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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 25 Nov 2010 13:07:11 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/25/t12906904275vrzsuj1kfex7td.htm/, Retrieved Fri, 29 Mar 2024 08:26:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=100960, Retrieved Fri, 29 Mar 2024 08:26:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact156
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [Workshop 7 mini-t...] [2010-11-20 16:10:06] [87d60b8864dc39f7ed759c345edfb471]
-   PD    [Multiple Regression] [Workshop 7 mini-t...] [2010-11-21 12:07:24] [87d60b8864dc39f7ed759c345edfb471]
- R PD      [Multiple Regression] [W7-model4] [2010-11-21 20:43:25] [48146708a479232c43a8f6e52fbf83b4]
-   PD        [Multiple Regression] [W7 - model 4] [2010-11-22 19:23:10] [48146708a479232c43a8f6e52fbf83b4]
- R PD            [Multiple Regression] [W7-interactie_gender] [2010-11-25 13:07:11] [6f3869f9d1e39c73f93153f1f7803f84] [Current]
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Dataseries X:
0	24	14	0	11	0	12	0	24	0	26	0
0	25	11	0	7	0	8	0	25	0	23	0
0	17	6	0	17	0	8	0	30	0	25	0
1	18	12	12	10	10	8	8	19	19	23	23
1	18	8	8	12	12	9	9	22	22	19	19
1	16	10	10	12	12	7	7	22	22	29	29
1	20	10	10	11	11	4	4	25	25	25	25
1	16	11	11	11	11	11	11	23	23	21	21
1	18	16	16	12	12	7	7	17	17	22	22
1	17	11	11	13	13	7	7	21	21	25	25
0	23	13	0	14	0	12	0	19	0	24	0
0	30	12	0	16	0	10	0	19	0	18	0
1	23	8	8	11	11	10	10	15	15	22	22
1	18	12	12	10	10	8	8	16	16	15	15
1	15	11	11	11	11	8	8	23	23	22	22
1	12	4	4	15	15	4	4	27	27	28	28
0	21	9	0	9	0	9	0	22	0	20	0
1	15	8	8	11	11	8	8	14	14	12	12
1	20	8	8	17	17	7	7	22	22	24	24
0	31	14	0	17	0	11	0	23	0	20	0
0	27	15	0	11	0	9	0	23	0	21	0
1	34	16	16	18	18	11	11	21	21	20	20
1	21	9	9	14	14	13	13	19	19	21	21
1	31	14	14	10	10	8	8	18	18	23	23
1	19	11	11	11	11	8	8	20	20	28	28
0	16	8	0	15	0	9	0	23	0	24	0
1	20	9	9	15	15	6	6	25	25	24	24
1	21	9	9	13	13	9	9	19	19	24	24
1	22	9	9	16	16	9	9	24	24	23	23
1	17	9	9	13	13	6	6	22	22	23	23
1	24	10	10	9	9	6	6	25	25	29	29
0	25	16	0	18	0	16	0	26	0	24	0
0	26	11	0	18	0	5	0	29	0	18	0
1	25	8	8	12	12	7	7	32	32	25	25
1	17	9	9	17	17	9	9	25	25	21	21
1	32	16	16	9	9	6	6	29	29	26	26
1	33	11	11	9	9	6	6	28	28	22	22
1	13	16	16	12	12	5	5	17	17	22	22
1	32	12	12	18	18	12	12	28	28	22	22
1	25	12	12	12	12	7	7	29	29	23	23
1	29	14	14	18	18	10	10	26	26	30	30
1	22	9	9	14	14	9	9	25	25	23	23
1	18	10	10	15	15	8	8	14	14	17	17
1	17	9	9	16	16	5	5	25	25	23	23
0	20	10	0	10	0	8	0	26	0	23	0
1	15	12	12	11	11	8	8	20	20	25	25
1	20	14	14	14	14	10	10	18	18	24	24
1	33	14	14	9	9	6	6	32	32	24	24
0	29	10	0	12	0	8	0	25	0	23	0
1	23	14	14	17	17	7	7	25	25	21	21
0	26	16	0	5	0	4	0	23	0	24	0
1	18	9	9	12	12	8	8	21	21	24	24
0	20	10	0	12	0	8	0	20	0	28	0
	11	6	0	6	0	4	0	15	0	16	0
1	28	8	8	24	24	20	20	30	30	20	20
1	26	13	13	12	12	8	8	24	24	29	29
0	22	10	0	12	0	8	0	26	0	27	0
1	17	8	8	14	14	6	6	24	24	22	22
0	12	7	0	7	0	4	0	22	0	28	0
1	14	15	15	13	13	8	8	14	14	16	16
1	17	9	9	12	12	9	9	24	24	25	25
1	21	10	10	13	13	6	6	24	24	24	24
1	19	12	12	14	14	7	7	24	24	28	28
1	18	13	13	8	8	9	9	24	24	24	24
0	10	10	0	11	0	5	0	19	0	23	0
0	29	11	0	9	0	5	0	31	0	30	0
1	31	8	8	11	11	8	8	22	22	24	24
0	19	9	0	13	0	8	0	27	0	21	0
1	9	13	13	10	10	6	6	19	19	25	25
1	20	11	11	11	11	8	8	25	25	25	25
1	28	8	8	12	12	7	7	20	20	22	22
0	19	9	0	9	0	7	0	21	0	23	0
0	30	9	0	15	0	9	0	27	0	26	0
0	29	15	0	18	0	11	0	23	0	23	0
0	26	9	0	15	0	6	0	25	0	25	0
0	23	10	0	12	0	8	0	20	0	21	0
1	13	14	14	13	13	6	6	21	21	25	25
1	21	12	12	14	14	9	9	22	22	24	24
1	19	12	12	10	10	8	8	23	23	29	29
1	28	11	11	13	13	6	6	25	25	22	22
1	23	14	14	13	13	10	10	25	25	27	27
1	18	6	6	11	11	8	8	17	17	26	26
0	21	12	0	13	0	8	0	19	0	22	0
1	20	8	8	16	16	10	10	25	25	24	24
1	23	14	14	8	8	5	5	19	19	27	27
1	21	11	11	16	16	7	7	20	20	24	24
1	21	10	10	11	11	5	5	26	26	24	24
1	15	14	14	9	9	8	8	23	23	29	29
1	28	12	12	16	16	14	14	27	27	22	22
1	19	10	10	12	12	7	7	17	17	21	21
1	26	14	14	14	14	8	8	17	17	24	24
1	10	5	5	8	8	6	6	19	19	24	24
0	16	11	0	9	0	5	0	17	0	23	0
1	22	10	10	15	15	6	6	22	22	20	20
1	19	9	9	11	11	10	10	21	21	27	27
1	31	10	10	21	21	12	12	32	32	26	26
0	31	16	0	14	0	9	0	21	0	25	0
1	29	13	13	18	18	12	12	21	21	21	21
0	19	9	0	12	0	7	0	18	0	21	0
1	22	10	10	13	13	8	8	18	18	19	19
1	23	10	10	15	15	10	10	23	23	21	21
0	15	7	0	12	0	6	0	19	0	21	0
0	20	9	0	19	0	10	0	20	0	16	0
1	18	8	8	15	15	10	10	21	21	22	22
1	23	14	14	11	11	10	10	20	20	29	29
1	25	14	14	11	11	5	5	17	17	15	15
1	21	8	8	10	10	7	7	18	18	17	17
1	24	9	9	13	13	10	10	19	19	15	15
1	25	14	14	15	15	11	11	22	22	21	21
1	17	14	14	12	12	6	6	15	15	21	21
1	13	8	8	12	12	7	7	14	14	19	19
1	28	8	8	16	16	12	12	18	18	24	24
0	21	8	0	9	0	11	0	24	0	20	0
1	25	7	7	18	18	11	11	35	35	17	17
0	9	6	0	8	0	11	0	29	0	23	0
1	16	8	8	13	13	5	5	21	21	24	24
1	19	6	6	17	17	8	8	25	25	14	14
1	17	11	11	9	9	6	6	20	20	19	19
1	25	14	14	15	15	9	9	22	22	24	24
1	20	11	11	8	8	4	4	13	13	13	13
1	29	11	11	7	7	4	4	26	26	22	22
1	14	11	11	12	12	7	7	17	17	16	16
1	22	14	14	14	14	11	11	25	25	19	19
1	15	8	8	6	6	6	6	20	20	25	25
0	19	20	0	8	0	7	0	19	0	25	0
1	20	11	11	17	17	8	8	21	21	23	23
0	15	8	0	10	0	4	0	22	0	24	0
1	20	11	11	11	11	8	8	24	24	26	26
1	18	10	10	14	14	9	9	21	21	26	26
1	33	14	14	11	11	8	8	26	26	25	25
1	22	11	11	13	13	11	11	24	24	18	18
1	16	9	9	12	12	8	8	16	16	21	21
1	17	9	9	11	11	5	5	23	23	26	26
1	16	8	8	9	9	4	4	18	18	23	23
0	21	10	0	12	0	8	0	16	0	23	0
0	26	13	0	20	0	10	0	26	0	22	0
1	18	13	13	12	12	6	6	19	19	20	20
1	18	12	12	13	13	9	9	21	21	13	13
1	17	8	8	12	12	9	9	21	21	24	24
1	22	13	13	12	12	13	13	22	22	15	15
1	30	14	14	9	9	9	9	23	23	14	14
0	30	12	0	15	0	10	0	29	0	22	0
1	24	14	14	24	24	20	20	21	21	10	10
1	21	15	15	7	7	5	5	21	21	24	24
1	21	13	13	17	17	11	11	23	23	22	22
1	29	16	16	11	11	6	6	27	27	24	24
1	31	9	9	17	17	9	9	25	25	19	19
1	20	9	9	11	11	7	7	21	21	20	20
0	16	9	0	12	0	9	0	10	0	13	0
0	22	8	0	14	0	10	0	20	0	20	0
1	20	7	7	11	11	9	9	26	26	22	22
1	28	16	16	16	16	8	8	24	24	24	24
1	38	11	11	21	21	7	7	29	29	29	29
0	22	9	0	14	0	6	0	19	0	12	0
1	20	11	11	20	20	13	13	24	24	20	20
0	17	9	0	13	0	6	0	19	0	21	0
1	28	14	14	11	11	8	8	24	24	24	24
1	22	13	13	15	15	10	10	22	22	22	22
0	31	16	0	19	0	16	0	17	0	20	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time14 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
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 & 14 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \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=100960&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]14 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/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=100960&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100960&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 time14 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
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
YT[t] = + 18.5439219142398 + 0.0622101960213318G[t] + 0.730621258268541X1[t] -0.0488988052248012X1_G[t] -0.23865725478243X2[t] -0.105245492279363X2_G[t] + 0.247229909963782X3[t] -0.501008474019651X3_G[t] + 0.334674427648451X4[t] + 0.172083243211216X4_G[t] -0.52672682818197X5[t] + 0.133226980233375`X5_G `[t] -0.0211658458013016t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
YT[t] =  +  18.5439219142398 +  0.0622101960213318G[t] +  0.730621258268541X1[t] -0.0488988052248012X1_G[t] -0.23865725478243X2[t] -0.105245492279363X2_G[t] +  0.247229909963782X3[t] -0.501008474019651X3_G[t] +  0.334674427648451X4[t] +  0.172083243211216X4_G[t] -0.52672682818197X5[t] +  0.133226980233375`X5_G
`[t] -0.0211658458013016t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100960&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]YT[t] =  +  18.5439219142398 +  0.0622101960213318G[t] +  0.730621258268541X1[t] -0.0488988052248012X1_G[t] -0.23865725478243X2[t] -0.105245492279363X2_G[t] +  0.247229909963782X3[t] -0.501008474019651X3_G[t] +  0.334674427648451X4[t] +  0.172083243211216X4_G[t] -0.52672682818197X5[t] +  0.133226980233375`X5_G
`[t] -0.0211658458013016t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100960&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100960&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
YT[t] = + 18.5439219142398 + 0.0622101960213318G[t] + 0.730621258268541X1[t] -0.0488988052248012X1_G[t] -0.23865725478243X2[t] -0.105245492279363X2_G[t] + 0.247229909963782X3[t] -0.501008474019651X3_G[t] + 0.334674427648451X4[t] + 0.172083243211216X4_G[t] -0.52672682818197X5[t] + 0.133226980233375`X5_G `[t] -0.0211658458013016t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)18.54392191423981.19709115.490800
G0.06221019602133180.0745090.83490.4051210.20256
X10.7306212582685410.1173886.22400
X1_G-0.04889880522480120.121642-0.4020.6882810.34414
X2-0.238657254782430.137157-1.740.0839610.041981
X2_G-0.1052454922793630.166405-0.63250.5280710.264036
X30.2472299099637820.1862111.32770.1863540.093177
X3_G-0.5010084740196510.097517-5.13761e-060
X40.3346744276484510.0893263.74670.0002570.000129
X4_G0.1720832432112160.0865761.98770.048720.02436
X5-0.526726828181970.093813-5.614600
`X5_G `0.1332269802333750.0918531.45040.1490820.074541
t-0.02116584580130160.010826-1.95510.0524820.026241

\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) & 18.5439219142398 & 1.197091 & 15.4908 & 0 & 0 \tabularnewline
G & 0.0622101960213318 & 0.074509 & 0.8349 & 0.405121 & 0.20256 \tabularnewline
X1 & 0.730621258268541 & 0.117388 & 6.224 & 0 & 0 \tabularnewline
X1_G & -0.0488988052248012 & 0.121642 & -0.402 & 0.688281 & 0.34414 \tabularnewline
X2 & -0.23865725478243 & 0.137157 & -1.74 & 0.083961 & 0.041981 \tabularnewline
X2_G & -0.105245492279363 & 0.166405 & -0.6325 & 0.528071 & 0.264036 \tabularnewline
X3 & 0.247229909963782 & 0.186211 & 1.3277 & 0.186354 & 0.093177 \tabularnewline
X3_G & -0.501008474019651 & 0.097517 & -5.1376 & 1e-06 & 0 \tabularnewline
X4 & 0.334674427648451 & 0.089326 & 3.7467 & 0.000257 & 0.000129 \tabularnewline
X4_G & 0.172083243211216 & 0.086576 & 1.9877 & 0.04872 & 0.02436 \tabularnewline
X5 & -0.52672682818197 & 0.093813 & -5.6146 & 0 & 0 \tabularnewline
`X5_G
` & 0.133226980233375 & 0.091853 & 1.4504 & 0.149082 & 0.074541 \tabularnewline
t & -0.0211658458013016 & 0.010826 & -1.9551 & 0.052482 & 0.026241 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100960&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]18.5439219142398[/C][C]1.197091[/C][C]15.4908[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]G[/C][C]0.0622101960213318[/C][C]0.074509[/C][C]0.8349[/C][C]0.405121[/C][C]0.20256[/C][/ROW]
[ROW][C]X1[/C][C]0.730621258268541[/C][C]0.117388[/C][C]6.224[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X1_G[/C][C]-0.0488988052248012[/C][C]0.121642[/C][C]-0.402[/C][C]0.688281[/C][C]0.34414[/C][/ROW]
[ROW][C]X2[/C][C]-0.23865725478243[/C][C]0.137157[/C][C]-1.74[/C][C]0.083961[/C][C]0.041981[/C][/ROW]
[ROW][C]X2_G[/C][C]-0.105245492279363[/C][C]0.166405[/C][C]-0.6325[/C][C]0.528071[/C][C]0.264036[/C][/ROW]
[ROW][C]X3[/C][C]0.247229909963782[/C][C]0.186211[/C][C]1.3277[/C][C]0.186354[/C][C]0.093177[/C][/ROW]
[ROW][C]X3_G[/C][C]-0.501008474019651[/C][C]0.097517[/C][C]-5.1376[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]X4[/C][C]0.334674427648451[/C][C]0.089326[/C][C]3.7467[/C][C]0.000257[/C][C]0.000129[/C][/ROW]
[ROW][C]X4_G[/C][C]0.172083243211216[/C][C]0.086576[/C][C]1.9877[/C][C]0.04872[/C][C]0.02436[/C][/ROW]
[ROW][C]X5[/C][C]-0.52672682818197[/C][C]0.093813[/C][C]-5.6146[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`X5_G
`[/C][C]0.133226980233375[/C][C]0.091853[/C][C]1.4504[/C][C]0.149082[/C][C]0.074541[/C][/ROW]
[ROW][C]t[/C][C]-0.0211658458013016[/C][C]0.010826[/C][C]-1.9551[/C][C]0.052482[/C][C]0.026241[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100960&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100960&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)18.54392191423981.19709115.490800
G0.06221019602133180.0745090.83490.4051210.20256
X10.7306212582685410.1173886.22400
X1_G-0.04889880522480120.121642-0.4020.6882810.34414
X2-0.238657254782430.137157-1.740.0839610.041981
X2_G-0.1052454922793630.166405-0.63250.5280710.264036
X30.2472299099637820.1862111.32770.1863540.093177
X3_G-0.5010084740196510.097517-5.13761e-060
X40.3346744276484510.0893263.74670.0002570.000129
X4_G0.1720832432112160.0865761.98770.048720.02436
X5-0.526726828181970.093813-5.614600
`X5_G `0.1332269802333750.0918531.45040.1490820.074541
t-0.02116584580130160.010826-1.95510.0524820.026241







Multiple Linear Regression - Regression Statistics
Multiple R0.849135511888232
R-squared0.72103111754969
Adjusted R-squared0.6981021683072
F-TEST (value)31.4463218494766
F-TEST (DF numerator)12
F-TEST (DF denominator)146
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.65450296177352
Sum Squared Residuals1949.88721705127

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.849135511888232 \tabularnewline
R-squared & 0.72103111754969 \tabularnewline
Adjusted R-squared & 0.6981021683072 \tabularnewline
F-TEST (value) & 31.4463218494766 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 146 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.65450296177352 \tabularnewline
Sum Squared Residuals & 1949.88721705127 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100960&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.849135511888232[/C][/ROW]
[ROW][C]R-squared[/C][C]0.72103111754969[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.6981021683072[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]31.4463218494766[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]146[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.65450296177352[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1949.88721705127[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100960&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100960&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 R0.849135511888232
R-squared0.72103111754969
Adjusted R-squared0.6981021683072
F-TEST (value)31.4463218494766
F-TEST (DF numerator)12
F-TEST (DF denominator)146
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.65450296177352
Sum Squared Residuals1949.88721705127







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12423.43027153198820.56972846801179
22523.09780620285041.90219379714961
31717.6568799997603-0.656879999760318
41821.8107814240319-3.81078142403191
51821.2154141122496-3.21541411224959
61619.1302518211615-3.13025182116155
72023.3085968189630-3.30859681896303
81622.7531875278894-6.75318752788943
91823.3777995833619-5.3777995833619
101720.450649864873-3.45064986487299
112321.15210156948121.84789843051883
123022.58890110501077.41109889498928
132316.40840828898166.59159171101837
141823.2268487370287-5.22684873702865
151522.9728624514993-7.97286245149932
161217.4851742981152-5.48517429811525
172121.6651486012932-0.665148601293227
181520.2383769967131-5.23837699671314
192017.73963642409122.26036357590884
203124.17463356454866.82536643545139
212725.29484585760091.70515414239911
223422.838143228686611.1618567713134
232117.50595888204663.49404111795336
243122.24415174323378.75584825676631
251918.87993189321570.120068106784265
261617.5398583170390-1.53985831703897
272020.7138891814829-0.713889181482937
282117.57864711247963.42135288752039
292219.45306122773992.54693877226014
301720.2114239735722-3.21142397357221
312421.40686549394922.59313450605075
322525.2764941967240-0.276494196724022
332623.04707730201562.95292269798441
342523.81573933302871.18426066697133
351720.2759207726271-3.27592077262712
363228.59890121048963.40109878951037
373326.23636482040436.76363517959566
381323.2715471832359-10.2715471832359
393222.25795947395419.74204052604586
402525.682360753714-0.682360753714005
412919.92511569124279.07488430875734
422220.37246839730621.62753160269381
431817.72956552977790.270434470222058
441720.6574454678035-3.65744546780348
452021.075756238427-1.07575623842701
461520.2976711282779-5.29767112827793
472019.48066932549630.519330674503749
483329.28873886326273.71126113673728
492920.17910391800858.8208960819915
502323.8746024789380-0.87460247893803
512624.00610523616011.99389476383986
521818.6818634660854-0.681863466085368
532015.78743425565124.21256574434882
54612.7423353580560-6.74233535805596
5588.95533275311095-0.95533275311095
561311.88269754774051.11730245225953
57109.030597637527660.969402362472339
5889.25079194001516-1.25079194001516
59711.2077571202430-4.20775712024302
601518.6321323173743-3.63213231737432
6199.98827725926862-0.988277259268624
621010.5877077277315-0.587707727731456
631210.33921802421871.66078197578125
641314.2811157957265-1.28111579572652
651011.1648831659835-1.16488316598348
66117.749216649513153.25078335048685
67810.7012600102935-2.70126001029350
6897.02873600713761.9712639928624
691313.2245830565789-0.224583056578933
701111.4248953476096-0.424895347609600
71811.0423808240166-3.04238082401656
72910.4619036874179-1.46190368741792
7398.131365067459480.868634932540524
74159.178585791848675.82141420815133
7598.985862773437720.0141372265622786
761010.6642081481749-0.664208148174871
771412.83938205975831.16061794024171
781212.1813619972361-0.181361997236140
791211.10446361051170.895536389488292
801111.9157682572962-0.915768257296198
811412.57013495207781.42986504792223
8268.23618032037408-2.23618032037408
831211.01581974752920.984180252470836
8488.13754185188919-0.137541851889191
851414.2113340587138-0.211334058713820
861110.75500110991630.244998890083714
871010.1590211922866-0.159021192286559
881412.41392879075901.58607120924102
891212.3964359720643-0.396435972064284
901012.5284532104547-2.52845321045466
911414.3681853126345-0.368185312634538
9257.75153745777384-2.75153745777384
931112.1785421983973-1.1785421983973
941011.1487411707221-1.14874117072209
9599.61231434093077-0.612314340930776
96106.86844243560473.1315575643953
971610.70168835061445.29831164938556
981313.3591147417237-0.359114741723721
99911.0358153509783-2.03581535097827
1001012.9008068217531-2.90080682175314
1011010.9765235052967-0.976523505296659
102710.1944870675154-3.19448706751538
10398.492900886287320.507099113712681
10489.25198395925403-1.25198395925403
1051412.75964983477551.24035016522452
1061417.6169946212989-3.61699462129891
107812.6591641208644-4.65916412086439
108913.6614883179975-4.6614883179975
1091414.2956476082921-0.295647608292068
1101415.0938846105181-1.09388461051811
111811.4577560650174-3.45775606501737
11289.35765991735726-1.35765991735726
11387.411494261030540.588505738969455
11477.33580614820812-0.335806148208119
11564.682746381932161.31725361806784
11688.02947627924661-0.0294762792466125
11768.8905271822193-2.89052718221930
1181113.4729791874093-2.47297918740928
1191412.73608955999801.26391044000205
1201116.9130651429320-5.91306514293199
1211112.3852122537675-1.38521225376751
1221114.0439343478287-3.04393434782866
1231414.3105362698450-0.310536269844989
12489.63127963571698-1.63127963571697
1252010.86842307986889.13157692013115
126119.755667997886261.24433200211374
12789.12008080529924-1.12008080529924
1281110.00896675253460.99103324746545
129108.911077633198371.08892236680163
1301412.98920687624321.01079312375677
1311112.7578794199781-1.75787941997814
132911.0305543045231-2.03055430452308
13397.995645212244911.00435478775509
13489.37714962101867-1.37714962101867
1351011.5059762561231-1.50597625612307
136136.145238960896946.85476103910306
1371313.5433031111217-0.543303111121712
1381215.0797502021508-3.07975020215077
13988.46036575258291-0.460365752582913
1401315.9967530673342-2.99675306733424
1411417.5537031156223-3.55370311562232
142125.008553274239516.99144672576049
1431416.2710673551707-2.27106735517067
1441514.587568744840.412431255160004
1451311.45812536573491.54187463426507
1461613.76729577910492.23270422089512
14799.5627044280725-0.562704428072497
148910.4760595709918-1.47605957099177
149912.0785763545246-3.07857635452458
15088.55535352463767-0.555353524637675
15177.82755793085548-0.827557930855484
1521612.92328118272203.07671881727798
153115.553231151127615.44676884887239
15498.016014638879840.983985361120157
155119.554040993847581.44595900615242
15699.26027996129633-0.260279961296329
1571412.79398973721461.20601026278541
1581311.71141433480721.28858566519280
159169.418584138643346.58141586135666

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 24 & 23.4302715319882 & 0.56972846801179 \tabularnewline
2 & 25 & 23.0978062028504 & 1.90219379714961 \tabularnewline
3 & 17 & 17.6568799997603 & -0.656879999760318 \tabularnewline
4 & 18 & 21.8107814240319 & -3.81078142403191 \tabularnewline
5 & 18 & 21.2154141122496 & -3.21541411224959 \tabularnewline
6 & 16 & 19.1302518211615 & -3.13025182116155 \tabularnewline
7 & 20 & 23.3085968189630 & -3.30859681896303 \tabularnewline
8 & 16 & 22.7531875278894 & -6.75318752788943 \tabularnewline
9 & 18 & 23.3777995833619 & -5.3777995833619 \tabularnewline
10 & 17 & 20.450649864873 & -3.45064986487299 \tabularnewline
11 & 23 & 21.1521015694812 & 1.84789843051883 \tabularnewline
12 & 30 & 22.5889011050107 & 7.41109889498928 \tabularnewline
13 & 23 & 16.4084082889816 & 6.59159171101837 \tabularnewline
14 & 18 & 23.2268487370287 & -5.22684873702865 \tabularnewline
15 & 15 & 22.9728624514993 & -7.97286245149932 \tabularnewline
16 & 12 & 17.4851742981152 & -5.48517429811525 \tabularnewline
17 & 21 & 21.6651486012932 & -0.665148601293227 \tabularnewline
18 & 15 & 20.2383769967131 & -5.23837699671314 \tabularnewline
19 & 20 & 17.7396364240912 & 2.26036357590884 \tabularnewline
20 & 31 & 24.1746335645486 & 6.82536643545139 \tabularnewline
21 & 27 & 25.2948458576009 & 1.70515414239911 \tabularnewline
22 & 34 & 22.8381432286866 & 11.1618567713134 \tabularnewline
23 & 21 & 17.5059588820466 & 3.49404111795336 \tabularnewline
24 & 31 & 22.2441517432337 & 8.75584825676631 \tabularnewline
25 & 19 & 18.8799318932157 & 0.120068106784265 \tabularnewline
26 & 16 & 17.5398583170390 & -1.53985831703897 \tabularnewline
27 & 20 & 20.7138891814829 & -0.713889181482937 \tabularnewline
28 & 21 & 17.5786471124796 & 3.42135288752039 \tabularnewline
29 & 22 & 19.4530612277399 & 2.54693877226014 \tabularnewline
30 & 17 & 20.2114239735722 & -3.21142397357221 \tabularnewline
31 & 24 & 21.4068654939492 & 2.59313450605075 \tabularnewline
32 & 25 & 25.2764941967240 & -0.276494196724022 \tabularnewline
33 & 26 & 23.0470773020156 & 2.95292269798441 \tabularnewline
34 & 25 & 23.8157393330287 & 1.18426066697133 \tabularnewline
35 & 17 & 20.2759207726271 & -3.27592077262712 \tabularnewline
36 & 32 & 28.5989012104896 & 3.40109878951037 \tabularnewline
37 & 33 & 26.2363648204043 & 6.76363517959566 \tabularnewline
38 & 13 & 23.2715471832359 & -10.2715471832359 \tabularnewline
39 & 32 & 22.2579594739541 & 9.74204052604586 \tabularnewline
40 & 25 & 25.682360753714 & -0.682360753714005 \tabularnewline
41 & 29 & 19.9251156912427 & 9.07488430875734 \tabularnewline
42 & 22 & 20.3724683973062 & 1.62753160269381 \tabularnewline
43 & 18 & 17.7295655297779 & 0.270434470222058 \tabularnewline
44 & 17 & 20.6574454678035 & -3.65744546780348 \tabularnewline
45 & 20 & 21.075756238427 & -1.07575623842701 \tabularnewline
46 & 15 & 20.2976711282779 & -5.29767112827793 \tabularnewline
47 & 20 & 19.4806693254963 & 0.519330674503749 \tabularnewline
48 & 33 & 29.2887388632627 & 3.71126113673728 \tabularnewline
49 & 29 & 20.1791039180085 & 8.8208960819915 \tabularnewline
50 & 23 & 23.8746024789380 & -0.87460247893803 \tabularnewline
51 & 26 & 24.0061052361601 & 1.99389476383986 \tabularnewline
52 & 18 & 18.6818634660854 & -0.681863466085368 \tabularnewline
53 & 20 & 15.7874342556512 & 4.21256574434882 \tabularnewline
54 & 6 & 12.7423353580560 & -6.74233535805596 \tabularnewline
55 & 8 & 8.95533275311095 & -0.95533275311095 \tabularnewline
56 & 13 & 11.8826975477405 & 1.11730245225953 \tabularnewline
57 & 10 & 9.03059763752766 & 0.969402362472339 \tabularnewline
58 & 8 & 9.25079194001516 & -1.25079194001516 \tabularnewline
59 & 7 & 11.2077571202430 & -4.20775712024302 \tabularnewline
60 & 15 & 18.6321323173743 & -3.63213231737432 \tabularnewline
61 & 9 & 9.98827725926862 & -0.988277259268624 \tabularnewline
62 & 10 & 10.5877077277315 & -0.587707727731456 \tabularnewline
63 & 12 & 10.3392180242187 & 1.66078197578125 \tabularnewline
64 & 13 & 14.2811157957265 & -1.28111579572652 \tabularnewline
65 & 10 & 11.1648831659835 & -1.16488316598348 \tabularnewline
66 & 11 & 7.74921664951315 & 3.25078335048685 \tabularnewline
67 & 8 & 10.7012600102935 & -2.70126001029350 \tabularnewline
68 & 9 & 7.0287360071376 & 1.9712639928624 \tabularnewline
69 & 13 & 13.2245830565789 & -0.224583056578933 \tabularnewline
70 & 11 & 11.4248953476096 & -0.424895347609600 \tabularnewline
71 & 8 & 11.0423808240166 & -3.04238082401656 \tabularnewline
72 & 9 & 10.4619036874179 & -1.46190368741792 \tabularnewline
73 & 9 & 8.13136506745948 & 0.868634932540524 \tabularnewline
74 & 15 & 9.17858579184867 & 5.82141420815133 \tabularnewline
75 & 9 & 8.98586277343772 & 0.0141372265622786 \tabularnewline
76 & 10 & 10.6642081481749 & -0.664208148174871 \tabularnewline
77 & 14 & 12.8393820597583 & 1.16061794024171 \tabularnewline
78 & 12 & 12.1813619972361 & -0.181361997236140 \tabularnewline
79 & 12 & 11.1044636105117 & 0.895536389488292 \tabularnewline
80 & 11 & 11.9157682572962 & -0.915768257296198 \tabularnewline
81 & 14 & 12.5701349520778 & 1.42986504792223 \tabularnewline
82 & 6 & 8.23618032037408 & -2.23618032037408 \tabularnewline
83 & 12 & 11.0158197475292 & 0.984180252470836 \tabularnewline
84 & 8 & 8.13754185188919 & -0.137541851889191 \tabularnewline
85 & 14 & 14.2113340587138 & -0.211334058713820 \tabularnewline
86 & 11 & 10.7550011099163 & 0.244998890083714 \tabularnewline
87 & 10 & 10.1590211922866 & -0.159021192286559 \tabularnewline
88 & 14 & 12.4139287907590 & 1.58607120924102 \tabularnewline
89 & 12 & 12.3964359720643 & -0.396435972064284 \tabularnewline
90 & 10 & 12.5284532104547 & -2.52845321045466 \tabularnewline
91 & 14 & 14.3681853126345 & -0.368185312634538 \tabularnewline
92 & 5 & 7.75153745777384 & -2.75153745777384 \tabularnewline
93 & 11 & 12.1785421983973 & -1.1785421983973 \tabularnewline
94 & 10 & 11.1487411707221 & -1.14874117072209 \tabularnewline
95 & 9 & 9.61231434093077 & -0.612314340930776 \tabularnewline
96 & 10 & 6.8684424356047 & 3.1315575643953 \tabularnewline
97 & 16 & 10.7016883506144 & 5.29831164938556 \tabularnewline
98 & 13 & 13.3591147417237 & -0.359114741723721 \tabularnewline
99 & 9 & 11.0358153509783 & -2.03581535097827 \tabularnewline
100 & 10 & 12.9008068217531 & -2.90080682175314 \tabularnewline
101 & 10 & 10.9765235052967 & -0.976523505296659 \tabularnewline
102 & 7 & 10.1944870675154 & -3.19448706751538 \tabularnewline
103 & 9 & 8.49290088628732 & 0.507099113712681 \tabularnewline
104 & 8 & 9.25198395925403 & -1.25198395925403 \tabularnewline
105 & 14 & 12.7596498347755 & 1.24035016522452 \tabularnewline
106 & 14 & 17.6169946212989 & -3.61699462129891 \tabularnewline
107 & 8 & 12.6591641208644 & -4.65916412086439 \tabularnewline
108 & 9 & 13.6614883179975 & -4.6614883179975 \tabularnewline
109 & 14 & 14.2956476082921 & -0.295647608292068 \tabularnewline
110 & 14 & 15.0938846105181 & -1.09388461051811 \tabularnewline
111 & 8 & 11.4577560650174 & -3.45775606501737 \tabularnewline
112 & 8 & 9.35765991735726 & -1.35765991735726 \tabularnewline
113 & 8 & 7.41149426103054 & 0.588505738969455 \tabularnewline
114 & 7 & 7.33580614820812 & -0.335806148208119 \tabularnewline
115 & 6 & 4.68274638193216 & 1.31725361806784 \tabularnewline
116 & 8 & 8.02947627924661 & -0.0294762792466125 \tabularnewline
117 & 6 & 8.8905271822193 & -2.89052718221930 \tabularnewline
118 & 11 & 13.4729791874093 & -2.47297918740928 \tabularnewline
119 & 14 & 12.7360895599980 & 1.26391044000205 \tabularnewline
120 & 11 & 16.9130651429320 & -5.91306514293199 \tabularnewline
121 & 11 & 12.3852122537675 & -1.38521225376751 \tabularnewline
122 & 11 & 14.0439343478287 & -3.04393434782866 \tabularnewline
123 & 14 & 14.3105362698450 & -0.310536269844989 \tabularnewline
124 & 8 & 9.63127963571698 & -1.63127963571697 \tabularnewline
125 & 20 & 10.8684230798688 & 9.13157692013115 \tabularnewline
126 & 11 & 9.75566799788626 & 1.24433200211374 \tabularnewline
127 & 8 & 9.12008080529924 & -1.12008080529924 \tabularnewline
128 & 11 & 10.0089667525346 & 0.99103324746545 \tabularnewline
129 & 10 & 8.91107763319837 & 1.08892236680163 \tabularnewline
130 & 14 & 12.9892068762432 & 1.01079312375677 \tabularnewline
131 & 11 & 12.7578794199781 & -1.75787941997814 \tabularnewline
132 & 9 & 11.0305543045231 & -2.03055430452308 \tabularnewline
133 & 9 & 7.99564521224491 & 1.00435478775509 \tabularnewline
134 & 8 & 9.37714962101867 & -1.37714962101867 \tabularnewline
135 & 10 & 11.5059762561231 & -1.50597625612307 \tabularnewline
136 & 13 & 6.14523896089694 & 6.85476103910306 \tabularnewline
137 & 13 & 13.5433031111217 & -0.543303111121712 \tabularnewline
138 & 12 & 15.0797502021508 & -3.07975020215077 \tabularnewline
139 & 8 & 8.46036575258291 & -0.460365752582913 \tabularnewline
140 & 13 & 15.9967530673342 & -2.99675306733424 \tabularnewline
141 & 14 & 17.5537031156223 & -3.55370311562232 \tabularnewline
142 & 12 & 5.00855327423951 & 6.99144672576049 \tabularnewline
143 & 14 & 16.2710673551707 & -2.27106735517067 \tabularnewline
144 & 15 & 14.58756874484 & 0.412431255160004 \tabularnewline
145 & 13 & 11.4581253657349 & 1.54187463426507 \tabularnewline
146 & 16 & 13.7672957791049 & 2.23270422089512 \tabularnewline
147 & 9 & 9.5627044280725 & -0.562704428072497 \tabularnewline
148 & 9 & 10.4760595709918 & -1.47605957099177 \tabularnewline
149 & 9 & 12.0785763545246 & -3.07857635452458 \tabularnewline
150 & 8 & 8.55535352463767 & -0.555353524637675 \tabularnewline
151 & 7 & 7.82755793085548 & -0.827557930855484 \tabularnewline
152 & 16 & 12.9232811827220 & 3.07671881727798 \tabularnewline
153 & 11 & 5.55323115112761 & 5.44676884887239 \tabularnewline
154 & 9 & 8.01601463887984 & 0.983985361120157 \tabularnewline
155 & 11 & 9.55404099384758 & 1.44595900615242 \tabularnewline
156 & 9 & 9.26027996129633 & -0.260279961296329 \tabularnewline
157 & 14 & 12.7939897372146 & 1.20601026278541 \tabularnewline
158 & 13 & 11.7114143348072 & 1.28858566519280 \tabularnewline
159 & 16 & 9.41858413864334 & 6.58141586135666 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100960&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]24[/C][C]23.4302715319882[/C][C]0.56972846801179[/C][/ROW]
[ROW][C]2[/C][C]25[/C][C]23.0978062028504[/C][C]1.90219379714961[/C][/ROW]
[ROW][C]3[/C][C]17[/C][C]17.6568799997603[/C][C]-0.656879999760318[/C][/ROW]
[ROW][C]4[/C][C]18[/C][C]21.8107814240319[/C][C]-3.81078142403191[/C][/ROW]
[ROW][C]5[/C][C]18[/C][C]21.2154141122496[/C][C]-3.21541411224959[/C][/ROW]
[ROW][C]6[/C][C]16[/C][C]19.1302518211615[/C][C]-3.13025182116155[/C][/ROW]
[ROW][C]7[/C][C]20[/C][C]23.3085968189630[/C][C]-3.30859681896303[/C][/ROW]
[ROW][C]8[/C][C]16[/C][C]22.7531875278894[/C][C]-6.75318752788943[/C][/ROW]
[ROW][C]9[/C][C]18[/C][C]23.3777995833619[/C][C]-5.3777995833619[/C][/ROW]
[ROW][C]10[/C][C]17[/C][C]20.450649864873[/C][C]-3.45064986487299[/C][/ROW]
[ROW][C]11[/C][C]23[/C][C]21.1521015694812[/C][C]1.84789843051883[/C][/ROW]
[ROW][C]12[/C][C]30[/C][C]22.5889011050107[/C][C]7.41109889498928[/C][/ROW]
[ROW][C]13[/C][C]23[/C][C]16.4084082889816[/C][C]6.59159171101837[/C][/ROW]
[ROW][C]14[/C][C]18[/C][C]23.2268487370287[/C][C]-5.22684873702865[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]22.9728624514993[/C][C]-7.97286245149932[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]17.4851742981152[/C][C]-5.48517429811525[/C][/ROW]
[ROW][C]17[/C][C]21[/C][C]21.6651486012932[/C][C]-0.665148601293227[/C][/ROW]
[ROW][C]18[/C][C]15[/C][C]20.2383769967131[/C][C]-5.23837699671314[/C][/ROW]
[ROW][C]19[/C][C]20[/C][C]17.7396364240912[/C][C]2.26036357590884[/C][/ROW]
[ROW][C]20[/C][C]31[/C][C]24.1746335645486[/C][C]6.82536643545139[/C][/ROW]
[ROW][C]21[/C][C]27[/C][C]25.2948458576009[/C][C]1.70515414239911[/C][/ROW]
[ROW][C]22[/C][C]34[/C][C]22.8381432286866[/C][C]11.1618567713134[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]17.5059588820466[/C][C]3.49404111795336[/C][/ROW]
[ROW][C]24[/C][C]31[/C][C]22.2441517432337[/C][C]8.75584825676631[/C][/ROW]
[ROW][C]25[/C][C]19[/C][C]18.8799318932157[/C][C]0.120068106784265[/C][/ROW]
[ROW][C]26[/C][C]16[/C][C]17.5398583170390[/C][C]-1.53985831703897[/C][/ROW]
[ROW][C]27[/C][C]20[/C][C]20.7138891814829[/C][C]-0.713889181482937[/C][/ROW]
[ROW][C]28[/C][C]21[/C][C]17.5786471124796[/C][C]3.42135288752039[/C][/ROW]
[ROW][C]29[/C][C]22[/C][C]19.4530612277399[/C][C]2.54693877226014[/C][/ROW]
[ROW][C]30[/C][C]17[/C][C]20.2114239735722[/C][C]-3.21142397357221[/C][/ROW]
[ROW][C]31[/C][C]24[/C][C]21.4068654939492[/C][C]2.59313450605075[/C][/ROW]
[ROW][C]32[/C][C]25[/C][C]25.2764941967240[/C][C]-0.276494196724022[/C][/ROW]
[ROW][C]33[/C][C]26[/C][C]23.0470773020156[/C][C]2.95292269798441[/C][/ROW]
[ROW][C]34[/C][C]25[/C][C]23.8157393330287[/C][C]1.18426066697133[/C][/ROW]
[ROW][C]35[/C][C]17[/C][C]20.2759207726271[/C][C]-3.27592077262712[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]28.5989012104896[/C][C]3.40109878951037[/C][/ROW]
[ROW][C]37[/C][C]33[/C][C]26.2363648204043[/C][C]6.76363517959566[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]23.2715471832359[/C][C]-10.2715471832359[/C][/ROW]
[ROW][C]39[/C][C]32[/C][C]22.2579594739541[/C][C]9.74204052604586[/C][/ROW]
[ROW][C]40[/C][C]25[/C][C]25.682360753714[/C][C]-0.682360753714005[/C][/ROW]
[ROW][C]41[/C][C]29[/C][C]19.9251156912427[/C][C]9.07488430875734[/C][/ROW]
[ROW][C]42[/C][C]22[/C][C]20.3724683973062[/C][C]1.62753160269381[/C][/ROW]
[ROW][C]43[/C][C]18[/C][C]17.7295655297779[/C][C]0.270434470222058[/C][/ROW]
[ROW][C]44[/C][C]17[/C][C]20.6574454678035[/C][C]-3.65744546780348[/C][/ROW]
[ROW][C]45[/C][C]20[/C][C]21.075756238427[/C][C]-1.07575623842701[/C][/ROW]
[ROW][C]46[/C][C]15[/C][C]20.2976711282779[/C][C]-5.29767112827793[/C][/ROW]
[ROW][C]47[/C][C]20[/C][C]19.4806693254963[/C][C]0.519330674503749[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]29.2887388632627[/C][C]3.71126113673728[/C][/ROW]
[ROW][C]49[/C][C]29[/C][C]20.1791039180085[/C][C]8.8208960819915[/C][/ROW]
[ROW][C]50[/C][C]23[/C][C]23.8746024789380[/C][C]-0.87460247893803[/C][/ROW]
[ROW][C]51[/C][C]26[/C][C]24.0061052361601[/C][C]1.99389476383986[/C][/ROW]
[ROW][C]52[/C][C]18[/C][C]18.6818634660854[/C][C]-0.681863466085368[/C][/ROW]
[ROW][C]53[/C][C]20[/C][C]15.7874342556512[/C][C]4.21256574434882[/C][/ROW]
[ROW][C]54[/C][C]6[/C][C]12.7423353580560[/C][C]-6.74233535805596[/C][/ROW]
[ROW][C]55[/C][C]8[/C][C]8.95533275311095[/C][C]-0.95533275311095[/C][/ROW]
[ROW][C]56[/C][C]13[/C][C]11.8826975477405[/C][C]1.11730245225953[/C][/ROW]
[ROW][C]57[/C][C]10[/C][C]9.03059763752766[/C][C]0.969402362472339[/C][/ROW]
[ROW][C]58[/C][C]8[/C][C]9.25079194001516[/C][C]-1.25079194001516[/C][/ROW]
[ROW][C]59[/C][C]7[/C][C]11.2077571202430[/C][C]-4.20775712024302[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]18.6321323173743[/C][C]-3.63213231737432[/C][/ROW]
[ROW][C]61[/C][C]9[/C][C]9.98827725926862[/C][C]-0.988277259268624[/C][/ROW]
[ROW][C]62[/C][C]10[/C][C]10.5877077277315[/C][C]-0.587707727731456[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]10.3392180242187[/C][C]1.66078197578125[/C][/ROW]
[ROW][C]64[/C][C]13[/C][C]14.2811157957265[/C][C]-1.28111579572652[/C][/ROW]
[ROW][C]65[/C][C]10[/C][C]11.1648831659835[/C][C]-1.16488316598348[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]7.74921664951315[/C][C]3.25078335048685[/C][/ROW]
[ROW][C]67[/C][C]8[/C][C]10.7012600102935[/C][C]-2.70126001029350[/C][/ROW]
[ROW][C]68[/C][C]9[/C][C]7.0287360071376[/C][C]1.9712639928624[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]13.2245830565789[/C][C]-0.224583056578933[/C][/ROW]
[ROW][C]70[/C][C]11[/C][C]11.4248953476096[/C][C]-0.424895347609600[/C][/ROW]
[ROW][C]71[/C][C]8[/C][C]11.0423808240166[/C][C]-3.04238082401656[/C][/ROW]
[ROW][C]72[/C][C]9[/C][C]10.4619036874179[/C][C]-1.46190368741792[/C][/ROW]
[ROW][C]73[/C][C]9[/C][C]8.13136506745948[/C][C]0.868634932540524[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]9.17858579184867[/C][C]5.82141420815133[/C][/ROW]
[ROW][C]75[/C][C]9[/C][C]8.98586277343772[/C][C]0.0141372265622786[/C][/ROW]
[ROW][C]76[/C][C]10[/C][C]10.6642081481749[/C][C]-0.664208148174871[/C][/ROW]
[ROW][C]77[/C][C]14[/C][C]12.8393820597583[/C][C]1.16061794024171[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]12.1813619972361[/C][C]-0.181361997236140[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]11.1044636105117[/C][C]0.895536389488292[/C][/ROW]
[ROW][C]80[/C][C]11[/C][C]11.9157682572962[/C][C]-0.915768257296198[/C][/ROW]
[ROW][C]81[/C][C]14[/C][C]12.5701349520778[/C][C]1.42986504792223[/C][/ROW]
[ROW][C]82[/C][C]6[/C][C]8.23618032037408[/C][C]-2.23618032037408[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]11.0158197475292[/C][C]0.984180252470836[/C][/ROW]
[ROW][C]84[/C][C]8[/C][C]8.13754185188919[/C][C]-0.137541851889191[/C][/ROW]
[ROW][C]85[/C][C]14[/C][C]14.2113340587138[/C][C]-0.211334058713820[/C][/ROW]
[ROW][C]86[/C][C]11[/C][C]10.7550011099163[/C][C]0.244998890083714[/C][/ROW]
[ROW][C]87[/C][C]10[/C][C]10.1590211922866[/C][C]-0.159021192286559[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]12.4139287907590[/C][C]1.58607120924102[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]12.3964359720643[/C][C]-0.396435972064284[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]12.5284532104547[/C][C]-2.52845321045466[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]14.3681853126345[/C][C]-0.368185312634538[/C][/ROW]
[ROW][C]92[/C][C]5[/C][C]7.75153745777384[/C][C]-2.75153745777384[/C][/ROW]
[ROW][C]93[/C][C]11[/C][C]12.1785421983973[/C][C]-1.1785421983973[/C][/ROW]
[ROW][C]94[/C][C]10[/C][C]11.1487411707221[/C][C]-1.14874117072209[/C][/ROW]
[ROW][C]95[/C][C]9[/C][C]9.61231434093077[/C][C]-0.612314340930776[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]6.8684424356047[/C][C]3.1315575643953[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]10.7016883506144[/C][C]5.29831164938556[/C][/ROW]
[ROW][C]98[/C][C]13[/C][C]13.3591147417237[/C][C]-0.359114741723721[/C][/ROW]
[ROW][C]99[/C][C]9[/C][C]11.0358153509783[/C][C]-2.03581535097827[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]12.9008068217531[/C][C]-2.90080682175314[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]10.9765235052967[/C][C]-0.976523505296659[/C][/ROW]
[ROW][C]102[/C][C]7[/C][C]10.1944870675154[/C][C]-3.19448706751538[/C][/ROW]
[ROW][C]103[/C][C]9[/C][C]8.49290088628732[/C][C]0.507099113712681[/C][/ROW]
[ROW][C]104[/C][C]8[/C][C]9.25198395925403[/C][C]-1.25198395925403[/C][/ROW]
[ROW][C]105[/C][C]14[/C][C]12.7596498347755[/C][C]1.24035016522452[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]17.6169946212989[/C][C]-3.61699462129891[/C][/ROW]
[ROW][C]107[/C][C]8[/C][C]12.6591641208644[/C][C]-4.65916412086439[/C][/ROW]
[ROW][C]108[/C][C]9[/C][C]13.6614883179975[/C][C]-4.6614883179975[/C][/ROW]
[ROW][C]109[/C][C]14[/C][C]14.2956476082921[/C][C]-0.295647608292068[/C][/ROW]
[ROW][C]110[/C][C]14[/C][C]15.0938846105181[/C][C]-1.09388461051811[/C][/ROW]
[ROW][C]111[/C][C]8[/C][C]11.4577560650174[/C][C]-3.45775606501737[/C][/ROW]
[ROW][C]112[/C][C]8[/C][C]9.35765991735726[/C][C]-1.35765991735726[/C][/ROW]
[ROW][C]113[/C][C]8[/C][C]7.41149426103054[/C][C]0.588505738969455[/C][/ROW]
[ROW][C]114[/C][C]7[/C][C]7.33580614820812[/C][C]-0.335806148208119[/C][/ROW]
[ROW][C]115[/C][C]6[/C][C]4.68274638193216[/C][C]1.31725361806784[/C][/ROW]
[ROW][C]116[/C][C]8[/C][C]8.02947627924661[/C][C]-0.0294762792466125[/C][/ROW]
[ROW][C]117[/C][C]6[/C][C]8.8905271822193[/C][C]-2.89052718221930[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]13.4729791874093[/C][C]-2.47297918740928[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]12.7360895599980[/C][C]1.26391044000205[/C][/ROW]
[ROW][C]120[/C][C]11[/C][C]16.9130651429320[/C][C]-5.91306514293199[/C][/ROW]
[ROW][C]121[/C][C]11[/C][C]12.3852122537675[/C][C]-1.38521225376751[/C][/ROW]
[ROW][C]122[/C][C]11[/C][C]14.0439343478287[/C][C]-3.04393434782866[/C][/ROW]
[ROW][C]123[/C][C]14[/C][C]14.3105362698450[/C][C]-0.310536269844989[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]9.63127963571698[/C][C]-1.63127963571697[/C][/ROW]
[ROW][C]125[/C][C]20[/C][C]10.8684230798688[/C][C]9.13157692013115[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]9.75566799788626[/C][C]1.24433200211374[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]9.12008080529924[/C][C]-1.12008080529924[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]10.0089667525346[/C][C]0.99103324746545[/C][/ROW]
[ROW][C]129[/C][C]10[/C][C]8.91107763319837[/C][C]1.08892236680163[/C][/ROW]
[ROW][C]130[/C][C]14[/C][C]12.9892068762432[/C][C]1.01079312375677[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]12.7578794199781[/C][C]-1.75787941997814[/C][/ROW]
[ROW][C]132[/C][C]9[/C][C]11.0305543045231[/C][C]-2.03055430452308[/C][/ROW]
[ROW][C]133[/C][C]9[/C][C]7.99564521224491[/C][C]1.00435478775509[/C][/ROW]
[ROW][C]134[/C][C]8[/C][C]9.37714962101867[/C][C]-1.37714962101867[/C][/ROW]
[ROW][C]135[/C][C]10[/C][C]11.5059762561231[/C][C]-1.50597625612307[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]6.14523896089694[/C][C]6.85476103910306[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]13.5433031111217[/C][C]-0.543303111121712[/C][/ROW]
[ROW][C]138[/C][C]12[/C][C]15.0797502021508[/C][C]-3.07975020215077[/C][/ROW]
[ROW][C]139[/C][C]8[/C][C]8.46036575258291[/C][C]-0.460365752582913[/C][/ROW]
[ROW][C]140[/C][C]13[/C][C]15.9967530673342[/C][C]-2.99675306733424[/C][/ROW]
[ROW][C]141[/C][C]14[/C][C]17.5537031156223[/C][C]-3.55370311562232[/C][/ROW]
[ROW][C]142[/C][C]12[/C][C]5.00855327423951[/C][C]6.99144672576049[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]16.2710673551707[/C][C]-2.27106735517067[/C][/ROW]
[ROW][C]144[/C][C]15[/C][C]14.58756874484[/C][C]0.412431255160004[/C][/ROW]
[ROW][C]145[/C][C]13[/C][C]11.4581253657349[/C][C]1.54187463426507[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]13.7672957791049[/C][C]2.23270422089512[/C][/ROW]
[ROW][C]147[/C][C]9[/C][C]9.5627044280725[/C][C]-0.562704428072497[/C][/ROW]
[ROW][C]148[/C][C]9[/C][C]10.4760595709918[/C][C]-1.47605957099177[/C][/ROW]
[ROW][C]149[/C][C]9[/C][C]12.0785763545246[/C][C]-3.07857635452458[/C][/ROW]
[ROW][C]150[/C][C]8[/C][C]8.55535352463767[/C][C]-0.555353524637675[/C][/ROW]
[ROW][C]151[/C][C]7[/C][C]7.82755793085548[/C][C]-0.827557930855484[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]12.9232811827220[/C][C]3.07671881727798[/C][/ROW]
[ROW][C]153[/C][C]11[/C][C]5.55323115112761[/C][C]5.44676884887239[/C][/ROW]
[ROW][C]154[/C][C]9[/C][C]8.01601463887984[/C][C]0.983985361120157[/C][/ROW]
[ROW][C]155[/C][C]11[/C][C]9.55404099384758[/C][C]1.44595900615242[/C][/ROW]
[ROW][C]156[/C][C]9[/C][C]9.26027996129633[/C][C]-0.260279961296329[/C][/ROW]
[ROW][C]157[/C][C]14[/C][C]12.7939897372146[/C][C]1.20601026278541[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.7114143348072[/C][C]1.28858566519280[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]9.41858413864334[/C][C]6.58141586135666[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100960&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100960&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
12423.43027153198820.56972846801179
22523.09780620285041.90219379714961
31717.6568799997603-0.656879999760318
41821.8107814240319-3.81078142403191
51821.2154141122496-3.21541411224959
61619.1302518211615-3.13025182116155
72023.3085968189630-3.30859681896303
81622.7531875278894-6.75318752788943
91823.3777995833619-5.3777995833619
101720.450649864873-3.45064986487299
112321.15210156948121.84789843051883
123022.58890110501077.41109889498928
132316.40840828898166.59159171101837
141823.2268487370287-5.22684873702865
151522.9728624514993-7.97286245149932
161217.4851742981152-5.48517429811525
172121.6651486012932-0.665148601293227
181520.2383769967131-5.23837699671314
192017.73963642409122.26036357590884
203124.17463356454866.82536643545139
212725.29484585760091.70515414239911
223422.838143228686611.1618567713134
232117.50595888204663.49404111795336
243122.24415174323378.75584825676631
251918.87993189321570.120068106784265
261617.5398583170390-1.53985831703897
272020.7138891814829-0.713889181482937
282117.57864711247963.42135288752039
292219.45306122773992.54693877226014
301720.2114239735722-3.21142397357221
312421.40686549394922.59313450605075
322525.2764941967240-0.276494196724022
332623.04707730201562.95292269798441
342523.81573933302871.18426066697133
351720.2759207726271-3.27592077262712
363228.59890121048963.40109878951037
373326.23636482040436.76363517959566
381323.2715471832359-10.2715471832359
393222.25795947395419.74204052604586
402525.682360753714-0.682360753714005
412919.92511569124279.07488430875734
422220.37246839730621.62753160269381
431817.72956552977790.270434470222058
441720.6574454678035-3.65744546780348
452021.075756238427-1.07575623842701
461520.2976711282779-5.29767112827793
472019.48066932549630.519330674503749
483329.28873886326273.71126113673728
492920.17910391800858.8208960819915
502323.8746024789380-0.87460247893803
512624.00610523616011.99389476383986
521818.6818634660854-0.681863466085368
532015.78743425565124.21256574434882
54612.7423353580560-6.74233535805596
5588.95533275311095-0.95533275311095
561311.88269754774051.11730245225953
57109.030597637527660.969402362472339
5889.25079194001516-1.25079194001516
59711.2077571202430-4.20775712024302
601518.6321323173743-3.63213231737432
6199.98827725926862-0.988277259268624
621010.5877077277315-0.587707727731456
631210.33921802421871.66078197578125
641314.2811157957265-1.28111579572652
651011.1648831659835-1.16488316598348
66117.749216649513153.25078335048685
67810.7012600102935-2.70126001029350
6897.02873600713761.9712639928624
691313.2245830565789-0.224583056578933
701111.4248953476096-0.424895347609600
71811.0423808240166-3.04238082401656
72910.4619036874179-1.46190368741792
7398.131365067459480.868634932540524
74159.178585791848675.82141420815133
7598.985862773437720.0141372265622786
761010.6642081481749-0.664208148174871
771412.83938205975831.16061794024171
781212.1813619972361-0.181361997236140
791211.10446361051170.895536389488292
801111.9157682572962-0.915768257296198
811412.57013495207781.42986504792223
8268.23618032037408-2.23618032037408
831211.01581974752920.984180252470836
8488.13754185188919-0.137541851889191
851414.2113340587138-0.211334058713820
861110.75500110991630.244998890083714
871010.1590211922866-0.159021192286559
881412.41392879075901.58607120924102
891212.3964359720643-0.396435972064284
901012.5284532104547-2.52845321045466
911414.3681853126345-0.368185312634538
9257.75153745777384-2.75153745777384
931112.1785421983973-1.1785421983973
941011.1487411707221-1.14874117072209
9599.61231434093077-0.612314340930776
96106.86844243560473.1315575643953
971610.70168835061445.29831164938556
981313.3591147417237-0.359114741723721
99911.0358153509783-2.03581535097827
1001012.9008068217531-2.90080682175314
1011010.9765235052967-0.976523505296659
102710.1944870675154-3.19448706751538
10398.492900886287320.507099113712681
10489.25198395925403-1.25198395925403
1051412.75964983477551.24035016522452
1061417.6169946212989-3.61699462129891
107812.6591641208644-4.65916412086439
108913.6614883179975-4.6614883179975
1091414.2956476082921-0.295647608292068
1101415.0938846105181-1.09388461051811
111811.4577560650174-3.45775606501737
11289.35765991735726-1.35765991735726
11387.411494261030540.588505738969455
11477.33580614820812-0.335806148208119
11564.682746381932161.31725361806784
11688.02947627924661-0.0294762792466125
11768.8905271822193-2.89052718221930
1181113.4729791874093-2.47297918740928
1191412.73608955999801.26391044000205
1201116.9130651429320-5.91306514293199
1211112.3852122537675-1.38521225376751
1221114.0439343478287-3.04393434782866
1231414.3105362698450-0.310536269844989
12489.63127963571698-1.63127963571697
1252010.86842307986889.13157692013115
126119.755667997886261.24433200211374
12789.12008080529924-1.12008080529924
1281110.00896675253460.99103324746545
129108.911077633198371.08892236680163
1301412.98920687624321.01079312375677
1311112.7578794199781-1.75787941997814
132911.0305543045231-2.03055430452308
13397.995645212244911.00435478775509
13489.37714962101867-1.37714962101867
1351011.5059762561231-1.50597625612307
136136.145238960896946.85476103910306
1371313.5433031111217-0.543303111121712
1381215.0797502021508-3.07975020215077
13988.46036575258291-0.460365752582913
1401315.9967530673342-2.99675306733424
1411417.5537031156223-3.55370311562232
142125.008553274239516.99144672576049
1431416.2710673551707-2.27106735517067
1441514.587568744840.412431255160004
1451311.45812536573491.54187463426507
1461613.76729577910492.23270422089512
14799.5627044280725-0.562704428072497
148910.4760595709918-1.47605957099177
149912.0785763545246-3.07857635452458
15088.55535352463767-0.555353524637675
15177.82755793085548-0.827557930855484
1521612.92328118272203.07671881727798
153115.553231151127615.44676884887239
15498.016014638879840.983985361120157
155119.554040993847581.44595900615242
15699.26027996129633-0.260279961296329
1571412.79398973721461.20601026278541
1581311.71141433480721.28858566519280
159169.418584138643346.58141586135666







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.4715656327671650.943131265534330.528434367232835
170.3027195215338640.6054390430677280.697280478466136
180.3386961391482100.6773922782964190.66130386085179
190.5176417205549050.964716558890190.482358279445095
200.4302532663162340.8605065326324690.569746733683766
210.3754245258626440.7508490517252890.624575474137356
220.8168955773792590.3662088452414830.183104422620741
230.7580993897872490.4838012204255020.241900610212751
240.9822875598120340.0354248803759330.0177124401879665
250.9768652767249160.04626944655016860.0231347232750843
260.9713814277781620.05723714444367670.0286185722218384
270.9583061531734980.08338769365300470.0416938468265024
280.9426136080239470.1147727839521050.0573863919760526
290.9203398213201330.1593203573597350.0796601786798673
300.9023034749208010.1953930501583990.0976965250791993
310.9308276085598360.1383447828803280.0691723914401639
320.9443501266944760.1112997466110490.0556498733055245
330.94924346330510.1015130733898010.0507565366949007
340.958061313196710.08387737360658130.0419386868032907
350.9709776525554420.05804469488911660.0290223474445583
360.9682909353959780.06341812920804340.0317090646040217
370.990940882828740.01811823434251970.00905911717125983
380.9996703213501130.0006593572997746130.000329678649887306
390.9999084049558130.0001831900883739859.15950441869923e-05
400.9998626163854290.0002747672291424990.000137383614571249
410.9999317877882080.0001364244235838246.82122117919118e-05
420.9999149997505160.0001700004989683918.50002494841957e-05
430.9998807696481780.0002384607036448890.000119230351822444
440.9998471232100180.0003057535799639210.000152876789981961
450.999810112433730.0003797751325415630.000189887566270782
460.999947139972510.0001057200549784405.28600274892201e-05
470.9999282326415180.0001435347169647637.17673584823816e-05
480.9999352256078090.0001295487843817946.47743921908971e-05
490.9999999484436141.03112771071824e-075.15563855359121e-08
500.9999999100234821.79953035256824e-078.99765176284121e-08
510.999999991212091.75758217465388e-088.7879108732694e-09
520.9999999845907453.08185105395242e-081.54092552697621e-08
5314.13042206721116e-202.06521103360558e-20
5411.61407830244153e-218.07039151220767e-22
5515.41881111997112e-212.70940555998556e-21
5619.0346137332179e-214.51730686660895e-21
5712.93737393785734e-211.46868696892867e-21
5819.41630729015494e-224.70815364507747e-22
5911.84883782631852e-229.24418913159262e-23
6013.73408504858449e-221.86704252429224e-22
6117.98758032810882e-223.99379016405441e-22
6212.39152756674451e-211.19576378337226e-21
6318.0204340155742e-214.0102170077871e-21
6418.08119412246517e-214.04059706123258e-21
6511.68583341087901e-208.42916705439504e-21
6612.37751439084313e-201.18875719542157e-20
6717.75201220833773e-203.87600610416886e-20
6811.06412017355714e-195.3206008677857e-20
6912.53580484305394e-191.26790242152697e-19
7016.34077608395306e-193.17038804197653e-19
7111.99086374697876e-189.95431873489381e-19
7215.85144612171303e-182.92572306085652e-18
7312.15685416246753e-181.07842708123376e-18
7411.11650308536899e-195.58251542684495e-20
7512.18913775673321e-191.09456887836660e-19
7616.40664076159268e-193.20332038079634e-19
7711.52880768868587e-187.64403844342937e-19
7814.72195226910635e-182.36097613455318e-18
7911.47965742767146e-177.39828713835732e-18
8013.47951318368306e-171.73975659184153e-17
8111.07578157052363e-165.37890785261814e-17
8213.28861076059634e-161.64430538029817e-16
8319.34709095341143e-164.67354547670571e-16
840.9999999999999992.70491200723316e-151.35245600361658e-15
850.9999999999999967.95887286825814e-153.97943643412907e-15
860.9999999999999882.32208433615442e-141.16104216807721e-14
870.999999999999976.00192793533862e-143.00096396766931e-14
880.9999999999999321.36875964486213e-136.84379822431067e-14
890.999999999999852.98711975569631e-131.49355987784816e-13
900.9999999999996626.75153401008661e-133.37576700504331e-13
910.9999999999990871.82555563785638e-129.12777818928189e-13
920.999999999998313.38139596094978e-121.69069798047489e-12
930.9999999999956578.6862205832746e-124.3431102916373e-12
940.9999999999895532.08929926882147e-111.04464963441073e-11
950.9999999999726975.4606338191621e-112.73031690958105e-11
960.9999999999340031.31994866958659e-106.59974334793295e-11
970.999999999946841.06318730825807e-105.31593654129034e-11
980.999999999874222.51559896088936e-101.25779948044468e-10
990.9999999997993084.01383144919376e-102.00691572459688e-10
1000.9999999996100577.79885850501357e-103.89942925250678e-10
1010.9999999991689371.66212566333608e-098.31062831668041e-10
1020.9999999991672141.66557246761023e-098.32786233805115e-10
1030.9999999981072683.78546322949954e-091.89273161474977e-09
1040.9999999954230139.15397310637772e-094.57698655318886e-09
1050.999999988924952.21501004832171e-081.10750502416086e-08
1060.9999999823939633.52120739764536e-081.76060369882268e-08
1070.9999999712442195.75115623426902e-082.87557811713451e-08
1080.9999999541060669.17878683520928e-084.58939341760464e-08
1090.9999998925342962.14931407832742e-071.07465703916371e-07
1100.9999997515456864.96908627531213e-072.48454313765607e-07
1110.9999994650968921.06980621522865e-065.34903107614326e-07
1120.999999038572881.92285424098849e-069.61427120494247e-07
1130.999998451184233.09763154039969e-061.54881577019984e-06
1140.9999970523179075.8953641850845e-062.94768209254225e-06
1150.999999611812057.7637590059848e-073.8818795029924e-07
1160.9999990750433831.84991323416342e-069.24956617081712e-07
1170.9999979393989524.12120209575748e-062.06060104787874e-06
1180.9999954878812469.02423750692174e-064.51211875346087e-06
1190.999990002877111.99942457814962e-059.99712289074812e-06
1200.9999840203475493.19593049019695e-051.59796524509847e-05
1210.9999661896667976.7620666406061e-053.38103332030305e-05
1220.9999317446233650.0001365107532696796.82553766348395e-05
1230.999855018031890.0002899639362206840.000144981968110342
1240.999700823584910.0005983528301825490.000299176415091275
1250.9999999999990191.96256737557483e-129.81283687787415e-13
1260.9999999999948561.02872480078326e-115.14362400391629e-12
1270.999999999985812.83778742639275e-111.41889371319637e-11
1280.9999999999196531.60693740333872e-108.03468701669359e-11
1290.9999999995606658.78669633664959e-104.39334816832479e-10
1300.9999999978792714.24145745854309e-092.12072872927154e-09
1310.9999999891320072.17359862647687e-081.08679931323843e-08
1320.9999999457125131.08574974077297e-075.42874870386484e-08
1330.9999997458480785.08303843869452e-072.54151921934726e-07
1340.9999988446894062.31062118806774e-061.15531059403387e-06
1350.999996728707466.54258508140731e-063.27129254070366e-06
1360.999994131585131.17368297411935e-055.86841487059675e-06
1370.9999736878567665.26242864681908e-052.63121432340954e-05
1380.999908417838140.0001831643237200039.15821618600016e-05
1390.9996230560375050.0007538879249894320.000376943962494716
1400.9985619607957810.002876078408437980.00143803920421899
1410.9960098353412790.007980329317442840.00399016465872142
1420.9996219040802450.0007561918395104620.000378095919755231
1430.997652347566480.00469530486703870.00234765243351935

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.471565632767165 & 0.94313126553433 & 0.528434367232835 \tabularnewline
17 & 0.302719521533864 & 0.605439043067728 & 0.697280478466136 \tabularnewline
18 & 0.338696139148210 & 0.677392278296419 & 0.66130386085179 \tabularnewline
19 & 0.517641720554905 & 0.96471655889019 & 0.482358279445095 \tabularnewline
20 & 0.430253266316234 & 0.860506532632469 & 0.569746733683766 \tabularnewline
21 & 0.375424525862644 & 0.750849051725289 & 0.624575474137356 \tabularnewline
22 & 0.816895577379259 & 0.366208845241483 & 0.183104422620741 \tabularnewline
23 & 0.758099389787249 & 0.483801220425502 & 0.241900610212751 \tabularnewline
24 & 0.982287559812034 & 0.035424880375933 & 0.0177124401879665 \tabularnewline
25 & 0.976865276724916 & 0.0462694465501686 & 0.0231347232750843 \tabularnewline
26 & 0.971381427778162 & 0.0572371444436767 & 0.0286185722218384 \tabularnewline
27 & 0.958306153173498 & 0.0833876936530047 & 0.0416938468265024 \tabularnewline
28 & 0.942613608023947 & 0.114772783952105 & 0.0573863919760526 \tabularnewline
29 & 0.920339821320133 & 0.159320357359735 & 0.0796601786798673 \tabularnewline
30 & 0.902303474920801 & 0.195393050158399 & 0.0976965250791993 \tabularnewline
31 & 0.930827608559836 & 0.138344782880328 & 0.0691723914401639 \tabularnewline
32 & 0.944350126694476 & 0.111299746611049 & 0.0556498733055245 \tabularnewline
33 & 0.9492434633051 & 0.101513073389801 & 0.0507565366949007 \tabularnewline
34 & 0.95806131319671 & 0.0838773736065813 & 0.0419386868032907 \tabularnewline
35 & 0.970977652555442 & 0.0580446948891166 & 0.0290223474445583 \tabularnewline
36 & 0.968290935395978 & 0.0634181292080434 & 0.0317090646040217 \tabularnewline
37 & 0.99094088282874 & 0.0181182343425197 & 0.00905911717125983 \tabularnewline
38 & 0.999670321350113 & 0.000659357299774613 & 0.000329678649887306 \tabularnewline
39 & 0.999908404955813 & 0.000183190088373985 & 9.15950441869923e-05 \tabularnewline
40 & 0.999862616385429 & 0.000274767229142499 & 0.000137383614571249 \tabularnewline
41 & 0.999931787788208 & 0.000136424423583824 & 6.82122117919118e-05 \tabularnewline
42 & 0.999914999750516 & 0.000170000498968391 & 8.50002494841957e-05 \tabularnewline
43 & 0.999880769648178 & 0.000238460703644889 & 0.000119230351822444 \tabularnewline
44 & 0.999847123210018 & 0.000305753579963921 & 0.000152876789981961 \tabularnewline
45 & 0.99981011243373 & 0.000379775132541563 & 0.000189887566270782 \tabularnewline
46 & 0.99994713997251 & 0.000105720054978440 & 5.28600274892201e-05 \tabularnewline
47 & 0.999928232641518 & 0.000143534716964763 & 7.17673584823816e-05 \tabularnewline
48 & 0.999935225607809 & 0.000129548784381794 & 6.47743921908971e-05 \tabularnewline
49 & 0.999999948443614 & 1.03112771071824e-07 & 5.15563855359121e-08 \tabularnewline
50 & 0.999999910023482 & 1.79953035256824e-07 & 8.99765176284121e-08 \tabularnewline
51 & 0.99999999121209 & 1.75758217465388e-08 & 8.7879108732694e-09 \tabularnewline
52 & 0.999999984590745 & 3.08185105395242e-08 & 1.54092552697621e-08 \tabularnewline
53 & 1 & 4.13042206721116e-20 & 2.06521103360558e-20 \tabularnewline
54 & 1 & 1.61407830244153e-21 & 8.07039151220767e-22 \tabularnewline
55 & 1 & 5.41881111997112e-21 & 2.70940555998556e-21 \tabularnewline
56 & 1 & 9.0346137332179e-21 & 4.51730686660895e-21 \tabularnewline
57 & 1 & 2.93737393785734e-21 & 1.46868696892867e-21 \tabularnewline
58 & 1 & 9.41630729015494e-22 & 4.70815364507747e-22 \tabularnewline
59 & 1 & 1.84883782631852e-22 & 9.24418913159262e-23 \tabularnewline
60 & 1 & 3.73408504858449e-22 & 1.86704252429224e-22 \tabularnewline
61 & 1 & 7.98758032810882e-22 & 3.99379016405441e-22 \tabularnewline
62 & 1 & 2.39152756674451e-21 & 1.19576378337226e-21 \tabularnewline
63 & 1 & 8.0204340155742e-21 & 4.0102170077871e-21 \tabularnewline
64 & 1 & 8.08119412246517e-21 & 4.04059706123258e-21 \tabularnewline
65 & 1 & 1.68583341087901e-20 & 8.42916705439504e-21 \tabularnewline
66 & 1 & 2.37751439084313e-20 & 1.18875719542157e-20 \tabularnewline
67 & 1 & 7.75201220833773e-20 & 3.87600610416886e-20 \tabularnewline
68 & 1 & 1.06412017355714e-19 & 5.3206008677857e-20 \tabularnewline
69 & 1 & 2.53580484305394e-19 & 1.26790242152697e-19 \tabularnewline
70 & 1 & 6.34077608395306e-19 & 3.17038804197653e-19 \tabularnewline
71 & 1 & 1.99086374697876e-18 & 9.95431873489381e-19 \tabularnewline
72 & 1 & 5.85144612171303e-18 & 2.92572306085652e-18 \tabularnewline
73 & 1 & 2.15685416246753e-18 & 1.07842708123376e-18 \tabularnewline
74 & 1 & 1.11650308536899e-19 & 5.58251542684495e-20 \tabularnewline
75 & 1 & 2.18913775673321e-19 & 1.09456887836660e-19 \tabularnewline
76 & 1 & 6.40664076159268e-19 & 3.20332038079634e-19 \tabularnewline
77 & 1 & 1.52880768868587e-18 & 7.64403844342937e-19 \tabularnewline
78 & 1 & 4.72195226910635e-18 & 2.36097613455318e-18 \tabularnewline
79 & 1 & 1.47965742767146e-17 & 7.39828713835732e-18 \tabularnewline
80 & 1 & 3.47951318368306e-17 & 1.73975659184153e-17 \tabularnewline
81 & 1 & 1.07578157052363e-16 & 5.37890785261814e-17 \tabularnewline
82 & 1 & 3.28861076059634e-16 & 1.64430538029817e-16 \tabularnewline
83 & 1 & 9.34709095341143e-16 & 4.67354547670571e-16 \tabularnewline
84 & 0.999999999999999 & 2.70491200723316e-15 & 1.35245600361658e-15 \tabularnewline
85 & 0.999999999999996 & 7.95887286825814e-15 & 3.97943643412907e-15 \tabularnewline
86 & 0.999999999999988 & 2.32208433615442e-14 & 1.16104216807721e-14 \tabularnewline
87 & 0.99999999999997 & 6.00192793533862e-14 & 3.00096396766931e-14 \tabularnewline
88 & 0.999999999999932 & 1.36875964486213e-13 & 6.84379822431067e-14 \tabularnewline
89 & 0.99999999999985 & 2.98711975569631e-13 & 1.49355987784816e-13 \tabularnewline
90 & 0.999999999999662 & 6.75153401008661e-13 & 3.37576700504331e-13 \tabularnewline
91 & 0.999999999999087 & 1.82555563785638e-12 & 9.12777818928189e-13 \tabularnewline
92 & 0.99999999999831 & 3.38139596094978e-12 & 1.69069798047489e-12 \tabularnewline
93 & 0.999999999995657 & 8.6862205832746e-12 & 4.3431102916373e-12 \tabularnewline
94 & 0.999999999989553 & 2.08929926882147e-11 & 1.04464963441073e-11 \tabularnewline
95 & 0.999999999972697 & 5.4606338191621e-11 & 2.73031690958105e-11 \tabularnewline
96 & 0.999999999934003 & 1.31994866958659e-10 & 6.59974334793295e-11 \tabularnewline
97 & 0.99999999994684 & 1.06318730825807e-10 & 5.31593654129034e-11 \tabularnewline
98 & 0.99999999987422 & 2.51559896088936e-10 & 1.25779948044468e-10 \tabularnewline
99 & 0.999999999799308 & 4.01383144919376e-10 & 2.00691572459688e-10 \tabularnewline
100 & 0.999999999610057 & 7.79885850501357e-10 & 3.89942925250678e-10 \tabularnewline
101 & 0.999999999168937 & 1.66212566333608e-09 & 8.31062831668041e-10 \tabularnewline
102 & 0.999999999167214 & 1.66557246761023e-09 & 8.32786233805115e-10 \tabularnewline
103 & 0.999999998107268 & 3.78546322949954e-09 & 1.89273161474977e-09 \tabularnewline
104 & 0.999999995423013 & 9.15397310637772e-09 & 4.57698655318886e-09 \tabularnewline
105 & 0.99999998892495 & 2.21501004832171e-08 & 1.10750502416086e-08 \tabularnewline
106 & 0.999999982393963 & 3.52120739764536e-08 & 1.76060369882268e-08 \tabularnewline
107 & 0.999999971244219 & 5.75115623426902e-08 & 2.87557811713451e-08 \tabularnewline
108 & 0.999999954106066 & 9.17878683520928e-08 & 4.58939341760464e-08 \tabularnewline
109 & 0.999999892534296 & 2.14931407832742e-07 & 1.07465703916371e-07 \tabularnewline
110 & 0.999999751545686 & 4.96908627531213e-07 & 2.48454313765607e-07 \tabularnewline
111 & 0.999999465096892 & 1.06980621522865e-06 & 5.34903107614326e-07 \tabularnewline
112 & 0.99999903857288 & 1.92285424098849e-06 & 9.61427120494247e-07 \tabularnewline
113 & 0.99999845118423 & 3.09763154039969e-06 & 1.54881577019984e-06 \tabularnewline
114 & 0.999997052317907 & 5.8953641850845e-06 & 2.94768209254225e-06 \tabularnewline
115 & 0.99999961181205 & 7.7637590059848e-07 & 3.8818795029924e-07 \tabularnewline
116 & 0.999999075043383 & 1.84991323416342e-06 & 9.24956617081712e-07 \tabularnewline
117 & 0.999997939398952 & 4.12120209575748e-06 & 2.06060104787874e-06 \tabularnewline
118 & 0.999995487881246 & 9.02423750692174e-06 & 4.51211875346087e-06 \tabularnewline
119 & 0.99999000287711 & 1.99942457814962e-05 & 9.99712289074812e-06 \tabularnewline
120 & 0.999984020347549 & 3.19593049019695e-05 & 1.59796524509847e-05 \tabularnewline
121 & 0.999966189666797 & 6.7620666406061e-05 & 3.38103332030305e-05 \tabularnewline
122 & 0.999931744623365 & 0.000136510753269679 & 6.82553766348395e-05 \tabularnewline
123 & 0.99985501803189 & 0.000289963936220684 & 0.000144981968110342 \tabularnewline
124 & 0.99970082358491 & 0.000598352830182549 & 0.000299176415091275 \tabularnewline
125 & 0.999999999999019 & 1.96256737557483e-12 & 9.81283687787415e-13 \tabularnewline
126 & 0.999999999994856 & 1.02872480078326e-11 & 5.14362400391629e-12 \tabularnewline
127 & 0.99999999998581 & 2.83778742639275e-11 & 1.41889371319637e-11 \tabularnewline
128 & 0.999999999919653 & 1.60693740333872e-10 & 8.03468701669359e-11 \tabularnewline
129 & 0.999999999560665 & 8.78669633664959e-10 & 4.39334816832479e-10 \tabularnewline
130 & 0.999999997879271 & 4.24145745854309e-09 & 2.12072872927154e-09 \tabularnewline
131 & 0.999999989132007 & 2.17359862647687e-08 & 1.08679931323843e-08 \tabularnewline
132 & 0.999999945712513 & 1.08574974077297e-07 & 5.42874870386484e-08 \tabularnewline
133 & 0.999999745848078 & 5.08303843869452e-07 & 2.54151921934726e-07 \tabularnewline
134 & 0.999998844689406 & 2.31062118806774e-06 & 1.15531059403387e-06 \tabularnewline
135 & 0.99999672870746 & 6.54258508140731e-06 & 3.27129254070366e-06 \tabularnewline
136 & 0.99999413158513 & 1.17368297411935e-05 & 5.86841487059675e-06 \tabularnewline
137 & 0.999973687856766 & 5.26242864681908e-05 & 2.63121432340954e-05 \tabularnewline
138 & 0.99990841783814 & 0.000183164323720003 & 9.15821618600016e-05 \tabularnewline
139 & 0.999623056037505 & 0.000753887924989432 & 0.000376943962494716 \tabularnewline
140 & 0.998561960795781 & 0.00287607840843798 & 0.00143803920421899 \tabularnewline
141 & 0.996009835341279 & 0.00798032931744284 & 0.00399016465872142 \tabularnewline
142 & 0.999621904080245 & 0.000756191839510462 & 0.000378095919755231 \tabularnewline
143 & 0.99765234756648 & 0.0046953048670387 & 0.00234765243351935 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100960&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]16[/C][C]0.471565632767165[/C][C]0.94313126553433[/C][C]0.528434367232835[/C][/ROW]
[ROW][C]17[/C][C]0.302719521533864[/C][C]0.605439043067728[/C][C]0.697280478466136[/C][/ROW]
[ROW][C]18[/C][C]0.338696139148210[/C][C]0.677392278296419[/C][C]0.66130386085179[/C][/ROW]
[ROW][C]19[/C][C]0.517641720554905[/C][C]0.96471655889019[/C][C]0.482358279445095[/C][/ROW]
[ROW][C]20[/C][C]0.430253266316234[/C][C]0.860506532632469[/C][C]0.569746733683766[/C][/ROW]
[ROW][C]21[/C][C]0.375424525862644[/C][C]0.750849051725289[/C][C]0.624575474137356[/C][/ROW]
[ROW][C]22[/C][C]0.816895577379259[/C][C]0.366208845241483[/C][C]0.183104422620741[/C][/ROW]
[ROW][C]23[/C][C]0.758099389787249[/C][C]0.483801220425502[/C][C]0.241900610212751[/C][/ROW]
[ROW][C]24[/C][C]0.982287559812034[/C][C]0.035424880375933[/C][C]0.0177124401879665[/C][/ROW]
[ROW][C]25[/C][C]0.976865276724916[/C][C]0.0462694465501686[/C][C]0.0231347232750843[/C][/ROW]
[ROW][C]26[/C][C]0.971381427778162[/C][C]0.0572371444436767[/C][C]0.0286185722218384[/C][/ROW]
[ROW][C]27[/C][C]0.958306153173498[/C][C]0.0833876936530047[/C][C]0.0416938468265024[/C][/ROW]
[ROW][C]28[/C][C]0.942613608023947[/C][C]0.114772783952105[/C][C]0.0573863919760526[/C][/ROW]
[ROW][C]29[/C][C]0.920339821320133[/C][C]0.159320357359735[/C][C]0.0796601786798673[/C][/ROW]
[ROW][C]30[/C][C]0.902303474920801[/C][C]0.195393050158399[/C][C]0.0976965250791993[/C][/ROW]
[ROW][C]31[/C][C]0.930827608559836[/C][C]0.138344782880328[/C][C]0.0691723914401639[/C][/ROW]
[ROW][C]32[/C][C]0.944350126694476[/C][C]0.111299746611049[/C][C]0.0556498733055245[/C][/ROW]
[ROW][C]33[/C][C]0.9492434633051[/C][C]0.101513073389801[/C][C]0.0507565366949007[/C][/ROW]
[ROW][C]34[/C][C]0.95806131319671[/C][C]0.0838773736065813[/C][C]0.0419386868032907[/C][/ROW]
[ROW][C]35[/C][C]0.970977652555442[/C][C]0.0580446948891166[/C][C]0.0290223474445583[/C][/ROW]
[ROW][C]36[/C][C]0.968290935395978[/C][C]0.0634181292080434[/C][C]0.0317090646040217[/C][/ROW]
[ROW][C]37[/C][C]0.99094088282874[/C][C]0.0181182343425197[/C][C]0.00905911717125983[/C][/ROW]
[ROW][C]38[/C][C]0.999670321350113[/C][C]0.000659357299774613[/C][C]0.000329678649887306[/C][/ROW]
[ROW][C]39[/C][C]0.999908404955813[/C][C]0.000183190088373985[/C][C]9.15950441869923e-05[/C][/ROW]
[ROW][C]40[/C][C]0.999862616385429[/C][C]0.000274767229142499[/C][C]0.000137383614571249[/C][/ROW]
[ROW][C]41[/C][C]0.999931787788208[/C][C]0.000136424423583824[/C][C]6.82122117919118e-05[/C][/ROW]
[ROW][C]42[/C][C]0.999914999750516[/C][C]0.000170000498968391[/C][C]8.50002494841957e-05[/C][/ROW]
[ROW][C]43[/C][C]0.999880769648178[/C][C]0.000238460703644889[/C][C]0.000119230351822444[/C][/ROW]
[ROW][C]44[/C][C]0.999847123210018[/C][C]0.000305753579963921[/C][C]0.000152876789981961[/C][/ROW]
[ROW][C]45[/C][C]0.99981011243373[/C][C]0.000379775132541563[/C][C]0.000189887566270782[/C][/ROW]
[ROW][C]46[/C][C]0.99994713997251[/C][C]0.000105720054978440[/C][C]5.28600274892201e-05[/C][/ROW]
[ROW][C]47[/C][C]0.999928232641518[/C][C]0.000143534716964763[/C][C]7.17673584823816e-05[/C][/ROW]
[ROW][C]48[/C][C]0.999935225607809[/C][C]0.000129548784381794[/C][C]6.47743921908971e-05[/C][/ROW]
[ROW][C]49[/C][C]0.999999948443614[/C][C]1.03112771071824e-07[/C][C]5.15563855359121e-08[/C][/ROW]
[ROW][C]50[/C][C]0.999999910023482[/C][C]1.79953035256824e-07[/C][C]8.99765176284121e-08[/C][/ROW]
[ROW][C]51[/C][C]0.99999999121209[/C][C]1.75758217465388e-08[/C][C]8.7879108732694e-09[/C][/ROW]
[ROW][C]52[/C][C]0.999999984590745[/C][C]3.08185105395242e-08[/C][C]1.54092552697621e-08[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]4.13042206721116e-20[/C][C]2.06521103360558e-20[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]1.61407830244153e-21[/C][C]8.07039151220767e-22[/C][/ROW]
[ROW][C]55[/C][C]1[/C][C]5.41881111997112e-21[/C][C]2.70940555998556e-21[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]9.0346137332179e-21[/C][C]4.51730686660895e-21[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]2.93737393785734e-21[/C][C]1.46868696892867e-21[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]9.41630729015494e-22[/C][C]4.70815364507747e-22[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]1.84883782631852e-22[/C][C]9.24418913159262e-23[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]3.73408504858449e-22[/C][C]1.86704252429224e-22[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]7.98758032810882e-22[/C][C]3.99379016405441e-22[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]2.39152756674451e-21[/C][C]1.19576378337226e-21[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]8.0204340155742e-21[/C][C]4.0102170077871e-21[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]8.08119412246517e-21[/C][C]4.04059706123258e-21[/C][/ROW]
[ROW][C]65[/C][C]1[/C][C]1.68583341087901e-20[/C][C]8.42916705439504e-21[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]2.37751439084313e-20[/C][C]1.18875719542157e-20[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]7.75201220833773e-20[/C][C]3.87600610416886e-20[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]1.06412017355714e-19[/C][C]5.3206008677857e-20[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]2.53580484305394e-19[/C][C]1.26790242152697e-19[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]6.34077608395306e-19[/C][C]3.17038804197653e-19[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]1.99086374697876e-18[/C][C]9.95431873489381e-19[/C][/ROW]
[ROW][C]72[/C][C]1[/C][C]5.85144612171303e-18[/C][C]2.92572306085652e-18[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]2.15685416246753e-18[/C][C]1.07842708123376e-18[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]1.11650308536899e-19[/C][C]5.58251542684495e-20[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]2.18913775673321e-19[/C][C]1.09456887836660e-19[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]6.40664076159268e-19[/C][C]3.20332038079634e-19[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]1.52880768868587e-18[/C][C]7.64403844342937e-19[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]4.72195226910635e-18[/C][C]2.36097613455318e-18[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]1.47965742767146e-17[/C][C]7.39828713835732e-18[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]3.47951318368306e-17[/C][C]1.73975659184153e-17[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]1.07578157052363e-16[/C][C]5.37890785261814e-17[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]3.28861076059634e-16[/C][C]1.64430538029817e-16[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]9.34709095341143e-16[/C][C]4.67354547670571e-16[/C][/ROW]
[ROW][C]84[/C][C]0.999999999999999[/C][C]2.70491200723316e-15[/C][C]1.35245600361658e-15[/C][/ROW]
[ROW][C]85[/C][C]0.999999999999996[/C][C]7.95887286825814e-15[/C][C]3.97943643412907e-15[/C][/ROW]
[ROW][C]86[/C][C]0.999999999999988[/C][C]2.32208433615442e-14[/C][C]1.16104216807721e-14[/C][/ROW]
[ROW][C]87[/C][C]0.99999999999997[/C][C]6.00192793533862e-14[/C][C]3.00096396766931e-14[/C][/ROW]
[ROW][C]88[/C][C]0.999999999999932[/C][C]1.36875964486213e-13[/C][C]6.84379822431067e-14[/C][/ROW]
[ROW][C]89[/C][C]0.99999999999985[/C][C]2.98711975569631e-13[/C][C]1.49355987784816e-13[/C][/ROW]
[ROW][C]90[/C][C]0.999999999999662[/C][C]6.75153401008661e-13[/C][C]3.37576700504331e-13[/C][/ROW]
[ROW][C]91[/C][C]0.999999999999087[/C][C]1.82555563785638e-12[/C][C]9.12777818928189e-13[/C][/ROW]
[ROW][C]92[/C][C]0.99999999999831[/C][C]3.38139596094978e-12[/C][C]1.69069798047489e-12[/C][/ROW]
[ROW][C]93[/C][C]0.999999999995657[/C][C]8.6862205832746e-12[/C][C]4.3431102916373e-12[/C][/ROW]
[ROW][C]94[/C][C]0.999999999989553[/C][C]2.08929926882147e-11[/C][C]1.04464963441073e-11[/C][/ROW]
[ROW][C]95[/C][C]0.999999999972697[/C][C]5.4606338191621e-11[/C][C]2.73031690958105e-11[/C][/ROW]
[ROW][C]96[/C][C]0.999999999934003[/C][C]1.31994866958659e-10[/C][C]6.59974334793295e-11[/C][/ROW]
[ROW][C]97[/C][C]0.99999999994684[/C][C]1.06318730825807e-10[/C][C]5.31593654129034e-11[/C][/ROW]
[ROW][C]98[/C][C]0.99999999987422[/C][C]2.51559896088936e-10[/C][C]1.25779948044468e-10[/C][/ROW]
[ROW][C]99[/C][C]0.999999999799308[/C][C]4.01383144919376e-10[/C][C]2.00691572459688e-10[/C][/ROW]
[ROW][C]100[/C][C]0.999999999610057[/C][C]7.79885850501357e-10[/C][C]3.89942925250678e-10[/C][/ROW]
[ROW][C]101[/C][C]0.999999999168937[/C][C]1.66212566333608e-09[/C][C]8.31062831668041e-10[/C][/ROW]
[ROW][C]102[/C][C]0.999999999167214[/C][C]1.66557246761023e-09[/C][C]8.32786233805115e-10[/C][/ROW]
[ROW][C]103[/C][C]0.999999998107268[/C][C]3.78546322949954e-09[/C][C]1.89273161474977e-09[/C][/ROW]
[ROW][C]104[/C][C]0.999999995423013[/C][C]9.15397310637772e-09[/C][C]4.57698655318886e-09[/C][/ROW]
[ROW][C]105[/C][C]0.99999998892495[/C][C]2.21501004832171e-08[/C][C]1.10750502416086e-08[/C][/ROW]
[ROW][C]106[/C][C]0.999999982393963[/C][C]3.52120739764536e-08[/C][C]1.76060369882268e-08[/C][/ROW]
[ROW][C]107[/C][C]0.999999971244219[/C][C]5.75115623426902e-08[/C][C]2.87557811713451e-08[/C][/ROW]
[ROW][C]108[/C][C]0.999999954106066[/C][C]9.17878683520928e-08[/C][C]4.58939341760464e-08[/C][/ROW]
[ROW][C]109[/C][C]0.999999892534296[/C][C]2.14931407832742e-07[/C][C]1.07465703916371e-07[/C][/ROW]
[ROW][C]110[/C][C]0.999999751545686[/C][C]4.96908627531213e-07[/C][C]2.48454313765607e-07[/C][/ROW]
[ROW][C]111[/C][C]0.999999465096892[/C][C]1.06980621522865e-06[/C][C]5.34903107614326e-07[/C][/ROW]
[ROW][C]112[/C][C]0.99999903857288[/C][C]1.92285424098849e-06[/C][C]9.61427120494247e-07[/C][/ROW]
[ROW][C]113[/C][C]0.99999845118423[/C][C]3.09763154039969e-06[/C][C]1.54881577019984e-06[/C][/ROW]
[ROW][C]114[/C][C]0.999997052317907[/C][C]5.8953641850845e-06[/C][C]2.94768209254225e-06[/C][/ROW]
[ROW][C]115[/C][C]0.99999961181205[/C][C]7.7637590059848e-07[/C][C]3.8818795029924e-07[/C][/ROW]
[ROW][C]116[/C][C]0.999999075043383[/C][C]1.84991323416342e-06[/C][C]9.24956617081712e-07[/C][/ROW]
[ROW][C]117[/C][C]0.999997939398952[/C][C]4.12120209575748e-06[/C][C]2.06060104787874e-06[/C][/ROW]
[ROW][C]118[/C][C]0.999995487881246[/C][C]9.02423750692174e-06[/C][C]4.51211875346087e-06[/C][/ROW]
[ROW][C]119[/C][C]0.99999000287711[/C][C]1.99942457814962e-05[/C][C]9.99712289074812e-06[/C][/ROW]
[ROW][C]120[/C][C]0.999984020347549[/C][C]3.19593049019695e-05[/C][C]1.59796524509847e-05[/C][/ROW]
[ROW][C]121[/C][C]0.999966189666797[/C][C]6.7620666406061e-05[/C][C]3.38103332030305e-05[/C][/ROW]
[ROW][C]122[/C][C]0.999931744623365[/C][C]0.000136510753269679[/C][C]6.82553766348395e-05[/C][/ROW]
[ROW][C]123[/C][C]0.99985501803189[/C][C]0.000289963936220684[/C][C]0.000144981968110342[/C][/ROW]
[ROW][C]124[/C][C]0.99970082358491[/C][C]0.000598352830182549[/C][C]0.000299176415091275[/C][/ROW]
[ROW][C]125[/C][C]0.999999999999019[/C][C]1.96256737557483e-12[/C][C]9.81283687787415e-13[/C][/ROW]
[ROW][C]126[/C][C]0.999999999994856[/C][C]1.02872480078326e-11[/C][C]5.14362400391629e-12[/C][/ROW]
[ROW][C]127[/C][C]0.99999999998581[/C][C]2.83778742639275e-11[/C][C]1.41889371319637e-11[/C][/ROW]
[ROW][C]128[/C][C]0.999999999919653[/C][C]1.60693740333872e-10[/C][C]8.03468701669359e-11[/C][/ROW]
[ROW][C]129[/C][C]0.999999999560665[/C][C]8.78669633664959e-10[/C][C]4.39334816832479e-10[/C][/ROW]
[ROW][C]130[/C][C]0.999999997879271[/C][C]4.24145745854309e-09[/C][C]2.12072872927154e-09[/C][/ROW]
[ROW][C]131[/C][C]0.999999989132007[/C][C]2.17359862647687e-08[/C][C]1.08679931323843e-08[/C][/ROW]
[ROW][C]132[/C][C]0.999999945712513[/C][C]1.08574974077297e-07[/C][C]5.42874870386484e-08[/C][/ROW]
[ROW][C]133[/C][C]0.999999745848078[/C][C]5.08303843869452e-07[/C][C]2.54151921934726e-07[/C][/ROW]
[ROW][C]134[/C][C]0.999998844689406[/C][C]2.31062118806774e-06[/C][C]1.15531059403387e-06[/C][/ROW]
[ROW][C]135[/C][C]0.99999672870746[/C][C]6.54258508140731e-06[/C][C]3.27129254070366e-06[/C][/ROW]
[ROW][C]136[/C][C]0.99999413158513[/C][C]1.17368297411935e-05[/C][C]5.86841487059675e-06[/C][/ROW]
[ROW][C]137[/C][C]0.999973687856766[/C][C]5.26242864681908e-05[/C][C]2.63121432340954e-05[/C][/ROW]
[ROW][C]138[/C][C]0.99990841783814[/C][C]0.000183164323720003[/C][C]9.15821618600016e-05[/C][/ROW]
[ROW][C]139[/C][C]0.999623056037505[/C][C]0.000753887924989432[/C][C]0.000376943962494716[/C][/ROW]
[ROW][C]140[/C][C]0.998561960795781[/C][C]0.00287607840843798[/C][C]0.00143803920421899[/C][/ROW]
[ROW][C]141[/C][C]0.996009835341279[/C][C]0.00798032931744284[/C][C]0.00399016465872142[/C][/ROW]
[ROW][C]142[/C][C]0.999621904080245[/C][C]0.000756191839510462[/C][C]0.000378095919755231[/C][/ROW]
[ROW][C]143[/C][C]0.99765234756648[/C][C]0.0046953048670387[/C][C]0.00234765243351935[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100960&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100960&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
160.4715656327671650.943131265534330.528434367232835
170.3027195215338640.6054390430677280.697280478466136
180.3386961391482100.6773922782964190.66130386085179
190.5176417205549050.964716558890190.482358279445095
200.4302532663162340.8605065326324690.569746733683766
210.3754245258626440.7508490517252890.624575474137356
220.8168955773792590.3662088452414830.183104422620741
230.7580993897872490.4838012204255020.241900610212751
240.9822875598120340.0354248803759330.0177124401879665
250.9768652767249160.04626944655016860.0231347232750843
260.9713814277781620.05723714444367670.0286185722218384
270.9583061531734980.08338769365300470.0416938468265024
280.9426136080239470.1147727839521050.0573863919760526
290.9203398213201330.1593203573597350.0796601786798673
300.9023034749208010.1953930501583990.0976965250791993
310.9308276085598360.1383447828803280.0691723914401639
320.9443501266944760.1112997466110490.0556498733055245
330.94924346330510.1015130733898010.0507565366949007
340.958061313196710.08387737360658130.0419386868032907
350.9709776525554420.05804469488911660.0290223474445583
360.9682909353959780.06341812920804340.0317090646040217
370.990940882828740.01811823434251970.00905911717125983
380.9996703213501130.0006593572997746130.000329678649887306
390.9999084049558130.0001831900883739859.15950441869923e-05
400.9998626163854290.0002747672291424990.000137383614571249
410.9999317877882080.0001364244235838246.82122117919118e-05
420.9999149997505160.0001700004989683918.50002494841957e-05
430.9998807696481780.0002384607036448890.000119230351822444
440.9998471232100180.0003057535799639210.000152876789981961
450.999810112433730.0003797751325415630.000189887566270782
460.999947139972510.0001057200549784405.28600274892201e-05
470.9999282326415180.0001435347169647637.17673584823816e-05
480.9999352256078090.0001295487843817946.47743921908971e-05
490.9999999484436141.03112771071824e-075.15563855359121e-08
500.9999999100234821.79953035256824e-078.99765176284121e-08
510.999999991212091.75758217465388e-088.7879108732694e-09
520.9999999845907453.08185105395242e-081.54092552697621e-08
5314.13042206721116e-202.06521103360558e-20
5411.61407830244153e-218.07039151220767e-22
5515.41881111997112e-212.70940555998556e-21
5619.0346137332179e-214.51730686660895e-21
5712.93737393785734e-211.46868696892867e-21
5819.41630729015494e-224.70815364507747e-22
5911.84883782631852e-229.24418913159262e-23
6013.73408504858449e-221.86704252429224e-22
6117.98758032810882e-223.99379016405441e-22
6212.39152756674451e-211.19576378337226e-21
6318.0204340155742e-214.0102170077871e-21
6418.08119412246517e-214.04059706123258e-21
6511.68583341087901e-208.42916705439504e-21
6612.37751439084313e-201.18875719542157e-20
6717.75201220833773e-203.87600610416886e-20
6811.06412017355714e-195.3206008677857e-20
6912.53580484305394e-191.26790242152697e-19
7016.34077608395306e-193.17038804197653e-19
7111.99086374697876e-189.95431873489381e-19
7215.85144612171303e-182.92572306085652e-18
7312.15685416246753e-181.07842708123376e-18
7411.11650308536899e-195.58251542684495e-20
7512.18913775673321e-191.09456887836660e-19
7616.40664076159268e-193.20332038079634e-19
7711.52880768868587e-187.64403844342937e-19
7814.72195226910635e-182.36097613455318e-18
7911.47965742767146e-177.39828713835732e-18
8013.47951318368306e-171.73975659184153e-17
8111.07578157052363e-165.37890785261814e-17
8213.28861076059634e-161.64430538029817e-16
8319.34709095341143e-164.67354547670571e-16
840.9999999999999992.70491200723316e-151.35245600361658e-15
850.9999999999999967.95887286825814e-153.97943643412907e-15
860.9999999999999882.32208433615442e-141.16104216807721e-14
870.999999999999976.00192793533862e-143.00096396766931e-14
880.9999999999999321.36875964486213e-136.84379822431067e-14
890.999999999999852.98711975569631e-131.49355987784816e-13
900.9999999999996626.75153401008661e-133.37576700504331e-13
910.9999999999990871.82555563785638e-129.12777818928189e-13
920.999999999998313.38139596094978e-121.69069798047489e-12
930.9999999999956578.6862205832746e-124.3431102916373e-12
940.9999999999895532.08929926882147e-111.04464963441073e-11
950.9999999999726975.4606338191621e-112.73031690958105e-11
960.9999999999340031.31994866958659e-106.59974334793295e-11
970.999999999946841.06318730825807e-105.31593654129034e-11
980.999999999874222.51559896088936e-101.25779948044468e-10
990.9999999997993084.01383144919376e-102.00691572459688e-10
1000.9999999996100577.79885850501357e-103.89942925250678e-10
1010.9999999991689371.66212566333608e-098.31062831668041e-10
1020.9999999991672141.66557246761023e-098.32786233805115e-10
1030.9999999981072683.78546322949954e-091.89273161474977e-09
1040.9999999954230139.15397310637772e-094.57698655318886e-09
1050.999999988924952.21501004832171e-081.10750502416086e-08
1060.9999999823939633.52120739764536e-081.76060369882268e-08
1070.9999999712442195.75115623426902e-082.87557811713451e-08
1080.9999999541060669.17878683520928e-084.58939341760464e-08
1090.9999998925342962.14931407832742e-071.07465703916371e-07
1100.9999997515456864.96908627531213e-072.48454313765607e-07
1110.9999994650968921.06980621522865e-065.34903107614326e-07
1120.999999038572881.92285424098849e-069.61427120494247e-07
1130.999998451184233.09763154039969e-061.54881577019984e-06
1140.9999970523179075.8953641850845e-062.94768209254225e-06
1150.999999611812057.7637590059848e-073.8818795029924e-07
1160.9999990750433831.84991323416342e-069.24956617081712e-07
1170.9999979393989524.12120209575748e-062.06060104787874e-06
1180.9999954878812469.02423750692174e-064.51211875346087e-06
1190.999990002877111.99942457814962e-059.99712289074812e-06
1200.9999840203475493.19593049019695e-051.59796524509847e-05
1210.9999661896667976.7620666406061e-053.38103332030305e-05
1220.9999317446233650.0001365107532696796.82553766348395e-05
1230.999855018031890.0002899639362206840.000144981968110342
1240.999700823584910.0005983528301825490.000299176415091275
1250.9999999999990191.96256737557483e-129.81283687787415e-13
1260.9999999999948561.02872480078326e-115.14362400391629e-12
1270.999999999985812.83778742639275e-111.41889371319637e-11
1280.9999999999196531.60693740333872e-108.03468701669359e-11
1290.9999999995606658.78669633664959e-104.39334816832479e-10
1300.9999999978792714.24145745854309e-092.12072872927154e-09
1310.9999999891320072.17359862647687e-081.08679931323843e-08
1320.9999999457125131.08574974077297e-075.42874870386484e-08
1330.9999997458480785.08303843869452e-072.54151921934726e-07
1340.9999988446894062.31062118806774e-061.15531059403387e-06
1350.999996728707466.54258508140731e-063.27129254070366e-06
1360.999994131585131.17368297411935e-055.86841487059675e-06
1370.9999736878567665.26242864681908e-052.63121432340954e-05
1380.999908417838140.0001831643237200039.15821618600016e-05
1390.9996230560375050.0007538879249894320.000376943962494716
1400.9985619607957810.002876078408437980.00143803920421899
1410.9960098353412790.007980329317442840.00399016465872142
1420.9996219040802450.0007561918395104620.000378095919755231
1430.997652347566480.00469530486703870.00234765243351935







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1060.828125NOK
5% type I error level1090.8515625NOK
10% type I error level1140.890625NOK

\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 & 106 & 0.828125 & NOK \tabularnewline
5% type I error level & 109 & 0.8515625 & NOK \tabularnewline
10% type I error level & 114 & 0.890625 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100960&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]106[/C][C]0.828125[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]109[/C][C]0.8515625[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]114[/C][C]0.890625[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100960&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100960&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 level1060.828125NOK
5% type I error level1090.8515625NOK
10% type I error level1140.890625NOK



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