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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 computationSun, 09 Dec 2012 09:26:28 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/09/t1355063618idoaiw75xicfd16.htm/, Retrieved Fri, 29 Mar 2024 09:26:06 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197889, Retrieved Fri, 29 Mar 2024 09:26:06 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact80
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataseries X:
1	1	14	12	26	21	21	23	17	23	127
1	1	18	11	20	16	15	24	17	20	108
1	1	11	14	19	19	18	22	18	20	110
1	0	12	12	19	18	11	20	21	21	102
1	1	16	21	20	16	8	24	20	24	104
1	1	18	12	25	23	19	27	28	22	140
1	0	14	22	25	17	4	28	19	23	112
1	1	14	11	22	12	20	27	22	20	115
1	1	15	10	26	19	16	24	16	25	121
1	1	15	13	22	16	14	23	18	23	112
1	0	17	10	17	19	10	24	25	27	118
1	0	19	8	22	20	13	27	17	27	122
1	1	10	15	19	13	14	27	14	22	105
1	1	16	14	24	20	8	28	11	24	111
1	1	18	10	26	27	23	27	27	25	151
1	0	14	14	21	17	11	23	20	22	106
1	1	14	14	13	8	9	24	22	28	100
1	0	17	11	26	25	24	28	22	28	149
1	0	14	10	20	26	5	27	21	27	122
1	1	16	13	22	13	15	25	23	25	115
1	0	18	7	14	19	5	19	17	16	86
1	1	11	14	21	15	19	24	24	28	124
1	1	14	12	7	5	6	20	14	21	69
1	0	12	14	23	16	13	28	17	24	117
1	1	17	11	17	14	11	26	23	27	113
1	1	9	9	25	24	17	23	24	14	123
1	1	16	11	25	24	17	23	24	14	123
1	1	14	15	19	9	5	20	8	27	84
1	0	15	14	20	19	9	11	22	20	97
1	1	11	13	23	19	15	24	23	21	121
1	0	16	9	22	25	17	25	25	22	132
1	1	13	15	22	19	17	23	21	21	119
1	1	17	10	21	18	20	18	24	12	98
1	0	15	11	15	15	12	20	15	20	87
1	0	14	13	20	12	7	20	22	24	101
1	0	16	8	22	21	16	24	21	19	115
1	1	9	20	18	12	7	23	25	28	109
1	0	15	12	20	15	14	25	16	23	109
1	0	17	10	28	28	24	28	28	27	159
1	1	13	10	22	25	15	26	23	22	129
1	1	15	9	18	19	15	26	21	27	119
1	1	16	14	23	20	10	23	21	26	119
1	1	16	8	20	24	14	22	26	22	122
1	0	12	14	25	26	18	24	22	21	131
1	0	12	11	26	25	12	21	21	19	120
1	1	11	13	15	12	9	20	18	24	82
1	0	15	9	17	12	9	22	12	19	86
1	0	15	11	23	15	8	20	25	26	105
1	1	17	15	21	17	18	25	17	22	114
1	0	13	11	13	14	10	20	24	28	100
1	1	16	10	18	16	17	22	15	21	100
1	1	14	14	19	11	14	23	13	23	99
1	1	11	18	22	20	16	25	26	28	132
1	1	12	14	16	11	10	23	16	10	82
1	0	12	11	24	22	19	23	24	24	132
1	1	15	12	18	20	10	22	21	21	107
1	1	16	13	20	19	14	24	20	21	114
1	1	15	9	24	17	10	25	14	24	110
1	0	12	10	14	21	4	21	25	24	105
1	0	12	15	22	23	19	12	25	25	121
1	1	8	20	24	18	9	17	20	25	109
1	1	13	12	18	17	12	20	22	23	106
1	1	11	12	21	27	16	23	20	21	124
1	0	14	14	23	25	11	23	26	16	120
1	1	15	13	17	19	18	20	18	17	91
1	0	10	11	22	22	11	28	22	25	126
1	0	11	17	24	24	24	24	24	24	138
1	0	12	12	21	20	17	24	17	23	118
1	1	15	13	22	19	18	24	24	25	128
1	1	15	14	16	11	9	24	20	23	98
1	1	14	13	21	22	19	28	19	28	133
1	0	16	15	23	22	18	25	20	26	130
1	0	15	13	22	16	12	21	15	22	103
1	1	15	10	24	20	23	25	23	19	124
1	1	13	11	24	24	22	25	26	26	142
1	1	12	19	16	16	14	18	22	18	96
1	1	17	13	16	16	14	17	20	18	93
1	0	13	17	21	22	16	26	24	25	129
1	0	15	13	26	24	23	28	26	27	150
1	0	13	9	15	16	7	21	21	12	88
1	0	15	11	25	27	10	27	25	15	125
1	1	16	10	18	11	12	22	13	21	92
1	0	15	9	23	21	12	21	20	23	0
1	1	16	12	20	20	12	25	22	22	117
1	0	15	12	17	20	17	22	23	21	112
1	0	14	13	25	27	21	23	28	24	144
1	1	15	13	24	20	16	26	22	27	130
1	1	14	12	17	12	11	19	20	22	87
1	1	13	15	19	8	14	25	6	28	92
1	1	7	22	20	21	13	21	21	26	114
1	1	17	13	15	18	9	13	20	10	81
1	0	13	15	27	24	19	24	18	19	127
1	1	15	13	22	16	13	25	23	22	115
1	1	14	15	23	18	19	26	20	21	123
1	1	13	10	16	20	13	25	24	24	115
1	1	16	11	19	20	13	25	22	25	117
1	0	12	16	25	19	13	22	21	21	117
1	1	14	11	19	17	14	21	18	20	103
1	0	17	11	19	16	12	23	21	21	108
1	0	15	10	26	26	22	25	23	24	139
1	1	17	10	21	15	11	24	23	23	113
1	0	12	16	20	22	5	21	15	18	97
1	1	16	12	24	17	18	21	21	24	117
1	1	11	11	22	23	19	25	24	24	133
1	0	15	16	20	21	14	22	23	19	115
1	1	9	19	18	19	15	20	21	20	103
1	0	16	11	18	14	12	20	21	18	95
1	1	15	16	24	17	19	23	20	20	117
1	1	10	15	24	12	15	28	11	27	113
1	1	10	24	22	24	17	23	22	23	127
1	1	15	14	23	18	8	28	27	26	126
1	1	11	15	22	20	10	24	25	23	119
1	1	13	11	20	16	12	18	18	17	97
1	1	14	15	18	20	12	20	20	21	105
1	1	18	12	25	22	20	28	24	25	140
1	0	16	10	18	12	12	21	10	23	91
1	1	14	14	16	16	12	21	27	27	112
1	1	14	13	20	17	14	25	21	24	113
1	0	14	9	19	22	6	19	21	20	102
1	1	14	15	15	12	10	18	18	27	92
1	1	12	15	19	14	18	21	15	21	98
1	1	14	14	19	23	18	22	24	24	122
1	1	15	11	16	15	7	24	22	21	100
1	1	15	8	17	17	18	15	14	15	84
1	1	15	11	28	28	9	28	28	25	142
1	0	13	11	23	20	17	26	18	25	124
1	1	17	8	25	23	22	23	26	22	137
1	1	17	10	20	13	11	26	17	24	105
1	0	19	11	17	18	15	20	19	21	106
1	0	15	13	23	23	17	22	22	22	125
1	1	13	11	16	19	15	20	18	23	104
1	0	9	20	23	23	22	23	24	22	130
1	0	15	10	11	12	9	22	15	20	79
1	0	15	15	18	16	13	24	18	23	108
1	0	15	12	24	23	20	23	26	25	136
1	1	16	14	23	13	14	22	11	23	98
1	1	11	23	21	22	14	26	26	22	120
1	0	14	14	16	18	12	23	21	25	108
1	0	11	16	24	23	20	27	23	26	139
1	1	15	11	23	20	20	23	23	22	123
1	1	13	12	18	10	8	21	15	24	90
1	1	15	10	20	17	17	26	22	24	119
1	1	16	14	9	18	9	23	26	25	105
1	0	14	12	24	15	18	21	16	20	110
1	1	15	12	25	23	22	27	20	26	135
1	1	16	11	20	17	10	19	18	21	101
1	0	16	12	21	17	13	23	22	26	114
1	0	11	13	25	22	15	25	16	21	118
1	0	12	11	22	20	18	23	19	22	120
1	0	9	19	21	20	18	22	20	16	108
1	1	16	12	21	19	12	22	19	26	114
1	1	13	17	22	18	12	25	23	28	122
1	1	16	9	27	22	20	25	24	18	132
1	0	12	12	24	20	12	28	25	25	130
1	0	9	19	24	22	16	28	21	23	130
1	0	13	18	21	18	16	20	21	21	112
1	1	13	15	18	16	18	25	23	20	114
1	1	14	14	16	16	16	19	27	25	103
0	1	19	11	22	16	13	25	23	22	115
0	1	13	9	20	16	17	22	18	21	108
0	0	12	18	18	17	13	18	16	16	94
0	1	13	16	20	18	17	20	16	18	105




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=197889&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=197889&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197889&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 time10 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
TotaleMotivatie [t] = -17.4611169237435 -1.70969896505393Pop[t] + 0.715234976636848Gender[t] + 0.096653943965633Happiness[t] + 0.334392934710763Depression[t] + 0.87259328201138I1[t] + 1.08065320213306I2[t] + 0.965844182688753I3[t] + 1.53093629880875E1[t] + 0.980166590737826E2[t] + 0.834917466193323E3[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TotaleMotivatie
[t] =  -17.4611169237435 -1.70969896505393Pop[t] +  0.715234976636848Gender[t] +  0.096653943965633Happiness[t] +  0.334392934710763Depression[t] +  0.87259328201138I1[t] +  1.08065320213306I2[t] +  0.965844182688753I3[t] +  1.53093629880875E1[t] +  0.980166590737826E2[t] +  0.834917466193323E3[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197889&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TotaleMotivatie
[t] =  -17.4611169237435 -1.70969896505393Pop[t] +  0.715234976636848Gender[t] +  0.096653943965633Happiness[t] +  0.334392934710763Depression[t] +  0.87259328201138I1[t] +  1.08065320213306I2[t] +  0.965844182688753I3[t] +  1.53093629880875E1[t] +  0.980166590737826E2[t] +  0.834917466193323E3[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197889&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197889&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
TotaleMotivatie [t] = -17.4611169237435 -1.70969896505393Pop[t] + 0.715234976636848Gender[t] + 0.096653943965633Happiness[t] + 0.334392934710763Depression[t] + 0.87259328201138I1[t] + 1.08065320213306I2[t] + 0.965844182688753I3[t] + 1.53093629880875E1[t] + 0.980166590737826E2[t] + 0.834917466193323E3[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-17.461116923743511.112986-1.57120.1182210.059111
Pop-1.709698965053934.890024-0.34960.7271040.363552
Gender0.7152349766368481.6336430.43780.6621450.331073
Happiness0.0966539439656330.3868190.24990.8030290.401514
Depression0.3343929347107630.2888361.15770.2488030.124402
I10.872593282011380.3011422.89760.004320.00216
I21.080653202133060.2719693.97340.0001095.5e-05
I30.9658441826887530.1982664.87153e-061e-06
E11.530936298808750.2923865.2361e-060
E20.9801665907378260.226854.32082.8e-051.4e-05
E30.8349174661933230.2357483.54160.0005290.000265

\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) & -17.4611169237435 & 11.112986 & -1.5712 & 0.118221 & 0.059111 \tabularnewline
Pop & -1.70969896505393 & 4.890024 & -0.3496 & 0.727104 & 0.363552 \tabularnewline
Gender & 0.715234976636848 & 1.633643 & 0.4378 & 0.662145 & 0.331073 \tabularnewline
Happiness & 0.096653943965633 & 0.386819 & 0.2499 & 0.803029 & 0.401514 \tabularnewline
Depression & 0.334392934710763 & 0.288836 & 1.1577 & 0.248803 & 0.124402 \tabularnewline
I1 & 0.87259328201138 & 0.301142 & 2.8976 & 0.00432 & 0.00216 \tabularnewline
I2 & 1.08065320213306 & 0.271969 & 3.9734 & 0.000109 & 5.5e-05 \tabularnewline
I3 & 0.965844182688753 & 0.198266 & 4.8715 & 3e-06 & 1e-06 \tabularnewline
E1 & 1.53093629880875 & 0.292386 & 5.236 & 1e-06 & 0 \tabularnewline
E2 & 0.980166590737826 & 0.22685 & 4.3208 & 2.8e-05 & 1.4e-05 \tabularnewline
E3 & 0.834917466193323 & 0.235748 & 3.5416 & 0.000529 & 0.000265 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197889&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]-17.4611169237435[/C][C]11.112986[/C][C]-1.5712[/C][C]0.118221[/C][C]0.059111[/C][/ROW]
[ROW][C]Pop[/C][C]-1.70969896505393[/C][C]4.890024[/C][C]-0.3496[/C][C]0.727104[/C][C]0.363552[/C][/ROW]
[ROW][C]Gender[/C][C]0.715234976636848[/C][C]1.633643[/C][C]0.4378[/C][C]0.662145[/C][C]0.331073[/C][/ROW]
[ROW][C]Happiness[/C][C]0.096653943965633[/C][C]0.386819[/C][C]0.2499[/C][C]0.803029[/C][C]0.401514[/C][/ROW]
[ROW][C]Depression[/C][C]0.334392934710763[/C][C]0.288836[/C][C]1.1577[/C][C]0.248803[/C][C]0.124402[/C][/ROW]
[ROW][C]I1[/C][C]0.87259328201138[/C][C]0.301142[/C][C]2.8976[/C][C]0.00432[/C][C]0.00216[/C][/ROW]
[ROW][C]I2[/C][C]1.08065320213306[/C][C]0.271969[/C][C]3.9734[/C][C]0.000109[/C][C]5.5e-05[/C][/ROW]
[ROW][C]I3[/C][C]0.965844182688753[/C][C]0.198266[/C][C]4.8715[/C][C]3e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]E1[/C][C]1.53093629880875[/C][C]0.292386[/C][C]5.236[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]E2[/C][C]0.980166590737826[/C][C]0.22685[/C][C]4.3208[/C][C]2.8e-05[/C][C]1.4e-05[/C][/ROW]
[ROW][C]E3[/C][C]0.834917466193323[/C][C]0.235748[/C][C]3.5416[/C][C]0.000529[/C][C]0.000265[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197889&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197889&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)-17.461116923743511.112986-1.57120.1182210.059111
Pop-1.709698965053934.890024-0.34960.7271040.363552
Gender0.7152349766368481.6336430.43780.6621450.331073
Happiness0.0966539439656330.3868190.24990.8030290.401514
Depression0.3343929347107630.2888361.15770.2488030.124402
I10.872593282011380.3011422.89760.004320.00216
I21.080653202133060.2719693.97340.0001095.5e-05
I30.9658441826887530.1982664.87153e-061e-06
E11.530936298808750.2923865.2361e-060
E20.9801665907378260.226854.32082.8e-051.4e-05
E30.8349174661933230.2357483.54160.0005290.000265







Multiple Linear Regression - Regression Statistics
Multiple R0.866427473942803
R-squared0.750696567602907
Adjusted R-squared0.734186406517007
F-TEST (value)45.4687609464942
F-TEST (DF numerator)10
F-TEST (DF denominator)151
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.48838606764833
Sum Squared Residuals13594.4499954802

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.866427473942803 \tabularnewline
R-squared & 0.750696567602907 \tabularnewline
Adjusted R-squared & 0.734186406517007 \tabularnewline
F-TEST (value) & 45.4687609464942 \tabularnewline
F-TEST (DF numerator) & 10 \tabularnewline
F-TEST (DF denominator) & 151 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9.48838606764833 \tabularnewline
Sum Squared Residuals & 13594.4499954802 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197889&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.866427473942803[/C][/ROW]
[ROW][C]R-squared[/C][C]0.750696567602907[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.734186406517007[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]45.4687609464942[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]10[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]151[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]9.48838606764833[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]13594.4499954802[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197889&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197889&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 R0.866427473942803
R-squared0.750696567602907
Adjusted R-squared0.734186406517007
F-TEST (value)45.4687609464942
F-TEST (DF numerator)10
F-TEST (DF denominator)151
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.48838606764833
Sum Squared Residuals13594.4499954802







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1127123.6516285710323.34837142896806
2108106.2961445135461.70385548645353
3110109.8079385754940.192061424506283
4102101.3925538332360.60744616676405
5104108.966026330888-4.96602633088834
6140139.465929330930.534070669070478
7112114.28078042077-2.28078042076979
8115117.654965256734-2.65496525673362
9121118.3095739683242.6904260316762
10112109.4082936229142.59170637708583
11118114.630576494413.36942350558963
12122119.24772684372.75227315630004
13105105.102231686165-0.102231686164574
14111111.740507603085-0.740507603085309
15151149.3803120906491.61968790935123
16106107.36674072436-1.36674072436019
1710097.94443653777532.05556346222469
18149146.8422096268792.15779037312072
19122120.365888543511.63411145648981
20115116.861372626863-1.86137262686265
218681.59694452669294.4030554733071
22124123.8185683852640.181431614735964
236966.09109863732922.90890136267079
24117116.1536712179130.84632878208696
25113112.3443219939230.655678006076705
26123120.0180785801292.98192141987078
27123121.363442057311.63655794268983
288480.05067070417453.94932929582549
299788.73968208858718.26031791141293
30121118.8640092382622.13599076173784
31132129.1636734852532.83632651474738
32119117.2939285986971.70607140130332
3398104.724426847357-6.72442684735705
348788.8657171680459-1.86571716804586
3510195.93047098365385.0695290163462
36115115.584468498981-0.584468498980854
37109107.6309787528281.36902124717192
38109106.6343653615532.36563463844743
39159156.5410449408242.45895505917592
40129127.5622543166591.43774568334108
41119119.661131078527-0.661131078526524
42119116.6164220321732.3835779678274
43122120.2085009072681.79149909273179
44131129.8069460765841.1930539234157
45120115.5578318366474.44216816335289
468290.0037997207631-8.00379972076305
478683.08908106739242.91091893260764
48105106.79425739792-1.7942573979201
49114115.58861257183-1.58861257182999
5010099.41572019476830.584279805231704
51100101.767657179359-1.76765717935937
529996.72415380327072.27584619672927
53132132.025736349002-0.0257363490018999
548282.1362620502506-0.136262050250589
55132127.5085546458444.49144535415597
56107105.7824921789531.21750782104682
57114112.8231551571541.17684484284604
58110107.0093086203312.99069137966857
59105100.7982069416754.20179305832455
60121113.1563777928327.8436222071677
61109104.5942889339854.40571106601501
62106103.8670419755072.13295802449278
63124121.924063468262.07593653174037
64120118.6286476527141.37135234728557
6591102.548349856425-11.5483498564253
66126124.3725725111411.6274274888586
67138137.9397219266620.0602780733383974
68118114.9670256623283.03297433767232
69128125.5954007356912.40459926430931
709897.7659094217320.234090578268007
71133132.5616219389270.438378061072565
72130128.2053458629021.79465413709768
7310399.92408044487553.0759195551245
74124127.788547522069-3.78854752206897
75142140.0713232302591.92867676974132
769697.9805272451545-1.9805272451545
779392.96616987643370.0338301235663122
78129129.523980166593-0.523980166593414
79150148.3569391203061.64306087969429
808884.98765181307153.01234818692851
81125124.9714327164520.0285672835482621
829289.57483707377472.42516292622534
830110.598118418592-110.598118418592
84117115.9639140056761.03608599432353
85112112.915906380602-0.91590638060187
86144140.4988624242023.5011375757977
87130129.2609264849970.739073515002503
888792.3958054976297-5.39580549762971
899294.095226981036-2.09522698103602
90114116.720213320716-2.72021332071569
918176.62257716046314.37742283953692
92127125.1972160023681.80278399763185
93115115.570237525339-0.570237525338711
94123122.7268532936070.273146706393437
95115116.110805472864-1.11080547286358
96117118.227524370223-1.22752437022305
97117114.0398994292752.96010057072466
98103101.5391021694281.46089783057155
99108104.9387772932023.06122270679777
100139138.5111614705310.488838529468914
101113110.1794129272.82058707299968
1029795.27545092229191.72454907770808
103117116.5733157698690.426684230130839
104133130.5244749142432.47552508575696
105115113.3845436871591.61545631284078
106103107.395096705635-4.39509670563461
1079594.71066236795290.289337632047106
108117117.522113889542-0.522113889541712
109113112.1154129343390.884587065660946
110127128.066770712986-1.06677071298641
111126125.9624543570340.0375456429659519
112119118.5418022282240.458197771776096
1139792.20513864495744.79486135504258
114105104.5786662161420.421333783857983
115140139.4661426459230.533857354077363
1169187.13865416063533.86134583936471
117112111.578081140010.421918859990271
118113115.484396153083-2.48439615308312
11910297.71022104712134.28977895287868
1209291.37127140573270.6287285942673
12198101.199200838676-3.19920083867592
122122123.641182625123-1.64118262512309
12310099.44415330984340.555846690156567
1248485.4698959892236-1.46989598922363
125142141.2398672915230.760132708477511
126124122.1863473563471.81365264365273
127137132.8451578194764.15484218052354
128105105.161303760107-0.161303760106685
129106101.8925954915414.10740450845878
130125121.5825694892363.41743051076442
131104102.9256350661551.07436493384524
132130131.663886762145-1.66388676214529
1337981.9633315484417-2.96333154844165
134108106.4365635037731.56343649622669
135136132.9746574449423.02534255505752
1369899.0778717432292-1.07787174322923
137120129.576157281082-9.57615728108176
138108108.535190688443-0.535190688442895
139139137.9437762971381.05622370286216
140123123.795694427665-0.795694427664565
1419087.94378045965812.05621954034191
142119119.986506730529-0.986506730529295
143105105.338880984554-0.338880984554217
144110105.2629636825734.73703631742749
145135137.57183718597-2.57183718596958
14610196.51467147719194.48532852280809
147114114.122954154496-0.122954154496377
148118117.8056905956910.194309404308786
149120116.0655496087913.93445039120937
150108112.017863467338-4.01786346733759
151114111.5622152816882.43778471831164
152122122.919468394988-0.919468394987799
153132129.5776113581022.42238864189766
154130128.3904974484421.6095025515577
155130130.875467999204-0.875467999203919
156112110.0699728629331.93002713706731
157114115.714728359841-1.71472835984057
158103110.709750340761-7.7097503407606
159115116.997766396834-1.99776639683364
160108107.5386877140420.461312285958128
1619492.94975940551431.05024059448572
162105104.513786483610.486213516389789

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 127 & 123.651628571032 & 3.34837142896806 \tabularnewline
2 & 108 & 106.296144513546 & 1.70385548645353 \tabularnewline
3 & 110 & 109.807938575494 & 0.192061424506283 \tabularnewline
4 & 102 & 101.392553833236 & 0.60744616676405 \tabularnewline
5 & 104 & 108.966026330888 & -4.96602633088834 \tabularnewline
6 & 140 & 139.46592933093 & 0.534070669070478 \tabularnewline
7 & 112 & 114.28078042077 & -2.28078042076979 \tabularnewline
8 & 115 & 117.654965256734 & -2.65496525673362 \tabularnewline
9 & 121 & 118.309573968324 & 2.6904260316762 \tabularnewline
10 & 112 & 109.408293622914 & 2.59170637708583 \tabularnewline
11 & 118 & 114.63057649441 & 3.36942350558963 \tabularnewline
12 & 122 & 119.2477268437 & 2.75227315630004 \tabularnewline
13 & 105 & 105.102231686165 & -0.102231686164574 \tabularnewline
14 & 111 & 111.740507603085 & -0.740507603085309 \tabularnewline
15 & 151 & 149.380312090649 & 1.61968790935123 \tabularnewline
16 & 106 & 107.36674072436 & -1.36674072436019 \tabularnewline
17 & 100 & 97.9444365377753 & 2.05556346222469 \tabularnewline
18 & 149 & 146.842209626879 & 2.15779037312072 \tabularnewline
19 & 122 & 120.36588854351 & 1.63411145648981 \tabularnewline
20 & 115 & 116.861372626863 & -1.86137262686265 \tabularnewline
21 & 86 & 81.5969445266929 & 4.4030554733071 \tabularnewline
22 & 124 & 123.818568385264 & 0.181431614735964 \tabularnewline
23 & 69 & 66.0910986373292 & 2.90890136267079 \tabularnewline
24 & 117 & 116.153671217913 & 0.84632878208696 \tabularnewline
25 & 113 & 112.344321993923 & 0.655678006076705 \tabularnewline
26 & 123 & 120.018078580129 & 2.98192141987078 \tabularnewline
27 & 123 & 121.36344205731 & 1.63655794268983 \tabularnewline
28 & 84 & 80.0506707041745 & 3.94932929582549 \tabularnewline
29 & 97 & 88.7396820885871 & 8.26031791141293 \tabularnewline
30 & 121 & 118.864009238262 & 2.13599076173784 \tabularnewline
31 & 132 & 129.163673485253 & 2.83632651474738 \tabularnewline
32 & 119 & 117.293928598697 & 1.70607140130332 \tabularnewline
33 & 98 & 104.724426847357 & -6.72442684735705 \tabularnewline
34 & 87 & 88.8657171680459 & -1.86571716804586 \tabularnewline
35 & 101 & 95.9304709836538 & 5.0695290163462 \tabularnewline
36 & 115 & 115.584468498981 & -0.584468498980854 \tabularnewline
37 & 109 & 107.630978752828 & 1.36902124717192 \tabularnewline
38 & 109 & 106.634365361553 & 2.36563463844743 \tabularnewline
39 & 159 & 156.541044940824 & 2.45895505917592 \tabularnewline
40 & 129 & 127.562254316659 & 1.43774568334108 \tabularnewline
41 & 119 & 119.661131078527 & -0.661131078526524 \tabularnewline
42 & 119 & 116.616422032173 & 2.3835779678274 \tabularnewline
43 & 122 & 120.208500907268 & 1.79149909273179 \tabularnewline
44 & 131 & 129.806946076584 & 1.1930539234157 \tabularnewline
45 & 120 & 115.557831836647 & 4.44216816335289 \tabularnewline
46 & 82 & 90.0037997207631 & -8.00379972076305 \tabularnewline
47 & 86 & 83.0890810673924 & 2.91091893260764 \tabularnewline
48 & 105 & 106.79425739792 & -1.7942573979201 \tabularnewline
49 & 114 & 115.58861257183 & -1.58861257182999 \tabularnewline
50 & 100 & 99.4157201947683 & 0.584279805231704 \tabularnewline
51 & 100 & 101.767657179359 & -1.76765717935937 \tabularnewline
52 & 99 & 96.7241538032707 & 2.27584619672927 \tabularnewline
53 & 132 & 132.025736349002 & -0.0257363490018999 \tabularnewline
54 & 82 & 82.1362620502506 & -0.136262050250589 \tabularnewline
55 & 132 & 127.508554645844 & 4.49144535415597 \tabularnewline
56 & 107 & 105.782492178953 & 1.21750782104682 \tabularnewline
57 & 114 & 112.823155157154 & 1.17684484284604 \tabularnewline
58 & 110 & 107.009308620331 & 2.99069137966857 \tabularnewline
59 & 105 & 100.798206941675 & 4.20179305832455 \tabularnewline
60 & 121 & 113.156377792832 & 7.8436222071677 \tabularnewline
61 & 109 & 104.594288933985 & 4.40571106601501 \tabularnewline
62 & 106 & 103.867041975507 & 2.13295802449278 \tabularnewline
63 & 124 & 121.92406346826 & 2.07593653174037 \tabularnewline
64 & 120 & 118.628647652714 & 1.37135234728557 \tabularnewline
65 & 91 & 102.548349856425 & -11.5483498564253 \tabularnewline
66 & 126 & 124.372572511141 & 1.6274274888586 \tabularnewline
67 & 138 & 137.939721926662 & 0.0602780733383974 \tabularnewline
68 & 118 & 114.967025662328 & 3.03297433767232 \tabularnewline
69 & 128 & 125.595400735691 & 2.40459926430931 \tabularnewline
70 & 98 & 97.765909421732 & 0.234090578268007 \tabularnewline
71 & 133 & 132.561621938927 & 0.438378061072565 \tabularnewline
72 & 130 & 128.205345862902 & 1.79465413709768 \tabularnewline
73 & 103 & 99.9240804448755 & 3.0759195551245 \tabularnewline
74 & 124 & 127.788547522069 & -3.78854752206897 \tabularnewline
75 & 142 & 140.071323230259 & 1.92867676974132 \tabularnewline
76 & 96 & 97.9805272451545 & -1.9805272451545 \tabularnewline
77 & 93 & 92.9661698764337 & 0.0338301235663122 \tabularnewline
78 & 129 & 129.523980166593 & -0.523980166593414 \tabularnewline
79 & 150 & 148.356939120306 & 1.64306087969429 \tabularnewline
80 & 88 & 84.9876518130715 & 3.01234818692851 \tabularnewline
81 & 125 & 124.971432716452 & 0.0285672835482621 \tabularnewline
82 & 92 & 89.5748370737747 & 2.42516292622534 \tabularnewline
83 & 0 & 110.598118418592 & -110.598118418592 \tabularnewline
84 & 117 & 115.963914005676 & 1.03608599432353 \tabularnewline
85 & 112 & 112.915906380602 & -0.91590638060187 \tabularnewline
86 & 144 & 140.498862424202 & 3.5011375757977 \tabularnewline
87 & 130 & 129.260926484997 & 0.739073515002503 \tabularnewline
88 & 87 & 92.3958054976297 & -5.39580549762971 \tabularnewline
89 & 92 & 94.095226981036 & -2.09522698103602 \tabularnewline
90 & 114 & 116.720213320716 & -2.72021332071569 \tabularnewline
91 & 81 & 76.6225771604631 & 4.37742283953692 \tabularnewline
92 & 127 & 125.197216002368 & 1.80278399763185 \tabularnewline
93 & 115 & 115.570237525339 & -0.570237525338711 \tabularnewline
94 & 123 & 122.726853293607 & 0.273146706393437 \tabularnewline
95 & 115 & 116.110805472864 & -1.11080547286358 \tabularnewline
96 & 117 & 118.227524370223 & -1.22752437022305 \tabularnewline
97 & 117 & 114.039899429275 & 2.96010057072466 \tabularnewline
98 & 103 & 101.539102169428 & 1.46089783057155 \tabularnewline
99 & 108 & 104.938777293202 & 3.06122270679777 \tabularnewline
100 & 139 & 138.511161470531 & 0.488838529468914 \tabularnewline
101 & 113 & 110.179412927 & 2.82058707299968 \tabularnewline
102 & 97 & 95.2754509222919 & 1.72454907770808 \tabularnewline
103 & 117 & 116.573315769869 & 0.426684230130839 \tabularnewline
104 & 133 & 130.524474914243 & 2.47552508575696 \tabularnewline
105 & 115 & 113.384543687159 & 1.61545631284078 \tabularnewline
106 & 103 & 107.395096705635 & -4.39509670563461 \tabularnewline
107 & 95 & 94.7106623679529 & 0.289337632047106 \tabularnewline
108 & 117 & 117.522113889542 & -0.522113889541712 \tabularnewline
109 & 113 & 112.115412934339 & 0.884587065660946 \tabularnewline
110 & 127 & 128.066770712986 & -1.06677071298641 \tabularnewline
111 & 126 & 125.962454357034 & 0.0375456429659519 \tabularnewline
112 & 119 & 118.541802228224 & 0.458197771776096 \tabularnewline
113 & 97 & 92.2051386449574 & 4.79486135504258 \tabularnewline
114 & 105 & 104.578666216142 & 0.421333783857983 \tabularnewline
115 & 140 & 139.466142645923 & 0.533857354077363 \tabularnewline
116 & 91 & 87.1386541606353 & 3.86134583936471 \tabularnewline
117 & 112 & 111.57808114001 & 0.421918859990271 \tabularnewline
118 & 113 & 115.484396153083 & -2.48439615308312 \tabularnewline
119 & 102 & 97.7102210471213 & 4.28977895287868 \tabularnewline
120 & 92 & 91.3712714057327 & 0.6287285942673 \tabularnewline
121 & 98 & 101.199200838676 & -3.19920083867592 \tabularnewline
122 & 122 & 123.641182625123 & -1.64118262512309 \tabularnewline
123 & 100 & 99.4441533098434 & 0.555846690156567 \tabularnewline
124 & 84 & 85.4698959892236 & -1.46989598922363 \tabularnewline
125 & 142 & 141.239867291523 & 0.760132708477511 \tabularnewline
126 & 124 & 122.186347356347 & 1.81365264365273 \tabularnewline
127 & 137 & 132.845157819476 & 4.15484218052354 \tabularnewline
128 & 105 & 105.161303760107 & -0.161303760106685 \tabularnewline
129 & 106 & 101.892595491541 & 4.10740450845878 \tabularnewline
130 & 125 & 121.582569489236 & 3.41743051076442 \tabularnewline
131 & 104 & 102.925635066155 & 1.07436493384524 \tabularnewline
132 & 130 & 131.663886762145 & -1.66388676214529 \tabularnewline
133 & 79 & 81.9633315484417 & -2.96333154844165 \tabularnewline
134 & 108 & 106.436563503773 & 1.56343649622669 \tabularnewline
135 & 136 & 132.974657444942 & 3.02534255505752 \tabularnewline
136 & 98 & 99.0778717432292 & -1.07787174322923 \tabularnewline
137 & 120 & 129.576157281082 & -9.57615728108176 \tabularnewline
138 & 108 & 108.535190688443 & -0.535190688442895 \tabularnewline
139 & 139 & 137.943776297138 & 1.05622370286216 \tabularnewline
140 & 123 & 123.795694427665 & -0.795694427664565 \tabularnewline
141 & 90 & 87.9437804596581 & 2.05621954034191 \tabularnewline
142 & 119 & 119.986506730529 & -0.986506730529295 \tabularnewline
143 & 105 & 105.338880984554 & -0.338880984554217 \tabularnewline
144 & 110 & 105.262963682573 & 4.73703631742749 \tabularnewline
145 & 135 & 137.57183718597 & -2.57183718596958 \tabularnewline
146 & 101 & 96.5146714771919 & 4.48532852280809 \tabularnewline
147 & 114 & 114.122954154496 & -0.122954154496377 \tabularnewline
148 & 118 & 117.805690595691 & 0.194309404308786 \tabularnewline
149 & 120 & 116.065549608791 & 3.93445039120937 \tabularnewline
150 & 108 & 112.017863467338 & -4.01786346733759 \tabularnewline
151 & 114 & 111.562215281688 & 2.43778471831164 \tabularnewline
152 & 122 & 122.919468394988 & -0.919468394987799 \tabularnewline
153 & 132 & 129.577611358102 & 2.42238864189766 \tabularnewline
154 & 130 & 128.390497448442 & 1.6095025515577 \tabularnewline
155 & 130 & 130.875467999204 & -0.875467999203919 \tabularnewline
156 & 112 & 110.069972862933 & 1.93002713706731 \tabularnewline
157 & 114 & 115.714728359841 & -1.71472835984057 \tabularnewline
158 & 103 & 110.709750340761 & -7.7097503407606 \tabularnewline
159 & 115 & 116.997766396834 & -1.99776639683364 \tabularnewline
160 & 108 & 107.538687714042 & 0.461312285958128 \tabularnewline
161 & 94 & 92.9497594055143 & 1.05024059448572 \tabularnewline
162 & 105 & 104.51378648361 & 0.486213516389789 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197889&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]127[/C][C]123.651628571032[/C][C]3.34837142896806[/C][/ROW]
[ROW][C]2[/C][C]108[/C][C]106.296144513546[/C][C]1.70385548645353[/C][/ROW]
[ROW][C]3[/C][C]110[/C][C]109.807938575494[/C][C]0.192061424506283[/C][/ROW]
[ROW][C]4[/C][C]102[/C][C]101.392553833236[/C][C]0.60744616676405[/C][/ROW]
[ROW][C]5[/C][C]104[/C][C]108.966026330888[/C][C]-4.96602633088834[/C][/ROW]
[ROW][C]6[/C][C]140[/C][C]139.46592933093[/C][C]0.534070669070478[/C][/ROW]
[ROW][C]7[/C][C]112[/C][C]114.28078042077[/C][C]-2.28078042076979[/C][/ROW]
[ROW][C]8[/C][C]115[/C][C]117.654965256734[/C][C]-2.65496525673362[/C][/ROW]
[ROW][C]9[/C][C]121[/C][C]118.309573968324[/C][C]2.6904260316762[/C][/ROW]
[ROW][C]10[/C][C]112[/C][C]109.408293622914[/C][C]2.59170637708583[/C][/ROW]
[ROW][C]11[/C][C]118[/C][C]114.63057649441[/C][C]3.36942350558963[/C][/ROW]
[ROW][C]12[/C][C]122[/C][C]119.2477268437[/C][C]2.75227315630004[/C][/ROW]
[ROW][C]13[/C][C]105[/C][C]105.102231686165[/C][C]-0.102231686164574[/C][/ROW]
[ROW][C]14[/C][C]111[/C][C]111.740507603085[/C][C]-0.740507603085309[/C][/ROW]
[ROW][C]15[/C][C]151[/C][C]149.380312090649[/C][C]1.61968790935123[/C][/ROW]
[ROW][C]16[/C][C]106[/C][C]107.36674072436[/C][C]-1.36674072436019[/C][/ROW]
[ROW][C]17[/C][C]100[/C][C]97.9444365377753[/C][C]2.05556346222469[/C][/ROW]
[ROW][C]18[/C][C]149[/C][C]146.842209626879[/C][C]2.15779037312072[/C][/ROW]
[ROW][C]19[/C][C]122[/C][C]120.36588854351[/C][C]1.63411145648981[/C][/ROW]
[ROW][C]20[/C][C]115[/C][C]116.861372626863[/C][C]-1.86137262686265[/C][/ROW]
[ROW][C]21[/C][C]86[/C][C]81.5969445266929[/C][C]4.4030554733071[/C][/ROW]
[ROW][C]22[/C][C]124[/C][C]123.818568385264[/C][C]0.181431614735964[/C][/ROW]
[ROW][C]23[/C][C]69[/C][C]66.0910986373292[/C][C]2.90890136267079[/C][/ROW]
[ROW][C]24[/C][C]117[/C][C]116.153671217913[/C][C]0.84632878208696[/C][/ROW]
[ROW][C]25[/C][C]113[/C][C]112.344321993923[/C][C]0.655678006076705[/C][/ROW]
[ROW][C]26[/C][C]123[/C][C]120.018078580129[/C][C]2.98192141987078[/C][/ROW]
[ROW][C]27[/C][C]123[/C][C]121.36344205731[/C][C]1.63655794268983[/C][/ROW]
[ROW][C]28[/C][C]84[/C][C]80.0506707041745[/C][C]3.94932929582549[/C][/ROW]
[ROW][C]29[/C][C]97[/C][C]88.7396820885871[/C][C]8.26031791141293[/C][/ROW]
[ROW][C]30[/C][C]121[/C][C]118.864009238262[/C][C]2.13599076173784[/C][/ROW]
[ROW][C]31[/C][C]132[/C][C]129.163673485253[/C][C]2.83632651474738[/C][/ROW]
[ROW][C]32[/C][C]119[/C][C]117.293928598697[/C][C]1.70607140130332[/C][/ROW]
[ROW][C]33[/C][C]98[/C][C]104.724426847357[/C][C]-6.72442684735705[/C][/ROW]
[ROW][C]34[/C][C]87[/C][C]88.8657171680459[/C][C]-1.86571716804586[/C][/ROW]
[ROW][C]35[/C][C]101[/C][C]95.9304709836538[/C][C]5.0695290163462[/C][/ROW]
[ROW][C]36[/C][C]115[/C][C]115.584468498981[/C][C]-0.584468498980854[/C][/ROW]
[ROW][C]37[/C][C]109[/C][C]107.630978752828[/C][C]1.36902124717192[/C][/ROW]
[ROW][C]38[/C][C]109[/C][C]106.634365361553[/C][C]2.36563463844743[/C][/ROW]
[ROW][C]39[/C][C]159[/C][C]156.541044940824[/C][C]2.45895505917592[/C][/ROW]
[ROW][C]40[/C][C]129[/C][C]127.562254316659[/C][C]1.43774568334108[/C][/ROW]
[ROW][C]41[/C][C]119[/C][C]119.661131078527[/C][C]-0.661131078526524[/C][/ROW]
[ROW][C]42[/C][C]119[/C][C]116.616422032173[/C][C]2.3835779678274[/C][/ROW]
[ROW][C]43[/C][C]122[/C][C]120.208500907268[/C][C]1.79149909273179[/C][/ROW]
[ROW][C]44[/C][C]131[/C][C]129.806946076584[/C][C]1.1930539234157[/C][/ROW]
[ROW][C]45[/C][C]120[/C][C]115.557831836647[/C][C]4.44216816335289[/C][/ROW]
[ROW][C]46[/C][C]82[/C][C]90.0037997207631[/C][C]-8.00379972076305[/C][/ROW]
[ROW][C]47[/C][C]86[/C][C]83.0890810673924[/C][C]2.91091893260764[/C][/ROW]
[ROW][C]48[/C][C]105[/C][C]106.79425739792[/C][C]-1.7942573979201[/C][/ROW]
[ROW][C]49[/C][C]114[/C][C]115.58861257183[/C][C]-1.58861257182999[/C][/ROW]
[ROW][C]50[/C][C]100[/C][C]99.4157201947683[/C][C]0.584279805231704[/C][/ROW]
[ROW][C]51[/C][C]100[/C][C]101.767657179359[/C][C]-1.76765717935937[/C][/ROW]
[ROW][C]52[/C][C]99[/C][C]96.7241538032707[/C][C]2.27584619672927[/C][/ROW]
[ROW][C]53[/C][C]132[/C][C]132.025736349002[/C][C]-0.0257363490018999[/C][/ROW]
[ROW][C]54[/C][C]82[/C][C]82.1362620502506[/C][C]-0.136262050250589[/C][/ROW]
[ROW][C]55[/C][C]132[/C][C]127.508554645844[/C][C]4.49144535415597[/C][/ROW]
[ROW][C]56[/C][C]107[/C][C]105.782492178953[/C][C]1.21750782104682[/C][/ROW]
[ROW][C]57[/C][C]114[/C][C]112.823155157154[/C][C]1.17684484284604[/C][/ROW]
[ROW][C]58[/C][C]110[/C][C]107.009308620331[/C][C]2.99069137966857[/C][/ROW]
[ROW][C]59[/C][C]105[/C][C]100.798206941675[/C][C]4.20179305832455[/C][/ROW]
[ROW][C]60[/C][C]121[/C][C]113.156377792832[/C][C]7.8436222071677[/C][/ROW]
[ROW][C]61[/C][C]109[/C][C]104.594288933985[/C][C]4.40571106601501[/C][/ROW]
[ROW][C]62[/C][C]106[/C][C]103.867041975507[/C][C]2.13295802449278[/C][/ROW]
[ROW][C]63[/C][C]124[/C][C]121.92406346826[/C][C]2.07593653174037[/C][/ROW]
[ROW][C]64[/C][C]120[/C][C]118.628647652714[/C][C]1.37135234728557[/C][/ROW]
[ROW][C]65[/C][C]91[/C][C]102.548349856425[/C][C]-11.5483498564253[/C][/ROW]
[ROW][C]66[/C][C]126[/C][C]124.372572511141[/C][C]1.6274274888586[/C][/ROW]
[ROW][C]67[/C][C]138[/C][C]137.939721926662[/C][C]0.0602780733383974[/C][/ROW]
[ROW][C]68[/C][C]118[/C][C]114.967025662328[/C][C]3.03297433767232[/C][/ROW]
[ROW][C]69[/C][C]128[/C][C]125.595400735691[/C][C]2.40459926430931[/C][/ROW]
[ROW][C]70[/C][C]98[/C][C]97.765909421732[/C][C]0.234090578268007[/C][/ROW]
[ROW][C]71[/C][C]133[/C][C]132.561621938927[/C][C]0.438378061072565[/C][/ROW]
[ROW][C]72[/C][C]130[/C][C]128.205345862902[/C][C]1.79465413709768[/C][/ROW]
[ROW][C]73[/C][C]103[/C][C]99.9240804448755[/C][C]3.0759195551245[/C][/ROW]
[ROW][C]74[/C][C]124[/C][C]127.788547522069[/C][C]-3.78854752206897[/C][/ROW]
[ROW][C]75[/C][C]142[/C][C]140.071323230259[/C][C]1.92867676974132[/C][/ROW]
[ROW][C]76[/C][C]96[/C][C]97.9805272451545[/C][C]-1.9805272451545[/C][/ROW]
[ROW][C]77[/C][C]93[/C][C]92.9661698764337[/C][C]0.0338301235663122[/C][/ROW]
[ROW][C]78[/C][C]129[/C][C]129.523980166593[/C][C]-0.523980166593414[/C][/ROW]
[ROW][C]79[/C][C]150[/C][C]148.356939120306[/C][C]1.64306087969429[/C][/ROW]
[ROW][C]80[/C][C]88[/C][C]84.9876518130715[/C][C]3.01234818692851[/C][/ROW]
[ROW][C]81[/C][C]125[/C][C]124.971432716452[/C][C]0.0285672835482621[/C][/ROW]
[ROW][C]82[/C][C]92[/C][C]89.5748370737747[/C][C]2.42516292622534[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]110.598118418592[/C][C]-110.598118418592[/C][/ROW]
[ROW][C]84[/C][C]117[/C][C]115.963914005676[/C][C]1.03608599432353[/C][/ROW]
[ROW][C]85[/C][C]112[/C][C]112.915906380602[/C][C]-0.91590638060187[/C][/ROW]
[ROW][C]86[/C][C]144[/C][C]140.498862424202[/C][C]3.5011375757977[/C][/ROW]
[ROW][C]87[/C][C]130[/C][C]129.260926484997[/C][C]0.739073515002503[/C][/ROW]
[ROW][C]88[/C][C]87[/C][C]92.3958054976297[/C][C]-5.39580549762971[/C][/ROW]
[ROW][C]89[/C][C]92[/C][C]94.095226981036[/C][C]-2.09522698103602[/C][/ROW]
[ROW][C]90[/C][C]114[/C][C]116.720213320716[/C][C]-2.72021332071569[/C][/ROW]
[ROW][C]91[/C][C]81[/C][C]76.6225771604631[/C][C]4.37742283953692[/C][/ROW]
[ROW][C]92[/C][C]127[/C][C]125.197216002368[/C][C]1.80278399763185[/C][/ROW]
[ROW][C]93[/C][C]115[/C][C]115.570237525339[/C][C]-0.570237525338711[/C][/ROW]
[ROW][C]94[/C][C]123[/C][C]122.726853293607[/C][C]0.273146706393437[/C][/ROW]
[ROW][C]95[/C][C]115[/C][C]116.110805472864[/C][C]-1.11080547286358[/C][/ROW]
[ROW][C]96[/C][C]117[/C][C]118.227524370223[/C][C]-1.22752437022305[/C][/ROW]
[ROW][C]97[/C][C]117[/C][C]114.039899429275[/C][C]2.96010057072466[/C][/ROW]
[ROW][C]98[/C][C]103[/C][C]101.539102169428[/C][C]1.46089783057155[/C][/ROW]
[ROW][C]99[/C][C]108[/C][C]104.938777293202[/C][C]3.06122270679777[/C][/ROW]
[ROW][C]100[/C][C]139[/C][C]138.511161470531[/C][C]0.488838529468914[/C][/ROW]
[ROW][C]101[/C][C]113[/C][C]110.179412927[/C][C]2.82058707299968[/C][/ROW]
[ROW][C]102[/C][C]97[/C][C]95.2754509222919[/C][C]1.72454907770808[/C][/ROW]
[ROW][C]103[/C][C]117[/C][C]116.573315769869[/C][C]0.426684230130839[/C][/ROW]
[ROW][C]104[/C][C]133[/C][C]130.524474914243[/C][C]2.47552508575696[/C][/ROW]
[ROW][C]105[/C][C]115[/C][C]113.384543687159[/C][C]1.61545631284078[/C][/ROW]
[ROW][C]106[/C][C]103[/C][C]107.395096705635[/C][C]-4.39509670563461[/C][/ROW]
[ROW][C]107[/C][C]95[/C][C]94.7106623679529[/C][C]0.289337632047106[/C][/ROW]
[ROW][C]108[/C][C]117[/C][C]117.522113889542[/C][C]-0.522113889541712[/C][/ROW]
[ROW][C]109[/C][C]113[/C][C]112.115412934339[/C][C]0.884587065660946[/C][/ROW]
[ROW][C]110[/C][C]127[/C][C]128.066770712986[/C][C]-1.06677071298641[/C][/ROW]
[ROW][C]111[/C][C]126[/C][C]125.962454357034[/C][C]0.0375456429659519[/C][/ROW]
[ROW][C]112[/C][C]119[/C][C]118.541802228224[/C][C]0.458197771776096[/C][/ROW]
[ROW][C]113[/C][C]97[/C][C]92.2051386449574[/C][C]4.79486135504258[/C][/ROW]
[ROW][C]114[/C][C]105[/C][C]104.578666216142[/C][C]0.421333783857983[/C][/ROW]
[ROW][C]115[/C][C]140[/C][C]139.466142645923[/C][C]0.533857354077363[/C][/ROW]
[ROW][C]116[/C][C]91[/C][C]87.1386541606353[/C][C]3.86134583936471[/C][/ROW]
[ROW][C]117[/C][C]112[/C][C]111.57808114001[/C][C]0.421918859990271[/C][/ROW]
[ROW][C]118[/C][C]113[/C][C]115.484396153083[/C][C]-2.48439615308312[/C][/ROW]
[ROW][C]119[/C][C]102[/C][C]97.7102210471213[/C][C]4.28977895287868[/C][/ROW]
[ROW][C]120[/C][C]92[/C][C]91.3712714057327[/C][C]0.6287285942673[/C][/ROW]
[ROW][C]121[/C][C]98[/C][C]101.199200838676[/C][C]-3.19920083867592[/C][/ROW]
[ROW][C]122[/C][C]122[/C][C]123.641182625123[/C][C]-1.64118262512309[/C][/ROW]
[ROW][C]123[/C][C]100[/C][C]99.4441533098434[/C][C]0.555846690156567[/C][/ROW]
[ROW][C]124[/C][C]84[/C][C]85.4698959892236[/C][C]-1.46989598922363[/C][/ROW]
[ROW][C]125[/C][C]142[/C][C]141.239867291523[/C][C]0.760132708477511[/C][/ROW]
[ROW][C]126[/C][C]124[/C][C]122.186347356347[/C][C]1.81365264365273[/C][/ROW]
[ROW][C]127[/C][C]137[/C][C]132.845157819476[/C][C]4.15484218052354[/C][/ROW]
[ROW][C]128[/C][C]105[/C][C]105.161303760107[/C][C]-0.161303760106685[/C][/ROW]
[ROW][C]129[/C][C]106[/C][C]101.892595491541[/C][C]4.10740450845878[/C][/ROW]
[ROW][C]130[/C][C]125[/C][C]121.582569489236[/C][C]3.41743051076442[/C][/ROW]
[ROW][C]131[/C][C]104[/C][C]102.925635066155[/C][C]1.07436493384524[/C][/ROW]
[ROW][C]132[/C][C]130[/C][C]131.663886762145[/C][C]-1.66388676214529[/C][/ROW]
[ROW][C]133[/C][C]79[/C][C]81.9633315484417[/C][C]-2.96333154844165[/C][/ROW]
[ROW][C]134[/C][C]108[/C][C]106.436563503773[/C][C]1.56343649622669[/C][/ROW]
[ROW][C]135[/C][C]136[/C][C]132.974657444942[/C][C]3.02534255505752[/C][/ROW]
[ROW][C]136[/C][C]98[/C][C]99.0778717432292[/C][C]-1.07787174322923[/C][/ROW]
[ROW][C]137[/C][C]120[/C][C]129.576157281082[/C][C]-9.57615728108176[/C][/ROW]
[ROW][C]138[/C][C]108[/C][C]108.535190688443[/C][C]-0.535190688442895[/C][/ROW]
[ROW][C]139[/C][C]139[/C][C]137.943776297138[/C][C]1.05622370286216[/C][/ROW]
[ROW][C]140[/C][C]123[/C][C]123.795694427665[/C][C]-0.795694427664565[/C][/ROW]
[ROW][C]141[/C][C]90[/C][C]87.9437804596581[/C][C]2.05621954034191[/C][/ROW]
[ROW][C]142[/C][C]119[/C][C]119.986506730529[/C][C]-0.986506730529295[/C][/ROW]
[ROW][C]143[/C][C]105[/C][C]105.338880984554[/C][C]-0.338880984554217[/C][/ROW]
[ROW][C]144[/C][C]110[/C][C]105.262963682573[/C][C]4.73703631742749[/C][/ROW]
[ROW][C]145[/C][C]135[/C][C]137.57183718597[/C][C]-2.57183718596958[/C][/ROW]
[ROW][C]146[/C][C]101[/C][C]96.5146714771919[/C][C]4.48532852280809[/C][/ROW]
[ROW][C]147[/C][C]114[/C][C]114.122954154496[/C][C]-0.122954154496377[/C][/ROW]
[ROW][C]148[/C][C]118[/C][C]117.805690595691[/C][C]0.194309404308786[/C][/ROW]
[ROW][C]149[/C][C]120[/C][C]116.065549608791[/C][C]3.93445039120937[/C][/ROW]
[ROW][C]150[/C][C]108[/C][C]112.017863467338[/C][C]-4.01786346733759[/C][/ROW]
[ROW][C]151[/C][C]114[/C][C]111.562215281688[/C][C]2.43778471831164[/C][/ROW]
[ROW][C]152[/C][C]122[/C][C]122.919468394988[/C][C]-0.919468394987799[/C][/ROW]
[ROW][C]153[/C][C]132[/C][C]129.577611358102[/C][C]2.42238864189766[/C][/ROW]
[ROW][C]154[/C][C]130[/C][C]128.390497448442[/C][C]1.6095025515577[/C][/ROW]
[ROW][C]155[/C][C]130[/C][C]130.875467999204[/C][C]-0.875467999203919[/C][/ROW]
[ROW][C]156[/C][C]112[/C][C]110.069972862933[/C][C]1.93002713706731[/C][/ROW]
[ROW][C]157[/C][C]114[/C][C]115.714728359841[/C][C]-1.71472835984057[/C][/ROW]
[ROW][C]158[/C][C]103[/C][C]110.709750340761[/C][C]-7.7097503407606[/C][/ROW]
[ROW][C]159[/C][C]115[/C][C]116.997766396834[/C][C]-1.99776639683364[/C][/ROW]
[ROW][C]160[/C][C]108[/C][C]107.538687714042[/C][C]0.461312285958128[/C][/ROW]
[ROW][C]161[/C][C]94[/C][C]92.9497594055143[/C][C]1.05024059448572[/C][/ROW]
[ROW][C]162[/C][C]105[/C][C]104.51378648361[/C][C]0.486213516389789[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197889&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197889&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
1127123.6516285710323.34837142896806
2108106.2961445135461.70385548645353
3110109.8079385754940.192061424506283
4102101.3925538332360.60744616676405
5104108.966026330888-4.96602633088834
6140139.465929330930.534070669070478
7112114.28078042077-2.28078042076979
8115117.654965256734-2.65496525673362
9121118.3095739683242.6904260316762
10112109.4082936229142.59170637708583
11118114.630576494413.36942350558963
12122119.24772684372.75227315630004
13105105.102231686165-0.102231686164574
14111111.740507603085-0.740507603085309
15151149.3803120906491.61968790935123
16106107.36674072436-1.36674072436019
1710097.94443653777532.05556346222469
18149146.8422096268792.15779037312072
19122120.365888543511.63411145648981
20115116.861372626863-1.86137262686265
218681.59694452669294.4030554733071
22124123.8185683852640.181431614735964
236966.09109863732922.90890136267079
24117116.1536712179130.84632878208696
25113112.3443219939230.655678006076705
26123120.0180785801292.98192141987078
27123121.363442057311.63655794268983
288480.05067070417453.94932929582549
299788.73968208858718.26031791141293
30121118.8640092382622.13599076173784
31132129.1636734852532.83632651474738
32119117.2939285986971.70607140130332
3398104.724426847357-6.72442684735705
348788.8657171680459-1.86571716804586
3510195.93047098365385.0695290163462
36115115.584468498981-0.584468498980854
37109107.6309787528281.36902124717192
38109106.6343653615532.36563463844743
39159156.5410449408242.45895505917592
40129127.5622543166591.43774568334108
41119119.661131078527-0.661131078526524
42119116.6164220321732.3835779678274
43122120.2085009072681.79149909273179
44131129.8069460765841.1930539234157
45120115.5578318366474.44216816335289
468290.0037997207631-8.00379972076305
478683.08908106739242.91091893260764
48105106.79425739792-1.7942573979201
49114115.58861257183-1.58861257182999
5010099.41572019476830.584279805231704
51100101.767657179359-1.76765717935937
529996.72415380327072.27584619672927
53132132.025736349002-0.0257363490018999
548282.1362620502506-0.136262050250589
55132127.5085546458444.49144535415597
56107105.7824921789531.21750782104682
57114112.8231551571541.17684484284604
58110107.0093086203312.99069137966857
59105100.7982069416754.20179305832455
60121113.1563777928327.8436222071677
61109104.5942889339854.40571106601501
62106103.8670419755072.13295802449278
63124121.924063468262.07593653174037
64120118.6286476527141.37135234728557
6591102.548349856425-11.5483498564253
66126124.3725725111411.6274274888586
67138137.9397219266620.0602780733383974
68118114.9670256623283.03297433767232
69128125.5954007356912.40459926430931
709897.7659094217320.234090578268007
71133132.5616219389270.438378061072565
72130128.2053458629021.79465413709768
7310399.92408044487553.0759195551245
74124127.788547522069-3.78854752206897
75142140.0713232302591.92867676974132
769697.9805272451545-1.9805272451545
779392.96616987643370.0338301235663122
78129129.523980166593-0.523980166593414
79150148.3569391203061.64306087969429
808884.98765181307153.01234818692851
81125124.9714327164520.0285672835482621
829289.57483707377472.42516292622534
830110.598118418592-110.598118418592
84117115.9639140056761.03608599432353
85112112.915906380602-0.91590638060187
86144140.4988624242023.5011375757977
87130129.2609264849970.739073515002503
888792.3958054976297-5.39580549762971
899294.095226981036-2.09522698103602
90114116.720213320716-2.72021332071569
918176.62257716046314.37742283953692
92127125.1972160023681.80278399763185
93115115.570237525339-0.570237525338711
94123122.7268532936070.273146706393437
95115116.110805472864-1.11080547286358
96117118.227524370223-1.22752437022305
97117114.0398994292752.96010057072466
98103101.5391021694281.46089783057155
99108104.9387772932023.06122270679777
100139138.5111614705310.488838529468914
101113110.1794129272.82058707299968
1029795.27545092229191.72454907770808
103117116.5733157698690.426684230130839
104133130.5244749142432.47552508575696
105115113.3845436871591.61545631284078
106103107.395096705635-4.39509670563461
1079594.71066236795290.289337632047106
108117117.522113889542-0.522113889541712
109113112.1154129343390.884587065660946
110127128.066770712986-1.06677071298641
111126125.9624543570340.0375456429659519
112119118.5418022282240.458197771776096
1139792.20513864495744.79486135504258
114105104.5786662161420.421333783857983
115140139.4661426459230.533857354077363
1169187.13865416063533.86134583936471
117112111.578081140010.421918859990271
118113115.484396153083-2.48439615308312
11910297.71022104712134.28977895287868
1209291.37127140573270.6287285942673
12198101.199200838676-3.19920083867592
122122123.641182625123-1.64118262512309
12310099.44415330984340.555846690156567
1248485.4698959892236-1.46989598922363
125142141.2398672915230.760132708477511
126124122.1863473563471.81365264365273
127137132.8451578194764.15484218052354
128105105.161303760107-0.161303760106685
129106101.8925954915414.10740450845878
130125121.5825694892363.41743051076442
131104102.9256350661551.07436493384524
132130131.663886762145-1.66388676214529
1337981.9633315484417-2.96333154844165
134108106.4365635037731.56343649622669
135136132.9746574449423.02534255505752
1369899.0778717432292-1.07787174322923
137120129.576157281082-9.57615728108176
138108108.535190688443-0.535190688442895
139139137.9437762971381.05622370286216
140123123.795694427665-0.795694427664565
1419087.94378045965812.05621954034191
142119119.986506730529-0.986506730529295
143105105.338880984554-0.338880984554217
144110105.2629636825734.73703631742749
145135137.57183718597-2.57183718596958
14610196.51467147719194.48532852280809
147114114.122954154496-0.122954154496377
148118117.8056905956910.194309404308786
149120116.0655496087913.93445039120937
150108112.017863467338-4.01786346733759
151114111.5622152816882.43778471831164
152122122.919468394988-0.919468394987799
153132129.5776113581022.42238864189766
154130128.3904974484421.6095025515577
155130130.875467999204-0.875467999203919
156112110.0699728629331.93002713706731
157114115.714728359841-1.71472835984057
158103110.709750340761-7.7097503407606
159115116.997766396834-1.99776639683364
160108107.5386877140420.461312285958128
1619492.94975940551431.05024059448572
162105104.513786483610.486213516389789







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
140.02396965441007120.04793930882014240.976030345589929
150.005358602432547970.01071720486509590.994641397567452
160.001454378166585210.002908756333170420.998545621833415
170.0003429973946402260.0006859947892804530.99965700260536
186.3778812419201e-050.0001275576248384020.999936221187581
191.21534677326947e-052.43069354653894e-050.999987846532267
204.4938452928295e-068.98769058565901e-060.999995506154707
211.00232791600189e-062.00465583200378e-060.999998997672084
221.70831615159062e-073.41663230318124e-070.999999829168385
232.97945546462807e-085.95891092925613e-080.999999970205445
246.37967948613228e-091.27593589722646e-080.999999993620321
251.02837696928725e-092.0567539385745e-090.999999998971623
263.01633777584794e-106.03267555169588e-100.999999999698366
275.38249265779344e-111.07649853155869e-100.999999999946175
281.3738833775545e-112.747766755109e-110.999999999986261
297.89820095938901e-121.5796401918778e-110.999999999992102
301.68570439826079e-123.37140879652159e-120.999999999998314
312.72058850487401e-135.44117700974803e-130.999999999999728
325.20217083963275e-141.04043416792655e-130.999999999999948
331.97352421224507e-113.94704842449013e-110.999999999980265
341.40832449061252e-112.81664898122504e-110.999999999985917
354.51578589817503e-129.03157179635006e-120.999999999995484
361.25152470122671e-122.50304940245343e-120.999999999998748
372.6259665244754e-135.25193304895081e-130.999999999999737
388.729590610734e-141.7459181221468e-130.999999999999913
391.91835212637623e-143.83670425275245e-140.999999999999981
404.04660911888305e-158.0932182377661e-150.999999999999996
412.16326911059621e-154.32653822119243e-150.999999999999998
424.39977569155814e-168.79955138311629e-161
431.02773023469436e-162.05546046938872e-161
441.98289796310771e-173.96579592621543e-171
453.96279693739336e-187.92559387478672e-181
464.1691340533617e-158.3382681067234e-150.999999999999996
471.09108829756167e-152.18217659512333e-150.999999999999999
482.09945687304526e-154.19891374609052e-150.999999999999998
495.25854445757348e-161.0517088915147e-150.999999999999999
501.50543850995029e-163.01087701990058e-161
515.43052195220989e-171.08610439044198e-161
521.74852261415951e-173.49704522831903e-171
534.07013531644018e-188.14027063288036e-181
541.36461009661788e-182.72922019323577e-181
554.39234843890346e-198.78469687780692e-191
561.00325472170232e-192.00650944340463e-191
572.46369629210669e-204.92739258421339e-201
585.6669832561266e-211.13339665122532e-201
591.49847206757507e-212.99694413515014e-211
607.53859673051674e-221.50771934610335e-211
612.00890103940634e-224.01780207881268e-221
624.48193792483507e-238.96387584967014e-231
639.94008575526246e-241.98801715105249e-231
642.12444919434184e-244.24889838868367e-241
651.30527969005526e-212.61055938011052e-211
663.5034271201901e-227.0068542403802e-221
678.13332776162886e-231.62666555232577e-221
682.15509241618001e-234.31018483236001e-231
696.39270057495006e-241.27854011499001e-231
701.60628318888788e-243.21256637777576e-241
713.56983424713697e-257.13966849427395e-251
728.37694963296418e-261.67538992659284e-251
732.0322452059574e-264.0644904119148e-261
749.15269773864942e-271.83053954772988e-261
752.04386568220199e-274.08773136440397e-271
764.47416900739293e-288.94833801478586e-281
779.47120686545846e-291.89424137309169e-281
781.93986343811808e-293.87972687623617e-291
794.16235063250455e-308.32470126500911e-301
801.39223722575159e-302.78447445150319e-301
812.86162189356279e-315.72324378712557e-311
826.77694904524301e-321.3553898090486e-311
8314.00972730454917e-372.00486365227458e-37
8412.38751738223774e-361.19375869111887e-36
8511.2549036027188e-356.27451801359401e-36
8616.26770044767725e-353.13385022383863e-35
8713.78844255908413e-341.89422127954207e-34
8815.57293891558337e-352.78646945779168e-35
8913.37974275993359e-341.68987137996679e-34
9011.91855174146776e-339.59275870733879e-34
9114.86266127268583e-332.43133063634291e-33
9213.00597553131362e-321.50298776565681e-32
9311.85286146499672e-319.2643073249836e-32
9416.72431208801648e-313.36215604400824e-31
9514.04995519422284e-302.02497759711142e-30
9612.06090055710717e-291.03045027855358e-29
9711.1224603987225e-285.61230199361251e-29
9816.01684849523579e-283.00842424761789e-28
9912.83908493051307e-271.41954246525653e-27
10017.96854059295178e-273.98427029647589e-27
10112.99835581513583e-261.49917790756791e-26
10211.5276422736053e-257.6382113680265e-26
10317.52410698451603e-253.76205349225802e-25
10411.78948197273128e-248.94740986365641e-25
10518.42692973510711e-244.21346486755355e-24
10613.35706534161458e-231.67853267080729e-23
10711.45883489504598e-227.29417447522988e-23
10817.5749766001614e-223.7874883000807e-22
10912.4442290672494e-211.2221145336247e-21
11014.81677609008802e-212.40838804504401e-21
11112.46254919129892e-201.23127459564946e-20
11211.11211846965019e-195.56059234825094e-20
11311.66429205595709e-198.32146027978544e-20
11417.41428031001483e-193.70714015500742e-19
11513.46729132258764e-181.73364566129382e-18
11611.68760367668775e-178.43801838343876e-18
11716.10908308505733e-173.05454154252866e-17
11812.77476519560267e-161.38738259780133e-16
1190.9999999999999991.19183293754775e-155.95916468773876e-16
1200.9999999999999984.74706712939387e-152.37353356469694e-15
1210.9999999999999892.13752811737051e-141.06876405868526e-14
1220.9999999999999519.87435391904034e-144.93717695952017e-14
1230.9999999999998283.43035755733082e-131.71517877866541e-13
1240.9999999999998343.3139743066492e-131.6569871533246e-13
1250.9999999999994021.19619006444394e-125.98095032221969e-13
1260.9999999999973115.37792000926836e-122.68896000463418e-12
1270.9999999999907971.84059090932105e-119.20295454660524e-12
1280.999999999958798.24194577411218e-114.12097288705609e-11
1290.9999999998393663.21267525878348e-101.60633762939174e-10
1300.999999999329051.34189908846504e-096.70949544232521e-10
1310.9999999972195125.5609766994329e-092.78048834971645e-09
1320.99999998907612.18477995180862e-081.09238997590431e-08
1330.9999999942097361.15805272116874e-085.79026360584372e-09
1340.9999999755214184.89571640270534e-082.44785820135267e-08
1350.9999999231228081.53754384859687e-077.68771924298436e-08
1360.9999998436354773.12729045850635e-071.56364522925317e-07
1370.9999997606666634.78666674482053e-072.39333337241027e-07
1380.9999991374760261.7250479484851e-068.62523974242551e-07
1390.9999983613175163.2773649676723e-061.63868248383615e-06
1400.9999928488446851.43023106307818e-057.15115531539089e-06
1410.9999758399495994.83201008010809e-052.41600504005405e-05
1420.9999090691080760.0001818617838477069.09308919238528e-05
1430.9997904086694890.0004191826610215190.000209591330510759
1440.9992862259139860.001427548172028890.000713774086014446
1450.9974822719330270.005035456133945570.00251772806697278
1460.991601266767430.01679746646514090.00839873323257045
1470.9768914475458940.04621710490821160.0231085524541058
1480.9773883830275430.04522323394491480.0226116169724574

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
14 & 0.0239696544100712 & 0.0479393088201424 & 0.976030345589929 \tabularnewline
15 & 0.00535860243254797 & 0.0107172048650959 & 0.994641397567452 \tabularnewline
16 & 0.00145437816658521 & 0.00290875633317042 & 0.998545621833415 \tabularnewline
17 & 0.000342997394640226 & 0.000685994789280453 & 0.99965700260536 \tabularnewline
18 & 6.3778812419201e-05 & 0.000127557624838402 & 0.999936221187581 \tabularnewline
19 & 1.21534677326947e-05 & 2.43069354653894e-05 & 0.999987846532267 \tabularnewline
20 & 4.4938452928295e-06 & 8.98769058565901e-06 & 0.999995506154707 \tabularnewline
21 & 1.00232791600189e-06 & 2.00465583200378e-06 & 0.999998997672084 \tabularnewline
22 & 1.70831615159062e-07 & 3.41663230318124e-07 & 0.999999829168385 \tabularnewline
23 & 2.97945546462807e-08 & 5.95891092925613e-08 & 0.999999970205445 \tabularnewline
24 & 6.37967948613228e-09 & 1.27593589722646e-08 & 0.999999993620321 \tabularnewline
25 & 1.02837696928725e-09 & 2.0567539385745e-09 & 0.999999998971623 \tabularnewline
26 & 3.01633777584794e-10 & 6.03267555169588e-10 & 0.999999999698366 \tabularnewline
27 & 5.38249265779344e-11 & 1.07649853155869e-10 & 0.999999999946175 \tabularnewline
28 & 1.3738833775545e-11 & 2.747766755109e-11 & 0.999999999986261 \tabularnewline
29 & 7.89820095938901e-12 & 1.5796401918778e-11 & 0.999999999992102 \tabularnewline
30 & 1.68570439826079e-12 & 3.37140879652159e-12 & 0.999999999998314 \tabularnewline
31 & 2.72058850487401e-13 & 5.44117700974803e-13 & 0.999999999999728 \tabularnewline
32 & 5.20217083963275e-14 & 1.04043416792655e-13 & 0.999999999999948 \tabularnewline
33 & 1.97352421224507e-11 & 3.94704842449013e-11 & 0.999999999980265 \tabularnewline
34 & 1.40832449061252e-11 & 2.81664898122504e-11 & 0.999999999985917 \tabularnewline
35 & 4.51578589817503e-12 & 9.03157179635006e-12 & 0.999999999995484 \tabularnewline
36 & 1.25152470122671e-12 & 2.50304940245343e-12 & 0.999999999998748 \tabularnewline
37 & 2.6259665244754e-13 & 5.25193304895081e-13 & 0.999999999999737 \tabularnewline
38 & 8.729590610734e-14 & 1.7459181221468e-13 & 0.999999999999913 \tabularnewline
39 & 1.91835212637623e-14 & 3.83670425275245e-14 & 0.999999999999981 \tabularnewline
40 & 4.04660911888305e-15 & 8.0932182377661e-15 & 0.999999999999996 \tabularnewline
41 & 2.16326911059621e-15 & 4.32653822119243e-15 & 0.999999999999998 \tabularnewline
42 & 4.39977569155814e-16 & 8.79955138311629e-16 & 1 \tabularnewline
43 & 1.02773023469436e-16 & 2.05546046938872e-16 & 1 \tabularnewline
44 & 1.98289796310771e-17 & 3.96579592621543e-17 & 1 \tabularnewline
45 & 3.96279693739336e-18 & 7.92559387478672e-18 & 1 \tabularnewline
46 & 4.1691340533617e-15 & 8.3382681067234e-15 & 0.999999999999996 \tabularnewline
47 & 1.09108829756167e-15 & 2.18217659512333e-15 & 0.999999999999999 \tabularnewline
48 & 2.09945687304526e-15 & 4.19891374609052e-15 & 0.999999999999998 \tabularnewline
49 & 5.25854445757348e-16 & 1.0517088915147e-15 & 0.999999999999999 \tabularnewline
50 & 1.50543850995029e-16 & 3.01087701990058e-16 & 1 \tabularnewline
51 & 5.43052195220989e-17 & 1.08610439044198e-16 & 1 \tabularnewline
52 & 1.74852261415951e-17 & 3.49704522831903e-17 & 1 \tabularnewline
53 & 4.07013531644018e-18 & 8.14027063288036e-18 & 1 \tabularnewline
54 & 1.36461009661788e-18 & 2.72922019323577e-18 & 1 \tabularnewline
55 & 4.39234843890346e-19 & 8.78469687780692e-19 & 1 \tabularnewline
56 & 1.00325472170232e-19 & 2.00650944340463e-19 & 1 \tabularnewline
57 & 2.46369629210669e-20 & 4.92739258421339e-20 & 1 \tabularnewline
58 & 5.6669832561266e-21 & 1.13339665122532e-20 & 1 \tabularnewline
59 & 1.49847206757507e-21 & 2.99694413515014e-21 & 1 \tabularnewline
60 & 7.53859673051674e-22 & 1.50771934610335e-21 & 1 \tabularnewline
61 & 2.00890103940634e-22 & 4.01780207881268e-22 & 1 \tabularnewline
62 & 4.48193792483507e-23 & 8.96387584967014e-23 & 1 \tabularnewline
63 & 9.94008575526246e-24 & 1.98801715105249e-23 & 1 \tabularnewline
64 & 2.12444919434184e-24 & 4.24889838868367e-24 & 1 \tabularnewline
65 & 1.30527969005526e-21 & 2.61055938011052e-21 & 1 \tabularnewline
66 & 3.5034271201901e-22 & 7.0068542403802e-22 & 1 \tabularnewline
67 & 8.13332776162886e-23 & 1.62666555232577e-22 & 1 \tabularnewline
68 & 2.15509241618001e-23 & 4.31018483236001e-23 & 1 \tabularnewline
69 & 6.39270057495006e-24 & 1.27854011499001e-23 & 1 \tabularnewline
70 & 1.60628318888788e-24 & 3.21256637777576e-24 & 1 \tabularnewline
71 & 3.56983424713697e-25 & 7.13966849427395e-25 & 1 \tabularnewline
72 & 8.37694963296418e-26 & 1.67538992659284e-25 & 1 \tabularnewline
73 & 2.0322452059574e-26 & 4.0644904119148e-26 & 1 \tabularnewline
74 & 9.15269773864942e-27 & 1.83053954772988e-26 & 1 \tabularnewline
75 & 2.04386568220199e-27 & 4.08773136440397e-27 & 1 \tabularnewline
76 & 4.47416900739293e-28 & 8.94833801478586e-28 & 1 \tabularnewline
77 & 9.47120686545846e-29 & 1.89424137309169e-28 & 1 \tabularnewline
78 & 1.93986343811808e-29 & 3.87972687623617e-29 & 1 \tabularnewline
79 & 4.16235063250455e-30 & 8.32470126500911e-30 & 1 \tabularnewline
80 & 1.39223722575159e-30 & 2.78447445150319e-30 & 1 \tabularnewline
81 & 2.86162189356279e-31 & 5.72324378712557e-31 & 1 \tabularnewline
82 & 6.77694904524301e-32 & 1.3553898090486e-31 & 1 \tabularnewline
83 & 1 & 4.00972730454917e-37 & 2.00486365227458e-37 \tabularnewline
84 & 1 & 2.38751738223774e-36 & 1.19375869111887e-36 \tabularnewline
85 & 1 & 1.2549036027188e-35 & 6.27451801359401e-36 \tabularnewline
86 & 1 & 6.26770044767725e-35 & 3.13385022383863e-35 \tabularnewline
87 & 1 & 3.78844255908413e-34 & 1.89422127954207e-34 \tabularnewline
88 & 1 & 5.57293891558337e-35 & 2.78646945779168e-35 \tabularnewline
89 & 1 & 3.37974275993359e-34 & 1.68987137996679e-34 \tabularnewline
90 & 1 & 1.91855174146776e-33 & 9.59275870733879e-34 \tabularnewline
91 & 1 & 4.86266127268583e-33 & 2.43133063634291e-33 \tabularnewline
92 & 1 & 3.00597553131362e-32 & 1.50298776565681e-32 \tabularnewline
93 & 1 & 1.85286146499672e-31 & 9.2643073249836e-32 \tabularnewline
94 & 1 & 6.72431208801648e-31 & 3.36215604400824e-31 \tabularnewline
95 & 1 & 4.04995519422284e-30 & 2.02497759711142e-30 \tabularnewline
96 & 1 & 2.06090055710717e-29 & 1.03045027855358e-29 \tabularnewline
97 & 1 & 1.1224603987225e-28 & 5.61230199361251e-29 \tabularnewline
98 & 1 & 6.01684849523579e-28 & 3.00842424761789e-28 \tabularnewline
99 & 1 & 2.83908493051307e-27 & 1.41954246525653e-27 \tabularnewline
100 & 1 & 7.96854059295178e-27 & 3.98427029647589e-27 \tabularnewline
101 & 1 & 2.99835581513583e-26 & 1.49917790756791e-26 \tabularnewline
102 & 1 & 1.5276422736053e-25 & 7.6382113680265e-26 \tabularnewline
103 & 1 & 7.52410698451603e-25 & 3.76205349225802e-25 \tabularnewline
104 & 1 & 1.78948197273128e-24 & 8.94740986365641e-25 \tabularnewline
105 & 1 & 8.42692973510711e-24 & 4.21346486755355e-24 \tabularnewline
106 & 1 & 3.35706534161458e-23 & 1.67853267080729e-23 \tabularnewline
107 & 1 & 1.45883489504598e-22 & 7.29417447522988e-23 \tabularnewline
108 & 1 & 7.5749766001614e-22 & 3.7874883000807e-22 \tabularnewline
109 & 1 & 2.4442290672494e-21 & 1.2221145336247e-21 \tabularnewline
110 & 1 & 4.81677609008802e-21 & 2.40838804504401e-21 \tabularnewline
111 & 1 & 2.46254919129892e-20 & 1.23127459564946e-20 \tabularnewline
112 & 1 & 1.11211846965019e-19 & 5.56059234825094e-20 \tabularnewline
113 & 1 & 1.66429205595709e-19 & 8.32146027978544e-20 \tabularnewline
114 & 1 & 7.41428031001483e-19 & 3.70714015500742e-19 \tabularnewline
115 & 1 & 3.46729132258764e-18 & 1.73364566129382e-18 \tabularnewline
116 & 1 & 1.68760367668775e-17 & 8.43801838343876e-18 \tabularnewline
117 & 1 & 6.10908308505733e-17 & 3.05454154252866e-17 \tabularnewline
118 & 1 & 2.77476519560267e-16 & 1.38738259780133e-16 \tabularnewline
119 & 0.999999999999999 & 1.19183293754775e-15 & 5.95916468773876e-16 \tabularnewline
120 & 0.999999999999998 & 4.74706712939387e-15 & 2.37353356469694e-15 \tabularnewline
121 & 0.999999999999989 & 2.13752811737051e-14 & 1.06876405868526e-14 \tabularnewline
122 & 0.999999999999951 & 9.87435391904034e-14 & 4.93717695952017e-14 \tabularnewline
123 & 0.999999999999828 & 3.43035755733082e-13 & 1.71517877866541e-13 \tabularnewline
124 & 0.999999999999834 & 3.3139743066492e-13 & 1.6569871533246e-13 \tabularnewline
125 & 0.999999999999402 & 1.19619006444394e-12 & 5.98095032221969e-13 \tabularnewline
126 & 0.999999999997311 & 5.37792000926836e-12 & 2.68896000463418e-12 \tabularnewline
127 & 0.999999999990797 & 1.84059090932105e-11 & 9.20295454660524e-12 \tabularnewline
128 & 0.99999999995879 & 8.24194577411218e-11 & 4.12097288705609e-11 \tabularnewline
129 & 0.999999999839366 & 3.21267525878348e-10 & 1.60633762939174e-10 \tabularnewline
130 & 0.99999999932905 & 1.34189908846504e-09 & 6.70949544232521e-10 \tabularnewline
131 & 0.999999997219512 & 5.5609766994329e-09 & 2.78048834971645e-09 \tabularnewline
132 & 0.9999999890761 & 2.18477995180862e-08 & 1.09238997590431e-08 \tabularnewline
133 & 0.999999994209736 & 1.15805272116874e-08 & 5.79026360584372e-09 \tabularnewline
134 & 0.999999975521418 & 4.89571640270534e-08 & 2.44785820135267e-08 \tabularnewline
135 & 0.999999923122808 & 1.53754384859687e-07 & 7.68771924298436e-08 \tabularnewline
136 & 0.999999843635477 & 3.12729045850635e-07 & 1.56364522925317e-07 \tabularnewline
137 & 0.999999760666663 & 4.78666674482053e-07 & 2.39333337241027e-07 \tabularnewline
138 & 0.999999137476026 & 1.7250479484851e-06 & 8.62523974242551e-07 \tabularnewline
139 & 0.999998361317516 & 3.2773649676723e-06 & 1.63868248383615e-06 \tabularnewline
140 & 0.999992848844685 & 1.43023106307818e-05 & 7.15115531539089e-06 \tabularnewline
141 & 0.999975839949599 & 4.83201008010809e-05 & 2.41600504005405e-05 \tabularnewline
142 & 0.999909069108076 & 0.000181861783847706 & 9.09308919238528e-05 \tabularnewline
143 & 0.999790408669489 & 0.000419182661021519 & 0.000209591330510759 \tabularnewline
144 & 0.999286225913986 & 0.00142754817202889 & 0.000713774086014446 \tabularnewline
145 & 0.997482271933027 & 0.00503545613394557 & 0.00251772806697278 \tabularnewline
146 & 0.99160126676743 & 0.0167974664651409 & 0.00839873323257045 \tabularnewline
147 & 0.976891447545894 & 0.0462171049082116 & 0.0231085524541058 \tabularnewline
148 & 0.977388383027543 & 0.0452232339449148 & 0.0226116169724574 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197889&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]14[/C][C]0.0239696544100712[/C][C]0.0479393088201424[/C][C]0.976030345589929[/C][/ROW]
[ROW][C]15[/C][C]0.00535860243254797[/C][C]0.0107172048650959[/C][C]0.994641397567452[/C][/ROW]
[ROW][C]16[/C][C]0.00145437816658521[/C][C]0.00290875633317042[/C][C]0.998545621833415[/C][/ROW]
[ROW][C]17[/C][C]0.000342997394640226[/C][C]0.000685994789280453[/C][C]0.99965700260536[/C][/ROW]
[ROW][C]18[/C][C]6.3778812419201e-05[/C][C]0.000127557624838402[/C][C]0.999936221187581[/C][/ROW]
[ROW][C]19[/C][C]1.21534677326947e-05[/C][C]2.43069354653894e-05[/C][C]0.999987846532267[/C][/ROW]
[ROW][C]20[/C][C]4.4938452928295e-06[/C][C]8.98769058565901e-06[/C][C]0.999995506154707[/C][/ROW]
[ROW][C]21[/C][C]1.00232791600189e-06[/C][C]2.00465583200378e-06[/C][C]0.999998997672084[/C][/ROW]
[ROW][C]22[/C][C]1.70831615159062e-07[/C][C]3.41663230318124e-07[/C][C]0.999999829168385[/C][/ROW]
[ROW][C]23[/C][C]2.97945546462807e-08[/C][C]5.95891092925613e-08[/C][C]0.999999970205445[/C][/ROW]
[ROW][C]24[/C][C]6.37967948613228e-09[/C][C]1.27593589722646e-08[/C][C]0.999999993620321[/C][/ROW]
[ROW][C]25[/C][C]1.02837696928725e-09[/C][C]2.0567539385745e-09[/C][C]0.999999998971623[/C][/ROW]
[ROW][C]26[/C][C]3.01633777584794e-10[/C][C]6.03267555169588e-10[/C][C]0.999999999698366[/C][/ROW]
[ROW][C]27[/C][C]5.38249265779344e-11[/C][C]1.07649853155869e-10[/C][C]0.999999999946175[/C][/ROW]
[ROW][C]28[/C][C]1.3738833775545e-11[/C][C]2.747766755109e-11[/C][C]0.999999999986261[/C][/ROW]
[ROW][C]29[/C][C]7.89820095938901e-12[/C][C]1.5796401918778e-11[/C][C]0.999999999992102[/C][/ROW]
[ROW][C]30[/C][C]1.68570439826079e-12[/C][C]3.37140879652159e-12[/C][C]0.999999999998314[/C][/ROW]
[ROW][C]31[/C][C]2.72058850487401e-13[/C][C]5.44117700974803e-13[/C][C]0.999999999999728[/C][/ROW]
[ROW][C]32[/C][C]5.20217083963275e-14[/C][C]1.04043416792655e-13[/C][C]0.999999999999948[/C][/ROW]
[ROW][C]33[/C][C]1.97352421224507e-11[/C][C]3.94704842449013e-11[/C][C]0.999999999980265[/C][/ROW]
[ROW][C]34[/C][C]1.40832449061252e-11[/C][C]2.81664898122504e-11[/C][C]0.999999999985917[/C][/ROW]
[ROW][C]35[/C][C]4.51578589817503e-12[/C][C]9.03157179635006e-12[/C][C]0.999999999995484[/C][/ROW]
[ROW][C]36[/C][C]1.25152470122671e-12[/C][C]2.50304940245343e-12[/C][C]0.999999999998748[/C][/ROW]
[ROW][C]37[/C][C]2.6259665244754e-13[/C][C]5.25193304895081e-13[/C][C]0.999999999999737[/C][/ROW]
[ROW][C]38[/C][C]8.729590610734e-14[/C][C]1.7459181221468e-13[/C][C]0.999999999999913[/C][/ROW]
[ROW][C]39[/C][C]1.91835212637623e-14[/C][C]3.83670425275245e-14[/C][C]0.999999999999981[/C][/ROW]
[ROW][C]40[/C][C]4.04660911888305e-15[/C][C]8.0932182377661e-15[/C][C]0.999999999999996[/C][/ROW]
[ROW][C]41[/C][C]2.16326911059621e-15[/C][C]4.32653822119243e-15[/C][C]0.999999999999998[/C][/ROW]
[ROW][C]42[/C][C]4.39977569155814e-16[/C][C]8.79955138311629e-16[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]1.02773023469436e-16[/C][C]2.05546046938872e-16[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]1.98289796310771e-17[/C][C]3.96579592621543e-17[/C][C]1[/C][/ROW]
[ROW][C]45[/C][C]3.96279693739336e-18[/C][C]7.92559387478672e-18[/C][C]1[/C][/ROW]
[ROW][C]46[/C][C]4.1691340533617e-15[/C][C]8.3382681067234e-15[/C][C]0.999999999999996[/C][/ROW]
[ROW][C]47[/C][C]1.09108829756167e-15[/C][C]2.18217659512333e-15[/C][C]0.999999999999999[/C][/ROW]
[ROW][C]48[/C][C]2.09945687304526e-15[/C][C]4.19891374609052e-15[/C][C]0.999999999999998[/C][/ROW]
[ROW][C]49[/C][C]5.25854445757348e-16[/C][C]1.0517088915147e-15[/C][C]0.999999999999999[/C][/ROW]
[ROW][C]50[/C][C]1.50543850995029e-16[/C][C]3.01087701990058e-16[/C][C]1[/C][/ROW]
[ROW][C]51[/C][C]5.43052195220989e-17[/C][C]1.08610439044198e-16[/C][C]1[/C][/ROW]
[ROW][C]52[/C][C]1.74852261415951e-17[/C][C]3.49704522831903e-17[/C][C]1[/C][/ROW]
[ROW][C]53[/C][C]4.07013531644018e-18[/C][C]8.14027063288036e-18[/C][C]1[/C][/ROW]
[ROW][C]54[/C][C]1.36461009661788e-18[/C][C]2.72922019323577e-18[/C][C]1[/C][/ROW]
[ROW][C]55[/C][C]4.39234843890346e-19[/C][C]8.78469687780692e-19[/C][C]1[/C][/ROW]
[ROW][C]56[/C][C]1.00325472170232e-19[/C][C]2.00650944340463e-19[/C][C]1[/C][/ROW]
[ROW][C]57[/C][C]2.46369629210669e-20[/C][C]4.92739258421339e-20[/C][C]1[/C][/ROW]
[ROW][C]58[/C][C]5.6669832561266e-21[/C][C]1.13339665122532e-20[/C][C]1[/C][/ROW]
[ROW][C]59[/C][C]1.49847206757507e-21[/C][C]2.99694413515014e-21[/C][C]1[/C][/ROW]
[ROW][C]60[/C][C]7.53859673051674e-22[/C][C]1.50771934610335e-21[/C][C]1[/C][/ROW]
[ROW][C]61[/C][C]2.00890103940634e-22[/C][C]4.01780207881268e-22[/C][C]1[/C][/ROW]
[ROW][C]62[/C][C]4.48193792483507e-23[/C][C]8.96387584967014e-23[/C][C]1[/C][/ROW]
[ROW][C]63[/C][C]9.94008575526246e-24[/C][C]1.98801715105249e-23[/C][C]1[/C][/ROW]
[ROW][C]64[/C][C]2.12444919434184e-24[/C][C]4.24889838868367e-24[/C][C]1[/C][/ROW]
[ROW][C]65[/C][C]1.30527969005526e-21[/C][C]2.61055938011052e-21[/C][C]1[/C][/ROW]
[ROW][C]66[/C][C]3.5034271201901e-22[/C][C]7.0068542403802e-22[/C][C]1[/C][/ROW]
[ROW][C]67[/C][C]8.13332776162886e-23[/C][C]1.62666555232577e-22[/C][C]1[/C][/ROW]
[ROW][C]68[/C][C]2.15509241618001e-23[/C][C]4.31018483236001e-23[/C][C]1[/C][/ROW]
[ROW][C]69[/C][C]6.39270057495006e-24[/C][C]1.27854011499001e-23[/C][C]1[/C][/ROW]
[ROW][C]70[/C][C]1.60628318888788e-24[/C][C]3.21256637777576e-24[/C][C]1[/C][/ROW]
[ROW][C]71[/C][C]3.56983424713697e-25[/C][C]7.13966849427395e-25[/C][C]1[/C][/ROW]
[ROW][C]72[/C][C]8.37694963296418e-26[/C][C]1.67538992659284e-25[/C][C]1[/C][/ROW]
[ROW][C]73[/C][C]2.0322452059574e-26[/C][C]4.0644904119148e-26[/C][C]1[/C][/ROW]
[ROW][C]74[/C][C]9.15269773864942e-27[/C][C]1.83053954772988e-26[/C][C]1[/C][/ROW]
[ROW][C]75[/C][C]2.04386568220199e-27[/C][C]4.08773136440397e-27[/C][C]1[/C][/ROW]
[ROW][C]76[/C][C]4.47416900739293e-28[/C][C]8.94833801478586e-28[/C][C]1[/C][/ROW]
[ROW][C]77[/C][C]9.47120686545846e-29[/C][C]1.89424137309169e-28[/C][C]1[/C][/ROW]
[ROW][C]78[/C][C]1.93986343811808e-29[/C][C]3.87972687623617e-29[/C][C]1[/C][/ROW]
[ROW][C]79[/C][C]4.16235063250455e-30[/C][C]8.32470126500911e-30[/C][C]1[/C][/ROW]
[ROW][C]80[/C][C]1.39223722575159e-30[/C][C]2.78447445150319e-30[/C][C]1[/C][/ROW]
[ROW][C]81[/C][C]2.86162189356279e-31[/C][C]5.72324378712557e-31[/C][C]1[/C][/ROW]
[ROW][C]82[/C][C]6.77694904524301e-32[/C][C]1.3553898090486e-31[/C][C]1[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]4.00972730454917e-37[/C][C]2.00486365227458e-37[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]2.38751738223774e-36[/C][C]1.19375869111887e-36[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]1.2549036027188e-35[/C][C]6.27451801359401e-36[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]6.26770044767725e-35[/C][C]3.13385022383863e-35[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]3.78844255908413e-34[/C][C]1.89422127954207e-34[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]5.57293891558337e-35[/C][C]2.78646945779168e-35[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]3.37974275993359e-34[/C][C]1.68987137996679e-34[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]1.91855174146776e-33[/C][C]9.59275870733879e-34[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]4.86266127268583e-33[/C][C]2.43133063634291e-33[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]3.00597553131362e-32[/C][C]1.50298776565681e-32[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]1.85286146499672e-31[/C][C]9.2643073249836e-32[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]6.72431208801648e-31[/C][C]3.36215604400824e-31[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]4.04995519422284e-30[/C][C]2.02497759711142e-30[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]2.06090055710717e-29[/C][C]1.03045027855358e-29[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]1.1224603987225e-28[/C][C]5.61230199361251e-29[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]6.01684849523579e-28[/C][C]3.00842424761789e-28[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]2.83908493051307e-27[/C][C]1.41954246525653e-27[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]7.96854059295178e-27[/C][C]3.98427029647589e-27[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]2.99835581513583e-26[/C][C]1.49917790756791e-26[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]1.5276422736053e-25[/C][C]7.6382113680265e-26[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]7.52410698451603e-25[/C][C]3.76205349225802e-25[/C][/ROW]
[ROW][C]104[/C][C]1[/C][C]1.78948197273128e-24[/C][C]8.94740986365641e-25[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]8.42692973510711e-24[/C][C]4.21346486755355e-24[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]3.35706534161458e-23[/C][C]1.67853267080729e-23[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]1.45883489504598e-22[/C][C]7.29417447522988e-23[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]7.5749766001614e-22[/C][C]3.7874883000807e-22[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]2.4442290672494e-21[/C][C]1.2221145336247e-21[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]4.81677609008802e-21[/C][C]2.40838804504401e-21[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]2.46254919129892e-20[/C][C]1.23127459564946e-20[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]1.11211846965019e-19[/C][C]5.56059234825094e-20[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]1.66429205595709e-19[/C][C]8.32146027978544e-20[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]7.41428031001483e-19[/C][C]3.70714015500742e-19[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]3.46729132258764e-18[/C][C]1.73364566129382e-18[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]1.68760367668775e-17[/C][C]8.43801838343876e-18[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]6.10908308505733e-17[/C][C]3.05454154252866e-17[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]2.77476519560267e-16[/C][C]1.38738259780133e-16[/C][/ROW]
[ROW][C]119[/C][C]0.999999999999999[/C][C]1.19183293754775e-15[/C][C]5.95916468773876e-16[/C][/ROW]
[ROW][C]120[/C][C]0.999999999999998[/C][C]4.74706712939387e-15[/C][C]2.37353356469694e-15[/C][/ROW]
[ROW][C]121[/C][C]0.999999999999989[/C][C]2.13752811737051e-14[/C][C]1.06876405868526e-14[/C][/ROW]
[ROW][C]122[/C][C]0.999999999999951[/C][C]9.87435391904034e-14[/C][C]4.93717695952017e-14[/C][/ROW]
[ROW][C]123[/C][C]0.999999999999828[/C][C]3.43035755733082e-13[/C][C]1.71517877866541e-13[/C][/ROW]
[ROW][C]124[/C][C]0.999999999999834[/C][C]3.3139743066492e-13[/C][C]1.6569871533246e-13[/C][/ROW]
[ROW][C]125[/C][C]0.999999999999402[/C][C]1.19619006444394e-12[/C][C]5.98095032221969e-13[/C][/ROW]
[ROW][C]126[/C][C]0.999999999997311[/C][C]5.37792000926836e-12[/C][C]2.68896000463418e-12[/C][/ROW]
[ROW][C]127[/C][C]0.999999999990797[/C][C]1.84059090932105e-11[/C][C]9.20295454660524e-12[/C][/ROW]
[ROW][C]128[/C][C]0.99999999995879[/C][C]8.24194577411218e-11[/C][C]4.12097288705609e-11[/C][/ROW]
[ROW][C]129[/C][C]0.999999999839366[/C][C]3.21267525878348e-10[/C][C]1.60633762939174e-10[/C][/ROW]
[ROW][C]130[/C][C]0.99999999932905[/C][C]1.34189908846504e-09[/C][C]6.70949544232521e-10[/C][/ROW]
[ROW][C]131[/C][C]0.999999997219512[/C][C]5.5609766994329e-09[/C][C]2.78048834971645e-09[/C][/ROW]
[ROW][C]132[/C][C]0.9999999890761[/C][C]2.18477995180862e-08[/C][C]1.09238997590431e-08[/C][/ROW]
[ROW][C]133[/C][C]0.999999994209736[/C][C]1.15805272116874e-08[/C][C]5.79026360584372e-09[/C][/ROW]
[ROW][C]134[/C][C]0.999999975521418[/C][C]4.89571640270534e-08[/C][C]2.44785820135267e-08[/C][/ROW]
[ROW][C]135[/C][C]0.999999923122808[/C][C]1.53754384859687e-07[/C][C]7.68771924298436e-08[/C][/ROW]
[ROW][C]136[/C][C]0.999999843635477[/C][C]3.12729045850635e-07[/C][C]1.56364522925317e-07[/C][/ROW]
[ROW][C]137[/C][C]0.999999760666663[/C][C]4.78666674482053e-07[/C][C]2.39333337241027e-07[/C][/ROW]
[ROW][C]138[/C][C]0.999999137476026[/C][C]1.7250479484851e-06[/C][C]8.62523974242551e-07[/C][/ROW]
[ROW][C]139[/C][C]0.999998361317516[/C][C]3.2773649676723e-06[/C][C]1.63868248383615e-06[/C][/ROW]
[ROW][C]140[/C][C]0.999992848844685[/C][C]1.43023106307818e-05[/C][C]7.15115531539089e-06[/C][/ROW]
[ROW][C]141[/C][C]0.999975839949599[/C][C]4.83201008010809e-05[/C][C]2.41600504005405e-05[/C][/ROW]
[ROW][C]142[/C][C]0.999909069108076[/C][C]0.000181861783847706[/C][C]9.09308919238528e-05[/C][/ROW]
[ROW][C]143[/C][C]0.999790408669489[/C][C]0.000419182661021519[/C][C]0.000209591330510759[/C][/ROW]
[ROW][C]144[/C][C]0.999286225913986[/C][C]0.00142754817202889[/C][C]0.000713774086014446[/C][/ROW]
[ROW][C]145[/C][C]0.997482271933027[/C][C]0.00503545613394557[/C][C]0.00251772806697278[/C][/ROW]
[ROW][C]146[/C][C]0.99160126676743[/C][C]0.0167974664651409[/C][C]0.00839873323257045[/C][/ROW]
[ROW][C]147[/C][C]0.976891447545894[/C][C]0.0462171049082116[/C][C]0.0231085524541058[/C][/ROW]
[ROW][C]148[/C][C]0.977388383027543[/C][C]0.0452232339449148[/C][C]0.0226116169724574[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197889&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197889&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
140.02396965441007120.04793930882014240.976030345589929
150.005358602432547970.01071720486509590.994641397567452
160.001454378166585210.002908756333170420.998545621833415
170.0003429973946402260.0006859947892804530.99965700260536
186.3778812419201e-050.0001275576248384020.999936221187581
191.21534677326947e-052.43069354653894e-050.999987846532267
204.4938452928295e-068.98769058565901e-060.999995506154707
211.00232791600189e-062.00465583200378e-060.999998997672084
221.70831615159062e-073.41663230318124e-070.999999829168385
232.97945546462807e-085.95891092925613e-080.999999970205445
246.37967948613228e-091.27593589722646e-080.999999993620321
251.02837696928725e-092.0567539385745e-090.999999998971623
263.01633777584794e-106.03267555169588e-100.999999999698366
275.38249265779344e-111.07649853155869e-100.999999999946175
281.3738833775545e-112.747766755109e-110.999999999986261
297.89820095938901e-121.5796401918778e-110.999999999992102
301.68570439826079e-123.37140879652159e-120.999999999998314
312.72058850487401e-135.44117700974803e-130.999999999999728
325.20217083963275e-141.04043416792655e-130.999999999999948
331.97352421224507e-113.94704842449013e-110.999999999980265
341.40832449061252e-112.81664898122504e-110.999999999985917
354.51578589817503e-129.03157179635006e-120.999999999995484
361.25152470122671e-122.50304940245343e-120.999999999998748
372.6259665244754e-135.25193304895081e-130.999999999999737
388.729590610734e-141.7459181221468e-130.999999999999913
391.91835212637623e-143.83670425275245e-140.999999999999981
404.04660911888305e-158.0932182377661e-150.999999999999996
412.16326911059621e-154.32653822119243e-150.999999999999998
424.39977569155814e-168.79955138311629e-161
431.02773023469436e-162.05546046938872e-161
441.98289796310771e-173.96579592621543e-171
453.96279693739336e-187.92559387478672e-181
464.1691340533617e-158.3382681067234e-150.999999999999996
471.09108829756167e-152.18217659512333e-150.999999999999999
482.09945687304526e-154.19891374609052e-150.999999999999998
495.25854445757348e-161.0517088915147e-150.999999999999999
501.50543850995029e-163.01087701990058e-161
515.43052195220989e-171.08610439044198e-161
521.74852261415951e-173.49704522831903e-171
534.07013531644018e-188.14027063288036e-181
541.36461009661788e-182.72922019323577e-181
554.39234843890346e-198.78469687780692e-191
561.00325472170232e-192.00650944340463e-191
572.46369629210669e-204.92739258421339e-201
585.6669832561266e-211.13339665122532e-201
591.49847206757507e-212.99694413515014e-211
607.53859673051674e-221.50771934610335e-211
612.00890103940634e-224.01780207881268e-221
624.48193792483507e-238.96387584967014e-231
639.94008575526246e-241.98801715105249e-231
642.12444919434184e-244.24889838868367e-241
651.30527969005526e-212.61055938011052e-211
663.5034271201901e-227.0068542403802e-221
678.13332776162886e-231.62666555232577e-221
682.15509241618001e-234.31018483236001e-231
696.39270057495006e-241.27854011499001e-231
701.60628318888788e-243.21256637777576e-241
713.56983424713697e-257.13966849427395e-251
728.37694963296418e-261.67538992659284e-251
732.0322452059574e-264.0644904119148e-261
749.15269773864942e-271.83053954772988e-261
752.04386568220199e-274.08773136440397e-271
764.47416900739293e-288.94833801478586e-281
779.47120686545846e-291.89424137309169e-281
781.93986343811808e-293.87972687623617e-291
794.16235063250455e-308.32470126500911e-301
801.39223722575159e-302.78447445150319e-301
812.86162189356279e-315.72324378712557e-311
826.77694904524301e-321.3553898090486e-311
8314.00972730454917e-372.00486365227458e-37
8412.38751738223774e-361.19375869111887e-36
8511.2549036027188e-356.27451801359401e-36
8616.26770044767725e-353.13385022383863e-35
8713.78844255908413e-341.89422127954207e-34
8815.57293891558337e-352.78646945779168e-35
8913.37974275993359e-341.68987137996679e-34
9011.91855174146776e-339.59275870733879e-34
9114.86266127268583e-332.43133063634291e-33
9213.00597553131362e-321.50298776565681e-32
9311.85286146499672e-319.2643073249836e-32
9416.72431208801648e-313.36215604400824e-31
9514.04995519422284e-302.02497759711142e-30
9612.06090055710717e-291.03045027855358e-29
9711.1224603987225e-285.61230199361251e-29
9816.01684849523579e-283.00842424761789e-28
9912.83908493051307e-271.41954246525653e-27
10017.96854059295178e-273.98427029647589e-27
10112.99835581513583e-261.49917790756791e-26
10211.5276422736053e-257.6382113680265e-26
10317.52410698451603e-253.76205349225802e-25
10411.78948197273128e-248.94740986365641e-25
10518.42692973510711e-244.21346486755355e-24
10613.35706534161458e-231.67853267080729e-23
10711.45883489504598e-227.29417447522988e-23
10817.5749766001614e-223.7874883000807e-22
10912.4442290672494e-211.2221145336247e-21
11014.81677609008802e-212.40838804504401e-21
11112.46254919129892e-201.23127459564946e-20
11211.11211846965019e-195.56059234825094e-20
11311.66429205595709e-198.32146027978544e-20
11417.41428031001483e-193.70714015500742e-19
11513.46729132258764e-181.73364566129382e-18
11611.68760367668775e-178.43801838343876e-18
11716.10908308505733e-173.05454154252866e-17
11812.77476519560267e-161.38738259780133e-16
1190.9999999999999991.19183293754775e-155.95916468773876e-16
1200.9999999999999984.74706712939387e-152.37353356469694e-15
1210.9999999999999892.13752811737051e-141.06876405868526e-14
1220.9999999999999519.87435391904034e-144.93717695952017e-14
1230.9999999999998283.43035755733082e-131.71517877866541e-13
1240.9999999999998343.3139743066492e-131.6569871533246e-13
1250.9999999999994021.19619006444394e-125.98095032221969e-13
1260.9999999999973115.37792000926836e-122.68896000463418e-12
1270.9999999999907971.84059090932105e-119.20295454660524e-12
1280.999999999958798.24194577411218e-114.12097288705609e-11
1290.9999999998393663.21267525878348e-101.60633762939174e-10
1300.999999999329051.34189908846504e-096.70949544232521e-10
1310.9999999972195125.5609766994329e-092.78048834971645e-09
1320.99999998907612.18477995180862e-081.09238997590431e-08
1330.9999999942097361.15805272116874e-085.79026360584372e-09
1340.9999999755214184.89571640270534e-082.44785820135267e-08
1350.9999999231228081.53754384859687e-077.68771924298436e-08
1360.9999998436354773.12729045850635e-071.56364522925317e-07
1370.9999997606666634.78666674482053e-072.39333337241027e-07
1380.9999991374760261.7250479484851e-068.62523974242551e-07
1390.9999983613175163.2773649676723e-061.63868248383615e-06
1400.9999928488446851.43023106307818e-057.15115531539089e-06
1410.9999758399495994.83201008010809e-052.41600504005405e-05
1420.9999090691080760.0001818617838477069.09308919238528e-05
1430.9997904086694890.0004191826610215190.000209591330510759
1440.9992862259139860.001427548172028890.000713774086014446
1450.9974822719330270.005035456133945570.00251772806697278
1460.991601266767430.01679746646514090.00839873323257045
1470.9768914475458940.04621710490821160.0231085524541058
1480.9773883830275430.04522323394491480.0226116169724574







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1300.962962962962963NOK
5% type I error level1351NOK
10% type I error level1351NOK

\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 & 130 & 0.962962962962963 & NOK \tabularnewline
5% type I error level & 135 & 1 & NOK \tabularnewline
10% type I error level & 135 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197889&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]130[/C][C]0.962962962962963[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]135[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]135[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197889&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197889&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 level1300.962962962962963NOK
5% type I error level1351NOK
10% type I error level1351NOK



Parameters (Session):
par1 = kendall ;
Parameters (R input):
par1 = 11 ; 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')
}