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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 09:07:07 -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/19/t12586468853icbaux6fs2r2ft.htm/, Retrieved Thu, 28 Mar 2024 10:59:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57798, Retrieved Thu, 28 Mar 2024 10:59:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact131
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]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [SHWWS7model1b] [2009-11-19 16:07:07] [db49399df1e4a3dbe31268849cebfd7f] [Current]
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Dataseries X:
25.60	 161
23.70	 149
22.00	 139
21.30	 135
20.70	 130
20.40	 127
20.30	 122
20.40	 117
19.80	 112
19.50	 113
23.10	 149
23.50	 157
23.50	 157
22.90	 147
21.90	 137
21.50	 132
20.50	 125
20.20	 123
19.40	 117
19.20	 114
18.80	 111
18.80	 112
22.60	 144
23.30	 150
23.00	 149
21.40	 134
19.90	 123
18.80	 116
18.60	 117
18.40	 111
18.60	 105
19.90	 102
19.20	 95
18.40 93
21.10 124
20.50	 130
19.10	 124
18.10	 115
17.00	 106
17.10	 105
17.40	 105
16.80	 101
15.30	 95
14.30	 93
13.40	 84
15.30	 87
22.10	 116
23.70	 120
22.20	 117
19.50	 109
16.60	 105
17.30	 107
19.80	 109
21.20	 109
21.50	 108
20.60	 107
19.10	 99
19.60	 103
23.50	 131
24.00	 137




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=57798&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=57798&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57798&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 6.14943712562875 + 0.116613271853615X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  6.14943712562875 +  0.116613271853615X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57798&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  6.14943712562875 +  0.116613271853615X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57798&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57798&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
Y[t] = + 6.14943712562875 + 0.116613271853615X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.149437125628751.1645425.28062e-061e-06
X0.1166132718536150.00963212.106900

\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) & 6.14943712562875 & 1.164542 & 5.2806 & 2e-06 & 1e-06 \tabularnewline
X & 0.116613271853615 & 0.009632 & 12.1069 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57798&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]6.14943712562875[/C][C]1.164542[/C][C]5.2806[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]X[/C][C]0.116613271853615[/C][C]0.009632[/C][C]12.1069[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57798&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57798&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)6.149437125628751.1645425.28062e-061e-06
X0.1166132718536150.00963212.106900







Multiple Linear Regression - Regression Statistics
Multiple R0.846455334645394
R-squared0.716486633549646
Adjusted R-squared0.711598472059123
F-TEST (value)146.575892580205
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.36227282550261
Sum Squared Residuals107.635660563966

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.846455334645394 \tabularnewline
R-squared & 0.716486633549646 \tabularnewline
Adjusted R-squared & 0.711598472059123 \tabularnewline
F-TEST (value) & 146.575892580205 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.36227282550261 \tabularnewline
Sum Squared Residuals & 107.635660563966 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57798&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.846455334645394[/C][/ROW]
[ROW][C]R-squared[/C][C]0.716486633549646[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.711598472059123[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]146.575892580205[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/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]1.36227282550261[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]107.635660563966[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57798&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57798&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 R0.846455334645394
R-squared0.716486633549646
Adjusted R-squared0.711598472059123
F-TEST (value)146.575892580205
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.36227282550261
Sum Squared Residuals107.635660563966







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
125.624.92417389406080.675826105939228
223.723.52481463181740.175185368182579
32222.3586819132813-0.358681913281271
421.321.8922288258668-0.59222882586681
520.721.3091624665987-0.609162466598735
620.420.9593226510379-0.55932265103789
720.320.3762562917698-0.0762562917698112
820.419.79318993250170.606810067498263
919.819.21012357323370.589876426766341
1019.519.32673684508730.173263154912725
1123.123.5248146318174-0.424814631817422
1223.524.4577208066463-0.957720806646346
1323.524.4577208066463-0.957720806646346
1422.923.2915880881102-0.391588088110195
1521.922.1254553695740-0.225455369574042
1621.521.5423890103060-0.0423890103059643
1720.520.7260961073307-0.226096107330658
1820.220.4928695636234-0.292869563623428
1919.419.7931899325017-0.393189932501737
2019.219.4433501169409-0.243350116940891
2118.819.0935103013800-0.293510301380043
2218.819.2101235732337-0.410123573233659
2322.622.9417482725493-0.341748272549346
2423.323.6414279036710-0.341427903671038
252323.5248146318174-0.524814631817424
2621.421.7756155540132-0.375615554013196
2719.920.4928695636234-0.592869563623429
2818.819.6765766606481-0.87657666064812
2918.619.7931899325017-1.19318993250173
3018.419.0935103013800-0.693510301380046
3118.618.39383067025840.206169329741649
3219.918.04399085469751.85600914530249
3319.217.22769795172221.9723020482778
3418.416.99447140801501.40552859198503
3521.120.60948283547700.490517164522959
3620.521.3091624665987-0.809162466598734
3719.120.6094828354770-1.50948283547704
3818.119.5599633887945-1.45996338879450
391718.5104439421120-1.51044394211197
4017.118.3938306702584-1.29383067025835
4117.418.3938306702584-0.993830670258354
4216.817.9273775828439-1.12737758284389
4315.317.2276979517222-1.9276979517222
4414.316.9944714080150-2.69447140801497
4513.415.9449519613324-2.54495196133243
4615.316.2947917768933-0.994791776893277
4722.119.67657666064812.42342333935188
4823.720.14302974806263.55697025193742
4922.219.79318993250172.40681006749826
5019.518.86028375767280.639716242327186
5116.618.3938306702584-1.79383067025835
5217.318.6270572139656-1.32705721396558
5319.818.86028375767280.939716242327187
5421.218.86028375767282.33971624232719
5521.518.74367048581922.7563295141808
5620.618.62705721396561.97294278603442
5719.117.69415103913671.40584896086334
5819.618.16060412655111.43939587344888
5923.521.42577573845232.07422426154765
602422.12545536957401.87454463042596

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 25.6 & 24.9241738940608 & 0.675826105939228 \tabularnewline
2 & 23.7 & 23.5248146318174 & 0.175185368182579 \tabularnewline
3 & 22 & 22.3586819132813 & -0.358681913281271 \tabularnewline
4 & 21.3 & 21.8922288258668 & -0.59222882586681 \tabularnewline
5 & 20.7 & 21.3091624665987 & -0.609162466598735 \tabularnewline
6 & 20.4 & 20.9593226510379 & -0.55932265103789 \tabularnewline
7 & 20.3 & 20.3762562917698 & -0.0762562917698112 \tabularnewline
8 & 20.4 & 19.7931899325017 & 0.606810067498263 \tabularnewline
9 & 19.8 & 19.2101235732337 & 0.589876426766341 \tabularnewline
10 & 19.5 & 19.3267368450873 & 0.173263154912725 \tabularnewline
11 & 23.1 & 23.5248146318174 & -0.424814631817422 \tabularnewline
12 & 23.5 & 24.4577208066463 & -0.957720806646346 \tabularnewline
13 & 23.5 & 24.4577208066463 & -0.957720806646346 \tabularnewline
14 & 22.9 & 23.2915880881102 & -0.391588088110195 \tabularnewline
15 & 21.9 & 22.1254553695740 & -0.225455369574042 \tabularnewline
16 & 21.5 & 21.5423890103060 & -0.0423890103059643 \tabularnewline
17 & 20.5 & 20.7260961073307 & -0.226096107330658 \tabularnewline
18 & 20.2 & 20.4928695636234 & -0.292869563623428 \tabularnewline
19 & 19.4 & 19.7931899325017 & -0.393189932501737 \tabularnewline
20 & 19.2 & 19.4433501169409 & -0.243350116940891 \tabularnewline
21 & 18.8 & 19.0935103013800 & -0.293510301380043 \tabularnewline
22 & 18.8 & 19.2101235732337 & -0.410123573233659 \tabularnewline
23 & 22.6 & 22.9417482725493 & -0.341748272549346 \tabularnewline
24 & 23.3 & 23.6414279036710 & -0.341427903671038 \tabularnewline
25 & 23 & 23.5248146318174 & -0.524814631817424 \tabularnewline
26 & 21.4 & 21.7756155540132 & -0.375615554013196 \tabularnewline
27 & 19.9 & 20.4928695636234 & -0.592869563623429 \tabularnewline
28 & 18.8 & 19.6765766606481 & -0.87657666064812 \tabularnewline
29 & 18.6 & 19.7931899325017 & -1.19318993250173 \tabularnewline
30 & 18.4 & 19.0935103013800 & -0.693510301380046 \tabularnewline
31 & 18.6 & 18.3938306702584 & 0.206169329741649 \tabularnewline
32 & 19.9 & 18.0439908546975 & 1.85600914530249 \tabularnewline
33 & 19.2 & 17.2276979517222 & 1.9723020482778 \tabularnewline
34 & 18.4 & 16.9944714080150 & 1.40552859198503 \tabularnewline
35 & 21.1 & 20.6094828354770 & 0.490517164522959 \tabularnewline
36 & 20.5 & 21.3091624665987 & -0.809162466598734 \tabularnewline
37 & 19.1 & 20.6094828354770 & -1.50948283547704 \tabularnewline
38 & 18.1 & 19.5599633887945 & -1.45996338879450 \tabularnewline
39 & 17 & 18.5104439421120 & -1.51044394211197 \tabularnewline
40 & 17.1 & 18.3938306702584 & -1.29383067025835 \tabularnewline
41 & 17.4 & 18.3938306702584 & -0.993830670258354 \tabularnewline
42 & 16.8 & 17.9273775828439 & -1.12737758284389 \tabularnewline
43 & 15.3 & 17.2276979517222 & -1.9276979517222 \tabularnewline
44 & 14.3 & 16.9944714080150 & -2.69447140801497 \tabularnewline
45 & 13.4 & 15.9449519613324 & -2.54495196133243 \tabularnewline
46 & 15.3 & 16.2947917768933 & -0.994791776893277 \tabularnewline
47 & 22.1 & 19.6765766606481 & 2.42342333935188 \tabularnewline
48 & 23.7 & 20.1430297480626 & 3.55697025193742 \tabularnewline
49 & 22.2 & 19.7931899325017 & 2.40681006749826 \tabularnewline
50 & 19.5 & 18.8602837576728 & 0.639716242327186 \tabularnewline
51 & 16.6 & 18.3938306702584 & -1.79383067025835 \tabularnewline
52 & 17.3 & 18.6270572139656 & -1.32705721396558 \tabularnewline
53 & 19.8 & 18.8602837576728 & 0.939716242327187 \tabularnewline
54 & 21.2 & 18.8602837576728 & 2.33971624232719 \tabularnewline
55 & 21.5 & 18.7436704858192 & 2.7563295141808 \tabularnewline
56 & 20.6 & 18.6270572139656 & 1.97294278603442 \tabularnewline
57 & 19.1 & 17.6941510391367 & 1.40584896086334 \tabularnewline
58 & 19.6 & 18.1606041265511 & 1.43939587344888 \tabularnewline
59 & 23.5 & 21.4257757384523 & 2.07422426154765 \tabularnewline
60 & 24 & 22.1254553695740 & 1.87454463042596 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57798&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]25.6[/C][C]24.9241738940608[/C][C]0.675826105939228[/C][/ROW]
[ROW][C]2[/C][C]23.7[/C][C]23.5248146318174[/C][C]0.175185368182579[/C][/ROW]
[ROW][C]3[/C][C]22[/C][C]22.3586819132813[/C][C]-0.358681913281271[/C][/ROW]
[ROW][C]4[/C][C]21.3[/C][C]21.8922288258668[/C][C]-0.59222882586681[/C][/ROW]
[ROW][C]5[/C][C]20.7[/C][C]21.3091624665987[/C][C]-0.609162466598735[/C][/ROW]
[ROW][C]6[/C][C]20.4[/C][C]20.9593226510379[/C][C]-0.55932265103789[/C][/ROW]
[ROW][C]7[/C][C]20.3[/C][C]20.3762562917698[/C][C]-0.0762562917698112[/C][/ROW]
[ROW][C]8[/C][C]20.4[/C][C]19.7931899325017[/C][C]0.606810067498263[/C][/ROW]
[ROW][C]9[/C][C]19.8[/C][C]19.2101235732337[/C][C]0.589876426766341[/C][/ROW]
[ROW][C]10[/C][C]19.5[/C][C]19.3267368450873[/C][C]0.173263154912725[/C][/ROW]
[ROW][C]11[/C][C]23.1[/C][C]23.5248146318174[/C][C]-0.424814631817422[/C][/ROW]
[ROW][C]12[/C][C]23.5[/C][C]24.4577208066463[/C][C]-0.957720806646346[/C][/ROW]
[ROW][C]13[/C][C]23.5[/C][C]24.4577208066463[/C][C]-0.957720806646346[/C][/ROW]
[ROW][C]14[/C][C]22.9[/C][C]23.2915880881102[/C][C]-0.391588088110195[/C][/ROW]
[ROW][C]15[/C][C]21.9[/C][C]22.1254553695740[/C][C]-0.225455369574042[/C][/ROW]
[ROW][C]16[/C][C]21.5[/C][C]21.5423890103060[/C][C]-0.0423890103059643[/C][/ROW]
[ROW][C]17[/C][C]20.5[/C][C]20.7260961073307[/C][C]-0.226096107330658[/C][/ROW]
[ROW][C]18[/C][C]20.2[/C][C]20.4928695636234[/C][C]-0.292869563623428[/C][/ROW]
[ROW][C]19[/C][C]19.4[/C][C]19.7931899325017[/C][C]-0.393189932501737[/C][/ROW]
[ROW][C]20[/C][C]19.2[/C][C]19.4433501169409[/C][C]-0.243350116940891[/C][/ROW]
[ROW][C]21[/C][C]18.8[/C][C]19.0935103013800[/C][C]-0.293510301380043[/C][/ROW]
[ROW][C]22[/C][C]18.8[/C][C]19.2101235732337[/C][C]-0.410123573233659[/C][/ROW]
[ROW][C]23[/C][C]22.6[/C][C]22.9417482725493[/C][C]-0.341748272549346[/C][/ROW]
[ROW][C]24[/C][C]23.3[/C][C]23.6414279036710[/C][C]-0.341427903671038[/C][/ROW]
[ROW][C]25[/C][C]23[/C][C]23.5248146318174[/C][C]-0.524814631817424[/C][/ROW]
[ROW][C]26[/C][C]21.4[/C][C]21.7756155540132[/C][C]-0.375615554013196[/C][/ROW]
[ROW][C]27[/C][C]19.9[/C][C]20.4928695636234[/C][C]-0.592869563623429[/C][/ROW]
[ROW][C]28[/C][C]18.8[/C][C]19.6765766606481[/C][C]-0.87657666064812[/C][/ROW]
[ROW][C]29[/C][C]18.6[/C][C]19.7931899325017[/C][C]-1.19318993250173[/C][/ROW]
[ROW][C]30[/C][C]18.4[/C][C]19.0935103013800[/C][C]-0.693510301380046[/C][/ROW]
[ROW][C]31[/C][C]18.6[/C][C]18.3938306702584[/C][C]0.206169329741649[/C][/ROW]
[ROW][C]32[/C][C]19.9[/C][C]18.0439908546975[/C][C]1.85600914530249[/C][/ROW]
[ROW][C]33[/C][C]19.2[/C][C]17.2276979517222[/C][C]1.9723020482778[/C][/ROW]
[ROW][C]34[/C][C]18.4[/C][C]16.9944714080150[/C][C]1.40552859198503[/C][/ROW]
[ROW][C]35[/C][C]21.1[/C][C]20.6094828354770[/C][C]0.490517164522959[/C][/ROW]
[ROW][C]36[/C][C]20.5[/C][C]21.3091624665987[/C][C]-0.809162466598734[/C][/ROW]
[ROW][C]37[/C][C]19.1[/C][C]20.6094828354770[/C][C]-1.50948283547704[/C][/ROW]
[ROW][C]38[/C][C]18.1[/C][C]19.5599633887945[/C][C]-1.45996338879450[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]18.5104439421120[/C][C]-1.51044394211197[/C][/ROW]
[ROW][C]40[/C][C]17.1[/C][C]18.3938306702584[/C][C]-1.29383067025835[/C][/ROW]
[ROW][C]41[/C][C]17.4[/C][C]18.3938306702584[/C][C]-0.993830670258354[/C][/ROW]
[ROW][C]42[/C][C]16.8[/C][C]17.9273775828439[/C][C]-1.12737758284389[/C][/ROW]
[ROW][C]43[/C][C]15.3[/C][C]17.2276979517222[/C][C]-1.9276979517222[/C][/ROW]
[ROW][C]44[/C][C]14.3[/C][C]16.9944714080150[/C][C]-2.69447140801497[/C][/ROW]
[ROW][C]45[/C][C]13.4[/C][C]15.9449519613324[/C][C]-2.54495196133243[/C][/ROW]
[ROW][C]46[/C][C]15.3[/C][C]16.2947917768933[/C][C]-0.994791776893277[/C][/ROW]
[ROW][C]47[/C][C]22.1[/C][C]19.6765766606481[/C][C]2.42342333935188[/C][/ROW]
[ROW][C]48[/C][C]23.7[/C][C]20.1430297480626[/C][C]3.55697025193742[/C][/ROW]
[ROW][C]49[/C][C]22.2[/C][C]19.7931899325017[/C][C]2.40681006749826[/C][/ROW]
[ROW][C]50[/C][C]19.5[/C][C]18.8602837576728[/C][C]0.639716242327186[/C][/ROW]
[ROW][C]51[/C][C]16.6[/C][C]18.3938306702584[/C][C]-1.79383067025835[/C][/ROW]
[ROW][C]52[/C][C]17.3[/C][C]18.6270572139656[/C][C]-1.32705721396558[/C][/ROW]
[ROW][C]53[/C][C]19.8[/C][C]18.8602837576728[/C][C]0.939716242327187[/C][/ROW]
[ROW][C]54[/C][C]21.2[/C][C]18.8602837576728[/C][C]2.33971624232719[/C][/ROW]
[ROW][C]55[/C][C]21.5[/C][C]18.7436704858192[/C][C]2.7563295141808[/C][/ROW]
[ROW][C]56[/C][C]20.6[/C][C]18.6270572139656[/C][C]1.97294278603442[/C][/ROW]
[ROW][C]57[/C][C]19.1[/C][C]17.6941510391367[/C][C]1.40584896086334[/C][/ROW]
[ROW][C]58[/C][C]19.6[/C][C]18.1606041265511[/C][C]1.43939587344888[/C][/ROW]
[ROW][C]59[/C][C]23.5[/C][C]21.4257757384523[/C][C]2.07422426154765[/C][/ROW]
[ROW][C]60[/C][C]24[/C][C]22.1254553695740[/C][C]1.87454463042596[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57798&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57798&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
125.624.92417389406080.675826105939228
223.723.52481463181740.175185368182579
32222.3586819132813-0.358681913281271
421.321.8922288258668-0.59222882586681
520.721.3091624665987-0.609162466598735
620.420.9593226510379-0.55932265103789
720.320.3762562917698-0.0762562917698112
820.419.79318993250170.606810067498263
919.819.21012357323370.589876426766341
1019.519.32673684508730.173263154912725
1123.123.5248146318174-0.424814631817422
1223.524.4577208066463-0.957720806646346
1323.524.4577208066463-0.957720806646346
1422.923.2915880881102-0.391588088110195
1521.922.1254553695740-0.225455369574042
1621.521.5423890103060-0.0423890103059643
1720.520.7260961073307-0.226096107330658
1820.220.4928695636234-0.292869563623428
1919.419.7931899325017-0.393189932501737
2019.219.4433501169409-0.243350116940891
2118.819.0935103013800-0.293510301380043
2218.819.2101235732337-0.410123573233659
2322.622.9417482725493-0.341748272549346
2423.323.6414279036710-0.341427903671038
252323.5248146318174-0.524814631817424
2621.421.7756155540132-0.375615554013196
2719.920.4928695636234-0.592869563623429
2818.819.6765766606481-0.87657666064812
2918.619.7931899325017-1.19318993250173
3018.419.0935103013800-0.693510301380046
3118.618.39383067025840.206169329741649
3219.918.04399085469751.85600914530249
3319.217.22769795172221.9723020482778
3418.416.99447140801501.40552859198503
3521.120.60948283547700.490517164522959
3620.521.3091624665987-0.809162466598734
3719.120.6094828354770-1.50948283547704
3818.119.5599633887945-1.45996338879450
391718.5104439421120-1.51044394211197
4017.118.3938306702584-1.29383067025835
4117.418.3938306702584-0.993830670258354
4216.817.9273775828439-1.12737758284389
4315.317.2276979517222-1.9276979517222
4414.316.9944714080150-2.69447140801497
4513.415.9449519613324-2.54495196133243
4615.316.2947917768933-0.994791776893277
4722.119.67657666064812.42342333935188
4823.720.14302974806263.55697025193742
4922.219.79318993250172.40681006749826
5019.518.86028375767280.639716242327186
5116.618.3938306702584-1.79383067025835
5217.318.6270572139656-1.32705721396558
5319.818.86028375767280.939716242327187
5421.218.86028375767282.33971624232719
5521.518.74367048581922.7563295141808
5620.618.62705721396561.97294278603442
5719.117.69415103913671.40584896086334
5819.618.16060412655111.43939587344888
5923.521.42577573845232.07422426154765
602422.12545536957401.87454463042596







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.0003773230174921850.000754646034984370.999622676982508
60.0001805031177598480.0003610062355196960.99981949688224
70.001851879436960630.003703758873921260.99814812056304
80.009877498197799120.01975499639559820.990122501802201
90.007854355525873990.01570871105174800.992145644474126
100.002887194022165020.005774388044330040.997112805977835
110.001202998595405300.002405997190810600.998797001404595
120.0009354646825782760.001870929365156550.999064535317422
130.0005419076502123480.001083815300424700.999458092349788
140.0001886865461555480.0003773730923110970.999811313453844
156.16870598160802e-050.0001233741196321600.999938312940184
161.93352713260969e-053.86705426521937e-050.999980664728674
176.15581007632632e-061.23116201526526e-050.999993844189924
182.03159360363008e-064.06318720726017e-060.999997968406396
197.88004758656202e-071.57600951731240e-060.99999921199524
202.41378286122093e-074.82756572244186e-070.999999758621714
217.57489735315007e-081.51497947063001e-070.999999924251026
222.59850303200149e-085.19700606400298e-080.99999997401497
237.06287625799703e-091.41257525159941e-080.999999992937124
241.98766210129368e-093.97532420258736e-090.999999998012338
257.04497373975226e-101.40899474795045e-090.999999999295503
262.1658151526911e-104.3316303053822e-100.999999999783418
279.94916078270994e-111.98983215654199e-100.999999999900508
281.11123379809830e-102.22246759619659e-100.999999999888877
293.68971765142341e-107.37943530284683e-100.999999999631028
301.64563525944961e-103.29127051889922e-100.999999999835437
316.47151735189477e-111.29430347037895e-100.999999999935285
321.53717592232354e-083.07435184464708e-080.99999998462824
333.45172301716487e-076.90344603432974e-070.999999654827698
346.28157841093926e-071.25631568218785e-060.99999937184216
353.21575791409152e-076.43151582818305e-070.999999678424209
366.15726902084337e-071.23145380416867e-060.999999384273098
371.01412209083829e-052.02824418167658e-050.999989858779092
387.83290552814708e-050.0001566581105629420.999921670944719
390.0002880058876312190.0005760117752624380.999711994112369
400.0005271695952969190.001054339190593840.999472830404703
410.0005767500205004830.001153500041000970.9994232499795
420.0005988934755722640.001197786951144530.999401106524428
430.001463005636907040.002926011273814080.998536994363093
440.01073663539295900.02147327078591810.98926336460704
450.02605647458530950.0521129491706190.97394352541469
460.01917031266160770.03834062532321540.980829687338392
470.03918398441076320.07836796882152630.960816015589237
480.161651923530890.323303847061780.83834807646911
490.1876724770276090.3753449540552190.81232752297239
500.1353808591136270.2707617182272540.864619140886373
510.4172834729340110.8345669458680210.582716527065989
520.9557574015003710.08848519699925790.0442425984996289
530.9682592263500880.06348154729982460.0317407736499123
540.945661960929490.1086760781410210.0543380390705107
550.9904143200160360.01917135996792830.00958567998396414

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.000377323017492185 & 0.00075464603498437 & 0.999622676982508 \tabularnewline
6 & 0.000180503117759848 & 0.000361006235519696 & 0.99981949688224 \tabularnewline
7 & 0.00185187943696063 & 0.00370375887392126 & 0.99814812056304 \tabularnewline
8 & 0.00987749819779912 & 0.0197549963955982 & 0.990122501802201 \tabularnewline
9 & 0.00785435552587399 & 0.0157087110517480 & 0.992145644474126 \tabularnewline
10 & 0.00288719402216502 & 0.00577438804433004 & 0.997112805977835 \tabularnewline
11 & 0.00120299859540530 & 0.00240599719081060 & 0.998797001404595 \tabularnewline
12 & 0.000935464682578276 & 0.00187092936515655 & 0.999064535317422 \tabularnewline
13 & 0.000541907650212348 & 0.00108381530042470 & 0.999458092349788 \tabularnewline
14 & 0.000188686546155548 & 0.000377373092311097 & 0.999811313453844 \tabularnewline
15 & 6.16870598160802e-05 & 0.000123374119632160 & 0.999938312940184 \tabularnewline
16 & 1.93352713260969e-05 & 3.86705426521937e-05 & 0.999980664728674 \tabularnewline
17 & 6.15581007632632e-06 & 1.23116201526526e-05 & 0.999993844189924 \tabularnewline
18 & 2.03159360363008e-06 & 4.06318720726017e-06 & 0.999997968406396 \tabularnewline
19 & 7.88004758656202e-07 & 1.57600951731240e-06 & 0.99999921199524 \tabularnewline
20 & 2.41378286122093e-07 & 4.82756572244186e-07 & 0.999999758621714 \tabularnewline
21 & 7.57489735315007e-08 & 1.51497947063001e-07 & 0.999999924251026 \tabularnewline
22 & 2.59850303200149e-08 & 5.19700606400298e-08 & 0.99999997401497 \tabularnewline
23 & 7.06287625799703e-09 & 1.41257525159941e-08 & 0.999999992937124 \tabularnewline
24 & 1.98766210129368e-09 & 3.97532420258736e-09 & 0.999999998012338 \tabularnewline
25 & 7.04497373975226e-10 & 1.40899474795045e-09 & 0.999999999295503 \tabularnewline
26 & 2.1658151526911e-10 & 4.3316303053822e-10 & 0.999999999783418 \tabularnewline
27 & 9.94916078270994e-11 & 1.98983215654199e-10 & 0.999999999900508 \tabularnewline
28 & 1.11123379809830e-10 & 2.22246759619659e-10 & 0.999999999888877 \tabularnewline
29 & 3.68971765142341e-10 & 7.37943530284683e-10 & 0.999999999631028 \tabularnewline
30 & 1.64563525944961e-10 & 3.29127051889922e-10 & 0.999999999835437 \tabularnewline
31 & 6.47151735189477e-11 & 1.29430347037895e-10 & 0.999999999935285 \tabularnewline
32 & 1.53717592232354e-08 & 3.07435184464708e-08 & 0.99999998462824 \tabularnewline
33 & 3.45172301716487e-07 & 6.90344603432974e-07 & 0.999999654827698 \tabularnewline
34 & 6.28157841093926e-07 & 1.25631568218785e-06 & 0.99999937184216 \tabularnewline
35 & 3.21575791409152e-07 & 6.43151582818305e-07 & 0.999999678424209 \tabularnewline
36 & 6.15726902084337e-07 & 1.23145380416867e-06 & 0.999999384273098 \tabularnewline
37 & 1.01412209083829e-05 & 2.02824418167658e-05 & 0.999989858779092 \tabularnewline
38 & 7.83290552814708e-05 & 0.000156658110562942 & 0.999921670944719 \tabularnewline
39 & 0.000288005887631219 & 0.000576011775262438 & 0.999711994112369 \tabularnewline
40 & 0.000527169595296919 & 0.00105433919059384 & 0.999472830404703 \tabularnewline
41 & 0.000576750020500483 & 0.00115350004100097 & 0.9994232499795 \tabularnewline
42 & 0.000598893475572264 & 0.00119778695114453 & 0.999401106524428 \tabularnewline
43 & 0.00146300563690704 & 0.00292601127381408 & 0.998536994363093 \tabularnewline
44 & 0.0107366353929590 & 0.0214732707859181 & 0.98926336460704 \tabularnewline
45 & 0.0260564745853095 & 0.052112949170619 & 0.97394352541469 \tabularnewline
46 & 0.0191703126616077 & 0.0383406253232154 & 0.980829687338392 \tabularnewline
47 & 0.0391839844107632 & 0.0783679688215263 & 0.960816015589237 \tabularnewline
48 & 0.16165192353089 & 0.32330384706178 & 0.83834807646911 \tabularnewline
49 & 0.187672477027609 & 0.375344954055219 & 0.81232752297239 \tabularnewline
50 & 0.135380859113627 & 0.270761718227254 & 0.864619140886373 \tabularnewline
51 & 0.417283472934011 & 0.834566945868021 & 0.582716527065989 \tabularnewline
52 & 0.955757401500371 & 0.0884851969992579 & 0.0442425984996289 \tabularnewline
53 & 0.968259226350088 & 0.0634815472998246 & 0.0317407736499123 \tabularnewline
54 & 0.94566196092949 & 0.108676078141021 & 0.0543380390705107 \tabularnewline
55 & 0.990414320016036 & 0.0191713599679283 & 0.00958567998396414 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57798&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]5[/C][C]0.000377323017492185[/C][C]0.00075464603498437[/C][C]0.999622676982508[/C][/ROW]
[ROW][C]6[/C][C]0.000180503117759848[/C][C]0.000361006235519696[/C][C]0.99981949688224[/C][/ROW]
[ROW][C]7[/C][C]0.00185187943696063[/C][C]0.00370375887392126[/C][C]0.99814812056304[/C][/ROW]
[ROW][C]8[/C][C]0.00987749819779912[/C][C]0.0197549963955982[/C][C]0.990122501802201[/C][/ROW]
[ROW][C]9[/C][C]0.00785435552587399[/C][C]0.0157087110517480[/C][C]0.992145644474126[/C][/ROW]
[ROW][C]10[/C][C]0.00288719402216502[/C][C]0.00577438804433004[/C][C]0.997112805977835[/C][/ROW]
[ROW][C]11[/C][C]0.00120299859540530[/C][C]0.00240599719081060[/C][C]0.998797001404595[/C][/ROW]
[ROW][C]12[/C][C]0.000935464682578276[/C][C]0.00187092936515655[/C][C]0.999064535317422[/C][/ROW]
[ROW][C]13[/C][C]0.000541907650212348[/C][C]0.00108381530042470[/C][C]0.999458092349788[/C][/ROW]
[ROW][C]14[/C][C]0.000188686546155548[/C][C]0.000377373092311097[/C][C]0.999811313453844[/C][/ROW]
[ROW][C]15[/C][C]6.16870598160802e-05[/C][C]0.000123374119632160[/C][C]0.999938312940184[/C][/ROW]
[ROW][C]16[/C][C]1.93352713260969e-05[/C][C]3.86705426521937e-05[/C][C]0.999980664728674[/C][/ROW]
[ROW][C]17[/C][C]6.15581007632632e-06[/C][C]1.23116201526526e-05[/C][C]0.999993844189924[/C][/ROW]
[ROW][C]18[/C][C]2.03159360363008e-06[/C][C]4.06318720726017e-06[/C][C]0.999997968406396[/C][/ROW]
[ROW][C]19[/C][C]7.88004758656202e-07[/C][C]1.57600951731240e-06[/C][C]0.99999921199524[/C][/ROW]
[ROW][C]20[/C][C]2.41378286122093e-07[/C][C]4.82756572244186e-07[/C][C]0.999999758621714[/C][/ROW]
[ROW][C]21[/C][C]7.57489735315007e-08[/C][C]1.51497947063001e-07[/C][C]0.999999924251026[/C][/ROW]
[ROW][C]22[/C][C]2.59850303200149e-08[/C][C]5.19700606400298e-08[/C][C]0.99999997401497[/C][/ROW]
[ROW][C]23[/C][C]7.06287625799703e-09[/C][C]1.41257525159941e-08[/C][C]0.999999992937124[/C][/ROW]
[ROW][C]24[/C][C]1.98766210129368e-09[/C][C]3.97532420258736e-09[/C][C]0.999999998012338[/C][/ROW]
[ROW][C]25[/C][C]7.04497373975226e-10[/C][C]1.40899474795045e-09[/C][C]0.999999999295503[/C][/ROW]
[ROW][C]26[/C][C]2.1658151526911e-10[/C][C]4.3316303053822e-10[/C][C]0.999999999783418[/C][/ROW]
[ROW][C]27[/C][C]9.94916078270994e-11[/C][C]1.98983215654199e-10[/C][C]0.999999999900508[/C][/ROW]
[ROW][C]28[/C][C]1.11123379809830e-10[/C][C]2.22246759619659e-10[/C][C]0.999999999888877[/C][/ROW]
[ROW][C]29[/C][C]3.68971765142341e-10[/C][C]7.37943530284683e-10[/C][C]0.999999999631028[/C][/ROW]
[ROW][C]30[/C][C]1.64563525944961e-10[/C][C]3.29127051889922e-10[/C][C]0.999999999835437[/C][/ROW]
[ROW][C]31[/C][C]6.47151735189477e-11[/C][C]1.29430347037895e-10[/C][C]0.999999999935285[/C][/ROW]
[ROW][C]32[/C][C]1.53717592232354e-08[/C][C]3.07435184464708e-08[/C][C]0.99999998462824[/C][/ROW]
[ROW][C]33[/C][C]3.45172301716487e-07[/C][C]6.90344603432974e-07[/C][C]0.999999654827698[/C][/ROW]
[ROW][C]34[/C][C]6.28157841093926e-07[/C][C]1.25631568218785e-06[/C][C]0.99999937184216[/C][/ROW]
[ROW][C]35[/C][C]3.21575791409152e-07[/C][C]6.43151582818305e-07[/C][C]0.999999678424209[/C][/ROW]
[ROW][C]36[/C][C]6.15726902084337e-07[/C][C]1.23145380416867e-06[/C][C]0.999999384273098[/C][/ROW]
[ROW][C]37[/C][C]1.01412209083829e-05[/C][C]2.02824418167658e-05[/C][C]0.999989858779092[/C][/ROW]
[ROW][C]38[/C][C]7.83290552814708e-05[/C][C]0.000156658110562942[/C][C]0.999921670944719[/C][/ROW]
[ROW][C]39[/C][C]0.000288005887631219[/C][C]0.000576011775262438[/C][C]0.999711994112369[/C][/ROW]
[ROW][C]40[/C][C]0.000527169595296919[/C][C]0.00105433919059384[/C][C]0.999472830404703[/C][/ROW]
[ROW][C]41[/C][C]0.000576750020500483[/C][C]0.00115350004100097[/C][C]0.9994232499795[/C][/ROW]
[ROW][C]42[/C][C]0.000598893475572264[/C][C]0.00119778695114453[/C][C]0.999401106524428[/C][/ROW]
[ROW][C]43[/C][C]0.00146300563690704[/C][C]0.00292601127381408[/C][C]0.998536994363093[/C][/ROW]
[ROW][C]44[/C][C]0.0107366353929590[/C][C]0.0214732707859181[/C][C]0.98926336460704[/C][/ROW]
[ROW][C]45[/C][C]0.0260564745853095[/C][C]0.052112949170619[/C][C]0.97394352541469[/C][/ROW]
[ROW][C]46[/C][C]0.0191703126616077[/C][C]0.0383406253232154[/C][C]0.980829687338392[/C][/ROW]
[ROW][C]47[/C][C]0.0391839844107632[/C][C]0.0783679688215263[/C][C]0.960816015589237[/C][/ROW]
[ROW][C]48[/C][C]0.16165192353089[/C][C]0.32330384706178[/C][C]0.83834807646911[/C][/ROW]
[ROW][C]49[/C][C]0.187672477027609[/C][C]0.375344954055219[/C][C]0.81232752297239[/C][/ROW]
[ROW][C]50[/C][C]0.135380859113627[/C][C]0.270761718227254[/C][C]0.864619140886373[/C][/ROW]
[ROW][C]51[/C][C]0.417283472934011[/C][C]0.834566945868021[/C][C]0.582716527065989[/C][/ROW]
[ROW][C]52[/C][C]0.955757401500371[/C][C]0.0884851969992579[/C][C]0.0442425984996289[/C][/ROW]
[ROW][C]53[/C][C]0.968259226350088[/C][C]0.0634815472998246[/C][C]0.0317407736499123[/C][/ROW]
[ROW][C]54[/C][C]0.94566196092949[/C][C]0.108676078141021[/C][C]0.0543380390705107[/C][/ROW]
[ROW][C]55[/C][C]0.990414320016036[/C][C]0.0191713599679283[/C][C]0.00958567998396414[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57798&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57798&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
50.0003773230174921850.000754646034984370.999622676982508
60.0001805031177598480.0003610062355196960.99981949688224
70.001851879436960630.003703758873921260.99814812056304
80.009877498197799120.01975499639559820.990122501802201
90.007854355525873990.01570871105174800.992145644474126
100.002887194022165020.005774388044330040.997112805977835
110.001202998595405300.002405997190810600.998797001404595
120.0009354646825782760.001870929365156550.999064535317422
130.0005419076502123480.001083815300424700.999458092349788
140.0001886865461555480.0003773730923110970.999811313453844
156.16870598160802e-050.0001233741196321600.999938312940184
161.93352713260969e-053.86705426521937e-050.999980664728674
176.15581007632632e-061.23116201526526e-050.999993844189924
182.03159360363008e-064.06318720726017e-060.999997968406396
197.88004758656202e-071.57600951731240e-060.99999921199524
202.41378286122093e-074.82756572244186e-070.999999758621714
217.57489735315007e-081.51497947063001e-070.999999924251026
222.59850303200149e-085.19700606400298e-080.99999997401497
237.06287625799703e-091.41257525159941e-080.999999992937124
241.98766210129368e-093.97532420258736e-090.999999998012338
257.04497373975226e-101.40899474795045e-090.999999999295503
262.1658151526911e-104.3316303053822e-100.999999999783418
279.94916078270994e-111.98983215654199e-100.999999999900508
281.11123379809830e-102.22246759619659e-100.999999999888877
293.68971765142341e-107.37943530284683e-100.999999999631028
301.64563525944961e-103.29127051889922e-100.999999999835437
316.47151735189477e-111.29430347037895e-100.999999999935285
321.53717592232354e-083.07435184464708e-080.99999998462824
333.45172301716487e-076.90344603432974e-070.999999654827698
346.28157841093926e-071.25631568218785e-060.99999937184216
353.21575791409152e-076.43151582818305e-070.999999678424209
366.15726902084337e-071.23145380416867e-060.999999384273098
371.01412209083829e-052.02824418167658e-050.999989858779092
387.83290552814708e-050.0001566581105629420.999921670944719
390.0002880058876312190.0005760117752624380.999711994112369
400.0005271695952969190.001054339190593840.999472830404703
410.0005767500205004830.001153500041000970.9994232499795
420.0005988934755722640.001197786951144530.999401106524428
430.001463005636907040.002926011273814080.998536994363093
440.01073663539295900.02147327078591810.98926336460704
450.02605647458530950.0521129491706190.97394352541469
460.01917031266160770.03834062532321540.980829687338392
470.03918398441076320.07836796882152630.960816015589237
480.161651923530890.323303847061780.83834807646911
490.1876724770276090.3753449540552190.81232752297239
500.1353808591136270.2707617182272540.864619140886373
510.4172834729340110.8345669458680210.582716527065989
520.9557574015003710.08848519699925790.0442425984996289
530.9682592263500880.06348154729982460.0317407736499123
540.945661960929490.1086760781410210.0543380390705107
550.9904143200160360.01917135996792830.00958567998396414







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level370.725490196078431NOK
5% type I error level420.823529411764706NOK
10% type I error level460.901960784313726NOK

\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 & 37 & 0.725490196078431 & NOK \tabularnewline
5% type I error level & 42 & 0.823529411764706 & NOK \tabularnewline
10% type I error level & 46 & 0.901960784313726 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57798&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]37[/C][C]0.725490196078431[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]42[/C][C]0.823529411764706[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]46[/C][C]0.901960784313726[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57798&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57798&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 level370.725490196078431NOK
5% type I error level420.823529411764706NOK
10% type I error level460.901960784313726NOK



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