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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 21 Dec 2012 15:12:36 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/21/t135612079125od94uswihr3mt.htm/, Retrieved Tue, 23 Apr 2024 17:09:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=204206, Retrieved Tue, 23 Apr 2024 17:09:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact74

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Multiple regressi...] [2012-12-19 15:21:06] [5ba6a65192e86ec9d9b86886359e5312]
- R       [Multiple Regression] [multiple regressie] [2012-12-21 20:12:36] [b262fb17dc370e47870f909f9ce3690a] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1	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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time14 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\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 & 14 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204206&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]14 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204206&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204206&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time14 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 3.63882403788175 + 0.0580024189442229Connected[t] + 0.0399416808826553Seperate[t] + 0.608654260080512Software[t] + 0.0982509406962489Hapiness[t] -0.0410988809467352Depression[t] + 0.0145244673287915belonging[t] -0.0205284258383876Belonging_Fin[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  3.63882403788175 +  0.0580024189442229Connected[t] +  0.0399416808826553Seperate[t] +  0.608654260080512Software[t] +  0.0982509406962489Hapiness[t] -0.0410988809467352Depression[t] +  0.0145244673287915belonging[t] -0.0205284258383876Belonging_Fin[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204206&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  3.63882403788175 +  0.0580024189442229Connected[t] +  0.0399416808826553Seperate[t] +  0.608654260080512Software[t] +  0.0982509406962489Hapiness[t] -0.0410988809467352Depression[t] +  0.0145244673287915belonging[t] -0.0205284258383876Belonging_Fin[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204206&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204206&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 3.63882403788175 + 0.0580024189442229Connected[t] + 0.0399416808826553Seperate[t] + 0.608654260080512Software[t] + 0.0982509406962489Hapiness[t] -0.0410988809467352Depression[t] + 0.0145244673287915belonging[t] -0.0205284258383876Belonging_Fin[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.638824037881751.8741891.94150.0532890.026645
Connected0.05800241894422290.0293751.97460.0493940.024697
Seperate0.03994168088265530.0340531.17290.2419090.120955
Software0.6086542600805120.05160611.794300
Hapiness0.09825094069624890.0577211.70220.0899360.044968
Depression-0.04109888094673520.04233-0.97090.3325070.166253
belonging0.01452446732879150.0376250.3860.699790.349895
Belonging_Fin-0.02052842583838760.056144-0.36560.7149350.357468

\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) & 3.63882403788175 & 1.874189 & 1.9415 & 0.053289 & 0.026645 \tabularnewline
Connected & 0.0580024189442229 & 0.029375 & 1.9746 & 0.049394 & 0.024697 \tabularnewline
Seperate & 0.0399416808826553 & 0.034053 & 1.1729 & 0.241909 & 0.120955 \tabularnewline
Software & 0.608654260080512 & 0.051606 & 11.7943 & 0 & 0 \tabularnewline
Hapiness & 0.0982509406962489 & 0.057721 & 1.7022 & 0.089936 & 0.044968 \tabularnewline
Depression & -0.0410988809467352 & 0.04233 & -0.9709 & 0.332507 & 0.166253 \tabularnewline
belonging & 0.0145244673287915 & 0.037625 & 0.386 & 0.69979 & 0.349895 \tabularnewline
Belonging_Fin & -0.0205284258383876 & 0.056144 & -0.3656 & 0.714935 & 0.357468 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204206&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]3.63882403788175[/C][C]1.874189[/C][C]1.9415[/C][C]0.053289[/C][C]0.026645[/C][/ROW]
[ROW][C]Connected[/C][C]0.0580024189442229[/C][C]0.029375[/C][C]1.9746[/C][C]0.049394[/C][C]0.024697[/C][/ROW]
[ROW][C]Seperate[/C][C]0.0399416808826553[/C][C]0.034053[/C][C]1.1729[/C][C]0.241909[/C][C]0.120955[/C][/ROW]
[ROW][C]Software[/C][C]0.608654260080512[/C][C]0.051606[/C][C]11.7943[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Hapiness[/C][C]0.0982509406962489[/C][C]0.057721[/C][C]1.7022[/C][C]0.089936[/C][C]0.044968[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0410988809467352[/C][C]0.04233[/C][C]-0.9709[/C][C]0.332507[/C][C]0.166253[/C][/ROW]
[ROW][C]belonging[/C][C]0.0145244673287915[/C][C]0.037625[/C][C]0.386[/C][C]0.69979[/C][C]0.349895[/C][/ROW]
[ROW][C]Belonging_Fin[/C][C]-0.0205284258383876[/C][C]0.056144[/C][C]-0.3656[/C][C]0.714935[/C][C]0.357468[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204206&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204206&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.638824037881751.8741891.94150.0532890.026645
Connected0.05800241894422290.0293751.97460.0493940.024697
Seperate0.03994168088265530.0340531.17290.2419090.120955
Software0.6086542600805120.05160611.794300
Hapiness0.09825094069624890.0577211.70220.0899360.044968
Depression-0.04109888094673520.04233-0.97090.3325070.166253
belonging0.01452446732879150.0376250.3860.699790.349895
Belonging_Fin-0.02052842583838760.056144-0.36560.7149350.357468






Multiple Linear Regression - Regression Statistics
Multiple R0.656890909308643
R-squared0.431505666732336
Adjusted R-squared0.415960899807048
F-TEST (value)27.7589023242528
F-TEST (DF numerator)7
F-TEST (DF denominator)256
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.87691437456057
Sum Squared Residuals901.838737774612

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.656890909308643 \tabularnewline
R-squared & 0.431505666732336 \tabularnewline
Adjusted R-squared & 0.415960899807048 \tabularnewline
F-TEST (value) & 27.7589023242528 \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 & 1.87691437456057 \tabularnewline
Sum Squared Residuals & 901.838737774612 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204206&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.656890909308643[/C][/ROW]
[ROW][C]R-squared[/C][C]0.431505666732336[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.415960899807048[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]27.7589023242528[/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]1.87691437456057[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]901.838737774612[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204206&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204206&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.656890909308643
R-squared0.431505666732336
Adjusted R-squared0.415960899807048
F-TEST (value)27.7589023242528
F-TEST (DF numerator)7
F-TEST (DF denominator)256
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.87691437456057
Sum Squared Residuals901.838737774612






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11313.5136751913172-0.513675191317203
21615.34925933848740.650740661512645
31916.50846631120142.49153368879855
41511.22419862341273.77580137658735
51415.8697443353711-1.86974433537109
61314.3571628457448-1.35716284574484
71914.92454440635984.07545559364019
81516.6494564344438-1.6494564344438
91415.6425277923124-1.64252779231236
101513.85908791380361.14091208619639
111614.56846790119211.43153209880794
121616.033639399593-0.033639399592988
131615.02169752819950.978302471800496
141615.21741301210660.782586987893392
151717.6937101681425-0.693710168142468
161515.0203526455718-0.0203526455717886
171514.0572722323490.942727767651032
182016.05234738335213.94765261664788
191815.16632392108192.83367607891814
201615.20348725969960.796512740300425
211615.0032007219750.996799278025025
221614.56873130935431.43126869064568
231916.32041741276862.6795825872314
241614.69528132361991.30471867638015
251715.88270763401961.11729236598039
261715.91905229353121.08094770646877
271614.55247711823521.4475228817648
281516.4098731611705-1.40987316117053
291615.28233951924260.717660480757399
301413.63188782140920.368112178590778
311515.3371318963228-0.337131896322803
321212.119919416203-0.119919416202985
331414.5174402986478-0.517440298647832
341615.52579627598490.474203724015088
351415.1668522439893-1.16685224398934
361012.4447671808093-2.44476718080933
371012.2547451683762-2.25474516837619
381415.477930118344-1.47793011834398
391614.06445800622281.93554199377716
401614.07979455092051.92020544907949
411614.40449552939431.59550447060568
421415.4155479027351-1.41554790273509
432017.60097083386892.39902916613109
441413.79604482607010.203955173929875
451414.2094964037857-0.209496403785661
461115.1997251308506-4.19972513085057
471416.4783891404763-2.47838914047632
481514.77282175506020.227178244939811
491615.20629132905380.793708670946205
501415.4156312925424-1.41563129254239
511616.8373821112215-0.837382111221507
521413.73182558536840.268174414631628
531214.6132982105017-2.61329821050167
541615.60103187749280.398968122507185
55910.7192482422172-1.71924824221722
561411.8516517694942.14834823050599
571615.75154766611840.248452333881588
581615.19758206598050.802417934019503
591514.8635971943180.136402805682041
601613.80998692786362.19001307213636
611210.83476053971921.1652394602808
621615.48346470801860.516535291981448
631616.2941914763641-0.294191476364053
641414.4490746535316-0.449074653531554
651615.19931135153790.800688648462095
661715.74574251140521.25425748859483
671816.12452992194611.87547007805393
681814.05696422354783.94303577645219
691215.8768766509701-3.8768766509701
701615.48897615221770.511023847782292
711013.0611668417618-3.0611668417618
721414.7122947796061-0.71229477960609
731816.82038754906171.17961245093827
741817.24551102820570.754488971794263
751615.01199128911940.988008710880589
761713.13125421453.86874578549995
771616.4452320540163-0.445232054016302
781614.22181401198761.77818598801244
791314.8979616246973-1.89796162469735
801615.18144552723440.818554472765605
811615.51945724622120.480542753778823
821615.66193421409380.338065785906196
831515.663232412642-0.66323241264203
841514.67120390485960.328796095140369
851613.87241425856122.12758574143881
861413.98356439797850.0164356020215261
871615.23709309287850.762906907121482
881614.67054129441351.32945870558652
891514.09240205914520.907597940854827
901213.6046469436175-1.60464694361748
911716.90252100726460.0974789927354275
921615.72392379197180.276076208028154
931514.88179146604210.118208533957879
941314.7836433238262-1.78364332382616
951614.74241022191211.25758977808786
961615.73627287860070.26372712139925
971613.49701549142982.50298450857023
981615.85607752460610.143922475393937
991414.2416717860763-0.241671786076292
1001617.2413336472946-1.24133364729456
1011614.53600537607811.46399462392188
1022017.580497272872.41950272713003
1031514.03355898542950.966441014570535
1041614.80460519821661.19539480178343
1051314.790283752124-1.79028375212397
1061715.80367677293111.19632322706888
1071615.84319677457410.156803225425919
1081614.29297618750171.70702381249825
1091212.1194674563321-0.119467456332061
1101615.10624623372540.893753766274584
1111615.91197111430670.0880288856933299
1121714.80197146363092.19802853636914
1131314.5933208039284-1.5933208039284
1141214.643827153389-2.64382715338898
1151816.29767657062381.70232342937623
1161415.9700884989751-1.97008849897511
1171412.98892388951551.01107611048447
1181314.7916771565209-1.79167715652087
1191615.52183618453030.47816381546968
1201314.3953541738269-1.39535417382689
1211615.38751055494650.612489445053484
1221315.9103116501711-2.91031165017107
1231617.1078536189398-1.10785361893985
1241516.0357919805867-1.03579198058672
1251616.8925598386954-0.892559838695415
1261514.78520834849880.214791651501209
1271715.87468991454711.12531008545287
1281513.97184818255351.02815181744649
1291214.7641705998629-2.76417059986291
1301613.89373734779872.10626265220132
1311013.4359810749469-3.43598107494686
1321613.54284876568052.4571512343195
1331214.0785708758197-2.0785708758197
1341415.7176290661727-1.71762906617269
1351515.1002651820584-0.100265182058446
1361311.86186635743481.13813364256521
1371514.55703836675870.442961633241335
1381113.3524026218286-2.35240262182863
1391213.0262652271207-1.02626522712072
1401113.2573446407733-2.25734464077333
1411612.89181115881563.10818884118442
1421513.50436339533771.49563660466231
1431716.95009355753720.0499064424627691
1441614.20540848117341.79459151882662
1451013.3498885155341-3.34988851553409
1461815.70368419133832.29631580866168
1471315.1065255058354-2.10652550583544
1481614.90889573145981.09110426854024
1491312.64428908461330.355710915386657
1501012.8866229660412-2.88662296604125
1511516.2264513647711-1.22645136477105
1521613.99827533984052.0017246601595
1531611.70689544361434.29310455638573
1541412.29301285133081.70698714866919
1551012.2837711569455-2.28377115694548
1561716.90252100726460.0974789927354275
1571311.54426396836731.45573603163267
1581513.97184818255351.02815181744649
1591614.66471476950731.33528523049274
1601212.5147847932947-0.514784793294732
1611312.59560166542930.404398334570735
1621312.40194445002030.598055549979665
1631212.3563914844669-0.356391484466872
1641716.59677352926380.403226470736195
1651513.66094332375761.33905667624243
1661011.3407358686386-1.34073586863863
1671414.4220395460115-0.42203954601148
1681114.2244668740063-3.22446687400627
1691314.8238026766524-1.82380267665237
1701614.3496819278531.65031807214696
1711210.42603889988531.57396110011475
1721615.72994382406120.27005617593875
1731214.0605689784399-2.06056897843987
174911.297045950906-2.29704595090604
1751215.3293698151706-3.32936981517062
1761514.72508715116270.274912848837257
1771212.3038409270153-0.303840927015343
1781212.6547895910392-0.654789591039172
1791414.0541338266479-0.0541338266478555
1801213.5166392026678-1.5166392026678
1811615.49897141503680.501028584963152
1821111.557930840078-0.557930840078033
1831917.13975183429191.86024816570808
1841515.5660181733142-0.566018173314173
185814.6206369722122-6.62063697221216
1861614.9336453480011.06635465199902
1871714.77234223854282.22765776145723
1881212.576418357724-0.57641835772395
1891111.512185009998-0.512185009997973
1901110.27914100341150.720858996588508
1911415.1092450464616-1.1092450464616
1921615.8950429870850.104957012914992
193129.636214950183082.36378504981692
1941614.32397752199561.67602247800442
1951313.9004685211203-0.900468521120339
1961515.4058352264456-0.405835226445549
1971613.08368964056372.91631035943632
1981615.26924922038230.730750779617739
1991412.49496167884011.50503832115991
2001614.68698186381421.31301813618578
2011614.16551315262351.83448684737647
2021413.47730387106190.522696128938109
2031113.7723061959415-2.77230619594147
2041214.9605954184046-2.96059541840455
2051512.96487085034742.03512914965256
2061514.83451373238580.165486267614152
2071614.8297906546961.17020934530396
2081615.48508327071740.514916729282625
2091113.9468690617298-2.9468690617298
2101514.28798679539630.712013204603669
2111214.526839237758-2.52683923775798
2121216.3301843413077-4.33018434130769
2131514.24389801235550.756101987644503
2141512.1160284826462.88397151735404
2151614.90480030184641.09519969815361
2161413.38987996632520.610120033674782
2171715.03133943559291.96866056440707
2181414.365857370602-0.365857370602036
2191312.02290535302690.977094646973057
2201515.6103957498177-0.610395749817655
2211315.0379609957436-2.03796099574355
2221414.652225454202-0.652225454201975
2231514.56122811390410.438771886095865
2241213.4393062468293-1.43930624682928
2251312.87399417514210.126005824857865
226811.9343546211081-3.93435462110808
2271414.3231387801053-0.323138780105334
2281413.29170903397880.708290966021163
2291112.3440243141206-1.3440243141206
2301213.1672187509188-1.16721875091878
2311311.49192373662821.50807626337175
2321013.4537952826456-3.45379528264557
2331611.71211411140134.2878858885987
2341816.53619363691361.46380636308643
2351314.3202187044387-1.32021870443871
2361113.6910122444104-2.69101224441038
237411.19285806311-7.19285806310998
2381314.9009497151881-1.90094971518807
2391614.55639846876341.44360153123661
2401011.9738284729431-1.97382847294308
2411212.3104262526516-0.31042625265157
2421213.8390234702109-1.8390234702109
243108.852575007209431.14742499279056
2441311.29781673391221.7021832660878
2451514.11102777570120.888972224298776
2461211.98686418799960.013135812000393
2471413.13646909787180.863530902128218
2481012.9569243699695-2.95692436996951
2491210.683758005261.31624199474001
2501211.80896998056970.191030019430317
2511112.1083946007191-1.10839460071908
2521011.8382711620479-1.83827116204791
2531211.69001481245940.309985187540589
2541613.17388525369992.8261147463001
2551213.75427496248-1.75427496247999
2561414.2771236861309-0.2771236861309
2571614.67839671954741.32160328045257
2581411.80812544411632.19187455588371
2591314.8268822976117-1.82688229761175
26049.48489291173289-5.48489291173289
2611514.13014896231160.869851037688359
2621115.5591171345592-4.55911713455921
2631111.5408906987234-0.540890698723357
2641413.12228225124280.877717748757232

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 13.5136751913172 & -0.513675191317203 \tabularnewline
2 & 16 & 15.3492593384874 & 0.650740661512645 \tabularnewline
3 & 19 & 16.5084663112014 & 2.49153368879855 \tabularnewline
4 & 15 & 11.2241986234127 & 3.77580137658735 \tabularnewline
5 & 14 & 15.8697443353711 & -1.86974433537109 \tabularnewline
6 & 13 & 14.3571628457448 & -1.35716284574484 \tabularnewline
7 & 19 & 14.9245444063598 & 4.07545559364019 \tabularnewline
8 & 15 & 16.6494564344438 & -1.6494564344438 \tabularnewline
9 & 14 & 15.6425277923124 & -1.64252779231236 \tabularnewline
10 & 15 & 13.8590879138036 & 1.14091208619639 \tabularnewline
11 & 16 & 14.5684679011921 & 1.43153209880794 \tabularnewline
12 & 16 & 16.033639399593 & -0.033639399592988 \tabularnewline
13 & 16 & 15.0216975281995 & 0.978302471800496 \tabularnewline
14 & 16 & 15.2174130121066 & 0.782586987893392 \tabularnewline
15 & 17 & 17.6937101681425 & -0.693710168142468 \tabularnewline
16 & 15 & 15.0203526455718 & -0.0203526455717886 \tabularnewline
17 & 15 & 14.057272232349 & 0.942727767651032 \tabularnewline
18 & 20 & 16.0523473833521 & 3.94765261664788 \tabularnewline
19 & 18 & 15.1663239210819 & 2.83367607891814 \tabularnewline
20 & 16 & 15.2034872596996 & 0.796512740300425 \tabularnewline
21 & 16 & 15.003200721975 & 0.996799278025025 \tabularnewline
22 & 16 & 14.5687313093543 & 1.43126869064568 \tabularnewline
23 & 19 & 16.3204174127686 & 2.6795825872314 \tabularnewline
24 & 16 & 14.6952813236199 & 1.30471867638015 \tabularnewline
25 & 17 & 15.8827076340196 & 1.11729236598039 \tabularnewline
26 & 17 & 15.9190522935312 & 1.08094770646877 \tabularnewline
27 & 16 & 14.5524771182352 & 1.4475228817648 \tabularnewline
28 & 15 & 16.4098731611705 & -1.40987316117053 \tabularnewline
29 & 16 & 15.2823395192426 & 0.717660480757399 \tabularnewline
30 & 14 & 13.6318878214092 & 0.368112178590778 \tabularnewline
31 & 15 & 15.3371318963228 & -0.337131896322803 \tabularnewline
32 & 12 & 12.119919416203 & -0.119919416202985 \tabularnewline
33 & 14 & 14.5174402986478 & -0.517440298647832 \tabularnewline
34 & 16 & 15.5257962759849 & 0.474203724015088 \tabularnewline
35 & 14 & 15.1668522439893 & -1.16685224398934 \tabularnewline
36 & 10 & 12.4447671808093 & -2.44476718080933 \tabularnewline
37 & 10 & 12.2547451683762 & -2.25474516837619 \tabularnewline
38 & 14 & 15.477930118344 & -1.47793011834398 \tabularnewline
39 & 16 & 14.0644580062228 & 1.93554199377716 \tabularnewline
40 & 16 & 14.0797945509205 & 1.92020544907949 \tabularnewline
41 & 16 & 14.4044955293943 & 1.59550447060568 \tabularnewline
42 & 14 & 15.4155479027351 & -1.41554790273509 \tabularnewline
43 & 20 & 17.6009708338689 & 2.39902916613109 \tabularnewline
44 & 14 & 13.7960448260701 & 0.203955173929875 \tabularnewline
45 & 14 & 14.2094964037857 & -0.209496403785661 \tabularnewline
46 & 11 & 15.1997251308506 & -4.19972513085057 \tabularnewline
47 & 14 & 16.4783891404763 & -2.47838914047632 \tabularnewline
48 & 15 & 14.7728217550602 & 0.227178244939811 \tabularnewline
49 & 16 & 15.2062913290538 & 0.793708670946205 \tabularnewline
50 & 14 & 15.4156312925424 & -1.41563129254239 \tabularnewline
51 & 16 & 16.8373821112215 & -0.837382111221507 \tabularnewline
52 & 14 & 13.7318255853684 & 0.268174414631628 \tabularnewline
53 & 12 & 14.6132982105017 & -2.61329821050167 \tabularnewline
54 & 16 & 15.6010318774928 & 0.398968122507185 \tabularnewline
55 & 9 & 10.7192482422172 & -1.71924824221722 \tabularnewline
56 & 14 & 11.851651769494 & 2.14834823050599 \tabularnewline
57 & 16 & 15.7515476661184 & 0.248452333881588 \tabularnewline
58 & 16 & 15.1975820659805 & 0.802417934019503 \tabularnewline
59 & 15 & 14.863597194318 & 0.136402805682041 \tabularnewline
60 & 16 & 13.8099869278636 & 2.19001307213636 \tabularnewline
61 & 12 & 10.8347605397192 & 1.1652394602808 \tabularnewline
62 & 16 & 15.4834647080186 & 0.516535291981448 \tabularnewline
63 & 16 & 16.2941914763641 & -0.294191476364053 \tabularnewline
64 & 14 & 14.4490746535316 & -0.449074653531554 \tabularnewline
65 & 16 & 15.1993113515379 & 0.800688648462095 \tabularnewline
66 & 17 & 15.7457425114052 & 1.25425748859483 \tabularnewline
67 & 18 & 16.1245299219461 & 1.87547007805393 \tabularnewline
68 & 18 & 14.0569642235478 & 3.94303577645219 \tabularnewline
69 & 12 & 15.8768766509701 & -3.8768766509701 \tabularnewline
70 & 16 & 15.4889761522177 & 0.511023847782292 \tabularnewline
71 & 10 & 13.0611668417618 & -3.0611668417618 \tabularnewline
72 & 14 & 14.7122947796061 & -0.71229477960609 \tabularnewline
73 & 18 & 16.8203875490617 & 1.17961245093827 \tabularnewline
74 & 18 & 17.2455110282057 & 0.754488971794263 \tabularnewline
75 & 16 & 15.0119912891194 & 0.988008710880589 \tabularnewline
76 & 17 & 13.1312542145 & 3.86874578549995 \tabularnewline
77 & 16 & 16.4452320540163 & -0.445232054016302 \tabularnewline
78 & 16 & 14.2218140119876 & 1.77818598801244 \tabularnewline
79 & 13 & 14.8979616246973 & -1.89796162469735 \tabularnewline
80 & 16 & 15.1814455272344 & 0.818554472765605 \tabularnewline
81 & 16 & 15.5194572462212 & 0.480542753778823 \tabularnewline
82 & 16 & 15.6619342140938 & 0.338065785906196 \tabularnewline
83 & 15 & 15.663232412642 & -0.66323241264203 \tabularnewline
84 & 15 & 14.6712039048596 & 0.328796095140369 \tabularnewline
85 & 16 & 13.8724142585612 & 2.12758574143881 \tabularnewline
86 & 14 & 13.9835643979785 & 0.0164356020215261 \tabularnewline
87 & 16 & 15.2370930928785 & 0.762906907121482 \tabularnewline
88 & 16 & 14.6705412944135 & 1.32945870558652 \tabularnewline
89 & 15 & 14.0924020591452 & 0.907597940854827 \tabularnewline
90 & 12 & 13.6046469436175 & -1.60464694361748 \tabularnewline
91 & 17 & 16.9025210072646 & 0.0974789927354275 \tabularnewline
92 & 16 & 15.7239237919718 & 0.276076208028154 \tabularnewline
93 & 15 & 14.8817914660421 & 0.118208533957879 \tabularnewline
94 & 13 & 14.7836433238262 & -1.78364332382616 \tabularnewline
95 & 16 & 14.7424102219121 & 1.25758977808786 \tabularnewline
96 & 16 & 15.7362728786007 & 0.26372712139925 \tabularnewline
97 & 16 & 13.4970154914298 & 2.50298450857023 \tabularnewline
98 & 16 & 15.8560775246061 & 0.143922475393937 \tabularnewline
99 & 14 & 14.2416717860763 & -0.241671786076292 \tabularnewline
100 & 16 & 17.2413336472946 & -1.24133364729456 \tabularnewline
101 & 16 & 14.5360053760781 & 1.46399462392188 \tabularnewline
102 & 20 & 17.58049727287 & 2.41950272713003 \tabularnewline
103 & 15 & 14.0335589854295 & 0.966441014570535 \tabularnewline
104 & 16 & 14.8046051982166 & 1.19539480178343 \tabularnewline
105 & 13 & 14.790283752124 & -1.79028375212397 \tabularnewline
106 & 17 & 15.8036767729311 & 1.19632322706888 \tabularnewline
107 & 16 & 15.8431967745741 & 0.156803225425919 \tabularnewline
108 & 16 & 14.2929761875017 & 1.70702381249825 \tabularnewline
109 & 12 & 12.1194674563321 & -0.119467456332061 \tabularnewline
110 & 16 & 15.1062462337254 & 0.893753766274584 \tabularnewline
111 & 16 & 15.9119711143067 & 0.0880288856933299 \tabularnewline
112 & 17 & 14.8019714636309 & 2.19802853636914 \tabularnewline
113 & 13 & 14.5933208039284 & -1.5933208039284 \tabularnewline
114 & 12 & 14.643827153389 & -2.64382715338898 \tabularnewline
115 & 18 & 16.2976765706238 & 1.70232342937623 \tabularnewline
116 & 14 & 15.9700884989751 & -1.97008849897511 \tabularnewline
117 & 14 & 12.9889238895155 & 1.01107611048447 \tabularnewline
118 & 13 & 14.7916771565209 & -1.79167715652087 \tabularnewline
119 & 16 & 15.5218361845303 & 0.47816381546968 \tabularnewline
120 & 13 & 14.3953541738269 & -1.39535417382689 \tabularnewline
121 & 16 & 15.3875105549465 & 0.612489445053484 \tabularnewline
122 & 13 & 15.9103116501711 & -2.91031165017107 \tabularnewline
123 & 16 & 17.1078536189398 & -1.10785361893985 \tabularnewline
124 & 15 & 16.0357919805867 & -1.03579198058672 \tabularnewline
125 & 16 & 16.8925598386954 & -0.892559838695415 \tabularnewline
126 & 15 & 14.7852083484988 & 0.214791651501209 \tabularnewline
127 & 17 & 15.8746899145471 & 1.12531008545287 \tabularnewline
128 & 15 & 13.9718481825535 & 1.02815181744649 \tabularnewline
129 & 12 & 14.7641705998629 & -2.76417059986291 \tabularnewline
130 & 16 & 13.8937373477987 & 2.10626265220132 \tabularnewline
131 & 10 & 13.4359810749469 & -3.43598107494686 \tabularnewline
132 & 16 & 13.5428487656805 & 2.4571512343195 \tabularnewline
133 & 12 & 14.0785708758197 & -2.0785708758197 \tabularnewline
134 & 14 & 15.7176290661727 & -1.71762906617269 \tabularnewline
135 & 15 & 15.1002651820584 & -0.100265182058446 \tabularnewline
136 & 13 & 11.8618663574348 & 1.13813364256521 \tabularnewline
137 & 15 & 14.5570383667587 & 0.442961633241335 \tabularnewline
138 & 11 & 13.3524026218286 & -2.35240262182863 \tabularnewline
139 & 12 & 13.0262652271207 & -1.02626522712072 \tabularnewline
140 & 11 & 13.2573446407733 & -2.25734464077333 \tabularnewline
141 & 16 & 12.8918111588156 & 3.10818884118442 \tabularnewline
142 & 15 & 13.5043633953377 & 1.49563660466231 \tabularnewline
143 & 17 & 16.9500935575372 & 0.0499064424627691 \tabularnewline
144 & 16 & 14.2054084811734 & 1.79459151882662 \tabularnewline
145 & 10 & 13.3498885155341 & -3.34988851553409 \tabularnewline
146 & 18 & 15.7036841913383 & 2.29631580866168 \tabularnewline
147 & 13 & 15.1065255058354 & -2.10652550583544 \tabularnewline
148 & 16 & 14.9088957314598 & 1.09110426854024 \tabularnewline
149 & 13 & 12.6442890846133 & 0.355710915386657 \tabularnewline
150 & 10 & 12.8866229660412 & -2.88662296604125 \tabularnewline
151 & 15 & 16.2264513647711 & -1.22645136477105 \tabularnewline
152 & 16 & 13.9982753398405 & 2.0017246601595 \tabularnewline
153 & 16 & 11.7068954436143 & 4.29310455638573 \tabularnewline
154 & 14 & 12.2930128513308 & 1.70698714866919 \tabularnewline
155 & 10 & 12.2837711569455 & -2.28377115694548 \tabularnewline
156 & 17 & 16.9025210072646 & 0.0974789927354275 \tabularnewline
157 & 13 & 11.5442639683673 & 1.45573603163267 \tabularnewline
158 & 15 & 13.9718481825535 & 1.02815181744649 \tabularnewline
159 & 16 & 14.6647147695073 & 1.33528523049274 \tabularnewline
160 & 12 & 12.5147847932947 & -0.514784793294732 \tabularnewline
161 & 13 & 12.5956016654293 & 0.404398334570735 \tabularnewline
162 & 13 & 12.4019444500203 & 0.598055549979665 \tabularnewline
163 & 12 & 12.3563914844669 & -0.356391484466872 \tabularnewline
164 & 17 & 16.5967735292638 & 0.403226470736195 \tabularnewline
165 & 15 & 13.6609433237576 & 1.33905667624243 \tabularnewline
166 & 10 & 11.3407358686386 & -1.34073586863863 \tabularnewline
167 & 14 & 14.4220395460115 & -0.42203954601148 \tabularnewline
168 & 11 & 14.2244668740063 & -3.22446687400627 \tabularnewline
169 & 13 & 14.8238026766524 & -1.82380267665237 \tabularnewline
170 & 16 & 14.349681927853 & 1.65031807214696 \tabularnewline
171 & 12 & 10.4260388998853 & 1.57396110011475 \tabularnewline
172 & 16 & 15.7299438240612 & 0.27005617593875 \tabularnewline
173 & 12 & 14.0605689784399 & -2.06056897843987 \tabularnewline
174 & 9 & 11.297045950906 & -2.29704595090604 \tabularnewline
175 & 12 & 15.3293698151706 & -3.32936981517062 \tabularnewline
176 & 15 & 14.7250871511627 & 0.274912848837257 \tabularnewline
177 & 12 & 12.3038409270153 & -0.303840927015343 \tabularnewline
178 & 12 & 12.6547895910392 & -0.654789591039172 \tabularnewline
179 & 14 & 14.0541338266479 & -0.0541338266478555 \tabularnewline
180 & 12 & 13.5166392026678 & -1.5166392026678 \tabularnewline
181 & 16 & 15.4989714150368 & 0.501028584963152 \tabularnewline
182 & 11 & 11.557930840078 & -0.557930840078033 \tabularnewline
183 & 19 & 17.1397518342919 & 1.86024816570808 \tabularnewline
184 & 15 & 15.5660181733142 & -0.566018173314173 \tabularnewline
185 & 8 & 14.6206369722122 & -6.62063697221216 \tabularnewline
186 & 16 & 14.933645348001 & 1.06635465199902 \tabularnewline
187 & 17 & 14.7723422385428 & 2.22765776145723 \tabularnewline
188 & 12 & 12.576418357724 & -0.57641835772395 \tabularnewline
189 & 11 & 11.512185009998 & -0.512185009997973 \tabularnewline
190 & 11 & 10.2791410034115 & 0.720858996588508 \tabularnewline
191 & 14 & 15.1092450464616 & -1.1092450464616 \tabularnewline
192 & 16 & 15.895042987085 & 0.104957012914992 \tabularnewline
193 & 12 & 9.63621495018308 & 2.36378504981692 \tabularnewline
194 & 16 & 14.3239775219956 & 1.67602247800442 \tabularnewline
195 & 13 & 13.9004685211203 & -0.900468521120339 \tabularnewline
196 & 15 & 15.4058352264456 & -0.405835226445549 \tabularnewline
197 & 16 & 13.0836896405637 & 2.91631035943632 \tabularnewline
198 & 16 & 15.2692492203823 & 0.730750779617739 \tabularnewline
199 & 14 & 12.4949616788401 & 1.50503832115991 \tabularnewline
200 & 16 & 14.6869818638142 & 1.31301813618578 \tabularnewline
201 & 16 & 14.1655131526235 & 1.83448684737647 \tabularnewline
202 & 14 & 13.4773038710619 & 0.522696128938109 \tabularnewline
203 & 11 & 13.7723061959415 & -2.77230619594147 \tabularnewline
204 & 12 & 14.9605954184046 & -2.96059541840455 \tabularnewline
205 & 15 & 12.9648708503474 & 2.03512914965256 \tabularnewline
206 & 15 & 14.8345137323858 & 0.165486267614152 \tabularnewline
207 & 16 & 14.829790654696 & 1.17020934530396 \tabularnewline
208 & 16 & 15.4850832707174 & 0.514916729282625 \tabularnewline
209 & 11 & 13.9468690617298 & -2.9468690617298 \tabularnewline
210 & 15 & 14.2879867953963 & 0.712013204603669 \tabularnewline
211 & 12 & 14.526839237758 & -2.52683923775798 \tabularnewline
212 & 12 & 16.3301843413077 & -4.33018434130769 \tabularnewline
213 & 15 & 14.2438980123555 & 0.756101987644503 \tabularnewline
214 & 15 & 12.116028482646 & 2.88397151735404 \tabularnewline
215 & 16 & 14.9048003018464 & 1.09519969815361 \tabularnewline
216 & 14 & 13.3898799663252 & 0.610120033674782 \tabularnewline
217 & 17 & 15.0313394355929 & 1.96866056440707 \tabularnewline
218 & 14 & 14.365857370602 & -0.365857370602036 \tabularnewline
219 & 13 & 12.0229053530269 & 0.977094646973057 \tabularnewline
220 & 15 & 15.6103957498177 & -0.610395749817655 \tabularnewline
221 & 13 & 15.0379609957436 & -2.03796099574355 \tabularnewline
222 & 14 & 14.652225454202 & -0.652225454201975 \tabularnewline
223 & 15 & 14.5612281139041 & 0.438771886095865 \tabularnewline
224 & 12 & 13.4393062468293 & -1.43930624682928 \tabularnewline
225 & 13 & 12.8739941751421 & 0.126005824857865 \tabularnewline
226 & 8 & 11.9343546211081 & -3.93435462110808 \tabularnewline
227 & 14 & 14.3231387801053 & -0.323138780105334 \tabularnewline
228 & 14 & 13.2917090339788 & 0.708290966021163 \tabularnewline
229 & 11 & 12.3440243141206 & -1.3440243141206 \tabularnewline
230 & 12 & 13.1672187509188 & -1.16721875091878 \tabularnewline
231 & 13 & 11.4919237366282 & 1.50807626337175 \tabularnewline
232 & 10 & 13.4537952826456 & -3.45379528264557 \tabularnewline
233 & 16 & 11.7121141114013 & 4.2878858885987 \tabularnewline
234 & 18 & 16.5361936369136 & 1.46380636308643 \tabularnewline
235 & 13 & 14.3202187044387 & -1.32021870443871 \tabularnewline
236 & 11 & 13.6910122444104 & -2.69101224441038 \tabularnewline
237 & 4 & 11.19285806311 & -7.19285806310998 \tabularnewline
238 & 13 & 14.9009497151881 & -1.90094971518807 \tabularnewline
239 & 16 & 14.5563984687634 & 1.44360153123661 \tabularnewline
240 & 10 & 11.9738284729431 & -1.97382847294308 \tabularnewline
241 & 12 & 12.3104262526516 & -0.31042625265157 \tabularnewline
242 & 12 & 13.8390234702109 & -1.8390234702109 \tabularnewline
243 & 10 & 8.85257500720943 & 1.14742499279056 \tabularnewline
244 & 13 & 11.2978167339122 & 1.7021832660878 \tabularnewline
245 & 15 & 14.1110277757012 & 0.888972224298776 \tabularnewline
246 & 12 & 11.9868641879996 & 0.013135812000393 \tabularnewline
247 & 14 & 13.1364690978718 & 0.863530902128218 \tabularnewline
248 & 10 & 12.9569243699695 & -2.95692436996951 \tabularnewline
249 & 12 & 10.68375800526 & 1.31624199474001 \tabularnewline
250 & 12 & 11.8089699805697 & 0.191030019430317 \tabularnewline
251 & 11 & 12.1083946007191 & -1.10839460071908 \tabularnewline
252 & 10 & 11.8382711620479 & -1.83827116204791 \tabularnewline
253 & 12 & 11.6900148124594 & 0.309985187540589 \tabularnewline
254 & 16 & 13.1738852536999 & 2.8261147463001 \tabularnewline
255 & 12 & 13.75427496248 & -1.75427496247999 \tabularnewline
256 & 14 & 14.2771236861309 & -0.2771236861309 \tabularnewline
257 & 16 & 14.6783967195474 & 1.32160328045257 \tabularnewline
258 & 14 & 11.8081254441163 & 2.19187455588371 \tabularnewline
259 & 13 & 14.8268822976117 & -1.82688229761175 \tabularnewline
260 & 4 & 9.48489291173289 & -5.48489291173289 \tabularnewline
261 & 15 & 14.1301489623116 & 0.869851037688359 \tabularnewline
262 & 11 & 15.5591171345592 & -4.55911713455921 \tabularnewline
263 & 11 & 11.5408906987234 & -0.540890698723357 \tabularnewline
264 & 14 & 13.1222822512428 & 0.877717748757232 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204206&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]13.5136751913172[/C][C]-0.513675191317203[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.3492593384874[/C][C]0.650740661512645[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.5084663112014[/C][C]2.49153368879855[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]11.2241986234127[/C][C]3.77580137658735[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.8697443353711[/C][C]-1.86974433537109[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.3571628457448[/C][C]-1.35716284574484[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]14.9245444063598[/C][C]4.07545559364019[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.6494564344438[/C][C]-1.6494564344438[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.6425277923124[/C][C]-1.64252779231236[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]13.8590879138036[/C][C]1.14091208619639[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]14.5684679011921[/C][C]1.43153209880794[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.033639399593[/C][C]-0.033639399592988[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.0216975281995[/C][C]0.978302471800496[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.2174130121066[/C][C]0.782586987893392[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.6937101681425[/C][C]-0.693710168142468[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.0203526455718[/C][C]-0.0203526455717886[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.057272232349[/C][C]0.942727767651032[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.0523473833521[/C][C]3.94765261664788[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.1663239210819[/C][C]2.83367607891814[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.2034872596996[/C][C]0.796512740300425[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.003200721975[/C][C]0.996799278025025[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.5687313093543[/C][C]1.43126869064568[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.3204174127686[/C][C]2.6795825872314[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.6952813236199[/C][C]1.30471867638015[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.8827076340196[/C][C]1.11729236598039[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]15.9190522935312[/C][C]1.08094770646877[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.5524771182352[/C][C]1.4475228817648[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.4098731611705[/C][C]-1.40987316117053[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.2823395192426[/C][C]0.717660480757399[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]13.6318878214092[/C][C]0.368112178590778[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.3371318963228[/C][C]-0.337131896322803[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.119919416203[/C][C]-0.119919416202985[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.5174402986478[/C][C]-0.517440298647832[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.5257962759849[/C][C]0.474203724015088[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.1668522439893[/C][C]-1.16685224398934[/C][/ROW]
[ROW][C]36[/C][C]10[/C][C]12.4447671808093[/C][C]-2.44476718080933[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]12.2547451683762[/C][C]-2.25474516837619[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.477930118344[/C][C]-1.47793011834398[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.0644580062228[/C][C]1.93554199377716[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.0797945509205[/C][C]1.92020544907949[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]14.4044955293943[/C][C]1.59550447060568[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.4155479027351[/C][C]-1.41554790273509[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.6009708338689[/C][C]2.39902916613109[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]13.7960448260701[/C][C]0.203955173929875[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.2094964037857[/C][C]-0.209496403785661[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.1997251308506[/C][C]-4.19972513085057[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.4783891404763[/C][C]-2.47838914047632[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.7728217550602[/C][C]0.227178244939811[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.2062913290538[/C][C]0.793708670946205[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.4156312925424[/C][C]-1.41563129254239[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.8373821112215[/C][C]-0.837382111221507[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.7318255853684[/C][C]0.268174414631628[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.6132982105017[/C][C]-2.61329821050167[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.6010318774928[/C][C]0.398968122507185[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]10.7192482422172[/C][C]-1.71924824221722[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]11.851651769494[/C][C]2.14834823050599[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.7515476661184[/C][C]0.248452333881588[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.1975820659805[/C][C]0.802417934019503[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]14.863597194318[/C][C]0.136402805682041[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]13.8099869278636[/C][C]2.19001307213636[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]10.8347605397192[/C][C]1.1652394602808[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.4834647080186[/C][C]0.516535291981448[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.2941914763641[/C][C]-0.294191476364053[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.4490746535316[/C][C]-0.449074653531554[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.1993113515379[/C][C]0.800688648462095[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.7457425114052[/C][C]1.25425748859483[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.1245299219461[/C][C]1.87547007805393[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.0569642235478[/C][C]3.94303577645219[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.8768766509701[/C][C]-3.8768766509701[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.4889761522177[/C][C]0.511023847782292[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.0611668417618[/C][C]-3.0611668417618[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.7122947796061[/C][C]-0.71229477960609[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.8203875490617[/C][C]1.17961245093827[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.2455110282057[/C][C]0.754488971794263[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.0119912891194[/C][C]0.988008710880589[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.1312542145[/C][C]3.86874578549995[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.4452320540163[/C][C]-0.445232054016302[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.2218140119876[/C][C]1.77818598801244[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.8979616246973[/C][C]-1.89796162469735[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.1814455272344[/C][C]0.818554472765605[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.5194572462212[/C][C]0.480542753778823[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]15.6619342140938[/C][C]0.338065785906196[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.663232412642[/C][C]-0.66323241264203[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]14.6712039048596[/C][C]0.328796095140369[/C][/ROW]
[ROW][C]85[/C][C]16[/C][C]13.8724142585612[/C][C]2.12758574143881[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]13.9835643979785[/C][C]0.0164356020215261[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]15.2370930928785[/C][C]0.762906907121482[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]14.6705412944135[/C][C]1.32945870558652[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.0924020591452[/C][C]0.907597940854827[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]13.6046469436175[/C][C]-1.60464694361748[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]16.9025210072646[/C][C]0.0974789927354275[/C][/ROW]
[ROW][C]92[/C][C]16[/C][C]15.7239237919718[/C][C]0.276076208028154[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]14.8817914660421[/C][C]0.118208533957879[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]14.7836433238262[/C][C]-1.78364332382616[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]14.7424102219121[/C][C]1.25758977808786[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.7362728786007[/C][C]0.26372712139925[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]13.4970154914298[/C][C]2.50298450857023[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]15.8560775246061[/C][C]0.143922475393937[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]14.2416717860763[/C][C]-0.241671786076292[/C][/ROW]
[ROW][C]100[/C][C]16[/C][C]17.2413336472946[/C][C]-1.24133364729456[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]14.5360053760781[/C][C]1.46399462392188[/C][/ROW]
[ROW][C]102[/C][C]20[/C][C]17.58049727287[/C][C]2.41950272713003[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]14.0335589854295[/C][C]0.966441014570535[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]14.8046051982166[/C][C]1.19539480178343[/C][/ROW]
[ROW][C]105[/C][C]13[/C][C]14.790283752124[/C][C]-1.79028375212397[/C][/ROW]
[ROW][C]106[/C][C]17[/C][C]15.8036767729311[/C][C]1.19632322706888[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.8431967745741[/C][C]0.156803225425919[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]14.2929761875017[/C][C]1.70702381249825[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]12.1194674563321[/C][C]-0.119467456332061[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]15.1062462337254[/C][C]0.893753766274584[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.9119711143067[/C][C]0.0880288856933299[/C][/ROW]
[ROW][C]112[/C][C]17[/C][C]14.8019714636309[/C][C]2.19802853636914[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]14.5933208039284[/C][C]-1.5933208039284[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]14.643827153389[/C][C]-2.64382715338898[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]16.2976765706238[/C][C]1.70232342937623[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]15.9700884989751[/C][C]-1.97008849897511[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]12.9889238895155[/C][C]1.01107611048447[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]14.7916771565209[/C][C]-1.79167715652087[/C][/ROW]
[ROW][C]119[/C][C]16[/C][C]15.5218361845303[/C][C]0.47816381546968[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]14.3953541738269[/C][C]-1.39535417382689[/C][/ROW]
[ROW][C]121[/C][C]16[/C][C]15.3875105549465[/C][C]0.612489445053484[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]15.9103116501711[/C][C]-2.91031165017107[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]17.1078536189398[/C][C]-1.10785361893985[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]16.0357919805867[/C][C]-1.03579198058672[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]16.8925598386954[/C][C]-0.892559838695415[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]14.7852083484988[/C][C]0.214791651501209[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]15.8746899145471[/C][C]1.12531008545287[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]13.9718481825535[/C][C]1.02815181744649[/C][/ROW]
[ROW][C]129[/C][C]12[/C][C]14.7641705998629[/C][C]-2.76417059986291[/C][/ROW]
[ROW][C]130[/C][C]16[/C][C]13.8937373477987[/C][C]2.10626265220132[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]13.4359810749469[/C][C]-3.43598107494686[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]13.5428487656805[/C][C]2.4571512343195[/C][/ROW]
[ROW][C]133[/C][C]12[/C][C]14.0785708758197[/C][C]-2.0785708758197[/C][/ROW]
[ROW][C]134[/C][C]14[/C][C]15.7176290661727[/C][C]-1.71762906617269[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]15.1002651820584[/C][C]-0.100265182058446[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]11.8618663574348[/C][C]1.13813364256521[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]14.5570383667587[/C][C]0.442961633241335[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]13.3524026218286[/C][C]-2.35240262182863[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]13.0262652271207[/C][C]-1.02626522712072[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]13.2573446407733[/C][C]-2.25734464077333[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]12.8918111588156[/C][C]3.10818884118442[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]13.5043633953377[/C][C]1.49563660466231[/C][/ROW]
[ROW][C]143[/C][C]17[/C][C]16.9500935575372[/C][C]0.0499064424627691[/C][/ROW]
[ROW][C]144[/C][C]16[/C][C]14.2054084811734[/C][C]1.79459151882662[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]13.3498885155341[/C][C]-3.34988851553409[/C][/ROW]
[ROW][C]146[/C][C]18[/C][C]15.7036841913383[/C][C]2.29631580866168[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]15.1065255058354[/C][C]-2.10652550583544[/C][/ROW]
[ROW][C]148[/C][C]16[/C][C]14.9088957314598[/C][C]1.09110426854024[/C][/ROW]
[ROW][C]149[/C][C]13[/C][C]12.6442890846133[/C][C]0.355710915386657[/C][/ROW]
[ROW][C]150[/C][C]10[/C][C]12.8866229660412[/C][C]-2.88662296604125[/C][/ROW]
[ROW][C]151[/C][C]15[/C][C]16.2264513647711[/C][C]-1.22645136477105[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]13.9982753398405[/C][C]2.0017246601595[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]11.7068954436143[/C][C]4.29310455638573[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]12.2930128513308[/C][C]1.70698714866919[/C][/ROW]
[ROW][C]155[/C][C]10[/C][C]12.2837711569455[/C][C]-2.28377115694548[/C][/ROW]
[ROW][C]156[/C][C]17[/C][C]16.9025210072646[/C][C]0.0974789927354275[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]11.5442639683673[/C][C]1.45573603163267[/C][/ROW]
[ROW][C]158[/C][C]15[/C][C]13.9718481825535[/C][C]1.02815181744649[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]14.6647147695073[/C][C]1.33528523049274[/C][/ROW]
[ROW][C]160[/C][C]12[/C][C]12.5147847932947[/C][C]-0.514784793294732[/C][/ROW]
[ROW][C]161[/C][C]13[/C][C]12.5956016654293[/C][C]0.404398334570735[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.4019444500203[/C][C]0.598055549979665[/C][/ROW]
[ROW][C]163[/C][C]12[/C][C]12.3563914844669[/C][C]-0.356391484466872[/C][/ROW]
[ROW][C]164[/C][C]17[/C][C]16.5967735292638[/C][C]0.403226470736195[/C][/ROW]
[ROW][C]165[/C][C]15[/C][C]13.6609433237576[/C][C]1.33905667624243[/C][/ROW]
[ROW][C]166[/C][C]10[/C][C]11.3407358686386[/C][C]-1.34073586863863[/C][/ROW]
[ROW][C]167[/C][C]14[/C][C]14.4220395460115[/C][C]-0.42203954601148[/C][/ROW]
[ROW][C]168[/C][C]11[/C][C]14.2244668740063[/C][C]-3.22446687400627[/C][/ROW]
[ROW][C]169[/C][C]13[/C][C]14.8238026766524[/C][C]-1.82380267665237[/C][/ROW]
[ROW][C]170[/C][C]16[/C][C]14.349681927853[/C][C]1.65031807214696[/C][/ROW]
[ROW][C]171[/C][C]12[/C][C]10.4260388998853[/C][C]1.57396110011475[/C][/ROW]
[ROW][C]172[/C][C]16[/C][C]15.7299438240612[/C][C]0.27005617593875[/C][/ROW]
[ROW][C]173[/C][C]12[/C][C]14.0605689784399[/C][C]-2.06056897843987[/C][/ROW]
[ROW][C]174[/C][C]9[/C][C]11.297045950906[/C][C]-2.29704595090604[/C][/ROW]
[ROW][C]175[/C][C]12[/C][C]15.3293698151706[/C][C]-3.32936981517062[/C][/ROW]
[ROW][C]176[/C][C]15[/C][C]14.7250871511627[/C][C]0.274912848837257[/C][/ROW]
[ROW][C]177[/C][C]12[/C][C]12.3038409270153[/C][C]-0.303840927015343[/C][/ROW]
[ROW][C]178[/C][C]12[/C][C]12.6547895910392[/C][C]-0.654789591039172[/C][/ROW]
[ROW][C]179[/C][C]14[/C][C]14.0541338266479[/C][C]-0.0541338266478555[/C][/ROW]
[ROW][C]180[/C][C]12[/C][C]13.5166392026678[/C][C]-1.5166392026678[/C][/ROW]
[ROW][C]181[/C][C]16[/C][C]15.4989714150368[/C][C]0.501028584963152[/C][/ROW]
[ROW][C]182[/C][C]11[/C][C]11.557930840078[/C][C]-0.557930840078033[/C][/ROW]
[ROW][C]183[/C][C]19[/C][C]17.1397518342919[/C][C]1.86024816570808[/C][/ROW]
[ROW][C]184[/C][C]15[/C][C]15.5660181733142[/C][C]-0.566018173314173[/C][/ROW]
[ROW][C]185[/C][C]8[/C][C]14.6206369722122[/C][C]-6.62063697221216[/C][/ROW]
[ROW][C]186[/C][C]16[/C][C]14.933645348001[/C][C]1.06635465199902[/C][/ROW]
[ROW][C]187[/C][C]17[/C][C]14.7723422385428[/C][C]2.22765776145723[/C][/ROW]
[ROW][C]188[/C][C]12[/C][C]12.576418357724[/C][C]-0.57641835772395[/C][/ROW]
[ROW][C]189[/C][C]11[/C][C]11.512185009998[/C][C]-0.512185009997973[/C][/ROW]
[ROW][C]190[/C][C]11[/C][C]10.2791410034115[/C][C]0.720858996588508[/C][/ROW]
[ROW][C]191[/C][C]14[/C][C]15.1092450464616[/C][C]-1.1092450464616[/C][/ROW]
[ROW][C]192[/C][C]16[/C][C]15.895042987085[/C][C]0.104957012914992[/C][/ROW]
[ROW][C]193[/C][C]12[/C][C]9.63621495018308[/C][C]2.36378504981692[/C][/ROW]
[ROW][C]194[/C][C]16[/C][C]14.3239775219956[/C][C]1.67602247800442[/C][/ROW]
[ROW][C]195[/C][C]13[/C][C]13.9004685211203[/C][C]-0.900468521120339[/C][/ROW]
[ROW][C]196[/C][C]15[/C][C]15.4058352264456[/C][C]-0.405835226445549[/C][/ROW]
[ROW][C]197[/C][C]16[/C][C]13.0836896405637[/C][C]2.91631035943632[/C][/ROW]
[ROW][C]198[/C][C]16[/C][C]15.2692492203823[/C][C]0.730750779617739[/C][/ROW]
[ROW][C]199[/C][C]14[/C][C]12.4949616788401[/C][C]1.50503832115991[/C][/ROW]
[ROW][C]200[/C][C]16[/C][C]14.6869818638142[/C][C]1.31301813618578[/C][/ROW]
[ROW][C]201[/C][C]16[/C][C]14.1655131526235[/C][C]1.83448684737647[/C][/ROW]
[ROW][C]202[/C][C]14[/C][C]13.4773038710619[/C][C]0.522696128938109[/C][/ROW]
[ROW][C]203[/C][C]11[/C][C]13.7723061959415[/C][C]-2.77230619594147[/C][/ROW]
[ROW][C]204[/C][C]12[/C][C]14.9605954184046[/C][C]-2.96059541840455[/C][/ROW]
[ROW][C]205[/C][C]15[/C][C]12.9648708503474[/C][C]2.03512914965256[/C][/ROW]
[ROW][C]206[/C][C]15[/C][C]14.8345137323858[/C][C]0.165486267614152[/C][/ROW]
[ROW][C]207[/C][C]16[/C][C]14.829790654696[/C][C]1.17020934530396[/C][/ROW]
[ROW][C]208[/C][C]16[/C][C]15.4850832707174[/C][C]0.514916729282625[/C][/ROW]
[ROW][C]209[/C][C]11[/C][C]13.9468690617298[/C][C]-2.9468690617298[/C][/ROW]
[ROW][C]210[/C][C]15[/C][C]14.2879867953963[/C][C]0.712013204603669[/C][/ROW]
[ROW][C]211[/C][C]12[/C][C]14.526839237758[/C][C]-2.52683923775798[/C][/ROW]
[ROW][C]212[/C][C]12[/C][C]16.3301843413077[/C][C]-4.33018434130769[/C][/ROW]
[ROW][C]213[/C][C]15[/C][C]14.2438980123555[/C][C]0.756101987644503[/C][/ROW]
[ROW][C]214[/C][C]15[/C][C]12.116028482646[/C][C]2.88397151735404[/C][/ROW]
[ROW][C]215[/C][C]16[/C][C]14.9048003018464[/C][C]1.09519969815361[/C][/ROW]
[ROW][C]216[/C][C]14[/C][C]13.3898799663252[/C][C]0.610120033674782[/C][/ROW]
[ROW][C]217[/C][C]17[/C][C]15.0313394355929[/C][C]1.96866056440707[/C][/ROW]
[ROW][C]218[/C][C]14[/C][C]14.365857370602[/C][C]-0.365857370602036[/C][/ROW]
[ROW][C]219[/C][C]13[/C][C]12.0229053530269[/C][C]0.977094646973057[/C][/ROW]
[ROW][C]220[/C][C]15[/C][C]15.6103957498177[/C][C]-0.610395749817655[/C][/ROW]
[ROW][C]221[/C][C]13[/C][C]15.0379609957436[/C][C]-2.03796099574355[/C][/ROW]
[ROW][C]222[/C][C]14[/C][C]14.652225454202[/C][C]-0.652225454201975[/C][/ROW]
[ROW][C]223[/C][C]15[/C][C]14.5612281139041[/C][C]0.438771886095865[/C][/ROW]
[ROW][C]224[/C][C]12[/C][C]13.4393062468293[/C][C]-1.43930624682928[/C][/ROW]
[ROW][C]225[/C][C]13[/C][C]12.8739941751421[/C][C]0.126005824857865[/C][/ROW]
[ROW][C]226[/C][C]8[/C][C]11.9343546211081[/C][C]-3.93435462110808[/C][/ROW]
[ROW][C]227[/C][C]14[/C][C]14.3231387801053[/C][C]-0.323138780105334[/C][/ROW]
[ROW][C]228[/C][C]14[/C][C]13.2917090339788[/C][C]0.708290966021163[/C][/ROW]
[ROW][C]229[/C][C]11[/C][C]12.3440243141206[/C][C]-1.3440243141206[/C][/ROW]
[ROW][C]230[/C][C]12[/C][C]13.1672187509188[/C][C]-1.16721875091878[/C][/ROW]
[ROW][C]231[/C][C]13[/C][C]11.4919237366282[/C][C]1.50807626337175[/C][/ROW]
[ROW][C]232[/C][C]10[/C][C]13.4537952826456[/C][C]-3.45379528264557[/C][/ROW]
[ROW][C]233[/C][C]16[/C][C]11.7121141114013[/C][C]4.2878858885987[/C][/ROW]
[ROW][C]234[/C][C]18[/C][C]16.5361936369136[/C][C]1.46380636308643[/C][/ROW]
[ROW][C]235[/C][C]13[/C][C]14.3202187044387[/C][C]-1.32021870443871[/C][/ROW]
[ROW][C]236[/C][C]11[/C][C]13.6910122444104[/C][C]-2.69101224441038[/C][/ROW]
[ROW][C]237[/C][C]4[/C][C]11.19285806311[/C][C]-7.19285806310998[/C][/ROW]
[ROW][C]238[/C][C]13[/C][C]14.9009497151881[/C][C]-1.90094971518807[/C][/ROW]
[ROW][C]239[/C][C]16[/C][C]14.5563984687634[/C][C]1.44360153123661[/C][/ROW]
[ROW][C]240[/C][C]10[/C][C]11.9738284729431[/C][C]-1.97382847294308[/C][/ROW]
[ROW][C]241[/C][C]12[/C][C]12.3104262526516[/C][C]-0.31042625265157[/C][/ROW]
[ROW][C]242[/C][C]12[/C][C]13.8390234702109[/C][C]-1.8390234702109[/C][/ROW]
[ROW][C]243[/C][C]10[/C][C]8.85257500720943[/C][C]1.14742499279056[/C][/ROW]
[ROW][C]244[/C][C]13[/C][C]11.2978167339122[/C][C]1.7021832660878[/C][/ROW]
[ROW][C]245[/C][C]15[/C][C]14.1110277757012[/C][C]0.888972224298776[/C][/ROW]
[ROW][C]246[/C][C]12[/C][C]11.9868641879996[/C][C]0.013135812000393[/C][/ROW]
[ROW][C]247[/C][C]14[/C][C]13.1364690978718[/C][C]0.863530902128218[/C][/ROW]
[ROW][C]248[/C][C]10[/C][C]12.9569243699695[/C][C]-2.95692436996951[/C][/ROW]
[ROW][C]249[/C][C]12[/C][C]10.68375800526[/C][C]1.31624199474001[/C][/ROW]
[ROW][C]250[/C][C]12[/C][C]11.8089699805697[/C][C]0.191030019430317[/C][/ROW]
[ROW][C]251[/C][C]11[/C][C]12.1083946007191[/C][C]-1.10839460071908[/C][/ROW]
[ROW][C]252[/C][C]10[/C][C]11.8382711620479[/C][C]-1.83827116204791[/C][/ROW]
[ROW][C]253[/C][C]12[/C][C]11.6900148124594[/C][C]0.309985187540589[/C][/ROW]
[ROW][C]254[/C][C]16[/C][C]13.1738852536999[/C][C]2.8261147463001[/C][/ROW]
[ROW][C]255[/C][C]12[/C][C]13.75427496248[/C][C]-1.75427496247999[/C][/ROW]
[ROW][C]256[/C][C]14[/C][C]14.2771236861309[/C][C]-0.2771236861309[/C][/ROW]
[ROW][C]257[/C][C]16[/C][C]14.6783967195474[/C][C]1.32160328045257[/C][/ROW]
[ROW][C]258[/C][C]14[/C][C]11.8081254441163[/C][C]2.19187455588371[/C][/ROW]
[ROW][C]259[/C][C]13[/C][C]14.8268822976117[/C][C]-1.82688229761175[/C][/ROW]
[ROW][C]260[/C][C]4[/C][C]9.48489291173289[/C][C]-5.48489291173289[/C][/ROW]
[ROW][C]261[/C][C]15[/C][C]14.1301489623116[/C][C]0.869851037688359[/C][/ROW]
[ROW][C]262[/C][C]11[/C][C]15.5591171345592[/C][C]-4.55911713455921[/C][/ROW]
[ROW][C]263[/C][C]11[/C][C]11.5408906987234[/C][C]-0.540890698723357[/C][/ROW]
[ROW][C]264[/C][C]14[/C][C]13.1222822512428[/C][C]0.877717748757232[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204206&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204206&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11313.5136751913172-0.513675191317203
21615.34925933848740.650740661512645
31916.50846631120142.49153368879855
41511.22419862341273.77580137658735
51415.8697443353711-1.86974433537109
61314.3571628457448-1.35716284574484
71914.92454440635984.07545559364019
81516.6494564344438-1.6494564344438
91415.6425277923124-1.64252779231236
101513.85908791380361.14091208619639
111614.56846790119211.43153209880794
121616.033639399593-0.033639399592988
131615.02169752819950.978302471800496
141615.21741301210660.782586987893392
151717.6937101681425-0.693710168142468
161515.0203526455718-0.0203526455717886
171514.0572722323490.942727767651032
182016.05234738335213.94765261664788
191815.16632392108192.83367607891814
201615.20348725969960.796512740300425
211615.0032007219750.996799278025025
221614.56873130935431.43126869064568
231916.32041741276862.6795825872314
241614.69528132361991.30471867638015
251715.88270763401961.11729236598039
261715.91905229353121.08094770646877
271614.55247711823521.4475228817648
281516.4098731611705-1.40987316117053
291615.28233951924260.717660480757399
301413.63188782140920.368112178590778
311515.3371318963228-0.337131896322803
321212.119919416203-0.119919416202985
331414.5174402986478-0.517440298647832
341615.52579627598490.474203724015088
351415.1668522439893-1.16685224398934
361012.4447671808093-2.44476718080933
371012.2547451683762-2.25474516837619
381415.477930118344-1.47793011834398
391614.06445800622281.93554199377716
401614.07979455092051.92020544907949
411614.40449552939431.59550447060568
421415.4155479027351-1.41554790273509
432017.60097083386892.39902916613109
441413.79604482607010.203955173929875
451414.2094964037857-0.209496403785661
461115.1997251308506-4.19972513085057
471416.4783891404763-2.47838914047632
481514.77282175506020.227178244939811
491615.20629132905380.793708670946205
501415.4156312925424-1.41563129254239
511616.8373821112215-0.837382111221507
521413.73182558536840.268174414631628
531214.6132982105017-2.61329821050167
541615.60103187749280.398968122507185
55910.7192482422172-1.71924824221722
561411.8516517694942.14834823050599
571615.75154766611840.248452333881588
581615.19758206598050.802417934019503
591514.8635971943180.136402805682041
601613.80998692786362.19001307213636
611210.83476053971921.1652394602808
621615.48346470801860.516535291981448
631616.2941914763641-0.294191476364053
641414.4490746535316-0.449074653531554
651615.19931135153790.800688648462095
661715.74574251140521.25425748859483
671816.12452992194611.87547007805393
681814.05696422354783.94303577645219
691215.8768766509701-3.8768766509701
701615.48897615221770.511023847782292
711013.0611668417618-3.0611668417618
721414.7122947796061-0.71229477960609
731816.82038754906171.17961245093827
741817.24551102820570.754488971794263
751615.01199128911940.988008710880589
761713.13125421453.86874578549995
771616.4452320540163-0.445232054016302
781614.22181401198761.77818598801244
791314.8979616246973-1.89796162469735
801615.18144552723440.818554472765605
811615.51945724622120.480542753778823
821615.66193421409380.338065785906196
831515.663232412642-0.66323241264203
841514.67120390485960.328796095140369
851613.87241425856122.12758574143881
861413.98356439797850.0164356020215261
871615.23709309287850.762906907121482
881614.67054129441351.32945870558652
891514.09240205914520.907597940854827
901213.6046469436175-1.60464694361748
911716.90252100726460.0974789927354275
921615.72392379197180.276076208028154
931514.88179146604210.118208533957879
941314.7836433238262-1.78364332382616
951614.74241022191211.25758977808786
961615.73627287860070.26372712139925
971613.49701549142982.50298450857023
981615.85607752460610.143922475393937
991414.2416717860763-0.241671786076292
1001617.2413336472946-1.24133364729456
1011614.53600537607811.46399462392188
1022017.580497272872.41950272713003
1031514.03355898542950.966441014570535
1041614.80460519821661.19539480178343
1051314.790283752124-1.79028375212397
1061715.80367677293111.19632322706888
1071615.84319677457410.156803225425919
1081614.29297618750171.70702381249825
1091212.1194674563321-0.119467456332061
1101615.10624623372540.893753766274584
1111615.91197111430670.0880288856933299
1121714.80197146363092.19802853636914
1131314.5933208039284-1.5933208039284
1141214.643827153389-2.64382715338898
1151816.29767657062381.70232342937623
1161415.9700884989751-1.97008849897511
1171412.98892388951551.01107611048447
1181314.7916771565209-1.79167715652087
1191615.52183618453030.47816381546968
1201314.3953541738269-1.39535417382689
1211615.38751055494650.612489445053484
1221315.9103116501711-2.91031165017107
1231617.1078536189398-1.10785361893985
1241516.0357919805867-1.03579198058672
1251616.8925598386954-0.892559838695415
1261514.78520834849880.214791651501209
1271715.87468991454711.12531008545287
1281513.97184818255351.02815181744649
1291214.7641705998629-2.76417059986291
1301613.89373734779872.10626265220132
1311013.4359810749469-3.43598107494686
1321613.54284876568052.4571512343195
1331214.0785708758197-2.0785708758197
1341415.7176290661727-1.71762906617269
1351515.1002651820584-0.100265182058446
1361311.86186635743481.13813364256521
1371514.55703836675870.442961633241335
1381113.3524026218286-2.35240262182863
1391213.0262652271207-1.02626522712072
1401113.2573446407733-2.25734464077333
1411612.89181115881563.10818884118442
1421513.50436339533771.49563660466231
1431716.95009355753720.0499064424627691
1441614.20540848117341.79459151882662
1451013.3498885155341-3.34988851553409
1461815.70368419133832.29631580866168
1471315.1065255058354-2.10652550583544
1481614.90889573145981.09110426854024
1491312.64428908461330.355710915386657
1501012.8866229660412-2.88662296604125
1511516.2264513647711-1.22645136477105
1521613.99827533984052.0017246601595
1531611.70689544361434.29310455638573
1541412.29301285133081.70698714866919
1551012.2837711569455-2.28377115694548
1561716.90252100726460.0974789927354275
1571311.54426396836731.45573603163267
1581513.97184818255351.02815181744649
1591614.66471476950731.33528523049274
1601212.5147847932947-0.514784793294732
1611312.59560166542930.404398334570735
1621312.40194445002030.598055549979665
1631212.3563914844669-0.356391484466872
1641716.59677352926380.403226470736195
1651513.66094332375761.33905667624243
1661011.3407358686386-1.34073586863863
1671414.4220395460115-0.42203954601148
1681114.2244668740063-3.22446687400627
1691314.8238026766524-1.82380267665237
1701614.3496819278531.65031807214696
1711210.42603889988531.57396110011475
1721615.72994382406120.27005617593875
1731214.0605689784399-2.06056897843987
174911.297045950906-2.29704595090604
1751215.3293698151706-3.32936981517062
1761514.72508715116270.274912848837257
1771212.3038409270153-0.303840927015343
1781212.6547895910392-0.654789591039172
1791414.0541338266479-0.0541338266478555
1801213.5166392026678-1.5166392026678
1811615.49897141503680.501028584963152
1821111.557930840078-0.557930840078033
1831917.13975183429191.86024816570808
1841515.5660181733142-0.566018173314173
185814.6206369722122-6.62063697221216
1861614.9336453480011.06635465199902
1871714.77234223854282.22765776145723
1881212.576418357724-0.57641835772395
1891111.512185009998-0.512185009997973
1901110.27914100341150.720858996588508
1911415.1092450464616-1.1092450464616
1921615.8950429870850.104957012914992
193129.636214950183082.36378504981692
1941614.32397752199561.67602247800442
1951313.9004685211203-0.900468521120339
1961515.4058352264456-0.405835226445549
1971613.08368964056372.91631035943632
1981615.26924922038230.730750779617739
1991412.49496167884011.50503832115991
2001614.68698186381421.31301813618578
2011614.16551315262351.83448684737647
2021413.47730387106190.522696128938109
2031113.7723061959415-2.77230619594147
2041214.9605954184046-2.96059541840455
2051512.96487085034742.03512914965256
2061514.83451373238580.165486267614152
2071614.8297906546961.17020934530396
2081615.48508327071740.514916729282625
2091113.9468690617298-2.9468690617298
2101514.28798679539630.712013204603669
2111214.526839237758-2.52683923775798
2121216.3301843413077-4.33018434130769
2131514.24389801235550.756101987644503
2141512.1160284826462.88397151735404
2151614.90480030184641.09519969815361
2161413.38987996632520.610120033674782
2171715.03133943559291.96866056440707
2181414.365857370602-0.365857370602036
2191312.02290535302690.977094646973057
2201515.6103957498177-0.610395749817655
2211315.0379609957436-2.03796099574355
2221414.652225454202-0.652225454201975
2231514.56122811390410.438771886095865
2241213.4393062468293-1.43930624682928
2251312.87399417514210.126005824857865
226811.9343546211081-3.93435462110808
2271414.3231387801053-0.323138780105334
2281413.29170903397880.708290966021163
2291112.3440243141206-1.3440243141206
2301213.1672187509188-1.16721875091878
2311311.49192373662821.50807626337175
2321013.4537952826456-3.45379528264557
2331611.71211411140134.2878858885987
2341816.53619363691361.46380636308643
2351314.3202187044387-1.32021870443871
2361113.6910122444104-2.69101224441038
237411.19285806311-7.19285806310998
2381314.9009497151881-1.90094971518807
2391614.55639846876341.44360153123661
2401011.9738284729431-1.97382847294308
2411212.3104262526516-0.31042625265157
2421213.8390234702109-1.8390234702109
243108.852575007209431.14742499279056
2441311.29781673391221.7021832660878
2451514.11102777570120.888972224298776
2461211.98686418799960.013135812000393
2471413.13646909787180.863530902128218
2481012.9569243699695-2.95692436996951
2491210.683758005261.31624199474001
2501211.80896998056970.191030019430317
2511112.1083946007191-1.10839460071908
2521011.8382711620479-1.83827116204791
2531211.69001481245940.309985187540589
2541613.17388525369992.8261147463001
2551213.75427496248-1.75427496247999
2561414.2771236861309-0.2771236861309
2571614.67839671954741.32160328045257
2581411.80812544411632.19187455588371
2591314.8268822976117-1.82688229761175
26049.48489291173289-5.48489291173289
2611514.13014896231160.869851037688359
2621115.5591171345592-4.55911713455921
2631111.5408906987234-0.540890698723357
2641413.12228225124280.877717748757232






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.1701443393494670.3402886786989340.829855660650533
120.2341940448754850.468388089750970.765805955124515
130.1597919460880990.3195838921761980.840208053911901
140.09819726735351470.1963945347070290.901802732646485
150.0908368656677080.1816737313354160.909163134332292
160.05221412138546110.1044282427709220.947785878614539
170.04935036064146350.09870072128292710.950649639358536
180.2127865517472510.4255731034945010.787213448252749
190.1659216122843310.3318432245686630.834078387715669
200.1143125575388630.2286251150777270.885687442461137
210.07612854257853540.1522570851570710.923871457421465
220.06248320297161050.1249664059432210.93751679702839
230.1014654707935580.2029309415871150.898534529206443
240.1676887900489560.3353775800979110.832311209951044
250.1495483257417060.2990966514834120.850451674258294
260.1248234093827690.2496468187655370.875176590617231
270.1434027425000030.2868054850000050.856597257499997
280.1642308126946950.3284616253893890.835769187305305
290.1465822345903870.2931644691807730.853417765409613
300.1651417516810570.3302835033621140.834858248318943
310.1294351057934660.2588702115869330.870564894206534
320.1216759869671650.243351973934330.878324013032835
330.1069060906596020.2138121813192040.893093909340398
340.08402556097550650.1680511219510130.915974439024493
350.06630257230355590.1326051446071120.933697427696444
360.1515853086571110.3031706173142220.848414691342889
370.1919950102838590.3839900205677190.808004989716141
380.191315624467980.3826312489359590.80868437553202
390.2133211650627910.4266423301255830.786678834937208
400.1930855102971740.3861710205943480.806914489702826
410.1675502896075330.3351005792150660.832449710392467
420.145671779615430.2913435592308590.85432822038457
430.1423971139194130.2847942278388270.857602886080587
440.1180363179190580.2360726358381160.881963682080942
450.09860224295113330.1972044859022670.901397757048867
460.26436034700380.52872069400760.7356396529962
470.4462089554525540.8924179109051070.553791044547446
480.3978191164453570.7956382328907140.602180883554643
490.3720609308254680.7441218616509370.627939069174531
500.3559871327181640.7119742654363280.644012867281836
510.3198595490714120.6397190981428250.680140450928588
520.2785789423471150.557157884694230.721421057652885
530.2963575900777790.5927151801555580.703642409922221
540.2812497405165290.5624994810330580.718750259483471
550.2844063640881290.5688127281762580.715593635911871
560.2700640316469830.5401280632939650.729935968353017
570.2339322441172630.4678644882345260.766067755882737
580.204118806390740.408237612781480.79588119360926
590.1739008345207970.3478016690415940.826099165479203
600.1800606759286410.3601213518572820.819939324071359
610.160189896763080.3203797935261610.83981010323692
620.1385477288971780.2770954577943570.861452271102822
630.1158648733410340.2317297466820690.884135126658966
640.09718602547544170.1943720509508830.902813974524558
650.0816567507872730.1633135015745460.918343249212727
660.07427686451553150.1485537290310630.925723135484468
670.07144390113032680.1428878022606540.928556098869673
680.1696526454820770.3393052909641540.830347354517923
690.2688449507809730.5376899015619460.731155049219027
700.2386065354243060.4772130708486110.761393464575694
710.3470392329945120.6940784659890240.652960767005488
720.3115756849462030.6231513698924060.688424315053797
730.30105702578770.60211405157540.6989429742123
740.2805922258502540.5611844517005090.719407774149746
750.2510358285589980.5020716571179960.748964171441002
760.3256393660410750.651278732082150.674360633958925
770.2921811647278380.5843623294556760.707818835272162
780.2735188179946110.5470376359892210.726481182005389
790.2894481480716540.5788962961433080.710551851928346
800.2632914964284960.5265829928569930.736708503571504
810.2354027304734330.4708054609468670.764597269526567
820.2065762744937710.4131525489875420.793423725506229
830.1840914913716160.3681829827432330.815908508628384
840.1592172004083340.3184344008166670.840782799591666
850.1573377051932520.3146754103865040.842662294806748
860.1347996327389290.2695992654778570.865200367261071
870.1171252462354440.2342504924708880.882874753764556
880.1063716056372020.2127432112744040.893628394362798
890.09107432742323240.1821486548464650.908925672576768
900.08991881553792090.1798376310758420.910081184462079
910.07651631033286620.1530326206657320.923483689667134
920.06488649215597230.1297729843119450.935113507844028
930.05428409906230.10856819812460.9457159009377
940.05652610008312230.1130522001662450.943473899916878
950.05122274160170720.1024454832034140.948777258398293
960.04248767060194210.08497534120388420.957512329398058
970.04787306646065640.09574613292131280.952126933539344
980.03928462867247340.07856925734494680.960715371327527
990.03202938211097750.0640587642219550.967970617889022
1000.02793685627423820.05587371254847640.972063143725762
1010.0248244644538520.04964892890770390.975175535546148
1020.02975129511050440.05950259022100880.970248704889496
1030.02537256200437620.05074512400875240.974627437995624
1040.02248464709100830.04496929418201650.977515352908992
1050.02591357236735860.05182714473471710.974086427632641
1060.02271220503280010.04542441006560020.9772877949672
1070.01826981511144650.0365396302228930.981730184888553
1080.01778396008339330.03556792016678660.982216039916607
1090.01432802773318160.02865605546636320.985671972266818
1100.01163000499614910.02326000999229830.988369995003851
1110.00913299154358140.01826598308716280.990867008456419
1120.00939860420715290.01879720841430580.990601395792847
1130.008140129772357750.01628025954471550.991859870227642
1140.01187006374295290.02374012748590580.988129936257047
1150.01140763828458890.02281527656917770.988592361715411
1160.01128956763662530.02257913527325060.988710432363375
1170.009204579084222270.01840915816844450.990795420915778
1180.009426622596776780.01885324519355360.990573377403223
1190.007575939759601930.01515187951920390.992424060240398
1200.006792993298903590.01358598659780720.993207006701096
1210.005483286965585970.01096657393117190.994516713034414
1220.008563397512910190.01712679502582040.99143660248709
1230.007240993074796420.01448198614959280.992759006925204
1240.006397700466878260.01279540093375650.993602299533122
1250.00523317148484330.01046634296968660.994766828515157
1260.004205429957670060.008410859915340120.99579457004233
1270.003785729884304990.007571459768609990.996214270115695
1280.003027913011962850.00605582602392570.996972086988037
1290.004558689903379960.009117379806759910.99544131009662
1300.004908097672858880.009816195345717750.995091902327141
1310.01069128952216590.02138257904433170.989308710477834
1320.01307104953725150.0261420990745030.986928950462749
1330.01449755507582990.02899511015165990.98550244492417
1340.01409970760066030.02819941520132050.98590029239934
1350.01140195600413920.02280391200827850.988598043995861
1360.009607024633606460.01921404926721290.990392975366394
1370.007647105321995710.01529421064399140.992352894678004
1380.009800336192994720.01960067238598940.990199663807005
1390.008573448534553970.01714689706910790.991426551465446
1400.01008379076108390.02016758152216780.989916209238916
1410.01502657230880980.03005314461761960.98497342769119
1420.01348392186487580.02696784372975170.986516078135124
1430.01066684315606370.02133368631212740.989333156843936
1440.01141448695448750.0228289739089750.988585513045512
1450.02080129565878280.04160259131756560.979198704341217
1460.02580679901913160.05161359803826320.974193200980868
1470.02750609555806660.05501219111613320.972493904441933
1480.02474013916430440.04948027832860880.975259860835696
1490.02046441549816860.04092883099633720.979535584501831
1500.028576841086980.05715368217396010.97142315891302
1510.02441488959607060.04882977919214120.975585110403929
1520.02547325663459460.05094651326918920.974526743365405
1530.0534153879890310.1068307759780620.946584612010969
1540.05261836974215360.1052367394843070.947381630257846
1550.06027685670814660.1205537134162930.939723143291853
1560.05122387175518510.102447743510370.948776128244815
1570.04548751562444140.09097503124888290.954512484375559
1580.0396210469016830.07924209380336610.960378953098317
1590.03878411437735070.07756822875470130.961215885622649
1600.0330423420494650.06608468409892990.966957657950535
1610.02707353255012290.05414706510024590.972926467449877
1620.02212282909720580.04424565819441160.977877170902794
1630.01823798485543750.03647596971087490.981762015144563
1640.01533739803158840.03067479606317670.984662601968412
1650.01338129251421030.02676258502842060.98661870748579
1660.01364664884806430.02729329769612860.986353351151936
1670.01089867894802420.02179735789604830.989101321051976
1680.01638430676079590.03276861352159190.983615693239204
1690.01626977234593570.03253954469187140.983730227654064
1700.01501832949834560.03003665899669110.984981670501654
1710.0144551784296830.0289103568593660.985544821570317
1720.01162625335848270.02325250671696530.988373746641517
1730.01163971916669030.02327943833338060.98836028083331
1740.01304682352867460.02609364705734930.986953176471325
1750.01764414639300430.03528829278600850.982355853606996
1760.01408533159135490.02817066318270970.985914668408645
1770.01140119824755220.02280239649510450.988598801752448
1780.009246829236870730.01849365847374150.990753170763129
1790.007189208840384760.01437841768076950.992810791159615
1800.006258216666431780.01251643333286360.993741783333568
1810.0051209904863240.0102419809726480.994879009513676
1820.004126803900900160.008253607801800310.9958731960991
1830.00432270363315970.00864540726631940.99567729636684
1840.003398714093072190.006797428186144380.996601285906928
1850.08031836575375090.1606367315075020.919681634246249
1860.06933678969274120.1386735793854820.930663210307259
1870.07938574846991730.1587714969398350.920614251530083
1880.06794543077053210.1358908615410640.932054569229468
1890.05726395652315690.1145279130463140.942736043476843
1900.04714864395945630.09429728791891260.952851356040544
1910.04156208633188310.08312417266376630.958437913668117
1920.03489109593939060.06978219187878120.965108904060609
1930.04083516465791030.08167032931582050.95916483534209
1940.04387232178370740.08774464356741490.956127678216293
1950.03706064323307870.07412128646615750.962939356766921
1960.03079423913294950.0615884782658990.969205760867051
1970.04101039300616360.08202078601232720.958989606993836
1980.03341640857713140.06683281715426280.966583591422869
1990.03092810898628380.06185621797256750.969071891013716
2000.0259936294128320.05198725882566390.974006370587168
2010.02447076584951780.04894153169903560.975529234150482
2020.02028930334061660.04057860668123310.979710696659383
2030.02632664575141910.05265329150283830.973673354248581
2040.0304940411491720.06098808229834410.969505958850828
2050.0333543324639720.0667086649279440.966645667536028
2060.02631042413897240.05262084827794480.973689575861028
2070.02587545567790880.05175091135581770.974124544322091
2080.02112759178456480.04225518356912960.978872408215435
2090.02378129740532920.04756259481065840.976218702594671
2100.01915517839810210.03831035679620420.980844821601898
2110.02139035470256810.04278070940513630.978609645297432
2120.03506278954798910.07012557909597820.964937210452011
2130.02758112357998320.05516224715996630.972418876420017
2140.0390742092644910.07814841852898210.960925790735509
2150.03356397054534870.06712794109069740.966436029454651
2160.02610726965357250.05221453930714510.973892730346427
2170.02838166391165240.05676332782330490.971618336088348
2180.02419706732053630.04839413464107260.975802932679464
2190.02171291425954840.04342582851909680.978287085740452
2200.0165634963670420.03312699273408410.983436503632958
2210.01475456911706780.02950913823413550.985245430882932
2220.01080421994946140.02160843989892280.989195780050539
2230.008063122662500350.01612624532500070.9919368773375
2240.006484402945052410.01296880589010480.993515597054948
2250.005643196376983360.01128639275396670.994356803623017
2260.01320820823732670.02641641647465340.986791791762673
2270.01012979993777090.02025959987554180.989870200062229
2280.007288330409744780.01457666081948960.992711669590255
2290.005295989376231690.01059197875246340.994704010623768
2300.003819611421147790.007639222842295580.996180388578852
2310.00381614509713460.007632290194269210.996183854902865
2320.007214544870803660.01442908974160730.992785455129196
2330.0343472305702430.0686944611404860.965652769429757
2340.04984742900250510.09969485800501030.950152570997495
2350.03705404828101510.07410809656203030.962945951718985
2360.03303523992948680.06607047985897370.966964760070513
2370.2355211277066180.4710422554132350.764478872293382
2380.1914661743819790.3829323487639590.808533825618021
2390.1715649759491880.3431299518983760.828435024050812
2400.1350541089086740.2701082178173480.864945891091326
2410.1077019077884680.2154038155769370.892298092211532
2420.08950279867095360.1790055973419070.910497201329046
2430.08329965778462320.1665993155692460.916700342215377
2440.1471341254706990.2942682509413980.852865874529301
2450.1318481036803580.2636962073607170.868151896319642
2460.1052829482080390.2105658964160780.894717051791961
2470.07411728695437830.1482345739087570.925882713045622
2480.0637193287758310.1274386575516620.936280671224169
2490.05989079371037180.1197815874207440.940109206289628
2500.07455333108045210.1491066621609040.925446668919548
2510.04421809287507040.08843618575014070.95578190712493
2520.1091953087358590.2183906174717190.890804691264141
2530.2310653138259990.4621306276519980.768934686174001

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.170144339349467 & 0.340288678698934 & 0.829855660650533 \tabularnewline
12 & 0.234194044875485 & 0.46838808975097 & 0.765805955124515 \tabularnewline
13 & 0.159791946088099 & 0.319583892176198 & 0.840208053911901 \tabularnewline
14 & 0.0981972673535147 & 0.196394534707029 & 0.901802732646485 \tabularnewline
15 & 0.090836865667708 & 0.181673731335416 & 0.909163134332292 \tabularnewline
16 & 0.0522141213854611 & 0.104428242770922 & 0.947785878614539 \tabularnewline
17 & 0.0493503606414635 & 0.0987007212829271 & 0.950649639358536 \tabularnewline
18 & 0.212786551747251 & 0.425573103494501 & 0.787213448252749 \tabularnewline
19 & 0.165921612284331 & 0.331843224568663 & 0.834078387715669 \tabularnewline
20 & 0.114312557538863 & 0.228625115077727 & 0.885687442461137 \tabularnewline
21 & 0.0761285425785354 & 0.152257085157071 & 0.923871457421465 \tabularnewline
22 & 0.0624832029716105 & 0.124966405943221 & 0.93751679702839 \tabularnewline
23 & 0.101465470793558 & 0.202930941587115 & 0.898534529206443 \tabularnewline
24 & 0.167688790048956 & 0.335377580097911 & 0.832311209951044 \tabularnewline
25 & 0.149548325741706 & 0.299096651483412 & 0.850451674258294 \tabularnewline
26 & 0.124823409382769 & 0.249646818765537 & 0.875176590617231 \tabularnewline
27 & 0.143402742500003 & 0.286805485000005 & 0.856597257499997 \tabularnewline
28 & 0.164230812694695 & 0.328461625389389 & 0.835769187305305 \tabularnewline
29 & 0.146582234590387 & 0.293164469180773 & 0.853417765409613 \tabularnewline
30 & 0.165141751681057 & 0.330283503362114 & 0.834858248318943 \tabularnewline
31 & 0.129435105793466 & 0.258870211586933 & 0.870564894206534 \tabularnewline
32 & 0.121675986967165 & 0.24335197393433 & 0.878324013032835 \tabularnewline
33 & 0.106906090659602 & 0.213812181319204 & 0.893093909340398 \tabularnewline
34 & 0.0840255609755065 & 0.168051121951013 & 0.915974439024493 \tabularnewline
35 & 0.0663025723035559 & 0.132605144607112 & 0.933697427696444 \tabularnewline
36 & 0.151585308657111 & 0.303170617314222 & 0.848414691342889 \tabularnewline
37 & 0.191995010283859 & 0.383990020567719 & 0.808004989716141 \tabularnewline
38 & 0.19131562446798 & 0.382631248935959 & 0.80868437553202 \tabularnewline
39 & 0.213321165062791 & 0.426642330125583 & 0.786678834937208 \tabularnewline
40 & 0.193085510297174 & 0.386171020594348 & 0.806914489702826 \tabularnewline
41 & 0.167550289607533 & 0.335100579215066 & 0.832449710392467 \tabularnewline
42 & 0.14567177961543 & 0.291343559230859 & 0.85432822038457 \tabularnewline
43 & 0.142397113919413 & 0.284794227838827 & 0.857602886080587 \tabularnewline
44 & 0.118036317919058 & 0.236072635838116 & 0.881963682080942 \tabularnewline
45 & 0.0986022429511333 & 0.197204485902267 & 0.901397757048867 \tabularnewline
46 & 0.2643603470038 & 0.5287206940076 & 0.7356396529962 \tabularnewline
47 & 0.446208955452554 & 0.892417910905107 & 0.553791044547446 \tabularnewline
48 & 0.397819116445357 & 0.795638232890714 & 0.602180883554643 \tabularnewline
49 & 0.372060930825468 & 0.744121861650937 & 0.627939069174531 \tabularnewline
50 & 0.355987132718164 & 0.711974265436328 & 0.644012867281836 \tabularnewline
51 & 0.319859549071412 & 0.639719098142825 & 0.680140450928588 \tabularnewline
52 & 0.278578942347115 & 0.55715788469423 & 0.721421057652885 \tabularnewline
53 & 0.296357590077779 & 0.592715180155558 & 0.703642409922221 \tabularnewline
54 & 0.281249740516529 & 0.562499481033058 & 0.718750259483471 \tabularnewline
55 & 0.284406364088129 & 0.568812728176258 & 0.715593635911871 \tabularnewline
56 & 0.270064031646983 & 0.540128063293965 & 0.729935968353017 \tabularnewline
57 & 0.233932244117263 & 0.467864488234526 & 0.766067755882737 \tabularnewline
58 & 0.20411880639074 & 0.40823761278148 & 0.79588119360926 \tabularnewline
59 & 0.173900834520797 & 0.347801669041594 & 0.826099165479203 \tabularnewline
60 & 0.180060675928641 & 0.360121351857282 & 0.819939324071359 \tabularnewline
61 & 0.16018989676308 & 0.320379793526161 & 0.83981010323692 \tabularnewline
62 & 0.138547728897178 & 0.277095457794357 & 0.861452271102822 \tabularnewline
63 & 0.115864873341034 & 0.231729746682069 & 0.884135126658966 \tabularnewline
64 & 0.0971860254754417 & 0.194372050950883 & 0.902813974524558 \tabularnewline
65 & 0.081656750787273 & 0.163313501574546 & 0.918343249212727 \tabularnewline
66 & 0.0742768645155315 & 0.148553729031063 & 0.925723135484468 \tabularnewline
67 & 0.0714439011303268 & 0.142887802260654 & 0.928556098869673 \tabularnewline
68 & 0.169652645482077 & 0.339305290964154 & 0.830347354517923 \tabularnewline
69 & 0.268844950780973 & 0.537689901561946 & 0.731155049219027 \tabularnewline
70 & 0.238606535424306 & 0.477213070848611 & 0.761393464575694 \tabularnewline
71 & 0.347039232994512 & 0.694078465989024 & 0.652960767005488 \tabularnewline
72 & 0.311575684946203 & 0.623151369892406 & 0.688424315053797 \tabularnewline
73 & 0.3010570257877 & 0.6021140515754 & 0.6989429742123 \tabularnewline
74 & 0.280592225850254 & 0.561184451700509 & 0.719407774149746 \tabularnewline
75 & 0.251035828558998 & 0.502071657117996 & 0.748964171441002 \tabularnewline
76 & 0.325639366041075 & 0.65127873208215 & 0.674360633958925 \tabularnewline
77 & 0.292181164727838 & 0.584362329455676 & 0.707818835272162 \tabularnewline
78 & 0.273518817994611 & 0.547037635989221 & 0.726481182005389 \tabularnewline
79 & 0.289448148071654 & 0.578896296143308 & 0.710551851928346 \tabularnewline
80 & 0.263291496428496 & 0.526582992856993 & 0.736708503571504 \tabularnewline
81 & 0.235402730473433 & 0.470805460946867 & 0.764597269526567 \tabularnewline
82 & 0.206576274493771 & 0.413152548987542 & 0.793423725506229 \tabularnewline
83 & 0.184091491371616 & 0.368182982743233 & 0.815908508628384 \tabularnewline
84 & 0.159217200408334 & 0.318434400816667 & 0.840782799591666 \tabularnewline
85 & 0.157337705193252 & 0.314675410386504 & 0.842662294806748 \tabularnewline
86 & 0.134799632738929 & 0.269599265477857 & 0.865200367261071 \tabularnewline
87 & 0.117125246235444 & 0.234250492470888 & 0.882874753764556 \tabularnewline
88 & 0.106371605637202 & 0.212743211274404 & 0.893628394362798 \tabularnewline
89 & 0.0910743274232324 & 0.182148654846465 & 0.908925672576768 \tabularnewline
90 & 0.0899188155379209 & 0.179837631075842 & 0.910081184462079 \tabularnewline
91 & 0.0765163103328662 & 0.153032620665732 & 0.923483689667134 \tabularnewline
92 & 0.0648864921559723 & 0.129772984311945 & 0.935113507844028 \tabularnewline
93 & 0.0542840990623 & 0.1085681981246 & 0.9457159009377 \tabularnewline
94 & 0.0565261000831223 & 0.113052200166245 & 0.943473899916878 \tabularnewline
95 & 0.0512227416017072 & 0.102445483203414 & 0.948777258398293 \tabularnewline
96 & 0.0424876706019421 & 0.0849753412038842 & 0.957512329398058 \tabularnewline
97 & 0.0478730664606564 & 0.0957461329213128 & 0.952126933539344 \tabularnewline
98 & 0.0392846286724734 & 0.0785692573449468 & 0.960715371327527 \tabularnewline
99 & 0.0320293821109775 & 0.064058764221955 & 0.967970617889022 \tabularnewline
100 & 0.0279368562742382 & 0.0558737125484764 & 0.972063143725762 \tabularnewline
101 & 0.024824464453852 & 0.0496489289077039 & 0.975175535546148 \tabularnewline
102 & 0.0297512951105044 & 0.0595025902210088 & 0.970248704889496 \tabularnewline
103 & 0.0253725620043762 & 0.0507451240087524 & 0.974627437995624 \tabularnewline
104 & 0.0224846470910083 & 0.0449692941820165 & 0.977515352908992 \tabularnewline
105 & 0.0259135723673586 & 0.0518271447347171 & 0.974086427632641 \tabularnewline
106 & 0.0227122050328001 & 0.0454244100656002 & 0.9772877949672 \tabularnewline
107 & 0.0182698151114465 & 0.036539630222893 & 0.981730184888553 \tabularnewline
108 & 0.0177839600833933 & 0.0355679201667866 & 0.982216039916607 \tabularnewline
109 & 0.0143280277331816 & 0.0286560554663632 & 0.985671972266818 \tabularnewline
110 & 0.0116300049961491 & 0.0232600099922983 & 0.988369995003851 \tabularnewline
111 & 0.0091329915435814 & 0.0182659830871628 & 0.990867008456419 \tabularnewline
112 & 0.0093986042071529 & 0.0187972084143058 & 0.990601395792847 \tabularnewline
113 & 0.00814012977235775 & 0.0162802595447155 & 0.991859870227642 \tabularnewline
114 & 0.0118700637429529 & 0.0237401274859058 & 0.988129936257047 \tabularnewline
115 & 0.0114076382845889 & 0.0228152765691777 & 0.988592361715411 \tabularnewline
116 & 0.0112895676366253 & 0.0225791352732506 & 0.988710432363375 \tabularnewline
117 & 0.00920457908422227 & 0.0184091581684445 & 0.990795420915778 \tabularnewline
118 & 0.00942662259677678 & 0.0188532451935536 & 0.990573377403223 \tabularnewline
119 & 0.00757593975960193 & 0.0151518795192039 & 0.992424060240398 \tabularnewline
120 & 0.00679299329890359 & 0.0135859865978072 & 0.993207006701096 \tabularnewline
121 & 0.00548328696558597 & 0.0109665739311719 & 0.994516713034414 \tabularnewline
122 & 0.00856339751291019 & 0.0171267950258204 & 0.99143660248709 \tabularnewline
123 & 0.00724099307479642 & 0.0144819861495928 & 0.992759006925204 \tabularnewline
124 & 0.00639770046687826 & 0.0127954009337565 & 0.993602299533122 \tabularnewline
125 & 0.0052331714848433 & 0.0104663429696866 & 0.994766828515157 \tabularnewline
126 & 0.00420542995767006 & 0.00841085991534012 & 0.99579457004233 \tabularnewline
127 & 0.00378572988430499 & 0.00757145976860999 & 0.996214270115695 \tabularnewline
128 & 0.00302791301196285 & 0.0060558260239257 & 0.996972086988037 \tabularnewline
129 & 0.00455868990337996 & 0.00911737980675991 & 0.99544131009662 \tabularnewline
130 & 0.00490809767285888 & 0.00981619534571775 & 0.995091902327141 \tabularnewline
131 & 0.0106912895221659 & 0.0213825790443317 & 0.989308710477834 \tabularnewline
132 & 0.0130710495372515 & 0.026142099074503 & 0.986928950462749 \tabularnewline
133 & 0.0144975550758299 & 0.0289951101516599 & 0.98550244492417 \tabularnewline
134 & 0.0140997076006603 & 0.0281994152013205 & 0.98590029239934 \tabularnewline
135 & 0.0114019560041392 & 0.0228039120082785 & 0.988598043995861 \tabularnewline
136 & 0.00960702463360646 & 0.0192140492672129 & 0.990392975366394 \tabularnewline
137 & 0.00764710532199571 & 0.0152942106439914 & 0.992352894678004 \tabularnewline
138 & 0.00980033619299472 & 0.0196006723859894 & 0.990199663807005 \tabularnewline
139 & 0.00857344853455397 & 0.0171468970691079 & 0.991426551465446 \tabularnewline
140 & 0.0100837907610839 & 0.0201675815221678 & 0.989916209238916 \tabularnewline
141 & 0.0150265723088098 & 0.0300531446176196 & 0.98497342769119 \tabularnewline
142 & 0.0134839218648758 & 0.0269678437297517 & 0.986516078135124 \tabularnewline
143 & 0.0106668431560637 & 0.0213336863121274 & 0.989333156843936 \tabularnewline
144 & 0.0114144869544875 & 0.022828973908975 & 0.988585513045512 \tabularnewline
145 & 0.0208012956587828 & 0.0416025913175656 & 0.979198704341217 \tabularnewline
146 & 0.0258067990191316 & 0.0516135980382632 & 0.974193200980868 \tabularnewline
147 & 0.0275060955580666 & 0.0550121911161332 & 0.972493904441933 \tabularnewline
148 & 0.0247401391643044 & 0.0494802783286088 & 0.975259860835696 \tabularnewline
149 & 0.0204644154981686 & 0.0409288309963372 & 0.979535584501831 \tabularnewline
150 & 0.02857684108698 & 0.0571536821739601 & 0.97142315891302 \tabularnewline
151 & 0.0244148895960706 & 0.0488297791921412 & 0.975585110403929 \tabularnewline
152 & 0.0254732566345946 & 0.0509465132691892 & 0.974526743365405 \tabularnewline
153 & 0.053415387989031 & 0.106830775978062 & 0.946584612010969 \tabularnewline
154 & 0.0526183697421536 & 0.105236739484307 & 0.947381630257846 \tabularnewline
155 & 0.0602768567081466 & 0.120553713416293 & 0.939723143291853 \tabularnewline
156 & 0.0512238717551851 & 0.10244774351037 & 0.948776128244815 \tabularnewline
157 & 0.0454875156244414 & 0.0909750312488829 & 0.954512484375559 \tabularnewline
158 & 0.039621046901683 & 0.0792420938033661 & 0.960378953098317 \tabularnewline
159 & 0.0387841143773507 & 0.0775682287547013 & 0.961215885622649 \tabularnewline
160 & 0.033042342049465 & 0.0660846840989299 & 0.966957657950535 \tabularnewline
161 & 0.0270735325501229 & 0.0541470651002459 & 0.972926467449877 \tabularnewline
162 & 0.0221228290972058 & 0.0442456581944116 & 0.977877170902794 \tabularnewline
163 & 0.0182379848554375 & 0.0364759697108749 & 0.981762015144563 \tabularnewline
164 & 0.0153373980315884 & 0.0306747960631767 & 0.984662601968412 \tabularnewline
165 & 0.0133812925142103 & 0.0267625850284206 & 0.98661870748579 \tabularnewline
166 & 0.0136466488480643 & 0.0272932976961286 & 0.986353351151936 \tabularnewline
167 & 0.0108986789480242 & 0.0217973578960483 & 0.989101321051976 \tabularnewline
168 & 0.0163843067607959 & 0.0327686135215919 & 0.983615693239204 \tabularnewline
169 & 0.0162697723459357 & 0.0325395446918714 & 0.983730227654064 \tabularnewline
170 & 0.0150183294983456 & 0.0300366589966911 & 0.984981670501654 \tabularnewline
171 & 0.014455178429683 & 0.028910356859366 & 0.985544821570317 \tabularnewline
172 & 0.0116262533584827 & 0.0232525067169653 & 0.988373746641517 \tabularnewline
173 & 0.0116397191666903 & 0.0232794383333806 & 0.98836028083331 \tabularnewline
174 & 0.0130468235286746 & 0.0260936470573493 & 0.986953176471325 \tabularnewline
175 & 0.0176441463930043 & 0.0352882927860085 & 0.982355853606996 \tabularnewline
176 & 0.0140853315913549 & 0.0281706631827097 & 0.985914668408645 \tabularnewline
177 & 0.0114011982475522 & 0.0228023964951045 & 0.988598801752448 \tabularnewline
178 & 0.00924682923687073 & 0.0184936584737415 & 0.990753170763129 \tabularnewline
179 & 0.00718920884038476 & 0.0143784176807695 & 0.992810791159615 \tabularnewline
180 & 0.00625821666643178 & 0.0125164333328636 & 0.993741783333568 \tabularnewline
181 & 0.005120990486324 & 0.010241980972648 & 0.994879009513676 \tabularnewline
182 & 0.00412680390090016 & 0.00825360780180031 & 0.9958731960991 \tabularnewline
183 & 0.0043227036331597 & 0.0086454072663194 & 0.99567729636684 \tabularnewline
184 & 0.00339871409307219 & 0.00679742818614438 & 0.996601285906928 \tabularnewline
185 & 0.0803183657537509 & 0.160636731507502 & 0.919681634246249 \tabularnewline
186 & 0.0693367896927412 & 0.138673579385482 & 0.930663210307259 \tabularnewline
187 & 0.0793857484699173 & 0.158771496939835 & 0.920614251530083 \tabularnewline
188 & 0.0679454307705321 & 0.135890861541064 & 0.932054569229468 \tabularnewline
189 & 0.0572639565231569 & 0.114527913046314 & 0.942736043476843 \tabularnewline
190 & 0.0471486439594563 & 0.0942972879189126 & 0.952851356040544 \tabularnewline
191 & 0.0415620863318831 & 0.0831241726637663 & 0.958437913668117 \tabularnewline
192 & 0.0348910959393906 & 0.0697821918787812 & 0.965108904060609 \tabularnewline
193 & 0.0408351646579103 & 0.0816703293158205 & 0.95916483534209 \tabularnewline
194 & 0.0438723217837074 & 0.0877446435674149 & 0.956127678216293 \tabularnewline
195 & 0.0370606432330787 & 0.0741212864661575 & 0.962939356766921 \tabularnewline
196 & 0.0307942391329495 & 0.061588478265899 & 0.969205760867051 \tabularnewline
197 & 0.0410103930061636 & 0.0820207860123272 & 0.958989606993836 \tabularnewline
198 & 0.0334164085771314 & 0.0668328171542628 & 0.966583591422869 \tabularnewline
199 & 0.0309281089862838 & 0.0618562179725675 & 0.969071891013716 \tabularnewline
200 & 0.025993629412832 & 0.0519872588256639 & 0.974006370587168 \tabularnewline
201 & 0.0244707658495178 & 0.0489415316990356 & 0.975529234150482 \tabularnewline
202 & 0.0202893033406166 & 0.0405786066812331 & 0.979710696659383 \tabularnewline
203 & 0.0263266457514191 & 0.0526532915028383 & 0.973673354248581 \tabularnewline
204 & 0.030494041149172 & 0.0609880822983441 & 0.969505958850828 \tabularnewline
205 & 0.033354332463972 & 0.066708664927944 & 0.966645667536028 \tabularnewline
206 & 0.0263104241389724 & 0.0526208482779448 & 0.973689575861028 \tabularnewline
207 & 0.0258754556779088 & 0.0517509113558177 & 0.974124544322091 \tabularnewline
208 & 0.0211275917845648 & 0.0422551835691296 & 0.978872408215435 \tabularnewline
209 & 0.0237812974053292 & 0.0475625948106584 & 0.976218702594671 \tabularnewline
210 & 0.0191551783981021 & 0.0383103567962042 & 0.980844821601898 \tabularnewline
211 & 0.0213903547025681 & 0.0427807094051363 & 0.978609645297432 \tabularnewline
212 & 0.0350627895479891 & 0.0701255790959782 & 0.964937210452011 \tabularnewline
213 & 0.0275811235799832 & 0.0551622471599663 & 0.972418876420017 \tabularnewline
214 & 0.039074209264491 & 0.0781484185289821 & 0.960925790735509 \tabularnewline
215 & 0.0335639705453487 & 0.0671279410906974 & 0.966436029454651 \tabularnewline
216 & 0.0261072696535725 & 0.0522145393071451 & 0.973892730346427 \tabularnewline
217 & 0.0283816639116524 & 0.0567633278233049 & 0.971618336088348 \tabularnewline
218 & 0.0241970673205363 & 0.0483941346410726 & 0.975802932679464 \tabularnewline
219 & 0.0217129142595484 & 0.0434258285190968 & 0.978287085740452 \tabularnewline
220 & 0.016563496367042 & 0.0331269927340841 & 0.983436503632958 \tabularnewline
221 & 0.0147545691170678 & 0.0295091382341355 & 0.985245430882932 \tabularnewline
222 & 0.0108042199494614 & 0.0216084398989228 & 0.989195780050539 \tabularnewline
223 & 0.00806312266250035 & 0.0161262453250007 & 0.9919368773375 \tabularnewline
224 & 0.00648440294505241 & 0.0129688058901048 & 0.993515597054948 \tabularnewline
225 & 0.00564319637698336 & 0.0112863927539667 & 0.994356803623017 \tabularnewline
226 & 0.0132082082373267 & 0.0264164164746534 & 0.986791791762673 \tabularnewline
227 & 0.0101297999377709 & 0.0202595998755418 & 0.989870200062229 \tabularnewline
228 & 0.00728833040974478 & 0.0145766608194896 & 0.992711669590255 \tabularnewline
229 & 0.00529598937623169 & 0.0105919787524634 & 0.994704010623768 \tabularnewline
230 & 0.00381961142114779 & 0.00763922284229558 & 0.996180388578852 \tabularnewline
231 & 0.0038161450971346 & 0.00763229019426921 & 0.996183854902865 \tabularnewline
232 & 0.00721454487080366 & 0.0144290897416073 & 0.992785455129196 \tabularnewline
233 & 0.034347230570243 & 0.068694461140486 & 0.965652769429757 \tabularnewline
234 & 0.0498474290025051 & 0.0996948580050103 & 0.950152570997495 \tabularnewline
235 & 0.0370540482810151 & 0.0741080965620303 & 0.962945951718985 \tabularnewline
236 & 0.0330352399294868 & 0.0660704798589737 & 0.966964760070513 \tabularnewline
237 & 0.235521127706618 & 0.471042255413235 & 0.764478872293382 \tabularnewline
238 & 0.191466174381979 & 0.382932348763959 & 0.808533825618021 \tabularnewline
239 & 0.171564975949188 & 0.343129951898376 & 0.828435024050812 \tabularnewline
240 & 0.135054108908674 & 0.270108217817348 & 0.864945891091326 \tabularnewline
241 & 0.107701907788468 & 0.215403815576937 & 0.892298092211532 \tabularnewline
242 & 0.0895027986709536 & 0.179005597341907 & 0.910497201329046 \tabularnewline
243 & 0.0832996577846232 & 0.166599315569246 & 0.916700342215377 \tabularnewline
244 & 0.147134125470699 & 0.294268250941398 & 0.852865874529301 \tabularnewline
245 & 0.131848103680358 & 0.263696207360717 & 0.868151896319642 \tabularnewline
246 & 0.105282948208039 & 0.210565896416078 & 0.894717051791961 \tabularnewline
247 & 0.0741172869543783 & 0.148234573908757 & 0.925882713045622 \tabularnewline
248 & 0.063719328775831 & 0.127438657551662 & 0.936280671224169 \tabularnewline
249 & 0.0598907937103718 & 0.119781587420744 & 0.940109206289628 \tabularnewline
250 & 0.0745533310804521 & 0.149106662160904 & 0.925446668919548 \tabularnewline
251 & 0.0442180928750704 & 0.0884361857501407 & 0.95578190712493 \tabularnewline
252 & 0.109195308735859 & 0.218390617471719 & 0.890804691264141 \tabularnewline
253 & 0.231065313825999 & 0.462130627651998 & 0.768934686174001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204206&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]0.170144339349467[/C][C]0.340288678698934[/C][C]0.829855660650533[/C][/ROW]
[ROW][C]12[/C][C]0.234194044875485[/C][C]0.46838808975097[/C][C]0.765805955124515[/C][/ROW]
[ROW][C]13[/C][C]0.159791946088099[/C][C]0.319583892176198[/C][C]0.840208053911901[/C][/ROW]
[ROW][C]14[/C][C]0.0981972673535147[/C][C]0.196394534707029[/C][C]0.901802732646485[/C][/ROW]
[ROW][C]15[/C][C]0.090836865667708[/C][C]0.181673731335416[/C][C]0.909163134332292[/C][/ROW]
[ROW][C]16[/C][C]0.0522141213854611[/C][C]0.104428242770922[/C][C]0.947785878614539[/C][/ROW]
[ROW][C]17[/C][C]0.0493503606414635[/C][C]0.0987007212829271[/C][C]0.950649639358536[/C][/ROW]
[ROW][C]18[/C][C]0.212786551747251[/C][C]0.425573103494501[/C][C]0.787213448252749[/C][/ROW]
[ROW][C]19[/C][C]0.165921612284331[/C][C]0.331843224568663[/C][C]0.834078387715669[/C][/ROW]
[ROW][C]20[/C][C]0.114312557538863[/C][C]0.228625115077727[/C][C]0.885687442461137[/C][/ROW]
[ROW][C]21[/C][C]0.0761285425785354[/C][C]0.152257085157071[/C][C]0.923871457421465[/C][/ROW]
[ROW][C]22[/C][C]0.0624832029716105[/C][C]0.124966405943221[/C][C]0.93751679702839[/C][/ROW]
[ROW][C]23[/C][C]0.101465470793558[/C][C]0.202930941587115[/C][C]0.898534529206443[/C][/ROW]
[ROW][C]24[/C][C]0.167688790048956[/C][C]0.335377580097911[/C][C]0.832311209951044[/C][/ROW]
[ROW][C]25[/C][C]0.149548325741706[/C][C]0.299096651483412[/C][C]0.850451674258294[/C][/ROW]
[ROW][C]26[/C][C]0.124823409382769[/C][C]0.249646818765537[/C][C]0.875176590617231[/C][/ROW]
[ROW][C]27[/C][C]0.143402742500003[/C][C]0.286805485000005[/C][C]0.856597257499997[/C][/ROW]
[ROW][C]28[/C][C]0.164230812694695[/C][C]0.328461625389389[/C][C]0.835769187305305[/C][/ROW]
[ROW][C]29[/C][C]0.146582234590387[/C][C]0.293164469180773[/C][C]0.853417765409613[/C][/ROW]
[ROW][C]30[/C][C]0.165141751681057[/C][C]0.330283503362114[/C][C]0.834858248318943[/C][/ROW]
[ROW][C]31[/C][C]0.129435105793466[/C][C]0.258870211586933[/C][C]0.870564894206534[/C][/ROW]
[ROW][C]32[/C][C]0.121675986967165[/C][C]0.24335197393433[/C][C]0.878324013032835[/C][/ROW]
[ROW][C]33[/C][C]0.106906090659602[/C][C]0.213812181319204[/C][C]0.893093909340398[/C][/ROW]
[ROW][C]34[/C][C]0.0840255609755065[/C][C]0.168051121951013[/C][C]0.915974439024493[/C][/ROW]
[ROW][C]35[/C][C]0.0663025723035559[/C][C]0.132605144607112[/C][C]0.933697427696444[/C][/ROW]
[ROW][C]36[/C][C]0.151585308657111[/C][C]0.303170617314222[/C][C]0.848414691342889[/C][/ROW]
[ROW][C]37[/C][C]0.191995010283859[/C][C]0.383990020567719[/C][C]0.808004989716141[/C][/ROW]
[ROW][C]38[/C][C]0.19131562446798[/C][C]0.382631248935959[/C][C]0.80868437553202[/C][/ROW]
[ROW][C]39[/C][C]0.213321165062791[/C][C]0.426642330125583[/C][C]0.786678834937208[/C][/ROW]
[ROW][C]40[/C][C]0.193085510297174[/C][C]0.386171020594348[/C][C]0.806914489702826[/C][/ROW]
[ROW][C]41[/C][C]0.167550289607533[/C][C]0.335100579215066[/C][C]0.832449710392467[/C][/ROW]
[ROW][C]42[/C][C]0.14567177961543[/C][C]0.291343559230859[/C][C]0.85432822038457[/C][/ROW]
[ROW][C]43[/C][C]0.142397113919413[/C][C]0.284794227838827[/C][C]0.857602886080587[/C][/ROW]
[ROW][C]44[/C][C]0.118036317919058[/C][C]0.236072635838116[/C][C]0.881963682080942[/C][/ROW]
[ROW][C]45[/C][C]0.0986022429511333[/C][C]0.197204485902267[/C][C]0.901397757048867[/C][/ROW]
[ROW][C]46[/C][C]0.2643603470038[/C][C]0.5287206940076[/C][C]0.7356396529962[/C][/ROW]
[ROW][C]47[/C][C]0.446208955452554[/C][C]0.892417910905107[/C][C]0.553791044547446[/C][/ROW]
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[ROW][C]131[/C][C]0.0106912895221659[/C][C]0.0213825790443317[/C][C]0.989308710477834[/C][/ROW]
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[ROW][C]189[/C][C]0.0572639565231569[/C][C]0.114527913046314[/C][C]0.942736043476843[/C][/ROW]
[ROW][C]190[/C][C]0.0471486439594563[/C][C]0.0942972879189126[/C][C]0.952851356040544[/C][/ROW]
[ROW][C]191[/C][C]0.0415620863318831[/C][C]0.0831241726637663[/C][C]0.958437913668117[/C][/ROW]
[ROW][C]192[/C][C]0.0348910959393906[/C][C]0.0697821918787812[/C][C]0.965108904060609[/C][/ROW]
[ROW][C]193[/C][C]0.0408351646579103[/C][C]0.0816703293158205[/C][C]0.95916483534209[/C][/ROW]
[ROW][C]194[/C][C]0.0438723217837074[/C][C]0.0877446435674149[/C][C]0.956127678216293[/C][/ROW]
[ROW][C]195[/C][C]0.0370606432330787[/C][C]0.0741212864661575[/C][C]0.962939356766921[/C][/ROW]
[ROW][C]196[/C][C]0.0307942391329495[/C][C]0.061588478265899[/C][C]0.969205760867051[/C][/ROW]
[ROW][C]197[/C][C]0.0410103930061636[/C][C]0.0820207860123272[/C][C]0.958989606993836[/C][/ROW]
[ROW][C]198[/C][C]0.0334164085771314[/C][C]0.0668328171542628[/C][C]0.966583591422869[/C][/ROW]
[ROW][C]199[/C][C]0.0309281089862838[/C][C]0.0618562179725675[/C][C]0.969071891013716[/C][/ROW]
[ROW][C]200[/C][C]0.025993629412832[/C][C]0.0519872588256639[/C][C]0.974006370587168[/C][/ROW]
[ROW][C]201[/C][C]0.0244707658495178[/C][C]0.0489415316990356[/C][C]0.975529234150482[/C][/ROW]
[ROW][C]202[/C][C]0.0202893033406166[/C][C]0.0405786066812331[/C][C]0.979710696659383[/C][/ROW]
[ROW][C]203[/C][C]0.0263266457514191[/C][C]0.0526532915028383[/C][C]0.973673354248581[/C][/ROW]
[ROW][C]204[/C][C]0.030494041149172[/C][C]0.0609880822983441[/C][C]0.969505958850828[/C][/ROW]
[ROW][C]205[/C][C]0.033354332463972[/C][C]0.066708664927944[/C][C]0.966645667536028[/C][/ROW]
[ROW][C]206[/C][C]0.0263104241389724[/C][C]0.0526208482779448[/C][C]0.973689575861028[/C][/ROW]
[ROW][C]207[/C][C]0.0258754556779088[/C][C]0.0517509113558177[/C][C]0.974124544322091[/C][/ROW]
[ROW][C]208[/C][C]0.0211275917845648[/C][C]0.0422551835691296[/C][C]0.978872408215435[/C][/ROW]
[ROW][C]209[/C][C]0.0237812974053292[/C][C]0.0475625948106584[/C][C]0.976218702594671[/C][/ROW]
[ROW][C]210[/C][C]0.0191551783981021[/C][C]0.0383103567962042[/C][C]0.980844821601898[/C][/ROW]
[ROW][C]211[/C][C]0.0213903547025681[/C][C]0.0427807094051363[/C][C]0.978609645297432[/C][/ROW]
[ROW][C]212[/C][C]0.0350627895479891[/C][C]0.0701255790959782[/C][C]0.964937210452011[/C][/ROW]
[ROW][C]213[/C][C]0.0275811235799832[/C][C]0.0551622471599663[/C][C]0.972418876420017[/C][/ROW]
[ROW][C]214[/C][C]0.039074209264491[/C][C]0.0781484185289821[/C][C]0.960925790735509[/C][/ROW]
[ROW][C]215[/C][C]0.0335639705453487[/C][C]0.0671279410906974[/C][C]0.966436029454651[/C][/ROW]
[ROW][C]216[/C][C]0.0261072696535725[/C][C]0.0522145393071451[/C][C]0.973892730346427[/C][/ROW]
[ROW][C]217[/C][C]0.0283816639116524[/C][C]0.0567633278233049[/C][C]0.971618336088348[/C][/ROW]
[ROW][C]218[/C][C]0.0241970673205363[/C][C]0.0483941346410726[/C][C]0.975802932679464[/C][/ROW]
[ROW][C]219[/C][C]0.0217129142595484[/C][C]0.0434258285190968[/C][C]0.978287085740452[/C][/ROW]
[ROW][C]220[/C][C]0.016563496367042[/C][C]0.0331269927340841[/C][C]0.983436503632958[/C][/ROW]
[ROW][C]221[/C][C]0.0147545691170678[/C][C]0.0295091382341355[/C][C]0.985245430882932[/C][/ROW]
[ROW][C]222[/C][C]0.0108042199494614[/C][C]0.0216084398989228[/C][C]0.989195780050539[/C][/ROW]
[ROW][C]223[/C][C]0.00806312266250035[/C][C]0.0161262453250007[/C][C]0.9919368773375[/C][/ROW]
[ROW][C]224[/C][C]0.00648440294505241[/C][C]0.0129688058901048[/C][C]0.993515597054948[/C][/ROW]
[ROW][C]225[/C][C]0.00564319637698336[/C][C]0.0112863927539667[/C][C]0.994356803623017[/C][/ROW]
[ROW][C]226[/C][C]0.0132082082373267[/C][C]0.0264164164746534[/C][C]0.986791791762673[/C][/ROW]
[ROW][C]227[/C][C]0.0101297999377709[/C][C]0.0202595998755418[/C][C]0.989870200062229[/C][/ROW]
[ROW][C]228[/C][C]0.00728833040974478[/C][C]0.0145766608194896[/C][C]0.992711669590255[/C][/ROW]
[ROW][C]229[/C][C]0.00529598937623169[/C][C]0.0105919787524634[/C][C]0.994704010623768[/C][/ROW]
[ROW][C]230[/C][C]0.00381961142114779[/C][C]0.00763922284229558[/C][C]0.996180388578852[/C][/ROW]
[ROW][C]231[/C][C]0.0038161450971346[/C][C]0.00763229019426921[/C][C]0.996183854902865[/C][/ROW]
[ROW][C]232[/C][C]0.00721454487080366[/C][C]0.0144290897416073[/C][C]0.992785455129196[/C][/ROW]
[ROW][C]233[/C][C]0.034347230570243[/C][C]0.068694461140486[/C][C]0.965652769429757[/C][/ROW]
[ROW][C]234[/C][C]0.0498474290025051[/C][C]0.0996948580050103[/C][C]0.950152570997495[/C][/ROW]
[ROW][C]235[/C][C]0.0370540482810151[/C][C]0.0741080965620303[/C][C]0.962945951718985[/C][/ROW]
[ROW][C]236[/C][C]0.0330352399294868[/C][C]0.0660704798589737[/C][C]0.966964760070513[/C][/ROW]
[ROW][C]237[/C][C]0.235521127706618[/C][C]0.471042255413235[/C][C]0.764478872293382[/C][/ROW]
[ROW][C]238[/C][C]0.191466174381979[/C][C]0.382932348763959[/C][C]0.808533825618021[/C][/ROW]
[ROW][C]239[/C][C]0.171564975949188[/C][C]0.343129951898376[/C][C]0.828435024050812[/C][/ROW]
[ROW][C]240[/C][C]0.135054108908674[/C][C]0.270108217817348[/C][C]0.864945891091326[/C][/ROW]
[ROW][C]241[/C][C]0.107701907788468[/C][C]0.215403815576937[/C][C]0.892298092211532[/C][/ROW]
[ROW][C]242[/C][C]0.0895027986709536[/C][C]0.179005597341907[/C][C]0.910497201329046[/C][/ROW]
[ROW][C]243[/C][C]0.0832996577846232[/C][C]0.166599315569246[/C][C]0.916700342215377[/C][/ROW]
[ROW][C]244[/C][C]0.147134125470699[/C][C]0.294268250941398[/C][C]0.852865874529301[/C][/ROW]
[ROW][C]245[/C][C]0.131848103680358[/C][C]0.263696207360717[/C][C]0.868151896319642[/C][/ROW]
[ROW][C]246[/C][C]0.105282948208039[/C][C]0.210565896416078[/C][C]0.894717051791961[/C][/ROW]
[ROW][C]247[/C][C]0.0741172869543783[/C][C]0.148234573908757[/C][C]0.925882713045622[/C][/ROW]
[ROW][C]248[/C][C]0.063719328775831[/C][C]0.127438657551662[/C][C]0.936280671224169[/C][/ROW]
[ROW][C]249[/C][C]0.0598907937103718[/C][C]0.119781587420744[/C][C]0.940109206289628[/C][/ROW]
[ROW][C]250[/C][C]0.0745533310804521[/C][C]0.149106662160904[/C][C]0.925446668919548[/C][/ROW]
[ROW][C]251[/C][C]0.0442180928750704[/C][C]0.0884361857501407[/C][C]0.95578190712493[/C][/ROW]
[ROW][C]252[/C][C]0.109195308735859[/C][C]0.218390617471719[/C][C]0.890804691264141[/C][/ROW]
[ROW][C]253[/C][C]0.231065313825999[/C][C]0.462130627651998[/C][C]0.768934686174001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204206&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204206&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.1701443393494670.3402886786989340.829855660650533
120.2341940448754850.468388089750970.765805955124515
130.1597919460880990.3195838921761980.840208053911901
140.09819726735351470.1963945347070290.901802732646485
150.0908368656677080.1816737313354160.909163134332292
160.05221412138546110.1044282427709220.947785878614539
170.04935036064146350.09870072128292710.950649639358536
180.2127865517472510.4255731034945010.787213448252749
190.1659216122843310.3318432245686630.834078387715669
200.1143125575388630.2286251150777270.885687442461137
210.07612854257853540.1522570851570710.923871457421465
220.06248320297161050.1249664059432210.93751679702839
230.1014654707935580.2029309415871150.898534529206443
240.1676887900489560.3353775800979110.832311209951044
250.1495483257417060.2990966514834120.850451674258294
260.1248234093827690.2496468187655370.875176590617231
270.1434027425000030.2868054850000050.856597257499997
280.1642308126946950.3284616253893890.835769187305305
290.1465822345903870.2931644691807730.853417765409613
300.1651417516810570.3302835033621140.834858248318943
310.1294351057934660.2588702115869330.870564894206534
320.1216759869671650.243351973934330.878324013032835
330.1069060906596020.2138121813192040.893093909340398
340.08402556097550650.1680511219510130.915974439024493
350.06630257230355590.1326051446071120.933697427696444
360.1515853086571110.3031706173142220.848414691342889
370.1919950102838590.3839900205677190.808004989716141
380.191315624467980.3826312489359590.80868437553202
390.2133211650627910.4266423301255830.786678834937208
400.1930855102971740.3861710205943480.806914489702826
410.1675502896075330.3351005792150660.832449710392467
420.145671779615430.2913435592308590.85432822038457
430.1423971139194130.2847942278388270.857602886080587
440.1180363179190580.2360726358381160.881963682080942
450.09860224295113330.1972044859022670.901397757048867
460.26436034700380.52872069400760.7356396529962
470.4462089554525540.8924179109051070.553791044547446
480.3978191164453570.7956382328907140.602180883554643
490.3720609308254680.7441218616509370.627939069174531
500.3559871327181640.7119742654363280.644012867281836
510.3198595490714120.6397190981428250.680140450928588
520.2785789423471150.557157884694230.721421057652885
530.2963575900777790.5927151801555580.703642409922221
540.2812497405165290.5624994810330580.718750259483471
550.2844063640881290.5688127281762580.715593635911871
560.2700640316469830.5401280632939650.729935968353017
570.2339322441172630.4678644882345260.766067755882737
580.204118806390740.408237612781480.79588119360926
590.1739008345207970.3478016690415940.826099165479203
600.1800606759286410.3601213518572820.819939324071359
610.160189896763080.3203797935261610.83981010323692
620.1385477288971780.2770954577943570.861452271102822
630.1158648733410340.2317297466820690.884135126658966
640.09718602547544170.1943720509508830.902813974524558
650.0816567507872730.1633135015745460.918343249212727
660.07427686451553150.1485537290310630.925723135484468
670.07144390113032680.1428878022606540.928556098869673
680.1696526454820770.3393052909641540.830347354517923
690.2688449507809730.5376899015619460.731155049219027
700.2386065354243060.4772130708486110.761393464575694
710.3470392329945120.6940784659890240.652960767005488
720.3115756849462030.6231513698924060.688424315053797
730.30105702578770.60211405157540.6989429742123
740.2805922258502540.5611844517005090.719407774149746
750.2510358285589980.5020716571179960.748964171441002
760.3256393660410750.651278732082150.674360633958925
770.2921811647278380.5843623294556760.707818835272162
780.2735188179946110.5470376359892210.726481182005389
790.2894481480716540.5788962961433080.710551851928346
800.2632914964284960.5265829928569930.736708503571504
810.2354027304734330.4708054609468670.764597269526567
820.2065762744937710.4131525489875420.793423725506229
830.1840914913716160.3681829827432330.815908508628384
840.1592172004083340.3184344008166670.840782799591666
850.1573377051932520.3146754103865040.842662294806748
860.1347996327389290.2695992654778570.865200367261071
870.1171252462354440.2342504924708880.882874753764556
880.1063716056372020.2127432112744040.893628394362798
890.09107432742323240.1821486548464650.908925672576768
900.08991881553792090.1798376310758420.910081184462079
910.07651631033286620.1530326206657320.923483689667134
920.06488649215597230.1297729843119450.935113507844028
930.05428409906230.10856819812460.9457159009377
940.05652610008312230.1130522001662450.943473899916878
950.05122274160170720.1024454832034140.948777258398293
960.04248767060194210.08497534120388420.957512329398058
970.04787306646065640.09574613292131280.952126933539344
980.03928462867247340.07856925734494680.960715371327527
990.03202938211097750.0640587642219550.967970617889022
1000.02793685627423820.05587371254847640.972063143725762
1010.0248244644538520.04964892890770390.975175535546148
1020.02975129511050440.05950259022100880.970248704889496
1030.02537256200437620.05074512400875240.974627437995624
1040.02248464709100830.04496929418201650.977515352908992
1050.02591357236735860.05182714473471710.974086427632641
1060.02271220503280010.04542441006560020.9772877949672
1070.01826981511144650.0365396302228930.981730184888553
1080.01778396008339330.03556792016678660.982216039916607
1090.01432802773318160.02865605546636320.985671972266818
1100.01163000499614910.02326000999229830.988369995003851
1110.00913299154358140.01826598308716280.990867008456419
1120.00939860420715290.01879720841430580.990601395792847
1130.008140129772357750.01628025954471550.991859870227642
1140.01187006374295290.02374012748590580.988129936257047
1150.01140763828458890.02281527656917770.988592361715411
1160.01128956763662530.02257913527325060.988710432363375
1170.009204579084222270.01840915816844450.990795420915778
1180.009426622596776780.01885324519355360.990573377403223
1190.007575939759601930.01515187951920390.992424060240398
1200.006792993298903590.01358598659780720.993207006701096
1210.005483286965585970.01096657393117190.994516713034414
1220.008563397512910190.01712679502582040.99143660248709
1230.007240993074796420.01448198614959280.992759006925204
1240.006397700466878260.01279540093375650.993602299533122
1250.00523317148484330.01046634296968660.994766828515157
1260.004205429957670060.008410859915340120.99579457004233
1270.003785729884304990.007571459768609990.996214270115695
1280.003027913011962850.00605582602392570.996972086988037
1290.004558689903379960.009117379806759910.99544131009662
1300.004908097672858880.009816195345717750.995091902327141
1310.01069128952216590.02138257904433170.989308710477834
1320.01307104953725150.0261420990745030.986928950462749
1330.01449755507582990.02899511015165990.98550244492417
1340.01409970760066030.02819941520132050.98590029239934
1350.01140195600413920.02280391200827850.988598043995861
1360.009607024633606460.01921404926721290.990392975366394
1370.007647105321995710.01529421064399140.992352894678004
1380.009800336192994720.01960067238598940.990199663807005
1390.008573448534553970.01714689706910790.991426551465446
1400.01008379076108390.02016758152216780.989916209238916
1410.01502657230880980.03005314461761960.98497342769119
1420.01348392186487580.02696784372975170.986516078135124
1430.01066684315606370.02133368631212740.989333156843936
1440.01141448695448750.0228289739089750.988585513045512
1450.02080129565878280.04160259131756560.979198704341217
1460.02580679901913160.05161359803826320.974193200980868
1470.02750609555806660.05501219111613320.972493904441933
1480.02474013916430440.04948027832860880.975259860835696
1490.02046441549816860.04092883099633720.979535584501831
1500.028576841086980.05715368217396010.97142315891302
1510.02441488959607060.04882977919214120.975585110403929
1520.02547325663459460.05094651326918920.974526743365405
1530.0534153879890310.1068307759780620.946584612010969
1540.05261836974215360.1052367394843070.947381630257846
1550.06027685670814660.1205537134162930.939723143291853
1560.05122387175518510.102447743510370.948776128244815
1570.04548751562444140.09097503124888290.954512484375559
1580.0396210469016830.07924209380336610.960378953098317
1590.03878411437735070.07756822875470130.961215885622649
1600.0330423420494650.06608468409892990.966957657950535
1610.02707353255012290.05414706510024590.972926467449877
1620.02212282909720580.04424565819441160.977877170902794
1630.01823798485543750.03647596971087490.981762015144563
1640.01533739803158840.03067479606317670.984662601968412
1650.01338129251421030.02676258502842060.98661870748579
1660.01364664884806430.02729329769612860.986353351151936
1670.01089867894802420.02179735789604830.989101321051976
1680.01638430676079590.03276861352159190.983615693239204
1690.01626977234593570.03253954469187140.983730227654064
1700.01501832949834560.03003665899669110.984981670501654
1710.0144551784296830.0289103568593660.985544821570317
1720.01162625335848270.02325250671696530.988373746641517
1730.01163971916669030.02327943833338060.98836028083331
1740.01304682352867460.02609364705734930.986953176471325
1750.01764414639300430.03528829278600850.982355853606996
1760.01408533159135490.02817066318270970.985914668408645
1770.01140119824755220.02280239649510450.988598801752448
1780.009246829236870730.01849365847374150.990753170763129
1790.007189208840384760.01437841768076950.992810791159615
1800.006258216666431780.01251643333286360.993741783333568
1810.0051209904863240.0102419809726480.994879009513676
1820.004126803900900160.008253607801800310.9958731960991
1830.00432270363315970.00864540726631940.99567729636684
1840.003398714093072190.006797428186144380.996601285906928
1850.08031836575375090.1606367315075020.919681634246249
1860.06933678969274120.1386735793854820.930663210307259
1870.07938574846991730.1587714969398350.920614251530083
1880.06794543077053210.1358908615410640.932054569229468
1890.05726395652315690.1145279130463140.942736043476843
1900.04714864395945630.09429728791891260.952851356040544
1910.04156208633188310.08312417266376630.958437913668117
1920.03489109593939060.06978219187878120.965108904060609
1930.04083516465791030.08167032931582050.95916483534209
1940.04387232178370740.08774464356741490.956127678216293
1950.03706064323307870.07412128646615750.962939356766921
1960.03079423913294950.0615884782658990.969205760867051
1970.04101039300616360.08202078601232720.958989606993836
1980.03341640857713140.06683281715426280.966583591422869
1990.03092810898628380.06185621797256750.969071891013716
2000.0259936294128320.05198725882566390.974006370587168
2010.02447076584951780.04894153169903560.975529234150482
2020.02028930334061660.04057860668123310.979710696659383
2030.02632664575141910.05265329150283830.973673354248581
2040.0304940411491720.06098808229834410.969505958850828
2050.0333543324639720.0667086649279440.966645667536028
2060.02631042413897240.05262084827794480.973689575861028
2070.02587545567790880.05175091135581770.974124544322091
2080.02112759178456480.04225518356912960.978872408215435
2090.02378129740532920.04756259481065840.976218702594671
2100.01915517839810210.03831035679620420.980844821601898
2110.02139035470256810.04278070940513630.978609645297432
2120.03506278954798910.07012557909597820.964937210452011
2130.02758112357998320.05516224715996630.972418876420017
2140.0390742092644910.07814841852898210.960925790735509
2150.03356397054534870.06712794109069740.966436029454651
2160.02610726965357250.05221453930714510.973892730346427
2170.02838166391165240.05676332782330490.971618336088348
2180.02419706732053630.04839413464107260.975802932679464
2190.02171291425954840.04342582851909680.978287085740452
2200.0165634963670420.03312699273408410.983436503632958
2210.01475456911706780.02950913823413550.985245430882932
2220.01080421994946140.02160843989892280.989195780050539
2230.008063122662500350.01612624532500070.9919368773375
2240.006484402945052410.01296880589010480.993515597054948
2250.005643196376983360.01128639275396670.994356803623017
2260.01320820823732670.02641641647465340.986791791762673
2270.01012979993777090.02025959987554180.989870200062229
2280.007288330409744780.01457666081948960.992711669590255
2290.005295989376231690.01059197875246340.994704010623768
2300.003819611421147790.007639222842295580.996180388578852
2310.00381614509713460.007632290194269210.996183854902865
2320.007214544870803660.01442908974160730.992785455129196
2330.0343472305702430.0686944611404860.965652769429757
2340.04984742900250510.09969485800501030.950152570997495
2350.03705404828101510.07410809656203030.962945951718985
2360.03303523992948680.06607047985897370.966964760070513
2370.2355211277066180.4710422554132350.764478872293382
2380.1914661743819790.3829323487639590.808533825618021
2390.1715649759491880.3431299518983760.828435024050812
2400.1350541089086740.2701082178173480.864945891091326
2410.1077019077884680.2154038155769370.892298092211532
2420.08950279867095360.1790055973419070.910497201329046
2430.08329965778462320.1665993155692460.916700342215377
2440.1471341254706990.2942682509413980.852865874529301
2450.1318481036803580.2636962073607170.868151896319642
2460.1052829482080390.2105658964160780.894717051791961
2470.07411728695437830.1482345739087570.925882713045622
2480.0637193287758310.1274386575516620.936280671224169
2490.05989079371037180.1197815874207440.940109206289628
2500.07455333108045210.1491066621609040.925446668919548
2510.04421809287507040.08843618575014070.95578190712493
2520.1091953087358590.2183906174717190.890804691264141
2530.2310653138259990.4621306276519980.768934686174001






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level100.0411522633744856NOK
5% type I error level890.366255144032922NOK
10% type I error level1340.551440329218107NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 10 & 0.0411522633744856 & NOK \tabularnewline
5% type I error level & 89 & 0.366255144032922 & NOK \tabularnewline
10% type I error level & 134 & 0.551440329218107 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204206&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]10[/C][C]0.0411522633744856[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]89[/C][C]0.366255144032922[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]134[/C][C]0.551440329218107[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204206&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204206&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level100.0411522633744856NOK
5% type I error level890.366255144032922NOK
10% type I error level1340.551440329218107NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}