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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 24 Nov 2011 13:34:01 -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/2011/Nov/24/t1322159665vi1w4177bu3jq3v.htm/, Retrieved Thu, 28 Mar 2024 21:07:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147137, Retrieved Thu, 28 Mar 2024 21:07:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact62
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-11-24 18:34:01] [46e17293cd0520480fa187e99449b207] [Current]
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Dataseries X:
14	13	41	12	53
18	16	39	11	86
11	19	30	14	66
12	15	31	12	67
16	14	34	21	76
18	13	35	12	78
14	19	39	22	53
14	15	34	11	80
15	14	36	10	74
15	15	37	13	76
17	16	38	10	79
19	16	36	8	54
10	16	38	15	67
16	16	39	14	54
18	17	33	10	87
14	15	32	14	58
14	15	36	14	75
17	20	38	11	88
14	18	39	10	64
16	16	32	13	57
18	16	32	7	66
11	16	31	14	68
14	19	39	12	54
12	16	37	14	56
17	17	39	11	86
9	17	41	9	80
16	16	36	11	76
14	15	33	15	69
15	16	33	14	78
11	14	34	13	67
16	15	31	9	80
13	12	27	15	54
17	14	37	10	71
15	16	34	11	84
14	14	34	13	74
16	7	32	8	71
9	10	29	20	63
15	14	36	12	71
17	16	29	10	76
13	16	35	10	69
15	16	37	9	74
16	14	34	14	75
16	20	38	8	54
12	14	35	14	52
12	14	38	11	69
11	11	37	13	68
15	14	38	9	65
15	15	33	11	75
17	16	36	15	74
13	14	38	11	75
16	16	32	10	72
14	14	32	14	67
11	12	32	18	63
12	16	34	14	62
12	9	32	11	63
15	14	37	12	76
16	16	39	13	74
15	16	29	9	67
12	15	37	10	73
12	16	35	15	70
8	12	30	20	53
13	16	38	12	77
11	16	34	12	77
14	14	31	14	52
15	16	34	13	54
10	17	35	11	80
11	18	36	17	66
12	18	30	12	73
15	12	39	13	63
15	16	35	14	69
14	10	38	13	67
16	14	31	15	54
15	18	34	13	81
15	18	38	10	69
13	16	34	11	84
12	17	39	19	80
17	16	37	13	70
13	16	34	17	69
15	13	28	13	77
13	16	37	9	54
15	16	33	11	79
16	20	37	10	30
15	16	35	9	71
16	15	37	12	73
15	15	32	12	72
14	16	33	13	77
15	14	38	13	75
14	16	33	12	69
13	16	29	15	54
7	15	33	22	70
17	12	31	13	73
13	17	36	15	54
15	16	35	13	77
14	15	32	15	82
13	13	29	10	80
16	16	39	11	80
12	16	37	16	69
14	16	35	11	78
17	16	37	11	81
15	14	32	10	76
17	16	38	10	76
12	16	37	16	73
16	20	36	12	85
11	15	32	11	66
15	16	33	16	79
9	13	40	19	68
16	17	38	11	76
15	16	41	16	71
10	16	36	15	54
10	12	43	24	46
15	16	30	14	82
11	16	31	15	74
13	17	32	11	88
14	13	32	15	38
18	12	37	12	76
16	18	37	10	86
14	14	33	14	54
14	14	34	13	70
14	13	33	9	69
14	16	38	15	90
12	13	33	15	54
14	16	31	14	76
15	13	38	11	89
15	16	37	8	76
15	15	33	11	73
13	16	31	11	79
17	15	39	8	90
17	17	44	10	74
19	15	33	11	81
15	12	35	13	72
13	16	32	11	71
9	10	28	20	66
15	16	40	10	77
15	12	27	15	65
15	14	37	12	74
16	15	32	14	82
11	13	28	23	54
14	15	34	14	63
11	11	30	16	54
15	12	35	11	64
13	8	31	12	69
15	16	32	10	54
16	15	30	14	84
14	17	30	12	86
15	16	31	12	77
16	10	40	11	89
16	18	32	12	76
11	13	36	13	60
12	16	32	11	75
9	13	35	19	73
16	10	38	12	85
13	15	42	17	79
16	16	34	9	71
12	16	35	12	72
9	14	35	19	69
13	10	33	18	78
13	17	36	15	54
14	13	32	14	69
19	15	33	11	81
13	16	34	9	84
12	12	32	18	84
13	13	34	16	69




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 8 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=147137&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=147137&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147137&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 14.8142535198111 + 0.0562934670907469Learning[t] + 0.0478898476578772Connected[t] -0.341950967442765Depression[t] + 0.0264152274333828`Belonging `[t] -1.04946913607642M1[t] -0.72472249767663M2[t] + 0.00254117037458653M3[t] -1.16696732931539M4[t] -0.065593006997021M5[t] -1.20991410656088M6[t] + 0.0952095811044339M7[t] -0.92323399118414M8[t] + 0.306604585858594M9[t] -0.964929050551642M10[t] -1.11710048861157M11[t] -0.0018864249031574t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Happiness[t] =  +  14.8142535198111 +  0.0562934670907469Learning[t] +  0.0478898476578772Connected[t] -0.341950967442765Depression[t] +  0.0264152274333828`Belonging
`[t] -1.04946913607642M1[t] -0.72472249767663M2[t] +  0.00254117037458653M3[t] -1.16696732931539M4[t] -0.065593006997021M5[t] -1.20991410656088M6[t] +  0.0952095811044339M7[t] -0.92323399118414M8[t] +  0.306604585858594M9[t] -0.964929050551642M10[t] -1.11710048861157M11[t] -0.0018864249031574t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147137&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Happiness[t] =  +  14.8142535198111 +  0.0562934670907469Learning[t] +  0.0478898476578772Connected[t] -0.341950967442765Depression[t] +  0.0264152274333828`Belonging
`[t] -1.04946913607642M1[t] -0.72472249767663M2[t] +  0.00254117037458653M3[t] -1.16696732931539M4[t] -0.065593006997021M5[t] -1.20991410656088M6[t] +  0.0952095811044339M7[t] -0.92323399118414M8[t] +  0.306604585858594M9[t] -0.964929050551642M10[t] -1.11710048861157M11[t] -0.0018864249031574t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147137&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147137&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 14.8142535198111 + 0.0562934670907469Learning[t] + 0.0478898476578772Connected[t] -0.341950967442765Depression[t] + 0.0264152274333828`Belonging `[t] -1.04946913607642M1[t] -0.72472249767663M2[t] + 0.00254117037458653M3[t] -1.16696732931539M4[t] -0.065593006997021M5[t] -1.20991410656088M6[t] + 0.0952095811044339M7[t] -0.92323399118414M8[t] + 0.306604585858594M9[t] -0.964929050551642M10[t] -1.11710048861157M11[t] -0.0018864249031574t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)14.81425351981112.4015786.168500
Learning0.05629346709074690.0738540.76220.4471650.223582
Connected0.04788984765787720.0485850.98570.3259250.162963
Depression-0.3419509674427650.055195-6.195400
`Belonging `0.02641522743338280.0156971.68280.0945640.047282
M1-1.049469136076420.754703-1.39060.1664860.083243
M2-0.724722497676630.752213-0.96350.3369240.168462
M30.002541170374586530.7681750.00330.9973650.498683
M4-1.166967329315390.754424-1.54680.1240830.062042
M5-0.0655930069970210.755473-0.08680.9309310.465466
M6-1.209914106560880.754338-1.60390.1109030.055451
M70.09520958110443390.763110.12480.9008820.450441
M8-0.923233991184140.770852-1.19770.2329960.116498
M90.3066045858585940.7675530.39950.6901440.345072
M10-0.9649290505516420.766731-1.25850.2102340.105117
M11-1.117100488611570.767795-1.45490.1478450.073923
t-0.00188642490315740.003412-0.55280.5812290.290614

\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) & 14.8142535198111 & 2.401578 & 6.1685 & 0 & 0 \tabularnewline
Learning & 0.0562934670907469 & 0.073854 & 0.7622 & 0.447165 & 0.223582 \tabularnewline
Connected & 0.0478898476578772 & 0.048585 & 0.9857 & 0.325925 & 0.162963 \tabularnewline
Depression & -0.341950967442765 & 0.055195 & -6.1954 & 0 & 0 \tabularnewline
`Belonging
` & 0.0264152274333828 & 0.015697 & 1.6828 & 0.094564 & 0.047282 \tabularnewline
M1 & -1.04946913607642 & 0.754703 & -1.3906 & 0.166486 & 0.083243 \tabularnewline
M2 & -0.72472249767663 & 0.752213 & -0.9635 & 0.336924 & 0.168462 \tabularnewline
M3 & 0.00254117037458653 & 0.768175 & 0.0033 & 0.997365 & 0.498683 \tabularnewline
M4 & -1.16696732931539 & 0.754424 & -1.5468 & 0.124083 & 0.062042 \tabularnewline
M5 & -0.065593006997021 & 0.755473 & -0.0868 & 0.930931 & 0.465466 \tabularnewline
M6 & -1.20991410656088 & 0.754338 & -1.6039 & 0.110903 & 0.055451 \tabularnewline
M7 & 0.0952095811044339 & 0.76311 & 0.1248 & 0.900882 & 0.450441 \tabularnewline
M8 & -0.92323399118414 & 0.770852 & -1.1977 & 0.232996 & 0.116498 \tabularnewline
M9 & 0.306604585858594 & 0.767553 & 0.3995 & 0.690144 & 0.345072 \tabularnewline
M10 & -0.964929050551642 & 0.766731 & -1.2585 & 0.210234 & 0.105117 \tabularnewline
M11 & -1.11710048861157 & 0.767795 & -1.4549 & 0.147845 & 0.073923 \tabularnewline
t & -0.0018864249031574 & 0.003412 & -0.5528 & 0.581229 & 0.290614 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147137&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]14.8142535198111[/C][C]2.401578[/C][C]6.1685[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Learning[/C][C]0.0562934670907469[/C][C]0.073854[/C][C]0.7622[/C][C]0.447165[/C][C]0.223582[/C][/ROW]
[ROW][C]Connected[/C][C]0.0478898476578772[/C][C]0.048585[/C][C]0.9857[/C][C]0.325925[/C][C]0.162963[/C][/ROW]
[ROW][C]Depression[/C][C]-0.341950967442765[/C][C]0.055195[/C][C]-6.1954[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Belonging
`[/C][C]0.0264152274333828[/C][C]0.015697[/C][C]1.6828[/C][C]0.094564[/C][C]0.047282[/C][/ROW]
[ROW][C]M1[/C][C]-1.04946913607642[/C][C]0.754703[/C][C]-1.3906[/C][C]0.166486[/C][C]0.083243[/C][/ROW]
[ROW][C]M2[/C][C]-0.72472249767663[/C][C]0.752213[/C][C]-0.9635[/C][C]0.336924[/C][C]0.168462[/C][/ROW]
[ROW][C]M3[/C][C]0.00254117037458653[/C][C]0.768175[/C][C]0.0033[/C][C]0.997365[/C][C]0.498683[/C][/ROW]
[ROW][C]M4[/C][C]-1.16696732931539[/C][C]0.754424[/C][C]-1.5468[/C][C]0.124083[/C][C]0.062042[/C][/ROW]
[ROW][C]M5[/C][C]-0.065593006997021[/C][C]0.755473[/C][C]-0.0868[/C][C]0.930931[/C][C]0.465466[/C][/ROW]
[ROW][C]M6[/C][C]-1.20991410656088[/C][C]0.754338[/C][C]-1.6039[/C][C]0.110903[/C][C]0.055451[/C][/ROW]
[ROW][C]M7[/C][C]0.0952095811044339[/C][C]0.76311[/C][C]0.1248[/C][C]0.900882[/C][C]0.450441[/C][/ROW]
[ROW][C]M8[/C][C]-0.92323399118414[/C][C]0.770852[/C][C]-1.1977[/C][C]0.232996[/C][C]0.116498[/C][/ROW]
[ROW][C]M9[/C][C]0.306604585858594[/C][C]0.767553[/C][C]0.3995[/C][C]0.690144[/C][C]0.345072[/C][/ROW]
[ROW][C]M10[/C][C]-0.964929050551642[/C][C]0.766731[/C][C]-1.2585[/C][C]0.210234[/C][C]0.105117[/C][/ROW]
[ROW][C]M11[/C][C]-1.11710048861157[/C][C]0.767795[/C][C]-1.4549[/C][C]0.147845[/C][C]0.073923[/C][/ROW]
[ROW][C]t[/C][C]-0.0018864249031574[/C][C]0.003412[/C][C]-0.5528[/C][C]0.581229[/C][C]0.290614[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147137&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147137&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)14.81425351981112.4015786.168500
Learning0.05629346709074690.0738540.76220.4471650.223582
Connected0.04788984765787720.0485850.98570.3259250.162963
Depression-0.3419509674427650.055195-6.195400
`Belonging `0.02641522743338280.0156971.68280.0945640.047282
M1-1.049469136076420.754703-1.39060.1664860.083243
M2-0.724722497676630.752213-0.96350.3369240.168462
M30.002541170374586530.7681750.00330.9973650.498683
M4-1.166967329315390.754424-1.54680.1240830.062042
M5-0.0655930069970210.755473-0.08680.9309310.465466
M6-1.209914106560880.754338-1.60390.1109030.055451
M70.09520958110443390.763110.12480.9008820.450441
M8-0.923233991184140.770852-1.19770.2329960.116498
M90.3066045858585940.7675530.39950.6901440.345072
M10-0.9649290505516420.766731-1.25850.2102340.105117
M11-1.117100488611570.767795-1.45490.1478450.073923
t-0.00188642490315740.003412-0.55280.5812290.290614







Multiple Linear Regression - Regression Statistics
Multiple R0.616433194817533
R-squared0.379989883672951
Adjusted R-squared0.311574974285138
F-TEST (value)5.55419698824659
F-TEST (DF numerator)16
F-TEST (DF denominator)145
p-value3.63666030622767e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.93955402328856
Sum Squared Residuals545.471122341953

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.616433194817533 \tabularnewline
R-squared & 0.379989883672951 \tabularnewline
Adjusted R-squared & 0.311574974285138 \tabularnewline
F-TEST (value) & 5.55419698824659 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 145 \tabularnewline
p-value & 3.63666030622767e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.93955402328856 \tabularnewline
Sum Squared Residuals & 545.471122341953 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147137&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.616433194817533[/C][/ROW]
[ROW][C]R-squared[/C][C]0.379989883672951[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.311574974285138[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.55419698824659[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]145[/C][/ROW]
[ROW][C]p-value[/C][C]3.63666030622767e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.93955402328856[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]545.471122341953[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147137&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147137&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.616433194817533
R-squared0.379989883672951
Adjusted R-squared0.311574974285138
F-TEST (value)5.55419698824659
F-TEST (DF numerator)16
F-TEST (DF denominator)145
p-value3.63666030622767e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.93955402328856
Sum Squared Residuals545.471122341953







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11413.75479222964030.245207770359684
21815.36440662183782.63559337816221
31114.2734981863412-3.27349818634124
41213.6351364033619-1.63513640336191
51611.98217871657564.01782128342443
61813.95795673452734.04204326547267
71411.71062383020332.28937616979674
81414.7003425089309-0.700342508930904
91516.151240492138-1.15124049213796
101514.00898129811170.99101870188834
111715.06420533452561.93579466547437
121916.10716095196932.89283904803074
131013.1013262708401-3.10132627084006
141613.47062934280342.52937065719664
151816.20446734216761.79553265783241
161412.73875017039591.26124982960407
171414.4788563248102-0.478856324810153
181715.07914669007491.92085330992511
191416.025672375355-2.02567237535501
201613.34676701601472.65323298398533
211816.86416201971131.13583798028872
221113.2020257935074-2.20202579350743
231413.91405586389780.085944136102234
241214.1335383509994-2.13353835099942
251715.05256567775611.94743432224387
26915.9966161568537-6.99661615685375
271615.63668785000260.363312149997389
281412.71261945354041.28738054645964
291514.44808883238950.551911167610472
301113.2885676870744-2.28856768707445
311616.2156307003588-0.215630700358756
321312.09635919333870.903640806661264
331716.07461045981990.925389540180092
341514.77155477890560.228445221094412
351413.55685577254160.443144227458352
361615.81274502621280.187254973787242
37910.4718668947516-1.47186689475155
381514.30205946922550.697940530774509
391715.62077278500231.37922721499765
401314.5518103543228-1.5518103543228
411516.2211050516634-1.22110505166344
421613.13530144026092.86469855973915
431616.4648449247546-0.464844924754555
441212.8585483225214-0.858548322521357
451215.7050817863304-3.70508178633036
461113.5045743137679-2.50457431376794
471514.85584488720590.144155112794118
481515.368153519164-0.368153519163958
491713.12254187104433.87745812895568
501314.8228139428797-1.82281394287965
511615.63614431940460.363855680595442
521412.8522824536921.14771754630804
531112.3657186374211-1.36571863742109
541212.8818533189705-0.881853318970485
551214.7475247465433-2.74752474654334
561514.24955831228590.750441687714062
571615.29109567161320.708904328386767
581514.72167441145840.278325588541553
591214.7109822598252-2.71098225982516
601213.9977095757946-1.9977095757946
61810.3229172045813-2.32291720458132
621314.6236432656473-1.62364326564726
631115.1574611181638-4.15746111816381
641412.38552709569541.61447290430455
651514.13605289257530.863947107424682
661014.4647265310104-4.4647265310104
671113.4506281197972-2.45062811979724
681214.0376204659057-2.03762046590575
691514.75271720264510.247282797354854
701513.32945201622081.67054798377924
711413.27042340626280.729576593737176
721613.24828251320962.75171748679042
731513.96288343915351.03711656084651
741515.1861732164093-0.18617321640933
751315.6616815788024-2.66168157880236
761211.94476071031370.055239289686289
771714.67972897564522.32027102435482
781311.99563281100011.00436718899991
791514.42177627578090.578223724219132
801314.7615889475855-1.76158894758553
811515.7744604600426-0.774460460042635
821613.96537848093072.03462151906925
831514.91534234650040.0846576534996258
841615.09702019097230.90297980902773
851513.77980016426991.22019983573008
861413.99696886223930.00303113776067021
871514.79637795462850.203622045371485
881413.681580328770.318419671230044
891313.1674275217246-0.167427521724624
90710.1854727876331-3.18547278763314
911714.38085434309232.61914565690767
921312.69564966352390.30435033647608
931515.1108706667682-0.110870666768206
941413.08566179767180.914338202328181
951314.3322718399007-1.33227183990066
961615.75331381401730.246686185982674
971212.605856218741-0.605856218740962
981414.7804286210361-0.780428621036108
991715.68083124180011.31916875819993
1001514.36727497501190.632725024988095
1011715.86668889255591.13331110744413
1021212.5416400334742-0.541640033474242
1031615.70694791591320.293052084086837
1041114.0536528388447-3.05365283884468
1051514.0194314251530.980568574846966
106911.595939492077-2.59593949207704
1071614.51820536117041.48179463882964
1081513.87896452638091.12103547361908
1091012.4810518281872-2.48105182818722
110109.625086580474060.374913419525941
1111514.32352753446210.676472465537871
1121112.646749670617-1.64674967061704
1131315.5880379366193-2.5880379366193
1141411.52809130234912.47190869765091
1151815.04411588110672.95588411889327
1161615.30960089567880.690399104321161
1171413.90772864118460.0922713588153874
1181413.4467930339060.553206966094015
1191414.5299404985319-0.529940498531948
1201414.5564981732464-0.55649817324644
1211212.1458647851035-0.145864785103458
1221413.46491167553380.535088324466238
1231515.725888309977-0.72588830997699
1241515.3579388846925-0.357938884692536
1251515.1044753397571-0.104475339757052
1261314.0772729516653-1.07727295166532
1271717.0237559326953-0.0237559326952548
1281715.24891653415481.75108346584525
1291915.68044905246713.3195509475329
1301513.41228930341121.58771069658876
1311313.9972224732897-0.997222473289658
132911.3734814996703-2.37348149967029
1331514.94464208932460.0553579106754226
1341512.39302284849142.6069771515086
1351514.97347545162850.0265245483715351
1361613.14634464041822.85365535958178
1371110.12450113790080.875498862099166
1381412.69351538744791.3064846125521
1391112.6583804094296-1.65838040942959
1401513.90770022916561.09229977083436
1411314.5090442920349-1.50904429203487
1421514.02153533849010.978464661509882
1431613.1400572663512.85994273364905
1441415.1045906539932-1.10459065399316
1451513.80709442668031.19290557331973
1461614.88213616319671.11786383680333
1471614.98939343773091.01060656226906
1481112.9634959619387-1.96349596193869
1491215.1204352163809-3.12043521638091
150911.1605786392064-2.1605786392064
1511615.14924454496990.850755455030106
1521312.73369507204930.266304927950715
1531616.1591078300916-0.159107830091647
1541213.9341399415412-1.93413994154122
155911.1945926899971-2.19459268999714
1561312.568541204370.43145879562998
1571312.44679689992640.553203100073588
1581413.09110323337210.908896766627947
1591915.31979288988843.68020711011163
1601315.0157288972295-2.01572889722954
1611212.7167045239811-0.716704523981125
1621312.01024368530540.989756314694604

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 13.7547922296403 & 0.245207770359684 \tabularnewline
2 & 18 & 15.3644066218378 & 2.63559337816221 \tabularnewline
3 & 11 & 14.2734981863412 & -3.27349818634124 \tabularnewline
4 & 12 & 13.6351364033619 & -1.63513640336191 \tabularnewline
5 & 16 & 11.9821787165756 & 4.01782128342443 \tabularnewline
6 & 18 & 13.9579567345273 & 4.04204326547267 \tabularnewline
7 & 14 & 11.7106238302033 & 2.28937616979674 \tabularnewline
8 & 14 & 14.7003425089309 & -0.700342508930904 \tabularnewline
9 & 15 & 16.151240492138 & -1.15124049213796 \tabularnewline
10 & 15 & 14.0089812981117 & 0.99101870188834 \tabularnewline
11 & 17 & 15.0642053345256 & 1.93579466547437 \tabularnewline
12 & 19 & 16.1071609519693 & 2.89283904803074 \tabularnewline
13 & 10 & 13.1013262708401 & -3.10132627084006 \tabularnewline
14 & 16 & 13.4706293428034 & 2.52937065719664 \tabularnewline
15 & 18 & 16.2044673421676 & 1.79553265783241 \tabularnewline
16 & 14 & 12.7387501703959 & 1.26124982960407 \tabularnewline
17 & 14 & 14.4788563248102 & -0.478856324810153 \tabularnewline
18 & 17 & 15.0791466900749 & 1.92085330992511 \tabularnewline
19 & 14 & 16.025672375355 & -2.02567237535501 \tabularnewline
20 & 16 & 13.3467670160147 & 2.65323298398533 \tabularnewline
21 & 18 & 16.8641620197113 & 1.13583798028872 \tabularnewline
22 & 11 & 13.2020257935074 & -2.20202579350743 \tabularnewline
23 & 14 & 13.9140558638978 & 0.085944136102234 \tabularnewline
24 & 12 & 14.1335383509994 & -2.13353835099942 \tabularnewline
25 & 17 & 15.0525656777561 & 1.94743432224387 \tabularnewline
26 & 9 & 15.9966161568537 & -6.99661615685375 \tabularnewline
27 & 16 & 15.6366878500026 & 0.363312149997389 \tabularnewline
28 & 14 & 12.7126194535404 & 1.28738054645964 \tabularnewline
29 & 15 & 14.4480888323895 & 0.551911167610472 \tabularnewline
30 & 11 & 13.2885676870744 & -2.28856768707445 \tabularnewline
31 & 16 & 16.2156307003588 & -0.215630700358756 \tabularnewline
32 & 13 & 12.0963591933387 & 0.903640806661264 \tabularnewline
33 & 17 & 16.0746104598199 & 0.925389540180092 \tabularnewline
34 & 15 & 14.7715547789056 & 0.228445221094412 \tabularnewline
35 & 14 & 13.5568557725416 & 0.443144227458352 \tabularnewline
36 & 16 & 15.8127450262128 & 0.187254973787242 \tabularnewline
37 & 9 & 10.4718668947516 & -1.47186689475155 \tabularnewline
38 & 15 & 14.3020594692255 & 0.697940530774509 \tabularnewline
39 & 17 & 15.6207727850023 & 1.37922721499765 \tabularnewline
40 & 13 & 14.5518103543228 & -1.5518103543228 \tabularnewline
41 & 15 & 16.2211050516634 & -1.22110505166344 \tabularnewline
42 & 16 & 13.1353014402609 & 2.86469855973915 \tabularnewline
43 & 16 & 16.4648449247546 & -0.464844924754555 \tabularnewline
44 & 12 & 12.8585483225214 & -0.858548322521357 \tabularnewline
45 & 12 & 15.7050817863304 & -3.70508178633036 \tabularnewline
46 & 11 & 13.5045743137679 & -2.50457431376794 \tabularnewline
47 & 15 & 14.8558448872059 & 0.144155112794118 \tabularnewline
48 & 15 & 15.368153519164 & -0.368153519163958 \tabularnewline
49 & 17 & 13.1225418710443 & 3.87745812895568 \tabularnewline
50 & 13 & 14.8228139428797 & -1.82281394287965 \tabularnewline
51 & 16 & 15.6361443194046 & 0.363855680595442 \tabularnewline
52 & 14 & 12.852282453692 & 1.14771754630804 \tabularnewline
53 & 11 & 12.3657186374211 & -1.36571863742109 \tabularnewline
54 & 12 & 12.8818533189705 & -0.881853318970485 \tabularnewline
55 & 12 & 14.7475247465433 & -2.74752474654334 \tabularnewline
56 & 15 & 14.2495583122859 & 0.750441687714062 \tabularnewline
57 & 16 & 15.2910956716132 & 0.708904328386767 \tabularnewline
58 & 15 & 14.7216744114584 & 0.278325588541553 \tabularnewline
59 & 12 & 14.7109822598252 & -2.71098225982516 \tabularnewline
60 & 12 & 13.9977095757946 & -1.9977095757946 \tabularnewline
61 & 8 & 10.3229172045813 & -2.32291720458132 \tabularnewline
62 & 13 & 14.6236432656473 & -1.62364326564726 \tabularnewline
63 & 11 & 15.1574611181638 & -4.15746111816381 \tabularnewline
64 & 14 & 12.3855270956954 & 1.61447290430455 \tabularnewline
65 & 15 & 14.1360528925753 & 0.863947107424682 \tabularnewline
66 & 10 & 14.4647265310104 & -4.4647265310104 \tabularnewline
67 & 11 & 13.4506281197972 & -2.45062811979724 \tabularnewline
68 & 12 & 14.0376204659057 & -2.03762046590575 \tabularnewline
69 & 15 & 14.7527172026451 & 0.247282797354854 \tabularnewline
70 & 15 & 13.3294520162208 & 1.67054798377924 \tabularnewline
71 & 14 & 13.2704234062628 & 0.729576593737176 \tabularnewline
72 & 16 & 13.2482825132096 & 2.75171748679042 \tabularnewline
73 & 15 & 13.9628834391535 & 1.03711656084651 \tabularnewline
74 & 15 & 15.1861732164093 & -0.18617321640933 \tabularnewline
75 & 13 & 15.6616815788024 & -2.66168157880236 \tabularnewline
76 & 12 & 11.9447607103137 & 0.055239289686289 \tabularnewline
77 & 17 & 14.6797289756452 & 2.32027102435482 \tabularnewline
78 & 13 & 11.9956328110001 & 1.00436718899991 \tabularnewline
79 & 15 & 14.4217762757809 & 0.578223724219132 \tabularnewline
80 & 13 & 14.7615889475855 & -1.76158894758553 \tabularnewline
81 & 15 & 15.7744604600426 & -0.774460460042635 \tabularnewline
82 & 16 & 13.9653784809307 & 2.03462151906925 \tabularnewline
83 & 15 & 14.9153423465004 & 0.0846576534996258 \tabularnewline
84 & 16 & 15.0970201909723 & 0.90297980902773 \tabularnewline
85 & 15 & 13.7798001642699 & 1.22019983573008 \tabularnewline
86 & 14 & 13.9969688622393 & 0.00303113776067021 \tabularnewline
87 & 15 & 14.7963779546285 & 0.203622045371485 \tabularnewline
88 & 14 & 13.68158032877 & 0.318419671230044 \tabularnewline
89 & 13 & 13.1674275217246 & -0.167427521724624 \tabularnewline
90 & 7 & 10.1854727876331 & -3.18547278763314 \tabularnewline
91 & 17 & 14.3808543430923 & 2.61914565690767 \tabularnewline
92 & 13 & 12.6956496635239 & 0.30435033647608 \tabularnewline
93 & 15 & 15.1108706667682 & -0.110870666768206 \tabularnewline
94 & 14 & 13.0856617976718 & 0.914338202328181 \tabularnewline
95 & 13 & 14.3322718399007 & -1.33227183990066 \tabularnewline
96 & 16 & 15.7533138140173 & 0.246686185982674 \tabularnewline
97 & 12 & 12.605856218741 & -0.605856218740962 \tabularnewline
98 & 14 & 14.7804286210361 & -0.780428621036108 \tabularnewline
99 & 17 & 15.6808312418001 & 1.31916875819993 \tabularnewline
100 & 15 & 14.3672749750119 & 0.632725024988095 \tabularnewline
101 & 17 & 15.8666888925559 & 1.13331110744413 \tabularnewline
102 & 12 & 12.5416400334742 & -0.541640033474242 \tabularnewline
103 & 16 & 15.7069479159132 & 0.293052084086837 \tabularnewline
104 & 11 & 14.0536528388447 & -3.05365283884468 \tabularnewline
105 & 15 & 14.019431425153 & 0.980568574846966 \tabularnewline
106 & 9 & 11.595939492077 & -2.59593949207704 \tabularnewline
107 & 16 & 14.5182053611704 & 1.48179463882964 \tabularnewline
108 & 15 & 13.8789645263809 & 1.12103547361908 \tabularnewline
109 & 10 & 12.4810518281872 & -2.48105182818722 \tabularnewline
110 & 10 & 9.62508658047406 & 0.374913419525941 \tabularnewline
111 & 15 & 14.3235275344621 & 0.676472465537871 \tabularnewline
112 & 11 & 12.646749670617 & -1.64674967061704 \tabularnewline
113 & 13 & 15.5880379366193 & -2.5880379366193 \tabularnewline
114 & 14 & 11.5280913023491 & 2.47190869765091 \tabularnewline
115 & 18 & 15.0441158811067 & 2.95588411889327 \tabularnewline
116 & 16 & 15.3096008956788 & 0.690399104321161 \tabularnewline
117 & 14 & 13.9077286411846 & 0.0922713588153874 \tabularnewline
118 & 14 & 13.446793033906 & 0.553206966094015 \tabularnewline
119 & 14 & 14.5299404985319 & -0.529940498531948 \tabularnewline
120 & 14 & 14.5564981732464 & -0.55649817324644 \tabularnewline
121 & 12 & 12.1458647851035 & -0.145864785103458 \tabularnewline
122 & 14 & 13.4649116755338 & 0.535088324466238 \tabularnewline
123 & 15 & 15.725888309977 & -0.72588830997699 \tabularnewline
124 & 15 & 15.3579388846925 & -0.357938884692536 \tabularnewline
125 & 15 & 15.1044753397571 & -0.104475339757052 \tabularnewline
126 & 13 & 14.0772729516653 & -1.07727295166532 \tabularnewline
127 & 17 & 17.0237559326953 & -0.0237559326952548 \tabularnewline
128 & 17 & 15.2489165341548 & 1.75108346584525 \tabularnewline
129 & 19 & 15.6804490524671 & 3.3195509475329 \tabularnewline
130 & 15 & 13.4122893034112 & 1.58771069658876 \tabularnewline
131 & 13 & 13.9972224732897 & -0.997222473289658 \tabularnewline
132 & 9 & 11.3734814996703 & -2.37348149967029 \tabularnewline
133 & 15 & 14.9446420893246 & 0.0553579106754226 \tabularnewline
134 & 15 & 12.3930228484914 & 2.6069771515086 \tabularnewline
135 & 15 & 14.9734754516285 & 0.0265245483715351 \tabularnewline
136 & 16 & 13.1463446404182 & 2.85365535958178 \tabularnewline
137 & 11 & 10.1245011379008 & 0.875498862099166 \tabularnewline
138 & 14 & 12.6935153874479 & 1.3064846125521 \tabularnewline
139 & 11 & 12.6583804094296 & -1.65838040942959 \tabularnewline
140 & 15 & 13.9077002291656 & 1.09229977083436 \tabularnewline
141 & 13 & 14.5090442920349 & -1.50904429203487 \tabularnewline
142 & 15 & 14.0215353384901 & 0.978464661509882 \tabularnewline
143 & 16 & 13.140057266351 & 2.85994273364905 \tabularnewline
144 & 14 & 15.1045906539932 & -1.10459065399316 \tabularnewline
145 & 15 & 13.8070944266803 & 1.19290557331973 \tabularnewline
146 & 16 & 14.8821361631967 & 1.11786383680333 \tabularnewline
147 & 16 & 14.9893934377309 & 1.01060656226906 \tabularnewline
148 & 11 & 12.9634959619387 & -1.96349596193869 \tabularnewline
149 & 12 & 15.1204352163809 & -3.12043521638091 \tabularnewline
150 & 9 & 11.1605786392064 & -2.1605786392064 \tabularnewline
151 & 16 & 15.1492445449699 & 0.850755455030106 \tabularnewline
152 & 13 & 12.7336950720493 & 0.266304927950715 \tabularnewline
153 & 16 & 16.1591078300916 & -0.159107830091647 \tabularnewline
154 & 12 & 13.9341399415412 & -1.93413994154122 \tabularnewline
155 & 9 & 11.1945926899971 & -2.19459268999714 \tabularnewline
156 & 13 & 12.56854120437 & 0.43145879562998 \tabularnewline
157 & 13 & 12.4467968999264 & 0.553203100073588 \tabularnewline
158 & 14 & 13.0911032333721 & 0.908896766627947 \tabularnewline
159 & 19 & 15.3197928898884 & 3.68020711011163 \tabularnewline
160 & 13 & 15.0157288972295 & -2.01572889722954 \tabularnewline
161 & 12 & 12.7167045239811 & -0.716704523981125 \tabularnewline
162 & 13 & 12.0102436853054 & 0.989756314694604 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147137&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]14[/C][C]13.7547922296403[/C][C]0.245207770359684[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]15.3644066218378[/C][C]2.63559337816221[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]14.2734981863412[/C][C]-3.27349818634124[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]13.6351364033619[/C][C]-1.63513640336191[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]11.9821787165756[/C][C]4.01782128342443[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]13.9579567345273[/C][C]4.04204326547267[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]11.7106238302033[/C][C]2.28937616979674[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]14.7003425089309[/C][C]-0.700342508930904[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]16.151240492138[/C][C]-1.15124049213796[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.0089812981117[/C][C]0.99101870188834[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]15.0642053345256[/C][C]1.93579466547437[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]16.1071609519693[/C][C]2.89283904803074[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]13.1013262708401[/C][C]-3.10132627084006[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]13.4706293428034[/C][C]2.52937065719664[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]16.2044673421676[/C][C]1.79553265783241[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]12.7387501703959[/C][C]1.26124982960407[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]14.4788563248102[/C][C]-0.478856324810153[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]15.0791466900749[/C][C]1.92085330992511[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]16.025672375355[/C][C]-2.02567237535501[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]13.3467670160147[/C][C]2.65323298398533[/C][/ROW]
[ROW][C]21[/C][C]18[/C][C]16.8641620197113[/C][C]1.13583798028872[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]13.2020257935074[/C][C]-2.20202579350743[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]13.9140558638978[/C][C]0.085944136102234[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]14.1335383509994[/C][C]-2.13353835099942[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.0525656777561[/C][C]1.94743432224387[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]15.9966161568537[/C][C]-6.99661615685375[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.6366878500026[/C][C]0.363312149997389[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]12.7126194535404[/C][C]1.28738054645964[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]14.4480888323895[/C][C]0.551911167610472[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]13.2885676870744[/C][C]-2.28856768707445[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]16.2156307003588[/C][C]-0.215630700358756[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]12.0963591933387[/C][C]0.903640806661264[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]16.0746104598199[/C][C]0.925389540180092[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]14.7715547789056[/C][C]0.228445221094412[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]13.5568557725416[/C][C]0.443144227458352[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]15.8127450262128[/C][C]0.187254973787242[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]10.4718668947516[/C][C]-1.47186689475155[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]14.3020594692255[/C][C]0.697940530774509[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]15.6207727850023[/C][C]1.37922721499765[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]14.5518103543228[/C][C]-1.5518103543228[/C][/ROW]
[ROW][C]41[/C][C]15[/C][C]16.2211050516634[/C][C]-1.22110505166344[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]13.1353014402609[/C][C]2.86469855973915[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]16.4648449247546[/C][C]-0.464844924754555[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]12.8585483225214[/C][C]-0.858548322521357[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]15.7050817863304[/C][C]-3.70508178633036[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]13.5045743137679[/C][C]-2.50457431376794[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]14.8558448872059[/C][C]0.144155112794118[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.368153519164[/C][C]-0.368153519163958[/C][/ROW]
[ROW][C]49[/C][C]17[/C][C]13.1225418710443[/C][C]3.87745812895568[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.8228139428797[/C][C]-1.82281394287965[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]15.6361443194046[/C][C]0.363855680595442[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]12.852282453692[/C][C]1.14771754630804[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]12.3657186374211[/C][C]-1.36571863742109[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]12.8818533189705[/C][C]-0.881853318970485[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]14.7475247465433[/C][C]-2.74752474654334[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]14.2495583122859[/C][C]0.750441687714062[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.2910956716132[/C][C]0.708904328386767[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]14.7216744114584[/C][C]0.278325588541553[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]14.7109822598252[/C][C]-2.71098225982516[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]13.9977095757946[/C][C]-1.9977095757946[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]10.3229172045813[/C][C]-2.32291720458132[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]14.6236432656473[/C][C]-1.62364326564726[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]15.1574611181638[/C][C]-4.15746111816381[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]12.3855270956954[/C][C]1.61447290430455[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]14.1360528925753[/C][C]0.863947107424682[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]14.4647265310104[/C][C]-4.4647265310104[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]13.4506281197972[/C][C]-2.45062811979724[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]14.0376204659057[/C][C]-2.03762046590575[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]14.7527172026451[/C][C]0.247282797354854[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]13.3294520162208[/C][C]1.67054798377924[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]13.2704234062628[/C][C]0.729576593737176[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]13.2482825132096[/C][C]2.75171748679042[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]13.9628834391535[/C][C]1.03711656084651[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]15.1861732164093[/C][C]-0.18617321640933[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]15.6616815788024[/C][C]-2.66168157880236[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]11.9447607103137[/C][C]0.055239289686289[/C][/ROW]
[ROW][C]77[/C][C]17[/C][C]14.6797289756452[/C][C]2.32027102435482[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]11.9956328110001[/C][C]1.00436718899991[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]14.4217762757809[/C][C]0.578223724219132[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]14.7615889475855[/C][C]-1.76158894758553[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]15.7744604600426[/C][C]-0.774460460042635[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]13.9653784809307[/C][C]2.03462151906925[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]14.9153423465004[/C][C]0.0846576534996258[/C][/ROW]
[ROW][C]84[/C][C]16[/C][C]15.0970201909723[/C][C]0.90297980902773[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]13.7798001642699[/C][C]1.22019983573008[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]13.9969688622393[/C][C]0.00303113776067021[/C][/ROW]
[ROW][C]87[/C][C]15[/C][C]14.7963779546285[/C][C]0.203622045371485[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]13.68158032877[/C][C]0.318419671230044[/C][/ROW]
[ROW][C]89[/C][C]13[/C][C]13.1674275217246[/C][C]-0.167427521724624[/C][/ROW]
[ROW][C]90[/C][C]7[/C][C]10.1854727876331[/C][C]-3.18547278763314[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]14.3808543430923[/C][C]2.61914565690767[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]12.6956496635239[/C][C]0.30435033647608[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]15.1108706667682[/C][C]-0.110870666768206[/C][/ROW]
[ROW][C]94[/C][C]14[/C][C]13.0856617976718[/C][C]0.914338202328181[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]14.3322718399007[/C][C]-1.33227183990066[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.7533138140173[/C][C]0.246686185982674[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]12.605856218741[/C][C]-0.605856218740962[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]14.7804286210361[/C][C]-0.780428621036108[/C][/ROW]
[ROW][C]99[/C][C]17[/C][C]15.6808312418001[/C][C]1.31916875819993[/C][/ROW]
[ROW][C]100[/C][C]15[/C][C]14.3672749750119[/C][C]0.632725024988095[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]15.8666888925559[/C][C]1.13331110744413[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]12.5416400334742[/C][C]-0.541640033474242[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]15.7069479159132[/C][C]0.293052084086837[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]14.0536528388447[/C][C]-3.05365283884468[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]14.019431425153[/C][C]0.980568574846966[/C][/ROW]
[ROW][C]106[/C][C]9[/C][C]11.595939492077[/C][C]-2.59593949207704[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]14.5182053611704[/C][C]1.48179463882964[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]13.8789645263809[/C][C]1.12103547361908[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]12.4810518281872[/C][C]-2.48105182818722[/C][/ROW]
[ROW][C]110[/C][C]10[/C][C]9.62508658047406[/C][C]0.374913419525941[/C][/ROW]
[ROW][C]111[/C][C]15[/C][C]14.3235275344621[/C][C]0.676472465537871[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]12.646749670617[/C][C]-1.64674967061704[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]15.5880379366193[/C][C]-2.5880379366193[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]11.5280913023491[/C][C]2.47190869765091[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]15.0441158811067[/C][C]2.95588411889327[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]15.3096008956788[/C][C]0.690399104321161[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.9077286411846[/C][C]0.0922713588153874[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.446793033906[/C][C]0.553206966094015[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]14.5299404985319[/C][C]-0.529940498531948[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]14.5564981732464[/C][C]-0.55649817324644[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]12.1458647851035[/C][C]-0.145864785103458[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]13.4649116755338[/C][C]0.535088324466238[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]15.725888309977[/C][C]-0.72588830997699[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]15.3579388846925[/C][C]-0.357938884692536[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.1044753397571[/C][C]-0.104475339757052[/C][/ROW]
[ROW][C]126[/C][C]13[/C][C]14.0772729516653[/C][C]-1.07727295166532[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]17.0237559326953[/C][C]-0.0237559326952548[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]15.2489165341548[/C][C]1.75108346584525[/C][/ROW]
[ROW][C]129[/C][C]19[/C][C]15.6804490524671[/C][C]3.3195509475329[/C][/ROW]
[ROW][C]130[/C][C]15[/C][C]13.4122893034112[/C][C]1.58771069658876[/C][/ROW]
[ROW][C]131[/C][C]13[/C][C]13.9972224732897[/C][C]-0.997222473289658[/C][/ROW]
[ROW][C]132[/C][C]9[/C][C]11.3734814996703[/C][C]-2.37348149967029[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]14.9446420893246[/C][C]0.0553579106754226[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]12.3930228484914[/C][C]2.6069771515086[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]14.9734754516285[/C][C]0.0265245483715351[/C][/ROW]
[ROW][C]136[/C][C]16[/C][C]13.1463446404182[/C][C]2.85365535958178[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]10.1245011379008[/C][C]0.875498862099166[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]12.6935153874479[/C][C]1.3064846125521[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]12.6583804094296[/C][C]-1.65838040942959[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]13.9077002291656[/C][C]1.09229977083436[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]14.5090442920349[/C][C]-1.50904429203487[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]14.0215353384901[/C][C]0.978464661509882[/C][/ROW]
[ROW][C]143[/C][C]16[/C][C]13.140057266351[/C][C]2.85994273364905[/C][/ROW]
[ROW][C]144[/C][C]14[/C][C]15.1045906539932[/C][C]-1.10459065399316[/C][/ROW]
[ROW][C]145[/C][C]15[/C][C]13.8070944266803[/C][C]1.19290557331973[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]14.8821361631967[/C][C]1.11786383680333[/C][/ROW]
[ROW][C]147[/C][C]16[/C][C]14.9893934377309[/C][C]1.01060656226906[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]12.9634959619387[/C][C]-1.96349596193869[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]15.1204352163809[/C][C]-3.12043521638091[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]11.1605786392064[/C][C]-2.1605786392064[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]15.1492445449699[/C][C]0.850755455030106[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]12.7336950720493[/C][C]0.266304927950715[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]16.1591078300916[/C][C]-0.159107830091647[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]13.9341399415412[/C][C]-1.93413994154122[/C][/ROW]
[ROW][C]155[/C][C]9[/C][C]11.1945926899971[/C][C]-2.19459268999714[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]12.56854120437[/C][C]0.43145879562998[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]12.4467968999264[/C][C]0.553203100073588[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.0911032333721[/C][C]0.908896766627947[/C][/ROW]
[ROW][C]159[/C][C]19[/C][C]15.3197928898884[/C][C]3.68020711011163[/C][/ROW]
[ROW][C]160[/C][C]13[/C][C]15.0157288972295[/C][C]-2.01572889722954[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.7167045239811[/C][C]-0.716704523981125[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.0102436853054[/C][C]0.989756314694604[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147137&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147137&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11413.75479222964030.245207770359684
21815.36440662183782.63559337816221
31114.2734981863412-3.27349818634124
41213.6351364033619-1.63513640336191
51611.98217871657564.01782128342443
61813.95795673452734.04204326547267
71411.71062383020332.28937616979674
81414.7003425089309-0.700342508930904
91516.151240492138-1.15124049213796
101514.00898129811170.99101870188834
111715.06420533452561.93579466547437
121916.10716095196932.89283904803074
131013.1013262708401-3.10132627084006
141613.47062934280342.52937065719664
151816.20446734216761.79553265783241
161412.73875017039591.26124982960407
171414.4788563248102-0.478856324810153
181715.07914669007491.92085330992511
191416.025672375355-2.02567237535501
201613.34676701601472.65323298398533
211816.86416201971131.13583798028872
221113.2020257935074-2.20202579350743
231413.91405586389780.085944136102234
241214.1335383509994-2.13353835099942
251715.05256567775611.94743432224387
26915.9966161568537-6.99661615685375
271615.63668785000260.363312149997389
281412.71261945354041.28738054645964
291514.44808883238950.551911167610472
301113.2885676870744-2.28856768707445
311616.2156307003588-0.215630700358756
321312.09635919333870.903640806661264
331716.07461045981990.925389540180092
341514.77155477890560.228445221094412
351413.55685577254160.443144227458352
361615.81274502621280.187254973787242
37910.4718668947516-1.47186689475155
381514.30205946922550.697940530774509
391715.62077278500231.37922721499765
401314.5518103543228-1.5518103543228
411516.2211050516634-1.22110505166344
421613.13530144026092.86469855973915
431616.4648449247546-0.464844924754555
441212.8585483225214-0.858548322521357
451215.7050817863304-3.70508178633036
461113.5045743137679-2.50457431376794
471514.85584488720590.144155112794118
481515.368153519164-0.368153519163958
491713.12254187104433.87745812895568
501314.8228139428797-1.82281394287965
511615.63614431940460.363855680595442
521412.8522824536921.14771754630804
531112.3657186374211-1.36571863742109
541212.8818533189705-0.881853318970485
551214.7475247465433-2.74752474654334
561514.24955831228590.750441687714062
571615.29109567161320.708904328386767
581514.72167441145840.278325588541553
591214.7109822598252-2.71098225982516
601213.9977095757946-1.9977095757946
61810.3229172045813-2.32291720458132
621314.6236432656473-1.62364326564726
631115.1574611181638-4.15746111816381
641412.38552709569541.61447290430455
651514.13605289257530.863947107424682
661014.4647265310104-4.4647265310104
671113.4506281197972-2.45062811979724
681214.0376204659057-2.03762046590575
691514.75271720264510.247282797354854
701513.32945201622081.67054798377924
711413.27042340626280.729576593737176
721613.24828251320962.75171748679042
731513.96288343915351.03711656084651
741515.1861732164093-0.18617321640933
751315.6616815788024-2.66168157880236
761211.94476071031370.055239289686289
771714.67972897564522.32027102435482
781311.99563281100011.00436718899991
791514.42177627578090.578223724219132
801314.7615889475855-1.76158894758553
811515.7744604600426-0.774460460042635
821613.96537848093072.03462151906925
831514.91534234650040.0846576534996258
841615.09702019097230.90297980902773
851513.77980016426991.22019983573008
861413.99696886223930.00303113776067021
871514.79637795462850.203622045371485
881413.681580328770.318419671230044
891313.1674275217246-0.167427521724624
90710.1854727876331-3.18547278763314
911714.38085434309232.61914565690767
921312.69564966352390.30435033647608
931515.1108706667682-0.110870666768206
941413.08566179767180.914338202328181
951314.3322718399007-1.33227183990066
961615.75331381401730.246686185982674
971212.605856218741-0.605856218740962
981414.7804286210361-0.780428621036108
991715.68083124180011.31916875819993
1001514.36727497501190.632725024988095
1011715.86668889255591.13331110744413
1021212.5416400334742-0.541640033474242
1031615.70694791591320.293052084086837
1041114.0536528388447-3.05365283884468
1051514.0194314251530.980568574846966
106911.595939492077-2.59593949207704
1071614.51820536117041.48179463882964
1081513.87896452638091.12103547361908
1091012.4810518281872-2.48105182818722
110109.625086580474060.374913419525941
1111514.32352753446210.676472465537871
1121112.646749670617-1.64674967061704
1131315.5880379366193-2.5880379366193
1141411.52809130234912.47190869765091
1151815.04411588110672.95588411889327
1161615.30960089567880.690399104321161
1171413.90772864118460.0922713588153874
1181413.4467930339060.553206966094015
1191414.5299404985319-0.529940498531948
1201414.5564981732464-0.55649817324644
1211212.1458647851035-0.145864785103458
1221413.46491167553380.535088324466238
1231515.725888309977-0.72588830997699
1241515.3579388846925-0.357938884692536
1251515.1044753397571-0.104475339757052
1261314.0772729516653-1.07727295166532
1271717.0237559326953-0.0237559326952548
1281715.24891653415481.75108346584525
1291915.68044905246713.3195509475329
1301513.41228930341121.58771069658876
1311313.9972224732897-0.997222473289658
132911.3734814996703-2.37348149967029
1331514.94464208932460.0553579106754226
1341512.39302284849142.6069771515086
1351514.97347545162850.0265245483715351
1361613.14634464041822.85365535958178
1371110.12450113790080.875498862099166
1381412.69351538744791.3064846125521
1391112.6583804094296-1.65838040942959
1401513.90770022916561.09229977083436
1411314.5090442920349-1.50904429203487
1421514.02153533849010.978464661509882
1431613.1400572663512.85994273364905
1441415.1045906539932-1.10459065399316
1451513.80709442668031.19290557331973
1461614.88213616319671.11786383680333
1471614.98939343773091.01060656226906
1481112.9634959619387-1.96349596193869
1491215.1204352163809-3.12043521638091
150911.1605786392064-2.1605786392064
1511615.14924454496990.850755455030106
1521312.73369507204930.266304927950715
1531616.1591078300916-0.159107830091647
1541213.9341399415412-1.93413994154122
155911.1945926899971-2.19459268999714
1561312.568541204370.43145879562998
1571312.44679689992640.553203100073588
1581413.09110323337210.908896766627947
1591915.31979288988843.68020711011163
1601315.0157288972295-2.01572889722954
1611212.7167045239811-0.716704523981125
1621312.01024368530540.989756314694604







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.8078531125347910.3842937749304180.192146887465209
210.9429386789454380.1141226421091230.0570613210545616
220.9320189797639630.1359620404720750.0679810202360373
230.9001484937072930.1997030125854140.0998515062927068
240.9817914184932830.03641716301343460.0182085815067173
250.9920334445882220.01593311082355640.00796655541177822
260.9999475648663860.0001048702672273145.24351336136572e-05
270.9999163077172580.0001673845654844068.36922827422029e-05
280.9998665524103640.0002668951792722480.000133447589636124
290.9997446362960590.0005107274078829070.000255363703941454
300.99988634033240.000227319335199750.000113659667599875
310.9997885398641590.000422920271682410.000211460135841205
320.9996337926311760.000732414737648030.000366207368824015
330.9994324667841550.001135066431689320.000567533215844662
340.9991616285052420.001676742989516630.000838371494758313
350.9986537146437750.002692570712450360.00134628535622518
360.9978075454931780.004384909013644160.00219245450682208
370.996626919724320.00674616055136050.00337308027568025
380.9960733639457960.007853272108408420.00392663605420421
390.9963011555616270.007397688876745790.00369884443837289
400.9945555252667060.01088894946658770.00544447473329384
410.991894251227710.01621149754457860.00810574877228931
420.9937470316054170.01250593678916690.00625296839458347
430.991725338442760.01654932311448250.00827466155724124
440.9883904009922640.02321919801547150.0116095990077357
450.992501714351080.01499657129784180.0074982856489209
460.9910471773455190.01790564530896260.00895282265448132
470.9872645556809690.02547088863806240.0127354443190312
480.981820961797070.0363580764058610.0181790382029305
490.996031761973550.007936476052901280.00396823802645064
500.994785359868480.01042928026303910.00521464013151955
510.9930877551114020.01382448977719560.00691224488859782
520.9921219027594230.01575619448115320.00787809724057661
530.9896753426538010.02064931469239770.0103246573461989
540.9864735316196790.02705293676064280.0135264683803214
550.9869624698602980.02607506027940410.0130375301397021
560.9836808464225480.03263830715490460.0163191535774523
570.9797101444497010.04057971110059750.0202898555502987
580.9765124421599930.04697511568001470.0234875578400074
590.9795025266404170.04099494671916510.0204974733595826
600.97885190233680.04229619532640010.0211480976632
610.9767743495370120.04645130092597570.0232256504629878
620.972082589121480.05583482175704080.0279174108785204
630.9858931107739770.02821377845204520.0141068892260226
640.9873175164163880.02536496716722420.0126824835836121
650.9852821184108950.02943576317821010.0147178815891051
660.9955396647615250.008920670476950550.00446033523847527
670.9959522919265210.0080954161469580.004047708073479
680.9954150216813230.009169956637353880.00458497831867694
690.9943756410784750.01124871784304960.00562435892152478
700.995238254482030.009523491035941510.00476174551797076
710.9940064518794930.01198709624101480.00599354812050742
720.9963384534266680.007323093146664680.00366154657333234
730.9958770295335120.008245940932976250.00412297046648813
740.9949402448304260.01011951033914890.00505975516957444
750.9963312884431180.007337423113763650.00366871155688183
760.9948676159255130.01026476814897320.00513238407448659
770.996477033585980.007045932828038360.00352296641401918
780.9957298853769580.008540229246084320.00427011462304216
790.994581131304870.01083773739025890.00541886869512947
800.994276564234220.0114468715315620.005723435765781
810.992458578183870.0150828436322610.00754142181613052
820.9925117505529250.01497649889415090.00748824944707547
830.9895229187544320.0209541624911350.0104770812455675
840.986902640771510.02619471845697850.0130973592284893
850.985225858548770.02954828290245860.0147741414512293
860.9806590376191440.03868192476171250.0193409623808563
870.9765458892946070.04690822141078680.0234541107053934
880.9696300236925890.06073995261482270.0303699763074113
890.9608613182422960.07827736351540760.0391386817577038
900.9721148412627170.05577031747456670.0278851587372834
910.9793482657515590.04130346849688270.0206517342484414
920.972584126869750.05483174626049950.0274158731302497
930.9640664881121730.07186702377565330.0359335118878266
940.9577653633843250.08446927323135080.0422346366156754
950.9495695949512240.1008608100975530.0504304050487764
960.935265919039180.1294681619216410.0647340809608204
970.9171926401659490.1656147196681020.0828073598340508
980.9109221827219480.1781556345561050.0890778172780523
990.8986789947800010.2026420104399980.101321005219999
1000.8814714832261020.2370570335477960.118528516773898
1010.877031426779410.2459371464411790.12296857322059
1020.8498121638032640.3003756723934720.150187836196736
1030.8184753889327320.3630492221345360.181524611067268
1040.8848717278127270.2302565443745460.115128272187273
1050.8612276954056020.2775446091887960.138772304594398
1060.8736227592007470.2527544815985060.126377240799253
1070.8629827639871150.2740344720257690.137017236012885
1080.8667797606238560.2664404787522880.133220239376144
1090.8857135053707280.2285729892585450.114286494629272
1100.8605297880822730.2789404238354540.139470211917727
1110.835081295413110.329837409173780.16491870458689
1120.8324667942012820.3350664115974360.167533205798718
1130.8481600483436560.3036799033126870.151839951656344
1140.8870554771780040.2258890456439920.112944522821996
1150.9216391856445250.1567216287109490.0783608143554746
1160.9112976933113170.1774046133773660.0887023066886829
1170.8833651006950170.2332697986099670.116634899304983
1180.8505918439755210.2988163120489580.149408156024479
1190.8107245732483690.3785508535032620.189275426751631
1200.7668713277838750.4662573444322510.233128672216125
1210.7179308107613850.5641383784772290.282069189238615
1220.7110196148311090.5779607703377820.288980385168891
1230.7295128944462870.5409742111074260.270487105553713
1240.6693499900476870.6613000199046250.330650009952313
1250.6183454688656620.7633090622686760.381654531134338
1260.6363337049454890.7273325901090220.363666295054511
1270.5706514772909370.8586970454181260.429348522709063
1280.5320365697675780.9359268604648450.467963430232422
1290.5819600374123560.8360799251752880.418039962587644
1300.5324033354284530.9351933291430950.467596664571547
1310.4663207263094820.9326414526189640.533679273690518
1320.4832153505114750.966430701022950.516784649488525
1330.3992286947348460.7984573894696920.600771305265154
1340.336475949304360.6729518986087210.66352405069564
1350.3043953293373480.6087906586746970.695604670662652
1360.3889042452737120.7778084905474230.611095754726288
1370.563930727263410.872138545473180.43606927273659
1380.5725023622195670.8549952755608670.427497637780433
1390.465717969539570.931435939079140.53428203046043
1400.4185834978273640.8371669956547290.581416502172636
1410.6519833371474330.6960333257051340.348016662852567
1420.5112050161627390.9775899676745220.488794983837261

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 & 0.807853112534791 & 0.384293774930418 & 0.192146887465209 \tabularnewline
21 & 0.942938678945438 & 0.114122642109123 & 0.0570613210545616 \tabularnewline
22 & 0.932018979763963 & 0.135962040472075 & 0.0679810202360373 \tabularnewline
23 & 0.900148493707293 & 0.199703012585414 & 0.0998515062927068 \tabularnewline
24 & 0.981791418493283 & 0.0364171630134346 & 0.0182085815067173 \tabularnewline
25 & 0.992033444588222 & 0.0159331108235564 & 0.00796655541177822 \tabularnewline
26 & 0.999947564866386 & 0.000104870267227314 & 5.24351336136572e-05 \tabularnewline
27 & 0.999916307717258 & 0.000167384565484406 & 8.36922827422029e-05 \tabularnewline
28 & 0.999866552410364 & 0.000266895179272248 & 0.000133447589636124 \tabularnewline
29 & 0.999744636296059 & 0.000510727407882907 & 0.000255363703941454 \tabularnewline
30 & 0.9998863403324 & 0.00022731933519975 & 0.000113659667599875 \tabularnewline
31 & 0.999788539864159 & 0.00042292027168241 & 0.000211460135841205 \tabularnewline
32 & 0.999633792631176 & 0.00073241473764803 & 0.000366207368824015 \tabularnewline
33 & 0.999432466784155 & 0.00113506643168932 & 0.000567533215844662 \tabularnewline
34 & 0.999161628505242 & 0.00167674298951663 & 0.000838371494758313 \tabularnewline
35 & 0.998653714643775 & 0.00269257071245036 & 0.00134628535622518 \tabularnewline
36 & 0.997807545493178 & 0.00438490901364416 & 0.00219245450682208 \tabularnewline
37 & 0.99662691972432 & 0.0067461605513605 & 0.00337308027568025 \tabularnewline
38 & 0.996073363945796 & 0.00785327210840842 & 0.00392663605420421 \tabularnewline
39 & 0.996301155561627 & 0.00739768887674579 & 0.00369884443837289 \tabularnewline
40 & 0.994555525266706 & 0.0108889494665877 & 0.00544447473329384 \tabularnewline
41 & 0.99189425122771 & 0.0162114975445786 & 0.00810574877228931 \tabularnewline
42 & 0.993747031605417 & 0.0125059367891669 & 0.00625296839458347 \tabularnewline
43 & 0.99172533844276 & 0.0165493231144825 & 0.00827466155724124 \tabularnewline
44 & 0.988390400992264 & 0.0232191980154715 & 0.0116095990077357 \tabularnewline
45 & 0.99250171435108 & 0.0149965712978418 & 0.0074982856489209 \tabularnewline
46 & 0.991047177345519 & 0.0179056453089626 & 0.00895282265448132 \tabularnewline
47 & 0.987264555680969 & 0.0254708886380624 & 0.0127354443190312 \tabularnewline
48 & 0.98182096179707 & 0.036358076405861 & 0.0181790382029305 \tabularnewline
49 & 0.99603176197355 & 0.00793647605290128 & 0.00396823802645064 \tabularnewline
50 & 0.99478535986848 & 0.0104292802630391 & 0.00521464013151955 \tabularnewline
51 & 0.993087755111402 & 0.0138244897771956 & 0.00691224488859782 \tabularnewline
52 & 0.992121902759423 & 0.0157561944811532 & 0.00787809724057661 \tabularnewline
53 & 0.989675342653801 & 0.0206493146923977 & 0.0103246573461989 \tabularnewline
54 & 0.986473531619679 & 0.0270529367606428 & 0.0135264683803214 \tabularnewline
55 & 0.986962469860298 & 0.0260750602794041 & 0.0130375301397021 \tabularnewline
56 & 0.983680846422548 & 0.0326383071549046 & 0.0163191535774523 \tabularnewline
57 & 0.979710144449701 & 0.0405797111005975 & 0.0202898555502987 \tabularnewline
58 & 0.976512442159993 & 0.0469751156800147 & 0.0234875578400074 \tabularnewline
59 & 0.979502526640417 & 0.0409949467191651 & 0.0204974733595826 \tabularnewline
60 & 0.9788519023368 & 0.0422961953264001 & 0.0211480976632 \tabularnewline
61 & 0.976774349537012 & 0.0464513009259757 & 0.0232256504629878 \tabularnewline
62 & 0.97208258912148 & 0.0558348217570408 & 0.0279174108785204 \tabularnewline
63 & 0.985893110773977 & 0.0282137784520452 & 0.0141068892260226 \tabularnewline
64 & 0.987317516416388 & 0.0253649671672242 & 0.0126824835836121 \tabularnewline
65 & 0.985282118410895 & 0.0294357631782101 & 0.0147178815891051 \tabularnewline
66 & 0.995539664761525 & 0.00892067047695055 & 0.00446033523847527 \tabularnewline
67 & 0.995952291926521 & 0.008095416146958 & 0.004047708073479 \tabularnewline
68 & 0.995415021681323 & 0.00916995663735388 & 0.00458497831867694 \tabularnewline
69 & 0.994375641078475 & 0.0112487178430496 & 0.00562435892152478 \tabularnewline
70 & 0.99523825448203 & 0.00952349103594151 & 0.00476174551797076 \tabularnewline
71 & 0.994006451879493 & 0.0119870962410148 & 0.00599354812050742 \tabularnewline
72 & 0.996338453426668 & 0.00732309314666468 & 0.00366154657333234 \tabularnewline
73 & 0.995877029533512 & 0.00824594093297625 & 0.00412297046648813 \tabularnewline
74 & 0.994940244830426 & 0.0101195103391489 & 0.00505975516957444 \tabularnewline
75 & 0.996331288443118 & 0.00733742311376365 & 0.00366871155688183 \tabularnewline
76 & 0.994867615925513 & 0.0102647681489732 & 0.00513238407448659 \tabularnewline
77 & 0.99647703358598 & 0.00704593282803836 & 0.00352296641401918 \tabularnewline
78 & 0.995729885376958 & 0.00854022924608432 & 0.00427011462304216 \tabularnewline
79 & 0.99458113130487 & 0.0108377373902589 & 0.00541886869512947 \tabularnewline
80 & 0.99427656423422 & 0.011446871531562 & 0.005723435765781 \tabularnewline
81 & 0.99245857818387 & 0.015082843632261 & 0.00754142181613052 \tabularnewline
82 & 0.992511750552925 & 0.0149764988941509 & 0.00748824944707547 \tabularnewline
83 & 0.989522918754432 & 0.020954162491135 & 0.0104770812455675 \tabularnewline
84 & 0.98690264077151 & 0.0261947184569785 & 0.0130973592284893 \tabularnewline
85 & 0.98522585854877 & 0.0295482829024586 & 0.0147741414512293 \tabularnewline
86 & 0.980659037619144 & 0.0386819247617125 & 0.0193409623808563 \tabularnewline
87 & 0.976545889294607 & 0.0469082214107868 & 0.0234541107053934 \tabularnewline
88 & 0.969630023692589 & 0.0607399526148227 & 0.0303699763074113 \tabularnewline
89 & 0.960861318242296 & 0.0782773635154076 & 0.0391386817577038 \tabularnewline
90 & 0.972114841262717 & 0.0557703174745667 & 0.0278851587372834 \tabularnewline
91 & 0.979348265751559 & 0.0413034684968827 & 0.0206517342484414 \tabularnewline
92 & 0.97258412686975 & 0.0548317462604995 & 0.0274158731302497 \tabularnewline
93 & 0.964066488112173 & 0.0718670237756533 & 0.0359335118878266 \tabularnewline
94 & 0.957765363384325 & 0.0844692732313508 & 0.0422346366156754 \tabularnewline
95 & 0.949569594951224 & 0.100860810097553 & 0.0504304050487764 \tabularnewline
96 & 0.93526591903918 & 0.129468161921641 & 0.0647340809608204 \tabularnewline
97 & 0.917192640165949 & 0.165614719668102 & 0.0828073598340508 \tabularnewline
98 & 0.910922182721948 & 0.178155634556105 & 0.0890778172780523 \tabularnewline
99 & 0.898678994780001 & 0.202642010439998 & 0.101321005219999 \tabularnewline
100 & 0.881471483226102 & 0.237057033547796 & 0.118528516773898 \tabularnewline
101 & 0.87703142677941 & 0.245937146441179 & 0.12296857322059 \tabularnewline
102 & 0.849812163803264 & 0.300375672393472 & 0.150187836196736 \tabularnewline
103 & 0.818475388932732 & 0.363049222134536 & 0.181524611067268 \tabularnewline
104 & 0.884871727812727 & 0.230256544374546 & 0.115128272187273 \tabularnewline
105 & 0.861227695405602 & 0.277544609188796 & 0.138772304594398 \tabularnewline
106 & 0.873622759200747 & 0.252754481598506 & 0.126377240799253 \tabularnewline
107 & 0.862982763987115 & 0.274034472025769 & 0.137017236012885 \tabularnewline
108 & 0.866779760623856 & 0.266440478752288 & 0.133220239376144 \tabularnewline
109 & 0.885713505370728 & 0.228572989258545 & 0.114286494629272 \tabularnewline
110 & 0.860529788082273 & 0.278940423835454 & 0.139470211917727 \tabularnewline
111 & 0.83508129541311 & 0.32983740917378 & 0.16491870458689 \tabularnewline
112 & 0.832466794201282 & 0.335066411597436 & 0.167533205798718 \tabularnewline
113 & 0.848160048343656 & 0.303679903312687 & 0.151839951656344 \tabularnewline
114 & 0.887055477178004 & 0.225889045643992 & 0.112944522821996 \tabularnewline
115 & 0.921639185644525 & 0.156721628710949 & 0.0783608143554746 \tabularnewline
116 & 0.911297693311317 & 0.177404613377366 & 0.0887023066886829 \tabularnewline
117 & 0.883365100695017 & 0.233269798609967 & 0.116634899304983 \tabularnewline
118 & 0.850591843975521 & 0.298816312048958 & 0.149408156024479 \tabularnewline
119 & 0.810724573248369 & 0.378550853503262 & 0.189275426751631 \tabularnewline
120 & 0.766871327783875 & 0.466257344432251 & 0.233128672216125 \tabularnewline
121 & 0.717930810761385 & 0.564138378477229 & 0.282069189238615 \tabularnewline
122 & 0.711019614831109 & 0.577960770337782 & 0.288980385168891 \tabularnewline
123 & 0.729512894446287 & 0.540974211107426 & 0.270487105553713 \tabularnewline
124 & 0.669349990047687 & 0.661300019904625 & 0.330650009952313 \tabularnewline
125 & 0.618345468865662 & 0.763309062268676 & 0.381654531134338 \tabularnewline
126 & 0.636333704945489 & 0.727332590109022 & 0.363666295054511 \tabularnewline
127 & 0.570651477290937 & 0.858697045418126 & 0.429348522709063 \tabularnewline
128 & 0.532036569767578 & 0.935926860464845 & 0.467963430232422 \tabularnewline
129 & 0.581960037412356 & 0.836079925175288 & 0.418039962587644 \tabularnewline
130 & 0.532403335428453 & 0.935193329143095 & 0.467596664571547 \tabularnewline
131 & 0.466320726309482 & 0.932641452618964 & 0.533679273690518 \tabularnewline
132 & 0.483215350511475 & 0.96643070102295 & 0.516784649488525 \tabularnewline
133 & 0.399228694734846 & 0.798457389469692 & 0.600771305265154 \tabularnewline
134 & 0.33647594930436 & 0.672951898608721 & 0.66352405069564 \tabularnewline
135 & 0.304395329337348 & 0.608790658674697 & 0.695604670662652 \tabularnewline
136 & 0.388904245273712 & 0.777808490547423 & 0.611095754726288 \tabularnewline
137 & 0.56393072726341 & 0.87213854547318 & 0.43606927273659 \tabularnewline
138 & 0.572502362219567 & 0.854995275560867 & 0.427497637780433 \tabularnewline
139 & 0.46571796953957 & 0.93143593907914 & 0.53428203046043 \tabularnewline
140 & 0.418583497827364 & 0.837166995654729 & 0.581416502172636 \tabularnewline
141 & 0.651983337147433 & 0.696033325705134 & 0.348016662852567 \tabularnewline
142 & 0.511205016162739 & 0.977589967674522 & 0.488794983837261 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147137&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]20[/C][C]0.807853112534791[/C][C]0.384293774930418[/C][C]0.192146887465209[/C][/ROW]
[ROW][C]21[/C][C]0.942938678945438[/C][C]0.114122642109123[/C][C]0.0570613210545616[/C][/ROW]
[ROW][C]22[/C][C]0.932018979763963[/C][C]0.135962040472075[/C][C]0.0679810202360373[/C][/ROW]
[ROW][C]23[/C][C]0.900148493707293[/C][C]0.199703012585414[/C][C]0.0998515062927068[/C][/ROW]
[ROW][C]24[/C][C]0.981791418493283[/C][C]0.0364171630134346[/C][C]0.0182085815067173[/C][/ROW]
[ROW][C]25[/C][C]0.992033444588222[/C][C]0.0159331108235564[/C][C]0.00796655541177822[/C][/ROW]
[ROW][C]26[/C][C]0.999947564866386[/C][C]0.000104870267227314[/C][C]5.24351336136572e-05[/C][/ROW]
[ROW][C]27[/C][C]0.999916307717258[/C][C]0.000167384565484406[/C][C]8.36922827422029e-05[/C][/ROW]
[ROW][C]28[/C][C]0.999866552410364[/C][C]0.000266895179272248[/C][C]0.000133447589636124[/C][/ROW]
[ROW][C]29[/C][C]0.999744636296059[/C][C]0.000510727407882907[/C][C]0.000255363703941454[/C][/ROW]
[ROW][C]30[/C][C]0.9998863403324[/C][C]0.00022731933519975[/C][C]0.000113659667599875[/C][/ROW]
[ROW][C]31[/C][C]0.999788539864159[/C][C]0.00042292027168241[/C][C]0.000211460135841205[/C][/ROW]
[ROW][C]32[/C][C]0.999633792631176[/C][C]0.00073241473764803[/C][C]0.000366207368824015[/C][/ROW]
[ROW][C]33[/C][C]0.999432466784155[/C][C]0.00113506643168932[/C][C]0.000567533215844662[/C][/ROW]
[ROW][C]34[/C][C]0.999161628505242[/C][C]0.00167674298951663[/C][C]0.000838371494758313[/C][/ROW]
[ROW][C]35[/C][C]0.998653714643775[/C][C]0.00269257071245036[/C][C]0.00134628535622518[/C][/ROW]
[ROW][C]36[/C][C]0.997807545493178[/C][C]0.00438490901364416[/C][C]0.00219245450682208[/C][/ROW]
[ROW][C]37[/C][C]0.99662691972432[/C][C]0.0067461605513605[/C][C]0.00337308027568025[/C][/ROW]
[ROW][C]38[/C][C]0.996073363945796[/C][C]0.00785327210840842[/C][C]0.00392663605420421[/C][/ROW]
[ROW][C]39[/C][C]0.996301155561627[/C][C]0.00739768887674579[/C][C]0.00369884443837289[/C][/ROW]
[ROW][C]40[/C][C]0.994555525266706[/C][C]0.0108889494665877[/C][C]0.00544447473329384[/C][/ROW]
[ROW][C]41[/C][C]0.99189425122771[/C][C]0.0162114975445786[/C][C]0.00810574877228931[/C][/ROW]
[ROW][C]42[/C][C]0.993747031605417[/C][C]0.0125059367891669[/C][C]0.00625296839458347[/C][/ROW]
[ROW][C]43[/C][C]0.99172533844276[/C][C]0.0165493231144825[/C][C]0.00827466155724124[/C][/ROW]
[ROW][C]44[/C][C]0.988390400992264[/C][C]0.0232191980154715[/C][C]0.0116095990077357[/C][/ROW]
[ROW][C]45[/C][C]0.99250171435108[/C][C]0.0149965712978418[/C][C]0.0074982856489209[/C][/ROW]
[ROW][C]46[/C][C]0.991047177345519[/C][C]0.0179056453089626[/C][C]0.00895282265448132[/C][/ROW]
[ROW][C]47[/C][C]0.987264555680969[/C][C]0.0254708886380624[/C][C]0.0127354443190312[/C][/ROW]
[ROW][C]48[/C][C]0.98182096179707[/C][C]0.036358076405861[/C][C]0.0181790382029305[/C][/ROW]
[ROW][C]49[/C][C]0.99603176197355[/C][C]0.00793647605290128[/C][C]0.00396823802645064[/C][/ROW]
[ROW][C]50[/C][C]0.99478535986848[/C][C]0.0104292802630391[/C][C]0.00521464013151955[/C][/ROW]
[ROW][C]51[/C][C]0.993087755111402[/C][C]0.0138244897771956[/C][C]0.00691224488859782[/C][/ROW]
[ROW][C]52[/C][C]0.992121902759423[/C][C]0.0157561944811532[/C][C]0.00787809724057661[/C][/ROW]
[ROW][C]53[/C][C]0.989675342653801[/C][C]0.0206493146923977[/C][C]0.0103246573461989[/C][/ROW]
[ROW][C]54[/C][C]0.986473531619679[/C][C]0.0270529367606428[/C][C]0.0135264683803214[/C][/ROW]
[ROW][C]55[/C][C]0.986962469860298[/C][C]0.0260750602794041[/C][C]0.0130375301397021[/C][/ROW]
[ROW][C]56[/C][C]0.983680846422548[/C][C]0.0326383071549046[/C][C]0.0163191535774523[/C][/ROW]
[ROW][C]57[/C][C]0.979710144449701[/C][C]0.0405797111005975[/C][C]0.0202898555502987[/C][/ROW]
[ROW][C]58[/C][C]0.976512442159993[/C][C]0.0469751156800147[/C][C]0.0234875578400074[/C][/ROW]
[ROW][C]59[/C][C]0.979502526640417[/C][C]0.0409949467191651[/C][C]0.0204974733595826[/C][/ROW]
[ROW][C]60[/C][C]0.9788519023368[/C][C]0.0422961953264001[/C][C]0.0211480976632[/C][/ROW]
[ROW][C]61[/C][C]0.976774349537012[/C][C]0.0464513009259757[/C][C]0.0232256504629878[/C][/ROW]
[ROW][C]62[/C][C]0.97208258912148[/C][C]0.0558348217570408[/C][C]0.0279174108785204[/C][/ROW]
[ROW][C]63[/C][C]0.985893110773977[/C][C]0.0282137784520452[/C][C]0.0141068892260226[/C][/ROW]
[ROW][C]64[/C][C]0.987317516416388[/C][C]0.0253649671672242[/C][C]0.0126824835836121[/C][/ROW]
[ROW][C]65[/C][C]0.985282118410895[/C][C]0.0294357631782101[/C][C]0.0147178815891051[/C][/ROW]
[ROW][C]66[/C][C]0.995539664761525[/C][C]0.00892067047695055[/C][C]0.00446033523847527[/C][/ROW]
[ROW][C]67[/C][C]0.995952291926521[/C][C]0.008095416146958[/C][C]0.004047708073479[/C][/ROW]
[ROW][C]68[/C][C]0.995415021681323[/C][C]0.00916995663735388[/C][C]0.00458497831867694[/C][/ROW]
[ROW][C]69[/C][C]0.994375641078475[/C][C]0.0112487178430496[/C][C]0.00562435892152478[/C][/ROW]
[ROW][C]70[/C][C]0.99523825448203[/C][C]0.00952349103594151[/C][C]0.00476174551797076[/C][/ROW]
[ROW][C]71[/C][C]0.994006451879493[/C][C]0.0119870962410148[/C][C]0.00599354812050742[/C][/ROW]
[ROW][C]72[/C][C]0.996338453426668[/C][C]0.00732309314666468[/C][C]0.00366154657333234[/C][/ROW]
[ROW][C]73[/C][C]0.995877029533512[/C][C]0.00824594093297625[/C][C]0.00412297046648813[/C][/ROW]
[ROW][C]74[/C][C]0.994940244830426[/C][C]0.0101195103391489[/C][C]0.00505975516957444[/C][/ROW]
[ROW][C]75[/C][C]0.996331288443118[/C][C]0.00733742311376365[/C][C]0.00366871155688183[/C][/ROW]
[ROW][C]76[/C][C]0.994867615925513[/C][C]0.0102647681489732[/C][C]0.00513238407448659[/C][/ROW]
[ROW][C]77[/C][C]0.99647703358598[/C][C]0.00704593282803836[/C][C]0.00352296641401918[/C][/ROW]
[ROW][C]78[/C][C]0.995729885376958[/C][C]0.00854022924608432[/C][C]0.00427011462304216[/C][/ROW]
[ROW][C]79[/C][C]0.99458113130487[/C][C]0.0108377373902589[/C][C]0.00541886869512947[/C][/ROW]
[ROW][C]80[/C][C]0.99427656423422[/C][C]0.011446871531562[/C][C]0.005723435765781[/C][/ROW]
[ROW][C]81[/C][C]0.99245857818387[/C][C]0.015082843632261[/C][C]0.00754142181613052[/C][/ROW]
[ROW][C]82[/C][C]0.992511750552925[/C][C]0.0149764988941509[/C][C]0.00748824944707547[/C][/ROW]
[ROW][C]83[/C][C]0.989522918754432[/C][C]0.020954162491135[/C][C]0.0104770812455675[/C][/ROW]
[ROW][C]84[/C][C]0.98690264077151[/C][C]0.0261947184569785[/C][C]0.0130973592284893[/C][/ROW]
[ROW][C]85[/C][C]0.98522585854877[/C][C]0.0295482829024586[/C][C]0.0147741414512293[/C][/ROW]
[ROW][C]86[/C][C]0.980659037619144[/C][C]0.0386819247617125[/C][C]0.0193409623808563[/C][/ROW]
[ROW][C]87[/C][C]0.976545889294607[/C][C]0.0469082214107868[/C][C]0.0234541107053934[/C][/ROW]
[ROW][C]88[/C][C]0.969630023692589[/C][C]0.0607399526148227[/C][C]0.0303699763074113[/C][/ROW]
[ROW][C]89[/C][C]0.960861318242296[/C][C]0.0782773635154076[/C][C]0.0391386817577038[/C][/ROW]
[ROW][C]90[/C][C]0.972114841262717[/C][C]0.0557703174745667[/C][C]0.0278851587372834[/C][/ROW]
[ROW][C]91[/C][C]0.979348265751559[/C][C]0.0413034684968827[/C][C]0.0206517342484414[/C][/ROW]
[ROW][C]92[/C][C]0.97258412686975[/C][C]0.0548317462604995[/C][C]0.0274158731302497[/C][/ROW]
[ROW][C]93[/C][C]0.964066488112173[/C][C]0.0718670237756533[/C][C]0.0359335118878266[/C][/ROW]
[ROW][C]94[/C][C]0.957765363384325[/C][C]0.0844692732313508[/C][C]0.0422346366156754[/C][/ROW]
[ROW][C]95[/C][C]0.949569594951224[/C][C]0.100860810097553[/C][C]0.0504304050487764[/C][/ROW]
[ROW][C]96[/C][C]0.93526591903918[/C][C]0.129468161921641[/C][C]0.0647340809608204[/C][/ROW]
[ROW][C]97[/C][C]0.917192640165949[/C][C]0.165614719668102[/C][C]0.0828073598340508[/C][/ROW]
[ROW][C]98[/C][C]0.910922182721948[/C][C]0.178155634556105[/C][C]0.0890778172780523[/C][/ROW]
[ROW][C]99[/C][C]0.898678994780001[/C][C]0.202642010439998[/C][C]0.101321005219999[/C][/ROW]
[ROW][C]100[/C][C]0.881471483226102[/C][C]0.237057033547796[/C][C]0.118528516773898[/C][/ROW]
[ROW][C]101[/C][C]0.87703142677941[/C][C]0.245937146441179[/C][C]0.12296857322059[/C][/ROW]
[ROW][C]102[/C][C]0.849812163803264[/C][C]0.300375672393472[/C][C]0.150187836196736[/C][/ROW]
[ROW][C]103[/C][C]0.818475388932732[/C][C]0.363049222134536[/C][C]0.181524611067268[/C][/ROW]
[ROW][C]104[/C][C]0.884871727812727[/C][C]0.230256544374546[/C][C]0.115128272187273[/C][/ROW]
[ROW][C]105[/C][C]0.861227695405602[/C][C]0.277544609188796[/C][C]0.138772304594398[/C][/ROW]
[ROW][C]106[/C][C]0.873622759200747[/C][C]0.252754481598506[/C][C]0.126377240799253[/C][/ROW]
[ROW][C]107[/C][C]0.862982763987115[/C][C]0.274034472025769[/C][C]0.137017236012885[/C][/ROW]
[ROW][C]108[/C][C]0.866779760623856[/C][C]0.266440478752288[/C][C]0.133220239376144[/C][/ROW]
[ROW][C]109[/C][C]0.885713505370728[/C][C]0.228572989258545[/C][C]0.114286494629272[/C][/ROW]
[ROW][C]110[/C][C]0.860529788082273[/C][C]0.278940423835454[/C][C]0.139470211917727[/C][/ROW]
[ROW][C]111[/C][C]0.83508129541311[/C][C]0.32983740917378[/C][C]0.16491870458689[/C][/ROW]
[ROW][C]112[/C][C]0.832466794201282[/C][C]0.335066411597436[/C][C]0.167533205798718[/C][/ROW]
[ROW][C]113[/C][C]0.848160048343656[/C][C]0.303679903312687[/C][C]0.151839951656344[/C][/ROW]
[ROW][C]114[/C][C]0.887055477178004[/C][C]0.225889045643992[/C][C]0.112944522821996[/C][/ROW]
[ROW][C]115[/C][C]0.921639185644525[/C][C]0.156721628710949[/C][C]0.0783608143554746[/C][/ROW]
[ROW][C]116[/C][C]0.911297693311317[/C][C]0.177404613377366[/C][C]0.0887023066886829[/C][/ROW]
[ROW][C]117[/C][C]0.883365100695017[/C][C]0.233269798609967[/C][C]0.116634899304983[/C][/ROW]
[ROW][C]118[/C][C]0.850591843975521[/C][C]0.298816312048958[/C][C]0.149408156024479[/C][/ROW]
[ROW][C]119[/C][C]0.810724573248369[/C][C]0.378550853503262[/C][C]0.189275426751631[/C][/ROW]
[ROW][C]120[/C][C]0.766871327783875[/C][C]0.466257344432251[/C][C]0.233128672216125[/C][/ROW]
[ROW][C]121[/C][C]0.717930810761385[/C][C]0.564138378477229[/C][C]0.282069189238615[/C][/ROW]
[ROW][C]122[/C][C]0.711019614831109[/C][C]0.577960770337782[/C][C]0.288980385168891[/C][/ROW]
[ROW][C]123[/C][C]0.729512894446287[/C][C]0.540974211107426[/C][C]0.270487105553713[/C][/ROW]
[ROW][C]124[/C][C]0.669349990047687[/C][C]0.661300019904625[/C][C]0.330650009952313[/C][/ROW]
[ROW][C]125[/C][C]0.618345468865662[/C][C]0.763309062268676[/C][C]0.381654531134338[/C][/ROW]
[ROW][C]126[/C][C]0.636333704945489[/C][C]0.727332590109022[/C][C]0.363666295054511[/C][/ROW]
[ROW][C]127[/C][C]0.570651477290937[/C][C]0.858697045418126[/C][C]0.429348522709063[/C][/ROW]
[ROW][C]128[/C][C]0.532036569767578[/C][C]0.935926860464845[/C][C]0.467963430232422[/C][/ROW]
[ROW][C]129[/C][C]0.581960037412356[/C][C]0.836079925175288[/C][C]0.418039962587644[/C][/ROW]
[ROW][C]130[/C][C]0.532403335428453[/C][C]0.935193329143095[/C][C]0.467596664571547[/C][/ROW]
[ROW][C]131[/C][C]0.466320726309482[/C][C]0.932641452618964[/C][C]0.533679273690518[/C][/ROW]
[ROW][C]132[/C][C]0.483215350511475[/C][C]0.96643070102295[/C][C]0.516784649488525[/C][/ROW]
[ROW][C]133[/C][C]0.399228694734846[/C][C]0.798457389469692[/C][C]0.600771305265154[/C][/ROW]
[ROW][C]134[/C][C]0.33647594930436[/C][C]0.672951898608721[/C][C]0.66352405069564[/C][/ROW]
[ROW][C]135[/C][C]0.304395329337348[/C][C]0.608790658674697[/C][C]0.695604670662652[/C][/ROW]
[ROW][C]136[/C][C]0.388904245273712[/C][C]0.777808490547423[/C][C]0.611095754726288[/C][/ROW]
[ROW][C]137[/C][C]0.56393072726341[/C][C]0.87213854547318[/C][C]0.43606927273659[/C][/ROW]
[ROW][C]138[/C][C]0.572502362219567[/C][C]0.854995275560867[/C][C]0.427497637780433[/C][/ROW]
[ROW][C]139[/C][C]0.46571796953957[/C][C]0.93143593907914[/C][C]0.53428203046043[/C][/ROW]
[ROW][C]140[/C][C]0.418583497827364[/C][C]0.837166995654729[/C][C]0.581416502172636[/C][/ROW]
[ROW][C]141[/C][C]0.651983337147433[/C][C]0.696033325705134[/C][C]0.348016662852567[/C][/ROW]
[ROW][C]142[/C][C]0.511205016162739[/C][C]0.977589967674522[/C][C]0.488794983837261[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147137&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147137&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.8078531125347910.3842937749304180.192146887465209
210.9429386789454380.1141226421091230.0570613210545616
220.9320189797639630.1359620404720750.0679810202360373
230.9001484937072930.1997030125854140.0998515062927068
240.9817914184932830.03641716301343460.0182085815067173
250.9920334445882220.01593311082355640.00796655541177822
260.9999475648663860.0001048702672273145.24351336136572e-05
270.9999163077172580.0001673845654844068.36922827422029e-05
280.9998665524103640.0002668951792722480.000133447589636124
290.9997446362960590.0005107274078829070.000255363703941454
300.99988634033240.000227319335199750.000113659667599875
310.9997885398641590.000422920271682410.000211460135841205
320.9996337926311760.000732414737648030.000366207368824015
330.9994324667841550.001135066431689320.000567533215844662
340.9991616285052420.001676742989516630.000838371494758313
350.9986537146437750.002692570712450360.00134628535622518
360.9978075454931780.004384909013644160.00219245450682208
370.996626919724320.00674616055136050.00337308027568025
380.9960733639457960.007853272108408420.00392663605420421
390.9963011555616270.007397688876745790.00369884443837289
400.9945555252667060.01088894946658770.00544447473329384
410.991894251227710.01621149754457860.00810574877228931
420.9937470316054170.01250593678916690.00625296839458347
430.991725338442760.01654932311448250.00827466155724124
440.9883904009922640.02321919801547150.0116095990077357
450.992501714351080.01499657129784180.0074982856489209
460.9910471773455190.01790564530896260.00895282265448132
470.9872645556809690.02547088863806240.0127354443190312
480.981820961797070.0363580764058610.0181790382029305
490.996031761973550.007936476052901280.00396823802645064
500.994785359868480.01042928026303910.00521464013151955
510.9930877551114020.01382448977719560.00691224488859782
520.9921219027594230.01575619448115320.00787809724057661
530.9896753426538010.02064931469239770.0103246573461989
540.9864735316196790.02705293676064280.0135264683803214
550.9869624698602980.02607506027940410.0130375301397021
560.9836808464225480.03263830715490460.0163191535774523
570.9797101444497010.04057971110059750.0202898555502987
580.9765124421599930.04697511568001470.0234875578400074
590.9795025266404170.04099494671916510.0204974733595826
600.97885190233680.04229619532640010.0211480976632
610.9767743495370120.04645130092597570.0232256504629878
620.972082589121480.05583482175704080.0279174108785204
630.9858931107739770.02821377845204520.0141068892260226
640.9873175164163880.02536496716722420.0126824835836121
650.9852821184108950.02943576317821010.0147178815891051
660.9955396647615250.008920670476950550.00446033523847527
670.9959522919265210.0080954161469580.004047708073479
680.9954150216813230.009169956637353880.00458497831867694
690.9943756410784750.01124871784304960.00562435892152478
700.995238254482030.009523491035941510.00476174551797076
710.9940064518794930.01198709624101480.00599354812050742
720.9963384534266680.007323093146664680.00366154657333234
730.9958770295335120.008245940932976250.00412297046648813
740.9949402448304260.01011951033914890.00505975516957444
750.9963312884431180.007337423113763650.00366871155688183
760.9948676159255130.01026476814897320.00513238407448659
770.996477033585980.007045932828038360.00352296641401918
780.9957298853769580.008540229246084320.00427011462304216
790.994581131304870.01083773739025890.00541886869512947
800.994276564234220.0114468715315620.005723435765781
810.992458578183870.0150828436322610.00754142181613052
820.9925117505529250.01497649889415090.00748824944707547
830.9895229187544320.0209541624911350.0104770812455675
840.986902640771510.02619471845697850.0130973592284893
850.985225858548770.02954828290245860.0147741414512293
860.9806590376191440.03868192476171250.0193409623808563
870.9765458892946070.04690822141078680.0234541107053934
880.9696300236925890.06073995261482270.0303699763074113
890.9608613182422960.07827736351540760.0391386817577038
900.9721148412627170.05577031747456670.0278851587372834
910.9793482657515590.04130346849688270.0206517342484414
920.972584126869750.05483174626049950.0274158731302497
930.9640664881121730.07186702377565330.0359335118878266
940.9577653633843250.08446927323135080.0422346366156754
950.9495695949512240.1008608100975530.0504304050487764
960.935265919039180.1294681619216410.0647340809608204
970.9171926401659490.1656147196681020.0828073598340508
980.9109221827219480.1781556345561050.0890778172780523
990.8986789947800010.2026420104399980.101321005219999
1000.8814714832261020.2370570335477960.118528516773898
1010.877031426779410.2459371464411790.12296857322059
1020.8498121638032640.3003756723934720.150187836196736
1030.8184753889327320.3630492221345360.181524611067268
1040.8848717278127270.2302565443745460.115128272187273
1050.8612276954056020.2775446091887960.138772304594398
1060.8736227592007470.2527544815985060.126377240799253
1070.8629827639871150.2740344720257690.137017236012885
1080.8667797606238560.2664404787522880.133220239376144
1090.8857135053707280.2285729892585450.114286494629272
1100.8605297880822730.2789404238354540.139470211917727
1110.835081295413110.329837409173780.16491870458689
1120.8324667942012820.3350664115974360.167533205798718
1130.8481600483436560.3036799033126870.151839951656344
1140.8870554771780040.2258890456439920.112944522821996
1150.9216391856445250.1567216287109490.0783608143554746
1160.9112976933113170.1774046133773660.0887023066886829
1170.8833651006950170.2332697986099670.116634899304983
1180.8505918439755210.2988163120489580.149408156024479
1190.8107245732483690.3785508535032620.189275426751631
1200.7668713277838750.4662573444322510.233128672216125
1210.7179308107613850.5641383784772290.282069189238615
1220.7110196148311090.5779607703377820.288980385168891
1230.7295128944462870.5409742111074260.270487105553713
1240.6693499900476870.6613000199046250.330650009952313
1250.6183454688656620.7633090622686760.381654531134338
1260.6363337049454890.7273325901090220.363666295054511
1270.5706514772909370.8586970454181260.429348522709063
1280.5320365697675780.9359268604648450.467963430232422
1290.5819600374123560.8360799251752880.418039962587644
1300.5324033354284530.9351933291430950.467596664571547
1310.4663207263094820.9326414526189640.533679273690518
1320.4832153505114750.966430701022950.516784649488525
1330.3992286947348460.7984573894696920.600771305265154
1340.336475949304360.6729518986087210.66352405069564
1350.3043953293373480.6087906586746970.695604670662652
1360.3889042452737120.7778084905474230.611095754726288
1370.563930727263410.872138545473180.43606927273659
1380.5725023622195670.8549952755608670.427497637780433
1390.465717969539570.931435939079140.53428203046043
1400.4185834978273640.8371669956547290.581416502172636
1410.6519833371474330.6960333257051340.348016662852567
1420.5112050161627390.9775899676745220.488794983837261







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level240.195121951219512NOK
5% type I error level640.520325203252033NOK
10% type I error level710.577235772357724NOK

\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 & 24 & 0.195121951219512 & NOK \tabularnewline
5% type I error level & 64 & 0.520325203252033 & NOK \tabularnewline
10% type I error level & 71 & 0.577235772357724 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147137&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]24[/C][C]0.195121951219512[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]64[/C][C]0.520325203252033[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]71[/C][C]0.577235772357724[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147137&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147137&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level240.195121951219512NOK
5% type I error level640.520325203252033NOK
10% type I error level710.577235772357724NOK



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