FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationSat, 04 Dec 2010 13:53:29 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/04/t1291470725yzjitxpjtz8kr01.htm/, Retrieved Thu, 06 Nov 2025 03:07:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=105143, Retrieved Thu, 06 Nov 2025 03:07:59 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact443

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:06:21] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [WS7_3] [2009-11-18 18:28:36] [8b1aef4e7013bd33fbc2a5833375c5f5]
-    D        [Multiple Regression] [Paper Multiple re...] [2010-12-04 13:53:29] [da925928e5a77063c5ecc7b801d712e1] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1,79	194,9
1,95	195,5
2,26	196,0
2,04	196,2
2,16	196,2
2,75	196,2
2,79	196,2
2,88	197,0
3,36	197,7
2,97	198,0
3,10	198,2
2,49	198,5
2,2	198,6
2,25	199,5
2,09	200
2,79	201,3
3,14	202,2
2,93	202,9
2,65	203,5
2,67	203,5
2,26	204
2,35	204,1
2,13	204,3
2,18	204,5
2,9	204,8
2,63	205,1
2,67	205,7
1,81	206,5
1,33	206,9
0,88	207,1
1,28	207,8
1,26	208
1,26	208,5
1,29	208,6
1,1	209
1,37	209,1
1,21	209,7
1,74	209,8
1,76	209,9
1,48	210
1,04	210,8
1,62	211,4
1,49	211,7
1,79	212
1,8	212,2
1,58	212,4
1,86	212,9
1,74	213,4
1,59	213,7
1,26	214
1,13	214,3
1,92	214,8
2,61	215
2,26	215,9
2,41	216,4
2,26	216,9
2,03	217,2
2,86	217,5
2,55	217,9
2,27	218,1
2,26	218,6
2,57	218,9
3,07	219,3
2,76	220,4
2,51	220,9
2,87	221
3,14	221,8
3,11	222
3,16	222,2
2,47	222,5
2,57	222,9
2,89	223,1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time113 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 113 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105143&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]113 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105143&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105143&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 time113 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 34.2655743166752 -0.165157690441535X[t] -0.169194606178226M1[t] -0.0910606501698221M2[t] + 0.00598734433123115M3[t] + 0.0204107229500253M4[t] + 0.0301358968138449M5[t] + 0.119936519488922M6[t] + 0.209080988193435M7[t] + 0.233451803331717M8[t] + 0.217166464499433M9[t] + 0.128935549086204M10[t] + 0.0860589923985128M11[t] + 0.0656817483422296t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  34.2655743166752 -0.165157690441535X[t] -0.169194606178226M1[t] -0.0910606501698221M2[t] +  0.00598734433123115M3[t] +  0.0204107229500253M4[t] +  0.0301358968138449M5[t] +  0.119936519488922M6[t] +  0.209080988193435M7[t] +  0.233451803331717M8[t] +  0.217166464499433M9[t] +  0.128935549086204M10[t] +  0.0860589923985128M11[t] +  0.0656817483422296t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105143&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  34.2655743166752 -0.165157690441535X[t] -0.169194606178226M1[t] -0.0910606501698221M2[t] +  0.00598734433123115M3[t] +  0.0204107229500253M4[t] +  0.0301358968138449M5[t] +  0.119936519488922M6[t] +  0.209080988193435M7[t] +  0.233451803331717M8[t] +  0.217166464499433M9[t] +  0.128935549086204M10[t] +  0.0860589923985128M11[t] +  0.0656817483422296t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105143&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105143&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
Y[t] = + 34.2655743166752 -0.165157690441535X[t] -0.169194606178226M1[t] -0.0910606501698221M2[t] + 0.00598734433123115M3[t] + 0.0204107229500253M4[t] + 0.0301358968138449M5[t] + 0.119936519488922M6[t] + 0.209080988193435M7[t] + 0.233451803331717M8[t] + 0.217166464499433M9[t] + 0.128935549086204M10[t] + 0.0860589923985128M11[t] + 0.0656817483422296t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)34.265574316675230.2731291.13190.2623430.131171
X-0.1651576904415350.155704-1.06070.2932150.146607
M1-0.1691946061782260.400678-0.42230.6743890.337194
M2-0.09106065016982210.400246-0.22750.8208260.410413
M30.005987344331231150.3998720.0150.9881050.494052
M40.02041072295002530.401650.05080.9596460.479823
M50.03013589681384490.40260.07490.9405890.470295
M60.1199365194889220.402740.29780.7669190.383459
M70.2090809881934350.404560.51680.6072530.303627
M80.2334518033317170.4028170.57950.5644640.282232
M90.2171664644994330.4027340.53920.5917920.295896
M100.1289355490862040.3995110.32270.7480580.374029
M110.08605899239851280.3989570.21570.829970.414985
t0.06568174834222960.0620381.05870.2941080.147054

\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) & 34.2655743166752 & 30.273129 & 1.1319 & 0.262343 & 0.131171 \tabularnewline
X & -0.165157690441535 & 0.155704 & -1.0607 & 0.293215 & 0.146607 \tabularnewline
M1 & -0.169194606178226 & 0.400678 & -0.4223 & 0.674389 & 0.337194 \tabularnewline
M2 & -0.0910606501698221 & 0.400246 & -0.2275 & 0.820826 & 0.410413 \tabularnewline
M3 & 0.00598734433123115 & 0.399872 & 0.015 & 0.988105 & 0.494052 \tabularnewline
M4 & 0.0204107229500253 & 0.40165 & 0.0508 & 0.959646 & 0.479823 \tabularnewline
M5 & 0.0301358968138449 & 0.4026 & 0.0749 & 0.940589 & 0.470295 \tabularnewline
M6 & 0.119936519488922 & 0.40274 & 0.2978 & 0.766919 & 0.383459 \tabularnewline
M7 & 0.209080988193435 & 0.40456 & 0.5168 & 0.607253 & 0.303627 \tabularnewline
M8 & 0.233451803331717 & 0.402817 & 0.5795 & 0.564464 & 0.282232 \tabularnewline
M9 & 0.217166464499433 & 0.402734 & 0.5392 & 0.591792 & 0.295896 \tabularnewline
M10 & 0.128935549086204 & 0.399511 & 0.3227 & 0.748058 & 0.374029 \tabularnewline
M11 & 0.0860589923985128 & 0.398957 & 0.2157 & 0.82997 & 0.414985 \tabularnewline
t & 0.0656817483422296 & 0.062038 & 1.0587 & 0.294108 & 0.147054 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105143&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]34.2655743166752[/C][C]30.273129[/C][C]1.1319[/C][C]0.262343[/C][C]0.131171[/C][/ROW]
[ROW][C]X[/C][C]-0.165157690441535[/C][C]0.155704[/C][C]-1.0607[/C][C]0.293215[/C][C]0.146607[/C][/ROW]
[ROW][C]M1[/C][C]-0.169194606178226[/C][C]0.400678[/C][C]-0.4223[/C][C]0.674389[/C][C]0.337194[/C][/ROW]
[ROW][C]M2[/C][C]-0.0910606501698221[/C][C]0.400246[/C][C]-0.2275[/C][C]0.820826[/C][C]0.410413[/C][/ROW]
[ROW][C]M3[/C][C]0.00598734433123115[/C][C]0.399872[/C][C]0.015[/C][C]0.988105[/C][C]0.494052[/C][/ROW]
[ROW][C]M4[/C][C]0.0204107229500253[/C][C]0.40165[/C][C]0.0508[/C][C]0.959646[/C][C]0.479823[/C][/ROW]
[ROW][C]M5[/C][C]0.0301358968138449[/C][C]0.4026[/C][C]0.0749[/C][C]0.940589[/C][C]0.470295[/C][/ROW]
[ROW][C]M6[/C][C]0.119936519488922[/C][C]0.40274[/C][C]0.2978[/C][C]0.766919[/C][C]0.383459[/C][/ROW]
[ROW][C]M7[/C][C]0.209080988193435[/C][C]0.40456[/C][C]0.5168[/C][C]0.607253[/C][C]0.303627[/C][/ROW]
[ROW][C]M8[/C][C]0.233451803331717[/C][C]0.402817[/C][C]0.5795[/C][C]0.564464[/C][C]0.282232[/C][/ROW]
[ROW][C]M9[/C][C]0.217166464499433[/C][C]0.402734[/C][C]0.5392[/C][C]0.591792[/C][C]0.295896[/C][/ROW]
[ROW][C]M10[/C][C]0.128935549086204[/C][C]0.399511[/C][C]0.3227[/C][C]0.748058[/C][C]0.374029[/C][/ROW]
[ROW][C]M11[/C][C]0.0860589923985128[/C][C]0.398957[/C][C]0.2157[/C][C]0.82997[/C][C]0.414985[/C][/ROW]
[ROW][C]t[/C][C]0.0656817483422296[/C][C]0.062038[/C][C]1.0587[/C][C]0.294108[/C][C]0.147054[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105143&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105143&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)34.265574316675230.2731291.13190.2623430.131171
X-0.1651576904415350.155704-1.06070.2932150.146607
M1-0.1691946061782260.400678-0.42230.6743890.337194
M2-0.09106065016982210.400246-0.22750.8208260.410413
M30.005987344331231150.3998720.0150.9881050.494052
M40.02041072295002530.401650.05080.9596460.479823
M50.03013589681384490.40260.07490.9405890.470295
M60.1199365194889220.402740.29780.7669190.383459
M70.2090809881934350.404560.51680.6072530.303627
M80.2334518033317170.4028170.57950.5644640.282232
M90.2171664644994330.4027340.53920.5917920.295896
M100.1289355490862040.3995110.32270.7480580.374029
M110.08605899239851280.3989570.21570.829970.414985
t0.06568174834222960.0620381.05870.2941080.147054






Multiple Linear Regression - Regression Statistics
Multiple R0.205311131411143
R-squared0.0421526606813234
Adjusted R-squared-0.172537260200449
F-TEST (value)0.196342056991751
F-TEST (DF numerator)13
F-TEST (DF denominator)58
p-value0.998747705775358
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.689831138014099
Sum Squared Residuals27.6002859404819

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.205311131411143 \tabularnewline
R-squared & 0.0421526606813234 \tabularnewline
Adjusted R-squared & -0.172537260200449 \tabularnewline
F-TEST (value) & 0.196342056991751 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0.998747705775358 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.689831138014099 \tabularnewline
Sum Squared Residuals & 27.6002859404819 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105143&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.205311131411143[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0421526606813234[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.172537260200449[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.196342056991751[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]0.998747705775358[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.689831138014099[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]27.6002859404819[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105143&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105143&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 R0.205311131411143
R-squared0.0421526606813234
Adjusted R-squared-0.172537260200449
F-TEST (value)0.196342056991751
F-TEST (DF numerator)13
F-TEST (DF denominator)58
p-value0.998747705775358
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.689831138014099
Sum Squared Residuals27.6002859404819






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.791.97282759178389-0.182827591783894
21.952.01754868186965-0.0675486818696511
32.262.097699579492160.162300420507844
42.042.14477316836487-0.104773168364875
52.162.22018009057092-0.0601800905709236
62.752.375662461588230.374337538411769
72.792.530488678634970.259511321365028
82.882.488415089762250.391584910237746
93.362.422201115963130.937798884036872
102.972.350104641759660.619895358240335
113.12.339878295325900.760121704674103
122.492.269953744137150.220046255862848
132.22.149925117257000.0500748827429965
142.252.145098900210250.104901099789747
152.092.22524979783277-0.135249797832769
162.792.090649927219790.699350072780206
173.142.017414928028471.12258507197153
182.932.057286915736690.872713084263306
192.652.113018518518520.536981481481484
202.672.203071081999030.466928918000972
212.262.169888646288210.0901113537117932
222.352.130823710173050.219176289826946
232.132.120597363739280.00940263626071736
242.182.067188581594690.112811418405306
252.91.914128416626240.985871583373764
262.632.008396813844410.621603186155589
272.672.072031942422770.597968057577227
281.812.02001091703057-0.210010917030567
291.332.02935476306-0.699354763060001
300.882.151805595989-1.27180559598900
311.282.19102142972667-0.911021429726667
321.262.24804245511887-0.988042455118874
331.262.21486001940805-0.954860019408051
341.292.1757950832929-0.8857950832929
351.12.13253719877082-1.03253719877082
361.372.09564418567039-0.725644185670386
371.211.89303671356947-0.68303671356947
381.742.02033664887595-0.280336648875946
391.762.16655062267508-0.406550622675076
401.482.23013998059195-0.750139980591947
411.042.17342075044477-1.13342075044477
421.622.22980850719715-0.609808507197153
431.492.33508741711144-0.845087417111437
441.792.37559267345949-0.585592673459486
451.82.39195754488113-0.591957544881127
461.582.33637683972182-0.756376839721818
471.862.27660318615559-0.416603186155588
481.742.17364709687854-0.433647096878537
491.592.02058693191008-0.430586931910083
501.262.11485532912825-0.854855329128254
511.132.22803776483907-1.09803776483907
521.922.22556404657933-0.30556404657933
532.612.267939430697070.342060569302926
542.262.274779880317-0.0147798803169983
552.412.347027252142970.0629727478570277
562.262.35450097040272-0.094500970402717
572.032.35435007278020-0.324350072780205
582.862.282253598576740.577746401423257
592.552.238995714054670.311004285945334
602.272.185586931910080.0844130680899229
612.261.999495228853310.260504771146687
622.572.093763626071480.476236373928516
633.072.190430292738150.879569707261848
642.762.088861960213490.671138039786513
652.512.081690037198770.428309962801231
662.872.220656639171920.649343360828077
673.142.243356703865430.896643296134566
683.112.300377729257640.809622270742358
693.162.316742600679280.843257399320717
702.472.244646126475820.225353873524180
712.572.201388241953740.368611758046257
722.892.147979459809160.742020540190845

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.79 & 1.97282759178389 & -0.182827591783894 \tabularnewline
2 & 1.95 & 2.01754868186965 & -0.0675486818696511 \tabularnewline
3 & 2.26 & 2.09769957949216 & 0.162300420507844 \tabularnewline
4 & 2.04 & 2.14477316836487 & -0.104773168364875 \tabularnewline
5 & 2.16 & 2.22018009057092 & -0.0601800905709236 \tabularnewline
6 & 2.75 & 2.37566246158823 & 0.374337538411769 \tabularnewline
7 & 2.79 & 2.53048867863497 & 0.259511321365028 \tabularnewline
8 & 2.88 & 2.48841508976225 & 0.391584910237746 \tabularnewline
9 & 3.36 & 2.42220111596313 & 0.937798884036872 \tabularnewline
10 & 2.97 & 2.35010464175966 & 0.619895358240335 \tabularnewline
11 & 3.1 & 2.33987829532590 & 0.760121704674103 \tabularnewline
12 & 2.49 & 2.26995374413715 & 0.220046255862848 \tabularnewline
13 & 2.2 & 2.14992511725700 & 0.0500748827429965 \tabularnewline
14 & 2.25 & 2.14509890021025 & 0.104901099789747 \tabularnewline
15 & 2.09 & 2.22524979783277 & -0.135249797832769 \tabularnewline
16 & 2.79 & 2.09064992721979 & 0.699350072780206 \tabularnewline
17 & 3.14 & 2.01741492802847 & 1.12258507197153 \tabularnewline
18 & 2.93 & 2.05728691573669 & 0.872713084263306 \tabularnewline
19 & 2.65 & 2.11301851851852 & 0.536981481481484 \tabularnewline
20 & 2.67 & 2.20307108199903 & 0.466928918000972 \tabularnewline
21 & 2.26 & 2.16988864628821 & 0.0901113537117932 \tabularnewline
22 & 2.35 & 2.13082371017305 & 0.219176289826946 \tabularnewline
23 & 2.13 & 2.12059736373928 & 0.00940263626071736 \tabularnewline
24 & 2.18 & 2.06718858159469 & 0.112811418405306 \tabularnewline
25 & 2.9 & 1.91412841662624 & 0.985871583373764 \tabularnewline
26 & 2.63 & 2.00839681384441 & 0.621603186155589 \tabularnewline
27 & 2.67 & 2.07203194242277 & 0.597968057577227 \tabularnewline
28 & 1.81 & 2.02001091703057 & -0.210010917030567 \tabularnewline
29 & 1.33 & 2.02935476306 & -0.699354763060001 \tabularnewline
30 & 0.88 & 2.151805595989 & -1.27180559598900 \tabularnewline
31 & 1.28 & 2.19102142972667 & -0.911021429726667 \tabularnewline
32 & 1.26 & 2.24804245511887 & -0.988042455118874 \tabularnewline
33 & 1.26 & 2.21486001940805 & -0.954860019408051 \tabularnewline
34 & 1.29 & 2.1757950832929 & -0.8857950832929 \tabularnewline
35 & 1.1 & 2.13253719877082 & -1.03253719877082 \tabularnewline
36 & 1.37 & 2.09564418567039 & -0.725644185670386 \tabularnewline
37 & 1.21 & 1.89303671356947 & -0.68303671356947 \tabularnewline
38 & 1.74 & 2.02033664887595 & -0.280336648875946 \tabularnewline
39 & 1.76 & 2.16655062267508 & -0.406550622675076 \tabularnewline
40 & 1.48 & 2.23013998059195 & -0.750139980591947 \tabularnewline
41 & 1.04 & 2.17342075044477 & -1.13342075044477 \tabularnewline
42 & 1.62 & 2.22980850719715 & -0.609808507197153 \tabularnewline
43 & 1.49 & 2.33508741711144 & -0.845087417111437 \tabularnewline
44 & 1.79 & 2.37559267345949 & -0.585592673459486 \tabularnewline
45 & 1.8 & 2.39195754488113 & -0.591957544881127 \tabularnewline
46 & 1.58 & 2.33637683972182 & -0.756376839721818 \tabularnewline
47 & 1.86 & 2.27660318615559 & -0.416603186155588 \tabularnewline
48 & 1.74 & 2.17364709687854 & -0.433647096878537 \tabularnewline
49 & 1.59 & 2.02058693191008 & -0.430586931910083 \tabularnewline
50 & 1.26 & 2.11485532912825 & -0.854855329128254 \tabularnewline
51 & 1.13 & 2.22803776483907 & -1.09803776483907 \tabularnewline
52 & 1.92 & 2.22556404657933 & -0.30556404657933 \tabularnewline
53 & 2.61 & 2.26793943069707 & 0.342060569302926 \tabularnewline
54 & 2.26 & 2.274779880317 & -0.0147798803169983 \tabularnewline
55 & 2.41 & 2.34702725214297 & 0.0629727478570277 \tabularnewline
56 & 2.26 & 2.35450097040272 & -0.094500970402717 \tabularnewline
57 & 2.03 & 2.35435007278020 & -0.324350072780205 \tabularnewline
58 & 2.86 & 2.28225359857674 & 0.577746401423257 \tabularnewline
59 & 2.55 & 2.23899571405467 & 0.311004285945334 \tabularnewline
60 & 2.27 & 2.18558693191008 & 0.0844130680899229 \tabularnewline
61 & 2.26 & 1.99949522885331 & 0.260504771146687 \tabularnewline
62 & 2.57 & 2.09376362607148 & 0.476236373928516 \tabularnewline
63 & 3.07 & 2.19043029273815 & 0.879569707261848 \tabularnewline
64 & 2.76 & 2.08886196021349 & 0.671138039786513 \tabularnewline
65 & 2.51 & 2.08169003719877 & 0.428309962801231 \tabularnewline
66 & 2.87 & 2.22065663917192 & 0.649343360828077 \tabularnewline
67 & 3.14 & 2.24335670386543 & 0.896643296134566 \tabularnewline
68 & 3.11 & 2.30037772925764 & 0.809622270742358 \tabularnewline
69 & 3.16 & 2.31674260067928 & 0.843257399320717 \tabularnewline
70 & 2.47 & 2.24464612647582 & 0.225353873524180 \tabularnewline
71 & 2.57 & 2.20138824195374 & 0.368611758046257 \tabularnewline
72 & 2.89 & 2.14797945980916 & 0.742020540190845 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105143&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]1.79[/C][C]1.97282759178389[/C][C]-0.182827591783894[/C][/ROW]
[ROW][C]2[/C][C]1.95[/C][C]2.01754868186965[/C][C]-0.0675486818696511[/C][/ROW]
[ROW][C]3[/C][C]2.26[/C][C]2.09769957949216[/C][C]0.162300420507844[/C][/ROW]
[ROW][C]4[/C][C]2.04[/C][C]2.14477316836487[/C][C]-0.104773168364875[/C][/ROW]
[ROW][C]5[/C][C]2.16[/C][C]2.22018009057092[/C][C]-0.0601800905709236[/C][/ROW]
[ROW][C]6[/C][C]2.75[/C][C]2.37566246158823[/C][C]0.374337538411769[/C][/ROW]
[ROW][C]7[/C][C]2.79[/C][C]2.53048867863497[/C][C]0.259511321365028[/C][/ROW]
[ROW][C]8[/C][C]2.88[/C][C]2.48841508976225[/C][C]0.391584910237746[/C][/ROW]
[ROW][C]9[/C][C]3.36[/C][C]2.42220111596313[/C][C]0.937798884036872[/C][/ROW]
[ROW][C]10[/C][C]2.97[/C][C]2.35010464175966[/C][C]0.619895358240335[/C][/ROW]
[ROW][C]11[/C][C]3.1[/C][C]2.33987829532590[/C][C]0.760121704674103[/C][/ROW]
[ROW][C]12[/C][C]2.49[/C][C]2.26995374413715[/C][C]0.220046255862848[/C][/ROW]
[ROW][C]13[/C][C]2.2[/C][C]2.14992511725700[/C][C]0.0500748827429965[/C][/ROW]
[ROW][C]14[/C][C]2.25[/C][C]2.14509890021025[/C][C]0.104901099789747[/C][/ROW]
[ROW][C]15[/C][C]2.09[/C][C]2.22524979783277[/C][C]-0.135249797832769[/C][/ROW]
[ROW][C]16[/C][C]2.79[/C][C]2.09064992721979[/C][C]0.699350072780206[/C][/ROW]
[ROW][C]17[/C][C]3.14[/C][C]2.01741492802847[/C][C]1.12258507197153[/C][/ROW]
[ROW][C]18[/C][C]2.93[/C][C]2.05728691573669[/C][C]0.872713084263306[/C][/ROW]
[ROW][C]19[/C][C]2.65[/C][C]2.11301851851852[/C][C]0.536981481481484[/C][/ROW]
[ROW][C]20[/C][C]2.67[/C][C]2.20307108199903[/C][C]0.466928918000972[/C][/ROW]
[ROW][C]21[/C][C]2.26[/C][C]2.16988864628821[/C][C]0.0901113537117932[/C][/ROW]
[ROW][C]22[/C][C]2.35[/C][C]2.13082371017305[/C][C]0.219176289826946[/C][/ROW]
[ROW][C]23[/C][C]2.13[/C][C]2.12059736373928[/C][C]0.00940263626071736[/C][/ROW]
[ROW][C]24[/C][C]2.18[/C][C]2.06718858159469[/C][C]0.112811418405306[/C][/ROW]
[ROW][C]25[/C][C]2.9[/C][C]1.91412841662624[/C][C]0.985871583373764[/C][/ROW]
[ROW][C]26[/C][C]2.63[/C][C]2.00839681384441[/C][C]0.621603186155589[/C][/ROW]
[ROW][C]27[/C][C]2.67[/C][C]2.07203194242277[/C][C]0.597968057577227[/C][/ROW]
[ROW][C]28[/C][C]1.81[/C][C]2.02001091703057[/C][C]-0.210010917030567[/C][/ROW]
[ROW][C]29[/C][C]1.33[/C][C]2.02935476306[/C][C]-0.699354763060001[/C][/ROW]
[ROW][C]30[/C][C]0.88[/C][C]2.151805595989[/C][C]-1.27180559598900[/C][/ROW]
[ROW][C]31[/C][C]1.28[/C][C]2.19102142972667[/C][C]-0.911021429726667[/C][/ROW]
[ROW][C]32[/C][C]1.26[/C][C]2.24804245511887[/C][C]-0.988042455118874[/C][/ROW]
[ROW][C]33[/C][C]1.26[/C][C]2.21486001940805[/C][C]-0.954860019408051[/C][/ROW]
[ROW][C]34[/C][C]1.29[/C][C]2.1757950832929[/C][C]-0.8857950832929[/C][/ROW]
[ROW][C]35[/C][C]1.1[/C][C]2.13253719877082[/C][C]-1.03253719877082[/C][/ROW]
[ROW][C]36[/C][C]1.37[/C][C]2.09564418567039[/C][C]-0.725644185670386[/C][/ROW]
[ROW][C]37[/C][C]1.21[/C][C]1.89303671356947[/C][C]-0.68303671356947[/C][/ROW]
[ROW][C]38[/C][C]1.74[/C][C]2.02033664887595[/C][C]-0.280336648875946[/C][/ROW]
[ROW][C]39[/C][C]1.76[/C][C]2.16655062267508[/C][C]-0.406550622675076[/C][/ROW]
[ROW][C]40[/C][C]1.48[/C][C]2.23013998059195[/C][C]-0.750139980591947[/C][/ROW]
[ROW][C]41[/C][C]1.04[/C][C]2.17342075044477[/C][C]-1.13342075044477[/C][/ROW]
[ROW][C]42[/C][C]1.62[/C][C]2.22980850719715[/C][C]-0.609808507197153[/C][/ROW]
[ROW][C]43[/C][C]1.49[/C][C]2.33508741711144[/C][C]-0.845087417111437[/C][/ROW]
[ROW][C]44[/C][C]1.79[/C][C]2.37559267345949[/C][C]-0.585592673459486[/C][/ROW]
[ROW][C]45[/C][C]1.8[/C][C]2.39195754488113[/C][C]-0.591957544881127[/C][/ROW]
[ROW][C]46[/C][C]1.58[/C][C]2.33637683972182[/C][C]-0.756376839721818[/C][/ROW]
[ROW][C]47[/C][C]1.86[/C][C]2.27660318615559[/C][C]-0.416603186155588[/C][/ROW]
[ROW][C]48[/C][C]1.74[/C][C]2.17364709687854[/C][C]-0.433647096878537[/C][/ROW]
[ROW][C]49[/C][C]1.59[/C][C]2.02058693191008[/C][C]-0.430586931910083[/C][/ROW]
[ROW][C]50[/C][C]1.26[/C][C]2.11485532912825[/C][C]-0.854855329128254[/C][/ROW]
[ROW][C]51[/C][C]1.13[/C][C]2.22803776483907[/C][C]-1.09803776483907[/C][/ROW]
[ROW][C]52[/C][C]1.92[/C][C]2.22556404657933[/C][C]-0.30556404657933[/C][/ROW]
[ROW][C]53[/C][C]2.61[/C][C]2.26793943069707[/C][C]0.342060569302926[/C][/ROW]
[ROW][C]54[/C][C]2.26[/C][C]2.274779880317[/C][C]-0.0147798803169983[/C][/ROW]
[ROW][C]55[/C][C]2.41[/C][C]2.34702725214297[/C][C]0.0629727478570277[/C][/ROW]
[ROW][C]56[/C][C]2.26[/C][C]2.35450097040272[/C][C]-0.094500970402717[/C][/ROW]
[ROW][C]57[/C][C]2.03[/C][C]2.35435007278020[/C][C]-0.324350072780205[/C][/ROW]
[ROW][C]58[/C][C]2.86[/C][C]2.28225359857674[/C][C]0.577746401423257[/C][/ROW]
[ROW][C]59[/C][C]2.55[/C][C]2.23899571405467[/C][C]0.311004285945334[/C][/ROW]
[ROW][C]60[/C][C]2.27[/C][C]2.18558693191008[/C][C]0.0844130680899229[/C][/ROW]
[ROW][C]61[/C][C]2.26[/C][C]1.99949522885331[/C][C]0.260504771146687[/C][/ROW]
[ROW][C]62[/C][C]2.57[/C][C]2.09376362607148[/C][C]0.476236373928516[/C][/ROW]
[ROW][C]63[/C][C]3.07[/C][C]2.19043029273815[/C][C]0.879569707261848[/C][/ROW]
[ROW][C]64[/C][C]2.76[/C][C]2.08886196021349[/C][C]0.671138039786513[/C][/ROW]
[ROW][C]65[/C][C]2.51[/C][C]2.08169003719877[/C][C]0.428309962801231[/C][/ROW]
[ROW][C]66[/C][C]2.87[/C][C]2.22065663917192[/C][C]0.649343360828077[/C][/ROW]
[ROW][C]67[/C][C]3.14[/C][C]2.24335670386543[/C][C]0.896643296134566[/C][/ROW]
[ROW][C]68[/C][C]3.11[/C][C]2.30037772925764[/C][C]0.809622270742358[/C][/ROW]
[ROW][C]69[/C][C]3.16[/C][C]2.31674260067928[/C][C]0.843257399320717[/C][/ROW]
[ROW][C]70[/C][C]2.47[/C][C]2.24464612647582[/C][C]0.225353873524180[/C][/ROW]
[ROW][C]71[/C][C]2.57[/C][C]2.20138824195374[/C][C]0.368611758046257[/C][/ROW]
[ROW][C]72[/C][C]2.89[/C][C]2.14797945980916[/C][C]0.742020540190845[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105143&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105143&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
11.791.97282759178389-0.182827591783894
21.952.01754868186965-0.0675486818696511
32.262.097699579492160.162300420507844
42.042.14477316836487-0.104773168364875
52.162.22018009057092-0.0601800905709236
62.752.375662461588230.374337538411769
72.792.530488678634970.259511321365028
82.882.488415089762250.391584910237746
93.362.422201115963130.937798884036872
102.972.350104641759660.619895358240335
113.12.339878295325900.760121704674103
122.492.269953744137150.220046255862848
132.22.149925117257000.0500748827429965
142.252.145098900210250.104901099789747
152.092.22524979783277-0.135249797832769
162.792.090649927219790.699350072780206
173.142.017414928028471.12258507197153
182.932.057286915736690.872713084263306
192.652.113018518518520.536981481481484
202.672.203071081999030.466928918000972
212.262.169888646288210.0901113537117932
222.352.130823710173050.219176289826946
232.132.120597363739280.00940263626071736
242.182.067188581594690.112811418405306
252.91.914128416626240.985871583373764
262.632.008396813844410.621603186155589
272.672.072031942422770.597968057577227
281.812.02001091703057-0.210010917030567
291.332.02935476306-0.699354763060001
300.882.151805595989-1.27180559598900
311.282.19102142972667-0.911021429726667
321.262.24804245511887-0.988042455118874
331.262.21486001940805-0.954860019408051
341.292.1757950832929-0.8857950832929
351.12.13253719877082-1.03253719877082
361.372.09564418567039-0.725644185670386
371.211.89303671356947-0.68303671356947
381.742.02033664887595-0.280336648875946
391.762.16655062267508-0.406550622675076
401.482.23013998059195-0.750139980591947
411.042.17342075044477-1.13342075044477
421.622.22980850719715-0.609808507197153
431.492.33508741711144-0.845087417111437
441.792.37559267345949-0.585592673459486
451.82.39195754488113-0.591957544881127
461.582.33637683972182-0.756376839721818
471.862.27660318615559-0.416603186155588
481.742.17364709687854-0.433647096878537
491.592.02058693191008-0.430586931910083
501.262.11485532912825-0.854855329128254
511.132.22803776483907-1.09803776483907
521.922.22556404657933-0.30556404657933
532.612.267939430697070.342060569302926
542.262.274779880317-0.0147798803169983
552.412.347027252142970.0629727478570277
562.262.35450097040272-0.094500970402717
572.032.35435007278020-0.324350072780205
582.862.282253598576740.577746401423257
592.552.238995714054670.311004285945334
602.272.185586931910080.0844130680899229
612.261.999495228853310.260504771146687
622.572.093763626071480.476236373928516
633.072.190430292738150.879569707261848
642.762.088861960213490.671138039786513
652.512.081690037198770.428309962801231
662.872.220656639171920.649343360828077
673.142.243356703865430.896643296134566
683.112.300377729257640.809622270742358
693.162.316742600679280.843257399320717
702.472.244646126475820.225353873524180
712.572.201388241953740.368611758046257
722.892.147979459809160.742020540190845






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.04756686472769720.09513372945539440.952433135272303
180.09175438473180080.1835087694636020.9082456152682
190.09130759497799080.1826151899559820.90869240502201
200.07387856012961860.1477571202592370.926121439870381
210.2226830042983510.4453660085967030.777316995701649
220.2119155608681560.4238311217363110.788084439131844
230.2593456282204980.5186912564409950.740654371779502
240.2154218389004960.4308436778009930.784578161099504
250.491616973542690.983233947085380.50838302645731
260.6575375116771190.6849249766457610.342462488322881
270.8547408350097410.2905183299805170.145259164990259
280.928840869457150.1423182610857000.0711591305428499
290.9843677961444130.03126440771117490.0156322038555874
300.9984869744336860.003026051132628150.00151302556631408
310.9987581191943050.002483761611390780.00124188080569539
320.998846196264180.002307607471640200.00115380373582010
330.9987464590442350.0025070819115310.0012535409557655
340.9981703404083670.00365931918326520.0018296595916326
350.9975947077262190.004810584547562390.00240529227378119
360.9955825050156820.008834989968636830.00441749498431842
370.9918128035550850.01637439288982970.00818719644491483
380.992873158306260.01425368338748020.00712684169374009
390.9947085700599980.01058285988000370.00529142994000184
400.9901143374234840.01977132515303110.00988566257651555
410.986285051119470.02742989776105860.0137149488805293
420.977120435730480.04575912853903940.0228795642695197
430.9639672330475360.07206553390492810.0360327669524640
440.9419612977724210.1160774044551570.0580387022275786
450.9116807395353120.1766385209293750.0883192604646876
460.865816657326630.2683666853467420.134183342673371
470.8234122717225870.3531754565548260.176587728277413
480.7816701670230960.4366596659538070.218329832976904
490.7044212629547350.591157474090530.295578737045265
500.6368209994214880.7263580011570250.363179000578512
510.8373306904718090.3253386190563830.162669309528191
520.8063646514728570.3872706970542870.193635348527143
530.8146769817030640.3706460365938720.185323018296936
540.7060427575048040.5879144849903920.293957242495196
550.5963625585454170.8072748829091660.403637441454583

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.0475668647276972 & 0.0951337294553944 & 0.952433135272303 \tabularnewline
18 & 0.0917543847318008 & 0.183508769463602 & 0.9082456152682 \tabularnewline
19 & 0.0913075949779908 & 0.182615189955982 & 0.90869240502201 \tabularnewline
20 & 0.0738785601296186 & 0.147757120259237 & 0.926121439870381 \tabularnewline
21 & 0.222683004298351 & 0.445366008596703 & 0.777316995701649 \tabularnewline
22 & 0.211915560868156 & 0.423831121736311 & 0.788084439131844 \tabularnewline
23 & 0.259345628220498 & 0.518691256440995 & 0.740654371779502 \tabularnewline
24 & 0.215421838900496 & 0.430843677800993 & 0.784578161099504 \tabularnewline
25 & 0.49161697354269 & 0.98323394708538 & 0.50838302645731 \tabularnewline
26 & 0.657537511677119 & 0.684924976645761 & 0.342462488322881 \tabularnewline
27 & 0.854740835009741 & 0.290518329980517 & 0.145259164990259 \tabularnewline
28 & 0.92884086945715 & 0.142318261085700 & 0.0711591305428499 \tabularnewline
29 & 0.984367796144413 & 0.0312644077111749 & 0.0156322038555874 \tabularnewline
30 & 0.998486974433686 & 0.00302605113262815 & 0.00151302556631408 \tabularnewline
31 & 0.998758119194305 & 0.00248376161139078 & 0.00124188080569539 \tabularnewline
32 & 0.99884619626418 & 0.00230760747164020 & 0.00115380373582010 \tabularnewline
33 & 0.998746459044235 & 0.002507081911531 & 0.0012535409557655 \tabularnewline
34 & 0.998170340408367 & 0.0036593191832652 & 0.0018296595916326 \tabularnewline
35 & 0.997594707726219 & 0.00481058454756239 & 0.00240529227378119 \tabularnewline
36 & 0.995582505015682 & 0.00883498996863683 & 0.00441749498431842 \tabularnewline
37 & 0.991812803555085 & 0.0163743928898297 & 0.00818719644491483 \tabularnewline
38 & 0.99287315830626 & 0.0142536833874802 & 0.00712684169374009 \tabularnewline
39 & 0.994708570059998 & 0.0105828598800037 & 0.00529142994000184 \tabularnewline
40 & 0.990114337423484 & 0.0197713251530311 & 0.00988566257651555 \tabularnewline
41 & 0.98628505111947 & 0.0274298977610586 & 0.0137149488805293 \tabularnewline
42 & 0.97712043573048 & 0.0457591285390394 & 0.0228795642695197 \tabularnewline
43 & 0.963967233047536 & 0.0720655339049281 & 0.0360327669524640 \tabularnewline
44 & 0.941961297772421 & 0.116077404455157 & 0.0580387022275786 \tabularnewline
45 & 0.911680739535312 & 0.176638520929375 & 0.0883192604646876 \tabularnewline
46 & 0.86581665732663 & 0.268366685346742 & 0.134183342673371 \tabularnewline
47 & 0.823412271722587 & 0.353175456554826 & 0.176587728277413 \tabularnewline
48 & 0.781670167023096 & 0.436659665953807 & 0.218329832976904 \tabularnewline
49 & 0.704421262954735 & 0.59115747409053 & 0.295578737045265 \tabularnewline
50 & 0.636820999421488 & 0.726358001157025 & 0.363179000578512 \tabularnewline
51 & 0.837330690471809 & 0.325338619056383 & 0.162669309528191 \tabularnewline
52 & 0.806364651472857 & 0.387270697054287 & 0.193635348527143 \tabularnewline
53 & 0.814676981703064 & 0.370646036593872 & 0.185323018296936 \tabularnewline
54 & 0.706042757504804 & 0.587914484990392 & 0.293957242495196 \tabularnewline
55 & 0.596362558545417 & 0.807274882909166 & 0.403637441454583 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105143&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]17[/C][C]0.0475668647276972[/C][C]0.0951337294553944[/C][C]0.952433135272303[/C][/ROW]
[ROW][C]18[/C][C]0.0917543847318008[/C][C]0.183508769463602[/C][C]0.9082456152682[/C][/ROW]
[ROW][C]19[/C][C]0.0913075949779908[/C][C]0.182615189955982[/C][C]0.90869240502201[/C][/ROW]
[ROW][C]20[/C][C]0.0738785601296186[/C][C]0.147757120259237[/C][C]0.926121439870381[/C][/ROW]
[ROW][C]21[/C][C]0.222683004298351[/C][C]0.445366008596703[/C][C]0.777316995701649[/C][/ROW]
[ROW][C]22[/C][C]0.211915560868156[/C][C]0.423831121736311[/C][C]0.788084439131844[/C][/ROW]
[ROW][C]23[/C][C]0.259345628220498[/C][C]0.518691256440995[/C][C]0.740654371779502[/C][/ROW]
[ROW][C]24[/C][C]0.215421838900496[/C][C]0.430843677800993[/C][C]0.784578161099504[/C][/ROW]
[ROW][C]25[/C][C]0.49161697354269[/C][C]0.98323394708538[/C][C]0.50838302645731[/C][/ROW]
[ROW][C]26[/C][C]0.657537511677119[/C][C]0.684924976645761[/C][C]0.342462488322881[/C][/ROW]
[ROW][C]27[/C][C]0.854740835009741[/C][C]0.290518329980517[/C][C]0.145259164990259[/C][/ROW]
[ROW][C]28[/C][C]0.92884086945715[/C][C]0.142318261085700[/C][C]0.0711591305428499[/C][/ROW]
[ROW][C]29[/C][C]0.984367796144413[/C][C]0.0312644077111749[/C][C]0.0156322038555874[/C][/ROW]
[ROW][C]30[/C][C]0.998486974433686[/C][C]0.00302605113262815[/C][C]0.00151302556631408[/C][/ROW]
[ROW][C]31[/C][C]0.998758119194305[/C][C]0.00248376161139078[/C][C]0.00124188080569539[/C][/ROW]
[ROW][C]32[/C][C]0.99884619626418[/C][C]0.00230760747164020[/C][C]0.00115380373582010[/C][/ROW]
[ROW][C]33[/C][C]0.998746459044235[/C][C]0.002507081911531[/C][C]0.0012535409557655[/C][/ROW]
[ROW][C]34[/C][C]0.998170340408367[/C][C]0.0036593191832652[/C][C]0.0018296595916326[/C][/ROW]
[ROW][C]35[/C][C]0.997594707726219[/C][C]0.00481058454756239[/C][C]0.00240529227378119[/C][/ROW]
[ROW][C]36[/C][C]0.995582505015682[/C][C]0.00883498996863683[/C][C]0.00441749498431842[/C][/ROW]
[ROW][C]37[/C][C]0.991812803555085[/C][C]0.0163743928898297[/C][C]0.00818719644491483[/C][/ROW]
[ROW][C]38[/C][C]0.99287315830626[/C][C]0.0142536833874802[/C][C]0.00712684169374009[/C][/ROW]
[ROW][C]39[/C][C]0.994708570059998[/C][C]0.0105828598800037[/C][C]0.00529142994000184[/C][/ROW]
[ROW][C]40[/C][C]0.990114337423484[/C][C]0.0197713251530311[/C][C]0.00988566257651555[/C][/ROW]
[ROW][C]41[/C][C]0.98628505111947[/C][C]0.0274298977610586[/C][C]0.0137149488805293[/C][/ROW]
[ROW][C]42[/C][C]0.97712043573048[/C][C]0.0457591285390394[/C][C]0.0228795642695197[/C][/ROW]
[ROW][C]43[/C][C]0.963967233047536[/C][C]0.0720655339049281[/C][C]0.0360327669524640[/C][/ROW]
[ROW][C]44[/C][C]0.941961297772421[/C][C]0.116077404455157[/C][C]0.0580387022275786[/C][/ROW]
[ROW][C]45[/C][C]0.911680739535312[/C][C]0.176638520929375[/C][C]0.0883192604646876[/C][/ROW]
[ROW][C]46[/C][C]0.86581665732663[/C][C]0.268366685346742[/C][C]0.134183342673371[/C][/ROW]
[ROW][C]47[/C][C]0.823412271722587[/C][C]0.353175456554826[/C][C]0.176587728277413[/C][/ROW]
[ROW][C]48[/C][C]0.781670167023096[/C][C]0.436659665953807[/C][C]0.218329832976904[/C][/ROW]
[ROW][C]49[/C][C]0.704421262954735[/C][C]0.59115747409053[/C][C]0.295578737045265[/C][/ROW]
[ROW][C]50[/C][C]0.636820999421488[/C][C]0.726358001157025[/C][C]0.363179000578512[/C][/ROW]
[ROW][C]51[/C][C]0.837330690471809[/C][C]0.325338619056383[/C][C]0.162669309528191[/C][/ROW]
[ROW][C]52[/C][C]0.806364651472857[/C][C]0.387270697054287[/C][C]0.193635348527143[/C][/ROW]
[ROW][C]53[/C][C]0.814676981703064[/C][C]0.370646036593872[/C][C]0.185323018296936[/C][/ROW]
[ROW][C]54[/C][C]0.706042757504804[/C][C]0.587914484990392[/C][C]0.293957242495196[/C][/ROW]
[ROW][C]55[/C][C]0.596362558545417[/C][C]0.807274882909166[/C][C]0.403637441454583[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105143&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105143&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
170.04756686472769720.09513372945539440.952433135272303
180.09175438473180080.1835087694636020.9082456152682
190.09130759497799080.1826151899559820.90869240502201
200.07387856012961860.1477571202592370.926121439870381
210.2226830042983510.4453660085967030.777316995701649
220.2119155608681560.4238311217363110.788084439131844
230.2593456282204980.5186912564409950.740654371779502
240.2154218389004960.4308436778009930.784578161099504
250.491616973542690.983233947085380.50838302645731
260.6575375116771190.6849249766457610.342462488322881
270.8547408350097410.2905183299805170.145259164990259
280.928840869457150.1423182610857000.0711591305428499
290.9843677961444130.03126440771117490.0156322038555874
300.9984869744336860.003026051132628150.00151302556631408
310.9987581191943050.002483761611390780.00124188080569539
320.998846196264180.002307607471640200.00115380373582010
330.9987464590442350.0025070819115310.0012535409557655
340.9981703404083670.00365931918326520.0018296595916326
350.9975947077262190.004810584547562390.00240529227378119
360.9955825050156820.008834989968636830.00441749498431842
370.9918128035550850.01637439288982970.00818719644491483
380.992873158306260.01425368338748020.00712684169374009
390.9947085700599980.01058285988000370.00529142994000184
400.9901143374234840.01977132515303110.00988566257651555
410.986285051119470.02742989776105860.0137149488805293
420.977120435730480.04575912853903940.0228795642695197
430.9639672330475360.07206553390492810.0360327669524640
440.9419612977724210.1160774044551570.0580387022275786
450.9116807395353120.1766385209293750.0883192604646876
460.865816657326630.2683666853467420.134183342673371
470.8234122717225870.3531754565548260.176587728277413
480.7816701670230960.4366596659538070.218329832976904
490.7044212629547350.591157474090530.295578737045265
500.6368209994214880.7263580011570250.363179000578512
510.8373306904718090.3253386190563830.162669309528191
520.8063646514728570.3872706970542870.193635348527143
530.8146769817030640.3706460365938720.185323018296936
540.7060427575048040.5879144849903920.293957242495196
550.5963625585454170.8072748829091660.403637441454583






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level70.179487179487179NOK
5% type I error level140.358974358974359NOK
10% type I error level160.41025641025641NOK

\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 & 7 & 0.179487179487179 & NOK \tabularnewline
5% type I error level & 14 & 0.358974358974359 & NOK \tabularnewline
10% type I error level & 16 & 0.41025641025641 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105143&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]7[/C][C]0.179487179487179[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]14[/C][C]0.358974358974359[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.41025641025641[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105143&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105143&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 level70.179487179487179NOK
5% type I error level140.358974358974359NOK
10% type I error level160.41025641025641NOK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

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