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 computationSun, 20 Dec 2009 05:23:34 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/20/t12613138645a0dapt181sssrp.htm/, Retrieved Sat, 27 Apr 2024 05:29:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=69867, Retrieved Sat, 27 Apr 2024 05:29:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact152

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] [multiple regressi...] [2009-11-14 12:58:47] [d46757a0a8c9b00540ab7e7e0c34bfc4]
-    D        [Multiple Regression] [Multiple Regressi...] [2009-12-20 12:23:34] [8cd69d0f4298074aa572ca2f9b39b6ae] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-1,2	23,6
-2,4	25,7
0,8	32,5
-0,1	33,5
-1,5	34,5
-4,4	27,9
-4,2	45,3
3,5	40,8
10	58,5
8,6	32,5
9,5	35,5
9,9	46,7
10,4	53,2
16	36,1
12,7	54
10,2	58,1
8,9	41,8
12,6	43,1
13,6	76
14,8	42,8
9,5	41
13,7	61,4
17	34,2
14,7	53,8
17,4	80,7
9	79,5
9,1	96,5
12,2	108,3
15,9	100,1
12,9	108,5
10,9	127,4
10,6	86,5
13,2	71,4
9,6	88,2
6,4	135,6
5,8	70,5
-1	87,5
-0,2	73,3
2,7	92,2
3,6	61,1
-0,9	45,7
0,3	30,5
-1,1	34,8
-2,5	29,2
-3,4	56,7
-3,5	67,1
-3,9	41,8
-4,6	46,8
-0,1	50,1
4,3	81,9
10,2	115,8
8,7	102,5
13,3	106,6
15	101,4
20,7	136,1
20,7	143,4
26,4	127,5
31,2	113,8
31,4	75,3
26,6	98,5
26,6	113,7
19,2	103,7
6,5	73,9
3,1	52,5
-0,2	63,9
-4	44,9
-12,6	31,3
-13	24,9
-17,6	22,8
-21,7	24,8
-23,2	22,8
-16,8	20,9
-19,8	21,5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69867&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]4 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=69867&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69867&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
werkl[t] = -0.395712991636488 + 0.269567934887980afzetp[t] -3.79013568721123M1[t] -3.21395655816536M2[t] -6.56078146518992M3[t] -4.87045347166873M4[t] -3.97578920142145M5[t] -2.65155387116502M6[t] -7.54172565371467M7[t] -2.45587483350240M8[t] -2.04195046420927M9[t] -2.31005489925694M10[t] -0.302773237368129M11[t] -0.210015990850824t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
werkl[t] =  -0.395712991636488 +  0.269567934887980afzetp[t] -3.79013568721123M1[t] -3.21395655816536M2[t] -6.56078146518992M3[t] -4.87045347166873M4[t] -3.97578920142145M5[t] -2.65155387116502M6[t] -7.54172565371467M7[t] -2.45587483350240M8[t] -2.04195046420927M9[t] -2.31005489925694M10[t] -0.302773237368129M11[t] -0.210015990850824t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69867&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]werkl[t] =  -0.395712991636488 +  0.269567934887980afzetp[t] -3.79013568721123M1[t] -3.21395655816536M2[t] -6.56078146518992M3[t] -4.87045347166873M4[t] -3.97578920142145M5[t] -2.65155387116502M6[t] -7.54172565371467M7[t] -2.45587483350240M8[t] -2.04195046420927M9[t] -2.31005489925694M10[t] -0.302773237368129M11[t] -0.210015990850824t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69867&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69867&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
werkl[t] = -0.395712991636488 + 0.269567934887980afzetp[t] -3.79013568721123M1[t] -3.21395655816536M2[t] -6.56078146518992M3[t] -4.87045347166873M4[t] -3.97578920142145M5[t] -2.65155387116502M6[t] -7.54172565371467M7[t] -2.45587483350240M8[t] -2.04195046420927M9[t] -2.31005489925694M10[t] -0.302773237368129M11[t] -0.210015990850824t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.3957129916364884.145033-0.09550.9242680.462134
afzetp0.2695679348879800.0310788.673800
M1-3.790135687211234.676195-0.81050.42090.21045
M2-3.213956558165364.884167-0.6580.5130750.256537
M3-6.560781465189924.919882-1.33350.1874860.093743
M4-4.870453471668734.881645-0.99770.3224960.161248
M5-3.975789201421454.866318-0.8170.4172140.208607
M6-2.651553871165024.852244-0.54650.5868110.293405
M7-7.541725653714674.889913-1.54230.1283470.064174
M8-2.455874833502404.849025-0.50650.6144160.307208
M9-2.041950464209274.849357-0.42110.6752290.337615
M10-2.310054899256944.850378-0.47630.6356460.317823
M11-0.3027732373681294.841556-0.06250.9503470.475174
t-0.2100159908508240.04853-4.32755.9e-053e-05

\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) & -0.395712991636488 & 4.145033 & -0.0955 & 0.924268 & 0.462134 \tabularnewline
afzetp & 0.269567934887980 & 0.031078 & 8.6738 & 0 & 0 \tabularnewline
M1 & -3.79013568721123 & 4.676195 & -0.8105 & 0.4209 & 0.21045 \tabularnewline
M2 & -3.21395655816536 & 4.884167 & -0.658 & 0.513075 & 0.256537 \tabularnewline
M3 & -6.56078146518992 & 4.919882 & -1.3335 & 0.187486 & 0.093743 \tabularnewline
M4 & -4.87045347166873 & 4.881645 & -0.9977 & 0.322496 & 0.161248 \tabularnewline
M5 & -3.97578920142145 & 4.866318 & -0.817 & 0.417214 & 0.208607 \tabularnewline
M6 & -2.65155387116502 & 4.852244 & -0.5465 & 0.586811 & 0.293405 \tabularnewline
M7 & -7.54172565371467 & 4.889913 & -1.5423 & 0.128347 & 0.064174 \tabularnewline
M8 & -2.45587483350240 & 4.849025 & -0.5065 & 0.614416 & 0.307208 \tabularnewline
M9 & -2.04195046420927 & 4.849357 & -0.4211 & 0.675229 & 0.337615 \tabularnewline
M10 & -2.31005489925694 & 4.850378 & -0.4763 & 0.635646 & 0.317823 \tabularnewline
M11 & -0.302773237368129 & 4.841556 & -0.0625 & 0.950347 & 0.475174 \tabularnewline
t & -0.210015990850824 & 0.04853 & -4.3275 & 5.9e-05 & 3e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69867&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]-0.395712991636488[/C][C]4.145033[/C][C]-0.0955[/C][C]0.924268[/C][C]0.462134[/C][/ROW]
[ROW][C]afzetp[/C][C]0.269567934887980[/C][C]0.031078[/C][C]8.6738[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-3.79013568721123[/C][C]4.676195[/C][C]-0.8105[/C][C]0.4209[/C][C]0.21045[/C][/ROW]
[ROW][C]M2[/C][C]-3.21395655816536[/C][C]4.884167[/C][C]-0.658[/C][C]0.513075[/C][C]0.256537[/C][/ROW]
[ROW][C]M3[/C][C]-6.56078146518992[/C][C]4.919882[/C][C]-1.3335[/C][C]0.187486[/C][C]0.093743[/C][/ROW]
[ROW][C]M4[/C][C]-4.87045347166873[/C][C]4.881645[/C][C]-0.9977[/C][C]0.322496[/C][C]0.161248[/C][/ROW]
[ROW][C]M5[/C][C]-3.97578920142145[/C][C]4.866318[/C][C]-0.817[/C][C]0.417214[/C][C]0.208607[/C][/ROW]
[ROW][C]M6[/C][C]-2.65155387116502[/C][C]4.852244[/C][C]-0.5465[/C][C]0.586811[/C][C]0.293405[/C][/ROW]
[ROW][C]M7[/C][C]-7.54172565371467[/C][C]4.889913[/C][C]-1.5423[/C][C]0.128347[/C][C]0.064174[/C][/ROW]
[ROW][C]M8[/C][C]-2.45587483350240[/C][C]4.849025[/C][C]-0.5065[/C][C]0.614416[/C][C]0.307208[/C][/ROW]
[ROW][C]M9[/C][C]-2.04195046420927[/C][C]4.849357[/C][C]-0.4211[/C][C]0.675229[/C][C]0.337615[/C][/ROW]
[ROW][C]M10[/C][C]-2.31005489925694[/C][C]4.850378[/C][C]-0.4763[/C][C]0.635646[/C][C]0.317823[/C][/ROW]
[ROW][C]M11[/C][C]-0.302773237368129[/C][C]4.841556[/C][C]-0.0625[/C][C]0.950347[/C][C]0.475174[/C][/ROW]
[ROW][C]t[/C][C]-0.210015990850824[/C][C]0.04853[/C][C]-4.3275[/C][C]5.9e-05[/C][C]3e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69867&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69867&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)-0.3957129916364884.145033-0.09550.9242680.462134
afzetp0.2695679348879800.0310788.673800
M1-3.790135687211234.676195-0.81050.42090.21045
M2-3.213956558165364.884167-0.6580.5130750.256537
M3-6.560781465189924.919882-1.33350.1874860.093743
M4-4.870453471668734.881645-0.99770.3224960.161248
M5-3.975789201421454.866318-0.8170.4172140.208607
M6-2.651553871165024.852244-0.54650.5868110.293405
M7-7.541725653714674.889913-1.54230.1283470.064174
M8-2.455874833502404.849025-0.50650.6144160.307208
M9-2.041950464209274.849357-0.42110.6752290.337615
M10-2.310054899256944.850378-0.47630.6356460.317823
M11-0.3027732373681294.841556-0.06250.9503470.475174
t-0.2100159908508240.04853-4.32755.9e-053e-05






Multiple Linear Regression - Regression Statistics
Multiple R0.76096849416809
R-squared0.57907304911645
Adjusted R-squared0.486326432820075
F-TEST (value)6.24360297162756
F-TEST (DF numerator)13
F-TEST (DF denominator)59
p-value3.46713284971045e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.38491643678452
Sum Squared Residuals4148.10259545969

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.76096849416809 \tabularnewline
R-squared & 0.57907304911645 \tabularnewline
Adjusted R-squared & 0.486326432820075 \tabularnewline
F-TEST (value) & 6.24360297162756 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 3.46713284971045e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 8.38491643678452 \tabularnewline
Sum Squared Residuals & 4148.10259545969 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69867&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.76096849416809[/C][/ROW]
[ROW][C]R-squared[/C][C]0.57907304911645[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.486326432820075[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.24360297162756[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]59[/C][/ROW]
[ROW][C]p-value[/C][C]3.46713284971045e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]8.38491643678452[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4148.10259545969[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69867&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69867&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 R0.76096849416809
R-squared0.57907304911645
Adjusted R-squared0.486326432820075
F-TEST (value)6.24360297162756
F-TEST (DF numerator)13
F-TEST (DF denominator)59
p-value3.46713284971045e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.38491643678452
Sum Squared Residuals4148.10259545969






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-1.21.96593859365773-3.16593859365773
2-2.42.89819439511755-5.29819439511755
30.81.17441545448043-0.374415454480434
4-0.12.92429539203879-3.02429539203879
5-1.53.87851160632323-5.37851160632323
6-4.43.21358257546817-7.61358257546817
7-4.22.80387686911853-7.00387686911853
83.56.46665599148408-2.96665599148408
91011.4419168174436-1.44191681744361
108.63.955030084457644.64496991554236
119.56.560999560159582.93900043984042
129.99.672917677422270.227082322577732
1310.47.424957576132072.97504242386793
14163.1815090277426512.8184909722573
1512.74.449934164362128.25006583563788
1610.27.03547470007323.1645252999268
178.93.326165640795595.57383435920441
1812.64.790823295555577.80917670444443
1913.68.559420579969625.04057942003038
2014.84.4855999710501510.3144000289499
219.54.204286066694095.29571393330591
2213.79.225351512510374.47464848748963
23173.6903693545953113.3096306454047
2414.79.066658124917025.63334187508298
2517.412.31788389534165.08211610465838
26912.3605655116711-3.36056551167109
279.113.3863795068914-4.28637950689136
2812.218.0475931412399-5.84759314123989
2915.916.5217843545549-0.621784354554908
3012.919.9003743470195-7.00037434701954
3110.919.8950205430019-8.99502054300188
3210.613.7455268354450-3.14552683544497
3313.29.878959397078783.32104060292122
349.613.9295802772983-4.32958027729833
356.428.5043660620266-22.1043660620266
365.811.0482507473364-5.24825074733639
37-111.63075396237-12.63075396237
38-0.28.16905242515572-8.36905242515572
392.79.70704549666316-7.00704549666316
403.62.803794724317360.796205275682643
41-0.9-0.662903193561076-0.237096806438924
420.3-3.646116464452763.94611646445276
43-1.1-7.587162117834936.48716211783492
44-2.5-4.220907723846161.72090772384616
45-3.43.39611886401558-6.79611886401558
46-3.55.72150496095207-9.22150496095207
47-3.90.698701879324174-4.59870187932417
48-4.62.13929880028138-6.73929880028138
49-0.1-0.971278692650340.871278692650341
504.37.96714477498246-3.66714477498246
5110.213.5486568698096-3.34865686980959
528.711.4437153384698-2.74371533846983
5313.313.23359215090700.0664078490930123
541512.94605822889512.05394177110489
5520.717.19987779610753.50012220389248
5620.724.0435585501512-3.34355855015123
5726.419.96133676387466.43866323612535
5831.215.790135630010815.4098643699892
5931.47.209035807861624.1909641921384
6026.613.555769143780013.0442308562200
6126.613.653050076015312.9469499239847
6219.211.32353386533057.87646613466948
636.5-0.2664314922066556.76643149220665
643.1-4.554873296139057.65487329613905
65-0.2-0.7971505590196340.597150559019634
66-4-4.804721982485640.80472198248564
67-12.6-13.57103367036260.971033670362639
68-13-10.4204336242843-2.57956637571574
69-17.6-10.7826179091067-6.81738209089328
70-21.7-10.7216024652293-10.9783975347707
71-23.2-9.46347266396723-13.7365273360328
72-16.8-9.88289449373708-6.91710550626292
73-19.8-13.7213054108663-6.07869458913365

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -1.2 & 1.96593859365773 & -3.16593859365773 \tabularnewline
2 & -2.4 & 2.89819439511755 & -5.29819439511755 \tabularnewline
3 & 0.8 & 1.17441545448043 & -0.374415454480434 \tabularnewline
4 & -0.1 & 2.92429539203879 & -3.02429539203879 \tabularnewline
5 & -1.5 & 3.87851160632323 & -5.37851160632323 \tabularnewline
6 & -4.4 & 3.21358257546817 & -7.61358257546817 \tabularnewline
7 & -4.2 & 2.80387686911853 & -7.00387686911853 \tabularnewline
8 & 3.5 & 6.46665599148408 & -2.96665599148408 \tabularnewline
9 & 10 & 11.4419168174436 & -1.44191681744361 \tabularnewline
10 & 8.6 & 3.95503008445764 & 4.64496991554236 \tabularnewline
11 & 9.5 & 6.56099956015958 & 2.93900043984042 \tabularnewline
12 & 9.9 & 9.67291767742227 & 0.227082322577732 \tabularnewline
13 & 10.4 & 7.42495757613207 & 2.97504242386793 \tabularnewline
14 & 16 & 3.18150902774265 & 12.8184909722573 \tabularnewline
15 & 12.7 & 4.44993416436212 & 8.25006583563788 \tabularnewline
16 & 10.2 & 7.0354747000732 & 3.1645252999268 \tabularnewline
17 & 8.9 & 3.32616564079559 & 5.57383435920441 \tabularnewline
18 & 12.6 & 4.79082329555557 & 7.80917670444443 \tabularnewline
19 & 13.6 & 8.55942057996962 & 5.04057942003038 \tabularnewline
20 & 14.8 & 4.48559997105015 & 10.3144000289499 \tabularnewline
21 & 9.5 & 4.20428606669409 & 5.29571393330591 \tabularnewline
22 & 13.7 & 9.22535151251037 & 4.47464848748963 \tabularnewline
23 & 17 & 3.69036935459531 & 13.3096306454047 \tabularnewline
24 & 14.7 & 9.06665812491702 & 5.63334187508298 \tabularnewline
25 & 17.4 & 12.3178838953416 & 5.08211610465838 \tabularnewline
26 & 9 & 12.3605655116711 & -3.36056551167109 \tabularnewline
27 & 9.1 & 13.3863795068914 & -4.28637950689136 \tabularnewline
28 & 12.2 & 18.0475931412399 & -5.84759314123989 \tabularnewline
29 & 15.9 & 16.5217843545549 & -0.621784354554908 \tabularnewline
30 & 12.9 & 19.9003743470195 & -7.00037434701954 \tabularnewline
31 & 10.9 & 19.8950205430019 & -8.99502054300188 \tabularnewline
32 & 10.6 & 13.7455268354450 & -3.14552683544497 \tabularnewline
33 & 13.2 & 9.87895939707878 & 3.32104060292122 \tabularnewline
34 & 9.6 & 13.9295802772983 & -4.32958027729833 \tabularnewline
35 & 6.4 & 28.5043660620266 & -22.1043660620266 \tabularnewline
36 & 5.8 & 11.0482507473364 & -5.24825074733639 \tabularnewline
37 & -1 & 11.63075396237 & -12.63075396237 \tabularnewline
38 & -0.2 & 8.16905242515572 & -8.36905242515572 \tabularnewline
39 & 2.7 & 9.70704549666316 & -7.00704549666316 \tabularnewline
40 & 3.6 & 2.80379472431736 & 0.796205275682643 \tabularnewline
41 & -0.9 & -0.662903193561076 & -0.237096806438924 \tabularnewline
42 & 0.3 & -3.64611646445276 & 3.94611646445276 \tabularnewline
43 & -1.1 & -7.58716211783493 & 6.48716211783492 \tabularnewline
44 & -2.5 & -4.22090772384616 & 1.72090772384616 \tabularnewline
45 & -3.4 & 3.39611886401558 & -6.79611886401558 \tabularnewline
46 & -3.5 & 5.72150496095207 & -9.22150496095207 \tabularnewline
47 & -3.9 & 0.698701879324174 & -4.59870187932417 \tabularnewline
48 & -4.6 & 2.13929880028138 & -6.73929880028138 \tabularnewline
49 & -0.1 & -0.97127869265034 & 0.871278692650341 \tabularnewline
50 & 4.3 & 7.96714477498246 & -3.66714477498246 \tabularnewline
51 & 10.2 & 13.5486568698096 & -3.34865686980959 \tabularnewline
52 & 8.7 & 11.4437153384698 & -2.74371533846983 \tabularnewline
53 & 13.3 & 13.2335921509070 & 0.0664078490930123 \tabularnewline
54 & 15 & 12.9460582288951 & 2.05394177110489 \tabularnewline
55 & 20.7 & 17.1998777961075 & 3.50012220389248 \tabularnewline
56 & 20.7 & 24.0435585501512 & -3.34355855015123 \tabularnewline
57 & 26.4 & 19.9613367638746 & 6.43866323612535 \tabularnewline
58 & 31.2 & 15.7901356300108 & 15.4098643699892 \tabularnewline
59 & 31.4 & 7.2090358078616 & 24.1909641921384 \tabularnewline
60 & 26.6 & 13.5557691437800 & 13.0442308562200 \tabularnewline
61 & 26.6 & 13.6530500760153 & 12.9469499239847 \tabularnewline
62 & 19.2 & 11.3235338653305 & 7.87646613466948 \tabularnewline
63 & 6.5 & -0.266431492206655 & 6.76643149220665 \tabularnewline
64 & 3.1 & -4.55487329613905 & 7.65487329613905 \tabularnewline
65 & -0.2 & -0.797150559019634 & 0.597150559019634 \tabularnewline
66 & -4 & -4.80472198248564 & 0.80472198248564 \tabularnewline
67 & -12.6 & -13.5710336703626 & 0.971033670362639 \tabularnewline
68 & -13 & -10.4204336242843 & -2.57956637571574 \tabularnewline
69 & -17.6 & -10.7826179091067 & -6.81738209089328 \tabularnewline
70 & -21.7 & -10.7216024652293 & -10.9783975347707 \tabularnewline
71 & -23.2 & -9.46347266396723 & -13.7365273360328 \tabularnewline
72 & -16.8 & -9.88289449373708 & -6.91710550626292 \tabularnewline
73 & -19.8 & -13.7213054108663 & -6.07869458913365 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69867&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]-1.2[/C][C]1.96593859365773[/C][C]-3.16593859365773[/C][/ROW]
[ROW][C]2[/C][C]-2.4[/C][C]2.89819439511755[/C][C]-5.29819439511755[/C][/ROW]
[ROW][C]3[/C][C]0.8[/C][C]1.17441545448043[/C][C]-0.374415454480434[/C][/ROW]
[ROW][C]4[/C][C]-0.1[/C][C]2.92429539203879[/C][C]-3.02429539203879[/C][/ROW]
[ROW][C]5[/C][C]-1.5[/C][C]3.87851160632323[/C][C]-5.37851160632323[/C][/ROW]
[ROW][C]6[/C][C]-4.4[/C][C]3.21358257546817[/C][C]-7.61358257546817[/C][/ROW]
[ROW][C]7[/C][C]-4.2[/C][C]2.80387686911853[/C][C]-7.00387686911853[/C][/ROW]
[ROW][C]8[/C][C]3.5[/C][C]6.46665599148408[/C][C]-2.96665599148408[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]11.4419168174436[/C][C]-1.44191681744361[/C][/ROW]
[ROW][C]10[/C][C]8.6[/C][C]3.95503008445764[/C][C]4.64496991554236[/C][/ROW]
[ROW][C]11[/C][C]9.5[/C][C]6.56099956015958[/C][C]2.93900043984042[/C][/ROW]
[ROW][C]12[/C][C]9.9[/C][C]9.67291767742227[/C][C]0.227082322577732[/C][/ROW]
[ROW][C]13[/C][C]10.4[/C][C]7.42495757613207[/C][C]2.97504242386793[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]3.18150902774265[/C][C]12.8184909722573[/C][/ROW]
[ROW][C]15[/C][C]12.7[/C][C]4.44993416436212[/C][C]8.25006583563788[/C][/ROW]
[ROW][C]16[/C][C]10.2[/C][C]7.0354747000732[/C][C]3.1645252999268[/C][/ROW]
[ROW][C]17[/C][C]8.9[/C][C]3.32616564079559[/C][C]5.57383435920441[/C][/ROW]
[ROW][C]18[/C][C]12.6[/C][C]4.79082329555557[/C][C]7.80917670444443[/C][/ROW]
[ROW][C]19[/C][C]13.6[/C][C]8.55942057996962[/C][C]5.04057942003038[/C][/ROW]
[ROW][C]20[/C][C]14.8[/C][C]4.48559997105015[/C][C]10.3144000289499[/C][/ROW]
[ROW][C]21[/C][C]9.5[/C][C]4.20428606669409[/C][C]5.29571393330591[/C][/ROW]
[ROW][C]22[/C][C]13.7[/C][C]9.22535151251037[/C][C]4.47464848748963[/C][/ROW]
[ROW][C]23[/C][C]17[/C][C]3.69036935459531[/C][C]13.3096306454047[/C][/ROW]
[ROW][C]24[/C][C]14.7[/C][C]9.06665812491702[/C][C]5.63334187508298[/C][/ROW]
[ROW][C]25[/C][C]17.4[/C][C]12.3178838953416[/C][C]5.08211610465838[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]12.3605655116711[/C][C]-3.36056551167109[/C][/ROW]
[ROW][C]27[/C][C]9.1[/C][C]13.3863795068914[/C][C]-4.28637950689136[/C][/ROW]
[ROW][C]28[/C][C]12.2[/C][C]18.0475931412399[/C][C]-5.84759314123989[/C][/ROW]
[ROW][C]29[/C][C]15.9[/C][C]16.5217843545549[/C][C]-0.621784354554908[/C][/ROW]
[ROW][C]30[/C][C]12.9[/C][C]19.9003743470195[/C][C]-7.00037434701954[/C][/ROW]
[ROW][C]31[/C][C]10.9[/C][C]19.8950205430019[/C][C]-8.99502054300188[/C][/ROW]
[ROW][C]32[/C][C]10.6[/C][C]13.7455268354450[/C][C]-3.14552683544497[/C][/ROW]
[ROW][C]33[/C][C]13.2[/C][C]9.87895939707878[/C][C]3.32104060292122[/C][/ROW]
[ROW][C]34[/C][C]9.6[/C][C]13.9295802772983[/C][C]-4.32958027729833[/C][/ROW]
[ROW][C]35[/C][C]6.4[/C][C]28.5043660620266[/C][C]-22.1043660620266[/C][/ROW]
[ROW][C]36[/C][C]5.8[/C][C]11.0482507473364[/C][C]-5.24825074733639[/C][/ROW]
[ROW][C]37[/C][C]-1[/C][C]11.63075396237[/C][C]-12.63075396237[/C][/ROW]
[ROW][C]38[/C][C]-0.2[/C][C]8.16905242515572[/C][C]-8.36905242515572[/C][/ROW]
[ROW][C]39[/C][C]2.7[/C][C]9.70704549666316[/C][C]-7.00704549666316[/C][/ROW]
[ROW][C]40[/C][C]3.6[/C][C]2.80379472431736[/C][C]0.796205275682643[/C][/ROW]
[ROW][C]41[/C][C]-0.9[/C][C]-0.662903193561076[/C][C]-0.237096806438924[/C][/ROW]
[ROW][C]42[/C][C]0.3[/C][C]-3.64611646445276[/C][C]3.94611646445276[/C][/ROW]
[ROW][C]43[/C][C]-1.1[/C][C]-7.58716211783493[/C][C]6.48716211783492[/C][/ROW]
[ROW][C]44[/C][C]-2.5[/C][C]-4.22090772384616[/C][C]1.72090772384616[/C][/ROW]
[ROW][C]45[/C][C]-3.4[/C][C]3.39611886401558[/C][C]-6.79611886401558[/C][/ROW]
[ROW][C]46[/C][C]-3.5[/C][C]5.72150496095207[/C][C]-9.22150496095207[/C][/ROW]
[ROW][C]47[/C][C]-3.9[/C][C]0.698701879324174[/C][C]-4.59870187932417[/C][/ROW]
[ROW][C]48[/C][C]-4.6[/C][C]2.13929880028138[/C][C]-6.73929880028138[/C][/ROW]
[ROW][C]49[/C][C]-0.1[/C][C]-0.97127869265034[/C][C]0.871278692650341[/C][/ROW]
[ROW][C]50[/C][C]4.3[/C][C]7.96714477498246[/C][C]-3.66714477498246[/C][/ROW]
[ROW][C]51[/C][C]10.2[/C][C]13.5486568698096[/C][C]-3.34865686980959[/C][/ROW]
[ROW][C]52[/C][C]8.7[/C][C]11.4437153384698[/C][C]-2.74371533846983[/C][/ROW]
[ROW][C]53[/C][C]13.3[/C][C]13.2335921509070[/C][C]0.0664078490930123[/C][/ROW]
[ROW][C]54[/C][C]15[/C][C]12.9460582288951[/C][C]2.05394177110489[/C][/ROW]
[ROW][C]55[/C][C]20.7[/C][C]17.1998777961075[/C][C]3.50012220389248[/C][/ROW]
[ROW][C]56[/C][C]20.7[/C][C]24.0435585501512[/C][C]-3.34355855015123[/C][/ROW]
[ROW][C]57[/C][C]26.4[/C][C]19.9613367638746[/C][C]6.43866323612535[/C][/ROW]
[ROW][C]58[/C][C]31.2[/C][C]15.7901356300108[/C][C]15.4098643699892[/C][/ROW]
[ROW][C]59[/C][C]31.4[/C][C]7.2090358078616[/C][C]24.1909641921384[/C][/ROW]
[ROW][C]60[/C][C]26.6[/C][C]13.5557691437800[/C][C]13.0442308562200[/C][/ROW]
[ROW][C]61[/C][C]26.6[/C][C]13.6530500760153[/C][C]12.9469499239847[/C][/ROW]
[ROW][C]62[/C][C]19.2[/C][C]11.3235338653305[/C][C]7.87646613466948[/C][/ROW]
[ROW][C]63[/C][C]6.5[/C][C]-0.266431492206655[/C][C]6.76643149220665[/C][/ROW]
[ROW][C]64[/C][C]3.1[/C][C]-4.55487329613905[/C][C]7.65487329613905[/C][/ROW]
[ROW][C]65[/C][C]-0.2[/C][C]-0.797150559019634[/C][C]0.597150559019634[/C][/ROW]
[ROW][C]66[/C][C]-4[/C][C]-4.80472198248564[/C][C]0.80472198248564[/C][/ROW]
[ROW][C]67[/C][C]-12.6[/C][C]-13.5710336703626[/C][C]0.971033670362639[/C][/ROW]
[ROW][C]68[/C][C]-13[/C][C]-10.4204336242843[/C][C]-2.57956637571574[/C][/ROW]
[ROW][C]69[/C][C]-17.6[/C][C]-10.7826179091067[/C][C]-6.81738209089328[/C][/ROW]
[ROW][C]70[/C][C]-21.7[/C][C]-10.7216024652293[/C][C]-10.9783975347707[/C][/ROW]
[ROW][C]71[/C][C]-23.2[/C][C]-9.46347266396723[/C][C]-13.7365273360328[/C][/ROW]
[ROW][C]72[/C][C]-16.8[/C][C]-9.88289449373708[/C][C]-6.91710550626292[/C][/ROW]
[ROW][C]73[/C][C]-19.8[/C][C]-13.7213054108663[/C][C]-6.07869458913365[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69867&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69867&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
1-1.21.96593859365773-3.16593859365773
2-2.42.89819439511755-5.29819439511755
30.81.17441545448043-0.374415454480434
4-0.12.92429539203879-3.02429539203879
5-1.53.87851160632323-5.37851160632323
6-4.43.21358257546817-7.61358257546817
7-4.22.80387686911853-7.00387686911853
83.56.46665599148408-2.96665599148408
91011.4419168174436-1.44191681744361
108.63.955030084457644.64496991554236
119.56.560999560159582.93900043984042
129.99.672917677422270.227082322577732
1310.47.424957576132072.97504242386793
14163.1815090277426512.8184909722573
1512.74.449934164362128.25006583563788
1610.27.03547470007323.1645252999268
178.93.326165640795595.57383435920441
1812.64.790823295555577.80917670444443
1913.68.559420579969625.04057942003038
2014.84.4855999710501510.3144000289499
219.54.204286066694095.29571393330591
2213.79.225351512510374.47464848748963
23173.6903693545953113.3096306454047
2414.79.066658124917025.63334187508298
2517.412.31788389534165.08211610465838
26912.3605655116711-3.36056551167109
279.113.3863795068914-4.28637950689136
2812.218.0475931412399-5.84759314123989
2915.916.5217843545549-0.621784354554908
3012.919.9003743470195-7.00037434701954
3110.919.8950205430019-8.99502054300188
3210.613.7455268354450-3.14552683544497
3313.29.878959397078783.32104060292122
349.613.9295802772983-4.32958027729833
356.428.5043660620266-22.1043660620266
365.811.0482507473364-5.24825074733639
37-111.63075396237-12.63075396237
38-0.28.16905242515572-8.36905242515572
392.79.70704549666316-7.00704549666316
403.62.803794724317360.796205275682643
41-0.9-0.662903193561076-0.237096806438924
420.3-3.646116464452763.94611646445276
43-1.1-7.587162117834936.48716211783492
44-2.5-4.220907723846161.72090772384616
45-3.43.39611886401558-6.79611886401558
46-3.55.72150496095207-9.22150496095207
47-3.90.698701879324174-4.59870187932417
48-4.62.13929880028138-6.73929880028138
49-0.1-0.971278692650340.871278692650341
504.37.96714477498246-3.66714477498246
5110.213.5486568698096-3.34865686980959
528.711.4437153384698-2.74371533846983
5313.313.23359215090700.0664078490930123
541512.94605822889512.05394177110489
5520.717.19987779610753.50012220389248
5620.724.0435585501512-3.34355855015123
5726.419.96133676387466.43866323612535
5831.215.790135630010815.4098643699892
5931.47.209035807861624.1909641921384
6026.613.555769143780013.0442308562200
6126.613.653050076015312.9469499239847
6219.211.32353386533057.87646613466948
636.5-0.2664314922066556.76643149220665
643.1-4.554873296139057.65487329613905
65-0.2-0.7971505590196340.597150559019634
66-4-4.804721982485640.80472198248564
67-12.6-13.57103367036260.971033670362639
68-13-10.4204336242843-2.57956637571574
69-17.6-10.7826179091067-6.81738209089328
70-21.7-10.7216024652293-10.9783975347707
71-23.2-9.46347266396723-13.7365273360328
72-16.8-9.88289449373708-6.91710550626292
73-19.8-13.7213054108663-6.07869458913365






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.02695548926142310.05391097852284630.973044510738577
180.01048008895351890.02096017790703790.98951991104648
190.006653067028118550.01330613405623710.993346932971881
200.002294576483384510.004589152966769010.997705423516616
210.003466279640605000.006932559281209990.996533720359395
220.006329757503630370.01265951500726070.99367024249637
230.00378168463839590.00756336927679180.996218315361604
240.002406009340000370.004812018680000740.99759399066
250.002252571533356250.004505143066712500.997747428466644
260.01255872073186850.0251174414637370.987441279268132
270.01446581771214890.02893163542429780.985534182287851
280.007764796856661140.01552959371332230.992235203143339
290.004143904567574480.008287809135148970.995856095432426
300.002074347061791930.004148694123583860.997925652938208
310.001298578556317710.002597157112635420.998701421443682
320.001338812277831500.002677624555662990.998661187722168
330.001334096749030540.002668193498061080.99866590325097
340.001737135204957060.003474270409914120.998262864795043
350.009387243419869120.01877448683973820.99061275658013
360.02299856905887490.04599713811774990.977001430941125
370.09900252315095270.1980050463019050.900997476849047
380.1285077980995180.2570155961990350.871492201900482
390.1229446893250340.2458893786500690.877055310674966
400.09159403172260470.1831880634452090.908405968277395
410.0742627896714640.1485255793429280.925737210328536
420.05661448019333330.1132289603866670.943385519806667
430.05064573439182040.1012914687836410.94935426560818
440.07335803705775460.1467160741155090.926641962942245
450.06583284572468120.1316656914493620.934167154275319
460.05948018344628710.1189603668925740.940519816553713
470.04266318684318160.08532637368636310.957336813156818
480.03138457673724190.06276915347448370.968615423262758
490.01888467003973400.03776934007946790.981115329960266
500.01123338896024000.02246677792048000.98876661103976
510.01353688683177870.02707377366355740.986463113168221
520.03579326089621890.07158652179243780.964206739103781
530.1050089387700740.2100178775401480.894991061229926
540.857667766893430.2846644662131410.142332233106570
550.873848260600290.2523034787994180.126151739399709
560.8543020400085830.2913959199828340.145697959991417

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.0269554892614231 & 0.0539109785228463 & 0.973044510738577 \tabularnewline
18 & 0.0104800889535189 & 0.0209601779070379 & 0.98951991104648 \tabularnewline
19 & 0.00665306702811855 & 0.0133061340562371 & 0.993346932971881 \tabularnewline
20 & 0.00229457648338451 & 0.00458915296676901 & 0.997705423516616 \tabularnewline
21 & 0.00346627964060500 & 0.00693255928120999 & 0.996533720359395 \tabularnewline
22 & 0.00632975750363037 & 0.0126595150072607 & 0.99367024249637 \tabularnewline
23 & 0.0037816846383959 & 0.0075633692767918 & 0.996218315361604 \tabularnewline
24 & 0.00240600934000037 & 0.00481201868000074 & 0.99759399066 \tabularnewline
25 & 0.00225257153335625 & 0.00450514306671250 & 0.997747428466644 \tabularnewline
26 & 0.0125587207318685 & 0.025117441463737 & 0.987441279268132 \tabularnewline
27 & 0.0144658177121489 & 0.0289316354242978 & 0.985534182287851 \tabularnewline
28 & 0.00776479685666114 & 0.0155295937133223 & 0.992235203143339 \tabularnewline
29 & 0.00414390456757448 & 0.00828780913514897 & 0.995856095432426 \tabularnewline
30 & 0.00207434706179193 & 0.00414869412358386 & 0.997925652938208 \tabularnewline
31 & 0.00129857855631771 & 0.00259715711263542 & 0.998701421443682 \tabularnewline
32 & 0.00133881227783150 & 0.00267762455566299 & 0.998661187722168 \tabularnewline
33 & 0.00133409674903054 & 0.00266819349806108 & 0.99866590325097 \tabularnewline
34 & 0.00173713520495706 & 0.00347427040991412 & 0.998262864795043 \tabularnewline
35 & 0.00938724341986912 & 0.0187744868397382 & 0.99061275658013 \tabularnewline
36 & 0.0229985690588749 & 0.0459971381177499 & 0.977001430941125 \tabularnewline
37 & 0.0990025231509527 & 0.198005046301905 & 0.900997476849047 \tabularnewline
38 & 0.128507798099518 & 0.257015596199035 & 0.871492201900482 \tabularnewline
39 & 0.122944689325034 & 0.245889378650069 & 0.877055310674966 \tabularnewline
40 & 0.0915940317226047 & 0.183188063445209 & 0.908405968277395 \tabularnewline
41 & 0.074262789671464 & 0.148525579342928 & 0.925737210328536 \tabularnewline
42 & 0.0566144801933333 & 0.113228960386667 & 0.943385519806667 \tabularnewline
43 & 0.0506457343918204 & 0.101291468783641 & 0.94935426560818 \tabularnewline
44 & 0.0733580370577546 & 0.146716074115509 & 0.926641962942245 \tabularnewline
45 & 0.0658328457246812 & 0.131665691449362 & 0.934167154275319 \tabularnewline
46 & 0.0594801834462871 & 0.118960366892574 & 0.940519816553713 \tabularnewline
47 & 0.0426631868431816 & 0.0853263736863631 & 0.957336813156818 \tabularnewline
48 & 0.0313845767372419 & 0.0627691534744837 & 0.968615423262758 \tabularnewline
49 & 0.0188846700397340 & 0.0377693400794679 & 0.981115329960266 \tabularnewline
50 & 0.0112333889602400 & 0.0224667779204800 & 0.98876661103976 \tabularnewline
51 & 0.0135368868317787 & 0.0270737736635574 & 0.986463113168221 \tabularnewline
52 & 0.0357932608962189 & 0.0715865217924378 & 0.964206739103781 \tabularnewline
53 & 0.105008938770074 & 0.210017877540148 & 0.894991061229926 \tabularnewline
54 & 0.85766776689343 & 0.284664466213141 & 0.142332233106570 \tabularnewline
55 & 0.87384826060029 & 0.252303478799418 & 0.126151739399709 \tabularnewline
56 & 0.854302040008583 & 0.291395919982834 & 0.145697959991417 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69867&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]0.0269554892614231[/C][C]0.0539109785228463[/C][C]0.973044510738577[/C][/ROW]
[ROW][C]18[/C][C]0.0104800889535189[/C][C]0.0209601779070379[/C][C]0.98951991104648[/C][/ROW]
[ROW][C]19[/C][C]0.00665306702811855[/C][C]0.0133061340562371[/C][C]0.993346932971881[/C][/ROW]
[ROW][C]20[/C][C]0.00229457648338451[/C][C]0.00458915296676901[/C][C]0.997705423516616[/C][/ROW]
[ROW][C]21[/C][C]0.00346627964060500[/C][C]0.00693255928120999[/C][C]0.996533720359395[/C][/ROW]
[ROW][C]22[/C][C]0.00632975750363037[/C][C]0.0126595150072607[/C][C]0.99367024249637[/C][/ROW]
[ROW][C]23[/C][C]0.0037816846383959[/C][C]0.0075633692767918[/C][C]0.996218315361604[/C][/ROW]
[ROW][C]24[/C][C]0.00240600934000037[/C][C]0.00481201868000074[/C][C]0.99759399066[/C][/ROW]
[ROW][C]25[/C][C]0.00225257153335625[/C][C]0.00450514306671250[/C][C]0.997747428466644[/C][/ROW]
[ROW][C]26[/C][C]0.0125587207318685[/C][C]0.025117441463737[/C][C]0.987441279268132[/C][/ROW]
[ROW][C]27[/C][C]0.0144658177121489[/C][C]0.0289316354242978[/C][C]0.985534182287851[/C][/ROW]
[ROW][C]28[/C][C]0.00776479685666114[/C][C]0.0155295937133223[/C][C]0.992235203143339[/C][/ROW]
[ROW][C]29[/C][C]0.00414390456757448[/C][C]0.00828780913514897[/C][C]0.995856095432426[/C][/ROW]
[ROW][C]30[/C][C]0.00207434706179193[/C][C]0.00414869412358386[/C][C]0.997925652938208[/C][/ROW]
[ROW][C]31[/C][C]0.00129857855631771[/C][C]0.00259715711263542[/C][C]0.998701421443682[/C][/ROW]
[ROW][C]32[/C][C]0.00133881227783150[/C][C]0.00267762455566299[/C][C]0.998661187722168[/C][/ROW]
[ROW][C]33[/C][C]0.00133409674903054[/C][C]0.00266819349806108[/C][C]0.99866590325097[/C][/ROW]
[ROW][C]34[/C][C]0.00173713520495706[/C][C]0.00347427040991412[/C][C]0.998262864795043[/C][/ROW]
[ROW][C]35[/C][C]0.00938724341986912[/C][C]0.0187744868397382[/C][C]0.99061275658013[/C][/ROW]
[ROW][C]36[/C][C]0.0229985690588749[/C][C]0.0459971381177499[/C][C]0.977001430941125[/C][/ROW]
[ROW][C]37[/C][C]0.0990025231509527[/C][C]0.198005046301905[/C][C]0.900997476849047[/C][/ROW]
[ROW][C]38[/C][C]0.128507798099518[/C][C]0.257015596199035[/C][C]0.871492201900482[/C][/ROW]
[ROW][C]39[/C][C]0.122944689325034[/C][C]0.245889378650069[/C][C]0.877055310674966[/C][/ROW]
[ROW][C]40[/C][C]0.0915940317226047[/C][C]0.183188063445209[/C][C]0.908405968277395[/C][/ROW]
[ROW][C]41[/C][C]0.074262789671464[/C][C]0.148525579342928[/C][C]0.925737210328536[/C][/ROW]
[ROW][C]42[/C][C]0.0566144801933333[/C][C]0.113228960386667[/C][C]0.943385519806667[/C][/ROW]
[ROW][C]43[/C][C]0.0506457343918204[/C][C]0.101291468783641[/C][C]0.94935426560818[/C][/ROW]
[ROW][C]44[/C][C]0.0733580370577546[/C][C]0.146716074115509[/C][C]0.926641962942245[/C][/ROW]
[ROW][C]45[/C][C]0.0658328457246812[/C][C]0.131665691449362[/C][C]0.934167154275319[/C][/ROW]
[ROW][C]46[/C][C]0.0594801834462871[/C][C]0.118960366892574[/C][C]0.940519816553713[/C][/ROW]
[ROW][C]47[/C][C]0.0426631868431816[/C][C]0.0853263736863631[/C][C]0.957336813156818[/C][/ROW]
[ROW][C]48[/C][C]0.0313845767372419[/C][C]0.0627691534744837[/C][C]0.968615423262758[/C][/ROW]
[ROW][C]49[/C][C]0.0188846700397340[/C][C]0.0377693400794679[/C][C]0.981115329960266[/C][/ROW]
[ROW][C]50[/C][C]0.0112333889602400[/C][C]0.0224667779204800[/C][C]0.98876661103976[/C][/ROW]
[ROW][C]51[/C][C]0.0135368868317787[/C][C]0.0270737736635574[/C][C]0.986463113168221[/C][/ROW]
[ROW][C]52[/C][C]0.0357932608962189[/C][C]0.0715865217924378[/C][C]0.964206739103781[/C][/ROW]
[ROW][C]53[/C][C]0.105008938770074[/C][C]0.210017877540148[/C][C]0.894991061229926[/C][/ROW]
[ROW][C]54[/C][C]0.85766776689343[/C][C]0.284664466213141[/C][C]0.142332233106570[/C][/ROW]
[ROW][C]55[/C][C]0.87384826060029[/C][C]0.252303478799418[/C][C]0.126151739399709[/C][/ROW]
[ROW][C]56[/C][C]0.854302040008583[/C][C]0.291395919982834[/C][C]0.145697959991417[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69867&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69867&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
170.02695548926142310.05391097852284630.973044510738577
180.01048008895351890.02096017790703790.98951991104648
190.006653067028118550.01330613405623710.993346932971881
200.002294576483384510.004589152966769010.997705423516616
210.003466279640605000.006932559281209990.996533720359395
220.006329757503630370.01265951500726070.99367024249637
230.00378168463839590.00756336927679180.996218315361604
240.002406009340000370.004812018680000740.99759399066
250.002252571533356250.004505143066712500.997747428466644
260.01255872073186850.0251174414637370.987441279268132
270.01446581771214890.02893163542429780.985534182287851
280.007764796856661140.01552959371332230.992235203143339
290.004143904567574480.008287809135148970.995856095432426
300.002074347061791930.004148694123583860.997925652938208
310.001298578556317710.002597157112635420.998701421443682
320.001338812277831500.002677624555662990.998661187722168
330.001334096749030540.002668193498061080.99866590325097
340.001737135204957060.003474270409914120.998262864795043
350.009387243419869120.01877448683973820.99061275658013
360.02299856905887490.04599713811774990.977001430941125
370.09900252315095270.1980050463019050.900997476849047
380.1285077980995180.2570155961990350.871492201900482
390.1229446893250340.2458893786500690.877055310674966
400.09159403172260470.1831880634452090.908405968277395
410.0742627896714640.1485255793429280.925737210328536
420.05661448019333330.1132289603866670.943385519806667
430.05064573439182040.1012914687836410.94935426560818
440.07335803705775460.1467160741155090.926641962942245
450.06583284572468120.1316656914493620.934167154275319
460.05948018344628710.1189603668925740.940519816553713
470.04266318684318160.08532637368636310.957336813156818
480.03138457673724190.06276915347448370.968615423262758
490.01888467003973400.03776934007946790.981115329960266
500.01123338896024000.02246677792048000.98876661103976
510.01353688683177870.02707377366355740.986463113168221
520.03579326089621890.07158652179243780.964206739103781
530.1050089387700740.2100178775401480.894991061229926
540.857667766893430.2846644662131410.142332233106570
550.873848260600290.2523034787994180.126151739399709
560.8543020400085830.2913959199828340.145697959991417






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level110.275NOK
5% type I error level220.55NOK
10% type I error level260.65NOK

\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 & 11 & 0.275 & NOK \tabularnewline
5% type I error level & 22 & 0.55 & NOK \tabularnewline
10% type I error level & 26 & 0.65 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69867&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]11[/C][C]0.275[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]22[/C][C]0.55[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]26[/C][C]0.65[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69867&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69867&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 level110.275NOK
5% type I error level220.55NOK
10% type I error level260.65NOK


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')
}