977654064603[/C][C]0.937511172967699[/C][/ROW]
[ROW][C]250[/C][C]0.0773809930539605[/C][C]0.154761986107921[/C][C]0.92261900694604[/C][/ROW]
[ROW][C]251[/C][C]0.0460549764082260[/C][C]0.0921099528164521[/C][C]0.953945023591774[/C][/ROW]
[ROW][C]252[/C][C]0.112285664162278[/C][C]0.224571328324556[/C][C]0.887714335837722[/C][/ROW]
[ROW][C]253[/C][C]0.234998511095394[/C][C]0.469997022190787[/C][C]0.765001488904606[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96530&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96530&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-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.234449524103323 0.468899048206647 0.765550475896677 12 0.120293517840557 0.240587035681113 0.879706482159443 13 0.0815541573457802 0.163108314691560 0.91844584265422 14 0.102874776497918 0.205749552995836 0.897125223502082 15 0.0587519756515502 0.117503951303100 0.94124802434845 16 0.0391686675931634 0.0783373351863268 0.960831332406837 17 0.0630191184441749 0.126038236888350 0.936980881555825 18 0.25586432793531 0.51172865587062 0.74413567206469 19 0.194216632900151 0.388433265800301 0.80578336709985 20 0.137764883334639 0.275529766669278 0.862235116665361 21 0.0986062615513702 0.197212523102740 0.90139373844863 22 0.0968533990427727 0.193706798085545 0.903146600957227 23 0.257054377911139 0.514108755822278 0.742945622088861 24 0.321495418423947 0.642990836847893 0.678504581576054 25 0.294869538595019 0.589739077190038 0.705130461404981 26 0.277447857711977 0.554895715423954 0.722552142288023 27 0.352098888872558 0.704197777745116 0.647901111127442 28 0.363849648859342 0.727699297718684 0.636150351140658 29 0.346785592147590 0.693571184295181 0.65321440785241 30 0.380898386272267 0.761796772544533 0.619101613727733 31 0.328675803111161 0.657351606222323 0.671324196888839 32 0.289492867969932 0.578985735939864 0.710507132030068 33 0.262490777406898 0.524981554813796 0.737509222593102 34 0.226304417033047 0.452608834066093 0.773695582966953 35 0.187880541814586 0.375761083629173 0.812119458185414 36 0.304929814888216 0.609859629776433 0.695070185111784 37 0.334971074151198 0.669942148302397 0.665028925848802 38 0.325963622584243 0.651927245168487 0.674036377415757 39 0.35602273985543 0.71204547971086 0.64397726014457 40 0.334209449134650 0.668418898269301 0.66579055086535 41 0.298001365293739 0.596002730587478 0.701998634706261 42 0.263835728828234 0.527671457656468 0.736164271171766 43 0.278274265211429 0.556548530422857 0.721725734788571 44 0.236536521323935 0.47307304264787 0.763463478676065 45 0.214442280198149 0.428884560396298 0.78555771980185 46 0.392689717772571 0.785379435545142 0.607310282227429 47 0.545519033485654 0.908961933028692 0.454480966514346 48 0.496548776402329 0.993097552804658 0.503451223597671 49 0.483216160724748 0.966432321449497 0.516783839275252 50 0.468390230644651 0.936780461289303 0.531609769355349 51 0.42367172034991 0.84734344069982 0.57632827965009 52 0.378169197572531 0.756338395145061 0.621830802427469 53 0.386603609576097 0.773207219152193 0.613396390423903 54 0.369709551282599 0.739419102565198 0.630290448717401 55 0.360937557172319 0.721875114344638 0.639062442827681 56 0.339385238523737 0.678770477047474 0.660614761476263 57 0.299777754262648 0.599555508525295 0.700222245737352 58 0.271157720239429 0.542315440478858 0.728842279760571 59 0.23669426979653 0.47338853959306 0.76330573020347 60 0.242458614750553 0.484917229501107 0.757541385249447 61 0.218650159104958 0.437300318209917 0.781349840895042 62 0.191735670804926 0.383471341609851 0.808264329195074 63 0.163732057843763 0.327464115687526 0.836267942156237 64 0.13797888889491 0.27595777778982 0.86202111110509 65 0.119073866261549 0.238147732523097 0.880926133738452 66 0.110228851421388 0.220457702842776 0.889771148578612 67 0.105986150241298 0.211972300482595 0.894013849758702 68 0.223058679356041 0.446117358712083 0.776941320643959 69 0.313038852663478 0.626077705326957 0.686961147336522 70 0.282142509826931 0.564285019653863 0.717857490173069 71 0.397902377321231 0.795804754642462 0.602097622678769 72 0.359897835237819 0.719795670475638 0.640102164762181 73 0.351564646488890 0.703129292977779 0.64843535351111 74 0.336197027115224 0.672394054230448 0.663802972884776 75 0.304024154874081 0.608048309748162 0.695975845125919 76 0.369544725558949 0.739089451117898 0.630455274441051 77 0.333817781175649 0.667635562351299 0.66618221882435 78 0.31297705265364 0.62595410530728 0.68702294734636 79 0.326056568919713 0.652113137839427 0.673943431080287 80 0.298478347311551 0.596956694623103 0.701521652688449 81 0.270341906890842 0.540683813781685 0.729658093109158 82 0.239446042587499 0.478892085174997 0.760553957412501 83 0.215139662822704 0.430279325645408 0.784860337177296 84 0.187913722738409 0.375827445476819 0.81208627726159 85 0.184607191674081 0.369214383348162 0.815392808325919 86 0.1595725120416 0.3191450240832 0.8404274879584 87 0.140322537361321 0.280645074722642 0.859677462638679 88 0.130738801764796 0.261477603529592 0.869261198235204 89 0.112976437875891 0.225952875751782 0.887023562124109 90 0.110203114219269 0.220406228438538 0.889796885780731 91 0.0942919369448664 0.188583873889733 0.905708063055134 92 0.0814240906086628 0.162848181217326 0.918575909391337 93 0.0691622046865873 0.138324409373175 0.930837795313413 94 0.0710505619641158 0.142101123928232 0.928949438035884 95 0.0642047750978116 0.128409550195623 0.935795224902188 96 0.0537975865035424 0.107595173007085 0.946202413496458 97 0.0594315146399681 0.118863029279936 0.940568485360032 98 0.0492270511304384 0.0984541022608768 0.950772948869562 99 0.040363797123943 0.080727594247886 0.959636202876057 100 0.0353149770686564 0.0706299541373128 0.964685022931344 101 0.0313300139468171 0.0626600278936343 0.968669986053183 102 0.0368820710846457 0.0737641421692915 0.963117928915354 103 0.0316513902506383 0.0633027805012766 0.968348609749362 104 0.0285859141191522 0.0571718282383044 0.971414085880848 105 0.033817135813769 0.067634271627538 0.966182864186231 106 0.0297170878687317 0.0594341757374634 0.970282912131268 107 0.0241330786284951 0.0482661572569901 0.975866921371505 108 0.0240079113500846 0.0480158227001693 0.975992088649915 109 0.0194515049063904 0.0389030098127808 0.98054849509361 110 0.0159136661421487 0.0318273322842973 0.984086333857851 111 0.0126146522719215 0.0252293045438429 0.987385347728079 112 0.0130381277586815 0.0260762555173629 0.986961872241318 113 0.0116452146478809 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 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 4 0.0164609053497942 NOK 5% type I error level 79 0.325102880658436 NOK 10% type I error level 128 0.526748971193416 NOK

\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 & 4 & 0.0164609053497942 & NOK \tabularnewline
5% type I error level & 79 & 0.325102880658436 & NOK \tabularnewline
10% type I error level & 128 & 0.526748971193416 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96530&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]4[/C][C]0.0164609053497942[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]79[/C][C]0.325102880658436[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]128[/C][C]0.526748971193416[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96530&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96530&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 tests OK/NOK 1% type I error level 4 0.0164609053497942 NOK 5% type I error level 79 0.325102880658436 NOK 10% type I error level 128 0.526748971193416 NOK

library(lattice)library(lmtest)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt testpar1 <- 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 <- x1if (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'}xk <- length(x[1,])df <- as.data.frame(x)(mylm <- lm(df))(mysum <- summary(mylm))if (n > n25) {kp3 <- k + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)zplot(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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')}}myeq <- paste(myeq, ' + e[t]')a<-table.row.start(a)a<-table.element(a, myeq)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable1.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Variable',header=TRUE)a<-table.element(a,'Parameter',header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'T-STATH0: parameter = 0',header=TRUE)a<-table.element(a,'2-tail p-value',header=TRUE)a<-table.element(a,'1-tail p-value',header=TRUE)a<-table.row.end(a)for (i in 1:k){a<-table.row.start(a)a<-table.element(a,rownames(mysum$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$sigma)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Sum Squared Residuals',1,TRUE)a<-table.element(a, sum(myerror*myerror))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable3.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,x[i])a<-table.element(a,x[i]-mysum$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')}