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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 computationTue, 23 Nov 2010 17:27:29 +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/23/t1290533269bh6gjxoxafdux3p.htm/, Retrieved Fri, 29 Mar 2024 14:45:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99469, Retrieved Fri, 29 Mar 2024 14:45:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact168
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] [WS 7 - Minitutori...] [2010-11-23 17:27:29] [fca744d17b21beb005bf086e7071b2bb] [Current]
-   PD      [Multiple Regression] [WS7 - Minitutorai...] [2010-11-23 21:16:43] [19f9551d4d95750ef21e9f3cf8fe2131]
-    D      [Multiple Regression] [p_Stress_MR1] [2010-12-03 20:24:39] [19f9551d4d95750ef21e9f3cf8fe2131]
-   PD        [Multiple Regression] [p_Stress_MR3v2] [2010-12-04 13:59:58] [19f9551d4d95750ef21e9f3cf8fe2131]
-   PD        [Multiple Regression] [p_Stress_MR2v2] [2010-12-04 14:03:11] [19f9551d4d95750ef21e9f3cf8fe2131]
-   PD        [Multiple Regression] [p_Stress_MR3v3] [2010-12-04 14:09:41] [19f9551d4d95750ef21e9f3cf8fe2131]
-   PD        [Multiple Regression] [p_Stress_MR4] [2010-12-04 14:16:09] [19f9551d4d95750ef21e9f3cf8fe2131]
-    D          [Multiple Regression] [p_Stress_MR1v2] [2010-12-04 14:53:22] [19f9551d4d95750ef21e9f3cf8fe2131]
-   PD            [Multiple Regression] [p_Stress_MR2v3] [2010-12-05 16:08:01] [19f9551d4d95750ef21e9f3cf8fe2131]
-   PD              [Multiple Regression] [Multiple Regressi...] [2010-12-25 15:04:54] [8ec018d7298e4a3ae278d8b7199e08b6]
-                 [Multiple Regression] [PAPER BAEYENS (Mu...] [2010-12-21 11:01:30] [e4076051fbfb461c886b1e223cd7862f]
-    D            [Multiple Regression] [PAPER BAEYENS (Mu...] [2010-12-21 11:33:46] [e4076051fbfb461c886b1e223cd7862f]
-   P               [Multiple Regression] [PAPER BAEYENS (Mu...] [2010-12-21 12:37:56] [e4076051fbfb461c886b1e223cd7862f]
-    D                [Multiple Regression] [PAPER BAEYENS (Mu...] [2010-12-21 14:03:10] [e4076051fbfb461c886b1e223cd7862f]
-    D      [Multiple Regression] [p_Stress_MR2] [2010-12-03 20:39:07] [19f9551d4d95750ef21e9f3cf8fe2131]
-    D      [Multiple Regression] [p_Stress_MR3] [2010-12-03 20:44:44] [19f9551d4d95750ef21e9f3cf8fe2131]
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Dataseries X:
12	6	15	4	7	2	2	2	2
11	6	15	3	5	4	1	2	2
14	13	14	5	7	7	4	3	4
12	8	10	3	3	3	1	2	3
21	7	10	6	7	7	5	4	4
12	9	12	5	7	2	1	2	3
22	5	18	6	7	7	1	2	3
11	8	12	6	1	2	1	3	4
10	9	14	5	4	1	1	2	3
13	11	18	5	5	2	1	2	4
10	8	9	3	6	6	2	3	3
8	11	11	5	4	1	1	2	2
15	12	11	7	7	1	3	3	3
10	8	17	5	6	1	1	1	3
14	7	8	5	2	2	1	3	3
14	9	16	3	2	2	1	1	2
11	12	21	5	6	2	1	3	3
10	20	24	6	7	1	1	2	2
13	7	21	5	5	7	2	3	4
7	8	14	2	2	1	4	4	5
12	8	7	5	7	2	1	3	3
14	16	18	4	4	4	2	3	3
11	10	18	6	5	2	1	1	1
9	6	13	3	5	1	2	2	4
11	8	11	5	5	1	3	1	3
15	9	13	4	3	5	1	3	4
13	9	13	5	5	2	1	3	3
9	11	18	2	1	1	1	2	3
15	12	14	2	1	3	1	2	1
10	8	12	5	3	1	1	3	4
11	7	9	2	2	2	2	2	4
13	8	12	2	3	5	1	2	2
8	9	8	2	2	2	1	2	2
20	4	5	5	5	6	1	1	1
12	8	10	5	2	4	1	2	3
10	8	11	1	3	1	1	3	4
10	8	11	5	4	3	1	1	1
9	6	12	2	6	6	1	2	3
14	8	12	6	2	7	2	3	3
8	4	15	1	7	4	1	2	2
14	7	12	4	6	1	2	1	4
11	14	16	3	5	5	1	1	3
13	10	14	2	3	3	1	3	3
11	9	17	5	3	2	2	3	2
11	8	10	3	4	2	1	3	3
10	11	17	4	5	2	1	3	2
14	8	12	3	2	2	1	2	1
18	8	13	6	7	1	1	3	3
14	10	13	4	6	2	1	2	3
11	8	11	5	5	1	4	3	5
12	10	13	2	6	2	2	4	1
13	7	12	5	5	2	1	3	3
9	8	12	5	2	5	1	3	4
10	7	12	3	3	5	4	3	3
15	9	9	5	5	2	2	3	4
20	5	7	7	7	1	1	2	2
12	7	17	4	4	1	1	3	3
12	7	12	2	7	2	1	3	4
14	7	12	3	5	3	1	1	1
13	9	9	6	6	7	1	1	1
11	5	9	7	6	4	1	1	1
17	8	13	4	3	4	2	4	4
12	8	10	4	5	1	1	3	2
13	8	11	4	7	2	1	2	3
14	9	12	5	7	2	2	3	4
13	6	10	2	5	2	1	1	2
15	8	13	3	6	5	2	4	5
13	6	6	3	5	1	2	3	3
10	4	7	4	5	6	4	2	3
11	6	13	3	2	2	1	3	3
13	4	11	4	5	2	1	3	4
17	12	18	6	4	4	3	3	4
13	6	9	2	6	6	1	2	3
9	11	9	4	5	2	1	1	1
11	8	11	5	3	2	1	1	3
10	10	11	2	3	2	1	1	1
9	10	15	1	4	1	1	3	3
12	4	8	2	2	1	1	4	5
12	8	11	5	2	2	1	2	3
13	9	14	4	5	2	1	2	3
13	9	14	4	4	3	4	2	4
22	7	12	6	6	3	1	2	5
13	7	12	1	4	5	1	3	4
15	11	8	4	6	2	2	4	4
13	8	11	5	4	5	1	2	4
15	8	10	2	2	3	1	3	4
10	7	17	3	5	1	1	3	4
11	5	16	3	2	2	1	2	3
16	7	13	6	7	2	1	2	4
11	9	15	5	1	1	1	3	3
11	8	11	4	3	2	1	3	3
10	6	12	4	5	2	1	3	3
10	8	16	5	6	5	1	3	4
16	10	20	5	6	5	1	3	3
12	10	16	6	2	2	1	3	4
11	8	11	6	5	3	1	2	2
16	11	15	5	5	5	5	3	5
19	8	15	7	3	5	1	3	3
11	8	12	5	6	6	1	2	4
15	6	9	5	5	2	1	1	2
24	20	24	7	7	7	3	3	4
14	6	15	5	1	1	1	2	3
15	12	18	6	6	1	1	2	4
11	9	17	6	4	6	1	3	3
15	5	12	4	7	6	1	1	1
12	10	15	5	2	2	1	3	4
10	5	11	1	6	1	1	2	4
14	6	11	6	7	2	1	2	2
9	6	12	5	5	1	4	2	5
15	10	14	2	2	2	4	2	4
15	5	11	1	1	1	1	2	4
14	13	20	5	3	3	1	3	3
11	7	11	6	3	3	1	3	4
8	9	12	5	3	6	4	3	4
11	8	12	5	5	4	2	3	4
8	5	11	4	2	1	1	3	3
10	4	10	2	4	2	1	1	5
11	9	11	3	6	5	1	3	3
13	7	12	3	5	6	1	4	4
11	5	9	5	5	3	1	2	4
20	5	8	3	2	5	1	2	4
10	4	6	2	3	3	2	4	4
12	7	12	2	2	2	4	3	4
14	9	15	3	6	3	4	2	5
23	8	13	6	5	2	1	3	3
14	8	17	5	4	5	1	1	1
16	11	14	6	6	5	1	2	4
11	10	16	2	4	7	2	4	4
12	9	15	5	6	4	1	3	3
10	12	16	5	2	4	1	3	4
14	10	11	5	0	5	1	3	4
12	10	11	1	1	1	3	2	4
12	7	16	4	5	4	2	4	4
11	10	15	2	2	1	2	1	4
12	6	14	2	5	4	1	3	4
13	6	9	7	6	6	1	1	3
17	11	13	6	7	7	2	2	5
11	8	11	5	5	1	3	1	3
12	9	14	5	5	3	1	2	4
19	9	11	5	5	5	1	4	4
15	11	8	4	6	2	2	4	4
14	4	7	3	6	4	2	3	4
11	9	11	3	6	5	1	3	3
9	5	13	3	1	1	1	1	4
18	4	9	2	3	2	1	4	4




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99469&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99469&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99469&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 time9 seconds
R Server'George Udny Yule' @ 72.249.76.132







Multiple Linear Regression - Estimated Regression Equation
Depression[t] = + 7.49200704176759 + 0.0408955674052273CriticParents[t] -0.0669085142751476ExpecParents[t] + 0.586756102445572FutureWorrying[t] + 0.202166740732203SleepDepri[t] + 0.35634657786847ChangesLastYear[t] -0.121340486208485FreqSmoking[t] + 0.264713129723179FreqHighAlc[t] + 0.292590037590936FreqBeerOrWine[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Depression[t] =  +  7.49200704176759 +  0.0408955674052273CriticParents[t] -0.0669085142751476ExpecParents[t] +  0.586756102445572FutureWorrying[t] +  0.202166740732203SleepDepri[t] +  0.35634657786847ChangesLastYear[t] -0.121340486208485FreqSmoking[t] +  0.264713129723179FreqHighAlc[t] +  0.292590037590936FreqBeerOrWine[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99469&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Depression[t] =  +  7.49200704176759 +  0.0408955674052273CriticParents[t] -0.0669085142751476ExpecParents[t] +  0.586756102445572FutureWorrying[t] +  0.202166740732203SleepDepri[t] +  0.35634657786847ChangesLastYear[t] -0.121340486208485FreqSmoking[t] +  0.264713129723179FreqHighAlc[t] +  0.292590037590936FreqBeerOrWine[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99469&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99469&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
Depression[t] = + 7.49200704176759 + 0.0408955674052273CriticParents[t] -0.0669085142751476ExpecParents[t] + 0.586756102445572FutureWorrying[t] + 0.202166740732203SleepDepri[t] + 0.35634657786847ChangesLastYear[t] -0.121340486208485FreqSmoking[t] + 0.264713129723179FreqHighAlc[t] + 0.292590037590936FreqBeerOrWine[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.492007041767591.4452385.18391e-060
CriticParents0.04089556740522730.1157540.35330.7244120.362206
ExpecParents-0.06690851427514760.086583-0.77280.4410.2205
FutureWorrying0.5867561024455720.1682573.48730.0006580.000329
SleepDepri0.2021667407322030.1412081.43170.1545250.077263
ChangesLastYear0.356346577868470.1349222.64110.009230.004615
FreqSmoking-0.1213404862084850.273487-0.44370.657980.32899
FreqHighAlc0.2647131297231790.3146050.84140.4015930.200796
FreqBeerOrWine0.2925900375909360.2785321.05050.2953640.147682

\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) & 7.49200704176759 & 1.445238 & 5.1839 & 1e-06 & 0 \tabularnewline
CriticParents & 0.0408955674052273 & 0.115754 & 0.3533 & 0.724412 & 0.362206 \tabularnewline
ExpecParents & -0.0669085142751476 & 0.086583 & -0.7728 & 0.441 & 0.2205 \tabularnewline
FutureWorrying & 0.586756102445572 & 0.168257 & 3.4873 & 0.000658 & 0.000329 \tabularnewline
SleepDepri & 0.202166740732203 & 0.141208 & 1.4317 & 0.154525 & 0.077263 \tabularnewline
ChangesLastYear & 0.35634657786847 & 0.134922 & 2.6411 & 0.00923 & 0.004615 \tabularnewline
FreqSmoking & -0.121340486208485 & 0.273487 & -0.4437 & 0.65798 & 0.32899 \tabularnewline
FreqHighAlc & 0.264713129723179 & 0.314605 & 0.8414 & 0.401593 & 0.200796 \tabularnewline
FreqBeerOrWine & 0.292590037590936 & 0.278532 & 1.0505 & 0.295364 & 0.147682 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99469&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]7.49200704176759[/C][C]1.445238[/C][C]5.1839[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]CriticParents[/C][C]0.0408955674052273[/C][C]0.115754[/C][C]0.3533[/C][C]0.724412[/C][C]0.362206[/C][/ROW]
[ROW][C]ExpecParents[/C][C]-0.0669085142751476[/C][C]0.086583[/C][C]-0.7728[/C][C]0.441[/C][C]0.2205[/C][/ROW]
[ROW][C]FutureWorrying[/C][C]0.586756102445572[/C][C]0.168257[/C][C]3.4873[/C][C]0.000658[/C][C]0.000329[/C][/ROW]
[ROW][C]SleepDepri[/C][C]0.202166740732203[/C][C]0.141208[/C][C]1.4317[/C][C]0.154525[/C][C]0.077263[/C][/ROW]
[ROW][C]ChangesLastYear[/C][C]0.35634657786847[/C][C]0.134922[/C][C]2.6411[/C][C]0.00923[/C][C]0.004615[/C][/ROW]
[ROW][C]FreqSmoking[/C][C]-0.121340486208485[/C][C]0.273487[/C][C]-0.4437[/C][C]0.65798[/C][C]0.32899[/C][/ROW]
[ROW][C]FreqHighAlc[/C][C]0.264713129723179[/C][C]0.314605[/C][C]0.8414[/C][C]0.401593[/C][C]0.200796[/C][/ROW]
[ROW][C]FreqBeerOrWine[/C][C]0.292590037590936[/C][C]0.278532[/C][C]1.0505[/C][C]0.295364[/C][C]0.147682[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99469&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99469&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)7.492007041767591.4452385.18391e-060
CriticParents0.04089556740522730.1157540.35330.7244120.362206
ExpecParents-0.06690851427514760.086583-0.77280.4410.2205
FutureWorrying0.5867561024455720.1682573.48730.0006580.000329
SleepDepri0.2021667407322030.1412081.43170.1545250.077263
ChangesLastYear0.356346577868470.1349222.64110.009230.004615
FreqSmoking-0.1213404862084850.273487-0.44370.657980.32899
FreqHighAlc0.2647131297231790.3146050.84140.4015930.200796
FreqBeerOrWine0.2925900375909360.2785321.05050.2953640.147682







Multiple Linear Regression - Regression Statistics
Multiple R0.453459545887258
R-squared0.205625559756278
Adjusted R-squared0.158897651506647
F-TEST (value)4.40048714908833
F-TEST (DF numerator)8
F-TEST (DF denominator)136
p-value9.38949108088005e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.89966249185119
Sum Squared Residuals1143.49378906421

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.453459545887258 \tabularnewline
R-squared & 0.205625559756278 \tabularnewline
Adjusted R-squared & 0.158897651506647 \tabularnewline
F-TEST (value) & 4.40048714908833 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 136 \tabularnewline
p-value & 9.38949108088005e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.89966249185119 \tabularnewline
Sum Squared Residuals & 1143.49378906421 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99469&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.453459545887258[/C][/ROW]
[ROW][C]R-squared[/C][C]0.205625559756278[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.158897651506647[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.40048714908833[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]136[/C][/ROW]
[ROW][C]p-value[/C][C]9.38949108088005e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.89966249185119[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1143.49378906421[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99469&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99469&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 R0.453459545887258
R-squared0.205625559756278
Adjusted R-squared0.158897651506647
F-TEST (value)4.40048714908833
F-TEST (DF numerator)8
F-TEST (DF denominator)136
p-value9.38949108088005e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.89966249185119
Sum Squared Residuals1143.49378906421







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11212.0805628449277-0.0805628449276666
21111.9235069029631-0.923506902963089
31415.4094415553154-1.40944155531539
41211.87175058740730.128249412592660
52116.16183095394494.8381690460551
61213.4046617162138-1.40466171621376
72215.20811735272996.7918826472701
81113.294824974175-2.29482497417500
91012.3079978875984-2.30799788759839
101312.97325832149990.0267416785001346
111013.7575717009992-3.75757170099921
12812.2979245276434-4.29792452764335
131514.43345471703350.566545282966519
141012.2059971291089-2.20599712910895
151412.84438406456611.15561593543394
161410.39537858324693.6046214167531
171112.9877181789441-1.98771817894410
181012.9894302733557-2.98943027335566
191314.5340560419106-1.53405604191056
20710.8530854073948-3.85308540739482
211213.9630218499075-1.96302184990746
221412.95228907700891.04771092299107
231112.3766356140442-1.37663561404422
24911.4521237868814-2.45212378688137
251112.1626005016107-1.16260050161066
261513.56867303748381.43132696251624
271313.1981328501974-0.198132850197392
2899.75538643577493-0.755386435774928
291510.19142914083584.80857085916418
301012.7560557753254-2.75605577532537
311110.92374366461350.0762563353865289
321311.57128057455751.42871942544252
33810.6086037247257-2.60860372472569
342013.83970310421816.16029689578191
351213.1994426294348-1.19944262943476
361010.4759398798182-0.475939879818229
371012.3306278138505-2.33062781385050
38912.7449262774030-3.74492627740305
391414.8647940804501-0.86479408045014
40811.0725370447259-3.07253704472590
411412.08413758201631.91586241798366
421112.567986413666-1.56798641366600
431311.36386469239481.63613530760518
441112.1122347878330-1.11223478783297
451111.9822838799943-0.98228387999426
461012.1329437878707-2.13294378787075
471410.59424016507453.40575983492549
481813.79198028883374.20801971116633
491412.58972592616611.41027407383393
501113.1558663500304-2.15586635003041
511211.23911941933090.760880580669071
521313.1832502296621-0.183250229662084
53913.9792753460670-4.97927534606705
541012.3104228182865-2.31042281828649
551513.63701645868041.36298354131957
562014.10019760740035.89980239259967
571211.70343823724010.296561762759900
581212.1199054413807-0.119905441380711
591411.25147826801122.74852173198882
601314.9218163051899-1.92181630518988
611114.2759504044091-3.27595040440913
621713.31480353572483.68519646427524
631212.1222701077126-0.122270107712629
641312.84391856063810.156081439361885
651413.84062439731940.159375602680603
661310.69388708643312.30611291356686
671513.98348427093521.0165157290648
681311.89260647893961.10739352106036
691013.6050017195018-3.60500171950182
701111.2954337208940-0.295433720893954
711312.83330597686690.166694023133087
721714.13346856366162.86653143633839
731312.94565182022850.0543481797715108
74911.8461956050346-2.84619560503464
751112.3572945704317-1.35729457043169
761010.0936373227236-0.0936373227235615
77910.1996736606694-1.19967366066936
781211.45497568205020.545024317949755
791212.4198409594227-0.419840959422669
801312.27975510375350.720244896246507
811312.36250351985520.637496480144759
822214.64898659616707.35101340383298
831311.99568885034391.00431114965606
841513.66583987577581.33416012422415
851314.1858042120834-1.18580421208342
861511.64013091154373.35986908845631
871011.6114389131177-1.61143891311767
881110.78909948094010.210900519059895
891614.13530820716471.86469179283533
901111.8993022808498-0.899302280849812
911112.2999647274325-1.29996472743248
921012.5555985598113-2.55559855981128
931014.5203082518953-4.52030825189527
941614.04187529201421.9581247079858
951213.3111487926171-1.31114879261707
961113.6768538243424-2.67685382434239
971614.31496482041091.68503517958910
981914.8616387112744.13836128872598
991114.8795757571412-3.87957575714115
1001512.5210639080452.47893609195499
1012416.32147807550017.67852192449986
1021411.51190244891102.48809755108905
1031513.44673015421441.5532698457856
1041114.7404744662841-3.74047446628405
1051513.22981645071611.77018354928388
1061212.7913012044466-0.791301204446649
1071010.6950402700760-0.695040270075978
1081413.64304959312790.356950406872131
109912.7424535712216-3.74245357122163
1101510.46920682303644.53079317696355
111159.684206566414965.31579343358504
1121412.84536861629631.15463138370367
1131114.0815179803778-3.0815179803778
114814.2146627734475-6.21466277344749
1151114.1080885041867-3.10808850418670
116811.6187647066161-3.61876470661612
1171011.2876993236633-1.28769932366329
1181113.4296441481942-2.42964414819416
1191313.9924275035589-0.992427503558935
1201113.6864081234133-2.6864081234133
1212012.68599736633767.31400263366237
1221012.0897220832703-2.08972208327026
1231210.74505027909421.25494972090576
1241412.40576242218991.59423757781014
1252313.74399338523779.25600661476226
1261412.64186988393661.35813011606345
1271615.09885495538360.901145044616358
1281113.2935633971562-2.29356339715624
1291213.9791757181162-1.97917571811624
1301013.5188769807189-3.51887698071890
1311413.72364151368820.276358486311758
132129.646003431024132.35399656897587
1331213.4775159069585-1.47751590695849
1341110.02391957358660.97608042641338
1351212.2535525196977-0.253552519697721
1361315.6147192027332-2.61471920273317
1371716.25187291751040.748127082489616
1381112.1626005016107-1.16260050161066
1391213.5154478216585-1.51544782165847
1401914.95829277966724.04170722033279
1411513.66583987577581.33416012422415
1421413.30770334178260.692296658217409
1431113.4296441481942-2.42964414819416
144910.4591886130326-1.45918861303263
1451811.65399044878486.34600955121516

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 12.0805628449277 & -0.0805628449276666 \tabularnewline
2 & 11 & 11.9235069029631 & -0.923506902963089 \tabularnewline
3 & 14 & 15.4094415553154 & -1.40944155531539 \tabularnewline
4 & 12 & 11.8717505874073 & 0.128249412592660 \tabularnewline
5 & 21 & 16.1618309539449 & 4.8381690460551 \tabularnewline
6 & 12 & 13.4046617162138 & -1.40466171621376 \tabularnewline
7 & 22 & 15.2081173527299 & 6.7918826472701 \tabularnewline
8 & 11 & 13.294824974175 & -2.29482497417500 \tabularnewline
9 & 10 & 12.3079978875984 & -2.30799788759839 \tabularnewline
10 & 13 & 12.9732583214999 & 0.0267416785001346 \tabularnewline
11 & 10 & 13.7575717009992 & -3.75757170099921 \tabularnewline
12 & 8 & 12.2979245276434 & -4.29792452764335 \tabularnewline
13 & 15 & 14.4334547170335 & 0.566545282966519 \tabularnewline
14 & 10 & 12.2059971291089 & -2.20599712910895 \tabularnewline
15 & 14 & 12.8443840645661 & 1.15561593543394 \tabularnewline
16 & 14 & 10.3953785832469 & 3.6046214167531 \tabularnewline
17 & 11 & 12.9877181789441 & -1.98771817894410 \tabularnewline
18 & 10 & 12.9894302733557 & -2.98943027335566 \tabularnewline
19 & 13 & 14.5340560419106 & -1.53405604191056 \tabularnewline
20 & 7 & 10.8530854073948 & -3.85308540739482 \tabularnewline
21 & 12 & 13.9630218499075 & -1.96302184990746 \tabularnewline
22 & 14 & 12.9522890770089 & 1.04771092299107 \tabularnewline
23 & 11 & 12.3766356140442 & -1.37663561404422 \tabularnewline
24 & 9 & 11.4521237868814 & -2.45212378688137 \tabularnewline
25 & 11 & 12.1626005016107 & -1.16260050161066 \tabularnewline
26 & 15 & 13.5686730374838 & 1.43132696251624 \tabularnewline
27 & 13 & 13.1981328501974 & -0.198132850197392 \tabularnewline
28 & 9 & 9.75538643577493 & -0.755386435774928 \tabularnewline
29 & 15 & 10.1914291408358 & 4.80857085916418 \tabularnewline
30 & 10 & 12.7560557753254 & -2.75605577532537 \tabularnewline
31 & 11 & 10.9237436646135 & 0.0762563353865289 \tabularnewline
32 & 13 & 11.5712805745575 & 1.42871942544252 \tabularnewline
33 & 8 & 10.6086037247257 & -2.60860372472569 \tabularnewline
34 & 20 & 13.8397031042181 & 6.16029689578191 \tabularnewline
35 & 12 & 13.1994426294348 & -1.19944262943476 \tabularnewline
36 & 10 & 10.4759398798182 & -0.475939879818229 \tabularnewline
37 & 10 & 12.3306278138505 & -2.33062781385050 \tabularnewline
38 & 9 & 12.7449262774030 & -3.74492627740305 \tabularnewline
39 & 14 & 14.8647940804501 & -0.86479408045014 \tabularnewline
40 & 8 & 11.0725370447259 & -3.07253704472590 \tabularnewline
41 & 14 & 12.0841375820163 & 1.91586241798366 \tabularnewline
42 & 11 & 12.567986413666 & -1.56798641366600 \tabularnewline
43 & 13 & 11.3638646923948 & 1.63613530760518 \tabularnewline
44 & 11 & 12.1122347878330 & -1.11223478783297 \tabularnewline
45 & 11 & 11.9822838799943 & -0.98228387999426 \tabularnewline
46 & 10 & 12.1329437878707 & -2.13294378787075 \tabularnewline
47 & 14 & 10.5942401650745 & 3.40575983492549 \tabularnewline
48 & 18 & 13.7919802888337 & 4.20801971116633 \tabularnewline
49 & 14 & 12.5897259261661 & 1.41027407383393 \tabularnewline
50 & 11 & 13.1558663500304 & -2.15586635003041 \tabularnewline
51 & 12 & 11.2391194193309 & 0.760880580669071 \tabularnewline
52 & 13 & 13.1832502296621 & -0.183250229662084 \tabularnewline
53 & 9 & 13.9792753460670 & -4.97927534606705 \tabularnewline
54 & 10 & 12.3104228182865 & -2.31042281828649 \tabularnewline
55 & 15 & 13.6370164586804 & 1.36298354131957 \tabularnewline
56 & 20 & 14.1001976074003 & 5.89980239259967 \tabularnewline
57 & 12 & 11.7034382372401 & 0.296561762759900 \tabularnewline
58 & 12 & 12.1199054413807 & -0.119905441380711 \tabularnewline
59 & 14 & 11.2514782680112 & 2.74852173198882 \tabularnewline
60 & 13 & 14.9218163051899 & -1.92181630518988 \tabularnewline
61 & 11 & 14.2759504044091 & -3.27595040440913 \tabularnewline
62 & 17 & 13.3148035357248 & 3.68519646427524 \tabularnewline
63 & 12 & 12.1222701077126 & -0.122270107712629 \tabularnewline
64 & 13 & 12.8439185606381 & 0.156081439361885 \tabularnewline
65 & 14 & 13.8406243973194 & 0.159375602680603 \tabularnewline
66 & 13 & 10.6938870864331 & 2.30611291356686 \tabularnewline
67 & 15 & 13.9834842709352 & 1.0165157290648 \tabularnewline
68 & 13 & 11.8926064789396 & 1.10739352106036 \tabularnewline
69 & 10 & 13.6050017195018 & -3.60500171950182 \tabularnewline
70 & 11 & 11.2954337208940 & -0.295433720893954 \tabularnewline
71 & 13 & 12.8333059768669 & 0.166694023133087 \tabularnewline
72 & 17 & 14.1334685636616 & 2.86653143633839 \tabularnewline
73 & 13 & 12.9456518202285 & 0.0543481797715108 \tabularnewline
74 & 9 & 11.8461956050346 & -2.84619560503464 \tabularnewline
75 & 11 & 12.3572945704317 & -1.35729457043169 \tabularnewline
76 & 10 & 10.0936373227236 & -0.0936373227235615 \tabularnewline
77 & 9 & 10.1996736606694 & -1.19967366066936 \tabularnewline
78 & 12 & 11.4549756820502 & 0.545024317949755 \tabularnewline
79 & 12 & 12.4198409594227 & -0.419840959422669 \tabularnewline
80 & 13 & 12.2797551037535 & 0.720244896246507 \tabularnewline
81 & 13 & 12.3625035198552 & 0.637496480144759 \tabularnewline
82 & 22 & 14.6489865961670 & 7.35101340383298 \tabularnewline
83 & 13 & 11.9956888503439 & 1.00431114965606 \tabularnewline
84 & 15 & 13.6658398757758 & 1.33416012422415 \tabularnewline
85 & 13 & 14.1858042120834 & -1.18580421208342 \tabularnewline
86 & 15 & 11.6401309115437 & 3.35986908845631 \tabularnewline
87 & 10 & 11.6114389131177 & -1.61143891311767 \tabularnewline
88 & 11 & 10.7890994809401 & 0.210900519059895 \tabularnewline
89 & 16 & 14.1353082071647 & 1.86469179283533 \tabularnewline
90 & 11 & 11.8993022808498 & -0.899302280849812 \tabularnewline
91 & 11 & 12.2999647274325 & -1.29996472743248 \tabularnewline
92 & 10 & 12.5555985598113 & -2.55559855981128 \tabularnewline
93 & 10 & 14.5203082518953 & -4.52030825189527 \tabularnewline
94 & 16 & 14.0418752920142 & 1.9581247079858 \tabularnewline
95 & 12 & 13.3111487926171 & -1.31114879261707 \tabularnewline
96 & 11 & 13.6768538243424 & -2.67685382434239 \tabularnewline
97 & 16 & 14.3149648204109 & 1.68503517958910 \tabularnewline
98 & 19 & 14.861638711274 & 4.13836128872598 \tabularnewline
99 & 11 & 14.8795757571412 & -3.87957575714115 \tabularnewline
100 & 15 & 12.521063908045 & 2.47893609195499 \tabularnewline
101 & 24 & 16.3214780755001 & 7.67852192449986 \tabularnewline
102 & 14 & 11.5119024489110 & 2.48809755108905 \tabularnewline
103 & 15 & 13.4467301542144 & 1.5532698457856 \tabularnewline
104 & 11 & 14.7404744662841 & -3.74047446628405 \tabularnewline
105 & 15 & 13.2298164507161 & 1.77018354928388 \tabularnewline
106 & 12 & 12.7913012044466 & -0.791301204446649 \tabularnewline
107 & 10 & 10.6950402700760 & -0.695040270075978 \tabularnewline
108 & 14 & 13.6430495931279 & 0.356950406872131 \tabularnewline
109 & 9 & 12.7424535712216 & -3.74245357122163 \tabularnewline
110 & 15 & 10.4692068230364 & 4.53079317696355 \tabularnewline
111 & 15 & 9.68420656641496 & 5.31579343358504 \tabularnewline
112 & 14 & 12.8453686162963 & 1.15463138370367 \tabularnewline
113 & 11 & 14.0815179803778 & -3.0815179803778 \tabularnewline
114 & 8 & 14.2146627734475 & -6.21466277344749 \tabularnewline
115 & 11 & 14.1080885041867 & -3.10808850418670 \tabularnewline
116 & 8 & 11.6187647066161 & -3.61876470661612 \tabularnewline
117 & 10 & 11.2876993236633 & -1.28769932366329 \tabularnewline
118 & 11 & 13.4296441481942 & -2.42964414819416 \tabularnewline
119 & 13 & 13.9924275035589 & -0.992427503558935 \tabularnewline
120 & 11 & 13.6864081234133 & -2.6864081234133 \tabularnewline
121 & 20 & 12.6859973663376 & 7.31400263366237 \tabularnewline
122 & 10 & 12.0897220832703 & -2.08972208327026 \tabularnewline
123 & 12 & 10.7450502790942 & 1.25494972090576 \tabularnewline
124 & 14 & 12.4057624221899 & 1.59423757781014 \tabularnewline
125 & 23 & 13.7439933852377 & 9.25600661476226 \tabularnewline
126 & 14 & 12.6418698839366 & 1.35813011606345 \tabularnewline
127 & 16 & 15.0988549553836 & 0.901145044616358 \tabularnewline
128 & 11 & 13.2935633971562 & -2.29356339715624 \tabularnewline
129 & 12 & 13.9791757181162 & -1.97917571811624 \tabularnewline
130 & 10 & 13.5188769807189 & -3.51887698071890 \tabularnewline
131 & 14 & 13.7236415136882 & 0.276358486311758 \tabularnewline
132 & 12 & 9.64600343102413 & 2.35399656897587 \tabularnewline
133 & 12 & 13.4775159069585 & -1.47751590695849 \tabularnewline
134 & 11 & 10.0239195735866 & 0.97608042641338 \tabularnewline
135 & 12 & 12.2535525196977 & -0.253552519697721 \tabularnewline
136 & 13 & 15.6147192027332 & -2.61471920273317 \tabularnewline
137 & 17 & 16.2518729175104 & 0.748127082489616 \tabularnewline
138 & 11 & 12.1626005016107 & -1.16260050161066 \tabularnewline
139 & 12 & 13.5154478216585 & -1.51544782165847 \tabularnewline
140 & 19 & 14.9582927796672 & 4.04170722033279 \tabularnewline
141 & 15 & 13.6658398757758 & 1.33416012422415 \tabularnewline
142 & 14 & 13.3077033417826 & 0.692296658217409 \tabularnewline
143 & 11 & 13.4296441481942 & -2.42964414819416 \tabularnewline
144 & 9 & 10.4591886130326 & -1.45918861303263 \tabularnewline
145 & 18 & 11.6539904487848 & 6.34600955121516 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99469&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]12[/C][C]12.0805628449277[/C][C]-0.0805628449276666[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]11.9235069029631[/C][C]-0.923506902963089[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]15.4094415553154[/C][C]-1.40944155531539[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]11.8717505874073[/C][C]0.128249412592660[/C][/ROW]
[ROW][C]5[/C][C]21[/C][C]16.1618309539449[/C][C]4.8381690460551[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]13.4046617162138[/C][C]-1.40466171621376[/C][/ROW]
[ROW][C]7[/C][C]22[/C][C]15.2081173527299[/C][C]6.7918826472701[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]13.294824974175[/C][C]-2.29482497417500[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]12.3079978875984[/C][C]-2.30799788759839[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.9732583214999[/C][C]0.0267416785001346[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]13.7575717009992[/C][C]-3.75757170099921[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]12.2979245276434[/C][C]-4.29792452764335[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]14.4334547170335[/C][C]0.566545282966519[/C][/ROW]
[ROW][C]14[/C][C]10[/C][C]12.2059971291089[/C][C]-2.20599712910895[/C][/ROW]
[ROW][C]15[/C][C]14[/C][C]12.8443840645661[/C][C]1.15561593543394[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]10.3953785832469[/C][C]3.6046214167531[/C][/ROW]
[ROW][C]17[/C][C]11[/C][C]12.9877181789441[/C][C]-1.98771817894410[/C][/ROW]
[ROW][C]18[/C][C]10[/C][C]12.9894302733557[/C][C]-2.98943027335566[/C][/ROW]
[ROW][C]19[/C][C]13[/C][C]14.5340560419106[/C][C]-1.53405604191056[/C][/ROW]
[ROW][C]20[/C][C]7[/C][C]10.8530854073948[/C][C]-3.85308540739482[/C][/ROW]
[ROW][C]21[/C][C]12[/C][C]13.9630218499075[/C][C]-1.96302184990746[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]12.9522890770089[/C][C]1.04771092299107[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]12.3766356140442[/C][C]-1.37663561404422[/C][/ROW]
[ROW][C]24[/C][C]9[/C][C]11.4521237868814[/C][C]-2.45212378688137[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]12.1626005016107[/C][C]-1.16260050161066[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]13.5686730374838[/C][C]1.43132696251624[/C][/ROW]
[ROW][C]27[/C][C]13[/C][C]13.1981328501974[/C][C]-0.198132850197392[/C][/ROW]
[ROW][C]28[/C][C]9[/C][C]9.75538643577493[/C][C]-0.755386435774928[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]10.1914291408358[/C][C]4.80857085916418[/C][/ROW]
[ROW][C]30[/C][C]10[/C][C]12.7560557753254[/C][C]-2.75605577532537[/C][/ROW]
[ROW][C]31[/C][C]11[/C][C]10.9237436646135[/C][C]0.0762563353865289[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]11.5712805745575[/C][C]1.42871942544252[/C][/ROW]
[ROW][C]33[/C][C]8[/C][C]10.6086037247257[/C][C]-2.60860372472569[/C][/ROW]
[ROW][C]34[/C][C]20[/C][C]13.8397031042181[/C][C]6.16029689578191[/C][/ROW]
[ROW][C]35[/C][C]12[/C][C]13.1994426294348[/C][C]-1.19944262943476[/C][/ROW]
[ROW][C]36[/C][C]10[/C][C]10.4759398798182[/C][C]-0.475939879818229[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]12.3306278138505[/C][C]-2.33062781385050[/C][/ROW]
[ROW][C]38[/C][C]9[/C][C]12.7449262774030[/C][C]-3.74492627740305[/C][/ROW]
[ROW][C]39[/C][C]14[/C][C]14.8647940804501[/C][C]-0.86479408045014[/C][/ROW]
[ROW][C]40[/C][C]8[/C][C]11.0725370447259[/C][C]-3.07253704472590[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]12.0841375820163[/C][C]1.91586241798366[/C][/ROW]
[ROW][C]42[/C][C]11[/C][C]12.567986413666[/C][C]-1.56798641366600[/C][/ROW]
[ROW][C]43[/C][C]13[/C][C]11.3638646923948[/C][C]1.63613530760518[/C][/ROW]
[ROW][C]44[/C][C]11[/C][C]12.1122347878330[/C][C]-1.11223478783297[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]11.9822838799943[/C][C]-0.98228387999426[/C][/ROW]
[ROW][C]46[/C][C]10[/C][C]12.1329437878707[/C][C]-2.13294378787075[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]10.5942401650745[/C][C]3.40575983492549[/C][/ROW]
[ROW][C]48[/C][C]18[/C][C]13.7919802888337[/C][C]4.20801971116633[/C][/ROW]
[ROW][C]49[/C][C]14[/C][C]12.5897259261661[/C][C]1.41027407383393[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]13.1558663500304[/C][C]-2.15586635003041[/C][/ROW]
[ROW][C]51[/C][C]12[/C][C]11.2391194193309[/C][C]0.760880580669071[/C][/ROW]
[ROW][C]52[/C][C]13[/C][C]13.1832502296621[/C][C]-0.183250229662084[/C][/ROW]
[ROW][C]53[/C][C]9[/C][C]13.9792753460670[/C][C]-4.97927534606705[/C][/ROW]
[ROW][C]54[/C][C]10[/C][C]12.3104228182865[/C][C]-2.31042281828649[/C][/ROW]
[ROW][C]55[/C][C]15[/C][C]13.6370164586804[/C][C]1.36298354131957[/C][/ROW]
[ROW][C]56[/C][C]20[/C][C]14.1001976074003[/C][C]5.89980239259967[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]11.7034382372401[/C][C]0.296561762759900[/C][/ROW]
[ROW][C]58[/C][C]12[/C][C]12.1199054413807[/C][C]-0.119905441380711[/C][/ROW]
[ROW][C]59[/C][C]14[/C][C]11.2514782680112[/C][C]2.74852173198882[/C][/ROW]
[ROW][C]60[/C][C]13[/C][C]14.9218163051899[/C][C]-1.92181630518988[/C][/ROW]
[ROW][C]61[/C][C]11[/C][C]14.2759504044091[/C][C]-3.27595040440913[/C][/ROW]
[ROW][C]62[/C][C]17[/C][C]13.3148035357248[/C][C]3.68519646427524[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]12.1222701077126[/C][C]-0.122270107712629[/C][/ROW]
[ROW][C]64[/C][C]13[/C][C]12.8439185606381[/C][C]0.156081439361885[/C][/ROW]
[ROW][C]65[/C][C]14[/C][C]13.8406243973194[/C][C]0.159375602680603[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]10.6938870864331[/C][C]2.30611291356686[/C][/ROW]
[ROW][C]67[/C][C]15[/C][C]13.9834842709352[/C][C]1.0165157290648[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]11.8926064789396[/C][C]1.10739352106036[/C][/ROW]
[ROW][C]69[/C][C]10[/C][C]13.6050017195018[/C][C]-3.60500171950182[/C][/ROW]
[ROW][C]70[/C][C]11[/C][C]11.2954337208940[/C][C]-0.295433720893954[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]12.8333059768669[/C][C]0.166694023133087[/C][/ROW]
[ROW][C]72[/C][C]17[/C][C]14.1334685636616[/C][C]2.86653143633839[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]12.9456518202285[/C][C]0.0543481797715108[/C][/ROW]
[ROW][C]74[/C][C]9[/C][C]11.8461956050346[/C][C]-2.84619560503464[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]12.3572945704317[/C][C]-1.35729457043169[/C][/ROW]
[ROW][C]76[/C][C]10[/C][C]10.0936373227236[/C][C]-0.0936373227235615[/C][/ROW]
[ROW][C]77[/C][C]9[/C][C]10.1996736606694[/C][C]-1.19967366066936[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]11.4549756820502[/C][C]0.545024317949755[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]12.4198409594227[/C][C]-0.419840959422669[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]12.2797551037535[/C][C]0.720244896246507[/C][/ROW]
[ROW][C]81[/C][C]13[/C][C]12.3625035198552[/C][C]0.637496480144759[/C][/ROW]
[ROW][C]82[/C][C]22[/C][C]14.6489865961670[/C][C]7.35101340383298[/C][/ROW]
[ROW][C]83[/C][C]13[/C][C]11.9956888503439[/C][C]1.00431114965606[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]13.6658398757758[/C][C]1.33416012422415[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]14.1858042120834[/C][C]-1.18580421208342[/C][/ROW]
[ROW][C]86[/C][C]15[/C][C]11.6401309115437[/C][C]3.35986908845631[/C][/ROW]
[ROW][C]87[/C][C]10[/C][C]11.6114389131177[/C][C]-1.61143891311767[/C][/ROW]
[ROW][C]88[/C][C]11[/C][C]10.7890994809401[/C][C]0.210900519059895[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.1353082071647[/C][C]1.86469179283533[/C][/ROW]
[ROW][C]90[/C][C]11[/C][C]11.8993022808498[/C][C]-0.899302280849812[/C][/ROW]
[ROW][C]91[/C][C]11[/C][C]12.2999647274325[/C][C]-1.29996472743248[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]12.5555985598113[/C][C]-2.55559855981128[/C][/ROW]
[ROW][C]93[/C][C]10[/C][C]14.5203082518953[/C][C]-4.52030825189527[/C][/ROW]
[ROW][C]94[/C][C]16[/C][C]14.0418752920142[/C][C]1.9581247079858[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]13.3111487926171[/C][C]-1.31114879261707[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]13.6768538243424[/C][C]-2.67685382434239[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]14.3149648204109[/C][C]1.68503517958910[/C][/ROW]
[ROW][C]98[/C][C]19[/C][C]14.861638711274[/C][C]4.13836128872598[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]14.8795757571412[/C][C]-3.87957575714115[/C][/ROW]
[ROW][C]100[/C][C]15[/C][C]12.521063908045[/C][C]2.47893609195499[/C][/ROW]
[ROW][C]101[/C][C]24[/C][C]16.3214780755001[/C][C]7.67852192449986[/C][/ROW]
[ROW][C]102[/C][C]14[/C][C]11.5119024489110[/C][C]2.48809755108905[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]13.4467301542144[/C][C]1.5532698457856[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]14.7404744662841[/C][C]-3.74047446628405[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]13.2298164507161[/C][C]1.77018354928388[/C][/ROW]
[ROW][C]106[/C][C]12[/C][C]12.7913012044466[/C][C]-0.791301204446649[/C][/ROW]
[ROW][C]107[/C][C]10[/C][C]10.6950402700760[/C][C]-0.695040270075978[/C][/ROW]
[ROW][C]108[/C][C]14[/C][C]13.6430495931279[/C][C]0.356950406872131[/C][/ROW]
[ROW][C]109[/C][C]9[/C][C]12.7424535712216[/C][C]-3.74245357122163[/C][/ROW]
[ROW][C]110[/C][C]15[/C][C]10.4692068230364[/C][C]4.53079317696355[/C][/ROW]
[ROW][C]111[/C][C]15[/C][C]9.68420656641496[/C][C]5.31579343358504[/C][/ROW]
[ROW][C]112[/C][C]14[/C][C]12.8453686162963[/C][C]1.15463138370367[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]14.0815179803778[/C][C]-3.0815179803778[/C][/ROW]
[ROW][C]114[/C][C]8[/C][C]14.2146627734475[/C][C]-6.21466277344749[/C][/ROW]
[ROW][C]115[/C][C]11[/C][C]14.1080885041867[/C][C]-3.10808850418670[/C][/ROW]
[ROW][C]116[/C][C]8[/C][C]11.6187647066161[/C][C]-3.61876470661612[/C][/ROW]
[ROW][C]117[/C][C]10[/C][C]11.2876993236633[/C][C]-1.28769932366329[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]13.4296441481942[/C][C]-2.42964414819416[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]13.9924275035589[/C][C]-0.992427503558935[/C][/ROW]
[ROW][C]120[/C][C]11[/C][C]13.6864081234133[/C][C]-2.6864081234133[/C][/ROW]
[ROW][C]121[/C][C]20[/C][C]12.6859973663376[/C][C]7.31400263366237[/C][/ROW]
[ROW][C]122[/C][C]10[/C][C]12.0897220832703[/C][C]-2.08972208327026[/C][/ROW]
[ROW][C]123[/C][C]12[/C][C]10.7450502790942[/C][C]1.25494972090576[/C][/ROW]
[ROW][C]124[/C][C]14[/C][C]12.4057624221899[/C][C]1.59423757781014[/C][/ROW]
[ROW][C]125[/C][C]23[/C][C]13.7439933852377[/C][C]9.25600661476226[/C][/ROW]
[ROW][C]126[/C][C]14[/C][C]12.6418698839366[/C][C]1.35813011606345[/C][/ROW]
[ROW][C]127[/C][C]16[/C][C]15.0988549553836[/C][C]0.901145044616358[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]13.2935633971562[/C][C]-2.29356339715624[/C][/ROW]
[ROW][C]129[/C][C]12[/C][C]13.9791757181162[/C][C]-1.97917571811624[/C][/ROW]
[ROW][C]130[/C][C]10[/C][C]13.5188769807189[/C][C]-3.51887698071890[/C][/ROW]
[ROW][C]131[/C][C]14[/C][C]13.7236415136882[/C][C]0.276358486311758[/C][/ROW]
[ROW][C]132[/C][C]12[/C][C]9.64600343102413[/C][C]2.35399656897587[/C][/ROW]
[ROW][C]133[/C][C]12[/C][C]13.4775159069585[/C][C]-1.47751590695849[/C][/ROW]
[ROW][C]134[/C][C]11[/C][C]10.0239195735866[/C][C]0.97608042641338[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]12.2535525196977[/C][C]-0.253552519697721[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]15.6147192027332[/C][C]-2.61471920273317[/C][/ROW]
[ROW][C]137[/C][C]17[/C][C]16.2518729175104[/C][C]0.748127082489616[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]12.1626005016107[/C][C]-1.16260050161066[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]13.5154478216585[/C][C]-1.51544782165847[/C][/ROW]
[ROW][C]140[/C][C]19[/C][C]14.9582927796672[/C][C]4.04170722033279[/C][/ROW]
[ROW][C]141[/C][C]15[/C][C]13.6658398757758[/C][C]1.33416012422415[/C][/ROW]
[ROW][C]142[/C][C]14[/C][C]13.3077033417826[/C][C]0.692296658217409[/C][/ROW]
[ROW][C]143[/C][C]11[/C][C]13.4296441481942[/C][C]-2.42964414819416[/C][/ROW]
[ROW][C]144[/C][C]9[/C][C]10.4591886130326[/C][C]-1.45918861303263[/C][/ROW]
[ROW][C]145[/C][C]18[/C][C]11.6539904487848[/C][C]6.34600955121516[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99469&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99469&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
11212.0805628449277-0.0805628449276666
21111.9235069029631-0.923506902963089
31415.4094415553154-1.40944155531539
41211.87175058740730.128249412592660
52116.16183095394494.8381690460551
61213.4046617162138-1.40466171621376
72215.20811735272996.7918826472701
81113.294824974175-2.29482497417500
91012.3079978875984-2.30799788759839
101312.97325832149990.0267416785001346
111013.7575717009992-3.75757170099921
12812.2979245276434-4.29792452764335
131514.43345471703350.566545282966519
141012.2059971291089-2.20599712910895
151412.84438406456611.15561593543394
161410.39537858324693.6046214167531
171112.9877181789441-1.98771817894410
181012.9894302733557-2.98943027335566
191314.5340560419106-1.53405604191056
20710.8530854073948-3.85308540739482
211213.9630218499075-1.96302184990746
221412.95228907700891.04771092299107
231112.3766356140442-1.37663561404422
24911.4521237868814-2.45212378688137
251112.1626005016107-1.16260050161066
261513.56867303748381.43132696251624
271313.1981328501974-0.198132850197392
2899.75538643577493-0.755386435774928
291510.19142914083584.80857085916418
301012.7560557753254-2.75605577532537
311110.92374366461350.0762563353865289
321311.57128057455751.42871942544252
33810.6086037247257-2.60860372472569
342013.83970310421816.16029689578191
351213.1994426294348-1.19944262943476
361010.4759398798182-0.475939879818229
371012.3306278138505-2.33062781385050
38912.7449262774030-3.74492627740305
391414.8647940804501-0.86479408045014
40811.0725370447259-3.07253704472590
411412.08413758201631.91586241798366
421112.567986413666-1.56798641366600
431311.36386469239481.63613530760518
441112.1122347878330-1.11223478783297
451111.9822838799943-0.98228387999426
461012.1329437878707-2.13294378787075
471410.59424016507453.40575983492549
481813.79198028883374.20801971116633
491412.58972592616611.41027407383393
501113.1558663500304-2.15586635003041
511211.23911941933090.760880580669071
521313.1832502296621-0.183250229662084
53913.9792753460670-4.97927534606705
541012.3104228182865-2.31042281828649
551513.63701645868041.36298354131957
562014.10019760740035.89980239259967
571211.70343823724010.296561762759900
581212.1199054413807-0.119905441380711
591411.25147826801122.74852173198882
601314.9218163051899-1.92181630518988
611114.2759504044091-3.27595040440913
621713.31480353572483.68519646427524
631212.1222701077126-0.122270107712629
641312.84391856063810.156081439361885
651413.84062439731940.159375602680603
661310.69388708643312.30611291356686
671513.98348427093521.0165157290648
681311.89260647893961.10739352106036
691013.6050017195018-3.60500171950182
701111.2954337208940-0.295433720893954
711312.83330597686690.166694023133087
721714.13346856366162.86653143633839
731312.94565182022850.0543481797715108
74911.8461956050346-2.84619560503464
751112.3572945704317-1.35729457043169
761010.0936373227236-0.0936373227235615
77910.1996736606694-1.19967366066936
781211.45497568205020.545024317949755
791212.4198409594227-0.419840959422669
801312.27975510375350.720244896246507
811312.36250351985520.637496480144759
822214.64898659616707.35101340383298
831311.99568885034391.00431114965606
841513.66583987577581.33416012422415
851314.1858042120834-1.18580421208342
861511.64013091154373.35986908845631
871011.6114389131177-1.61143891311767
881110.78909948094010.210900519059895
891614.13530820716471.86469179283533
901111.8993022808498-0.899302280849812
911112.2999647274325-1.29996472743248
921012.5555985598113-2.55559855981128
931014.5203082518953-4.52030825189527
941614.04187529201421.9581247079858
951213.3111487926171-1.31114879261707
961113.6768538243424-2.67685382434239
971614.31496482041091.68503517958910
981914.8616387112744.13836128872598
991114.8795757571412-3.87957575714115
1001512.5210639080452.47893609195499
1012416.32147807550017.67852192449986
1021411.51190244891102.48809755108905
1031513.44673015421441.5532698457856
1041114.7404744662841-3.74047446628405
1051513.22981645071611.77018354928388
1061212.7913012044466-0.791301204446649
1071010.6950402700760-0.695040270075978
1081413.64304959312790.356950406872131
109912.7424535712216-3.74245357122163
1101510.46920682303644.53079317696355
111159.684206566414965.31579343358504
1121412.84536861629631.15463138370367
1131114.0815179803778-3.0815179803778
114814.2146627734475-6.21466277344749
1151114.1080885041867-3.10808850418670
116811.6187647066161-3.61876470661612
1171011.2876993236633-1.28769932366329
1181113.4296441481942-2.42964414819416
1191313.9924275035589-0.992427503558935
1201113.6864081234133-2.6864081234133
1212012.68599736633767.31400263366237
1221012.0897220832703-2.08972208327026
1231210.74505027909421.25494972090576
1241412.40576242218991.59423757781014
1252313.74399338523779.25600661476226
1261412.64186988393661.35813011606345
1271615.09885495538360.901145044616358
1281113.2935633971562-2.29356339715624
1291213.9791757181162-1.97917571811624
1301013.5188769807189-3.51887698071890
1311413.72364151368820.276358486311758
132129.646003431024132.35399656897587
1331213.4775159069585-1.47751590695849
1341110.02391957358660.97608042641338
1351212.2535525196977-0.253552519697721
1361315.6147192027332-2.61471920273317
1371716.25187291751040.748127082489616
1381112.1626005016107-1.16260050161066
1391213.5154478216585-1.51544782165847
1401914.95829277966724.04170722033279
1411513.66583987577581.33416012422415
1421413.30770334178260.692296658217409
1431113.4296441481942-2.42964414819416
144910.4591886130326-1.45918861303263
1451811.65399044878486.34600955121516







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.1926259162491300.3852518324982590.80737408375087
130.2991785534902830.5983571069805660.700821446509717
140.4558260158513850.911652031702770.544173984148615
150.4008522288423880.8017044576847770.599147771157612
160.5534259210343960.8931481579312090.446574078965604
170.4692885565420590.9385771130841170.530711443457941
180.3834390085294840.7668780170589680.616560991470516
190.48998776172620.97997552345240.5100122382738
200.4193359699864650.838671939972930.580664030013535
210.3523178483994790.7046356967989580.647682151600521
220.3982272284687390.7964544569374780.601772771531261
230.4443793023685790.8887586047371590.555620697631421
240.3723115061432550.744623012286510.627688493856745
250.3156463276307380.6312926552614750.684353672369262
260.2743959391834840.5487918783669670.725604060816516
270.2245671129031660.4491342258063320.775432887096834
280.1893649141441130.3787298282882260.810635085855887
290.2479709625405080.4959419250810160.752029037459492
300.2079402576804490.4158805153608990.79205974231955
310.1709762506716090.3419525013432180.829023749328391
320.1341917441517340.2683834883034680.865808255848266
330.1293315846030510.2586631692061030.870668415396949
340.1466330336950040.2932660673900090.853366966304996
350.1490755878789850.2981511757579690.850924412121015
360.1541337838135120.3082675676270250.845866216186488
370.1944183746565010.3888367493130020.805581625343499
380.2467681494256590.4935362988513190.753231850574341
390.2703915726816620.5407831453633250.729608427318338
400.2639926092850950.5279852185701910.736007390714905
410.2756575599020650.5513151198041310.724342440097935
420.2447134600569360.4894269201138730.755286539943063
430.2439744738468690.4879489476937390.75602552615313
440.2080684380716160.4161368761432320.791931561928384
450.1731295247408680.3462590494817370.826870475259132
460.1516488136185840.3032976272371670.848351186381416
470.1583846375761890.3167692751523770.841615362423811
480.2590579830664650.518115966132930.740942016933535
490.2429757833422140.4859515666844280.757024216657786
500.2148409012068930.4296818024137870.785159098793107
510.1816944151220130.3633888302440260.818305584877987
520.1482986211536260.2965972423072510.851701378846374
530.2121029651868040.4242059303736080.787897034813196
540.2046433136957150.409286627391430.795356686304285
550.1872056526830210.3744113053660430.812794347316979
560.2798109225848990.5596218451697990.7201890774151
570.2414022484016400.4828044968032800.75859775159836
580.2092396217707750.4184792435415490.790760378229225
590.1951271177351410.3902542354702810.80487288226486
600.2085960301245090.4171920602490170.791403969875491
610.2595876484504180.5191752969008370.740412351549582
620.2974081852942350.594816370588470.702591814705765
630.2553414305770980.5106828611541950.744658569422902
640.2168113134630580.4336226269261160.783188686536942
650.182770896272030.365541792544060.81722910372797
660.1704066738499620.3408133476999240.829593326150038
670.1462335250473820.2924670500947640.853766474952618
680.1224404944081830.2448809888163660.877559505591817
690.1314071430552250.2628142861104510.868592856944775
700.1065068826163670.2130137652327340.893493117383633
710.08521511601751080.1704302320350220.914784883982489
720.0915782888547660.1831565777095320.908421711145234
730.07266564656141450.1453312931228290.927334353438586
740.07298037838491860.1459607567698370.927019621615081
750.06020302347687550.1204060469537510.939796976523125
760.04826574808179110.09653149616358220.95173425191821
770.04192889282218530.08385778564437050.958071107177815
780.03234286149404210.06468572298808430.967657138505958
790.02456126063671600.04912252127343210.975438739363284
800.01872880996654330.03745761993308670.981271190033457
810.01458481194177640.02916962388355280.985415188058224
820.07723045767693320.1544609153538660.922769542323067
830.06179378552698830.1235875710539770.938206214473012
840.04958236561099090.09916473122198170.95041763438901
850.03951006032199330.07902012064398660.960489939678007
860.040453400239240.080906800478480.95954659976076
870.03380602643020430.06761205286040860.966193973569796
880.02529067089354000.05058134178708010.97470932910646
890.02184436262541980.04368872525083970.97815563737458
900.01711021760032640.03422043520065280.982889782399674
910.01400762393752920.02801524787505840.98599237606247
920.01354034075109750.02708068150219490.986459659248903
930.01943072272637640.03886144545275270.980569277273624
940.01564936285195520.03129872570391050.984350637148045
950.01225480177966040.02450960355932080.98774519822034
960.01278845728419350.02557691456838710.987211542715806
970.01131223229285640.02262446458571280.988687767707144
980.01789570004944740.03579140009889490.982104299950552
990.0202178402154090.0404356804308180.97978215978459
1000.01662351933674340.03324703867348670.983376480663257
1010.08264663511104620.1652932702220920.917353364888954
1020.07564854080849380.1512970816169880.924351459191506
1030.0620392089681170.1240784179362340.937960791031883
1040.06222811305003130.1244562261000630.937771886949969
1050.05084741924110140.1016948384822030.949152580758899
1060.0391488678128840.0782977356257680.960851132187116
1070.03333962819637460.06667925639274910.966660371803626
1080.02401551745519720.04803103491039450.975984482544803
1090.02232476416072050.04464952832144090.97767523583928
1100.03234146942549690.06468293885099380.967658530574503
1110.04323079648167760.08646159296335520.956769203518322
1120.03233701298296840.06467402596593670.967662987017032
1130.03164264599968550.0632852919993710.968357354000315
1140.05828248337040150.1165649667408030.941717516629599
1150.06003746294310440.1200749258862090.939962537056896
1160.09508091532778850.1901618306555770.904919084672211
1170.07326605719487470.1465321143897490.926733942805125
1180.06089459665887410.1217891933177480.939105403341126
1190.04420525888042920.08841051776085830.95579474111957
1200.05323289541165440.1064657908233090.946767104588346
1210.2177809617056280.4355619234112570.782219038294372
1220.2444192014128350.4888384028256710.755580798587165
1230.1898465513775900.3796931027551790.81015344862241
1240.1660343039381120.3320686078762250.833965696061888
1250.5811319003780310.8377361992439380.418868099621969
1260.8659211237662550.268157752467490.134078876233745
1270.85803508642070.2839298271586010.141964913579301
1280.7951455517234920.4097088965530170.204854448276508
1290.7327688946566020.5344622106867950.267231105343398
1300.710976262618150.5780474747636990.289023737381849
1310.7064989221703550.5870021556592910.293501077829645
1320.6132844378967280.7734311242065450.386715562103272
1330.7793676018636150.4412647962727690.220632398136385

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.192625916249130 & 0.385251832498259 & 0.80737408375087 \tabularnewline
13 & 0.299178553490283 & 0.598357106980566 & 0.700821446509717 \tabularnewline
14 & 0.455826015851385 & 0.91165203170277 & 0.544173984148615 \tabularnewline
15 & 0.400852228842388 & 0.801704457684777 & 0.599147771157612 \tabularnewline
16 & 0.553425921034396 & 0.893148157931209 & 0.446574078965604 \tabularnewline
17 & 0.469288556542059 & 0.938577113084117 & 0.530711443457941 \tabularnewline
18 & 0.383439008529484 & 0.766878017058968 & 0.616560991470516 \tabularnewline
19 & 0.4899877617262 & 0.9799755234524 & 0.5100122382738 \tabularnewline
20 & 0.419335969986465 & 0.83867193997293 & 0.580664030013535 \tabularnewline
21 & 0.352317848399479 & 0.704635696798958 & 0.647682151600521 \tabularnewline
22 & 0.398227228468739 & 0.796454456937478 & 0.601772771531261 \tabularnewline
23 & 0.444379302368579 & 0.888758604737159 & 0.555620697631421 \tabularnewline
24 & 0.372311506143255 & 0.74462301228651 & 0.627688493856745 \tabularnewline
25 & 0.315646327630738 & 0.631292655261475 & 0.684353672369262 \tabularnewline
26 & 0.274395939183484 & 0.548791878366967 & 0.725604060816516 \tabularnewline
27 & 0.224567112903166 & 0.449134225806332 & 0.775432887096834 \tabularnewline
28 & 0.189364914144113 & 0.378729828288226 & 0.810635085855887 \tabularnewline
29 & 0.247970962540508 & 0.495941925081016 & 0.752029037459492 \tabularnewline
30 & 0.207940257680449 & 0.415880515360899 & 0.79205974231955 \tabularnewline
31 & 0.170976250671609 & 0.341952501343218 & 0.829023749328391 \tabularnewline
32 & 0.134191744151734 & 0.268383488303468 & 0.865808255848266 \tabularnewline
33 & 0.129331584603051 & 0.258663169206103 & 0.870668415396949 \tabularnewline
34 & 0.146633033695004 & 0.293266067390009 & 0.853366966304996 \tabularnewline
35 & 0.149075587878985 & 0.298151175757969 & 0.850924412121015 \tabularnewline
36 & 0.154133783813512 & 0.308267567627025 & 0.845866216186488 \tabularnewline
37 & 0.194418374656501 & 0.388836749313002 & 0.805581625343499 \tabularnewline
38 & 0.246768149425659 & 0.493536298851319 & 0.753231850574341 \tabularnewline
39 & 0.270391572681662 & 0.540783145363325 & 0.729608427318338 \tabularnewline
40 & 0.263992609285095 & 0.527985218570191 & 0.736007390714905 \tabularnewline
41 & 0.275657559902065 & 0.551315119804131 & 0.724342440097935 \tabularnewline
42 & 0.244713460056936 & 0.489426920113873 & 0.755286539943063 \tabularnewline
43 & 0.243974473846869 & 0.487948947693739 & 0.75602552615313 \tabularnewline
44 & 0.208068438071616 & 0.416136876143232 & 0.791931561928384 \tabularnewline
45 & 0.173129524740868 & 0.346259049481737 & 0.826870475259132 \tabularnewline
46 & 0.151648813618584 & 0.303297627237167 & 0.848351186381416 \tabularnewline
47 & 0.158384637576189 & 0.316769275152377 & 0.841615362423811 \tabularnewline
48 & 0.259057983066465 & 0.51811596613293 & 0.740942016933535 \tabularnewline
49 & 0.242975783342214 & 0.485951566684428 & 0.757024216657786 \tabularnewline
50 & 0.214840901206893 & 0.429681802413787 & 0.785159098793107 \tabularnewline
51 & 0.181694415122013 & 0.363388830244026 & 0.818305584877987 \tabularnewline
52 & 0.148298621153626 & 0.296597242307251 & 0.851701378846374 \tabularnewline
53 & 0.212102965186804 & 0.424205930373608 & 0.787897034813196 \tabularnewline
54 & 0.204643313695715 & 0.40928662739143 & 0.795356686304285 \tabularnewline
55 & 0.187205652683021 & 0.374411305366043 & 0.812794347316979 \tabularnewline
56 & 0.279810922584899 & 0.559621845169799 & 0.7201890774151 \tabularnewline
57 & 0.241402248401640 & 0.482804496803280 & 0.75859775159836 \tabularnewline
58 & 0.209239621770775 & 0.418479243541549 & 0.790760378229225 \tabularnewline
59 & 0.195127117735141 & 0.390254235470281 & 0.80487288226486 \tabularnewline
60 & 0.208596030124509 & 0.417192060249017 & 0.791403969875491 \tabularnewline
61 & 0.259587648450418 & 0.519175296900837 & 0.740412351549582 \tabularnewline
62 & 0.297408185294235 & 0.59481637058847 & 0.702591814705765 \tabularnewline
63 & 0.255341430577098 & 0.510682861154195 & 0.744658569422902 \tabularnewline
64 & 0.216811313463058 & 0.433622626926116 & 0.783188686536942 \tabularnewline
65 & 0.18277089627203 & 0.36554179254406 & 0.81722910372797 \tabularnewline
66 & 0.170406673849962 & 0.340813347699924 & 0.829593326150038 \tabularnewline
67 & 0.146233525047382 & 0.292467050094764 & 0.853766474952618 \tabularnewline
68 & 0.122440494408183 & 0.244880988816366 & 0.877559505591817 \tabularnewline
69 & 0.131407143055225 & 0.262814286110451 & 0.868592856944775 \tabularnewline
70 & 0.106506882616367 & 0.213013765232734 & 0.893493117383633 \tabularnewline
71 & 0.0852151160175108 & 0.170430232035022 & 0.914784883982489 \tabularnewline
72 & 0.091578288854766 & 0.183156577709532 & 0.908421711145234 \tabularnewline
73 & 0.0726656465614145 & 0.145331293122829 & 0.927334353438586 \tabularnewline
74 & 0.0729803783849186 & 0.145960756769837 & 0.927019621615081 \tabularnewline
75 & 0.0602030234768755 & 0.120406046953751 & 0.939796976523125 \tabularnewline
76 & 0.0482657480817911 & 0.0965314961635822 & 0.95173425191821 \tabularnewline
77 & 0.0419288928221853 & 0.0838577856443705 & 0.958071107177815 \tabularnewline
78 & 0.0323428614940421 & 0.0646857229880843 & 0.967657138505958 \tabularnewline
79 & 0.0245612606367160 & 0.0491225212734321 & 0.975438739363284 \tabularnewline
80 & 0.0187288099665433 & 0.0374576199330867 & 0.981271190033457 \tabularnewline
81 & 0.0145848119417764 & 0.0291696238835528 & 0.985415188058224 \tabularnewline
82 & 0.0772304576769332 & 0.154460915353866 & 0.922769542323067 \tabularnewline
83 & 0.0617937855269883 & 0.123587571053977 & 0.938206214473012 \tabularnewline
84 & 0.0495823656109909 & 0.0991647312219817 & 0.95041763438901 \tabularnewline
85 & 0.0395100603219933 & 0.0790201206439866 & 0.960489939678007 \tabularnewline
86 & 0.04045340023924 & 0.08090680047848 & 0.95954659976076 \tabularnewline
87 & 0.0338060264302043 & 0.0676120528604086 & 0.966193973569796 \tabularnewline
88 & 0.0252906708935400 & 0.0505813417870801 & 0.97470932910646 \tabularnewline
89 & 0.0218443626254198 & 0.0436887252508397 & 0.97815563737458 \tabularnewline
90 & 0.0171102176003264 & 0.0342204352006528 & 0.982889782399674 \tabularnewline
91 & 0.0140076239375292 & 0.0280152478750584 & 0.98599237606247 \tabularnewline
92 & 0.0135403407510975 & 0.0270806815021949 & 0.986459659248903 \tabularnewline
93 & 0.0194307227263764 & 0.0388614454527527 & 0.980569277273624 \tabularnewline
94 & 0.0156493628519552 & 0.0312987257039105 & 0.984350637148045 \tabularnewline
95 & 0.0122548017796604 & 0.0245096035593208 & 0.98774519822034 \tabularnewline
96 & 0.0127884572841935 & 0.0255769145683871 & 0.987211542715806 \tabularnewline
97 & 0.0113122322928564 & 0.0226244645857128 & 0.988687767707144 \tabularnewline
98 & 0.0178957000494474 & 0.0357914000988949 & 0.982104299950552 \tabularnewline
99 & 0.020217840215409 & 0.040435680430818 & 0.97978215978459 \tabularnewline
100 & 0.0166235193367434 & 0.0332470386734867 & 0.983376480663257 \tabularnewline
101 & 0.0826466351110462 & 0.165293270222092 & 0.917353364888954 \tabularnewline
102 & 0.0756485408084938 & 0.151297081616988 & 0.924351459191506 \tabularnewline
103 & 0.062039208968117 & 0.124078417936234 & 0.937960791031883 \tabularnewline
104 & 0.0622281130500313 & 0.124456226100063 & 0.937771886949969 \tabularnewline
105 & 0.0508474192411014 & 0.101694838482203 & 0.949152580758899 \tabularnewline
106 & 0.039148867812884 & 0.078297735625768 & 0.960851132187116 \tabularnewline
107 & 0.0333396281963746 & 0.0666792563927491 & 0.966660371803626 \tabularnewline
108 & 0.0240155174551972 & 0.0480310349103945 & 0.975984482544803 \tabularnewline
109 & 0.0223247641607205 & 0.0446495283214409 & 0.97767523583928 \tabularnewline
110 & 0.0323414694254969 & 0.0646829388509938 & 0.967658530574503 \tabularnewline
111 & 0.0432307964816776 & 0.0864615929633552 & 0.956769203518322 \tabularnewline
112 & 0.0323370129829684 & 0.0646740259659367 & 0.967662987017032 \tabularnewline
113 & 0.0316426459996855 & 0.063285291999371 & 0.968357354000315 \tabularnewline
114 & 0.0582824833704015 & 0.116564966740803 & 0.941717516629599 \tabularnewline
115 & 0.0600374629431044 & 0.120074925886209 & 0.939962537056896 \tabularnewline
116 & 0.0950809153277885 & 0.190161830655577 & 0.904919084672211 \tabularnewline
117 & 0.0732660571948747 & 0.146532114389749 & 0.926733942805125 \tabularnewline
118 & 0.0608945966588741 & 0.121789193317748 & 0.939105403341126 \tabularnewline
119 & 0.0442052588804292 & 0.0884105177608583 & 0.95579474111957 \tabularnewline
120 & 0.0532328954116544 & 0.106465790823309 & 0.946767104588346 \tabularnewline
121 & 0.217780961705628 & 0.435561923411257 & 0.782219038294372 \tabularnewline
122 & 0.244419201412835 & 0.488838402825671 & 0.755580798587165 \tabularnewline
123 & 0.189846551377590 & 0.379693102755179 & 0.81015344862241 \tabularnewline
124 & 0.166034303938112 & 0.332068607876225 & 0.833965696061888 \tabularnewline
125 & 0.581131900378031 & 0.837736199243938 & 0.418868099621969 \tabularnewline
126 & 0.865921123766255 & 0.26815775246749 & 0.134078876233745 \tabularnewline
127 & 0.8580350864207 & 0.283929827158601 & 0.141964913579301 \tabularnewline
128 & 0.795145551723492 & 0.409708896553017 & 0.204854448276508 \tabularnewline
129 & 0.732768894656602 & 0.534462210686795 & 0.267231105343398 \tabularnewline
130 & 0.71097626261815 & 0.578047474763699 & 0.289023737381849 \tabularnewline
131 & 0.706498922170355 & 0.587002155659291 & 0.293501077829645 \tabularnewline
132 & 0.613284437896728 & 0.773431124206545 & 0.386715562103272 \tabularnewline
133 & 0.779367601863615 & 0.441264796272769 & 0.220632398136385 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99469&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]12[/C][C]0.192625916249130[/C][C]0.385251832498259[/C][C]0.80737408375087[/C][/ROW]
[ROW][C]13[/C][C]0.299178553490283[/C][C]0.598357106980566[/C][C]0.700821446509717[/C][/ROW]
[ROW][C]14[/C][C]0.455826015851385[/C][C]0.91165203170277[/C][C]0.544173984148615[/C][/ROW]
[ROW][C]15[/C][C]0.400852228842388[/C][C]0.801704457684777[/C][C]0.599147771157612[/C][/ROW]
[ROW][C]16[/C][C]0.553425921034396[/C][C]0.893148157931209[/C][C]0.446574078965604[/C][/ROW]
[ROW][C]17[/C][C]0.469288556542059[/C][C]0.938577113084117[/C][C]0.530711443457941[/C][/ROW]
[ROW][C]18[/C][C]0.383439008529484[/C][C]0.766878017058968[/C][C]0.616560991470516[/C][/ROW]
[ROW][C]19[/C][C]0.4899877617262[/C][C]0.9799755234524[/C][C]0.5100122382738[/C][/ROW]
[ROW][C]20[/C][C]0.419335969986465[/C][C]0.83867193997293[/C][C]0.580664030013535[/C][/ROW]
[ROW][C]21[/C][C]0.352317848399479[/C][C]0.704635696798958[/C][C]0.647682151600521[/C][/ROW]
[ROW][C]22[/C][C]0.398227228468739[/C][C]0.796454456937478[/C][C]0.601772771531261[/C][/ROW]
[ROW][C]23[/C][C]0.444379302368579[/C][C]0.888758604737159[/C][C]0.555620697631421[/C][/ROW]
[ROW][C]24[/C][C]0.372311506143255[/C][C]0.74462301228651[/C][C]0.627688493856745[/C][/ROW]
[ROW][C]25[/C][C]0.315646327630738[/C][C]0.631292655261475[/C][C]0.684353672369262[/C][/ROW]
[ROW][C]26[/C][C]0.274395939183484[/C][C]0.548791878366967[/C][C]0.725604060816516[/C][/ROW]
[ROW][C]27[/C][C]0.224567112903166[/C][C]0.449134225806332[/C][C]0.775432887096834[/C][/ROW]
[ROW][C]28[/C][C]0.189364914144113[/C][C]0.378729828288226[/C][C]0.810635085855887[/C][/ROW]
[ROW][C]29[/C][C]0.247970962540508[/C][C]0.495941925081016[/C][C]0.752029037459492[/C][/ROW]
[ROW][C]30[/C][C]0.207940257680449[/C][C]0.415880515360899[/C][C]0.79205974231955[/C][/ROW]
[ROW][C]31[/C][C]0.170976250671609[/C][C]0.341952501343218[/C][C]0.829023749328391[/C][/ROW]
[ROW][C]32[/C][C]0.134191744151734[/C][C]0.268383488303468[/C][C]0.865808255848266[/C][/ROW]
[ROW][C]33[/C][C]0.129331584603051[/C][C]0.258663169206103[/C][C]0.870668415396949[/C][/ROW]
[ROW][C]34[/C][C]0.146633033695004[/C][C]0.293266067390009[/C][C]0.853366966304996[/C][/ROW]
[ROW][C]35[/C][C]0.149075587878985[/C][C]0.298151175757969[/C][C]0.850924412121015[/C][/ROW]
[ROW][C]36[/C][C]0.154133783813512[/C][C]0.308267567627025[/C][C]0.845866216186488[/C][/ROW]
[ROW][C]37[/C][C]0.194418374656501[/C][C]0.388836749313002[/C][C]0.805581625343499[/C][/ROW]
[ROW][C]38[/C][C]0.246768149425659[/C][C]0.493536298851319[/C][C]0.753231850574341[/C][/ROW]
[ROW][C]39[/C][C]0.270391572681662[/C][C]0.540783145363325[/C][C]0.729608427318338[/C][/ROW]
[ROW][C]40[/C][C]0.263992609285095[/C][C]0.527985218570191[/C][C]0.736007390714905[/C][/ROW]
[ROW][C]41[/C][C]0.275657559902065[/C][C]0.551315119804131[/C][C]0.724342440097935[/C][/ROW]
[ROW][C]42[/C][C]0.244713460056936[/C][C]0.489426920113873[/C][C]0.755286539943063[/C][/ROW]
[ROW][C]43[/C][C]0.243974473846869[/C][C]0.487948947693739[/C][C]0.75602552615313[/C][/ROW]
[ROW][C]44[/C][C]0.208068438071616[/C][C]0.416136876143232[/C][C]0.791931561928384[/C][/ROW]
[ROW][C]45[/C][C]0.173129524740868[/C][C]0.346259049481737[/C][C]0.826870475259132[/C][/ROW]
[ROW][C]46[/C][C]0.151648813618584[/C][C]0.303297627237167[/C][C]0.848351186381416[/C][/ROW]
[ROW][C]47[/C][C]0.158384637576189[/C][C]0.316769275152377[/C][C]0.841615362423811[/C][/ROW]
[ROW][C]48[/C][C]0.259057983066465[/C][C]0.51811596613293[/C][C]0.740942016933535[/C][/ROW]
[ROW][C]49[/C][C]0.242975783342214[/C][C]0.485951566684428[/C][C]0.757024216657786[/C][/ROW]
[ROW][C]50[/C][C]0.214840901206893[/C][C]0.429681802413787[/C][C]0.785159098793107[/C][/ROW]
[ROW][C]51[/C][C]0.181694415122013[/C][C]0.363388830244026[/C][C]0.818305584877987[/C][/ROW]
[ROW][C]52[/C][C]0.148298621153626[/C][C]0.296597242307251[/C][C]0.851701378846374[/C][/ROW]
[ROW][C]53[/C][C]0.212102965186804[/C][C]0.424205930373608[/C][C]0.787897034813196[/C][/ROW]
[ROW][C]54[/C][C]0.204643313695715[/C][C]0.40928662739143[/C][C]0.795356686304285[/C][/ROW]
[ROW][C]55[/C][C]0.187205652683021[/C][C]0.374411305366043[/C][C]0.812794347316979[/C][/ROW]
[ROW][C]56[/C][C]0.279810922584899[/C][C]0.559621845169799[/C][C]0.7201890774151[/C][/ROW]
[ROW][C]57[/C][C]0.241402248401640[/C][C]0.482804496803280[/C][C]0.75859775159836[/C][/ROW]
[ROW][C]58[/C][C]0.209239621770775[/C][C]0.418479243541549[/C][C]0.790760378229225[/C][/ROW]
[ROW][C]59[/C][C]0.195127117735141[/C][C]0.390254235470281[/C][C]0.80487288226486[/C][/ROW]
[ROW][C]60[/C][C]0.208596030124509[/C][C]0.417192060249017[/C][C]0.791403969875491[/C][/ROW]
[ROW][C]61[/C][C]0.259587648450418[/C][C]0.519175296900837[/C][C]0.740412351549582[/C][/ROW]
[ROW][C]62[/C][C]0.297408185294235[/C][C]0.59481637058847[/C][C]0.702591814705765[/C][/ROW]
[ROW][C]63[/C][C]0.255341430577098[/C][C]0.510682861154195[/C][C]0.744658569422902[/C][/ROW]
[ROW][C]64[/C][C]0.216811313463058[/C][C]0.433622626926116[/C][C]0.783188686536942[/C][/ROW]
[ROW][C]65[/C][C]0.18277089627203[/C][C]0.36554179254406[/C][C]0.81722910372797[/C][/ROW]
[ROW][C]66[/C][C]0.170406673849962[/C][C]0.340813347699924[/C][C]0.829593326150038[/C][/ROW]
[ROW][C]67[/C][C]0.146233525047382[/C][C]0.292467050094764[/C][C]0.853766474952618[/C][/ROW]
[ROW][C]68[/C][C]0.122440494408183[/C][C]0.244880988816366[/C][C]0.877559505591817[/C][/ROW]
[ROW][C]69[/C][C]0.131407143055225[/C][C]0.262814286110451[/C][C]0.868592856944775[/C][/ROW]
[ROW][C]70[/C][C]0.106506882616367[/C][C]0.213013765232734[/C][C]0.893493117383633[/C][/ROW]
[ROW][C]71[/C][C]0.0852151160175108[/C][C]0.170430232035022[/C][C]0.914784883982489[/C][/ROW]
[ROW][C]72[/C][C]0.091578288854766[/C][C]0.183156577709532[/C][C]0.908421711145234[/C][/ROW]
[ROW][C]73[/C][C]0.0726656465614145[/C][C]0.145331293122829[/C][C]0.927334353438586[/C][/ROW]
[ROW][C]74[/C][C]0.0729803783849186[/C][C]0.145960756769837[/C][C]0.927019621615081[/C][/ROW]
[ROW][C]75[/C][C]0.0602030234768755[/C][C]0.120406046953751[/C][C]0.939796976523125[/C][/ROW]
[ROW][C]76[/C][C]0.0482657480817911[/C][C]0.0965314961635822[/C][C]0.95173425191821[/C][/ROW]
[ROW][C]77[/C][C]0.0419288928221853[/C][C]0.0838577856443705[/C][C]0.958071107177815[/C][/ROW]
[ROW][C]78[/C][C]0.0323428614940421[/C][C]0.0646857229880843[/C][C]0.967657138505958[/C][/ROW]
[ROW][C]79[/C][C]0.0245612606367160[/C][C]0.0491225212734321[/C][C]0.975438739363284[/C][/ROW]
[ROW][C]80[/C][C]0.0187288099665433[/C][C]0.0374576199330867[/C][C]0.981271190033457[/C][/ROW]
[ROW][C]81[/C][C]0.0145848119417764[/C][C]0.0291696238835528[/C][C]0.985415188058224[/C][/ROW]
[ROW][C]82[/C][C]0.0772304576769332[/C][C]0.154460915353866[/C][C]0.922769542323067[/C][/ROW]
[ROW][C]83[/C][C]0.0617937855269883[/C][C]0.123587571053977[/C][C]0.938206214473012[/C][/ROW]
[ROW][C]84[/C][C]0.0495823656109909[/C][C]0.0991647312219817[/C][C]0.95041763438901[/C][/ROW]
[ROW][C]85[/C][C]0.0395100603219933[/C][C]0.0790201206439866[/C][C]0.960489939678007[/C][/ROW]
[ROW][C]86[/C][C]0.04045340023924[/C][C]0.08090680047848[/C][C]0.95954659976076[/C][/ROW]
[ROW][C]87[/C][C]0.0338060264302043[/C][C]0.0676120528604086[/C][C]0.966193973569796[/C][/ROW]
[ROW][C]88[/C][C]0.0252906708935400[/C][C]0.0505813417870801[/C][C]0.97470932910646[/C][/ROW]
[ROW][C]89[/C][C]0.0218443626254198[/C][C]0.0436887252508397[/C][C]0.97815563737458[/C][/ROW]
[ROW][C]90[/C][C]0.0171102176003264[/C][C]0.0342204352006528[/C][C]0.982889782399674[/C][/ROW]
[ROW][C]91[/C][C]0.0140076239375292[/C][C]0.0280152478750584[/C][C]0.98599237606247[/C][/ROW]
[ROW][C]92[/C][C]0.0135403407510975[/C][C]0.0270806815021949[/C][C]0.986459659248903[/C][/ROW]
[ROW][C]93[/C][C]0.0194307227263764[/C][C]0.0388614454527527[/C][C]0.980569277273624[/C][/ROW]
[ROW][C]94[/C][C]0.0156493628519552[/C][C]0.0312987257039105[/C][C]0.984350637148045[/C][/ROW]
[ROW][C]95[/C][C]0.0122548017796604[/C][C]0.0245096035593208[/C][C]0.98774519822034[/C][/ROW]
[ROW][C]96[/C][C]0.0127884572841935[/C][C]0.0255769145683871[/C][C]0.987211542715806[/C][/ROW]
[ROW][C]97[/C][C]0.0113122322928564[/C][C]0.0226244645857128[/C][C]0.988687767707144[/C][/ROW]
[ROW][C]98[/C][C]0.0178957000494474[/C][C]0.0357914000988949[/C][C]0.982104299950552[/C][/ROW]
[ROW][C]99[/C][C]0.020217840215409[/C][C]0.040435680430818[/C][C]0.97978215978459[/C][/ROW]
[ROW][C]100[/C][C]0.0166235193367434[/C][C]0.0332470386734867[/C][C]0.983376480663257[/C][/ROW]
[ROW][C]101[/C][C]0.0826466351110462[/C][C]0.165293270222092[/C][C]0.917353364888954[/C][/ROW]
[ROW][C]102[/C][C]0.0756485408084938[/C][C]0.151297081616988[/C][C]0.924351459191506[/C][/ROW]
[ROW][C]103[/C][C]0.062039208968117[/C][C]0.124078417936234[/C][C]0.937960791031883[/C][/ROW]
[ROW][C]104[/C][C]0.0622281130500313[/C][C]0.124456226100063[/C][C]0.937771886949969[/C][/ROW]
[ROW][C]105[/C][C]0.0508474192411014[/C][C]0.101694838482203[/C][C]0.949152580758899[/C][/ROW]
[ROW][C]106[/C][C]0.039148867812884[/C][C]0.078297735625768[/C][C]0.960851132187116[/C][/ROW]
[ROW][C]107[/C][C]0.0333396281963746[/C][C]0.0666792563927491[/C][C]0.966660371803626[/C][/ROW]
[ROW][C]108[/C][C]0.0240155174551972[/C][C]0.0480310349103945[/C][C]0.975984482544803[/C][/ROW]
[ROW][C]109[/C][C]0.0223247641607205[/C][C]0.0446495283214409[/C][C]0.97767523583928[/C][/ROW]
[ROW][C]110[/C][C]0.0323414694254969[/C][C]0.0646829388509938[/C][C]0.967658530574503[/C][/ROW]
[ROW][C]111[/C][C]0.0432307964816776[/C][C]0.0864615929633552[/C][C]0.956769203518322[/C][/ROW]
[ROW][C]112[/C][C]0.0323370129829684[/C][C]0.0646740259659367[/C][C]0.967662987017032[/C][/ROW]
[ROW][C]113[/C][C]0.0316426459996855[/C][C]0.063285291999371[/C][C]0.968357354000315[/C][/ROW]
[ROW][C]114[/C][C]0.0582824833704015[/C][C]0.116564966740803[/C][C]0.941717516629599[/C][/ROW]
[ROW][C]115[/C][C]0.0600374629431044[/C][C]0.120074925886209[/C][C]0.939962537056896[/C][/ROW]
[ROW][C]116[/C][C]0.0950809153277885[/C][C]0.190161830655577[/C][C]0.904919084672211[/C][/ROW]
[ROW][C]117[/C][C]0.0732660571948747[/C][C]0.146532114389749[/C][C]0.926733942805125[/C][/ROW]
[ROW][C]118[/C][C]0.0608945966588741[/C][C]0.121789193317748[/C][C]0.939105403341126[/C][/ROW]
[ROW][C]119[/C][C]0.0442052588804292[/C][C]0.0884105177608583[/C][C]0.95579474111957[/C][/ROW]
[ROW][C]120[/C][C]0.0532328954116544[/C][C]0.106465790823309[/C][C]0.946767104588346[/C][/ROW]
[ROW][C]121[/C][C]0.217780961705628[/C][C]0.435561923411257[/C][C]0.782219038294372[/C][/ROW]
[ROW][C]122[/C][C]0.244419201412835[/C][C]0.488838402825671[/C][C]0.755580798587165[/C][/ROW]
[ROW][C]123[/C][C]0.189846551377590[/C][C]0.379693102755179[/C][C]0.81015344862241[/C][/ROW]
[ROW][C]124[/C][C]0.166034303938112[/C][C]0.332068607876225[/C][C]0.833965696061888[/C][/ROW]
[ROW][C]125[/C][C]0.581131900378031[/C][C]0.837736199243938[/C][C]0.418868099621969[/C][/ROW]
[ROW][C]126[/C][C]0.865921123766255[/C][C]0.26815775246749[/C][C]0.134078876233745[/C][/ROW]
[ROW][C]127[/C][C]0.8580350864207[/C][C]0.283929827158601[/C][C]0.141964913579301[/C][/ROW]
[ROW][C]128[/C][C]0.795145551723492[/C][C]0.409708896553017[/C][C]0.204854448276508[/C][/ROW]
[ROW][C]129[/C][C]0.732768894656602[/C][C]0.534462210686795[/C][C]0.267231105343398[/C][/ROW]
[ROW][C]130[/C][C]0.71097626261815[/C][C]0.578047474763699[/C][C]0.289023737381849[/C][/ROW]
[ROW][C]131[/C][C]0.706498922170355[/C][C]0.587002155659291[/C][C]0.293501077829645[/C][/ROW]
[ROW][C]132[/C][C]0.613284437896728[/C][C]0.773431124206545[/C][C]0.386715562103272[/C][/ROW]
[ROW][C]133[/C][C]0.779367601863615[/C][C]0.441264796272769[/C][C]0.220632398136385[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99469&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99469&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
120.1926259162491300.3852518324982590.80737408375087
130.2991785534902830.5983571069805660.700821446509717
140.4558260158513850.911652031702770.544173984148615
150.4008522288423880.8017044576847770.599147771157612
160.5534259210343960.8931481579312090.446574078965604
170.4692885565420590.9385771130841170.530711443457941
180.3834390085294840.7668780170589680.616560991470516
190.48998776172620.97997552345240.5100122382738
200.4193359699864650.838671939972930.580664030013535
210.3523178483994790.7046356967989580.647682151600521
220.3982272284687390.7964544569374780.601772771531261
230.4443793023685790.8887586047371590.555620697631421
240.3723115061432550.744623012286510.627688493856745
250.3156463276307380.6312926552614750.684353672369262
260.2743959391834840.5487918783669670.725604060816516
270.2245671129031660.4491342258063320.775432887096834
280.1893649141441130.3787298282882260.810635085855887
290.2479709625405080.4959419250810160.752029037459492
300.2079402576804490.4158805153608990.79205974231955
310.1709762506716090.3419525013432180.829023749328391
320.1341917441517340.2683834883034680.865808255848266
330.1293315846030510.2586631692061030.870668415396949
340.1466330336950040.2932660673900090.853366966304996
350.1490755878789850.2981511757579690.850924412121015
360.1541337838135120.3082675676270250.845866216186488
370.1944183746565010.3888367493130020.805581625343499
380.2467681494256590.4935362988513190.753231850574341
390.2703915726816620.5407831453633250.729608427318338
400.2639926092850950.5279852185701910.736007390714905
410.2756575599020650.5513151198041310.724342440097935
420.2447134600569360.4894269201138730.755286539943063
430.2439744738468690.4879489476937390.75602552615313
440.2080684380716160.4161368761432320.791931561928384
450.1731295247408680.3462590494817370.826870475259132
460.1516488136185840.3032976272371670.848351186381416
470.1583846375761890.3167692751523770.841615362423811
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1330.7793676018636150.4412647962727690.220632398136385







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level170.139344262295082NOK
10% type I error level320.262295081967213NOK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 17 & 0.139344262295082 & NOK \tabularnewline
10% type I error level & 32 & 0.262295081967213 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99469&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]17[/C][C]0.139344262295082[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]32[/C][C]0.262295081967213[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99469&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99469&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 level00OK
5% type I error level170.139344262295082NOK
10% type I error level320.262295081967213NOK



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