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of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 27 Feb 2020 20:21:30 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2020/Feb/27/t15828323330lliebdi9vrale6.htm/, Retrieved Wed, 21 Apr 2021 09:30:24 +0200
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Wed, 21 Apr 2021 09:30:24 +0200
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact0
Dataseries X:
.100  1.63
.070  0.91
.180  1.24
.030  1.39
-.077  0.50
.100  0.75
.123  0.23
.047  0.19
.063  0.40
-.043  0.15
-.070  1.25
-.083  1.42
.036  1.51
-.036  0.72
.020  0.59
.080  0.32
.003  0.54
.177  0.22
.280  0.06
.233  0.61
.227  0.31
.236  0.03
.250  -0.01
.137  -0.63
.337  -0.20
.193  1.47
-.017  1.46
.030  1.78
.050  1.86
.050  1.20
.150  1.00
.177  -1.26
.027  -0.37
.176  -0.30
.017  1.33
.190  -0.10
.060  0.70
-.010  1.03
.117  0.84
.043  1.30
.117  0.93
.113  0.97
.087  -.13
.073  0.80
.097  1.53
.056  1.37
.187  1.53
.083  1.47
.127  1.00
.050  1.06
.070  2.54
.143  2.66
.097  1.20
.200  0.94
.093  1.86
.167  3.00
.297  2.90
.290  1.84
.280  -.54
.266  0.50
.084  1.70
.200  2.40
.333  3.87
.367  2.93
.333  2.40
.333  3.17




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center
R Engine error message
Error in vif.default(mylm) : model contains fewer than 2 terms
Calls: vif -> vif.default
Execution halted

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Engine error message & 
Error in vif.default(mylm) : model contains fewer than 2 terms
Calls: vif -> vif.default
Execution halted
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]1 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [ROW]R Engine error message[/C][C]
Error in vif.default(mylm) : model contains fewer than 2 terms
Calls: vif -> vif.default
Execution halted
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center
R Engine error message
Error in vif.default(mylm) : model contains fewer than 2 terms
Calls: vif -> vif.default
Execution halted







Multiple Linear Regression - Estimated Regression Equation
^CPI[t] = + 0.10473 + 0.0186338`^M`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
^CPI[t] =  +  0.10473 +  0.0186338`^M`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]^CPI[t] =  +  0.10473 +  0.0186338`^M`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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
^CPI[t] = + 0.10473 + 0.0186338`^M`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+0.1047 0.01982+5.2850e+00 1.617e-06 8.083e-07
`^M`+0.01863 0.01366+1.3650e+00 0.1772 0.08858

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & +0.1047 &  0.01982 & +5.2850e+00 &  1.617e-06 &  8.083e-07 \tabularnewline
`^M` & +0.01863 &  0.01366 & +1.3650e+00 &  0.1772 &  0.08858 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]+0.1047[/C][C] 0.01982[/C][C]+5.2850e+00[/C][C] 1.617e-06[/C][C] 8.083e-07[/C][/ROW]
[ROW][C]`^M`[/C][C]+0.01863[/C][C] 0.01366[/C][C]+1.3650e+00[/C][C] 0.1772[/C][C] 0.08858[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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)+0.1047 0.01982+5.2850e+00 1.617e-06 8.083e-07
`^M`+0.01863 0.01366+1.3650e+00 0.1772 0.08858







Multiple Linear Regression - Regression Statistics
Multiple R 0.1681
R-squared 0.02827
Adjusted R-squared 0.01309
F-TEST (value) 1.862
F-TEST (DF numerator)1
F-TEST (DF denominator)64
p-value 0.1772
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 0.1099
Sum Squared Residuals 0.7734

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.1681 \tabularnewline
R-squared &  0.02827 \tabularnewline
Adjusted R-squared &  0.01309 \tabularnewline
F-TEST (value) &  1.862 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 64 \tabularnewline
p-value &  0.1772 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.1099 \tabularnewline
Sum Squared Residuals &  0.7734 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.1681[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.02827[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.01309[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.862[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]64[/C][/ROW]
[ROW][C]p-value[/C][C] 0.1772[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.1099[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 0.7734[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 R 0.1681
R-squared 0.02827
Adjusted R-squared 0.01309
F-TEST (value) 1.862
F-TEST (DF numerator)1
F-TEST (DF denominator)64
p-value 0.1772
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 0.1099
Sum Squared Residuals 0.7734







Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=4

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

As an alternative you can also use a QR Code:  

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

Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 0.1 0.1351-0.0351
2 0.07 0.1217-0.05169
3 0.18 0.1278 0.05216
4 0.03 0.1306-0.1006
5-0.077 0.114-0.191
6 0.1 0.1187-0.01871
7 0.123 0.109 0.01398
8 0.047 0.1083-0.06127
9 0.063 0.1122-0.04918
10-0.043 0.1075-0.1505
11-0.07 0.128-0.198
12-0.083 0.1312-0.2142
13 0.036 0.1329-0.09687
14-0.036 0.1181-0.1541
15 0.02 0.1157-0.09572
16 0.08 0.1107-0.03069
17 0.003 0.1148-0.1118
18 0.177 0.1088 0.06817
19 0.28 0.1058 0.1742
20 0.233 0.1161 0.1169
21 0.227 0.1105 0.1165
22 0.236 0.1053 0.1307
23 0.25 0.1045 0.1455
24 0.137 0.09299 0.04401
25 0.337 0.101 0.236
26 0.193 0.1321 0.06088
27-0.017 0.1319-0.1489
28 0.03 0.1379-0.1079
29 0.05 0.1394-0.08939
30 0.05 0.1271-0.07709
31 0.15 0.1234 0.02664
32 0.177 0.08125 0.09575
33 0.027 0.09784-0.07084
34 0.176 0.09914 0.07686
35 0.017 0.1295-0.1125
36 0.19 0.1029 0.08713
37 0.06 0.1178-0.05777
38-0.01 0.1239-0.1339
39 0.117 0.1204-0.003383
40 0.043 0.129-0.08595
41 0.117 0.1221-0.00506
42 0.113 0.1228-0.009805
43 0.087 0.1023-0.01531
44 0.073 0.1196-0.04664
45 0.097 0.1332-0.03624
46 0.056 0.1303-0.07426
47 0.187 0.1332 0.05376
48 0.083 0.1321-0.04912
49 0.127 0.1234 0.003636
50 0.05 0.1245-0.07448
51 0.07 0.1521-0.08206
52 0.143 0.1543-0.0113
53 0.097 0.1271-0.03009
54 0.2 0.1222 0.07775
55 0.093 0.1394-0.04639
56 0.167 0.1606 0.006368
57 0.297 0.1588 0.1382
58 0.29 0.139 0.151
59 0.28 0.09467 0.1853
60 0.266 0.114 0.152
61 0.084 0.1364-0.05241
62 0.2 0.1495 0.05055
63 0.333 0.1768 0.1562
64 0.367 0.1593 0.2077
65 0.333 0.1495 0.1835
66 0.333 0.1638 0.1692

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  0.1 &  0.1351 & -0.0351 \tabularnewline
2 &  0.07 &  0.1217 & -0.05169 \tabularnewline
3 &  0.18 &  0.1278 &  0.05216 \tabularnewline
4 &  0.03 &  0.1306 & -0.1006 \tabularnewline
5 & -0.077 &  0.114 & -0.191 \tabularnewline
6 &  0.1 &  0.1187 & -0.01871 \tabularnewline
7 &  0.123 &  0.109 &  0.01398 \tabularnewline
8 &  0.047 &  0.1083 & -0.06127 \tabularnewline
9 &  0.063 &  0.1122 & -0.04918 \tabularnewline
10 & -0.043 &  0.1075 & -0.1505 \tabularnewline
11 & -0.07 &  0.128 & -0.198 \tabularnewline
12 & -0.083 &  0.1312 & -0.2142 \tabularnewline
13 &  0.036 &  0.1329 & -0.09687 \tabularnewline
14 & -0.036 &  0.1181 & -0.1541 \tabularnewline
15 &  0.02 &  0.1157 & -0.09572 \tabularnewline
16 &  0.08 &  0.1107 & -0.03069 \tabularnewline
17 &  0.003 &  0.1148 & -0.1118 \tabularnewline
18 &  0.177 &  0.1088 &  0.06817 \tabularnewline
19 &  0.28 &  0.1058 &  0.1742 \tabularnewline
20 &  0.233 &  0.1161 &  0.1169 \tabularnewline
21 &  0.227 &  0.1105 &  0.1165 \tabularnewline
22 &  0.236 &  0.1053 &  0.1307 \tabularnewline
23 &  0.25 &  0.1045 &  0.1455 \tabularnewline
24 &  0.137 &  0.09299 &  0.04401 \tabularnewline
25 &  0.337 &  0.101 &  0.236 \tabularnewline
26 &  0.193 &  0.1321 &  0.06088 \tabularnewline
27 & -0.017 &  0.1319 & -0.1489 \tabularnewline
28 &  0.03 &  0.1379 & -0.1079 \tabularnewline
29 &  0.05 &  0.1394 & -0.08939 \tabularnewline
30 &  0.05 &  0.1271 & -0.07709 \tabularnewline
31 &  0.15 &  0.1234 &  0.02664 \tabularnewline
32 &  0.177 &  0.08125 &  0.09575 \tabularnewline
33 &  0.027 &  0.09784 & -0.07084 \tabularnewline
34 &  0.176 &  0.09914 &  0.07686 \tabularnewline
35 &  0.017 &  0.1295 & -0.1125 \tabularnewline
36 &  0.19 &  0.1029 &  0.08713 \tabularnewline
37 &  0.06 &  0.1178 & -0.05777 \tabularnewline
38 & -0.01 &  0.1239 & -0.1339 \tabularnewline
39 &  0.117 &  0.1204 & -0.003383 \tabularnewline
40 &  0.043 &  0.129 & -0.08595 \tabularnewline
41 &  0.117 &  0.1221 & -0.00506 \tabularnewline
42 &  0.113 &  0.1228 & -0.009805 \tabularnewline
43 &  0.087 &  0.1023 & -0.01531 \tabularnewline
44 &  0.073 &  0.1196 & -0.04664 \tabularnewline
45 &  0.097 &  0.1332 & -0.03624 \tabularnewline
46 &  0.056 &  0.1303 & -0.07426 \tabularnewline
47 &  0.187 &  0.1332 &  0.05376 \tabularnewline
48 &  0.083 &  0.1321 & -0.04912 \tabularnewline
49 &  0.127 &  0.1234 &  0.003636 \tabularnewline
50 &  0.05 &  0.1245 & -0.07448 \tabularnewline
51 &  0.07 &  0.1521 & -0.08206 \tabularnewline
52 &  0.143 &  0.1543 & -0.0113 \tabularnewline
53 &  0.097 &  0.1271 & -0.03009 \tabularnewline
54 &  0.2 &  0.1222 &  0.07775 \tabularnewline
55 &  0.093 &  0.1394 & -0.04639 \tabularnewline
56 &  0.167 &  0.1606 &  0.006368 \tabularnewline
57 &  0.297 &  0.1588 &  0.1382 \tabularnewline
58 &  0.29 &  0.139 &  0.151 \tabularnewline
59 &  0.28 &  0.09467 &  0.1853 \tabularnewline
60 &  0.266 &  0.114 &  0.152 \tabularnewline
61 &  0.084 &  0.1364 & -0.05241 \tabularnewline
62 &  0.2 &  0.1495 &  0.05055 \tabularnewline
63 &  0.333 &  0.1768 &  0.1562 \tabularnewline
64 &  0.367 &  0.1593 &  0.2077 \tabularnewline
65 &  0.333 &  0.1495 &  0.1835 \tabularnewline
66 &  0.333 &  0.1638 &  0.1692 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=5

[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] 0.1[/C][C] 0.1351[/C][C]-0.0351[/C][/ROW]
[ROW][C]2[/C][C] 0.07[/C][C] 0.1217[/C][C]-0.05169[/C][/ROW]
[ROW][C]3[/C][C] 0.18[/C][C] 0.1278[/C][C] 0.05216[/C][/ROW]
[ROW][C]4[/C][C] 0.03[/C][C] 0.1306[/C][C]-0.1006[/C][/ROW]
[ROW][C]5[/C][C]-0.077[/C][C] 0.114[/C][C]-0.191[/C][/ROW]
[ROW][C]6[/C][C] 0.1[/C][C] 0.1187[/C][C]-0.01871[/C][/ROW]
[ROW][C]7[/C][C] 0.123[/C][C] 0.109[/C][C] 0.01398[/C][/ROW]
[ROW][C]8[/C][C] 0.047[/C][C] 0.1083[/C][C]-0.06127[/C][/ROW]
[ROW][C]9[/C][C] 0.063[/C][C] 0.1122[/C][C]-0.04918[/C][/ROW]
[ROW][C]10[/C][C]-0.043[/C][C] 0.1075[/C][C]-0.1505[/C][/ROW]
[ROW][C]11[/C][C]-0.07[/C][C] 0.128[/C][C]-0.198[/C][/ROW]
[ROW][C]12[/C][C]-0.083[/C][C] 0.1312[/C][C]-0.2142[/C][/ROW]
[ROW][C]13[/C][C] 0.036[/C][C] 0.1329[/C][C]-0.09687[/C][/ROW]
[ROW][C]14[/C][C]-0.036[/C][C] 0.1181[/C][C]-0.1541[/C][/ROW]
[ROW][C]15[/C][C] 0.02[/C][C] 0.1157[/C][C]-0.09572[/C][/ROW]
[ROW][C]16[/C][C] 0.08[/C][C] 0.1107[/C][C]-0.03069[/C][/ROW]
[ROW][C]17[/C][C] 0.003[/C][C] 0.1148[/C][C]-0.1118[/C][/ROW]
[ROW][C]18[/C][C] 0.177[/C][C] 0.1088[/C][C] 0.06817[/C][/ROW]
[ROW][C]19[/C][C] 0.28[/C][C] 0.1058[/C][C] 0.1742[/C][/ROW]
[ROW][C]20[/C][C] 0.233[/C][C] 0.1161[/C][C] 0.1169[/C][/ROW]
[ROW][C]21[/C][C] 0.227[/C][C] 0.1105[/C][C] 0.1165[/C][/ROW]
[ROW][C]22[/C][C] 0.236[/C][C] 0.1053[/C][C] 0.1307[/C][/ROW]
[ROW][C]23[/C][C] 0.25[/C][C] 0.1045[/C][C] 0.1455[/C][/ROW]
[ROW][C]24[/C][C] 0.137[/C][C] 0.09299[/C][C] 0.04401[/C][/ROW]
[ROW][C]25[/C][C] 0.337[/C][C] 0.101[/C][C] 0.236[/C][/ROW]
[ROW][C]26[/C][C] 0.193[/C][C] 0.1321[/C][C] 0.06088[/C][/ROW]
[ROW][C]27[/C][C]-0.017[/C][C] 0.1319[/C][C]-0.1489[/C][/ROW]
[ROW][C]28[/C][C] 0.03[/C][C] 0.1379[/C][C]-0.1079[/C][/ROW]
[ROW][C]29[/C][C] 0.05[/C][C] 0.1394[/C][C]-0.08939[/C][/ROW]
[ROW][C]30[/C][C] 0.05[/C][C] 0.1271[/C][C]-0.07709[/C][/ROW]
[ROW][C]31[/C][C] 0.15[/C][C] 0.1234[/C][C] 0.02664[/C][/ROW]
[ROW][C]32[/C][C] 0.177[/C][C] 0.08125[/C][C] 0.09575[/C][/ROW]
[ROW][C]33[/C][C] 0.027[/C][C] 0.09784[/C][C]-0.07084[/C][/ROW]
[ROW][C]34[/C][C] 0.176[/C][C] 0.09914[/C][C] 0.07686[/C][/ROW]
[ROW][C]35[/C][C] 0.017[/C][C] 0.1295[/C][C]-0.1125[/C][/ROW]
[ROW][C]36[/C][C] 0.19[/C][C] 0.1029[/C][C] 0.08713[/C][/ROW]
[ROW][C]37[/C][C] 0.06[/C][C] 0.1178[/C][C]-0.05777[/C][/ROW]
[ROW][C]38[/C][C]-0.01[/C][C] 0.1239[/C][C]-0.1339[/C][/ROW]
[ROW][C]39[/C][C] 0.117[/C][C] 0.1204[/C][C]-0.003383[/C][/ROW]
[ROW][C]40[/C][C] 0.043[/C][C] 0.129[/C][C]-0.08595[/C][/ROW]
[ROW][C]41[/C][C] 0.117[/C][C] 0.1221[/C][C]-0.00506[/C][/ROW]
[ROW][C]42[/C][C] 0.113[/C][C] 0.1228[/C][C]-0.009805[/C][/ROW]
[ROW][C]43[/C][C] 0.087[/C][C] 0.1023[/C][C]-0.01531[/C][/ROW]
[ROW][C]44[/C][C] 0.073[/C][C] 0.1196[/C][C]-0.04664[/C][/ROW]
[ROW][C]45[/C][C] 0.097[/C][C] 0.1332[/C][C]-0.03624[/C][/ROW]
[ROW][C]46[/C][C] 0.056[/C][C] 0.1303[/C][C]-0.07426[/C][/ROW]
[ROW][C]47[/C][C] 0.187[/C][C] 0.1332[/C][C] 0.05376[/C][/ROW]
[ROW][C]48[/C][C] 0.083[/C][C] 0.1321[/C][C]-0.04912[/C][/ROW]
[ROW][C]49[/C][C] 0.127[/C][C] 0.1234[/C][C] 0.003636[/C][/ROW]
[ROW][C]50[/C][C] 0.05[/C][C] 0.1245[/C][C]-0.07448[/C][/ROW]
[ROW][C]51[/C][C] 0.07[/C][C] 0.1521[/C][C]-0.08206[/C][/ROW]
[ROW][C]52[/C][C] 0.143[/C][C] 0.1543[/C][C]-0.0113[/C][/ROW]
[ROW][C]53[/C][C] 0.097[/C][C] 0.1271[/C][C]-0.03009[/C][/ROW]
[ROW][C]54[/C][C] 0.2[/C][C] 0.1222[/C][C] 0.07775[/C][/ROW]
[ROW][C]55[/C][C] 0.093[/C][C] 0.1394[/C][C]-0.04639[/C][/ROW]
[ROW][C]56[/C][C] 0.167[/C][C] 0.1606[/C][C] 0.006368[/C][/ROW]
[ROW][C]57[/C][C] 0.297[/C][C] 0.1588[/C][C] 0.1382[/C][/ROW]
[ROW][C]58[/C][C] 0.29[/C][C] 0.139[/C][C] 0.151[/C][/ROW]
[ROW][C]59[/C][C] 0.28[/C][C] 0.09467[/C][C] 0.1853[/C][/ROW]
[ROW][C]60[/C][C] 0.266[/C][C] 0.114[/C][C] 0.152[/C][/ROW]
[ROW][C]61[/C][C] 0.084[/C][C] 0.1364[/C][C]-0.05241[/C][/ROW]
[ROW][C]62[/C][C] 0.2[/C][C] 0.1495[/C][C] 0.05055[/C][/ROW]
[ROW][C]63[/C][C] 0.333[/C][C] 0.1768[/C][C] 0.1562[/C][/ROW]
[ROW][C]64[/C][C] 0.367[/C][C] 0.1593[/C][C] 0.2077[/C][/ROW]
[ROW][C]65[/C][C] 0.333[/C][C] 0.1495[/C][C] 0.1835[/C][/ROW]
[ROW][C]66[/C][C] 0.333[/C][C] 0.1638[/C][C] 0.1692[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=5

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

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
1 0.1 0.1351-0.0351
2 0.07 0.1217-0.05169
3 0.18 0.1278 0.05216
4 0.03 0.1306-0.1006
5-0.077 0.114-0.191
6 0.1 0.1187-0.01871
7 0.123 0.109 0.01398
8 0.047 0.1083-0.06127
9 0.063 0.1122-0.04918
10-0.043 0.1075-0.1505
11-0.07 0.128-0.198
12-0.083 0.1312-0.2142
13 0.036 0.1329-0.09687
14-0.036 0.1181-0.1541
15 0.02 0.1157-0.09572
16 0.08 0.1107-0.03069
17 0.003 0.1148-0.1118
18 0.177 0.1088 0.06817
19 0.28 0.1058 0.1742
20 0.233 0.1161 0.1169
21 0.227 0.1105 0.1165
22 0.236 0.1053 0.1307
23 0.25 0.1045 0.1455
24 0.137 0.09299 0.04401
25 0.337 0.101 0.236
26 0.193 0.1321 0.06088
27-0.017 0.1319-0.1489
28 0.03 0.1379-0.1079
29 0.05 0.1394-0.08939
30 0.05 0.1271-0.07709
31 0.15 0.1234 0.02664
32 0.177 0.08125 0.09575
33 0.027 0.09784-0.07084
34 0.176 0.09914 0.07686
35 0.017 0.1295-0.1125
36 0.19 0.1029 0.08713
37 0.06 0.1178-0.05777
38-0.01 0.1239-0.1339
39 0.117 0.1204-0.003383
40 0.043 0.129-0.08595
41 0.117 0.1221-0.00506
42 0.113 0.1228-0.009805
43 0.087 0.1023-0.01531
44 0.073 0.1196-0.04664
45 0.097 0.1332-0.03624
46 0.056 0.1303-0.07426
47 0.187 0.1332 0.05376
48 0.083 0.1321-0.04912
49 0.127 0.1234 0.003636
50 0.05 0.1245-0.07448
51 0.07 0.1521-0.08206
52 0.143 0.1543-0.0113
53 0.097 0.1271-0.03009
54 0.2 0.1222 0.07775
55 0.093 0.1394-0.04639
56 0.167 0.1606 0.006368
57 0.297 0.1588 0.1382
58 0.29 0.139 0.151
59 0.28 0.09467 0.1853
60 0.266 0.114 0.152
61 0.084 0.1364-0.05241
62 0.2 0.1495 0.05055
63 0.333 0.1768 0.1562
64 0.367 0.1593 0.2077
65 0.333 0.1495 0.1835
66 0.333 0.1638 0.1692







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
5 0.3536 0.7072 0.6464
6 0.2912 0.5825 0.7088
7 0.2978 0.5956 0.7022
8 0.1885 0.377 0.8115
9 0.1123 0.2246 0.8877
10 0.1043 0.2087 0.8957
11 0.2059 0.4117 0.7941
12 0.312 0.6241 0.688
13 0.2412 0.4824 0.7588
14 0.2335 0.4669 0.7665
15 0.1821 0.3643 0.8179
16 0.1425 0.285 0.8575
17 0.1169 0.2338 0.8831
18 0.1581 0.3161 0.8419
19 0.3678 0.7355 0.6322
20 0.4664 0.9327 0.5336
21 0.5023 0.9954 0.4977
22 0.5149 0.9703 0.4851
23 0.5347 0.9305 0.4653
24 0.4761 0.9522 0.5239
25 0.6724 0.6551 0.3276
26 0.7169 0.5661 0.2831
27 0.7277 0.5447 0.2723
28 0.7134 0.5732 0.2866
29 0.6968 0.6064 0.3032
30 0.6606 0.6788 0.3394
31 0.6137 0.7727 0.3863
32 0.6082 0.7836 0.3918
33 0.608 0.7841 0.392
34 0.5728 0.8543 0.4272
35 0.573 0.854 0.427
36 0.5567 0.8867 0.4433
37 0.5007 0.9986 0.4993
38 0.5451 0.9098 0.4549
39 0.4772 0.9544 0.5228
40 0.4647 0.9295 0.5353
41 0.4004 0.8007 0.5996
42 0.3396 0.6791 0.6604
43 0.2827 0.5653 0.7173
44 0.2431 0.4861 0.7569
45 0.2179 0.4358 0.7821
46 0.2201 0.4402 0.7799
47 0.2047 0.4093 0.7953
48 0.1945 0.3889 0.8055
49 0.1571 0.3141 0.8429
50 0.1866 0.3732 0.8134
51 0.2755 0.5511 0.7245
52 0.311 0.622 0.689
53 0.3523 0.7046 0.6477
54 0.2958 0.5916 0.7042
55 0.4493 0.8986 0.5507
56 0.5527 0.8946 0.4473
57 0.5261 0.9478 0.4739
58 0.4591 0.9182 0.5409
59 0.4594 0.9188 0.5406
60 0.6237 0.7525 0.3763
61 0.712 0.576 0.288

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 &  0.3536 &  0.7072 &  0.6464 \tabularnewline
6 &  0.2912 &  0.5825 &  0.7088 \tabularnewline
7 &  0.2978 &  0.5956 &  0.7022 \tabularnewline
8 &  0.1885 &  0.377 &  0.8115 \tabularnewline
9 &  0.1123 &  0.2246 &  0.8877 \tabularnewline
10 &  0.1043 &  0.2087 &  0.8957 \tabularnewline
11 &  0.2059 &  0.4117 &  0.7941 \tabularnewline
12 &  0.312 &  0.6241 &  0.688 \tabularnewline
13 &  0.2412 &  0.4824 &  0.7588 \tabularnewline
14 &  0.2335 &  0.4669 &  0.7665 \tabularnewline
15 &  0.1821 &  0.3643 &  0.8179 \tabularnewline
16 &  0.1425 &  0.285 &  0.8575 \tabularnewline
17 &  0.1169 &  0.2338 &  0.8831 \tabularnewline
18 &  0.1581 &  0.3161 &  0.8419 \tabularnewline
19 &  0.3678 &  0.7355 &  0.6322 \tabularnewline
20 &  0.4664 &  0.9327 &  0.5336 \tabularnewline
21 &  0.5023 &  0.9954 &  0.4977 \tabularnewline
22 &  0.5149 &  0.9703 &  0.4851 \tabularnewline
23 &  0.5347 &  0.9305 &  0.4653 \tabularnewline
24 &  0.4761 &  0.9522 &  0.5239 \tabularnewline
25 &  0.6724 &  0.6551 &  0.3276 \tabularnewline
26 &  0.7169 &  0.5661 &  0.2831 \tabularnewline
27 &  0.7277 &  0.5447 &  0.2723 \tabularnewline
28 &  0.7134 &  0.5732 &  0.2866 \tabularnewline
29 &  0.6968 &  0.6064 &  0.3032 \tabularnewline
30 &  0.6606 &  0.6788 &  0.3394 \tabularnewline
31 &  0.6137 &  0.7727 &  0.3863 \tabularnewline
32 &  0.6082 &  0.7836 &  0.3918 \tabularnewline
33 &  0.608 &  0.7841 &  0.392 \tabularnewline
34 &  0.5728 &  0.8543 &  0.4272 \tabularnewline
35 &  0.573 &  0.854 &  0.427 \tabularnewline
36 &  0.5567 &  0.8867 &  0.4433 \tabularnewline
37 &  0.5007 &  0.9986 &  0.4993 \tabularnewline
38 &  0.5451 &  0.9098 &  0.4549 \tabularnewline
39 &  0.4772 &  0.9544 &  0.5228 \tabularnewline
40 &  0.4647 &  0.9295 &  0.5353 \tabularnewline
41 &  0.4004 &  0.8007 &  0.5996 \tabularnewline
42 &  0.3396 &  0.6791 &  0.6604 \tabularnewline
43 &  0.2827 &  0.5653 &  0.7173 \tabularnewline
44 &  0.2431 &  0.4861 &  0.7569 \tabularnewline
45 &  0.2179 &  0.4358 &  0.7821 \tabularnewline
46 &  0.2201 &  0.4402 &  0.7799 \tabularnewline
47 &  0.2047 &  0.4093 &  0.7953 \tabularnewline
48 &  0.1945 &  0.3889 &  0.8055 \tabularnewline
49 &  0.1571 &  0.3141 &  0.8429 \tabularnewline
50 &  0.1866 &  0.3732 &  0.8134 \tabularnewline
51 &  0.2755 &  0.5511 &  0.7245 \tabularnewline
52 &  0.311 &  0.622 &  0.689 \tabularnewline
53 &  0.3523 &  0.7046 &  0.6477 \tabularnewline
54 &  0.2958 &  0.5916 &  0.7042 \tabularnewline
55 &  0.4493 &  0.8986 &  0.5507 \tabularnewline
56 &  0.5527 &  0.8946 &  0.4473 \tabularnewline
57 &  0.5261 &  0.9478 &  0.4739 \tabularnewline
58 &  0.4591 &  0.9182 &  0.5409 \tabularnewline
59 &  0.4594 &  0.9188 &  0.5406 \tabularnewline
60 &  0.6237 &  0.7525 &  0.3763 \tabularnewline
61 &  0.712 &  0.576 &  0.288 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=6

[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] 0.3536[/C][C] 0.7072[/C][C] 0.6464[/C][/ROW]
[ROW][C]6[/C][C] 0.2912[/C][C] 0.5825[/C][C] 0.7088[/C][/ROW]
[ROW][C]7[/C][C] 0.2978[/C][C] 0.5956[/C][C] 0.7022[/C][/ROW]
[ROW][C]8[/C][C] 0.1885[/C][C] 0.377[/C][C] 0.8115[/C][/ROW]
[ROW][C]9[/C][C] 0.1123[/C][C] 0.2246[/C][C] 0.8877[/C][/ROW]
[ROW][C]10[/C][C] 0.1043[/C][C] 0.2087[/C][C] 0.8957[/C][/ROW]
[ROW][C]11[/C][C] 0.2059[/C][C] 0.4117[/C][C] 0.7941[/C][/ROW]
[ROW][C]12[/C][C] 0.312[/C][C] 0.6241[/C][C] 0.688[/C][/ROW]
[ROW][C]13[/C][C] 0.2412[/C][C] 0.4824[/C][C] 0.7588[/C][/ROW]
[ROW][C]14[/C][C] 0.2335[/C][C] 0.4669[/C][C] 0.7665[/C][/ROW]
[ROW][C]15[/C][C] 0.1821[/C][C] 0.3643[/C][C] 0.8179[/C][/ROW]
[ROW][C]16[/C][C] 0.1425[/C][C] 0.285[/C][C] 0.8575[/C][/ROW]
[ROW][C]17[/C][C] 0.1169[/C][C] 0.2338[/C][C] 0.8831[/C][/ROW]
[ROW][C]18[/C][C] 0.1581[/C][C] 0.3161[/C][C] 0.8419[/C][/ROW]
[ROW][C]19[/C][C] 0.3678[/C][C] 0.7355[/C][C] 0.6322[/C][/ROW]
[ROW][C]20[/C][C] 0.4664[/C][C] 0.9327[/C][C] 0.5336[/C][/ROW]
[ROW][C]21[/C][C] 0.5023[/C][C] 0.9954[/C][C] 0.4977[/C][/ROW]
[ROW][C]22[/C][C] 0.5149[/C][C] 0.9703[/C][C] 0.4851[/C][/ROW]
[ROW][C]23[/C][C] 0.5347[/C][C] 0.9305[/C][C] 0.4653[/C][/ROW]
[ROW][C]24[/C][C] 0.4761[/C][C] 0.9522[/C][C] 0.5239[/C][/ROW]
[ROW][C]25[/C][C] 0.6724[/C][C] 0.6551[/C][C] 0.3276[/C][/ROW]
[ROW][C]26[/C][C] 0.7169[/C][C] 0.5661[/C][C] 0.2831[/C][/ROW]
[ROW][C]27[/C][C] 0.7277[/C][C] 0.5447[/C][C] 0.2723[/C][/ROW]
[ROW][C]28[/C][C] 0.7134[/C][C] 0.5732[/C][C] 0.2866[/C][/ROW]
[ROW][C]29[/C][C] 0.6968[/C][C] 0.6064[/C][C] 0.3032[/C][/ROW]
[ROW][C]30[/C][C] 0.6606[/C][C] 0.6788[/C][C] 0.3394[/C][/ROW]
[ROW][C]31[/C][C] 0.6137[/C][C] 0.7727[/C][C] 0.3863[/C][/ROW]
[ROW][C]32[/C][C] 0.6082[/C][C] 0.7836[/C][C] 0.3918[/C][/ROW]
[ROW][C]33[/C][C] 0.608[/C][C] 0.7841[/C][C] 0.392[/C][/ROW]
[ROW][C]34[/C][C] 0.5728[/C][C] 0.8543[/C][C] 0.4272[/C][/ROW]
[ROW][C]35[/C][C] 0.573[/C][C] 0.854[/C][C] 0.427[/C][/ROW]
[ROW][C]36[/C][C] 0.5567[/C][C] 0.8867[/C][C] 0.4433[/C][/ROW]
[ROW][C]37[/C][C] 0.5007[/C][C] 0.9986[/C][C] 0.4993[/C][/ROW]
[ROW][C]38[/C][C] 0.5451[/C][C] 0.9098[/C][C] 0.4549[/C][/ROW]
[ROW][C]39[/C][C] 0.4772[/C][C] 0.9544[/C][C] 0.5228[/C][/ROW]
[ROW][C]40[/C][C] 0.4647[/C][C] 0.9295[/C][C] 0.5353[/C][/ROW]
[ROW][C]41[/C][C] 0.4004[/C][C] 0.8007[/C][C] 0.5996[/C][/ROW]
[ROW][C]42[/C][C] 0.3396[/C][C] 0.6791[/C][C] 0.6604[/C][/ROW]
[ROW][C]43[/C][C] 0.2827[/C][C] 0.5653[/C][C] 0.7173[/C][/ROW]
[ROW][C]44[/C][C] 0.2431[/C][C] 0.4861[/C][C] 0.7569[/C][/ROW]
[ROW][C]45[/C][C] 0.2179[/C][C] 0.4358[/C][C] 0.7821[/C][/ROW]
[ROW][C]46[/C][C] 0.2201[/C][C] 0.4402[/C][C] 0.7799[/C][/ROW]
[ROW][C]47[/C][C] 0.2047[/C][C] 0.4093[/C][C] 0.7953[/C][/ROW]
[ROW][C]48[/C][C] 0.1945[/C][C] 0.3889[/C][C] 0.8055[/C][/ROW]
[ROW][C]49[/C][C] 0.1571[/C][C] 0.3141[/C][C] 0.8429[/C][/ROW]
[ROW][C]50[/C][C] 0.1866[/C][C] 0.3732[/C][C] 0.8134[/C][/ROW]
[ROW][C]51[/C][C] 0.2755[/C][C] 0.5511[/C][C] 0.7245[/C][/ROW]
[ROW][C]52[/C][C] 0.311[/C][C] 0.622[/C][C] 0.689[/C][/ROW]
[ROW][C]53[/C][C] 0.3523[/C][C] 0.7046[/C][C] 0.6477[/C][/ROW]
[ROW][C]54[/C][C] 0.2958[/C][C] 0.5916[/C][C] 0.7042[/C][/ROW]
[ROW][C]55[/C][C] 0.4493[/C][C] 0.8986[/C][C] 0.5507[/C][/ROW]
[ROW][C]56[/C][C] 0.5527[/C][C] 0.8946[/C][C] 0.4473[/C][/ROW]
[ROW][C]57[/C][C] 0.5261[/C][C] 0.9478[/C][C] 0.4739[/C][/ROW]
[ROW][C]58[/C][C] 0.4591[/C][C] 0.9182[/C][C] 0.5409[/C][/ROW]
[ROW][C]59[/C][C] 0.4594[/C][C] 0.9188[/C][C] 0.5406[/C][/ROW]
[ROW][C]60[/C][C] 0.6237[/C][C] 0.7525[/C][C] 0.3763[/C][/ROW]
[ROW][C]61[/C][C] 0.712[/C][C] 0.576[/C][C] 0.288[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=6

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

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
5 0.3536 0.7072 0.6464
6 0.2912 0.5825 0.7088
7 0.2978 0.5956 0.7022
8 0.1885 0.377 0.8115
9 0.1123 0.2246 0.8877
10 0.1043 0.2087 0.8957
11 0.2059 0.4117 0.7941
12 0.312 0.6241 0.688
13 0.2412 0.4824 0.7588
14 0.2335 0.4669 0.7665
15 0.1821 0.3643 0.8179
16 0.1425 0.285 0.8575
17 0.1169 0.2338 0.8831
18 0.1581 0.3161 0.8419
19 0.3678 0.7355 0.6322
20 0.4664 0.9327 0.5336
21 0.5023 0.9954 0.4977
22 0.5149 0.9703 0.4851
23 0.5347 0.9305 0.4653
24 0.4761 0.9522 0.5239
25 0.6724 0.6551 0.3276
26 0.7169 0.5661 0.2831
27 0.7277 0.5447 0.2723
28 0.7134 0.5732 0.2866
29 0.6968 0.6064 0.3032
30 0.6606 0.6788 0.3394
31 0.6137 0.7727 0.3863
32 0.6082 0.7836 0.3918
33 0.608 0.7841 0.392
34 0.5728 0.8543 0.4272
35 0.573 0.854 0.427
36 0.5567 0.8867 0.4433
37 0.5007 0.9986 0.4993
38 0.5451 0.9098 0.4549
39 0.4772 0.9544 0.5228
40 0.4647 0.9295 0.5353
41 0.4004 0.8007 0.5996
42 0.3396 0.6791 0.6604
43 0.2827 0.5653 0.7173
44 0.2431 0.4861 0.7569
45 0.2179 0.4358 0.7821
46 0.2201 0.4402 0.7799
47 0.2047 0.4093 0.7953
48 0.1945 0.3889 0.8055
49 0.1571 0.3141 0.8429
50 0.1866 0.3732 0.8134
51 0.2755 0.5511 0.7245
52 0.311 0.622 0.689
53 0.3523 0.7046 0.6477
54 0.2958 0.5916 0.7042
55 0.4493 0.8986 0.5507
56 0.5527 0.8946 0.4473
57 0.5261 0.9478 0.4739
58 0.4591 0.9182 0.5409
59 0.4594 0.9188 0.5406
60 0.6237 0.7525 0.3763
61 0.712 0.576 0.288







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level00OK
10% type I error level00OK

\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 & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=7

[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]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=7

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 level0 0OK
5% type I error level00OK
10% type I error level00OK







Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 11.255, df1 = 2, df2 = 62, p-value = 6.759e-05
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 11.255, df1 = 2, df2 = 62, p-value = 6.759e-05
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 11.255, df1 = 2, df2 = 62, p-value = 6.759e-05

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 11.255, df1 = 2, df2 = 62, p-value = 6.759e-05
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 11.255, df1 = 2, df2 = 62, p-value = 6.759e-05
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 11.255, df1 = 2, df2 = 62, p-value = 6.759e-05
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=&T=8

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]
> reset_test_fitted
	RESET test
data:  mylm
RESET = 11.255, df1 = 2, df2 = 62, p-value = 6.759e-05
[/C][/ROW] [ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW] [ROW][C]
> reset_test_regressors
	RESET test
data:  mylm
RESET = 11.255, df1 = 2, df2 = 62, p-value = 6.759e-05
[/C][/ROW] [ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW] [ROW][C]
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 11.255, df1 = 2, df2 = 62, p-value = 6.759e-05
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=8

As an alternative you can also use a QR Code:  

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 11.255, df1 = 2, df2 = 62, p-value = 6.759e-05
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 11.255, df1 = 2, df2 = 62, p-value = 6.759e-05
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 11.255, df1 = 2, df2 = 62, p-value = 6.759e-05



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$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, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$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.row.start(a)
a<-table.element(a, mywarning)
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,'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$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
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,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('
',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('
',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('
',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('
',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')