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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 14:21:37 +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/t12905220037z7mff4likl49f1.htm/, Retrieved Thu, 28 Mar 2024 15:41:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99126, Retrieved Thu, 28 Mar 2024 15:41:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact160
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:55:05] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [Workshop 7 - Regr...] [2010-11-19 15:10:35] [8b017ffbf7b0eded54d8efebfb3e4cfa]
-           [Multiple Regression] [workshop 7 multip...] [2010-11-23 14:21:37] [86130087148d9c8eb48f66f03eaf10c2] [Current]
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Dataseries X:
1	26	24	24	14	14	11	11	12	12	24	24
1	23	25	25	11	11	7	7	8	8	25	25
0	25	17	0	6	0	17	0	8	0	30	0
1	23	18	18	12	12	10	10	8	8	19	19
1	19	18	18	8	8	12	12	9	9	22	22
0	29	16	0	10	0	12	0	7	0	22	0
1	25	20	20	10	10	11	11	4	4	25	25
1	21	16	16	11	11	11	11	11	11	23	23
1	22	18	18	16	16	12	12	7	7	17	17
1	25	17	17	11	11	13	13	7	7	21	21
1	24	23	23	13	13	14	14	12	12	19	19
1	18	30	30	12	12	16	16	10	10	19	19
1	22	23	23	8	8	11	11	10	10	15	15
1	15	18	18	12	12	10	10	8	8	16	16
1	22	15	15	11	11	11	11	8	8	23	23
1	28	12	12	4	4	15	15	4	4	27	27
1	20	21	21	9	9	9	9	9	9	22	22
1	12	15	15	8	8	11	11	8	8	14	14
1	24	20	20	8	8	17	17	7	7	22	22
1	20	31	31	14	14	17	17	11	11	23	23
1	21	27	27	15	15	11	11	9	9	23	23
1	20	34	34	16	16	18	18	11	11	21	21
1	21	21	21	9	9	14	14	13	13	19	19
1	23	31	31	14	14	10	10	8	8	18	18
1	28	19	19	11	11	11	11	8	8	20	20
1	24	16	16	8	8	15	15	9	9	23	23
1	24	20	20	9	9	15	15	6	6	25	25
1	24	21	21	9	9	13	13	9	9	19	19
1	23	22	22	9	9	16	16	9	9	24	24
1	23	17	17	9	9	13	13	6	6	22	22
1	29	24	24	10	10	9	9	6	6	25	25
1	24	25	25	16	16	18	18	16	16	26	26
1	18	26	26	11	11	18	18	5	5	29	29
1	25	25	25	8	8	12	12	7	7	32	32
1	21	17	17	9	9	17	17	9	9	25	25
1	26	32	32	16	16	9	9	6	6	29	29
1	22	33	33	11	11	9	9	6	6	28	28
1	22	13	13	16	16	12	12	5	5	17	17
0	22	32	0	12	0	18	0	12	0	28	0
1	23	25	25	12	12	12	12	7	7	29	29
1	30	29	29	14	14	18	18	10	10	26	26
1	23	22	22	9	9	14	14	9	9	25	25
1	17	18	18	10	10	15	15	8	8	14	14
1	23	17	17	9	9	16	16	5	5	25	25
1	23	20	20	10	10	10	10	8	8	26	26
1	25	15	15	12	12	11	11	8	8	20	20
1	24	20	20	14	14	14	14	10	10	18	18
1	24	33	33	14	14	9	9	6	6	32	32
1	23	29	29	10	10	12	12	8	8	25	25
1	21	23	23	14	14	17	17	7	7	25	25
1	24	26	26	16	16	5	5	4	4	23	23
1	24	18	18	9	9	12	12	8	8	21	21
1	28	20	20	10	10	12	12	8	8	20	20
1	16	11	11	6	6	6	6	4	4	15	15
1	20	28	28	8	8	24	24	20	20	30	30
1	29	26	26	13	13	12	12	8	8	24	24
1	27	22	22	10	10	12	12	8	8	26	26
1	22	17	17	8	8	14	14	6	6	24	24
1	28	12	12	7	7	7	7	4	4	22	22
1	16	14	14	15	15	13	13	8	8	14	14
1	25	17	17	9	9	12	12	9	9	24	24
1	24	21	21	10	10	13	13	6	6	24	24
0	28	19	0	12	0	14	0	7	0	24	0
1	24	18	18	13	13	8	8	9	9	24	24
1	23	10	10	10	10	11	11	5	5	19	19
1	30	29	29	11	11	9	9	5	5	31	31
1	24	31	31	8	8	11	11	8	8	22	22
1	21	19	19	9	9	13	13	8	8	27	27
1	25	9	9	13	13	10	10	6	6	19	19
0	25	20	0	11	0	11	0	8	0	25	0
1	22	28	28	8	8	12	12	7	7	20	20
1	23	19	19	9	9	9	9	7	7	21	21
1	26	30	30	9	9	15	15	9	9	27	27
1	23	29	29	15	15	18	18	11	11	23	23
1	25	26	26	9	9	15	15	6	6	25	25
1	21	23	23	10	10	12	12	8	8	20	20
1	25	13	13	14	14	13	13	6	6	21	21
1	24	21	21	12	12	14	14	9	9	22	22
1	29	19	19	12	12	10	10	8	8	23	23
1	22	28	28	11	11	13	13	6	6	25	25
1	27	23	23	14	14	13	13	10	10	25	25
0	26	18	0	6	0	11	0	8	0	17	0
1	22	21	21	12	12	13	13	8	8	19	19
1	24	20	20	8	8	16	16	10	10	25	25
0	27	23	0	14	0	8	0	5	0	19	0
1	24	21	21	11	11	16	16	7	7	20	20
1	24	21	21	10	10	11	11	5	5	26	26
1	29	15	15	14	14	9	9	8	8	23	23
1	22	28	28	12	12	16	16	14	14	27	27
0	21	19	0	10	0	12	0	7	0	17	0
1	24	26	26	14	14	14	14	8	8	17	17
1	24	10	10	5	5	8	8	6	6	19	19
0	23	16	0	11	0	9	0	5	0	17	0
1	20	22	22	10	10	15	15	6	6	22	22
1	27	19	19	9	9	11	11	10	10	21	21
1	26	31	31	10	10	21	21	12	12	32	32
1	25	31	31	16	16	14	14	9	9	21	21
1	21	29	29	13	13	18	18	12	12	21	21
1	21	19	19	9	9	12	12	7	7	18	18
1	19	22	22	10	10	13	13	8	8	18	18
1	21	23	23	10	10	15	15	10	10	23	23
1	21	15	15	7	7	12	12	6	6	19	19
1	16	20	20	9	9	19	19	10	10	20	20
1	22	18	18	8	8	15	15	10	10	21	21
1	29	23	23	14	14	11	11	10	10	20	20
0	15	25	0	14	0	11	0	5	0	17	0
1	17	21	21	8	8	10	10	7	7	18	18
1	15	24	24	9	9	13	13	10	10	19	19
1	21	25	25	14	14	15	15	11	11	22	22
0	21	17	0	14	0	12	0	6	0	15	0
1	19	13	13	8	8	12	12	7	7	14	14
1	24	28	28	8	8	16	16	12	12	18	18
1	20	21	21	8	8	9	9	11	11	24	24
0	17	25	0	7	0	18	0	11	0	35	0
1	23	9	9	6	6	8	8	11	11	29	29
1	24	16	16	8	8	13	13	5	5	21	21
1	14	19	19	6	6	17	17	8	8	25	25
1	19	17	17	11	11	9	9	6	6	20	20
1	24	25	25	14	14	15	15	9	9	22	22
1	13	20	20	11	11	8	8	4	4	13	13
1	22	29	29	11	11	7	7	4	4	26	26
1	16	14	14	11	11	12	12	7	7	17	17
0	19	22	0	14	0	14	0	11	0	25	0
1	25	15	15	8	8	6	6	6	6	20	20
1	25	19	19	20	20	8	8	7	7	19	19
1	23	20	20	11	11	17	17	8	8	21	21
0	24	15	0	8	0	10	0	4	0	22	0
1	26	20	20	11	11	11	11	8	8	24	24
1	26	18	18	10	10	14	14	9	9	21	21
1	25	33	33	14	14	11	11	8	8	26	26
1	18	22	22	11	11	13	13	11	11	24	24
1	21	16	16	9	9	12	12	8	8	16	16
1	26	17	17	9	9	11	11	5	5	23	23
1	23	16	16	8	8	9	9	4	4	18	18
1	23	21	21	10	10	12	12	8	8	16	16
1	22	26	26	13	13	20	20	10	10	26	26
1	20	18	18	13	13	12	12	6	6	19	19
1	13	18	18	12	12	13	13	9	9	21	21
1	24	17	17	8	8	12	12	9	9	21	21
1	15	22	22	13	13	12	12	13	13	22	22
1	14	30	30	14	14	9	9	9	9	23	23
0	22	30	0	12	0	15	0	10	0	29	0
1	10	24	24	14	14	24	24	20	20	21	21
1	24	21	21	15	15	7	7	5	5	21	21
1	22	21	21	13	13	17	17	11	11	23	23
1	24	29	29	16	16	11	11	6	6	27	27
1	19	31	31	9	9	17	17	9	9	25	25
0	20	20	0	9	0	11	0	7	0	21	0
1	13	16	16	9	9	12	12	9	9	10	10
1	20	22	22	8	8	14	14	10	10	20	20
1	22	20	20	7	7	11	11	9	9	26	26
1	24	28	28	16	16	16	16	8	8	24	24
1	29	38	38	11	11	21	21	7	7	29	29
1	12	22	22	9	9	14	14	6	6	19	19
1	20	20	20	11	11	20	20	13	13	24	24
1	21	17	17	9	9	13	13	6	6	19	19
1	24	28	28	14	14	11	11	8	8	24	24
1	22	22	22	13	13	15	15	10	10	22	22
1	20	31	31	16	16	19	19	16	16	17	17




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time26 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 26 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99126&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]26 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99126&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99126&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 time26 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Multiple Linear Regression - Estimated Regression Equation
O[t] = + 29.5494632738198 -15.2181794105889B[t] -0.380081606319416CM[t] + 0.317358735332185CM_B[t] + 0.0209368771515474D[t] + 0.207544690798781D_B[t] -0.541638217696423PE[t] + 0.410737731232436PE_B[t] + 0.275927135451116PC[t] -0.509480209169096PC_B[t] + 0.251134847979600PS[t] + 0.221717890089259PS_B[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
O[t] =  +  29.5494632738198 -15.2181794105889B[t] -0.380081606319416CM[t] +  0.317358735332185CM_B[t] +  0.0209368771515474D[t] +  0.207544690798781D_B[t] -0.541638217696423PE[t] +  0.410737731232436PE_B[t] +  0.275927135451116PC[t] -0.509480209169096PC_B[t] +  0.251134847979600PS[t] +  0.221717890089259PS_B[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99126&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]O[t] =  +  29.5494632738198 -15.2181794105889B[t] -0.380081606319416CM[t] +  0.317358735332185CM_B[t] +  0.0209368771515474D[t] +  0.207544690798781D_B[t] -0.541638217696423PE[t] +  0.410737731232436PE_B[t] +  0.275927135451116PC[t] -0.509480209169096PC_B[t] +  0.251134847979600PS[t] +  0.221717890089259PS_B[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99126&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99126&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
O[t] = + 29.5494632738198 -15.2181794105889B[t] -0.380081606319416CM[t] + 0.317358735332185CM_B[t] + 0.0209368771515474D[t] + 0.207544690798781D_B[t] -0.541638217696423PE[t] + 0.410737731232436PE_B[t] + 0.275927135451116PC[t] -0.509480209169096PC_B[t] + 0.251134847979600PS[t] + 0.221717890089259PS_B[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)29.54946327381986.4114444.60899e-064e-06
B-15.21817941058896.7603-2.25110.0258620.012931
CM-0.3800816063194160.283486-1.34070.1820720.091036
CM_B0.3173587353321850.2907921.09140.27690.13845
D0.02093687715154740.4221670.04960.9605130.480257
D_B0.2075446907987810.4380690.47380.6363650.318183
PE-0.5416382176964230.625067-0.86650.3876130.193806
PE_B0.4107377312324360.633940.64790.5180520.259026
PC0.2759271354511160.7447980.37050.7115640.355782
PC_B-0.5094802091690960.756683-0.67330.5018090.250904
PS0.2511348479796000.2897650.86670.3875270.193763
PS_B0.2217178900892590.3011740.73620.4627950.231397

\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) & 29.5494632738198 & 6.411444 & 4.6089 & 9e-06 & 4e-06 \tabularnewline
B & -15.2181794105889 & 6.7603 & -2.2511 & 0.025862 & 0.012931 \tabularnewline
CM & -0.380081606319416 & 0.283486 & -1.3407 & 0.182072 & 0.091036 \tabularnewline
CM_B & 0.317358735332185 & 0.290792 & 1.0914 & 0.2769 & 0.13845 \tabularnewline
D & 0.0209368771515474 & 0.422167 & 0.0496 & 0.960513 & 0.480257 \tabularnewline
D_B & 0.207544690798781 & 0.438069 & 0.4738 & 0.636365 & 0.318183 \tabularnewline
PE & -0.541638217696423 & 0.625067 & -0.8665 & 0.387613 & 0.193806 \tabularnewline
PE_B & 0.410737731232436 & 0.63394 & 0.6479 & 0.518052 & 0.259026 \tabularnewline
PC & 0.275927135451116 & 0.744798 & 0.3705 & 0.711564 & 0.355782 \tabularnewline
PC_B & -0.509480209169096 & 0.756683 & -0.6733 & 0.501809 & 0.250904 \tabularnewline
PS & 0.251134847979600 & 0.289765 & 0.8667 & 0.387527 & 0.193763 \tabularnewline
PS_B & 0.221717890089259 & 0.301174 & 0.7362 & 0.462795 & 0.231397 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99126&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]29.5494632738198[/C][C]6.411444[/C][C]4.6089[/C][C]9e-06[/C][C]4e-06[/C][/ROW]
[ROW][C]B[/C][C]-15.2181794105889[/C][C]6.7603[/C][C]-2.2511[/C][C]0.025862[/C][C]0.012931[/C][/ROW]
[ROW][C]CM[/C][C]-0.380081606319416[/C][C]0.283486[/C][C]-1.3407[/C][C]0.182072[/C][C]0.091036[/C][/ROW]
[ROW][C]CM_B[/C][C]0.317358735332185[/C][C]0.290792[/C][C]1.0914[/C][C]0.2769[/C][C]0.13845[/C][/ROW]
[ROW][C]D[/C][C]0.0209368771515474[/C][C]0.422167[/C][C]0.0496[/C][C]0.960513[/C][C]0.480257[/C][/ROW]
[ROW][C]D_B[/C][C]0.207544690798781[/C][C]0.438069[/C][C]0.4738[/C][C]0.636365[/C][C]0.318183[/C][/ROW]
[ROW][C]PE[/C][C]-0.541638217696423[/C][C]0.625067[/C][C]-0.8665[/C][C]0.387613[/C][C]0.193806[/C][/ROW]
[ROW][C]PE_B[/C][C]0.410737731232436[/C][C]0.63394[/C][C]0.6479[/C][C]0.518052[/C][C]0.259026[/C][/ROW]
[ROW][C]PC[/C][C]0.275927135451116[/C][C]0.744798[/C][C]0.3705[/C][C]0.711564[/C][C]0.355782[/C][/ROW]
[ROW][C]PC_B[/C][C]-0.509480209169096[/C][C]0.756683[/C][C]-0.6733[/C][C]0.501809[/C][C]0.250904[/C][/ROW]
[ROW][C]PS[/C][C]0.251134847979600[/C][C]0.289765[/C][C]0.8667[/C][C]0.387527[/C][C]0.193763[/C][/ROW]
[ROW][C]PS_B[/C][C]0.221717890089259[/C][C]0.301174[/C][C]0.7362[/C][C]0.462795[/C][C]0.231397[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99126&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99126&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)29.54946327381986.4114444.60899e-064e-06
B-15.21817941058896.7603-2.25110.0258620.012931
CM-0.3800816063194160.283486-1.34070.1820720.091036
CM_B0.3173587353321850.2907921.09140.27690.13845
D0.02093687715154740.4221670.04960.9605130.480257
D_B0.2075446907987810.4380690.47380.6363650.318183
PE-0.5416382176964230.625067-0.86650.3876130.193806
PE_B0.4107377312324360.633940.64790.5180520.259026
PC0.2759271354511160.7447980.37050.7115640.355782
PC_B-0.5094802091690960.756683-0.67330.5018090.250904
PS0.2511348479796000.2897650.86670.3875270.193763
PS_B0.2217178900892590.3011740.73620.4627950.231397







Multiple Linear Regression - Regression Statistics
Multiple R0.508836076808853
R-squared0.258914153062225
Adjusted R-squared0.203458749549874
F-TEST (value)4.66887150148609
F-TEST (DF numerator)11
F-TEST (DF denominator)147
p-value4.20709262227703e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.48517056052898
Sum Squared Residuals1785.52283388875

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.508836076808853 \tabularnewline
R-squared & 0.258914153062225 \tabularnewline
Adjusted R-squared & 0.203458749549874 \tabularnewline
F-TEST (value) & 4.66887150148609 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 147 \tabularnewline
p-value & 4.20709262227703e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.48517056052898 \tabularnewline
Sum Squared Residuals & 1785.52283388875 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99126&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.508836076808853[/C][/ROW]
[ROW][C]R-squared[/C][C]0.258914153062225[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.203458749549874[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.66887150148609[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]147[/C][/ROW]
[ROW][C]p-value[/C][C]4.20709262227703e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.48517056052898[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1785.52283388875[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99126&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99126&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 R0.508836076808853
R-squared0.258914153062225
Adjusted R-squared0.203458749549874
F-TEST (value)4.66887150148609
F-TEST (DF numerator)11
F-TEST (DF denominator)147
p-value4.20709262227703e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.48517056052898
Sum Squared Residuals1785.52283388875







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12623.13060038877492.86939961122509
22324.3130997927334-1.31309979273343
32523.74731005145671.25268994854331
42321.75082356978931.24917643021074
51921.7601014655486-2.76010146554857
62924.63432433557654.3656756644235
72524.80884292873520.191157071264768
82122.7076389884709-1.7076389884709
92221.69079646624290.309203533757135
102522.37162196328992.6283780367101
112420.20787654207563.79212345792443
121819.7456400517226-1.74564005172261
132218.03386535687643.96613464312358
141520.3322653555827-5.33226535558269
152223.4710210806121-1.47102108061207
162824.36184001921293.63815998078713
172022.1931158799292-2.19311587992917
181218.5299017341414-6.52990173414137
192421.44725943869012.55274056130986
202021.6668377087295-1.66683770872951
212123.3987198268486-2.39871982684864
222020.8590262690668-0.85902626906677
232119.18584293853071.81415706146926
242320.91953664478712.08046335521293
252821.80157138245666.19842861754342
262421.96569848619992.03430151380007
272423.5896532674930.410346732507007
282420.25095571986663.74904428013335
292322.15979507983170.840204920168254
302322.62106463917610.378935360823917
312924.35264627027834.64735372972168
322422.62003042970621.37996957029377
331825.4025417440717-7.40254174407172
342526.5166748967625-1.51667489676250
352122.8153616863728-1.81536168637277
362627.1131636623579-1.11316366235788
372225.4351802135502-3.43518021355015
382222.471516968615-0.471516968614977
392218.23150784772363.76849215227642
402326.0120429543572-3.01204295435724
413023.31349425216456.68650574783546
422322.89444879082860.105551209171422
431718.2750943112244-1.27509431122438
442323.8804744677087-0.880474467708677
452324.4783838583962-1.47838385839615
462522.28094443435582.71905556564417
472420.61878013235473.38121986764531
482428.0120358696766-4.01203586967657
492323.1792243085142-0.179224308514248
502124.048538447637-3.04853844763699
512425.64309255316-1.64309255316002
522421.7492833691482.25071663085198
532821.37946645705506.62053354294497
541620.3853975474503-4.38539754745034
552020.7758050117615-0.775805011761506
562923.57998488725815.42001511274193
572724.09113714349372.90886285650628
582223.2073880608995-1.20738806089948
592823.73022492443154.26977507556854
601619.9301946078529-3.93019460785292
612522.99701138062382.00298861937616
622423.54436019931520.455639800684793
632822.95494653148775.0450534685123
642424.3718167272939-0.371816727293876
652322.36540213647640.634597863523598
663027.33818298542362.66181701457637
672421.30915770289652.69084229710346
682124.3927764401100-3.39277644010995
692523.01091712406061.98908287593938
702524.70590468453670.294095315463281
712220.65427342697451.34572657302549
722322.31281503127070.687184968729269
732623.20747081260452.79252918739554
742321.88986453219031.11013546780969
752523.21331604156961.78668395843039
762121.1912978440933-0.191297844093338
772523.54151122480781.45848877519221
782422.22405815146021.77594184853978
792923.57951165107755.42048834892254
802223.8066344084238-1.80663440842378
812723.8714811723393.128518827661
822623.3523047275812.64769527241898
832221.16995349743560.83004650256439
842422.29605891820681.70394108179324
852722.91879465589144.08120534410856
862421.25517628188022.74482371811984
872424.9854197220989-0.985419722098878
882924.41826675739104.58173324260897
892222.719695403376-0.719695403376022
902122.2384052767203-1.23840527672025
912420.23669631579843.76330368420159
922421.38214268239872.61785731760126
932324.4726473550171-1.47264735501708
942022.2741308792623-2.27413087926229
952721.35035483718885.64964516281118
962624.2514310599741.74856894002601
972522.03790297532042.96209702467963
982120.25364284643400.746357153566045
992120.50155535767220.498444642327805
1001920.1774147524789-1.17741475247886
1012121.750048451472-0.750048451471991
1022121.0018895175073-0.00188951750729945
1031619.7675753364208-3.76757533642083
1042220.66099419436981.33900580563023
1052921.76901845492277.23098154507732
1061520.0314470942041-5.03144709420414
1071720.4094290206754-3.40942902067538
1081519.8292340331870-4.82923403318697
1092121.832123169512-0.832123169512005
1102122.3041191665550-1.30411916655496
1111918.75800006336980.241999936630185
1122418.01720063639095.98279936360905
1132022.4432336406806-2.44323364068060
1141722.2694115066078-5.26941150660783
1152325.234109133435-2.23410913343498
1162422.21600627786211.78399372213789
1171422.2380243142653-8.2380243142653
1181922.6559242447950-3.65592424479497
1192422.29922931694801.70077068305203
1201319.7557930992512-6.75579309925122
1212225.4692733417253-3.46927334172529
1221620.7992801104401-4.79928011044015
1231923.2114188566166-4.21141885661661
1242522.48862674231042.51137325768959
1252524.01130728905060.988692710949389
1262321.42629833075431.57370166924572
1272425.2280272166323-1.22802721663232
1282623.63025946374482.36974053625522
1292621.48241089045244.51758910954761
1302524.44601232089950.553987679100515
1311822.5423535276884-4.5423535276884
1322119.51046542077821.48953457922181
1332623.58927142389092.41072857610911
1342321.55460308322951.44539691677054
1352319.42533263379243.57466736620763
1362223.0113803242479-1.01138032424793
1372022.1846103122476-2.18461031224758
1381322.0702745128170-9.07027451281704
1392421.34997159846692.65002840153306
1401521.7174055264794-6.71740552647937
1411423.2438706188646-9.2438706188646
1422220.31586629052901.68413370947095
1431018.1419112607927-8.14191126079267
1442424.2871658173622-0.287165817362175
1452222.0655848506515-0.0655848506514833
1462426.0938258262539-2.09382582625388
1471921.9372414925515-2.93724149255154
1482023.3835644028641-3.38356440286411
1491316.4397959186471-3.43979591864706
1502020.0681504588160-0.0681504588159777
1512223.4284855943632-1.42848559436320
1522423.61638190327860.383618096721359
1532923.79005968539705.20994031460296
1541220.7579915835694-8.75799158356937
1552021.284389716979-1.28438971697899
1562121.2025064249695-0.202506424969507
1572423.81392119969790.18607880030208
1582222.0253632882413-0.0253632882413480
1592017.85711807469912.14288192530086

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 26 & 23.1306003887749 & 2.86939961122509 \tabularnewline
2 & 23 & 24.3130997927334 & -1.31309979273343 \tabularnewline
3 & 25 & 23.7473100514567 & 1.25268994854331 \tabularnewline
4 & 23 & 21.7508235697893 & 1.24917643021074 \tabularnewline
5 & 19 & 21.7601014655486 & -2.76010146554857 \tabularnewline
6 & 29 & 24.6343243355765 & 4.3656756644235 \tabularnewline
7 & 25 & 24.8088429287352 & 0.191157071264768 \tabularnewline
8 & 21 & 22.7076389884709 & -1.7076389884709 \tabularnewline
9 & 22 & 21.6907964662429 & 0.309203533757135 \tabularnewline
10 & 25 & 22.3716219632899 & 2.6283780367101 \tabularnewline
11 & 24 & 20.2078765420756 & 3.79212345792443 \tabularnewline
12 & 18 & 19.7456400517226 & -1.74564005172261 \tabularnewline
13 & 22 & 18.0338653568764 & 3.96613464312358 \tabularnewline
14 & 15 & 20.3322653555827 & -5.33226535558269 \tabularnewline
15 & 22 & 23.4710210806121 & -1.47102108061207 \tabularnewline
16 & 28 & 24.3618400192129 & 3.63815998078713 \tabularnewline
17 & 20 & 22.1931158799292 & -2.19311587992917 \tabularnewline
18 & 12 & 18.5299017341414 & -6.52990173414137 \tabularnewline
19 & 24 & 21.4472594386901 & 2.55274056130986 \tabularnewline
20 & 20 & 21.6668377087295 & -1.66683770872951 \tabularnewline
21 & 21 & 23.3987198268486 & -2.39871982684864 \tabularnewline
22 & 20 & 20.8590262690668 & -0.85902626906677 \tabularnewline
23 & 21 & 19.1858429385307 & 1.81415706146926 \tabularnewline
24 & 23 & 20.9195366447871 & 2.08046335521293 \tabularnewline
25 & 28 & 21.8015713824566 & 6.19842861754342 \tabularnewline
26 & 24 & 21.9656984861999 & 2.03430151380007 \tabularnewline
27 & 24 & 23.589653267493 & 0.410346732507007 \tabularnewline
28 & 24 & 20.2509557198666 & 3.74904428013335 \tabularnewline
29 & 23 & 22.1597950798317 & 0.840204920168254 \tabularnewline
30 & 23 & 22.6210646391761 & 0.378935360823917 \tabularnewline
31 & 29 & 24.3526462702783 & 4.64735372972168 \tabularnewline
32 & 24 & 22.6200304297062 & 1.37996957029377 \tabularnewline
33 & 18 & 25.4025417440717 & -7.40254174407172 \tabularnewline
34 & 25 & 26.5166748967625 & -1.51667489676250 \tabularnewline
35 & 21 & 22.8153616863728 & -1.81536168637277 \tabularnewline
36 & 26 & 27.1131636623579 & -1.11316366235788 \tabularnewline
37 & 22 & 25.4351802135502 & -3.43518021355015 \tabularnewline
38 & 22 & 22.471516968615 & -0.471516968614977 \tabularnewline
39 & 22 & 18.2315078477236 & 3.76849215227642 \tabularnewline
40 & 23 & 26.0120429543572 & -3.01204295435724 \tabularnewline
41 & 30 & 23.3134942521645 & 6.68650574783546 \tabularnewline
42 & 23 & 22.8944487908286 & 0.105551209171422 \tabularnewline
43 & 17 & 18.2750943112244 & -1.27509431122438 \tabularnewline
44 & 23 & 23.8804744677087 & -0.880474467708677 \tabularnewline
45 & 23 & 24.4783838583962 & -1.47838385839615 \tabularnewline
46 & 25 & 22.2809444343558 & 2.71905556564417 \tabularnewline
47 & 24 & 20.6187801323547 & 3.38121986764531 \tabularnewline
48 & 24 & 28.0120358696766 & -4.01203586967657 \tabularnewline
49 & 23 & 23.1792243085142 & -0.179224308514248 \tabularnewline
50 & 21 & 24.048538447637 & -3.04853844763699 \tabularnewline
51 & 24 & 25.64309255316 & -1.64309255316002 \tabularnewline
52 & 24 & 21.749283369148 & 2.25071663085198 \tabularnewline
53 & 28 & 21.3794664570550 & 6.62053354294497 \tabularnewline
54 & 16 & 20.3853975474503 & -4.38539754745034 \tabularnewline
55 & 20 & 20.7758050117615 & -0.775805011761506 \tabularnewline
56 & 29 & 23.5799848872581 & 5.42001511274193 \tabularnewline
57 & 27 & 24.0911371434937 & 2.90886285650628 \tabularnewline
58 & 22 & 23.2073880608995 & -1.20738806089948 \tabularnewline
59 & 28 & 23.7302249244315 & 4.26977507556854 \tabularnewline
60 & 16 & 19.9301946078529 & -3.93019460785292 \tabularnewline
61 & 25 & 22.9970113806238 & 2.00298861937616 \tabularnewline
62 & 24 & 23.5443601993152 & 0.455639800684793 \tabularnewline
63 & 28 & 22.9549465314877 & 5.0450534685123 \tabularnewline
64 & 24 & 24.3718167272939 & -0.371816727293876 \tabularnewline
65 & 23 & 22.3654021364764 & 0.634597863523598 \tabularnewline
66 & 30 & 27.3381829854236 & 2.66181701457637 \tabularnewline
67 & 24 & 21.3091577028965 & 2.69084229710346 \tabularnewline
68 & 21 & 24.3927764401100 & -3.39277644010995 \tabularnewline
69 & 25 & 23.0109171240606 & 1.98908287593938 \tabularnewline
70 & 25 & 24.7059046845367 & 0.294095315463281 \tabularnewline
71 & 22 & 20.6542734269745 & 1.34572657302549 \tabularnewline
72 & 23 & 22.3128150312707 & 0.687184968729269 \tabularnewline
73 & 26 & 23.2074708126045 & 2.79252918739554 \tabularnewline
74 & 23 & 21.8898645321903 & 1.11013546780969 \tabularnewline
75 & 25 & 23.2133160415696 & 1.78668395843039 \tabularnewline
76 & 21 & 21.1912978440933 & -0.191297844093338 \tabularnewline
77 & 25 & 23.5415112248078 & 1.45848877519221 \tabularnewline
78 & 24 & 22.2240581514602 & 1.77594184853978 \tabularnewline
79 & 29 & 23.5795116510775 & 5.42048834892254 \tabularnewline
80 & 22 & 23.8066344084238 & -1.80663440842378 \tabularnewline
81 & 27 & 23.871481172339 & 3.128518827661 \tabularnewline
82 & 26 & 23.352304727581 & 2.64769527241898 \tabularnewline
83 & 22 & 21.1699534974356 & 0.83004650256439 \tabularnewline
84 & 24 & 22.2960589182068 & 1.70394108179324 \tabularnewline
85 & 27 & 22.9187946558914 & 4.08120534410856 \tabularnewline
86 & 24 & 21.2551762818802 & 2.74482371811984 \tabularnewline
87 & 24 & 24.9854197220989 & -0.985419722098878 \tabularnewline
88 & 29 & 24.4182667573910 & 4.58173324260897 \tabularnewline
89 & 22 & 22.719695403376 & -0.719695403376022 \tabularnewline
90 & 21 & 22.2384052767203 & -1.23840527672025 \tabularnewline
91 & 24 & 20.2366963157984 & 3.76330368420159 \tabularnewline
92 & 24 & 21.3821426823987 & 2.61785731760126 \tabularnewline
93 & 23 & 24.4726473550171 & -1.47264735501708 \tabularnewline
94 & 20 & 22.2741308792623 & -2.27413087926229 \tabularnewline
95 & 27 & 21.3503548371888 & 5.64964516281118 \tabularnewline
96 & 26 & 24.251431059974 & 1.74856894002601 \tabularnewline
97 & 25 & 22.0379029753204 & 2.96209702467963 \tabularnewline
98 & 21 & 20.2536428464340 & 0.746357153566045 \tabularnewline
99 & 21 & 20.5015553576722 & 0.498444642327805 \tabularnewline
100 & 19 & 20.1774147524789 & -1.17741475247886 \tabularnewline
101 & 21 & 21.750048451472 & -0.750048451471991 \tabularnewline
102 & 21 & 21.0018895175073 & -0.00188951750729945 \tabularnewline
103 & 16 & 19.7675753364208 & -3.76757533642083 \tabularnewline
104 & 22 & 20.6609941943698 & 1.33900580563023 \tabularnewline
105 & 29 & 21.7690184549227 & 7.23098154507732 \tabularnewline
106 & 15 & 20.0314470942041 & -5.03144709420414 \tabularnewline
107 & 17 & 20.4094290206754 & -3.40942902067538 \tabularnewline
108 & 15 & 19.8292340331870 & -4.82923403318697 \tabularnewline
109 & 21 & 21.832123169512 & -0.832123169512005 \tabularnewline
110 & 21 & 22.3041191665550 & -1.30411916655496 \tabularnewline
111 & 19 & 18.7580000633698 & 0.241999936630185 \tabularnewline
112 & 24 & 18.0172006363909 & 5.98279936360905 \tabularnewline
113 & 20 & 22.4432336406806 & -2.44323364068060 \tabularnewline
114 & 17 & 22.2694115066078 & -5.26941150660783 \tabularnewline
115 & 23 & 25.234109133435 & -2.23410913343498 \tabularnewline
116 & 24 & 22.2160062778621 & 1.78399372213789 \tabularnewline
117 & 14 & 22.2380243142653 & -8.2380243142653 \tabularnewline
118 & 19 & 22.6559242447950 & -3.65592424479497 \tabularnewline
119 & 24 & 22.2992293169480 & 1.70077068305203 \tabularnewline
120 & 13 & 19.7557930992512 & -6.75579309925122 \tabularnewline
121 & 22 & 25.4692733417253 & -3.46927334172529 \tabularnewline
122 & 16 & 20.7992801104401 & -4.79928011044015 \tabularnewline
123 & 19 & 23.2114188566166 & -4.21141885661661 \tabularnewline
124 & 25 & 22.4886267423104 & 2.51137325768959 \tabularnewline
125 & 25 & 24.0113072890506 & 0.988692710949389 \tabularnewline
126 & 23 & 21.4262983307543 & 1.57370166924572 \tabularnewline
127 & 24 & 25.2280272166323 & -1.22802721663232 \tabularnewline
128 & 26 & 23.6302594637448 & 2.36974053625522 \tabularnewline
129 & 26 & 21.4824108904524 & 4.51758910954761 \tabularnewline
130 & 25 & 24.4460123208995 & 0.553987679100515 \tabularnewline
131 & 18 & 22.5423535276884 & -4.5423535276884 \tabularnewline
132 & 21 & 19.5104654207782 & 1.48953457922181 \tabularnewline
133 & 26 & 23.5892714238909 & 2.41072857610911 \tabularnewline
134 & 23 & 21.5546030832295 & 1.44539691677054 \tabularnewline
135 & 23 & 19.4253326337924 & 3.57466736620763 \tabularnewline
136 & 22 & 23.0113803242479 & -1.01138032424793 \tabularnewline
137 & 20 & 22.1846103122476 & -2.18461031224758 \tabularnewline
138 & 13 & 22.0702745128170 & -9.07027451281704 \tabularnewline
139 & 24 & 21.3499715984669 & 2.65002840153306 \tabularnewline
140 & 15 & 21.7174055264794 & -6.71740552647937 \tabularnewline
141 & 14 & 23.2438706188646 & -9.2438706188646 \tabularnewline
142 & 22 & 20.3158662905290 & 1.68413370947095 \tabularnewline
143 & 10 & 18.1419112607927 & -8.14191126079267 \tabularnewline
144 & 24 & 24.2871658173622 & -0.287165817362175 \tabularnewline
145 & 22 & 22.0655848506515 & -0.0655848506514833 \tabularnewline
146 & 24 & 26.0938258262539 & -2.09382582625388 \tabularnewline
147 & 19 & 21.9372414925515 & -2.93724149255154 \tabularnewline
148 & 20 & 23.3835644028641 & -3.38356440286411 \tabularnewline
149 & 13 & 16.4397959186471 & -3.43979591864706 \tabularnewline
150 & 20 & 20.0681504588160 & -0.0681504588159777 \tabularnewline
151 & 22 & 23.4284855943632 & -1.42848559436320 \tabularnewline
152 & 24 & 23.6163819032786 & 0.383618096721359 \tabularnewline
153 & 29 & 23.7900596853970 & 5.20994031460296 \tabularnewline
154 & 12 & 20.7579915835694 & -8.75799158356937 \tabularnewline
155 & 20 & 21.284389716979 & -1.28438971697899 \tabularnewline
156 & 21 & 21.2025064249695 & -0.202506424969507 \tabularnewline
157 & 24 & 23.8139211996979 & 0.18607880030208 \tabularnewline
158 & 22 & 22.0253632882413 & -0.0253632882413480 \tabularnewline
159 & 20 & 17.8571180746991 & 2.14288192530086 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99126&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]26[/C][C]23.1306003887749[/C][C]2.86939961122509[/C][/ROW]
[ROW][C]2[/C][C]23[/C][C]24.3130997927334[/C][C]-1.31309979273343[/C][/ROW]
[ROW][C]3[/C][C]25[/C][C]23.7473100514567[/C][C]1.25268994854331[/C][/ROW]
[ROW][C]4[/C][C]23[/C][C]21.7508235697893[/C][C]1.24917643021074[/C][/ROW]
[ROW][C]5[/C][C]19[/C][C]21.7601014655486[/C][C]-2.76010146554857[/C][/ROW]
[ROW][C]6[/C][C]29[/C][C]24.6343243355765[/C][C]4.3656756644235[/C][/ROW]
[ROW][C]7[/C][C]25[/C][C]24.8088429287352[/C][C]0.191157071264768[/C][/ROW]
[ROW][C]8[/C][C]21[/C][C]22.7076389884709[/C][C]-1.7076389884709[/C][/ROW]
[ROW][C]9[/C][C]22[/C][C]21.6907964662429[/C][C]0.309203533757135[/C][/ROW]
[ROW][C]10[/C][C]25[/C][C]22.3716219632899[/C][C]2.6283780367101[/C][/ROW]
[ROW][C]11[/C][C]24[/C][C]20.2078765420756[/C][C]3.79212345792443[/C][/ROW]
[ROW][C]12[/C][C]18[/C][C]19.7456400517226[/C][C]-1.74564005172261[/C][/ROW]
[ROW][C]13[/C][C]22[/C][C]18.0338653568764[/C][C]3.96613464312358[/C][/ROW]
[ROW][C]14[/C][C]15[/C][C]20.3322653555827[/C][C]-5.33226535558269[/C][/ROW]
[ROW][C]15[/C][C]22[/C][C]23.4710210806121[/C][C]-1.47102108061207[/C][/ROW]
[ROW][C]16[/C][C]28[/C][C]24.3618400192129[/C][C]3.63815998078713[/C][/ROW]
[ROW][C]17[/C][C]20[/C][C]22.1931158799292[/C][C]-2.19311587992917[/C][/ROW]
[ROW][C]18[/C][C]12[/C][C]18.5299017341414[/C][C]-6.52990173414137[/C][/ROW]
[ROW][C]19[/C][C]24[/C][C]21.4472594386901[/C][C]2.55274056130986[/C][/ROW]
[ROW][C]20[/C][C]20[/C][C]21.6668377087295[/C][C]-1.66683770872951[/C][/ROW]
[ROW][C]21[/C][C]21[/C][C]23.3987198268486[/C][C]-2.39871982684864[/C][/ROW]
[ROW][C]22[/C][C]20[/C][C]20.8590262690668[/C][C]-0.85902626906677[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]19.1858429385307[/C][C]1.81415706146926[/C][/ROW]
[ROW][C]24[/C][C]23[/C][C]20.9195366447871[/C][C]2.08046335521293[/C][/ROW]
[ROW][C]25[/C][C]28[/C][C]21.8015713824566[/C][C]6.19842861754342[/C][/ROW]
[ROW][C]26[/C][C]24[/C][C]21.9656984861999[/C][C]2.03430151380007[/C][/ROW]
[ROW][C]27[/C][C]24[/C][C]23.589653267493[/C][C]0.410346732507007[/C][/ROW]
[ROW][C]28[/C][C]24[/C][C]20.2509557198666[/C][C]3.74904428013335[/C][/ROW]
[ROW][C]29[/C][C]23[/C][C]22.1597950798317[/C][C]0.840204920168254[/C][/ROW]
[ROW][C]30[/C][C]23[/C][C]22.6210646391761[/C][C]0.378935360823917[/C][/ROW]
[ROW][C]31[/C][C]29[/C][C]24.3526462702783[/C][C]4.64735372972168[/C][/ROW]
[ROW][C]32[/C][C]24[/C][C]22.6200304297062[/C][C]1.37996957029377[/C][/ROW]
[ROW][C]33[/C][C]18[/C][C]25.4025417440717[/C][C]-7.40254174407172[/C][/ROW]
[ROW][C]34[/C][C]25[/C][C]26.5166748967625[/C][C]-1.51667489676250[/C][/ROW]
[ROW][C]35[/C][C]21[/C][C]22.8153616863728[/C][C]-1.81536168637277[/C][/ROW]
[ROW][C]36[/C][C]26[/C][C]27.1131636623579[/C][C]-1.11316366235788[/C][/ROW]
[ROW][C]37[/C][C]22[/C][C]25.4351802135502[/C][C]-3.43518021355015[/C][/ROW]
[ROW][C]38[/C][C]22[/C][C]22.471516968615[/C][C]-0.471516968614977[/C][/ROW]
[ROW][C]39[/C][C]22[/C][C]18.2315078477236[/C][C]3.76849215227642[/C][/ROW]
[ROW][C]40[/C][C]23[/C][C]26.0120429543572[/C][C]-3.01204295435724[/C][/ROW]
[ROW][C]41[/C][C]30[/C][C]23.3134942521645[/C][C]6.68650574783546[/C][/ROW]
[ROW][C]42[/C][C]23[/C][C]22.8944487908286[/C][C]0.105551209171422[/C][/ROW]
[ROW][C]43[/C][C]17[/C][C]18.2750943112244[/C][C]-1.27509431122438[/C][/ROW]
[ROW][C]44[/C][C]23[/C][C]23.8804744677087[/C][C]-0.880474467708677[/C][/ROW]
[ROW][C]45[/C][C]23[/C][C]24.4783838583962[/C][C]-1.47838385839615[/C][/ROW]
[ROW][C]46[/C][C]25[/C][C]22.2809444343558[/C][C]2.71905556564417[/C][/ROW]
[ROW][C]47[/C][C]24[/C][C]20.6187801323547[/C][C]3.38121986764531[/C][/ROW]
[ROW][C]48[/C][C]24[/C][C]28.0120358696766[/C][C]-4.01203586967657[/C][/ROW]
[ROW][C]49[/C][C]23[/C][C]23.1792243085142[/C][C]-0.179224308514248[/C][/ROW]
[ROW][C]50[/C][C]21[/C][C]24.048538447637[/C][C]-3.04853844763699[/C][/ROW]
[ROW][C]51[/C][C]24[/C][C]25.64309255316[/C][C]-1.64309255316002[/C][/ROW]
[ROW][C]52[/C][C]24[/C][C]21.749283369148[/C][C]2.25071663085198[/C][/ROW]
[ROW][C]53[/C][C]28[/C][C]21.3794664570550[/C][C]6.62053354294497[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]20.3853975474503[/C][C]-4.38539754745034[/C][/ROW]
[ROW][C]55[/C][C]20[/C][C]20.7758050117615[/C][C]-0.775805011761506[/C][/ROW]
[ROW][C]56[/C][C]29[/C][C]23.5799848872581[/C][C]5.42001511274193[/C][/ROW]
[ROW][C]57[/C][C]27[/C][C]24.0911371434937[/C][C]2.90886285650628[/C][/ROW]
[ROW][C]58[/C][C]22[/C][C]23.2073880608995[/C][C]-1.20738806089948[/C][/ROW]
[ROW][C]59[/C][C]28[/C][C]23.7302249244315[/C][C]4.26977507556854[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]19.9301946078529[/C][C]-3.93019460785292[/C][/ROW]
[ROW][C]61[/C][C]25[/C][C]22.9970113806238[/C][C]2.00298861937616[/C][/ROW]
[ROW][C]62[/C][C]24[/C][C]23.5443601993152[/C][C]0.455639800684793[/C][/ROW]
[ROW][C]63[/C][C]28[/C][C]22.9549465314877[/C][C]5.0450534685123[/C][/ROW]
[ROW][C]64[/C][C]24[/C][C]24.3718167272939[/C][C]-0.371816727293876[/C][/ROW]
[ROW][C]65[/C][C]23[/C][C]22.3654021364764[/C][C]0.634597863523598[/C][/ROW]
[ROW][C]66[/C][C]30[/C][C]27.3381829854236[/C][C]2.66181701457637[/C][/ROW]
[ROW][C]67[/C][C]24[/C][C]21.3091577028965[/C][C]2.69084229710346[/C][/ROW]
[ROW][C]68[/C][C]21[/C][C]24.3927764401100[/C][C]-3.39277644010995[/C][/ROW]
[ROW][C]69[/C][C]25[/C][C]23.0109171240606[/C][C]1.98908287593938[/C][/ROW]
[ROW][C]70[/C][C]25[/C][C]24.7059046845367[/C][C]0.294095315463281[/C][/ROW]
[ROW][C]71[/C][C]22[/C][C]20.6542734269745[/C][C]1.34572657302549[/C][/ROW]
[ROW][C]72[/C][C]23[/C][C]22.3128150312707[/C][C]0.687184968729269[/C][/ROW]
[ROW][C]73[/C][C]26[/C][C]23.2074708126045[/C][C]2.79252918739554[/C][/ROW]
[ROW][C]74[/C][C]23[/C][C]21.8898645321903[/C][C]1.11013546780969[/C][/ROW]
[ROW][C]75[/C][C]25[/C][C]23.2133160415696[/C][C]1.78668395843039[/C][/ROW]
[ROW][C]76[/C][C]21[/C][C]21.1912978440933[/C][C]-0.191297844093338[/C][/ROW]
[ROW][C]77[/C][C]25[/C][C]23.5415112248078[/C][C]1.45848877519221[/C][/ROW]
[ROW][C]78[/C][C]24[/C][C]22.2240581514602[/C][C]1.77594184853978[/C][/ROW]
[ROW][C]79[/C][C]29[/C][C]23.5795116510775[/C][C]5.42048834892254[/C][/ROW]
[ROW][C]80[/C][C]22[/C][C]23.8066344084238[/C][C]-1.80663440842378[/C][/ROW]
[ROW][C]81[/C][C]27[/C][C]23.871481172339[/C][C]3.128518827661[/C][/ROW]
[ROW][C]82[/C][C]26[/C][C]23.352304727581[/C][C]2.64769527241898[/C][/ROW]
[ROW][C]83[/C][C]22[/C][C]21.1699534974356[/C][C]0.83004650256439[/C][/ROW]
[ROW][C]84[/C][C]24[/C][C]22.2960589182068[/C][C]1.70394108179324[/C][/ROW]
[ROW][C]85[/C][C]27[/C][C]22.9187946558914[/C][C]4.08120534410856[/C][/ROW]
[ROW][C]86[/C][C]24[/C][C]21.2551762818802[/C][C]2.74482371811984[/C][/ROW]
[ROW][C]87[/C][C]24[/C][C]24.9854197220989[/C][C]-0.985419722098878[/C][/ROW]
[ROW][C]88[/C][C]29[/C][C]24.4182667573910[/C][C]4.58173324260897[/C][/ROW]
[ROW][C]89[/C][C]22[/C][C]22.719695403376[/C][C]-0.719695403376022[/C][/ROW]
[ROW][C]90[/C][C]21[/C][C]22.2384052767203[/C][C]-1.23840527672025[/C][/ROW]
[ROW][C]91[/C][C]24[/C][C]20.2366963157984[/C][C]3.76330368420159[/C][/ROW]
[ROW][C]92[/C][C]24[/C][C]21.3821426823987[/C][C]2.61785731760126[/C][/ROW]
[ROW][C]93[/C][C]23[/C][C]24.4726473550171[/C][C]-1.47264735501708[/C][/ROW]
[ROW][C]94[/C][C]20[/C][C]22.2741308792623[/C][C]-2.27413087926229[/C][/ROW]
[ROW][C]95[/C][C]27[/C][C]21.3503548371888[/C][C]5.64964516281118[/C][/ROW]
[ROW][C]96[/C][C]26[/C][C]24.251431059974[/C][C]1.74856894002601[/C][/ROW]
[ROW][C]97[/C][C]25[/C][C]22.0379029753204[/C][C]2.96209702467963[/C][/ROW]
[ROW][C]98[/C][C]21[/C][C]20.2536428464340[/C][C]0.746357153566045[/C][/ROW]
[ROW][C]99[/C][C]21[/C][C]20.5015553576722[/C][C]0.498444642327805[/C][/ROW]
[ROW][C]100[/C][C]19[/C][C]20.1774147524789[/C][C]-1.17741475247886[/C][/ROW]
[ROW][C]101[/C][C]21[/C][C]21.750048451472[/C][C]-0.750048451471991[/C][/ROW]
[ROW][C]102[/C][C]21[/C][C]21.0018895175073[/C][C]-0.00188951750729945[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]19.7675753364208[/C][C]-3.76757533642083[/C][/ROW]
[ROW][C]104[/C][C]22[/C][C]20.6609941943698[/C][C]1.33900580563023[/C][/ROW]
[ROW][C]105[/C][C]29[/C][C]21.7690184549227[/C][C]7.23098154507732[/C][/ROW]
[ROW][C]106[/C][C]15[/C][C]20.0314470942041[/C][C]-5.03144709420414[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]20.4094290206754[/C][C]-3.40942902067538[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]19.8292340331870[/C][C]-4.82923403318697[/C][/ROW]
[ROW][C]109[/C][C]21[/C][C]21.832123169512[/C][C]-0.832123169512005[/C][/ROW]
[ROW][C]110[/C][C]21[/C][C]22.3041191665550[/C][C]-1.30411916655496[/C][/ROW]
[ROW][C]111[/C][C]19[/C][C]18.7580000633698[/C][C]0.241999936630185[/C][/ROW]
[ROW][C]112[/C][C]24[/C][C]18.0172006363909[/C][C]5.98279936360905[/C][/ROW]
[ROW][C]113[/C][C]20[/C][C]22.4432336406806[/C][C]-2.44323364068060[/C][/ROW]
[ROW][C]114[/C][C]17[/C][C]22.2694115066078[/C][C]-5.26941150660783[/C][/ROW]
[ROW][C]115[/C][C]23[/C][C]25.234109133435[/C][C]-2.23410913343498[/C][/ROW]
[ROW][C]116[/C][C]24[/C][C]22.2160062778621[/C][C]1.78399372213789[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]22.2380243142653[/C][C]-8.2380243142653[/C][/ROW]
[ROW][C]118[/C][C]19[/C][C]22.6559242447950[/C][C]-3.65592424479497[/C][/ROW]
[ROW][C]119[/C][C]24[/C][C]22.2992293169480[/C][C]1.70077068305203[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]19.7557930992512[/C][C]-6.75579309925122[/C][/ROW]
[ROW][C]121[/C][C]22[/C][C]25.4692733417253[/C][C]-3.46927334172529[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]20.7992801104401[/C][C]-4.79928011044015[/C][/ROW]
[ROW][C]123[/C][C]19[/C][C]23.2114188566166[/C][C]-4.21141885661661[/C][/ROW]
[ROW][C]124[/C][C]25[/C][C]22.4886267423104[/C][C]2.51137325768959[/C][/ROW]
[ROW][C]125[/C][C]25[/C][C]24.0113072890506[/C][C]0.988692710949389[/C][/ROW]
[ROW][C]126[/C][C]23[/C][C]21.4262983307543[/C][C]1.57370166924572[/C][/ROW]
[ROW][C]127[/C][C]24[/C][C]25.2280272166323[/C][C]-1.22802721663232[/C][/ROW]
[ROW][C]128[/C][C]26[/C][C]23.6302594637448[/C][C]2.36974053625522[/C][/ROW]
[ROW][C]129[/C][C]26[/C][C]21.4824108904524[/C][C]4.51758910954761[/C][/ROW]
[ROW][C]130[/C][C]25[/C][C]24.4460123208995[/C][C]0.553987679100515[/C][/ROW]
[ROW][C]131[/C][C]18[/C][C]22.5423535276884[/C][C]-4.5423535276884[/C][/ROW]
[ROW][C]132[/C][C]21[/C][C]19.5104654207782[/C][C]1.48953457922181[/C][/ROW]
[ROW][C]133[/C][C]26[/C][C]23.5892714238909[/C][C]2.41072857610911[/C][/ROW]
[ROW][C]134[/C][C]23[/C][C]21.5546030832295[/C][C]1.44539691677054[/C][/ROW]
[ROW][C]135[/C][C]23[/C][C]19.4253326337924[/C][C]3.57466736620763[/C][/ROW]
[ROW][C]136[/C][C]22[/C][C]23.0113803242479[/C][C]-1.01138032424793[/C][/ROW]
[ROW][C]137[/C][C]20[/C][C]22.1846103122476[/C][C]-2.18461031224758[/C][/ROW]
[ROW][C]138[/C][C]13[/C][C]22.0702745128170[/C][C]-9.07027451281704[/C][/ROW]
[ROW][C]139[/C][C]24[/C][C]21.3499715984669[/C][C]2.65002840153306[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]21.7174055264794[/C][C]-6.71740552647937[/C][/ROW]
[ROW][C]141[/C][C]14[/C][C]23.2438706188646[/C][C]-9.2438706188646[/C][/ROW]
[ROW][C]142[/C][C]22[/C][C]20.3158662905290[/C][C]1.68413370947095[/C][/ROW]
[ROW][C]143[/C][C]10[/C][C]18.1419112607927[/C][C]-8.14191126079267[/C][/ROW]
[ROW][C]144[/C][C]24[/C][C]24.2871658173622[/C][C]-0.287165817362175[/C][/ROW]
[ROW][C]145[/C][C]22[/C][C]22.0655848506515[/C][C]-0.0655848506514833[/C][/ROW]
[ROW][C]146[/C][C]24[/C][C]26.0938258262539[/C][C]-2.09382582625388[/C][/ROW]
[ROW][C]147[/C][C]19[/C][C]21.9372414925515[/C][C]-2.93724149255154[/C][/ROW]
[ROW][C]148[/C][C]20[/C][C]23.3835644028641[/C][C]-3.38356440286411[/C][/ROW]
[ROW][C]149[/C][C]13[/C][C]16.4397959186471[/C][C]-3.43979591864706[/C][/ROW]
[ROW][C]150[/C][C]20[/C][C]20.0681504588160[/C][C]-0.0681504588159777[/C][/ROW]
[ROW][C]151[/C][C]22[/C][C]23.4284855943632[/C][C]-1.42848559436320[/C][/ROW]
[ROW][C]152[/C][C]24[/C][C]23.6163819032786[/C][C]0.383618096721359[/C][/ROW]
[ROW][C]153[/C][C]29[/C][C]23.7900596853970[/C][C]5.20994031460296[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]20.7579915835694[/C][C]-8.75799158356937[/C][/ROW]
[ROW][C]155[/C][C]20[/C][C]21.284389716979[/C][C]-1.28438971697899[/C][/ROW]
[ROW][C]156[/C][C]21[/C][C]21.2025064249695[/C][C]-0.202506424969507[/C][/ROW]
[ROW][C]157[/C][C]24[/C][C]23.8139211996979[/C][C]0.18607880030208[/C][/ROW]
[ROW][C]158[/C][C]22[/C][C]22.0253632882413[/C][C]-0.0253632882413480[/C][/ROW]
[ROW][C]159[/C][C]20[/C][C]17.8571180746991[/C][C]2.14288192530086[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99126&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99126&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
12623.13060038877492.86939961122509
22324.3130997927334-1.31309979273343
32523.74731005145671.25268994854331
42321.75082356978931.24917643021074
51921.7601014655486-2.76010146554857
62924.63432433557654.3656756644235
72524.80884292873520.191157071264768
82122.7076389884709-1.7076389884709
92221.69079646624290.309203533757135
102522.37162196328992.6283780367101
112420.20787654207563.79212345792443
121819.7456400517226-1.74564005172261
132218.03386535687643.96613464312358
141520.3322653555827-5.33226535558269
152223.4710210806121-1.47102108061207
162824.36184001921293.63815998078713
172022.1931158799292-2.19311587992917
181218.5299017341414-6.52990173414137
192421.44725943869012.55274056130986
202021.6668377087295-1.66683770872951
212123.3987198268486-2.39871982684864
222020.8590262690668-0.85902626906677
232119.18584293853071.81415706146926
242320.91953664478712.08046335521293
252821.80157138245666.19842861754342
262421.96569848619992.03430151380007
272423.5896532674930.410346732507007
282420.25095571986663.74904428013335
292322.15979507983170.840204920168254
302322.62106463917610.378935360823917
312924.35264627027834.64735372972168
322422.62003042970621.37996957029377
331825.4025417440717-7.40254174407172
342526.5166748967625-1.51667489676250
352122.8153616863728-1.81536168637277
362627.1131636623579-1.11316366235788
372225.4351802135502-3.43518021355015
382222.471516968615-0.471516968614977
392218.23150784772363.76849215227642
402326.0120429543572-3.01204295435724
413023.31349425216456.68650574783546
422322.89444879082860.105551209171422
431718.2750943112244-1.27509431122438
442323.8804744677087-0.880474467708677
452324.4783838583962-1.47838385839615
462522.28094443435582.71905556564417
472420.61878013235473.38121986764531
482428.0120358696766-4.01203586967657
492323.1792243085142-0.179224308514248
502124.048538447637-3.04853844763699
512425.64309255316-1.64309255316002
522421.7492833691482.25071663085198
532821.37946645705506.62053354294497
541620.3853975474503-4.38539754745034
552020.7758050117615-0.775805011761506
562923.57998488725815.42001511274193
572724.09113714349372.90886285650628
582223.2073880608995-1.20738806089948
592823.73022492443154.26977507556854
601619.9301946078529-3.93019460785292
612522.99701138062382.00298861937616
622423.54436019931520.455639800684793
632822.95494653148775.0450534685123
642424.3718167272939-0.371816727293876
652322.36540213647640.634597863523598
663027.33818298542362.66181701457637
672421.30915770289652.69084229710346
682124.3927764401100-3.39277644010995
692523.01091712406061.98908287593938
702524.70590468453670.294095315463281
712220.65427342697451.34572657302549
722322.31281503127070.687184968729269
732623.20747081260452.79252918739554
742321.88986453219031.11013546780969
752523.21331604156961.78668395843039
762121.1912978440933-0.191297844093338
772523.54151122480781.45848877519221
782422.22405815146021.77594184853978
792923.57951165107755.42048834892254
802223.8066344084238-1.80663440842378
812723.8714811723393.128518827661
822623.3523047275812.64769527241898
832221.16995349743560.83004650256439
842422.29605891820681.70394108179324
852722.91879465589144.08120534410856
862421.25517628188022.74482371811984
872424.9854197220989-0.985419722098878
882924.41826675739104.58173324260897
892222.719695403376-0.719695403376022
902122.2384052767203-1.23840527672025
912420.23669631579843.76330368420159
922421.38214268239872.61785731760126
932324.4726473550171-1.47264735501708
942022.2741308792623-2.27413087926229
952721.35035483718885.64964516281118
962624.2514310599741.74856894002601
972522.03790297532042.96209702467963
982120.25364284643400.746357153566045
992120.50155535767220.498444642327805
1001920.1774147524789-1.17741475247886
1012121.750048451472-0.750048451471991
1022121.0018895175073-0.00188951750729945
1031619.7675753364208-3.76757533642083
1042220.66099419436981.33900580563023
1052921.76901845492277.23098154507732
1061520.0314470942041-5.03144709420414
1071720.4094290206754-3.40942902067538
1081519.8292340331870-4.82923403318697
1092121.832123169512-0.832123169512005
1102122.3041191665550-1.30411916655496
1111918.75800006336980.241999936630185
1122418.01720063639095.98279936360905
1132022.4432336406806-2.44323364068060
1141722.2694115066078-5.26941150660783
1152325.234109133435-2.23410913343498
1162422.21600627786211.78399372213789
1171422.2380243142653-8.2380243142653
1181922.6559242447950-3.65592424479497
1192422.29922931694801.70077068305203
1201319.7557930992512-6.75579309925122
1212225.4692733417253-3.46927334172529
1221620.7992801104401-4.79928011044015
1231923.2114188566166-4.21141885661661
1242522.48862674231042.51137325768959
1252524.01130728905060.988692710949389
1262321.42629833075431.57370166924572
1272425.2280272166323-1.22802721663232
1282623.63025946374482.36974053625522
1292621.48241089045244.51758910954761
1302524.44601232089950.553987679100515
1311822.5423535276884-4.5423535276884
1322119.51046542077821.48953457922181
1332623.58927142389092.41072857610911
1342321.55460308322951.44539691677054
1352319.42533263379243.57466736620763
1362223.0113803242479-1.01138032424793
1372022.1846103122476-2.18461031224758
1381322.0702745128170-9.07027451281704
1392421.34997159846692.65002840153306
1401521.7174055264794-6.71740552647937
1411423.2438706188646-9.2438706188646
1422220.31586629052901.68413370947095
1431018.1419112607927-8.14191126079267
1442424.2871658173622-0.287165817362175
1452222.0655848506515-0.0655848506514833
1462426.0938258262539-2.09382582625388
1471921.9372414925515-2.93724149255154
1482023.3835644028641-3.38356440286411
1491316.4397959186471-3.43979591864706
1502020.0681504588160-0.0681504588159777
1512223.4284855943632-1.42848559436320
1522423.61638190327860.383618096721359
1532923.79005968539705.20994031460296
1541220.7579915835694-8.75799158356937
1552021.284389716979-1.28438971697899
1562121.2025064249695-0.202506424969507
1572423.81392119969790.18607880030208
1582222.0253632882413-0.0253632882413480
1592017.85711807469912.14288192530086







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.9109524040856740.1780951918286520.0890475959143262
160.8719804734681240.2560390530637530.128019526531876
170.8042686710876440.3914626578247120.195731328912356
180.8337641647236940.3324716705526120.166235835276306
190.7571437697566810.4857124604866380.242856230243319
200.78164833640170.43670332719660.2183516635983
210.7238612434730090.5522775130539820.276138756526991
220.6521619823985760.6956760352028480.347838017601424
230.5726633092117920.8546733815764160.427336690788208
240.5861428002619190.8277143994761620.413857199738081
250.7414110631144610.5171778737710780.258588936885539
260.6776374739570320.6447250520859350.322362526042968
270.6080045122562410.7839909754875180.391995487743759
280.5969709657651540.8060580684696910.403029034234846
290.5258358474491890.9483283051016220.474164152550811
300.452699709821360.905399419642720.54730029017864
310.4773982292800140.9547964585600280.522601770719986
320.4102706692918170.8205413385836350.589729330708183
330.6621384343692210.6757231312615580.337861565630779
340.6166384385416650.766723122916670.383361561458335
350.5740599940717170.8518800118565660.425940005928283
360.511436431145240.977127137709520.48856356885476
370.4883474166299380.9766948332598770.511652583370062
380.4261913826403010.8523827652806010.573808617359699
390.3855031682210570.7710063364421150.614496831778943
400.3536248979175970.7072497958351950.646375102082403
410.5166385960749120.9667228078501760.483361403925088
420.4575796031155130.9151592062310270.542420396884487
430.4136920446428170.8273840892856330.586307955357183
440.3599765236402110.7199530472804220.640023476359789
450.3152131684641650.6304263369283290.684786831535835
460.2930933098299780.5861866196599550.706906690170022
470.2785221319620180.5570442639240360.721477868037982
480.2670623403419890.5341246806839780.732937659658011
490.2238589094868000.4477178189736010.7761410905132
500.2110857055083550.4221714110167110.788914294491645
510.1790316183604830.3580632367209660.820968381639517
520.1562195235576440.3124390471152880.843780476442356
530.2486740899148110.4973481798296210.75132591008519
540.2719148542992990.5438297085985980.728085145700701
550.2648381020810660.5296762041621320.735161897918934
560.3342043325474870.6684086650949740.665795667452513
570.3207773305820860.6415546611641730.679222669417914
580.2806323654992110.5612647309984230.719367634500789
590.3033344799169190.6066689598338370.696665520083081
600.3268883740737800.6537767481475610.67311162592622
610.2927484786399510.5854969572799020.707251521360049
620.2513900772540970.5027801545081940.748609922745903
630.2882737382349710.5765474764699420.711726261765029
640.2480017952436860.4960035904873720.751998204756314
650.2106103631473370.4212207262946740.789389636852663
660.2011944712306380.4023889424612770.798805528769362
670.184919449142740.369838898285480.81508055085726
680.1870771404848330.3741542809696650.812922859515167
690.1654304776853140.3308609553706280.834569522314686
700.1363734214530630.2727468429061250.863626578546937
710.1140236170500860.2280472341001710.885976382949914
720.09259549199204330.1851909839840870.907404508007957
730.08491273501998730.1698254700399750.915087264980013
740.06870950797279030.1374190159455810.93129049202721
750.05731665265320190.1146333053064040.942683347346798
760.04490676782865960.08981353565731930.95509323217134
770.03628120722604390.07256241445208770.963718792773956
780.02936422974454230.05872845948908460.970635770255458
790.04347561031746970.08695122063493940.95652438968253
800.03573889967440.07147779934880.9642611003256
810.03393192810525310.06786385621050630.966068071894747
820.02770323002897060.05540646005794110.97229676997103
830.02111574117537750.0422314823507550.978884258824622
840.01684322487924830.03368644975849670.983156775120752
850.01390753370936260.02781506741872530.986092466290637
860.01235201998375550.02470403996751110.987647980016244
870.009121197971890250.01824239594378050.99087880202811
880.01231613227091920.02463226454183830.98768386772908
890.009442540977979470.01888508195595890.99055745902202
900.01118855215509410.02237710431018820.988811447844906
910.01174991982444350.0234998396488870.988250080175556
920.01055593996455690.02111187992911380.989444060035443
930.009030362130111780.01806072426022360.990969637869888
940.007431582921171220.01486316584234240.992568417078829
950.01377391799745190.02754783599490370.986226082002548
960.01101159043989840.02202318087979680.988988409560102
970.01018047399534050.02036094799068100.98981952600466
980.007571799888453590.01514359977690720.992428200111546
990.005485987026258840.01097197405251770.994514012973741
1000.004059026516962490.008118053033924970.995940973483038
1010.002908874290788300.005817748581576590.997091125709212
1020.002003864287638060.004007728575276130.997996135712362
1030.002154030274295810.004308060548591610.997845969725704
1040.001622749237575320.003245498475150630.998377250762425
1050.007481117524037380.01496223504807480.992518882475963
1060.01173297792077500.02346595584155010.988267022079225
1070.01114822851969030.02229645703938060.98885177148031
1080.01412479682610940.02824959365221880.98587520317389
1090.01053075645804420.02106151291608830.989469243541956
1100.007509778497235330.01501955699447070.992490221502765
1110.005305572843806240.01061114568761250.994694427156194
1120.01481731582583020.02963463165166030.98518268417417
1130.01244794147535740.02489588295071480.987552058524643
1140.01571316257928820.03142632515857650.984286837420712
1150.01245017887720140.02490035775440290.987549821122799
1160.009549473778547510.01909894755709500.990450526221452
1170.03903279479461470.07806558958922950.960967205205385
1180.03631025902865530.07262051805731050.963689740971345
1190.030152752268360.060305504536720.96984724773164
1200.05553186020893270.1110637204178650.944468139791067
1210.05242840845979290.1048568169195860.947571591540207
1220.06442661819303720.1288532363860740.935573381806963
1230.05881392343198080.1176278468639620.94118607656802
1240.05620024421682590.1124004884336520.943799755783174
1250.04854355667029810.09708711334059630.951456443329702
1260.03568177831539740.07136355663079490.964318221684603
1270.025476865288390.050953730576780.97452313471161
1280.02497964481874720.04995928963749450.975020355181253
1290.03880289398237630.07760578796475270.961197106017624
1300.02996200454405250.0599240090881050.970037995455947
1310.02456926250371480.04913852500742970.975430737496285
1320.01929227276532760.03858454553065510.980707727234672
1330.01617175476651610.03234350953303210.983828245233484
1340.01167618931648460.02335237863296930.988323810683515
1350.01712543606056190.03425087212112380.982874563939438
1360.01068470145416430.02136940290832870.989315298545836
1370.006361122659351530.01272224531870310.993638877340648
1380.02147609255685490.04295218511370990.978523907443145
1390.03253666467815460.06507332935630910.967463335321845
1400.02564654773474360.05129309546948730.974353452265256
1410.08979870892414620.1795974178482920.910201291075854
1420.05186324427502840.1037264885500570.948136755724972
1430.1572617930210360.3145235860420720.842738206978964
1440.1003605200639170.2007210401278350.899639479936083

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.910952404085674 & 0.178095191828652 & 0.0890475959143262 \tabularnewline
16 & 0.871980473468124 & 0.256039053063753 & 0.128019526531876 \tabularnewline
17 & 0.804268671087644 & 0.391462657824712 & 0.195731328912356 \tabularnewline
18 & 0.833764164723694 & 0.332471670552612 & 0.166235835276306 \tabularnewline
19 & 0.757143769756681 & 0.485712460486638 & 0.242856230243319 \tabularnewline
20 & 0.7816483364017 & 0.4367033271966 & 0.2183516635983 \tabularnewline
21 & 0.723861243473009 & 0.552277513053982 & 0.276138756526991 \tabularnewline
22 & 0.652161982398576 & 0.695676035202848 & 0.347838017601424 \tabularnewline
23 & 0.572663309211792 & 0.854673381576416 & 0.427336690788208 \tabularnewline
24 & 0.586142800261919 & 0.827714399476162 & 0.413857199738081 \tabularnewline
25 & 0.741411063114461 & 0.517177873771078 & 0.258588936885539 \tabularnewline
26 & 0.677637473957032 & 0.644725052085935 & 0.322362526042968 \tabularnewline
27 & 0.608004512256241 & 0.783990975487518 & 0.391995487743759 \tabularnewline
28 & 0.596970965765154 & 0.806058068469691 & 0.403029034234846 \tabularnewline
29 & 0.525835847449189 & 0.948328305101622 & 0.474164152550811 \tabularnewline
30 & 0.45269970982136 & 0.90539941964272 & 0.54730029017864 \tabularnewline
31 & 0.477398229280014 & 0.954796458560028 & 0.522601770719986 \tabularnewline
32 & 0.410270669291817 & 0.820541338583635 & 0.589729330708183 \tabularnewline
33 & 0.662138434369221 & 0.675723131261558 & 0.337861565630779 \tabularnewline
34 & 0.616638438541665 & 0.76672312291667 & 0.383361561458335 \tabularnewline
35 & 0.574059994071717 & 0.851880011856566 & 0.425940005928283 \tabularnewline
36 & 0.51143643114524 & 0.97712713770952 & 0.48856356885476 \tabularnewline
37 & 0.488347416629938 & 0.976694833259877 & 0.511652583370062 \tabularnewline
38 & 0.426191382640301 & 0.852382765280601 & 0.573808617359699 \tabularnewline
39 & 0.385503168221057 & 0.771006336442115 & 0.614496831778943 \tabularnewline
40 & 0.353624897917597 & 0.707249795835195 & 0.646375102082403 \tabularnewline
41 & 0.516638596074912 & 0.966722807850176 & 0.483361403925088 \tabularnewline
42 & 0.457579603115513 & 0.915159206231027 & 0.542420396884487 \tabularnewline
43 & 0.413692044642817 & 0.827384089285633 & 0.586307955357183 \tabularnewline
44 & 0.359976523640211 & 0.719953047280422 & 0.640023476359789 \tabularnewline
45 & 0.315213168464165 & 0.630426336928329 & 0.684786831535835 \tabularnewline
46 & 0.293093309829978 & 0.586186619659955 & 0.706906690170022 \tabularnewline
47 & 0.278522131962018 & 0.557044263924036 & 0.721477868037982 \tabularnewline
48 & 0.267062340341989 & 0.534124680683978 & 0.732937659658011 \tabularnewline
49 & 0.223858909486800 & 0.447717818973601 & 0.7761410905132 \tabularnewline
50 & 0.211085705508355 & 0.422171411016711 & 0.788914294491645 \tabularnewline
51 & 0.179031618360483 & 0.358063236720966 & 0.820968381639517 \tabularnewline
52 & 0.156219523557644 & 0.312439047115288 & 0.843780476442356 \tabularnewline
53 & 0.248674089914811 & 0.497348179829621 & 0.75132591008519 \tabularnewline
54 & 0.271914854299299 & 0.543829708598598 & 0.728085145700701 \tabularnewline
55 & 0.264838102081066 & 0.529676204162132 & 0.735161897918934 \tabularnewline
56 & 0.334204332547487 & 0.668408665094974 & 0.665795667452513 \tabularnewline
57 & 0.320777330582086 & 0.641554661164173 & 0.679222669417914 \tabularnewline
58 & 0.280632365499211 & 0.561264730998423 & 0.719367634500789 \tabularnewline
59 & 0.303334479916919 & 0.606668959833837 & 0.696665520083081 \tabularnewline
60 & 0.326888374073780 & 0.653776748147561 & 0.67311162592622 \tabularnewline
61 & 0.292748478639951 & 0.585496957279902 & 0.707251521360049 \tabularnewline
62 & 0.251390077254097 & 0.502780154508194 & 0.748609922745903 \tabularnewline
63 & 0.288273738234971 & 0.576547476469942 & 0.711726261765029 \tabularnewline
64 & 0.248001795243686 & 0.496003590487372 & 0.751998204756314 \tabularnewline
65 & 0.210610363147337 & 0.421220726294674 & 0.789389636852663 \tabularnewline
66 & 0.201194471230638 & 0.402388942461277 & 0.798805528769362 \tabularnewline
67 & 0.18491944914274 & 0.36983889828548 & 0.81508055085726 \tabularnewline
68 & 0.187077140484833 & 0.374154280969665 & 0.812922859515167 \tabularnewline
69 & 0.165430477685314 & 0.330860955370628 & 0.834569522314686 \tabularnewline
70 & 0.136373421453063 & 0.272746842906125 & 0.863626578546937 \tabularnewline
71 & 0.114023617050086 & 0.228047234100171 & 0.885976382949914 \tabularnewline
72 & 0.0925954919920433 & 0.185190983984087 & 0.907404508007957 \tabularnewline
73 & 0.0849127350199873 & 0.169825470039975 & 0.915087264980013 \tabularnewline
74 & 0.0687095079727903 & 0.137419015945581 & 0.93129049202721 \tabularnewline
75 & 0.0573166526532019 & 0.114633305306404 & 0.942683347346798 \tabularnewline
76 & 0.0449067678286596 & 0.0898135356573193 & 0.95509323217134 \tabularnewline
77 & 0.0362812072260439 & 0.0725624144520877 & 0.963718792773956 \tabularnewline
78 & 0.0293642297445423 & 0.0587284594890846 & 0.970635770255458 \tabularnewline
79 & 0.0434756103174697 & 0.0869512206349394 & 0.95652438968253 \tabularnewline
80 & 0.0357388996744 & 0.0714777993488 & 0.9642611003256 \tabularnewline
81 & 0.0339319281052531 & 0.0678638562105063 & 0.966068071894747 \tabularnewline
82 & 0.0277032300289706 & 0.0554064600579411 & 0.97229676997103 \tabularnewline
83 & 0.0211157411753775 & 0.042231482350755 & 0.978884258824622 \tabularnewline
84 & 0.0168432248792483 & 0.0336864497584967 & 0.983156775120752 \tabularnewline
85 & 0.0139075337093626 & 0.0278150674187253 & 0.986092466290637 \tabularnewline
86 & 0.0123520199837555 & 0.0247040399675111 & 0.987647980016244 \tabularnewline
87 & 0.00912119797189025 & 0.0182423959437805 & 0.99087880202811 \tabularnewline
88 & 0.0123161322709192 & 0.0246322645418383 & 0.98768386772908 \tabularnewline
89 & 0.00944254097797947 & 0.0188850819559589 & 0.99055745902202 \tabularnewline
90 & 0.0111885521550941 & 0.0223771043101882 & 0.988811447844906 \tabularnewline
91 & 0.0117499198244435 & 0.023499839648887 & 0.988250080175556 \tabularnewline
92 & 0.0105559399645569 & 0.0211118799291138 & 0.989444060035443 \tabularnewline
93 & 0.00903036213011178 & 0.0180607242602236 & 0.990969637869888 \tabularnewline
94 & 0.00743158292117122 & 0.0148631658423424 & 0.992568417078829 \tabularnewline
95 & 0.0137739179974519 & 0.0275478359949037 & 0.986226082002548 \tabularnewline
96 & 0.0110115904398984 & 0.0220231808797968 & 0.988988409560102 \tabularnewline
97 & 0.0101804739953405 & 0.0203609479906810 & 0.98981952600466 \tabularnewline
98 & 0.00757179988845359 & 0.0151435997769072 & 0.992428200111546 \tabularnewline
99 & 0.00548598702625884 & 0.0109719740525177 & 0.994514012973741 \tabularnewline
100 & 0.00405902651696249 & 0.00811805303392497 & 0.995940973483038 \tabularnewline
101 & 0.00290887429078830 & 0.00581774858157659 & 0.997091125709212 \tabularnewline
102 & 0.00200386428763806 & 0.00400772857527613 & 0.997996135712362 \tabularnewline
103 & 0.00215403027429581 & 0.00430806054859161 & 0.997845969725704 \tabularnewline
104 & 0.00162274923757532 & 0.00324549847515063 & 0.998377250762425 \tabularnewline
105 & 0.00748111752403738 & 0.0149622350480748 & 0.992518882475963 \tabularnewline
106 & 0.0117329779207750 & 0.0234659558415501 & 0.988267022079225 \tabularnewline
107 & 0.0111482285196903 & 0.0222964570393806 & 0.98885177148031 \tabularnewline
108 & 0.0141247968261094 & 0.0282495936522188 & 0.98587520317389 \tabularnewline
109 & 0.0105307564580442 & 0.0210615129160883 & 0.989469243541956 \tabularnewline
110 & 0.00750977849723533 & 0.0150195569944707 & 0.992490221502765 \tabularnewline
111 & 0.00530557284380624 & 0.0106111456876125 & 0.994694427156194 \tabularnewline
112 & 0.0148173158258302 & 0.0296346316516603 & 0.98518268417417 \tabularnewline
113 & 0.0124479414753574 & 0.0248958829507148 & 0.987552058524643 \tabularnewline
114 & 0.0157131625792882 & 0.0314263251585765 & 0.984286837420712 \tabularnewline
115 & 0.0124501788772014 & 0.0249003577544029 & 0.987549821122799 \tabularnewline
116 & 0.00954947377854751 & 0.0190989475570950 & 0.990450526221452 \tabularnewline
117 & 0.0390327947946147 & 0.0780655895892295 & 0.960967205205385 \tabularnewline
118 & 0.0363102590286553 & 0.0726205180573105 & 0.963689740971345 \tabularnewline
119 & 0.03015275226836 & 0.06030550453672 & 0.96984724773164 \tabularnewline
120 & 0.0555318602089327 & 0.111063720417865 & 0.944468139791067 \tabularnewline
121 & 0.0524284084597929 & 0.104856816919586 & 0.947571591540207 \tabularnewline
122 & 0.0644266181930372 & 0.128853236386074 & 0.935573381806963 \tabularnewline
123 & 0.0588139234319808 & 0.117627846863962 & 0.94118607656802 \tabularnewline
124 & 0.0562002442168259 & 0.112400488433652 & 0.943799755783174 \tabularnewline
125 & 0.0485435566702981 & 0.0970871133405963 & 0.951456443329702 \tabularnewline
126 & 0.0356817783153974 & 0.0713635566307949 & 0.964318221684603 \tabularnewline
127 & 0.02547686528839 & 0.05095373057678 & 0.97452313471161 \tabularnewline
128 & 0.0249796448187472 & 0.0499592896374945 & 0.975020355181253 \tabularnewline
129 & 0.0388028939823763 & 0.0776057879647527 & 0.961197106017624 \tabularnewline
130 & 0.0299620045440525 & 0.059924009088105 & 0.970037995455947 \tabularnewline
131 & 0.0245692625037148 & 0.0491385250074297 & 0.975430737496285 \tabularnewline
132 & 0.0192922727653276 & 0.0385845455306551 & 0.980707727234672 \tabularnewline
133 & 0.0161717547665161 & 0.0323435095330321 & 0.983828245233484 \tabularnewline
134 & 0.0116761893164846 & 0.0233523786329693 & 0.988323810683515 \tabularnewline
135 & 0.0171254360605619 & 0.0342508721211238 & 0.982874563939438 \tabularnewline
136 & 0.0106847014541643 & 0.0213694029083287 & 0.989315298545836 \tabularnewline
137 & 0.00636112265935153 & 0.0127222453187031 & 0.993638877340648 \tabularnewline
138 & 0.0214760925568549 & 0.0429521851137099 & 0.978523907443145 \tabularnewline
139 & 0.0325366646781546 & 0.0650733293563091 & 0.967463335321845 \tabularnewline
140 & 0.0256465477347436 & 0.0512930954694873 & 0.974353452265256 \tabularnewline
141 & 0.0897987089241462 & 0.179597417848292 & 0.910201291075854 \tabularnewline
142 & 0.0518632442750284 & 0.103726488550057 & 0.948136755724972 \tabularnewline
143 & 0.157261793021036 & 0.314523586042072 & 0.842738206978964 \tabularnewline
144 & 0.100360520063917 & 0.200721040127835 & 0.899639479936083 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99126&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]15[/C][C]0.910952404085674[/C][C]0.178095191828652[/C][C]0.0890475959143262[/C][/ROW]
[ROW][C]16[/C][C]0.871980473468124[/C][C]0.256039053063753[/C][C]0.128019526531876[/C][/ROW]
[ROW][C]17[/C][C]0.804268671087644[/C][C]0.391462657824712[/C][C]0.195731328912356[/C][/ROW]
[ROW][C]18[/C][C]0.833764164723694[/C][C]0.332471670552612[/C][C]0.166235835276306[/C][/ROW]
[ROW][C]19[/C][C]0.757143769756681[/C][C]0.485712460486638[/C][C]0.242856230243319[/C][/ROW]
[ROW][C]20[/C][C]0.7816483364017[/C][C]0.4367033271966[/C][C]0.2183516635983[/C][/ROW]
[ROW][C]21[/C][C]0.723861243473009[/C][C]0.552277513053982[/C][C]0.276138756526991[/C][/ROW]
[ROW][C]22[/C][C]0.652161982398576[/C][C]0.695676035202848[/C][C]0.347838017601424[/C][/ROW]
[ROW][C]23[/C][C]0.572663309211792[/C][C]0.854673381576416[/C][C]0.427336690788208[/C][/ROW]
[ROW][C]24[/C][C]0.586142800261919[/C][C]0.827714399476162[/C][C]0.413857199738081[/C][/ROW]
[ROW][C]25[/C][C]0.741411063114461[/C][C]0.517177873771078[/C][C]0.258588936885539[/C][/ROW]
[ROW][C]26[/C][C]0.677637473957032[/C][C]0.644725052085935[/C][C]0.322362526042968[/C][/ROW]
[ROW][C]27[/C][C]0.608004512256241[/C][C]0.783990975487518[/C][C]0.391995487743759[/C][/ROW]
[ROW][C]28[/C][C]0.596970965765154[/C][C]0.806058068469691[/C][C]0.403029034234846[/C][/ROW]
[ROW][C]29[/C][C]0.525835847449189[/C][C]0.948328305101622[/C][C]0.474164152550811[/C][/ROW]
[ROW][C]30[/C][C]0.45269970982136[/C][C]0.90539941964272[/C][C]0.54730029017864[/C][/ROW]
[ROW][C]31[/C][C]0.477398229280014[/C][C]0.954796458560028[/C][C]0.522601770719986[/C][/ROW]
[ROW][C]32[/C][C]0.410270669291817[/C][C]0.820541338583635[/C][C]0.589729330708183[/C][/ROW]
[ROW][C]33[/C][C]0.662138434369221[/C][C]0.675723131261558[/C][C]0.337861565630779[/C][/ROW]
[ROW][C]34[/C][C]0.616638438541665[/C][C]0.76672312291667[/C][C]0.383361561458335[/C][/ROW]
[ROW][C]35[/C][C]0.574059994071717[/C][C]0.851880011856566[/C][C]0.425940005928283[/C][/ROW]
[ROW][C]36[/C][C]0.51143643114524[/C][C]0.97712713770952[/C][C]0.48856356885476[/C][/ROW]
[ROW][C]37[/C][C]0.488347416629938[/C][C]0.976694833259877[/C][C]0.511652583370062[/C][/ROW]
[ROW][C]38[/C][C]0.426191382640301[/C][C]0.852382765280601[/C][C]0.573808617359699[/C][/ROW]
[ROW][C]39[/C][C]0.385503168221057[/C][C]0.771006336442115[/C][C]0.614496831778943[/C][/ROW]
[ROW][C]40[/C][C]0.353624897917597[/C][C]0.707249795835195[/C][C]0.646375102082403[/C][/ROW]
[ROW][C]41[/C][C]0.516638596074912[/C][C]0.966722807850176[/C][C]0.483361403925088[/C][/ROW]
[ROW][C]42[/C][C]0.457579603115513[/C][C]0.915159206231027[/C][C]0.542420396884487[/C][/ROW]
[ROW][C]43[/C][C]0.413692044642817[/C][C]0.827384089285633[/C][C]0.586307955357183[/C][/ROW]
[ROW][C]44[/C][C]0.359976523640211[/C][C]0.719953047280422[/C][C]0.640023476359789[/C][/ROW]
[ROW][C]45[/C][C]0.315213168464165[/C][C]0.630426336928329[/C][C]0.684786831535835[/C][/ROW]
[ROW][C]46[/C][C]0.293093309829978[/C][C]0.586186619659955[/C][C]0.706906690170022[/C][/ROW]
[ROW][C]47[/C][C]0.278522131962018[/C][C]0.557044263924036[/C][C]0.721477868037982[/C][/ROW]
[ROW][C]48[/C][C]0.267062340341989[/C][C]0.534124680683978[/C][C]0.732937659658011[/C][/ROW]
[ROW][C]49[/C][C]0.223858909486800[/C][C]0.447717818973601[/C][C]0.7761410905132[/C][/ROW]
[ROW][C]50[/C][C]0.211085705508355[/C][C]0.422171411016711[/C][C]0.788914294491645[/C][/ROW]
[ROW][C]51[/C][C]0.179031618360483[/C][C]0.358063236720966[/C][C]0.820968381639517[/C][/ROW]
[ROW][C]52[/C][C]0.156219523557644[/C][C]0.312439047115288[/C][C]0.843780476442356[/C][/ROW]
[ROW][C]53[/C][C]0.248674089914811[/C][C]0.497348179829621[/C][C]0.75132591008519[/C][/ROW]
[ROW][C]54[/C][C]0.271914854299299[/C][C]0.543829708598598[/C][C]0.728085145700701[/C][/ROW]
[ROW][C]55[/C][C]0.264838102081066[/C][C]0.529676204162132[/C][C]0.735161897918934[/C][/ROW]
[ROW][C]56[/C][C]0.334204332547487[/C][C]0.668408665094974[/C][C]0.665795667452513[/C][/ROW]
[ROW][C]57[/C][C]0.320777330582086[/C][C]0.641554661164173[/C][C]0.679222669417914[/C][/ROW]
[ROW][C]58[/C][C]0.280632365499211[/C][C]0.561264730998423[/C][C]0.719367634500789[/C][/ROW]
[ROW][C]59[/C][C]0.303334479916919[/C][C]0.606668959833837[/C][C]0.696665520083081[/C][/ROW]
[ROW][C]60[/C][C]0.326888374073780[/C][C]0.653776748147561[/C][C]0.67311162592622[/C][/ROW]
[ROW][C]61[/C][C]0.292748478639951[/C][C]0.585496957279902[/C][C]0.707251521360049[/C][/ROW]
[ROW][C]62[/C][C]0.251390077254097[/C][C]0.502780154508194[/C][C]0.748609922745903[/C][/ROW]
[ROW][C]63[/C][C]0.288273738234971[/C][C]0.576547476469942[/C][C]0.711726261765029[/C][/ROW]
[ROW][C]64[/C][C]0.248001795243686[/C][C]0.496003590487372[/C][C]0.751998204756314[/C][/ROW]
[ROW][C]65[/C][C]0.210610363147337[/C][C]0.421220726294674[/C][C]0.789389636852663[/C][/ROW]
[ROW][C]66[/C][C]0.201194471230638[/C][C]0.402388942461277[/C][C]0.798805528769362[/C][/ROW]
[ROW][C]67[/C][C]0.18491944914274[/C][C]0.36983889828548[/C][C]0.81508055085726[/C][/ROW]
[ROW][C]68[/C][C]0.187077140484833[/C][C]0.374154280969665[/C][C]0.812922859515167[/C][/ROW]
[ROW][C]69[/C][C]0.165430477685314[/C][C]0.330860955370628[/C][C]0.834569522314686[/C][/ROW]
[ROW][C]70[/C][C]0.136373421453063[/C][C]0.272746842906125[/C][C]0.863626578546937[/C][/ROW]
[ROW][C]71[/C][C]0.114023617050086[/C][C]0.228047234100171[/C][C]0.885976382949914[/C][/ROW]
[ROW][C]72[/C][C]0.0925954919920433[/C][C]0.185190983984087[/C][C]0.907404508007957[/C][/ROW]
[ROW][C]73[/C][C]0.0849127350199873[/C][C]0.169825470039975[/C][C]0.915087264980013[/C][/ROW]
[ROW][C]74[/C][C]0.0687095079727903[/C][C]0.137419015945581[/C][C]0.93129049202721[/C][/ROW]
[ROW][C]75[/C][C]0.0573166526532019[/C][C]0.114633305306404[/C][C]0.942683347346798[/C][/ROW]
[ROW][C]76[/C][C]0.0449067678286596[/C][C]0.0898135356573193[/C][C]0.95509323217134[/C][/ROW]
[ROW][C]77[/C][C]0.0362812072260439[/C][C]0.0725624144520877[/C][C]0.963718792773956[/C][/ROW]
[ROW][C]78[/C][C]0.0293642297445423[/C][C]0.0587284594890846[/C][C]0.970635770255458[/C][/ROW]
[ROW][C]79[/C][C]0.0434756103174697[/C][C]0.0869512206349394[/C][C]0.95652438968253[/C][/ROW]
[ROW][C]80[/C][C]0.0357388996744[/C][C]0.0714777993488[/C][C]0.9642611003256[/C][/ROW]
[ROW][C]81[/C][C]0.0339319281052531[/C][C]0.0678638562105063[/C][C]0.966068071894747[/C][/ROW]
[ROW][C]82[/C][C]0.0277032300289706[/C][C]0.0554064600579411[/C][C]0.97229676997103[/C][/ROW]
[ROW][C]83[/C][C]0.0211157411753775[/C][C]0.042231482350755[/C][C]0.978884258824622[/C][/ROW]
[ROW][C]84[/C][C]0.0168432248792483[/C][C]0.0336864497584967[/C][C]0.983156775120752[/C][/ROW]
[ROW][C]85[/C][C]0.0139075337093626[/C][C]0.0278150674187253[/C][C]0.986092466290637[/C][/ROW]
[ROW][C]86[/C][C]0.0123520199837555[/C][C]0.0247040399675111[/C][C]0.987647980016244[/C][/ROW]
[ROW][C]87[/C][C]0.00912119797189025[/C][C]0.0182423959437805[/C][C]0.99087880202811[/C][/ROW]
[ROW][C]88[/C][C]0.0123161322709192[/C][C]0.0246322645418383[/C][C]0.98768386772908[/C][/ROW]
[ROW][C]89[/C][C]0.00944254097797947[/C][C]0.0188850819559589[/C][C]0.99055745902202[/C][/ROW]
[ROW][C]90[/C][C]0.0111885521550941[/C][C]0.0223771043101882[/C][C]0.988811447844906[/C][/ROW]
[ROW][C]91[/C][C]0.0117499198244435[/C][C]0.023499839648887[/C][C]0.988250080175556[/C][/ROW]
[ROW][C]92[/C][C]0.0105559399645569[/C][C]0.0211118799291138[/C][C]0.989444060035443[/C][/ROW]
[ROW][C]93[/C][C]0.00903036213011178[/C][C]0.0180607242602236[/C][C]0.990969637869888[/C][/ROW]
[ROW][C]94[/C][C]0.00743158292117122[/C][C]0.0148631658423424[/C][C]0.992568417078829[/C][/ROW]
[ROW][C]95[/C][C]0.0137739179974519[/C][C]0.0275478359949037[/C][C]0.986226082002548[/C][/ROW]
[ROW][C]96[/C][C]0.0110115904398984[/C][C]0.0220231808797968[/C][C]0.988988409560102[/C][/ROW]
[ROW][C]97[/C][C]0.0101804739953405[/C][C]0.0203609479906810[/C][C]0.98981952600466[/C][/ROW]
[ROW][C]98[/C][C]0.00757179988845359[/C][C]0.0151435997769072[/C][C]0.992428200111546[/C][/ROW]
[ROW][C]99[/C][C]0.00548598702625884[/C][C]0.0109719740525177[/C][C]0.994514012973741[/C][/ROW]
[ROW][C]100[/C][C]0.00405902651696249[/C][C]0.00811805303392497[/C][C]0.995940973483038[/C][/ROW]
[ROW][C]101[/C][C]0.00290887429078830[/C][C]0.00581774858157659[/C][C]0.997091125709212[/C][/ROW]
[ROW][C]102[/C][C]0.00200386428763806[/C][C]0.00400772857527613[/C][C]0.997996135712362[/C][/ROW]
[ROW][C]103[/C][C]0.00215403027429581[/C][C]0.00430806054859161[/C][C]0.997845969725704[/C][/ROW]
[ROW][C]104[/C][C]0.00162274923757532[/C][C]0.00324549847515063[/C][C]0.998377250762425[/C][/ROW]
[ROW][C]105[/C][C]0.00748111752403738[/C][C]0.0149622350480748[/C][C]0.992518882475963[/C][/ROW]
[ROW][C]106[/C][C]0.0117329779207750[/C][C]0.0234659558415501[/C][C]0.988267022079225[/C][/ROW]
[ROW][C]107[/C][C]0.0111482285196903[/C][C]0.0222964570393806[/C][C]0.98885177148031[/C][/ROW]
[ROW][C]108[/C][C]0.0141247968261094[/C][C]0.0282495936522188[/C][C]0.98587520317389[/C][/ROW]
[ROW][C]109[/C][C]0.0105307564580442[/C][C]0.0210615129160883[/C][C]0.989469243541956[/C][/ROW]
[ROW][C]110[/C][C]0.00750977849723533[/C][C]0.0150195569944707[/C][C]0.992490221502765[/C][/ROW]
[ROW][C]111[/C][C]0.00530557284380624[/C][C]0.0106111456876125[/C][C]0.994694427156194[/C][/ROW]
[ROW][C]112[/C][C]0.0148173158258302[/C][C]0.0296346316516603[/C][C]0.98518268417417[/C][/ROW]
[ROW][C]113[/C][C]0.0124479414753574[/C][C]0.0248958829507148[/C][C]0.987552058524643[/C][/ROW]
[ROW][C]114[/C][C]0.0157131625792882[/C][C]0.0314263251585765[/C][C]0.984286837420712[/C][/ROW]
[ROW][C]115[/C][C]0.0124501788772014[/C][C]0.0249003577544029[/C][C]0.987549821122799[/C][/ROW]
[ROW][C]116[/C][C]0.00954947377854751[/C][C]0.0190989475570950[/C][C]0.990450526221452[/C][/ROW]
[ROW][C]117[/C][C]0.0390327947946147[/C][C]0.0780655895892295[/C][C]0.960967205205385[/C][/ROW]
[ROW][C]118[/C][C]0.0363102590286553[/C][C]0.0726205180573105[/C][C]0.963689740971345[/C][/ROW]
[ROW][C]119[/C][C]0.03015275226836[/C][C]0.06030550453672[/C][C]0.96984724773164[/C][/ROW]
[ROW][C]120[/C][C]0.0555318602089327[/C][C]0.111063720417865[/C][C]0.944468139791067[/C][/ROW]
[ROW][C]121[/C][C]0.0524284084597929[/C][C]0.104856816919586[/C][C]0.947571591540207[/C][/ROW]
[ROW][C]122[/C][C]0.0644266181930372[/C][C]0.128853236386074[/C][C]0.935573381806963[/C][/ROW]
[ROW][C]123[/C][C]0.0588139234319808[/C][C]0.117627846863962[/C][C]0.94118607656802[/C][/ROW]
[ROW][C]124[/C][C]0.0562002442168259[/C][C]0.112400488433652[/C][C]0.943799755783174[/C][/ROW]
[ROW][C]125[/C][C]0.0485435566702981[/C][C]0.0970871133405963[/C][C]0.951456443329702[/C][/ROW]
[ROW][C]126[/C][C]0.0356817783153974[/C][C]0.0713635566307949[/C][C]0.964318221684603[/C][/ROW]
[ROW][C]127[/C][C]0.02547686528839[/C][C]0.05095373057678[/C][C]0.97452313471161[/C][/ROW]
[ROW][C]128[/C][C]0.0249796448187472[/C][C]0.0499592896374945[/C][C]0.975020355181253[/C][/ROW]
[ROW][C]129[/C][C]0.0388028939823763[/C][C]0.0776057879647527[/C][C]0.961197106017624[/C][/ROW]
[ROW][C]130[/C][C]0.0299620045440525[/C][C]0.059924009088105[/C][C]0.970037995455947[/C][/ROW]
[ROW][C]131[/C][C]0.0245692625037148[/C][C]0.0491385250074297[/C][C]0.975430737496285[/C][/ROW]
[ROW][C]132[/C][C]0.0192922727653276[/C][C]0.0385845455306551[/C][C]0.980707727234672[/C][/ROW]
[ROW][C]133[/C][C]0.0161717547665161[/C][C]0.0323435095330321[/C][C]0.983828245233484[/C][/ROW]
[ROW][C]134[/C][C]0.0116761893164846[/C][C]0.0233523786329693[/C][C]0.988323810683515[/C][/ROW]
[ROW][C]135[/C][C]0.0171254360605619[/C][C]0.0342508721211238[/C][C]0.982874563939438[/C][/ROW]
[ROW][C]136[/C][C]0.0106847014541643[/C][C]0.0213694029083287[/C][C]0.989315298545836[/C][/ROW]
[ROW][C]137[/C][C]0.00636112265935153[/C][C]0.0127222453187031[/C][C]0.993638877340648[/C][/ROW]
[ROW][C]138[/C][C]0.0214760925568549[/C][C]0.0429521851137099[/C][C]0.978523907443145[/C][/ROW]
[ROW][C]139[/C][C]0.0325366646781546[/C][C]0.0650733293563091[/C][C]0.967463335321845[/C][/ROW]
[ROW][C]140[/C][C]0.0256465477347436[/C][C]0.0512930954694873[/C][C]0.974353452265256[/C][/ROW]
[ROW][C]141[/C][C]0.0897987089241462[/C][C]0.179597417848292[/C][C]0.910201291075854[/C][/ROW]
[ROW][C]142[/C][C]0.0518632442750284[/C][C]0.103726488550057[/C][C]0.948136755724972[/C][/ROW]
[ROW][C]143[/C][C]0.157261793021036[/C][C]0.314523586042072[/C][C]0.842738206978964[/C][/ROW]
[ROW][C]144[/C][C]0.100360520063917[/C][C]0.200721040127835[/C][C]0.899639479936083[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99126&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99126&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
150.9109524040856740.1780951918286520.0890475959143262
160.8719804734681240.2560390530637530.128019526531876
170.8042686710876440.3914626578247120.195731328912356
180.8337641647236940.3324716705526120.166235835276306
190.7571437697566810.4857124604866380.242856230243319
200.78164833640170.43670332719660.2183516635983
210.7238612434730090.5522775130539820.276138756526991
220.6521619823985760.6956760352028480.347838017601424
230.5726633092117920.8546733815764160.427336690788208
240.5861428002619190.8277143994761620.413857199738081
250.7414110631144610.5171778737710780.258588936885539
260.6776374739570320.6447250520859350.322362526042968
270.6080045122562410.7839909754875180.391995487743759
280.5969709657651540.8060580684696910.403029034234846
290.5258358474491890.9483283051016220.474164152550811
300.452699709821360.905399419642720.54730029017864
310.4773982292800140.9547964585600280.522601770719986
320.4102706692918170.8205413385836350.589729330708183
330.6621384343692210.6757231312615580.337861565630779
340.6166384385416650.766723122916670.383361561458335
350.5740599940717170.8518800118565660.425940005928283
360.511436431145240.977127137709520.48856356885476
370.4883474166299380.9766948332598770.511652583370062
380.4261913826403010.8523827652806010.573808617359699
390.3855031682210570.7710063364421150.614496831778943
400.3536248979175970.7072497958351950.646375102082403
410.5166385960749120.9667228078501760.483361403925088
420.4575796031155130.9151592062310270.542420396884487
430.4136920446428170.8273840892856330.586307955357183
440.3599765236402110.7199530472804220.640023476359789
450.3152131684641650.6304263369283290.684786831535835
460.2930933098299780.5861866196599550.706906690170022
470.2785221319620180.5570442639240360.721477868037982
480.2670623403419890.5341246806839780.732937659658011
490.2238589094868000.4477178189736010.7761410905132
500.2110857055083550.4221714110167110.788914294491645
510.1790316183604830.3580632367209660.820968381639517
520.1562195235576440.3124390471152880.843780476442356
530.2486740899148110.4973481798296210.75132591008519
540.2719148542992990.5438297085985980.728085145700701
550.2648381020810660.5296762041621320.735161897918934
560.3342043325474870.6684086650949740.665795667452513
570.3207773305820860.6415546611641730.679222669417914
580.2806323654992110.5612647309984230.719367634500789
590.3033344799169190.6066689598338370.696665520083081
600.3268883740737800.6537767481475610.67311162592622
610.2927484786399510.5854969572799020.707251521360049
620.2513900772540970.5027801545081940.748609922745903
630.2882737382349710.5765474764699420.711726261765029
640.2480017952436860.4960035904873720.751998204756314
650.2106103631473370.4212207262946740.789389636852663
660.2011944712306380.4023889424612770.798805528769362
670.184919449142740.369838898285480.81508055085726
680.1870771404848330.3741542809696650.812922859515167
690.1654304776853140.3308609553706280.834569522314686
700.1363734214530630.2727468429061250.863626578546937
710.1140236170500860.2280472341001710.885976382949914
720.09259549199204330.1851909839840870.907404508007957
730.08491273501998730.1698254700399750.915087264980013
740.06870950797279030.1374190159455810.93129049202721
750.05731665265320190.1146333053064040.942683347346798
760.04490676782865960.08981353565731930.95509323217134
770.03628120722604390.07256241445208770.963718792773956
780.02936422974454230.05872845948908460.970635770255458
790.04347561031746970.08695122063493940.95652438968253
800.03573889967440.07147779934880.9642611003256
810.03393192810525310.06786385621050630.966068071894747
820.02770323002897060.05540646005794110.97229676997103
830.02111574117537750.0422314823507550.978884258824622
840.01684322487924830.03368644975849670.983156775120752
850.01390753370936260.02781506741872530.986092466290637
860.01235201998375550.02470403996751110.987647980016244
870.009121197971890250.01824239594378050.99087880202811
880.01231613227091920.02463226454183830.98768386772908
890.009442540977979470.01888508195595890.99055745902202
900.01118855215509410.02237710431018820.988811447844906
910.01174991982444350.0234998396488870.988250080175556
920.01055593996455690.02111187992911380.989444060035443
930.009030362130111780.01806072426022360.990969637869888
940.007431582921171220.01486316584234240.992568417078829
950.01377391799745190.02754783599490370.986226082002548
960.01101159043989840.02202318087979680.988988409560102
970.01018047399534050.02036094799068100.98981952600466
980.007571799888453590.01514359977690720.992428200111546
990.005485987026258840.01097197405251770.994514012973741
1000.004059026516962490.008118053033924970.995940973483038
1010.002908874290788300.005817748581576590.997091125709212
1020.002003864287638060.004007728575276130.997996135712362
1030.002154030274295810.004308060548591610.997845969725704
1040.001622749237575320.003245498475150630.998377250762425
1050.007481117524037380.01496223504807480.992518882475963
1060.01173297792077500.02346595584155010.988267022079225
1070.01114822851969030.02229645703938060.98885177148031
1080.01412479682610940.02824959365221880.98587520317389
1090.01053075645804420.02106151291608830.989469243541956
1100.007509778497235330.01501955699447070.992490221502765
1110.005305572843806240.01061114568761250.994694427156194
1120.01481731582583020.02963463165166030.98518268417417
1130.01244794147535740.02489588295071480.987552058524643
1140.01571316257928820.03142632515857650.984286837420712
1150.01245017887720140.02490035775440290.987549821122799
1160.009549473778547510.01909894755709500.990450526221452
1170.03903279479461470.07806558958922950.960967205205385
1180.03631025902865530.07262051805731050.963689740971345
1190.030152752268360.060305504536720.96984724773164
1200.05553186020893270.1110637204178650.944468139791067
1210.05242840845979290.1048568169195860.947571591540207
1220.06442661819303720.1288532363860740.935573381806963
1230.05881392343198080.1176278468639620.94118607656802
1240.05620024421682590.1124004884336520.943799755783174
1250.04854355667029810.09708711334059630.951456443329702
1260.03568177831539740.07136355663079490.964318221684603
1270.025476865288390.050953730576780.97452313471161
1280.02497964481874720.04995928963749450.975020355181253
1290.03880289398237630.07760578796475270.961197106017624
1300.02996200454405250.0599240090881050.970037995455947
1310.02456926250371480.04913852500742970.975430737496285
1320.01929227276532760.03858454553065510.980707727234672
1330.01617175476651610.03234350953303210.983828245233484
1340.01167618931648460.02335237863296930.988323810683515
1350.01712543606056190.03425087212112380.982874563939438
1360.01068470145416430.02136940290832870.989315298545836
1370.006361122659351530.01272224531870310.993638877340648
1380.02147609255685490.04295218511370990.978523907443145
1390.03253666467815460.06507332935630910.967463335321845
1400.02564654773474360.05129309546948730.974353452265256
1410.08979870892414620.1795974178482920.910201291075854
1420.05186324427502840.1037264885500570.948136755724972
1430.1572617930210360.3145235860420720.842738206978964
1440.1003605200639170.2007210401278350.899639479936083







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.0384615384615385NOK
5% type I error level430.330769230769231NOK
10% type I error level600.461538461538462NOK

\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 & 5 & 0.0384615384615385 & NOK \tabularnewline
5% type I error level & 43 & 0.330769230769231 & NOK \tabularnewline
10% type I error level & 60 & 0.461538461538462 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99126&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]5[/C][C]0.0384615384615385[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]43[/C][C]0.330769230769231[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]60[/C][C]0.461538461538462[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99126&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99126&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 level50.0384615384615385NOK
5% type I error level430.330769230769231NOK
10% type I error level600.461538461538462NOK



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}