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Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 06:07:06 -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/Nov/20/t1258722457q3kojs0zvtd2hhs.htm/, Retrieved Fri, 29 Mar 2024 07:37:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58112, Retrieved Fri, 29 Mar 2024 07:37:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact116
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 14:14:11] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2009-11-20 13:07:06] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
89.1		72.7		103.5		8.2
82.6		79.7		104.6		8.3
102.7		115.8		118.6		8.1
91.8		87.8		106.3		7.4
94.1		99.2		110.7		7.3
103.1		111.4		121.6		7.7
93.2		102.3		107		8
91		94.4		107.6		8
94.3		118.5		125.6		7.7
99.4		112.1		113.5		6.9
115.7		136.5		129.2		6.6
116.8		139.8		130.9		6.9
99.8		104.5		104.7		7.5
96		123.3		115.2		7.9
115.9		156.6		124.5		7.7
109.1		136.2		112.3		6.5
117.3		147.5		127.5		6.1
109.8		143.8		120.6		6.4
112.8		135.8		117.5		6.8
110.7		121.6		117.7		7.1
100		128		120.4		7.3
113.3		129.7		125		7.2
122.4		136.2		131.6		7
112.5		130.5		121.1		7
104.2		99.2		114.2		7
92.5		110.4		112.1		7.3
117.2		151.6		127		7.5
109.3		129.6		116.8		7.2
106.1		123.6		112		7.7
118.8		142.7		129.7		8
105.3		119		113.6		7.9
106		118.1		115.7		8
102		120		119.5		8
112.9		124.3		125.8		7.9
116.5		123.3		129.6		7.9
114.8		122.4		128		8
100.5		90.5		112.8		8.1
85.4		91		101.6		8.1
114.6		137		123.9		8.2
109.9		127.7		118.8		8
100.7		105.1		109.1		8.3
115.5		135.6		130.6		8.5
100.7		112.4		112.4		8.6
99		102.5		111		8.7
102.3		112.6		116.2		8.7
108.8		110.8		119.8		8.5
105.9		103.4		117.2		8.4
113.2		117.6		127.3		8.5
95.7		87.5		107.7		8.7
80.9		87		97.5		8.7
113.9		130		120.1		8.6
98.1		102.9		110.6		7.9
102.8		111.1		111.3		8.1
104.7		128.9		119.8		8.2
95.9		106.3		105.5		8.5
94.6		99		108.7		8.6
101.6		109.9		128.7		8.5
103.9		104		119.5		8.3
110.3		112.9		121.1		8.2
114.1		113.6		128.4		8.7




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=58112&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=58112&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58112&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.







Multiple Linear Regression - Estimated Regression Equation
TotaleIndustrieleProductie[t] = + 9.66386270757074 + 0.297950401733887Investeringsgoederen[t] + 0.479360303731291Consumptiegoederen[t] + 0.57480776596739BrutoInflatie[t] + 3.14166191797486M1[t] -8.44973556391387M2[t] -2.96336432788968M3[t] -0.79174312654727M4[t] -1.03781182965360M5[t] -4.54220727709078M6[t] -1.99582303416013M7[t] -1.49535021102689M8[t] -9.69478239937884M9[t] -0.834699350408226M10[t] + 1.41278369982361M11[t] + 0.0554727893837077t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TotaleIndustrieleProductie[t] =  +  9.66386270757074 +  0.297950401733887Investeringsgoederen[t] +  0.479360303731291Consumptiegoederen[t] +  0.57480776596739BrutoInflatie[t] +  3.14166191797486M1[t] -8.44973556391387M2[t] -2.96336432788968M3[t] -0.79174312654727M4[t] -1.03781182965360M5[t] -4.54220727709078M6[t] -1.99582303416013M7[t] -1.49535021102689M8[t] -9.69478239937884M9[t] -0.834699350408226M10[t] +  1.41278369982361M11[t] +  0.0554727893837077t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58112&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TotaleIndustrieleProductie[t] =  +  9.66386270757074 +  0.297950401733887Investeringsgoederen[t] +  0.479360303731291Consumptiegoederen[t] +  0.57480776596739BrutoInflatie[t] +  3.14166191797486M1[t] -8.44973556391387M2[t] -2.96336432788968M3[t] -0.79174312654727M4[t] -1.03781182965360M5[t] -4.54220727709078M6[t] -1.99582303416013M7[t] -1.49535021102689M8[t] -9.69478239937884M9[t] -0.834699350408226M10[t] +  1.41278369982361M11[t] +  0.0554727893837077t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58112&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58112&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
TotaleIndustrieleProductie[t] = + 9.66386270757074 + 0.297950401733887Investeringsgoederen[t] + 0.479360303731291Consumptiegoederen[t] + 0.57480776596739BrutoInflatie[t] + 3.14166191797486M1[t] -8.44973556391387M2[t] -2.96336432788968M3[t] -0.79174312654727M4[t] -1.03781182965360M5[t] -4.54220727709078M6[t] -1.99582303416013M7[t] -1.49535021102689M8[t] -9.69478239937884M9[t] -0.834699350408226M10[t] + 1.41278369982361M11[t] + 0.0554727893837077t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.6638627075707412.3352080.78340.4375670.218784
Investeringsgoederen0.2979504017338870.0454356.557800
Consumptiegoederen0.4793603037312910.0922365.19715e-063e-06
BrutoInflatie0.574807765967390.997290.57640.5673020.283651
M13.141661917974861.9666251.59750.1173140.058657
M2-8.449735563913872.070683-4.08070.0001869.3e-05
M3-2.963364327889681.902854-1.55730.1265580.063279
M4-0.791743126547271.877271-0.42180.6752590.33763
M5-1.037811829653601.815701-0.57160.5705180.285259
M6-4.542207277090781.606294-2.82780.007030.003515
M7-1.995823034160131.957243-1.01970.313440.15672
M8-1.495350211026891.819997-0.82160.4157220.207861
M9-9.694782399378841.545148-6.274300
M10-0.8346993504082261.565288-0.53330.596540.29827
M111.412783699823611.5161760.93180.3565210.178261
t0.05547278938370770.0279741.9830.0536280.026814

\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) & 9.66386270757074 & 12.335208 & 0.7834 & 0.437567 & 0.218784 \tabularnewline
Investeringsgoederen & 0.297950401733887 & 0.045435 & 6.5578 & 0 & 0 \tabularnewline
Consumptiegoederen & 0.479360303731291 & 0.092236 & 5.1971 & 5e-06 & 3e-06 \tabularnewline
BrutoInflatie & 0.57480776596739 & 0.99729 & 0.5764 & 0.567302 & 0.283651 \tabularnewline
M1 & 3.14166191797486 & 1.966625 & 1.5975 & 0.117314 & 0.058657 \tabularnewline
M2 & -8.44973556391387 & 2.070683 & -4.0807 & 0.000186 & 9.3e-05 \tabularnewline
M3 & -2.96336432788968 & 1.902854 & -1.5573 & 0.126558 & 0.063279 \tabularnewline
M4 & -0.79174312654727 & 1.877271 & -0.4218 & 0.675259 & 0.33763 \tabularnewline
M5 & -1.03781182965360 & 1.815701 & -0.5716 & 0.570518 & 0.285259 \tabularnewline
M6 & -4.54220727709078 & 1.606294 & -2.8278 & 0.00703 & 0.003515 \tabularnewline
M7 & -1.99582303416013 & 1.957243 & -1.0197 & 0.31344 & 0.15672 \tabularnewline
M8 & -1.49535021102689 & 1.819997 & -0.8216 & 0.415722 & 0.207861 \tabularnewline
M9 & -9.69478239937884 & 1.545148 & -6.2743 & 0 & 0 \tabularnewline
M10 & -0.834699350408226 & 1.565288 & -0.5333 & 0.59654 & 0.29827 \tabularnewline
M11 & 1.41278369982361 & 1.516176 & 0.9318 & 0.356521 & 0.178261 \tabularnewline
t & 0.0554727893837077 & 0.027974 & 1.983 & 0.053628 & 0.026814 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58112&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]9.66386270757074[/C][C]12.335208[/C][C]0.7834[/C][C]0.437567[/C][C]0.218784[/C][/ROW]
[ROW][C]Investeringsgoederen[/C][C]0.297950401733887[/C][C]0.045435[/C][C]6.5578[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Consumptiegoederen[/C][C]0.479360303731291[/C][C]0.092236[/C][C]5.1971[/C][C]5e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]BrutoInflatie[/C][C]0.57480776596739[/C][C]0.99729[/C][C]0.5764[/C][C]0.567302[/C][C]0.283651[/C][/ROW]
[ROW][C]M1[/C][C]3.14166191797486[/C][C]1.966625[/C][C]1.5975[/C][C]0.117314[/C][C]0.058657[/C][/ROW]
[ROW][C]M2[/C][C]-8.44973556391387[/C][C]2.070683[/C][C]-4.0807[/C][C]0.000186[/C][C]9.3e-05[/C][/ROW]
[ROW][C]M3[/C][C]-2.96336432788968[/C][C]1.902854[/C][C]-1.5573[/C][C]0.126558[/C][C]0.063279[/C][/ROW]
[ROW][C]M4[/C][C]-0.79174312654727[/C][C]1.877271[/C][C]-0.4218[/C][C]0.675259[/C][C]0.33763[/C][/ROW]
[ROW][C]M5[/C][C]-1.03781182965360[/C][C]1.815701[/C][C]-0.5716[/C][C]0.570518[/C][C]0.285259[/C][/ROW]
[ROW][C]M6[/C][C]-4.54220727709078[/C][C]1.606294[/C][C]-2.8278[/C][C]0.00703[/C][C]0.003515[/C][/ROW]
[ROW][C]M7[/C][C]-1.99582303416013[/C][C]1.957243[/C][C]-1.0197[/C][C]0.31344[/C][C]0.15672[/C][/ROW]
[ROW][C]M8[/C][C]-1.49535021102689[/C][C]1.819997[/C][C]-0.8216[/C][C]0.415722[/C][C]0.207861[/C][/ROW]
[ROW][C]M9[/C][C]-9.69478239937884[/C][C]1.545148[/C][C]-6.2743[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M10[/C][C]-0.834699350408226[/C][C]1.565288[/C][C]-0.5333[/C][C]0.59654[/C][C]0.29827[/C][/ROW]
[ROW][C]M11[/C][C]1.41278369982361[/C][C]1.516176[/C][C]0.9318[/C][C]0.356521[/C][C]0.178261[/C][/ROW]
[ROW][C]t[/C][C]0.0554727893837077[/C][C]0.027974[/C][C]1.983[/C][C]0.053628[/C][C]0.026814[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58112&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58112&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.6638627075707412.3352080.78340.4375670.218784
Investeringsgoederen0.2979504017338870.0454356.557800
Consumptiegoederen0.4793603037312910.0922365.19715e-063e-06
BrutoInflatie0.574807765967390.997290.57640.5673020.283651
M13.141661917974861.9666251.59750.1173140.058657
M2-8.449735563913872.070683-4.08070.0001869.3e-05
M3-2.963364327889681.902854-1.55730.1265580.063279
M4-0.791743126547271.877271-0.42180.6752590.33763
M5-1.037811829653601.815701-0.57160.5705180.285259
M6-4.542207277090781.606294-2.82780.007030.003515
M7-1.995823034160131.957243-1.01970.313440.15672
M8-1.495350211026891.819997-0.82160.4157220.207861
M9-9.694782399378841.545148-6.274300
M10-0.8346993504082261.565288-0.53330.596540.29827
M111.412783699823611.5161760.93180.3565210.178261
t0.05547278938370770.0279741.9830.0536280.026814







Multiple Linear Regression - Regression Statistics
Multiple R0.97719137439449
R-squared0.954902982190994
Adjusted R-squared0.939528998847014
F-TEST (value)62.1116181035114
F-TEST (DF numerator)15
F-TEST (DF denominator)44
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.36700732314936
Sum Squared Residuals246.519841385079

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.97719137439449 \tabularnewline
R-squared & 0.954902982190994 \tabularnewline
Adjusted R-squared & 0.939528998847014 \tabularnewline
F-TEST (value) & 62.1116181035114 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 44 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.36700732314936 \tabularnewline
Sum Squared Residuals & 246.519841385079 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58112&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.97719137439449[/C][/ROW]
[ROW][C]R-squared[/C][C]0.954902982190994[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.939528998847014[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]62.1116181035114[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]44[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.36700732314936[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]246.519841385079[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58112&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58112&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.97719137439449
R-squared0.954902982190994
Adjusted R-squared0.939528998847014
F-TEST (value)62.1116181035114
F-TEST (DF numerator)15
F-TEST (DF denominator)44
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.36700732314936
Sum Squared Residuals246.519841385079







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
189.188.84920673810450.250793261895545
282.679.98371196843752.61628803156246
3102.7102.877648195483-0.177648195483286
491.890.46363376558861.33636623441144
594.195.7213769914532-1.62137699145319
6103.1101.3623996516111.73760034838884
793.294.4266899234605-1.22668992346050
89192.9164435445185-1.91644354451853
994.3100.40913196471-6.10913196470996
1099.4101.157699344045-1.75769934404488
11115.7118.084159424758-2.38415942475831
12116.8118.697439686174-1.89743968617365
1399.899.16256991414660.637430085853382
149698.4913190698042-2.49131906980418
15115.9118.298000744458-2.39800074445802
16109.1107.9089415151301.19105848486975
17117.3118.141538651329-0.841538651329209
18109.8110.455055740905-0.655055740904673
19112.8109.4172157241683.38278427583211
20110.7106.0105800226004.68941997739988
21100101.182737567997-1.18273756799672
22113.3112.7523857098660.547614290134157
23122.4120.0408356121852.35916438781531
24112.5111.9519242226830.548075777316919
25104.2102.5156252600251.68437473997492
2692.593.4825307588941-0.982530758894103
27117.2118.557361414528-1.35736141452783
28109.3109.1676291392590.132370860740929
29106.1105.1758052402070.92419475979337
30118.8116.0748549611042.72514503889555
31105.3103.8401058056551.45989419434482
32106105.1920334710440.807966528955923
3310299.4357489895492.56425101045088
34112.9112.5949806922700.305019307730453
35116.5116.421555284330.0784447156698831
36114.8114.0865933029560.713406697043607
37100.5100.550314354885-0.0503143548850813
3885.483.79452946145651.60547053854345
39114.6113.7893075164280.810692483572234
40109.9110.685763668806-0.785763668805665
41100.799.2841360594941.41586394050610
42115.5115.3439087377400.156091262259794
43100.7102.366439698516-1.66643969851565
449999.35905268524-0.359052685240045
45102.396.71706592318685.58293407681323
46108.8106.7070465786592.09295342134073
47105.9105.5013518791460.398648120854041
48113.2113.27395651761-0.0739565176100113
4995.798.2222837328388-2.52228373283876
5080.981.6479087414076-0.747908741407636
51113.9110.7776821291033.12231787089691
5298.199.9740319112165-1.87403191121646
53102.8102.6771430575170.122856942482923
54104.7108.663780908640-3.96378090863950
5595.997.8495488482008-1.94954884820078
5694.697.8218902765972-3.22189027659723
57101.6102.455315554557-0.855315554557427
58103.9105.087887675160-1.18788767516046
59110.3110.752097799581-0.452097799580933
60114.1113.3900862705770.709913729423133

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 89.1 & 88.8492067381045 & 0.250793261895545 \tabularnewline
2 & 82.6 & 79.9837119684375 & 2.61628803156246 \tabularnewline
3 & 102.7 & 102.877648195483 & -0.177648195483286 \tabularnewline
4 & 91.8 & 90.4636337655886 & 1.33636623441144 \tabularnewline
5 & 94.1 & 95.7213769914532 & -1.62137699145319 \tabularnewline
6 & 103.1 & 101.362399651611 & 1.73760034838884 \tabularnewline
7 & 93.2 & 94.4266899234605 & -1.22668992346050 \tabularnewline
8 & 91 & 92.9164435445185 & -1.91644354451853 \tabularnewline
9 & 94.3 & 100.40913196471 & -6.10913196470996 \tabularnewline
10 & 99.4 & 101.157699344045 & -1.75769934404488 \tabularnewline
11 & 115.7 & 118.084159424758 & -2.38415942475831 \tabularnewline
12 & 116.8 & 118.697439686174 & -1.89743968617365 \tabularnewline
13 & 99.8 & 99.1625699141466 & 0.637430085853382 \tabularnewline
14 & 96 & 98.4913190698042 & -2.49131906980418 \tabularnewline
15 & 115.9 & 118.298000744458 & -2.39800074445802 \tabularnewline
16 & 109.1 & 107.908941515130 & 1.19105848486975 \tabularnewline
17 & 117.3 & 118.141538651329 & -0.841538651329209 \tabularnewline
18 & 109.8 & 110.455055740905 & -0.655055740904673 \tabularnewline
19 & 112.8 & 109.417215724168 & 3.38278427583211 \tabularnewline
20 & 110.7 & 106.010580022600 & 4.68941997739988 \tabularnewline
21 & 100 & 101.182737567997 & -1.18273756799672 \tabularnewline
22 & 113.3 & 112.752385709866 & 0.547614290134157 \tabularnewline
23 & 122.4 & 120.040835612185 & 2.35916438781531 \tabularnewline
24 & 112.5 & 111.951924222683 & 0.548075777316919 \tabularnewline
25 & 104.2 & 102.515625260025 & 1.68437473997492 \tabularnewline
26 & 92.5 & 93.4825307588941 & -0.982530758894103 \tabularnewline
27 & 117.2 & 118.557361414528 & -1.35736141452783 \tabularnewline
28 & 109.3 & 109.167629139259 & 0.132370860740929 \tabularnewline
29 & 106.1 & 105.175805240207 & 0.92419475979337 \tabularnewline
30 & 118.8 & 116.074854961104 & 2.72514503889555 \tabularnewline
31 & 105.3 & 103.840105805655 & 1.45989419434482 \tabularnewline
32 & 106 & 105.192033471044 & 0.807966528955923 \tabularnewline
33 & 102 & 99.435748989549 & 2.56425101045088 \tabularnewline
34 & 112.9 & 112.594980692270 & 0.305019307730453 \tabularnewline
35 & 116.5 & 116.42155528433 & 0.0784447156698831 \tabularnewline
36 & 114.8 & 114.086593302956 & 0.713406697043607 \tabularnewline
37 & 100.5 & 100.550314354885 & -0.0503143548850813 \tabularnewline
38 & 85.4 & 83.7945294614565 & 1.60547053854345 \tabularnewline
39 & 114.6 & 113.789307516428 & 0.810692483572234 \tabularnewline
40 & 109.9 & 110.685763668806 & -0.785763668805665 \tabularnewline
41 & 100.7 & 99.284136059494 & 1.41586394050610 \tabularnewline
42 & 115.5 & 115.343908737740 & 0.156091262259794 \tabularnewline
43 & 100.7 & 102.366439698516 & -1.66643969851565 \tabularnewline
44 & 99 & 99.35905268524 & -0.359052685240045 \tabularnewline
45 & 102.3 & 96.7170659231868 & 5.58293407681323 \tabularnewline
46 & 108.8 & 106.707046578659 & 2.09295342134073 \tabularnewline
47 & 105.9 & 105.501351879146 & 0.398648120854041 \tabularnewline
48 & 113.2 & 113.27395651761 & -0.0739565176100113 \tabularnewline
49 & 95.7 & 98.2222837328388 & -2.52228373283876 \tabularnewline
50 & 80.9 & 81.6479087414076 & -0.747908741407636 \tabularnewline
51 & 113.9 & 110.777682129103 & 3.12231787089691 \tabularnewline
52 & 98.1 & 99.9740319112165 & -1.87403191121646 \tabularnewline
53 & 102.8 & 102.677143057517 & 0.122856942482923 \tabularnewline
54 & 104.7 & 108.663780908640 & -3.96378090863950 \tabularnewline
55 & 95.9 & 97.8495488482008 & -1.94954884820078 \tabularnewline
56 & 94.6 & 97.8218902765972 & -3.22189027659723 \tabularnewline
57 & 101.6 & 102.455315554557 & -0.855315554557427 \tabularnewline
58 & 103.9 & 105.087887675160 & -1.18788767516046 \tabularnewline
59 & 110.3 & 110.752097799581 & -0.452097799580933 \tabularnewline
60 & 114.1 & 113.390086270577 & 0.709913729423133 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58112&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]89.1[/C][C]88.8492067381045[/C][C]0.250793261895545[/C][/ROW]
[ROW][C]2[/C][C]82.6[/C][C]79.9837119684375[/C][C]2.61628803156246[/C][/ROW]
[ROW][C]3[/C][C]102.7[/C][C]102.877648195483[/C][C]-0.177648195483286[/C][/ROW]
[ROW][C]4[/C][C]91.8[/C][C]90.4636337655886[/C][C]1.33636623441144[/C][/ROW]
[ROW][C]5[/C][C]94.1[/C][C]95.7213769914532[/C][C]-1.62137699145319[/C][/ROW]
[ROW][C]6[/C][C]103.1[/C][C]101.362399651611[/C][C]1.73760034838884[/C][/ROW]
[ROW][C]7[/C][C]93.2[/C][C]94.4266899234605[/C][C]-1.22668992346050[/C][/ROW]
[ROW][C]8[/C][C]91[/C][C]92.9164435445185[/C][C]-1.91644354451853[/C][/ROW]
[ROW][C]9[/C][C]94.3[/C][C]100.40913196471[/C][C]-6.10913196470996[/C][/ROW]
[ROW][C]10[/C][C]99.4[/C][C]101.157699344045[/C][C]-1.75769934404488[/C][/ROW]
[ROW][C]11[/C][C]115.7[/C][C]118.084159424758[/C][C]-2.38415942475831[/C][/ROW]
[ROW][C]12[/C][C]116.8[/C][C]118.697439686174[/C][C]-1.89743968617365[/C][/ROW]
[ROW][C]13[/C][C]99.8[/C][C]99.1625699141466[/C][C]0.637430085853382[/C][/ROW]
[ROW][C]14[/C][C]96[/C][C]98.4913190698042[/C][C]-2.49131906980418[/C][/ROW]
[ROW][C]15[/C][C]115.9[/C][C]118.298000744458[/C][C]-2.39800074445802[/C][/ROW]
[ROW][C]16[/C][C]109.1[/C][C]107.908941515130[/C][C]1.19105848486975[/C][/ROW]
[ROW][C]17[/C][C]117.3[/C][C]118.141538651329[/C][C]-0.841538651329209[/C][/ROW]
[ROW][C]18[/C][C]109.8[/C][C]110.455055740905[/C][C]-0.655055740904673[/C][/ROW]
[ROW][C]19[/C][C]112.8[/C][C]109.417215724168[/C][C]3.38278427583211[/C][/ROW]
[ROW][C]20[/C][C]110.7[/C][C]106.010580022600[/C][C]4.68941997739988[/C][/ROW]
[ROW][C]21[/C][C]100[/C][C]101.182737567997[/C][C]-1.18273756799672[/C][/ROW]
[ROW][C]22[/C][C]113.3[/C][C]112.752385709866[/C][C]0.547614290134157[/C][/ROW]
[ROW][C]23[/C][C]122.4[/C][C]120.040835612185[/C][C]2.35916438781531[/C][/ROW]
[ROW][C]24[/C][C]112.5[/C][C]111.951924222683[/C][C]0.548075777316919[/C][/ROW]
[ROW][C]25[/C][C]104.2[/C][C]102.515625260025[/C][C]1.68437473997492[/C][/ROW]
[ROW][C]26[/C][C]92.5[/C][C]93.4825307588941[/C][C]-0.982530758894103[/C][/ROW]
[ROW][C]27[/C][C]117.2[/C][C]118.557361414528[/C][C]-1.35736141452783[/C][/ROW]
[ROW][C]28[/C][C]109.3[/C][C]109.167629139259[/C][C]0.132370860740929[/C][/ROW]
[ROW][C]29[/C][C]106.1[/C][C]105.175805240207[/C][C]0.92419475979337[/C][/ROW]
[ROW][C]30[/C][C]118.8[/C][C]116.074854961104[/C][C]2.72514503889555[/C][/ROW]
[ROW][C]31[/C][C]105.3[/C][C]103.840105805655[/C][C]1.45989419434482[/C][/ROW]
[ROW][C]32[/C][C]106[/C][C]105.192033471044[/C][C]0.807966528955923[/C][/ROW]
[ROW][C]33[/C][C]102[/C][C]99.435748989549[/C][C]2.56425101045088[/C][/ROW]
[ROW][C]34[/C][C]112.9[/C][C]112.594980692270[/C][C]0.305019307730453[/C][/ROW]
[ROW][C]35[/C][C]116.5[/C][C]116.42155528433[/C][C]0.0784447156698831[/C][/ROW]
[ROW][C]36[/C][C]114.8[/C][C]114.086593302956[/C][C]0.713406697043607[/C][/ROW]
[ROW][C]37[/C][C]100.5[/C][C]100.550314354885[/C][C]-0.0503143548850813[/C][/ROW]
[ROW][C]38[/C][C]85.4[/C][C]83.7945294614565[/C][C]1.60547053854345[/C][/ROW]
[ROW][C]39[/C][C]114.6[/C][C]113.789307516428[/C][C]0.810692483572234[/C][/ROW]
[ROW][C]40[/C][C]109.9[/C][C]110.685763668806[/C][C]-0.785763668805665[/C][/ROW]
[ROW][C]41[/C][C]100.7[/C][C]99.284136059494[/C][C]1.41586394050610[/C][/ROW]
[ROW][C]42[/C][C]115.5[/C][C]115.343908737740[/C][C]0.156091262259794[/C][/ROW]
[ROW][C]43[/C][C]100.7[/C][C]102.366439698516[/C][C]-1.66643969851565[/C][/ROW]
[ROW][C]44[/C][C]99[/C][C]99.35905268524[/C][C]-0.359052685240045[/C][/ROW]
[ROW][C]45[/C][C]102.3[/C][C]96.7170659231868[/C][C]5.58293407681323[/C][/ROW]
[ROW][C]46[/C][C]108.8[/C][C]106.707046578659[/C][C]2.09295342134073[/C][/ROW]
[ROW][C]47[/C][C]105.9[/C][C]105.501351879146[/C][C]0.398648120854041[/C][/ROW]
[ROW][C]48[/C][C]113.2[/C][C]113.27395651761[/C][C]-0.0739565176100113[/C][/ROW]
[ROW][C]49[/C][C]95.7[/C][C]98.2222837328388[/C][C]-2.52228373283876[/C][/ROW]
[ROW][C]50[/C][C]80.9[/C][C]81.6479087414076[/C][C]-0.747908741407636[/C][/ROW]
[ROW][C]51[/C][C]113.9[/C][C]110.777682129103[/C][C]3.12231787089691[/C][/ROW]
[ROW][C]52[/C][C]98.1[/C][C]99.9740319112165[/C][C]-1.87403191121646[/C][/ROW]
[ROW][C]53[/C][C]102.8[/C][C]102.677143057517[/C][C]0.122856942482923[/C][/ROW]
[ROW][C]54[/C][C]104.7[/C][C]108.663780908640[/C][C]-3.96378090863950[/C][/ROW]
[ROW][C]55[/C][C]95.9[/C][C]97.8495488482008[/C][C]-1.94954884820078[/C][/ROW]
[ROW][C]56[/C][C]94.6[/C][C]97.8218902765972[/C][C]-3.22189027659723[/C][/ROW]
[ROW][C]57[/C][C]101.6[/C][C]102.455315554557[/C][C]-0.855315554557427[/C][/ROW]
[ROW][C]58[/C][C]103.9[/C][C]105.087887675160[/C][C]-1.18788767516046[/C][/ROW]
[ROW][C]59[/C][C]110.3[/C][C]110.752097799581[/C][C]-0.452097799580933[/C][/ROW]
[ROW][C]60[/C][C]114.1[/C][C]113.390086270577[/C][C]0.709913729423133[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58112&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58112&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
189.188.84920673810450.250793261895545
282.679.98371196843752.61628803156246
3102.7102.877648195483-0.177648195483286
491.890.46363376558861.33636623441144
594.195.7213769914532-1.62137699145319
6103.1101.3623996516111.73760034838884
793.294.4266899234605-1.22668992346050
89192.9164435445185-1.91644354451853
994.3100.40913196471-6.10913196470996
1099.4101.157699344045-1.75769934404488
11115.7118.084159424758-2.38415942475831
12116.8118.697439686174-1.89743968617365
1399.899.16256991414660.637430085853382
149698.4913190698042-2.49131906980418
15115.9118.298000744458-2.39800074445802
16109.1107.9089415151301.19105848486975
17117.3118.141538651329-0.841538651329209
18109.8110.455055740905-0.655055740904673
19112.8109.4172157241683.38278427583211
20110.7106.0105800226004.68941997739988
21100101.182737567997-1.18273756799672
22113.3112.7523857098660.547614290134157
23122.4120.0408356121852.35916438781531
24112.5111.9519242226830.548075777316919
25104.2102.5156252600251.68437473997492
2692.593.4825307588941-0.982530758894103
27117.2118.557361414528-1.35736141452783
28109.3109.1676291392590.132370860740929
29106.1105.1758052402070.92419475979337
30118.8116.0748549611042.72514503889555
31105.3103.8401058056551.45989419434482
32106105.1920334710440.807966528955923
3310299.4357489895492.56425101045088
34112.9112.5949806922700.305019307730453
35116.5116.421555284330.0784447156698831
36114.8114.0865933029560.713406697043607
37100.5100.550314354885-0.0503143548850813
3885.483.79452946145651.60547053854345
39114.6113.7893075164280.810692483572234
40109.9110.685763668806-0.785763668805665
41100.799.2841360594941.41586394050610
42115.5115.3439087377400.156091262259794
43100.7102.366439698516-1.66643969851565
449999.35905268524-0.359052685240045
45102.396.71706592318685.58293407681323
46108.8106.7070465786592.09295342134073
47105.9105.5013518791460.398648120854041
48113.2113.27395651761-0.0739565176100113
4995.798.2222837328388-2.52228373283876
5080.981.6479087414076-0.747908741407636
51113.9110.7776821291033.12231787089691
5298.199.9740319112165-1.87403191121646
53102.8102.6771430575170.122856942482923
54104.7108.663780908640-3.96378090863950
5595.997.8495488482008-1.94954884820078
5694.697.8218902765972-3.22189027659723
57101.6102.455315554557-0.855315554557427
58103.9105.087887675160-1.18788767516046
59110.3110.752097799581-0.452097799580933
60114.1113.3900862705770.709913729423133







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.6813919635389210.6372160729221590.318608036461079
200.7154935897031820.5690128205936360.284506410296818
210.7049113586828640.5901772826342720.295088641317136
220.6381049638725880.7237900722548240.361895036127412
230.5470126419809460.9059747160381080.452987358019054
240.5225047436537420.9549905126925170.477495256346258
250.924669340219770.1506613195604580.0753306597802289
260.9446896914069150.1106206171861690.0553103085930845
270.9583457102925120.08330857941497640.0416542897074882
280.9375747029562690.1248505940874620.0624252970437309
290.9171966230794530.1656067538410930.0828033769205466
300.9176318617651390.1647362764697220.0823681382348612
310.9166517115716240.1666965768567530.0833482884283764
320.9110739268011630.1778521463976750.0889260731988373
330.9109204211639250.1781591576721490.0890795788360747
340.8650889585630040.2698220828739930.134911041436996
350.8115149185108880.3769701629782250.188485081489112
360.7373896753048250.525220649390350.262610324695175
370.7592301556824220.4815396886351560.240769844317578
380.9518722862523280.09625542749534380.0481277137476719
390.9143268903674850.171346219265030.085673109632515
400.967661210715210.06467757856958040.0323387892847902
410.9057326300270260.1885347399459480.0942673699729742

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.681391963538921 & 0.637216072922159 & 0.318608036461079 \tabularnewline
20 & 0.715493589703182 & 0.569012820593636 & 0.284506410296818 \tabularnewline
21 & 0.704911358682864 & 0.590177282634272 & 0.295088641317136 \tabularnewline
22 & 0.638104963872588 & 0.723790072254824 & 0.361895036127412 \tabularnewline
23 & 0.547012641980946 & 0.905974716038108 & 0.452987358019054 \tabularnewline
24 & 0.522504743653742 & 0.954990512692517 & 0.477495256346258 \tabularnewline
25 & 0.92466934021977 & 0.150661319560458 & 0.0753306597802289 \tabularnewline
26 & 0.944689691406915 & 0.110620617186169 & 0.0553103085930845 \tabularnewline
27 & 0.958345710292512 & 0.0833085794149764 & 0.0416542897074882 \tabularnewline
28 & 0.937574702956269 & 0.124850594087462 & 0.0624252970437309 \tabularnewline
29 & 0.917196623079453 & 0.165606753841093 & 0.0828033769205466 \tabularnewline
30 & 0.917631861765139 & 0.164736276469722 & 0.0823681382348612 \tabularnewline
31 & 0.916651711571624 & 0.166696576856753 & 0.0833482884283764 \tabularnewline
32 & 0.911073926801163 & 0.177852146397675 & 0.0889260731988373 \tabularnewline
33 & 0.910920421163925 & 0.178159157672149 & 0.0890795788360747 \tabularnewline
34 & 0.865088958563004 & 0.269822082873993 & 0.134911041436996 \tabularnewline
35 & 0.811514918510888 & 0.376970162978225 & 0.188485081489112 \tabularnewline
36 & 0.737389675304825 & 0.52522064939035 & 0.262610324695175 \tabularnewline
37 & 0.759230155682422 & 0.481539688635156 & 0.240769844317578 \tabularnewline
38 & 0.951872286252328 & 0.0962554274953438 & 0.0481277137476719 \tabularnewline
39 & 0.914326890367485 & 0.17134621926503 & 0.085673109632515 \tabularnewline
40 & 0.96766121071521 & 0.0646775785695804 & 0.0323387892847902 \tabularnewline
41 & 0.905732630027026 & 0.188534739945948 & 0.0942673699729742 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58112&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]19[/C][C]0.681391963538921[/C][C]0.637216072922159[/C][C]0.318608036461079[/C][/ROW]
[ROW][C]20[/C][C]0.715493589703182[/C][C]0.569012820593636[/C][C]0.284506410296818[/C][/ROW]
[ROW][C]21[/C][C]0.704911358682864[/C][C]0.590177282634272[/C][C]0.295088641317136[/C][/ROW]
[ROW][C]22[/C][C]0.638104963872588[/C][C]0.723790072254824[/C][C]0.361895036127412[/C][/ROW]
[ROW][C]23[/C][C]0.547012641980946[/C][C]0.905974716038108[/C][C]0.452987358019054[/C][/ROW]
[ROW][C]24[/C][C]0.522504743653742[/C][C]0.954990512692517[/C][C]0.477495256346258[/C][/ROW]
[ROW][C]25[/C][C]0.92466934021977[/C][C]0.150661319560458[/C][C]0.0753306597802289[/C][/ROW]
[ROW][C]26[/C][C]0.944689691406915[/C][C]0.110620617186169[/C][C]0.0553103085930845[/C][/ROW]
[ROW][C]27[/C][C]0.958345710292512[/C][C]0.0833085794149764[/C][C]0.0416542897074882[/C][/ROW]
[ROW][C]28[/C][C]0.937574702956269[/C][C]0.124850594087462[/C][C]0.0624252970437309[/C][/ROW]
[ROW][C]29[/C][C]0.917196623079453[/C][C]0.165606753841093[/C][C]0.0828033769205466[/C][/ROW]
[ROW][C]30[/C][C]0.917631861765139[/C][C]0.164736276469722[/C][C]0.0823681382348612[/C][/ROW]
[ROW][C]31[/C][C]0.916651711571624[/C][C]0.166696576856753[/C][C]0.0833482884283764[/C][/ROW]
[ROW][C]32[/C][C]0.911073926801163[/C][C]0.177852146397675[/C][C]0.0889260731988373[/C][/ROW]
[ROW][C]33[/C][C]0.910920421163925[/C][C]0.178159157672149[/C][C]0.0890795788360747[/C][/ROW]
[ROW][C]34[/C][C]0.865088958563004[/C][C]0.269822082873993[/C][C]0.134911041436996[/C][/ROW]
[ROW][C]35[/C][C]0.811514918510888[/C][C]0.376970162978225[/C][C]0.188485081489112[/C][/ROW]
[ROW][C]36[/C][C]0.737389675304825[/C][C]0.52522064939035[/C][C]0.262610324695175[/C][/ROW]
[ROW][C]37[/C][C]0.759230155682422[/C][C]0.481539688635156[/C][C]0.240769844317578[/C][/ROW]
[ROW][C]38[/C][C]0.951872286252328[/C][C]0.0962554274953438[/C][C]0.0481277137476719[/C][/ROW]
[ROW][C]39[/C][C]0.914326890367485[/C][C]0.17134621926503[/C][C]0.085673109632515[/C][/ROW]
[ROW][C]40[/C][C]0.96766121071521[/C][C]0.0646775785695804[/C][C]0.0323387892847902[/C][/ROW]
[ROW][C]41[/C][C]0.905732630027026[/C][C]0.188534739945948[/C][C]0.0942673699729742[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58112&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58112&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.6813919635389210.6372160729221590.318608036461079
200.7154935897031820.5690128205936360.284506410296818
210.7049113586828640.5901772826342720.295088641317136
220.6381049638725880.7237900722548240.361895036127412
230.5470126419809460.9059747160381080.452987358019054
240.5225047436537420.9549905126925170.477495256346258
250.924669340219770.1506613195604580.0753306597802289
260.9446896914069150.1106206171861690.0553103085930845
270.9583457102925120.08330857941497640.0416542897074882
280.9375747029562690.1248505940874620.0624252970437309
290.9171966230794530.1656067538410930.0828033769205466
300.9176318617651390.1647362764697220.0823681382348612
310.9166517115716240.1666965768567530.0833482884283764
320.9110739268011630.1778521463976750.0889260731988373
330.9109204211639250.1781591576721490.0890795788360747
340.8650889585630040.2698220828739930.134911041436996
350.8115149185108880.3769701629782250.188485081489112
360.7373896753048250.525220649390350.262610324695175
370.7592301556824220.4815396886351560.240769844317578
380.9518722862523280.09625542749534380.0481277137476719
390.9143268903674850.171346219265030.085673109632515
400.967661210715210.06467757856958040.0323387892847902
410.9057326300270260.1885347399459480.0942673699729742







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level30.130434782608696NOK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 3 & 0.130434782608696 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58112&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.130434782608696[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58112&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58112&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level30.130434782608696NOK



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')
}