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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 23 Nov 2011 11:14:14 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/23/t13220649063cch153inlewl00.htm/, Retrieved Sat, 20 Apr 2024 02:33:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146540, Retrieved Sat, 20 Apr 2024 02:33:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact133

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-    D  [Multiple Regression] [Multiple Regressi...] [2010-11-23 22:51:32] [b8e188bcc949964bed729335b3416734]
-   P     [Multiple Regression] [Multiple Regressi...] [2010-11-23 23:09:01] [b8e188bcc949964bed729335b3416734]
-    D        [Multiple Regression] [ws7] [2011-11-23 16:14:14] [9c3f7eb531442757fa35fbfef7e48a65] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1	7	7	7	7	7
1	5	5	5	5	5
1	6	5	6	4	5
1	5	5	6	5	6
1	6	7	5	6	7
1	6	5	6	5	7
1	6	3	7	7	7
1	6	6	6	5	6
1	4	5	6	4	5
1	6	3	6	6	6
1	6	7	7	7	7
1	3	7	7	4	7
1	5	6	7	6	6
1	5	7	7	5	7
1	2	4	5	2	6
1	3	7	7	5	7
1	6	7	6	6	5
1	6	7	6	6	5
1	5	3	6	5	7
1	7	5	6	5	6
1	5	5	5	6	6
1	5	5	3	5	1
1	5	7	7	5	7
1	5	7	6	5	6
1	5	6	7	5	7
1	6	6	7	7	6
1	5	7	6	5	6
1	5	6	6	3	6
1	6	5	6	5	6
1	4	5	6	4	5
1	4	3	5	6	5
1	6	7	7	5	7
1	3	6	4	4	3
1	6	5	5	5	6
1	5	5	6	5	5
1	6	7	7	6	6
1	7	6	7	5	7
1	4	6	6	5	6
1	5	7	6	5	5
1	4	5	4	4	5
1	5	6	7	5	6
1	3	5	7	5	7
1	5	5	7	5	7
1	6	6	5	6	5
1	6	7	7	6	7
1	4	6	5	4	5
1	4	5	5	4	5
1	6	6	6	5	5
1	6	6	6	6	6
1	5	7	6	6	6
1	6	7	7	6	7
1	4	5	5	4	7
1	4	3	7	6	7
1	5	6	6	5	7
1	3	6	5	4	2
1	6	6	7	6	6
1	6	6	7	6	6
1	4	6	6	4	6
1	5	7	7	5	7
1	5	6	5	5	5
1	4	6	6	6	7
1	6	5	6	6	6
1	5	6	6	6	6
1	4	6	5	5	5
1	6	6	7	5	6
1	5	4	7	7	7
1	6	6	6	6	6
1	5	7	7	7	7
1	6	7	7	6	7
1	5	5	4	5	5
1	4	5	5	4	6
1	6	7	7	6	7
1	5	7	7	3	7
1	5	5	6	5	7
1	3	5	7	5	7
1	5	3	0	5	7
1	4	6	6	5	6
1	5	5	6	5	5
1	5	4	3	3	5
1	7	7	7	7	7
1	7	7	7	6	6
1	5	2	6	4	6
1	4	6	6	4	6
1	6	4	6	6	6
1	5	7	7	5	7
1	5	6	7	6	6
1	4	2	6	5	7
1	5	7	7	5	5
1	2	7	7	2	5
1	7	5	7	6	7
1	4	6	6	5	5
1	5	5	7	5	7
1	5	6	7	6	7
1	7	7	5	7	5
1	2	6	6	6	6
1	4	7	7	4	7
1	6	6	7	6	6
1	5	5	6	6	5
1	5	5	6	5	5
1	4	4	5	5	7
1	4	4	6	5	7
2	4	5	6	5	6
2	7	7	7	7	6
2	5	7	7	4	7
2	5	6	7	6	7
2	5	5	6	6	6
2	7	7	7	6	7
2	3	7	7	6	7
2	3	5	5	4	4
2	6	7	6	6	7
2	5	7	6	5	6
2	6	7	6	6	6
2	4	4	3	4	5
2	4	5	5	6	7
2	6	6	6	5	5
2	5	5	7	5	5
2	7	7	7	7	7
2	6	7	6	7	5
2	7	6	5	6	6
2	5	4	6	4	5
2	5	7	7	6	7
2	2	6	7	4	7
2	6	6	7	6	6
2	1	7	7	6	6
2	5	7	7	6	7
2	6	7	6	5	4
2	6	7	6	5	6
2	6	6	6	6	6
2	5	5	7	6	7
2	6	6	7	6	7
2	5	6	6	6	6
2	6	7	7	5	6
2	7	7	7	6	7
2	4	6	2	3	3
2	5	7	6	7	4
2	3	6	5	5	6
2	7	7	6	6	6
2	7	5	6	7	5
2	6	6	6	6	5
2	6	6	5	4	6
2	6	7	6	7	6
2	5	5	6	5	4
2	5	6	5	5	5
2	4	5	5	5	5
2	4	3	7	4	7
2	6	7	5	5	5
2	5	6	6	6	7
2	4	5	5	4	6
2	6	6	6	6	6
2	4	6	7	6	6
2	4	2	6	2	5
2	4	6	7	5	6
2	6	7	6	5	7
2	3	7	7	4	7
2	6	6	7	6	6
2	5	5	6	6	6
2	4	5	7	6	7
2	7	6	6	7	5
2	6	6	5	5	6
2	5	6	4	5	5
2	6	7	7	7	7
2	6	6	6	6	6
2	5	6	5	5	6
2	5	5	5	4	5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 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=146540&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]5 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=146540&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146540&T=0

As an alternative you can also use a QR Code:  
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 time5 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
Gender[t] = + 1.14059725448889 -0.00831463730047959Q1[t] + 0.0574514258564551Q2[t] -0.0289323320190734Q3[t] + 0.05782147075238Q4[t] -0.0295178640144729`Q5 `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Gender[t] =  +  1.14059725448889 -0.00831463730047959Q1[t] +  0.0574514258564551Q2[t] -0.0289323320190734Q3[t] +  0.05782147075238Q4[t] -0.0295178640144729`Q5
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146540&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Gender[t] =  +  1.14059725448889 -0.00831463730047959Q1[t] +  0.0574514258564551Q2[t] -0.0289323320190734Q3[t] +  0.05782147075238Q4[t] -0.0295178640144729`Q5
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146540&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146540&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Gender[t] = + 1.14059725448889 -0.00831463730047959Q1[t] + 0.0574514258564551Q2[t] -0.0289323320190734Q3[t] + 0.05782147075238Q4[t] -0.0295178640144729`Q5 `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.140597254488890.3103243.67550.0003250.000162
Q1-0.008314637300479590.038158-0.21790.8277880.413894
Q20.05745142585645510.0353151.62680.1057690.052885
Q3-0.02893233201907340.045134-0.6410.5224340.261217
Q40.057821470752380.0459741.25770.2103560.105178
`Q5 `-0.02951786401447290.044053-0.67010.5037990.2519

\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) & 1.14059725448889 & 0.310324 & 3.6755 & 0.000325 & 0.000162 \tabularnewline
Q1 & -0.00831463730047959 & 0.038158 & -0.2179 & 0.827788 & 0.413894 \tabularnewline
Q2 & 0.0574514258564551 & 0.035315 & 1.6268 & 0.105769 & 0.052885 \tabularnewline
Q3 & -0.0289323320190734 & 0.045134 & -0.641 & 0.522434 & 0.261217 \tabularnewline
Q4 & 0.05782147075238 & 0.045974 & 1.2577 & 0.210356 & 0.105178 \tabularnewline
`Q5
` & -0.0295178640144729 & 0.044053 & -0.6701 & 0.503799 & 0.2519 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146540&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]1.14059725448889[/C][C]0.310324[/C][C]3.6755[/C][C]0.000325[/C][C]0.000162[/C][/ROW]
[ROW][C]Q1[/C][C]-0.00831463730047959[/C][C]0.038158[/C][C]-0.2179[/C][C]0.827788[/C][C]0.413894[/C][/ROW]
[ROW][C]Q2[/C][C]0.0574514258564551[/C][C]0.035315[/C][C]1.6268[/C][C]0.105769[/C][C]0.052885[/C][/ROW]
[ROW][C]Q3[/C][C]-0.0289323320190734[/C][C]0.045134[/C][C]-0.641[/C][C]0.522434[/C][C]0.261217[/C][/ROW]
[ROW][C]Q4[/C][C]0.05782147075238[/C][C]0.045974[/C][C]1.2577[/C][C]0.210356[/C][C]0.105178[/C][/ROW]
[ROW][C]`Q5
`[/C][C]-0.0295178640144729[/C][C]0.044053[/C][C]-0.6701[/C][C]0.503799[/C][C]0.2519[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146540&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146540&T=2

As an alternative you can also use a QR Code:  
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)1.140597254488890.3103243.67550.0003250.000162
Q1-0.008314637300479590.038158-0.21790.8277880.413894
Q20.05745142585645510.0353151.62680.1057690.052885
Q3-0.02893233201907340.045134-0.6410.5224340.261217
Q40.057821470752380.0459741.25770.2103560.105178
`Q5 `-0.02951786401447290.044053-0.67010.5037990.2519






Multiple Linear Regression - Regression Statistics
Multiple R0.190860354474215
R-squared0.036427674910023
Adjusted R-squared0.00593487981223895
F-TEST (value)1.19463220059712
F-TEST (DF numerator)5
F-TEST (DF denominator)158
p-value0.314249346156635
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.486432577745064
Sum Squared Residuals37.3854311252898

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.190860354474215 \tabularnewline
R-squared & 0.036427674910023 \tabularnewline
Adjusted R-squared & 0.00593487981223895 \tabularnewline
F-TEST (value) & 1.19463220059712 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 158 \tabularnewline
p-value & 0.314249346156635 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.486432577745064 \tabularnewline
Sum Squared Residuals & 37.3854311252898 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146540&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.190860354474215[/C][/ROW]
[ROW][C]R-squared[/C][C]0.036427674910023[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.00593487981223895[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.19463220059712[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]158[/C][/ROW]
[ROW][C]p-value[/C][C]0.314249346156635[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.486432577745064[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]37.3854311252898[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146540&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146540&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.190860354474215
R-squared0.036427674910023
Adjusted R-squared0.00593487981223895
F-TEST (value)1.19463220059712
F-TEST (DF numerator)5
F-TEST (DF denominator)158
p-value0.314249346156635
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.486432577745064
Sum Squared Residuals37.3854311252898






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
111.48015369741256-0.480153697412556
211.38313757086294-0.383137570862941
311.28806913079101-0.288069130791008
411.32468737482939-0.324687374829394
511.48851152799881-0.488511527998806
611.28685487351444-0.286854873514442
711.25866263128722-0.258662631287219
811.37382416338537-0.37382416338537
911.30469840539197-0.304698405391967
1011.25929135656838-0.259291356568385
1111.48846833471304-0.488468334713039
1211.33994783435734-0.339947834357338
1311.41102793941916-0.411027939419156
1411.38114003050876-0.381140030508758
1511.14764778063631-0.147647780636311
1611.39776930510972-0.397769305109718
1711.51861492400868-0.518614924008678
1811.51861492400868-0.518614924008678
1911.18026665910201-0.180266659102011
2011.30805810022844-0.308058100228435
2111.41144117760085-0.411441177600848
2211.55907369095898-0.559073690958979
2311.38114003050876-0.381140030508758
2411.4395902265423-0.439590226542305
2511.3236886046523-0.323688604652303
2611.46053477287106-0.460534772871057
2711.4395902265423-0.439590226542305
2811.26649585918109-0.26649585918109
2911.31637273752891-0.316372737528915
3011.30469840539197-0.304698405391967
3111.33437082720289-0.33437082720289
3211.37282539320828-0.372825393208279
3311.48736486061599-0.487364860615994
3411.34530506954799-0.345305069547988
3511.35420523884387-0.354205238843867
3611.46016472797513-0.460164727975132
3711.30705933005134-0.307059330051344
3811.39045343798633-0.390453437986329
3911.46910809055678-0.469108090556778
4011.36256306943011-0.362563069430114
4111.35320646866678-0.353206468666776
4211.28286645339681-0.282866453396807
4311.26623717879585-0.266237178795848
4411.4900958301713-0.490095830171296
4511.43064686396066-0.430646863960659
4611.3910821632675-0.391082163267495
4711.33363073741104-0.33363073741104
4811.40334202739984-0.403342027399843
4911.43164563413775-0.43164563413775
5011.49741169729468-0.497411697294685
5111.43064686396066-0.430646863960659
5211.27459500938209-0.274595009382095
5311.2174704351358-0.217470435135798
5411.35262093667138-0.352620936671377
5511.48795039261139-0.487950392611394
5611.40271330211868-0.402713302118677
5711.40271330211868-0.402713302118677
5811.33263196723395-0.332631967233949
5911.38114003050876-0.381140030508758
6011.4405889967194-0.440588996719396
6111.41875704472424-0.418757044724236
6211.3741942082813-0.374194208281295
6311.43996027143823-0.43996027143823
6411.44890363401988-0.448903634019875
6511.3448918313663-0.344891831366297
6611.32442869444415-0.324428694444153
6711.43164563413775-0.43164563413775
6811.49678297201352-0.496782972013519
6911.43064686396066-0.430646863960659
7011.41206990288201-0.412069902882014
7111.30411287339657-0.304112873396567
7211.43064686396066-0.430646863960659
7311.265497089004-0.265497089003998
7411.29516951081492-0.295169510814922
7511.28286645339681-0.282866453396807
7611.35386065121645-0.353860651216452
7711.39045343798633-0.390453437986329
7811.35420523884387-0.354205238843867
7911.26790786753987-0.267907867539872
8011.48015369741256-0.480153697412559
8111.45185009067465-0.451850090674652
8211.09451162650765-0.0945116265076492
8311.33263196723395-0.332631967233949
8411.31674278242484-0.31674278242484
8511.38114003050876-0.381140030508758
8611.41102793941916-0.411027939419156
8711.13112987054604-0.131129870546036
8811.4401757585377-0.440175758537704
8911.291655258182-0.291655258182003
9011.30742937494727-0.307429374947269
9111.4199713020008-0.419971302000802
9211.26623717879585-0.266237178795848
9311.38151007540468-0.381510075404683
9411.59705408947965-0.597054089479652
9511.46490418333967-0.464904183339668
9611.33163319705686-0.331633197056858
9711.40271330211868-0.402713302118677
9811.41202670959625-0.412026709596247
9911.35420523884387-0.354205238843867
10011.27496505427802-0.27496505427802
10111.24603272225895-0.246032722258946
10221.333002012129870.666997987870126
10321.509671561427030.490328438572968
10421.323318559756380.676681440243622
10521.381510075404680.618489924595317
10621.382508845581770.617491154418225
10721.422332226660180.577667773339821
10821.45559077586210.544409224137902
10921.371463238725990.628536761274007
11021.459579195979730.540420804020268
11121.43959022654230.560409773457695
11221.489097059994210.510902940005795
11321.334043975592730.665956024407268
11421.390237950886850.609762049113146
11521.403342027399840.596657972600157
11621.325272906824790.674727093175206
11721.480153697412560.519846302587441
11821.576436394761060.423563605238942
11921.452263328856340.547736671143656
12021.238932342235030.761067657764968
12121.438961501261140.561038498738862
12221.290811045801360.709188954198638
12321.402713302118680.597286697881323
12421.501737914477530.49826208552247
12521.438961501261140.561038498738862
12621.490311317270770.509688682729229
12721.431275589241820.568724410758175
12821.431645634137750.56835436586225
12921.324058649548230.675941350451772
13021.37319543810420.626804561895796
13121.439960271438230.56003972856177
13221.402343257222750.597656742777248
13321.422332226660180.577667773339821
13421.479093416601280.520906583398719
13521.614268896076010.38573110392399
13621.427700407305880.572299592694118
13721.480782422693730.519217577306274
13821.453218905747670.546781094252332
13921.461163498152220.538836501847777
14021.344935024652060.655064975347937
14121.546918530746590.453081469253415
14221.383723102858340.61627689714166
14321.44058899671940.559411003280604
14421.391452208163420.60854779183658
14521.101827493631040.898172506368963
14621.489725785275370.510274214724629
14721.410442407423760.589557592576243
14821.304112873396570.695887126603433
14921.431645634137750.56835436586225
15021.419342576719640.580657423280364
15121.016701186317840.983298813682158
15221.361521105967260.638478894032744
15321.401757725227350.598242274772648
15421.339947834357340.660052165642663
15521.402713302118680.597286697881323
15621.382508845581770.617491154418225
15721.332373286848710.667626713151292
15821.510670331604120.489329668395877
15921.402756495404440.597243504595557
16021.469521328738470.530478671261531
16121.488468334713040.511531665286961
16221.431645634137750.56835436586225
16321.411071132704920.588928867295077
16421.325316100110560.674683899889439

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 1.48015369741256 & -0.480153697412556 \tabularnewline
2 & 1 & 1.38313757086294 & -0.383137570862941 \tabularnewline
3 & 1 & 1.28806913079101 & -0.288069130791008 \tabularnewline
4 & 1 & 1.32468737482939 & -0.324687374829394 \tabularnewline
5 & 1 & 1.48851152799881 & -0.488511527998806 \tabularnewline
6 & 1 & 1.28685487351444 & -0.286854873514442 \tabularnewline
7 & 1 & 1.25866263128722 & -0.258662631287219 \tabularnewline
8 & 1 & 1.37382416338537 & -0.37382416338537 \tabularnewline
9 & 1 & 1.30469840539197 & -0.304698405391967 \tabularnewline
10 & 1 & 1.25929135656838 & -0.259291356568385 \tabularnewline
11 & 1 & 1.48846833471304 & -0.488468334713039 \tabularnewline
12 & 1 & 1.33994783435734 & -0.339947834357338 \tabularnewline
13 & 1 & 1.41102793941916 & -0.411027939419156 \tabularnewline
14 & 1 & 1.38114003050876 & -0.381140030508758 \tabularnewline
15 & 1 & 1.14764778063631 & -0.147647780636311 \tabularnewline
16 & 1 & 1.39776930510972 & -0.397769305109718 \tabularnewline
17 & 1 & 1.51861492400868 & -0.518614924008678 \tabularnewline
18 & 1 & 1.51861492400868 & -0.518614924008678 \tabularnewline
19 & 1 & 1.18026665910201 & -0.180266659102011 \tabularnewline
20 & 1 & 1.30805810022844 & -0.308058100228435 \tabularnewline
21 & 1 & 1.41144117760085 & -0.411441177600848 \tabularnewline
22 & 1 & 1.55907369095898 & -0.559073690958979 \tabularnewline
23 & 1 & 1.38114003050876 & -0.381140030508758 \tabularnewline
24 & 1 & 1.4395902265423 & -0.439590226542305 \tabularnewline
25 & 1 & 1.3236886046523 & -0.323688604652303 \tabularnewline
26 & 1 & 1.46053477287106 & -0.460534772871057 \tabularnewline
27 & 1 & 1.4395902265423 & -0.439590226542305 \tabularnewline
28 & 1 & 1.26649585918109 & -0.26649585918109 \tabularnewline
29 & 1 & 1.31637273752891 & -0.316372737528915 \tabularnewline
30 & 1 & 1.30469840539197 & -0.304698405391967 \tabularnewline
31 & 1 & 1.33437082720289 & -0.33437082720289 \tabularnewline
32 & 1 & 1.37282539320828 & -0.372825393208279 \tabularnewline
33 & 1 & 1.48736486061599 & -0.487364860615994 \tabularnewline
34 & 1 & 1.34530506954799 & -0.345305069547988 \tabularnewline
35 & 1 & 1.35420523884387 & -0.354205238843867 \tabularnewline
36 & 1 & 1.46016472797513 & -0.460164727975132 \tabularnewline
37 & 1 & 1.30705933005134 & -0.307059330051344 \tabularnewline
38 & 1 & 1.39045343798633 & -0.390453437986329 \tabularnewline
39 & 1 & 1.46910809055678 & -0.469108090556778 \tabularnewline
40 & 1 & 1.36256306943011 & -0.362563069430114 \tabularnewline
41 & 1 & 1.35320646866678 & -0.353206468666776 \tabularnewline
42 & 1 & 1.28286645339681 & -0.282866453396807 \tabularnewline
43 & 1 & 1.26623717879585 & -0.266237178795848 \tabularnewline
44 & 1 & 1.4900958301713 & -0.490095830171296 \tabularnewline
45 & 1 & 1.43064686396066 & -0.430646863960659 \tabularnewline
46 & 1 & 1.3910821632675 & -0.391082163267495 \tabularnewline
47 & 1 & 1.33363073741104 & -0.33363073741104 \tabularnewline
48 & 1 & 1.40334202739984 & -0.403342027399843 \tabularnewline
49 & 1 & 1.43164563413775 & -0.43164563413775 \tabularnewline
50 & 1 & 1.49741169729468 & -0.497411697294685 \tabularnewline
51 & 1 & 1.43064686396066 & -0.430646863960659 \tabularnewline
52 & 1 & 1.27459500938209 & -0.274595009382095 \tabularnewline
53 & 1 & 1.2174704351358 & -0.217470435135798 \tabularnewline
54 & 1 & 1.35262093667138 & -0.352620936671377 \tabularnewline
55 & 1 & 1.48795039261139 & -0.487950392611394 \tabularnewline
56 & 1 & 1.40271330211868 & -0.402713302118677 \tabularnewline
57 & 1 & 1.40271330211868 & -0.402713302118677 \tabularnewline
58 & 1 & 1.33263196723395 & -0.332631967233949 \tabularnewline
59 & 1 & 1.38114003050876 & -0.381140030508758 \tabularnewline
60 & 1 & 1.4405889967194 & -0.440588996719396 \tabularnewline
61 & 1 & 1.41875704472424 & -0.418757044724236 \tabularnewline
62 & 1 & 1.3741942082813 & -0.374194208281295 \tabularnewline
63 & 1 & 1.43996027143823 & -0.43996027143823 \tabularnewline
64 & 1 & 1.44890363401988 & -0.448903634019875 \tabularnewline
65 & 1 & 1.3448918313663 & -0.344891831366297 \tabularnewline
66 & 1 & 1.32442869444415 & -0.324428694444153 \tabularnewline
67 & 1 & 1.43164563413775 & -0.43164563413775 \tabularnewline
68 & 1 & 1.49678297201352 & -0.496782972013519 \tabularnewline
69 & 1 & 1.43064686396066 & -0.430646863960659 \tabularnewline
70 & 1 & 1.41206990288201 & -0.412069902882014 \tabularnewline
71 & 1 & 1.30411287339657 & -0.304112873396567 \tabularnewline
72 & 1 & 1.43064686396066 & -0.430646863960659 \tabularnewline
73 & 1 & 1.265497089004 & -0.265497089003998 \tabularnewline
74 & 1 & 1.29516951081492 & -0.295169510814922 \tabularnewline
75 & 1 & 1.28286645339681 & -0.282866453396807 \tabularnewline
76 & 1 & 1.35386065121645 & -0.353860651216452 \tabularnewline
77 & 1 & 1.39045343798633 & -0.390453437986329 \tabularnewline
78 & 1 & 1.35420523884387 & -0.354205238843867 \tabularnewline
79 & 1 & 1.26790786753987 & -0.267907867539872 \tabularnewline
80 & 1 & 1.48015369741256 & -0.480153697412559 \tabularnewline
81 & 1 & 1.45185009067465 & -0.451850090674652 \tabularnewline
82 & 1 & 1.09451162650765 & -0.0945116265076492 \tabularnewline
83 & 1 & 1.33263196723395 & -0.332631967233949 \tabularnewline
84 & 1 & 1.31674278242484 & -0.31674278242484 \tabularnewline
85 & 1 & 1.38114003050876 & -0.381140030508758 \tabularnewline
86 & 1 & 1.41102793941916 & -0.411027939419156 \tabularnewline
87 & 1 & 1.13112987054604 & -0.131129870546036 \tabularnewline
88 & 1 & 1.4401757585377 & -0.440175758537704 \tabularnewline
89 & 1 & 1.291655258182 & -0.291655258182003 \tabularnewline
90 & 1 & 1.30742937494727 & -0.307429374947269 \tabularnewline
91 & 1 & 1.4199713020008 & -0.419971302000802 \tabularnewline
92 & 1 & 1.26623717879585 & -0.266237178795848 \tabularnewline
93 & 1 & 1.38151007540468 & -0.381510075404683 \tabularnewline
94 & 1 & 1.59705408947965 & -0.597054089479652 \tabularnewline
95 & 1 & 1.46490418333967 & -0.464904183339668 \tabularnewline
96 & 1 & 1.33163319705686 & -0.331633197056858 \tabularnewline
97 & 1 & 1.40271330211868 & -0.402713302118677 \tabularnewline
98 & 1 & 1.41202670959625 & -0.412026709596247 \tabularnewline
99 & 1 & 1.35420523884387 & -0.354205238843867 \tabularnewline
100 & 1 & 1.27496505427802 & -0.27496505427802 \tabularnewline
101 & 1 & 1.24603272225895 & -0.246032722258946 \tabularnewline
102 & 2 & 1.33300201212987 & 0.666997987870126 \tabularnewline
103 & 2 & 1.50967156142703 & 0.490328438572968 \tabularnewline
104 & 2 & 1.32331855975638 & 0.676681440243622 \tabularnewline
105 & 2 & 1.38151007540468 & 0.618489924595317 \tabularnewline
106 & 2 & 1.38250884558177 & 0.617491154418225 \tabularnewline
107 & 2 & 1.42233222666018 & 0.577667773339821 \tabularnewline
108 & 2 & 1.4555907758621 & 0.544409224137902 \tabularnewline
109 & 2 & 1.37146323872599 & 0.628536761274007 \tabularnewline
110 & 2 & 1.45957919597973 & 0.540420804020268 \tabularnewline
111 & 2 & 1.4395902265423 & 0.560409773457695 \tabularnewline
112 & 2 & 1.48909705999421 & 0.510902940005795 \tabularnewline
113 & 2 & 1.33404397559273 & 0.665956024407268 \tabularnewline
114 & 2 & 1.39023795088685 & 0.609762049113146 \tabularnewline
115 & 2 & 1.40334202739984 & 0.596657972600157 \tabularnewline
116 & 2 & 1.32527290682479 & 0.674727093175206 \tabularnewline
117 & 2 & 1.48015369741256 & 0.519846302587441 \tabularnewline
118 & 2 & 1.57643639476106 & 0.423563605238942 \tabularnewline
119 & 2 & 1.45226332885634 & 0.547736671143656 \tabularnewline
120 & 2 & 1.23893234223503 & 0.761067657764968 \tabularnewline
121 & 2 & 1.43896150126114 & 0.561038498738862 \tabularnewline
122 & 2 & 1.29081104580136 & 0.709188954198638 \tabularnewline
123 & 2 & 1.40271330211868 & 0.597286697881323 \tabularnewline
124 & 2 & 1.50173791447753 & 0.49826208552247 \tabularnewline
125 & 2 & 1.43896150126114 & 0.561038498738862 \tabularnewline
126 & 2 & 1.49031131727077 & 0.509688682729229 \tabularnewline
127 & 2 & 1.43127558924182 & 0.568724410758175 \tabularnewline
128 & 2 & 1.43164563413775 & 0.56835436586225 \tabularnewline
129 & 2 & 1.32405864954823 & 0.675941350451772 \tabularnewline
130 & 2 & 1.3731954381042 & 0.626804561895796 \tabularnewline
131 & 2 & 1.43996027143823 & 0.56003972856177 \tabularnewline
132 & 2 & 1.40234325722275 & 0.597656742777248 \tabularnewline
133 & 2 & 1.42233222666018 & 0.577667773339821 \tabularnewline
134 & 2 & 1.47909341660128 & 0.520906583398719 \tabularnewline
135 & 2 & 1.61426889607601 & 0.38573110392399 \tabularnewline
136 & 2 & 1.42770040730588 & 0.572299592694118 \tabularnewline
137 & 2 & 1.48078242269373 & 0.519217577306274 \tabularnewline
138 & 2 & 1.45321890574767 & 0.546781094252332 \tabularnewline
139 & 2 & 1.46116349815222 & 0.538836501847777 \tabularnewline
140 & 2 & 1.34493502465206 & 0.655064975347937 \tabularnewline
141 & 2 & 1.54691853074659 & 0.453081469253415 \tabularnewline
142 & 2 & 1.38372310285834 & 0.61627689714166 \tabularnewline
143 & 2 & 1.4405889967194 & 0.559411003280604 \tabularnewline
144 & 2 & 1.39145220816342 & 0.60854779183658 \tabularnewline
145 & 2 & 1.10182749363104 & 0.898172506368963 \tabularnewline
146 & 2 & 1.48972578527537 & 0.510274214724629 \tabularnewline
147 & 2 & 1.41044240742376 & 0.589557592576243 \tabularnewline
148 & 2 & 1.30411287339657 & 0.695887126603433 \tabularnewline
149 & 2 & 1.43164563413775 & 0.56835436586225 \tabularnewline
150 & 2 & 1.41934257671964 & 0.580657423280364 \tabularnewline
151 & 2 & 1.01670118631784 & 0.983298813682158 \tabularnewline
152 & 2 & 1.36152110596726 & 0.638478894032744 \tabularnewline
153 & 2 & 1.40175772522735 & 0.598242274772648 \tabularnewline
154 & 2 & 1.33994783435734 & 0.660052165642663 \tabularnewline
155 & 2 & 1.40271330211868 & 0.597286697881323 \tabularnewline
156 & 2 & 1.38250884558177 & 0.617491154418225 \tabularnewline
157 & 2 & 1.33237328684871 & 0.667626713151292 \tabularnewline
158 & 2 & 1.51067033160412 & 0.489329668395877 \tabularnewline
159 & 2 & 1.40275649540444 & 0.597243504595557 \tabularnewline
160 & 2 & 1.46952132873847 & 0.530478671261531 \tabularnewline
161 & 2 & 1.48846833471304 & 0.511531665286961 \tabularnewline
162 & 2 & 1.43164563413775 & 0.56835436586225 \tabularnewline
163 & 2 & 1.41107113270492 & 0.588928867295077 \tabularnewline
164 & 2 & 1.32531610011056 & 0.674683899889439 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146540&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]1[/C][C]1.48015369741256[/C][C]-0.480153697412556[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]1.38313757086294[/C][C]-0.383137570862941[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]1.28806913079101[/C][C]-0.288069130791008[/C][/ROW]
[ROW][C]4[/C][C]1[/C][C]1.32468737482939[/C][C]-0.324687374829394[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]1.48851152799881[/C][C]-0.488511527998806[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]1.28685487351444[/C][C]-0.286854873514442[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]1.25866263128722[/C][C]-0.258662631287219[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]1.37382416338537[/C][C]-0.37382416338537[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]1.30469840539197[/C][C]-0.304698405391967[/C][/ROW]
[ROW][C]10[/C][C]1[/C][C]1.25929135656838[/C][C]-0.259291356568385[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]1.48846833471304[/C][C]-0.488468334713039[/C][/ROW]
[ROW][C]12[/C][C]1[/C][C]1.33994783435734[/C][C]-0.339947834357338[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]1.41102793941916[/C][C]-0.411027939419156[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]1.38114003050876[/C][C]-0.381140030508758[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]1.14764778063631[/C][C]-0.147647780636311[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]1.39776930510972[/C][C]-0.397769305109718[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]1.51861492400868[/C][C]-0.518614924008678[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]1.51861492400868[/C][C]-0.518614924008678[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]1.18026665910201[/C][C]-0.180266659102011[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]1.30805810022844[/C][C]-0.308058100228435[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]1.41144117760085[/C][C]-0.411441177600848[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]1.55907369095898[/C][C]-0.559073690958979[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]1.38114003050876[/C][C]-0.381140030508758[/C][/ROW]
[ROW][C]24[/C][C]1[/C][C]1.4395902265423[/C][C]-0.439590226542305[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]1.3236886046523[/C][C]-0.323688604652303[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]1.46053477287106[/C][C]-0.460534772871057[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]1.4395902265423[/C][C]-0.439590226542305[/C][/ROW]
[ROW][C]28[/C][C]1[/C][C]1.26649585918109[/C][C]-0.26649585918109[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]1.31637273752891[/C][C]-0.316372737528915[/C][/ROW]
[ROW][C]30[/C][C]1[/C][C]1.30469840539197[/C][C]-0.304698405391967[/C][/ROW]
[ROW][C]31[/C][C]1[/C][C]1.33437082720289[/C][C]-0.33437082720289[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]1.37282539320828[/C][C]-0.372825393208279[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]1.48736486061599[/C][C]-0.487364860615994[/C][/ROW]
[ROW][C]34[/C][C]1[/C][C]1.34530506954799[/C][C]-0.345305069547988[/C][/ROW]
[ROW][C]35[/C][C]1[/C][C]1.35420523884387[/C][C]-0.354205238843867[/C][/ROW]
[ROW][C]36[/C][C]1[/C][C]1.46016472797513[/C][C]-0.460164727975132[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]1.30705933005134[/C][C]-0.307059330051344[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]1.39045343798633[/C][C]-0.390453437986329[/C][/ROW]
[ROW][C]39[/C][C]1[/C][C]1.46910809055678[/C][C]-0.469108090556778[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]1.36256306943011[/C][C]-0.362563069430114[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]1.35320646866678[/C][C]-0.353206468666776[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]1.28286645339681[/C][C]-0.282866453396807[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]1.26623717879585[/C][C]-0.266237178795848[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]1.4900958301713[/C][C]-0.490095830171296[/C][/ROW]
[ROW][C]45[/C][C]1[/C][C]1.43064686396066[/C][C]-0.430646863960659[/C][/ROW]
[ROW][C]46[/C][C]1[/C][C]1.3910821632675[/C][C]-0.391082163267495[/C][/ROW]
[ROW][C]47[/C][C]1[/C][C]1.33363073741104[/C][C]-0.33363073741104[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]1.40334202739984[/C][C]-0.403342027399843[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]1.43164563413775[/C][C]-0.43164563413775[/C][/ROW]
[ROW][C]50[/C][C]1[/C][C]1.49741169729468[/C][C]-0.497411697294685[/C][/ROW]
[ROW][C]51[/C][C]1[/C][C]1.43064686396066[/C][C]-0.430646863960659[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]1.27459500938209[/C][C]-0.274595009382095[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]1.2174704351358[/C][C]-0.217470435135798[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]1.35262093667138[/C][C]-0.352620936671377[/C][/ROW]
[ROW][C]55[/C][C]1[/C][C]1.48795039261139[/C][C]-0.487950392611394[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]1.40271330211868[/C][C]-0.402713302118677[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]1.40271330211868[/C][C]-0.402713302118677[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]1.33263196723395[/C][C]-0.332631967233949[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]1.38114003050876[/C][C]-0.381140030508758[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]1.4405889967194[/C][C]-0.440588996719396[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]1.41875704472424[/C][C]-0.418757044724236[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]1.3741942082813[/C][C]-0.374194208281295[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]1.43996027143823[/C][C]-0.43996027143823[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]1.44890363401988[/C][C]-0.448903634019875[/C][/ROW]
[ROW][C]65[/C][C]1[/C][C]1.3448918313663[/C][C]-0.344891831366297[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]1.32442869444415[/C][C]-0.324428694444153[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]1.43164563413775[/C][C]-0.43164563413775[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]1.49678297201352[/C][C]-0.496782972013519[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]1.43064686396066[/C][C]-0.430646863960659[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]1.41206990288201[/C][C]-0.412069902882014[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]1.30411287339657[/C][C]-0.304112873396567[/C][/ROW]
[ROW][C]72[/C][C]1[/C][C]1.43064686396066[/C][C]-0.430646863960659[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]1.265497089004[/C][C]-0.265497089003998[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]1.29516951081492[/C][C]-0.295169510814922[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]1.28286645339681[/C][C]-0.282866453396807[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]1.35386065121645[/C][C]-0.353860651216452[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]1.39045343798633[/C][C]-0.390453437986329[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]1.35420523884387[/C][C]-0.354205238843867[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]1.26790786753987[/C][C]-0.267907867539872[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]1.48015369741256[/C][C]-0.480153697412559[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]1.45185009067465[/C][C]-0.451850090674652[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]1.09451162650765[/C][C]-0.0945116265076492[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]1.33263196723395[/C][C]-0.332631967233949[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]1.31674278242484[/C][C]-0.31674278242484[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]1.38114003050876[/C][C]-0.381140030508758[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]1.41102793941916[/C][C]-0.411027939419156[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]1.13112987054604[/C][C]-0.131129870546036[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]1.4401757585377[/C][C]-0.440175758537704[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]1.291655258182[/C][C]-0.291655258182003[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]1.30742937494727[/C][C]-0.307429374947269[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]1.4199713020008[/C][C]-0.419971302000802[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]1.26623717879585[/C][C]-0.266237178795848[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]1.38151007540468[/C][C]-0.381510075404683[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]1.59705408947965[/C][C]-0.597054089479652[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]1.46490418333967[/C][C]-0.464904183339668[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]1.33163319705686[/C][C]-0.331633197056858[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]1.40271330211868[/C][C]-0.402713302118677[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]1.41202670959625[/C][C]-0.412026709596247[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]1.35420523884387[/C][C]-0.354205238843867[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]1.27496505427802[/C][C]-0.27496505427802[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]1.24603272225895[/C][C]-0.246032722258946[/C][/ROW]
[ROW][C]102[/C][C]2[/C][C]1.33300201212987[/C][C]0.666997987870126[/C][/ROW]
[ROW][C]103[/C][C]2[/C][C]1.50967156142703[/C][C]0.490328438572968[/C][/ROW]
[ROW][C]104[/C][C]2[/C][C]1.32331855975638[/C][C]0.676681440243622[/C][/ROW]
[ROW][C]105[/C][C]2[/C][C]1.38151007540468[/C][C]0.618489924595317[/C][/ROW]
[ROW][C]106[/C][C]2[/C][C]1.38250884558177[/C][C]0.617491154418225[/C][/ROW]
[ROW][C]107[/C][C]2[/C][C]1.42233222666018[/C][C]0.577667773339821[/C][/ROW]
[ROW][C]108[/C][C]2[/C][C]1.4555907758621[/C][C]0.544409224137902[/C][/ROW]
[ROW][C]109[/C][C]2[/C][C]1.37146323872599[/C][C]0.628536761274007[/C][/ROW]
[ROW][C]110[/C][C]2[/C][C]1.45957919597973[/C][C]0.540420804020268[/C][/ROW]
[ROW][C]111[/C][C]2[/C][C]1.4395902265423[/C][C]0.560409773457695[/C][/ROW]
[ROW][C]112[/C][C]2[/C][C]1.48909705999421[/C][C]0.510902940005795[/C][/ROW]
[ROW][C]113[/C][C]2[/C][C]1.33404397559273[/C][C]0.665956024407268[/C][/ROW]
[ROW][C]114[/C][C]2[/C][C]1.39023795088685[/C][C]0.609762049113146[/C][/ROW]
[ROW][C]115[/C][C]2[/C][C]1.40334202739984[/C][C]0.596657972600157[/C][/ROW]
[ROW][C]116[/C][C]2[/C][C]1.32527290682479[/C][C]0.674727093175206[/C][/ROW]
[ROW][C]117[/C][C]2[/C][C]1.48015369741256[/C][C]0.519846302587441[/C][/ROW]
[ROW][C]118[/C][C]2[/C][C]1.57643639476106[/C][C]0.423563605238942[/C][/ROW]
[ROW][C]119[/C][C]2[/C][C]1.45226332885634[/C][C]0.547736671143656[/C][/ROW]
[ROW][C]120[/C][C]2[/C][C]1.23893234223503[/C][C]0.761067657764968[/C][/ROW]
[ROW][C]121[/C][C]2[/C][C]1.43896150126114[/C][C]0.561038498738862[/C][/ROW]
[ROW][C]122[/C][C]2[/C][C]1.29081104580136[/C][C]0.709188954198638[/C][/ROW]
[ROW][C]123[/C][C]2[/C][C]1.40271330211868[/C][C]0.597286697881323[/C][/ROW]
[ROW][C]124[/C][C]2[/C][C]1.50173791447753[/C][C]0.49826208552247[/C][/ROW]
[ROW][C]125[/C][C]2[/C][C]1.43896150126114[/C][C]0.561038498738862[/C][/ROW]
[ROW][C]126[/C][C]2[/C][C]1.49031131727077[/C][C]0.509688682729229[/C][/ROW]
[ROW][C]127[/C][C]2[/C][C]1.43127558924182[/C][C]0.568724410758175[/C][/ROW]
[ROW][C]128[/C][C]2[/C][C]1.43164563413775[/C][C]0.56835436586225[/C][/ROW]
[ROW][C]129[/C][C]2[/C][C]1.32405864954823[/C][C]0.675941350451772[/C][/ROW]
[ROW][C]130[/C][C]2[/C][C]1.3731954381042[/C][C]0.626804561895796[/C][/ROW]
[ROW][C]131[/C][C]2[/C][C]1.43996027143823[/C][C]0.56003972856177[/C][/ROW]
[ROW][C]132[/C][C]2[/C][C]1.40234325722275[/C][C]0.597656742777248[/C][/ROW]
[ROW][C]133[/C][C]2[/C][C]1.42233222666018[/C][C]0.577667773339821[/C][/ROW]
[ROW][C]134[/C][C]2[/C][C]1.47909341660128[/C][C]0.520906583398719[/C][/ROW]
[ROW][C]135[/C][C]2[/C][C]1.61426889607601[/C][C]0.38573110392399[/C][/ROW]
[ROW][C]136[/C][C]2[/C][C]1.42770040730588[/C][C]0.572299592694118[/C][/ROW]
[ROW][C]137[/C][C]2[/C][C]1.48078242269373[/C][C]0.519217577306274[/C][/ROW]
[ROW][C]138[/C][C]2[/C][C]1.45321890574767[/C][C]0.546781094252332[/C][/ROW]
[ROW][C]139[/C][C]2[/C][C]1.46116349815222[/C][C]0.538836501847777[/C][/ROW]
[ROW][C]140[/C][C]2[/C][C]1.34493502465206[/C][C]0.655064975347937[/C][/ROW]
[ROW][C]141[/C][C]2[/C][C]1.54691853074659[/C][C]0.453081469253415[/C][/ROW]
[ROW][C]142[/C][C]2[/C][C]1.38372310285834[/C][C]0.61627689714166[/C][/ROW]
[ROW][C]143[/C][C]2[/C][C]1.4405889967194[/C][C]0.559411003280604[/C][/ROW]
[ROW][C]144[/C][C]2[/C][C]1.39145220816342[/C][C]0.60854779183658[/C][/ROW]
[ROW][C]145[/C][C]2[/C][C]1.10182749363104[/C][C]0.898172506368963[/C][/ROW]
[ROW][C]146[/C][C]2[/C][C]1.48972578527537[/C][C]0.510274214724629[/C][/ROW]
[ROW][C]147[/C][C]2[/C][C]1.41044240742376[/C][C]0.589557592576243[/C][/ROW]
[ROW][C]148[/C][C]2[/C][C]1.30411287339657[/C][C]0.695887126603433[/C][/ROW]
[ROW][C]149[/C][C]2[/C][C]1.43164563413775[/C][C]0.56835436586225[/C][/ROW]
[ROW][C]150[/C][C]2[/C][C]1.41934257671964[/C][C]0.580657423280364[/C][/ROW]
[ROW][C]151[/C][C]2[/C][C]1.01670118631784[/C][C]0.983298813682158[/C][/ROW]
[ROW][C]152[/C][C]2[/C][C]1.36152110596726[/C][C]0.638478894032744[/C][/ROW]
[ROW][C]153[/C][C]2[/C][C]1.40175772522735[/C][C]0.598242274772648[/C][/ROW]
[ROW][C]154[/C][C]2[/C][C]1.33994783435734[/C][C]0.660052165642663[/C][/ROW]
[ROW][C]155[/C][C]2[/C][C]1.40271330211868[/C][C]0.597286697881323[/C][/ROW]
[ROW][C]156[/C][C]2[/C][C]1.38250884558177[/C][C]0.617491154418225[/C][/ROW]
[ROW][C]157[/C][C]2[/C][C]1.33237328684871[/C][C]0.667626713151292[/C][/ROW]
[ROW][C]158[/C][C]2[/C][C]1.51067033160412[/C][C]0.489329668395877[/C][/ROW]
[ROW][C]159[/C][C]2[/C][C]1.40275649540444[/C][C]0.597243504595557[/C][/ROW]
[ROW][C]160[/C][C]2[/C][C]1.46952132873847[/C][C]0.530478671261531[/C][/ROW]
[ROW][C]161[/C][C]2[/C][C]1.48846833471304[/C][C]0.511531665286961[/C][/ROW]
[ROW][C]162[/C][C]2[/C][C]1.43164563413775[/C][C]0.56835436586225[/C][/ROW]
[ROW][C]163[/C][C]2[/C][C]1.41107113270492[/C][C]0.588928867295077[/C][/ROW]
[ROW][C]164[/C][C]2[/C][C]1.32531610011056[/C][C]0.674683899889439[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146540&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146540&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
111.48015369741256-0.480153697412556
211.38313757086294-0.383137570862941
311.28806913079101-0.288069130791008
411.32468737482939-0.324687374829394
511.48851152799881-0.488511527998806
611.28685487351444-0.286854873514442
711.25866263128722-0.258662631287219
811.37382416338537-0.37382416338537
911.30469840539197-0.304698405391967
1011.25929135656838-0.259291356568385
1111.48846833471304-0.488468334713039
1211.33994783435734-0.339947834357338
1311.41102793941916-0.411027939419156
1411.38114003050876-0.381140030508758
1511.14764778063631-0.147647780636311
1611.39776930510972-0.397769305109718
1711.51861492400868-0.518614924008678
1811.51861492400868-0.518614924008678
1911.18026665910201-0.180266659102011
2011.30805810022844-0.308058100228435
2111.41144117760085-0.411441177600848
2211.55907369095898-0.559073690958979
2311.38114003050876-0.381140030508758
2411.4395902265423-0.439590226542305
2511.3236886046523-0.323688604652303
2611.46053477287106-0.460534772871057
2711.4395902265423-0.439590226542305
2811.26649585918109-0.26649585918109
2911.31637273752891-0.316372737528915
3011.30469840539197-0.304698405391967
3111.33437082720289-0.33437082720289
3211.37282539320828-0.372825393208279
3311.48736486061599-0.487364860615994
3411.34530506954799-0.345305069547988
3511.35420523884387-0.354205238843867
3611.46016472797513-0.460164727975132
3711.30705933005134-0.307059330051344
3811.39045343798633-0.390453437986329
3911.46910809055678-0.469108090556778
4011.36256306943011-0.362563069430114
4111.35320646866678-0.353206468666776
4211.28286645339681-0.282866453396807
4311.26623717879585-0.266237178795848
4411.4900958301713-0.490095830171296
4511.43064686396066-0.430646863960659
4611.3910821632675-0.391082163267495
4711.33363073741104-0.33363073741104
4811.40334202739984-0.403342027399843
4911.43164563413775-0.43164563413775
5011.49741169729468-0.497411697294685
5111.43064686396066-0.430646863960659
5211.27459500938209-0.274595009382095
5311.2174704351358-0.217470435135798
5411.35262093667138-0.352620936671377
5511.48795039261139-0.487950392611394
5611.40271330211868-0.402713302118677
5711.40271330211868-0.402713302118677
5811.33263196723395-0.332631967233949
5911.38114003050876-0.381140030508758
6011.4405889967194-0.440588996719396
6111.41875704472424-0.418757044724236
6211.3741942082813-0.374194208281295
6311.43996027143823-0.43996027143823
6411.44890363401988-0.448903634019875
6511.3448918313663-0.344891831366297
6611.32442869444415-0.324428694444153
6711.43164563413775-0.43164563413775
6811.49678297201352-0.496782972013519
6911.43064686396066-0.430646863960659
7011.41206990288201-0.412069902882014
7111.30411287339657-0.304112873396567
7211.43064686396066-0.430646863960659
7311.265497089004-0.265497089003998
7411.29516951081492-0.295169510814922
7511.28286645339681-0.282866453396807
7611.35386065121645-0.353860651216452
7711.39045343798633-0.390453437986329
7811.35420523884387-0.354205238843867
7911.26790786753987-0.267907867539872
8011.48015369741256-0.480153697412559
8111.45185009067465-0.451850090674652
8211.09451162650765-0.0945116265076492
8311.33263196723395-0.332631967233949
8411.31674278242484-0.31674278242484
8511.38114003050876-0.381140030508758
8611.41102793941916-0.411027939419156
8711.13112987054604-0.131129870546036
8811.4401757585377-0.440175758537704
8911.291655258182-0.291655258182003
9011.30742937494727-0.307429374947269
9111.4199713020008-0.419971302000802
9211.26623717879585-0.266237178795848
9311.38151007540468-0.381510075404683
9411.59705408947965-0.597054089479652
9511.46490418333967-0.464904183339668
9611.33163319705686-0.331633197056858
9711.40271330211868-0.402713302118677
9811.41202670959625-0.412026709596247
9911.35420523884387-0.354205238843867
10011.27496505427802-0.27496505427802
10111.24603272225895-0.246032722258946
10221.333002012129870.666997987870126
10321.509671561427030.490328438572968
10421.323318559756380.676681440243622
10521.381510075404680.618489924595317
10621.382508845581770.617491154418225
10721.422332226660180.577667773339821
10821.45559077586210.544409224137902
10921.371463238725990.628536761274007
11021.459579195979730.540420804020268
11121.43959022654230.560409773457695
11221.489097059994210.510902940005795
11321.334043975592730.665956024407268
11421.390237950886850.609762049113146
11521.403342027399840.596657972600157
11621.325272906824790.674727093175206
11721.480153697412560.519846302587441
11821.576436394761060.423563605238942
11921.452263328856340.547736671143656
12021.238932342235030.761067657764968
12121.438961501261140.561038498738862
12221.290811045801360.709188954198638
12321.402713302118680.597286697881323
12421.501737914477530.49826208552247
12521.438961501261140.561038498738862
12621.490311317270770.509688682729229
12721.431275589241820.568724410758175
12821.431645634137750.56835436586225
12921.324058649548230.675941350451772
13021.37319543810420.626804561895796
13121.439960271438230.56003972856177
13221.402343257222750.597656742777248
13321.422332226660180.577667773339821
13421.479093416601280.520906583398719
13521.614268896076010.38573110392399
13621.427700407305880.572299592694118
13721.480782422693730.519217577306274
13821.453218905747670.546781094252332
13921.461163498152220.538836501847777
14021.344935024652060.655064975347937
14121.546918530746590.453081469253415
14221.383723102858340.61627689714166
14321.44058899671940.559411003280604
14421.391452208163420.60854779183658
14521.101827493631040.898172506368963
14621.489725785275370.510274214724629
14721.410442407423760.589557592576243
14821.304112873396570.695887126603433
14921.431645634137750.56835436586225
15021.419342576719640.580657423280364
15121.016701186317840.983298813682158
15221.361521105967260.638478894032744
15321.401757725227350.598242274772648
15421.339947834357340.660052165642663
15521.402713302118680.597286697881323
15621.382508845581770.617491154418225
15721.332373286848710.667626713151292
15821.510670331604120.489329668395877
15921.402756495404440.597243504595557
16021.469521328738470.530478671261531
16121.488468334713040.511531665286961
16221.431645634137750.56835436586225
16321.411071132704920.588928867295077
16421.325316100110560.674683899889439






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
94.13123927445532e-498.26247854891065e-491
103.7567910305007e-657.5135820610014e-651
119.67719660874226e-841.93543932174845e-831
126.60645744956371e-941.32129148991274e-931
137.61659928897534e-1241.52331985779507e-1231
146.27481968750816e-1241.25496393750163e-1231
155.33133183252998e-1391.066266366506e-1381
16001
171.25344257100657e-1822.50688514201314e-1821
185.61891852667195e-1871.12378370533439e-1861
193.16218734325829e-2016.32437468651659e-2011
206.05983960380972e-2271.21196792076194e-2261
212.72569567431628e-2625.45139134863255e-2621
228.67909415812148e-2511.7358188316243e-2501
233.97817348734552e-2627.95634697469104e-2621
246.43317333030521e-2811.28663466606104e-2801
253.92433557202785e-3007.84867114405571e-3001
26001
27001
28001
29001
30001
31001
32001
33001
34001
35001
36001
37001
38001
39001
40001
41001
42001
43001
44001
45001
46001
47001
48001
49001
50001
51001
52001
53001
54001
55001
56001
57001
58001
59001
60001
61001
62001
63001
64001
65001
66001
67001
68001
69001
70001
71001
72001
73001
74001
75001
76001
77001
78001
79001
80001
81001
82001
83001
84001
85001
86001
87001
88001
89001
90001
91001
92001
93001
94001
95001
96001
97001
98001
99001
100001
1010.0003831782557914310.0007663565115828620.999616821744209
102100
103100
104100
105100
106100
107100
108100
109100
110100
111100
112100
113100
114100
115100
116100
117100
118100
119100
120100
121100
122100
123100
124100
125100
126100
127100
128100
129100
130100
131100
132100
133100
134100
135100
136100
13719.88131291682493e-3234.94065645841247e-323
138100
13912.08473907465332e-2931.04236953732666e-293
14015.15223859566959e-2742.5761192978348e-274
14119.8715859067122e-2574.9357929533561e-257
14211.29998579060299e-2446.49992895301494e-245
14314.50544774007755e-2572.25272387003878e-257
14419.91230235236303e-2234.95615117618151e-223
14516.00326499716819e-1973.00163249858409e-197
14611.11913961840113e-1825.59569809200563e-183
14711.21308754280509e-1786.06543771402546e-179
148100
14916.69780113066295e-1363.34890056533147e-136
15011.64919223175938e-1238.24596115879689e-124
15114.4547637871912e-1212.2273818935956e-121
15216.95694200048636e-923.47847100024318e-92
15311.61576495384289e-838.07882476921444e-84
15411.16801688694594e-625.84008443472972e-63
15515.76106924372664e-472.88053462186332e-47

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 4.13123927445532e-49 & 8.26247854891065e-49 & 1 \tabularnewline
10 & 3.7567910305007e-65 & 7.5135820610014e-65 & 1 \tabularnewline
11 & 9.67719660874226e-84 & 1.93543932174845e-83 & 1 \tabularnewline
12 & 6.60645744956371e-94 & 1.32129148991274e-93 & 1 \tabularnewline
13 & 7.61659928897534e-124 & 1.52331985779507e-123 & 1 \tabularnewline
14 & 6.27481968750816e-124 & 1.25496393750163e-123 & 1 \tabularnewline
15 & 5.33133183252998e-139 & 1.066266366506e-138 & 1 \tabularnewline
16 & 0 & 0 & 1 \tabularnewline
17 & 1.25344257100657e-182 & 2.50688514201314e-182 & 1 \tabularnewline
18 & 5.61891852667195e-187 & 1.12378370533439e-186 & 1 \tabularnewline
19 & 3.16218734325829e-201 & 6.32437468651659e-201 & 1 \tabularnewline
20 & 6.05983960380972e-227 & 1.21196792076194e-226 & 1 \tabularnewline
21 & 2.72569567431628e-262 & 5.45139134863255e-262 & 1 \tabularnewline
22 & 8.67909415812148e-251 & 1.7358188316243e-250 & 1 \tabularnewline
23 & 3.97817348734552e-262 & 7.95634697469104e-262 & 1 \tabularnewline
24 & 6.43317333030521e-281 & 1.28663466606104e-280 & 1 \tabularnewline
25 & 3.92433557202785e-300 & 7.84867114405571e-300 & 1 \tabularnewline
26 & 0 & 0 & 1 \tabularnewline
27 & 0 & 0 & 1 \tabularnewline
28 & 0 & 0 & 1 \tabularnewline
29 & 0 & 0 & 1 \tabularnewline
30 & 0 & 0 & 1 \tabularnewline
31 & 0 & 0 & 1 \tabularnewline
32 & 0 & 0 & 1 \tabularnewline
33 & 0 & 0 & 1 \tabularnewline
34 & 0 & 0 & 1 \tabularnewline
35 & 0 & 0 & 1 \tabularnewline
36 & 0 & 0 & 1 \tabularnewline
37 & 0 & 0 & 1 \tabularnewline
38 & 0 & 0 & 1 \tabularnewline
39 & 0 & 0 & 1 \tabularnewline
40 & 0 & 0 & 1 \tabularnewline
41 & 0 & 0 & 1 \tabularnewline
42 & 0 & 0 & 1 \tabularnewline
43 & 0 & 0 & 1 \tabularnewline
44 & 0 & 0 & 1 \tabularnewline
45 & 0 & 0 & 1 \tabularnewline
46 & 0 & 0 & 1 \tabularnewline
47 & 0 & 0 & 1 \tabularnewline
48 & 0 & 0 & 1 \tabularnewline
49 & 0 & 0 & 1 \tabularnewline
50 & 0 & 0 & 1 \tabularnewline
51 & 0 & 0 & 1 \tabularnewline
52 & 0 & 0 & 1 \tabularnewline
53 & 0 & 0 & 1 \tabularnewline
54 & 0 & 0 & 1 \tabularnewline
55 & 0 & 0 & 1 \tabularnewline
56 & 0 & 0 & 1 \tabularnewline
57 & 0 & 0 & 1 \tabularnewline
58 & 0 & 0 & 1 \tabularnewline
59 & 0 & 0 & 1 \tabularnewline
60 & 0 & 0 & 1 \tabularnewline
61 & 0 & 0 & 1 \tabularnewline
62 & 0 & 0 & 1 \tabularnewline
63 & 0 & 0 & 1 \tabularnewline
64 & 0 & 0 & 1 \tabularnewline
65 & 0 & 0 & 1 \tabularnewline
66 & 0 & 0 & 1 \tabularnewline
67 & 0 & 0 & 1 \tabularnewline
68 & 0 & 0 & 1 \tabularnewline
69 & 0 & 0 & 1 \tabularnewline
70 & 0 & 0 & 1 \tabularnewline
71 & 0 & 0 & 1 \tabularnewline
72 & 0 & 0 & 1 \tabularnewline
73 & 0 & 0 & 1 \tabularnewline
74 & 0 & 0 & 1 \tabularnewline
75 & 0 & 0 & 1 \tabularnewline
76 & 0 & 0 & 1 \tabularnewline
77 & 0 & 0 & 1 \tabularnewline
78 & 0 & 0 & 1 \tabularnewline
79 & 0 & 0 & 1 \tabularnewline
80 & 0 & 0 & 1 \tabularnewline
81 & 0 & 0 & 1 \tabularnewline
82 & 0 & 0 & 1 \tabularnewline
83 & 0 & 0 & 1 \tabularnewline
84 & 0 & 0 & 1 \tabularnewline
85 & 0 & 0 & 1 \tabularnewline
86 & 0 & 0 & 1 \tabularnewline
87 & 0 & 0 & 1 \tabularnewline
88 & 0 & 0 & 1 \tabularnewline
89 & 0 & 0 & 1 \tabularnewline
90 & 0 & 0 & 1 \tabularnewline
91 & 0 & 0 & 1 \tabularnewline
92 & 0 & 0 & 1 \tabularnewline
93 & 0 & 0 & 1 \tabularnewline
94 & 0 & 0 & 1 \tabularnewline
95 & 0 & 0 & 1 \tabularnewline
96 & 0 & 0 & 1 \tabularnewline
97 & 0 & 0 & 1 \tabularnewline
98 & 0 & 0 & 1 \tabularnewline
99 & 0 & 0 & 1 \tabularnewline
100 & 0 & 0 & 1 \tabularnewline
101 & 0.000383178255791431 & 0.000766356511582862 & 0.999616821744209 \tabularnewline
102 & 1 & 0 & 0 \tabularnewline
103 & 1 & 0 & 0 \tabularnewline
104 & 1 & 0 & 0 \tabularnewline
105 & 1 & 0 & 0 \tabularnewline
106 & 1 & 0 & 0 \tabularnewline
107 & 1 & 0 & 0 \tabularnewline
108 & 1 & 0 & 0 \tabularnewline
109 & 1 & 0 & 0 \tabularnewline
110 & 1 & 0 & 0 \tabularnewline
111 & 1 & 0 & 0 \tabularnewline
112 & 1 & 0 & 0 \tabularnewline
113 & 1 & 0 & 0 \tabularnewline
114 & 1 & 0 & 0 \tabularnewline
115 & 1 & 0 & 0 \tabularnewline
116 & 1 & 0 & 0 \tabularnewline
117 & 1 & 0 & 0 \tabularnewline
118 & 1 & 0 & 0 \tabularnewline
119 & 1 & 0 & 0 \tabularnewline
120 & 1 & 0 & 0 \tabularnewline
121 & 1 & 0 & 0 \tabularnewline
122 & 1 & 0 & 0 \tabularnewline
123 & 1 & 0 & 0 \tabularnewline
124 & 1 & 0 & 0 \tabularnewline
125 & 1 & 0 & 0 \tabularnewline
126 & 1 & 0 & 0 \tabularnewline
127 & 1 & 0 & 0 \tabularnewline
128 & 1 & 0 & 0 \tabularnewline
129 & 1 & 0 & 0 \tabularnewline
130 & 1 & 0 & 0 \tabularnewline
131 & 1 & 0 & 0 \tabularnewline
132 & 1 & 0 & 0 \tabularnewline
133 & 1 & 0 & 0 \tabularnewline
134 & 1 & 0 & 0 \tabularnewline
135 & 1 & 0 & 0 \tabularnewline
136 & 1 & 0 & 0 \tabularnewline
137 & 1 & 9.88131291682493e-323 & 4.94065645841247e-323 \tabularnewline
138 & 1 & 0 & 0 \tabularnewline
139 & 1 & 2.08473907465332e-293 & 1.04236953732666e-293 \tabularnewline
140 & 1 & 5.15223859566959e-274 & 2.5761192978348e-274 \tabularnewline
141 & 1 & 9.8715859067122e-257 & 4.9357929533561e-257 \tabularnewline
142 & 1 & 1.29998579060299e-244 & 6.49992895301494e-245 \tabularnewline
143 & 1 & 4.50544774007755e-257 & 2.25272387003878e-257 \tabularnewline
144 & 1 & 9.91230235236303e-223 & 4.95615117618151e-223 \tabularnewline
145 & 1 & 6.00326499716819e-197 & 3.00163249858409e-197 \tabularnewline
146 & 1 & 1.11913961840113e-182 & 5.59569809200563e-183 \tabularnewline
147 & 1 & 1.21308754280509e-178 & 6.06543771402546e-179 \tabularnewline
148 & 1 & 0 & 0 \tabularnewline
149 & 1 & 6.69780113066295e-136 & 3.34890056533147e-136 \tabularnewline
150 & 1 & 1.64919223175938e-123 & 8.24596115879689e-124 \tabularnewline
151 & 1 & 4.4547637871912e-121 & 2.2273818935956e-121 \tabularnewline
152 & 1 & 6.95694200048636e-92 & 3.47847100024318e-92 \tabularnewline
153 & 1 & 1.61576495384289e-83 & 8.07882476921444e-84 \tabularnewline
154 & 1 & 1.16801688694594e-62 & 5.84008443472972e-63 \tabularnewline
155 & 1 & 5.76106924372664e-47 & 2.88053462186332e-47 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146540&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]9[/C][C]4.13123927445532e-49[/C][C]8.26247854891065e-49[/C][C]1[/C][/ROW]
[ROW][C]10[/C][C]3.7567910305007e-65[/C][C]7.5135820610014e-65[/C][C]1[/C][/ROW]
[ROW][C]11[/C][C]9.67719660874226e-84[/C][C]1.93543932174845e-83[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]6.60645744956371e-94[/C][C]1.32129148991274e-93[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]7.61659928897534e-124[/C][C]1.52331985779507e-123[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]6.27481968750816e-124[/C][C]1.25496393750163e-123[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]5.33133183252998e-139[/C][C]1.066266366506e-138[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]1.25344257100657e-182[/C][C]2.50688514201314e-182[/C][C]1[/C][/ROW]
[ROW][C]18[/C][C]5.61891852667195e-187[/C][C]1.12378370533439e-186[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]3.16218734325829e-201[/C][C]6.32437468651659e-201[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]6.05983960380972e-227[/C][C]1.21196792076194e-226[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]2.72569567431628e-262[/C][C]5.45139134863255e-262[/C][C]1[/C][/ROW]
[ROW][C]22[/C][C]8.67909415812148e-251[/C][C]1.7358188316243e-250[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]3.97817348734552e-262[/C][C]7.95634697469104e-262[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]6.43317333030521e-281[/C][C]1.28663466606104e-280[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]3.92433557202785e-300[/C][C]7.84867114405571e-300[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]60[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]67[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]79[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]84[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]87[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]88[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]90[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]91[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]100[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]101[/C][C]0.000383178255791431[/C][C]0.000766356511582862[/C][C]0.999616821744209[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]104[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]119[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]126[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]9.88131291682493e-323[/C][C]4.94065645841247e-323[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]2.08473907465332e-293[/C][C]1.04236953732666e-293[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]5.15223859566959e-274[/C][C]2.5761192978348e-274[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]9.8715859067122e-257[/C][C]4.9357929533561e-257[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]1.29998579060299e-244[/C][C]6.49992895301494e-245[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]4.50544774007755e-257[/C][C]2.25272387003878e-257[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]9.91230235236303e-223[/C][C]4.95615117618151e-223[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]6.00326499716819e-197[/C][C]3.00163249858409e-197[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]1.11913961840113e-182[/C][C]5.59569809200563e-183[/C][/ROW]
[ROW][C]147[/C][C]1[/C][C]1.21308754280509e-178[/C][C]6.06543771402546e-179[/C][/ROW]
[ROW][C]148[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]6.69780113066295e-136[/C][C]3.34890056533147e-136[/C][/ROW]
[ROW][C]150[/C][C]1[/C][C]1.64919223175938e-123[/C][C]8.24596115879689e-124[/C][/ROW]
[ROW][C]151[/C][C]1[/C][C]4.4547637871912e-121[/C][C]2.2273818935956e-121[/C][/ROW]
[ROW][C]152[/C][C]1[/C][C]6.95694200048636e-92[/C][C]3.47847100024318e-92[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]1.61576495384289e-83[/C][C]8.07882476921444e-84[/C][/ROW]
[ROW][C]154[/C][C]1[/C][C]1.16801688694594e-62[/C][C]5.84008443472972e-63[/C][/ROW]
[ROW][C]155[/C][C]1[/C][C]5.76106924372664e-47[/C][C]2.88053462186332e-47[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146540&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146540&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
94.13123927445532e-498.26247854891065e-491
103.7567910305007e-657.5135820610014e-651
119.67719660874226e-841.93543932174845e-831
126.60645744956371e-941.32129148991274e-931
137.61659928897534e-1241.52331985779507e-1231
146.27481968750816e-1241.25496393750163e-1231
155.33133183252998e-1391.066266366506e-1381
16001
171.25344257100657e-1822.50688514201314e-1821
185.61891852667195e-1871.12378370533439e-1861
193.16218734325829e-2016.32437468651659e-2011
206.05983960380972e-2271.21196792076194e-2261
212.72569567431628e-2625.45139134863255e-2621
228.67909415812148e-2511.7358188316243e-2501
233.97817348734552e-2627.95634697469104e-2621
246.43317333030521e-2811.28663466606104e-2801
253.92433557202785e-3007.84867114405571e-3001
26001
27001
28001
29001
30001
31001
32001
33001
34001
35001
36001
37001
38001
39001
40001
41001
42001
43001
44001
45001
46001
47001
48001
49001
50001
51001
52001
53001
54001
55001
56001
57001
58001
59001
60001
61001
62001
63001
64001
65001
66001
67001
68001
69001
70001
71001
72001
73001
74001
75001
76001
77001
78001
79001
80001
81001
82001
83001
84001
85001
86001
87001
88001
89001
90001
91001
92001
93001
94001
95001
96001
97001
98001
99001
100001
1010.0003831782557914310.0007663565115828620.999616821744209
102100
103100
104100
105100
106100
107100
108100
109100
110100
111100
112100
113100
114100
115100
116100
117100
118100
119100
120100
121100
122100
123100
124100
125100
126100
127100
128100
129100
130100
131100
132100
133100
134100
135100
136100
13719.88131291682493e-3234.94065645841247e-323
138100
13912.08473907465332e-2931.04236953732666e-293
14015.15223859566959e-2742.5761192978348e-274
14119.8715859067122e-2574.9357929533561e-257
14211.29998579060299e-2446.49992895301494e-245
14314.50544774007755e-2572.25272387003878e-257
14419.91230235236303e-2234.95615117618151e-223
14516.00326499716819e-1973.00163249858409e-197
14611.11913961840113e-1825.59569809200563e-183
14711.21308754280509e-1786.06543771402546e-179
148100
14916.69780113066295e-1363.34890056533147e-136
15011.64919223175938e-1238.24596115879689e-124
15114.4547637871912e-1212.2273818935956e-121
15216.95694200048636e-923.47847100024318e-92
15311.61576495384289e-838.07882476921444e-84
15411.16801688694594e-625.84008443472972e-63
15515.76106924372664e-472.88053462186332e-47






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1471NOK
5% type I error level1471NOK
10% type I error level1471NOK

\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 & 147 & 1 & NOK \tabularnewline
5% type I error level & 147 & 1 & NOK \tabularnewline
10% type I error level & 147 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146540&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]147[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]147[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]147[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146540&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146540&T=6

As an alternative you can also use a QR Code:  
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 level1471NOK
5% type I error level1471NOK
10% type I error level1471NOK


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

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