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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 computationWed, 16 Sep 2020 23:53:15 +0200
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/Sep/17/t1600293813folzvjcl9pn36tm.htm/, Retrieved Wed, 21 Apr 2021 10:45:32 +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 10:45:32 +0200
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact0
Dataseries X:
955	1	895	2	0	1	-1	1039	1	4
960	0.2	1269	2	0	27	1	832	0	5
1156	1	1187	2	0	14	0	1076	1	5
1433	0.1	2010	3	0	27	0	1460	0	5
769	0.3	1063	2	0	25	1	771	0	6
1310	0.2	1379	2	0	10	0	1339	1	8
840	1	967	2	1	14	0	925	0	10
1590	1	1302	3	0	2	-1	1500	1	10
716	0.3	1038	2	0	25	0	718	0	10
892	0.5	1006	3	0	15	0	966	0	10
1717	1	1615	2	0	25	1	1406	1	15
930	0.5	955	3	1	12	0	904	1	17
920	0.4	1231	3	1	15	0	1158	0	17
838	0.2	901	2	0	5	0	901	0	25
1310	1	1164	3	1	4	0	1331	0	25
1275	0.5	1268	2	0	10	0	1253	1	31
1780	0.4	1910	3	0	11	0	1759	1	33
1148	0.2	1017	3	0	1	0	1126	1	33
900	0.2	845	2	0	4	0	935	1	34
839	0.3	812	2	0	4	0	838	1	36
1975	0.2	3931	4	0	26	0	2245	0	38
865	0.5	1001	3	0	15	0	943	0	38
1018	1	892	2	0	4	0	923	1	43
1550	0.2	2383	3	0	28	0	1628	0	44
1190	1	1378	2	1	13	0	1369	0	47
1182	0.2	1510	2	0	11	0.5	1213	1	50
783	0	856	2	0	10	0	819	0	51
920	1	907	2	0	1	0	1041	1	53
788	1	844	2	0	13	1	731	0	61
1193	1	1152	3	0	4	0	1292	0	67




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R ServerBig Analytics Cloud Computing Center

\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 time3 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \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]3 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/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 time3 seconds
R ServerBig Analytics Cloud Computing Center







Multiple Linear Regression - Estimated Regression Equation
SP[t] = -178.397 + 69.2751V[t] -0.175723SQ[t] + 25.7879BR[t] -100.782PH[t] + 6.02176AG[t] + 105.971Re[t] + 1.17152As[t] + 105.094C[t] -1.16872T[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SP[t] =  -178.397 +  69.2751V[t] -0.175723SQ[t] +  25.7879BR[t] -100.782PH[t] +  6.02176AG[t] +  105.971Re[t] +  1.17152As[t] +  105.094C[t] -1.16872T[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]SP[t] =  -178.397 +  69.2751V[t] -0.175723SQ[t] +  25.7879BR[t] -100.782PH[t] +  6.02176AG[t] +  105.971Re[t] +  1.17152As[t] +  105.094C[t] -1.16872T[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
SP[t] = -178.397 + 69.2751V[t] -0.175723SQ[t] + 25.7879BR[t] -100.782PH[t] + 6.02176AG[t] + 105.971Re[t] + 1.17152As[t] + 105.094C[t] -1.16872T[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-178.4 105.4-1.6930e+00 0.106 0.05299
V+69.28 44.05+1.5730e+00 0.1315 0.06574
SQ-0.1757 0.08091-2.1720e+00 0.04208 0.02104
BR+25.79 35.95+7.1720e-01 0.4815 0.2408
PH-100.8 41.47-2.4300e+00 0.02462 0.01231
AG+6.022 3.747+1.6070e+00 0.1237 0.06186
Re+106 45.24+2.3430e+00 0.02961 0.01481
As+1.171 0.127+9.2240e+00 1.209e-08 6.043e-09
C+105.1 39.14+2.6850e+00 0.01423 0.007114
T-1.169 0.9968-1.1720e+00 0.2548 0.1274

\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) & -178.4 &  105.4 & -1.6930e+00 &  0.106 &  0.05299 \tabularnewline
V & +69.28 &  44.05 & +1.5730e+00 &  0.1315 &  0.06574 \tabularnewline
SQ & -0.1757 &  0.08091 & -2.1720e+00 &  0.04208 &  0.02104 \tabularnewline
BR & +25.79 &  35.95 & +7.1720e-01 &  0.4815 &  0.2408 \tabularnewline
PH & -100.8 &  41.47 & -2.4300e+00 &  0.02462 &  0.01231 \tabularnewline
AG & +6.022 &  3.747 & +1.6070e+00 &  0.1237 &  0.06186 \tabularnewline
Re & +106 &  45.24 & +2.3430e+00 &  0.02961 &  0.01481 \tabularnewline
As & +1.171 &  0.127 & +9.2240e+00 &  1.209e-08 &  6.043e-09 \tabularnewline
C & +105.1 &  39.14 & +2.6850e+00 &  0.01423 &  0.007114 \tabularnewline
T & -1.169 &  0.9968 & -1.1720e+00 &  0.2548 &  0.1274 \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]-178.4[/C][C] 105.4[/C][C]-1.6930e+00[/C][C] 0.106[/C][C] 0.05299[/C][/ROW]
[ROW][C]V[/C][C]+69.28[/C][C] 44.05[/C][C]+1.5730e+00[/C][C] 0.1315[/C][C] 0.06574[/C][/ROW]
[ROW][C]SQ[/C][C]-0.1757[/C][C] 0.08091[/C][C]-2.1720e+00[/C][C] 0.04208[/C][C] 0.02104[/C][/ROW]
[ROW][C]BR[/C][C]+25.79[/C][C] 35.95[/C][C]+7.1720e-01[/C][C] 0.4815[/C][C] 0.2408[/C][/ROW]
[ROW][C]PH[/C][C]-100.8[/C][C] 41.47[/C][C]-2.4300e+00[/C][C] 0.02462[/C][C] 0.01231[/C][/ROW]
[ROW][C]AG[/C][C]+6.022[/C][C] 3.747[/C][C]+1.6070e+00[/C][C] 0.1237[/C][C] 0.06186[/C][/ROW]
[ROW][C]Re[/C][C]+106[/C][C] 45.24[/C][C]+2.3430e+00[/C][C] 0.02961[/C][C] 0.01481[/C][/ROW]
[ROW][C]As[/C][C]+1.171[/C][C] 0.127[/C][C]+9.2240e+00[/C][C] 1.209e-08[/C][C] 6.043e-09[/C][/ROW]
[ROW][C]C[/C][C]+105.1[/C][C] 39.14[/C][C]+2.6850e+00[/C][C] 0.01423[/C][C] 0.007114[/C][/ROW]
[ROW][C]T[/C][C]-1.169[/C][C] 0.9968[/C][C]-1.1720e+00[/C][C] 0.2548[/C][C] 0.1274[/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)-178.4 105.4-1.6930e+00 0.106 0.05299
V+69.28 44.05+1.5730e+00 0.1315 0.06574
SQ-0.1757 0.08091-2.1720e+00 0.04208 0.02104
BR+25.79 35.95+7.1720e-01 0.4815 0.2408
PH-100.8 41.47-2.4300e+00 0.02462 0.01231
AG+6.022 3.747+1.6070e+00 0.1237 0.06186
Re+106 45.24+2.3430e+00 0.02961 0.01481
As+1.171 0.127+9.2240e+00 1.209e-08 6.043e-09
C+105.1 39.14+2.6850e+00 0.01423 0.007114
T-1.169 0.9968-1.1720e+00 0.2548 0.1274







Multiple Linear Regression - Regression Statistics
Multiple R 0.9834
R-squared 0.9672
Adjusted R-squared 0.9524
F-TEST (value) 65.45
F-TEST (DF numerator)9
F-TEST (DF denominator)20
p-value 7.178e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 72.83
Sum Squared Residuals 1.061e+05

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9834 \tabularnewline
R-squared &  0.9672 \tabularnewline
Adjusted R-squared &  0.9524 \tabularnewline
F-TEST (value) &  65.45 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 20 \tabularnewline
p-value &  7.178e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  72.83 \tabularnewline
Sum Squared Residuals &  1.061e+05 \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] 0.9834[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.9672[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.9524[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 65.45[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]20[/C][/ROW]
[ROW][C]p-value[/C][C] 7.178e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 72.83[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1.061e+05[/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 0.9834
R-squared 0.9672
Adjusted R-squared 0.9524
F-TEST (value) 65.45
F-TEST (DF numerator)9
F-TEST (DF denominator)20
p-value 7.178e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 72.83
Sum Squared Residuals 1.061e+05







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
1 955 1003-47.86
2 960 901.5 58.54
3 1156 1178-21.98
4 1433 1420 13.14
5 769 859.9-90.91
6 1310 1369-59.34
7 840 828 11.98
8 1590 1496 93.79
9 716 691.6 24.43
10 892 967.2-75.16
11 1717 1650 67.1
12 930 881.5 48.45
13 920 1037-116.7
14 838 785.1 52.86
15 1310 1217 92.92
16 1275 1282-6.996
17 1780 1785-4.517
18 1148 1126 22.21
19 900 923.4-23.36
20 839 820.1 18.88
21 1975 1990-15.06
22 865 908.4-43.36
23 1018 945.9 72.05
24 1550 1518 31.5
25 1190 1227-36.69
26 1182 1209-26.63
27 783 682.8 100.2
28 920 1052-131.8
29 788 763.5 24.51
30 1193 1225-32.2

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  955 &  1003 & -47.86 \tabularnewline
2 &  960 &  901.5 &  58.54 \tabularnewline
3 &  1156 &  1178 & -21.98 \tabularnewline
4 &  1433 &  1420 &  13.14 \tabularnewline
5 &  769 &  859.9 & -90.91 \tabularnewline
6 &  1310 &  1369 & -59.34 \tabularnewline
7 &  840 &  828 &  11.98 \tabularnewline
8 &  1590 &  1496 &  93.79 \tabularnewline
9 &  716 &  691.6 &  24.43 \tabularnewline
10 &  892 &  967.2 & -75.16 \tabularnewline
11 &  1717 &  1650 &  67.1 \tabularnewline
12 &  930 &  881.5 &  48.45 \tabularnewline
13 &  920 &  1037 & -116.7 \tabularnewline
14 &  838 &  785.1 &  52.86 \tabularnewline
15 &  1310 &  1217 &  92.92 \tabularnewline
16 &  1275 &  1282 & -6.996 \tabularnewline
17 &  1780 &  1785 & -4.517 \tabularnewline
18 &  1148 &  1126 &  22.21 \tabularnewline
19 &  900 &  923.4 & -23.36 \tabularnewline
20 &  839 &  820.1 &  18.88 \tabularnewline
21 &  1975 &  1990 & -15.06 \tabularnewline
22 &  865 &  908.4 & -43.36 \tabularnewline
23 &  1018 &  945.9 &  72.05 \tabularnewline
24 &  1550 &  1518 &  31.5 \tabularnewline
25 &  1190 &  1227 & -36.69 \tabularnewline
26 &  1182 &  1209 & -26.63 \tabularnewline
27 &  783 &  682.8 &  100.2 \tabularnewline
28 &  920 &  1052 & -131.8 \tabularnewline
29 &  788 &  763.5 &  24.51 \tabularnewline
30 &  1193 &  1225 & -32.2 \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] 955[/C][C] 1003[/C][C]-47.86[/C][/ROW]
[ROW][C]2[/C][C] 960[/C][C] 901.5[/C][C] 58.54[/C][/ROW]
[ROW][C]3[/C][C] 1156[/C][C] 1178[/C][C]-21.98[/C][/ROW]
[ROW][C]4[/C][C] 1433[/C][C] 1420[/C][C] 13.14[/C][/ROW]
[ROW][C]5[/C][C] 769[/C][C] 859.9[/C][C]-90.91[/C][/ROW]
[ROW][C]6[/C][C] 1310[/C][C] 1369[/C][C]-59.34[/C][/ROW]
[ROW][C]7[/C][C] 840[/C][C] 828[/C][C] 11.98[/C][/ROW]
[ROW][C]8[/C][C] 1590[/C][C] 1496[/C][C] 93.79[/C][/ROW]
[ROW][C]9[/C][C] 716[/C][C] 691.6[/C][C] 24.43[/C][/ROW]
[ROW][C]10[/C][C] 892[/C][C] 967.2[/C][C]-75.16[/C][/ROW]
[ROW][C]11[/C][C] 1717[/C][C] 1650[/C][C] 67.1[/C][/ROW]
[ROW][C]12[/C][C] 930[/C][C] 881.5[/C][C] 48.45[/C][/ROW]
[ROW][C]13[/C][C] 920[/C][C] 1037[/C][C]-116.7[/C][/ROW]
[ROW][C]14[/C][C] 838[/C][C] 785.1[/C][C] 52.86[/C][/ROW]
[ROW][C]15[/C][C] 1310[/C][C] 1217[/C][C] 92.92[/C][/ROW]
[ROW][C]16[/C][C] 1275[/C][C] 1282[/C][C]-6.996[/C][/ROW]
[ROW][C]17[/C][C] 1780[/C][C] 1785[/C][C]-4.517[/C][/ROW]
[ROW][C]18[/C][C] 1148[/C][C] 1126[/C][C] 22.21[/C][/ROW]
[ROW][C]19[/C][C] 900[/C][C] 923.4[/C][C]-23.36[/C][/ROW]
[ROW][C]20[/C][C] 839[/C][C] 820.1[/C][C] 18.88[/C][/ROW]
[ROW][C]21[/C][C] 1975[/C][C] 1990[/C][C]-15.06[/C][/ROW]
[ROW][C]22[/C][C] 865[/C][C] 908.4[/C][C]-43.36[/C][/ROW]
[ROW][C]23[/C][C] 1018[/C][C] 945.9[/C][C] 72.05[/C][/ROW]
[ROW][C]24[/C][C] 1550[/C][C] 1518[/C][C] 31.5[/C][/ROW]
[ROW][C]25[/C][C] 1190[/C][C] 1227[/C][C]-36.69[/C][/ROW]
[ROW][C]26[/C][C] 1182[/C][C] 1209[/C][C]-26.63[/C][/ROW]
[ROW][C]27[/C][C] 783[/C][C] 682.8[/C][C] 100.2[/C][/ROW]
[ROW][C]28[/C][C] 920[/C][C] 1052[/C][C]-131.8[/C][/ROW]
[ROW][C]29[/C][C] 788[/C][C] 763.5[/C][C] 24.51[/C][/ROW]
[ROW][C]30[/C][C] 1193[/C][C] 1225[/C][C]-32.2[/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
1 955 1003-47.86
2 960 901.5 58.54
3 1156 1178-21.98
4 1433 1420 13.14
5 769 859.9-90.91
6 1310 1369-59.34
7 840 828 11.98
8 1590 1496 93.79
9 716 691.6 24.43
10 892 967.2-75.16
11 1717 1650 67.1
12 930 881.5 48.45
13 920 1037-116.7
14 838 785.1 52.86
15 1310 1217 92.92
16 1275 1282-6.996
17 1780 1785-4.517
18 1148 1126 22.21
19 900 923.4-23.36
20 839 820.1 18.88
21 1975 1990-15.06
22 865 908.4-43.36
23 1018 945.9 72.05
24 1550 1518 31.5
25 1190 1227-36.69
26 1182 1209-26.63
27 783 682.8 100.2
28 920 1052-131.8
29 788 763.5 24.51
30 1193 1225-32.2







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
13 0.7982 0.4036 0.2018
14 0.6754 0.6491 0.3246
15 0.546 0.9081 0.454
16 0.4613 0.9225 0.5387
17 0.3492 0.6983 0.6508

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 &  0.7982 &  0.4036 &  0.2018 \tabularnewline
14 &  0.6754 &  0.6491 &  0.3246 \tabularnewline
15 &  0.546 &  0.9081 &  0.454 \tabularnewline
16 &  0.4613 &  0.9225 &  0.5387 \tabularnewline
17 &  0.3492 &  0.6983 &  0.6508 \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]13[/C][C] 0.7982[/C][C] 0.4036[/C][C] 0.2018[/C][/ROW]
[ROW][C]14[/C][C] 0.6754[/C][C] 0.6491[/C][C] 0.3246[/C][/ROW]
[ROW][C]15[/C][C] 0.546[/C][C] 0.9081[/C][C] 0.454[/C][/ROW]
[ROW][C]16[/C][C] 0.4613[/C][C] 0.9225[/C][C] 0.5387[/C][/ROW]
[ROW][C]17[/C][C] 0.3492[/C][C] 0.6983[/C][C] 0.6508[/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
13 0.7982 0.4036 0.2018
14 0.6754 0.6491 0.3246
15 0.546 0.9081 0.454
16 0.4613 0.9225 0.5387
17 0.3492 0.6983 0.6508







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 = 9.0364, df1 = 2, df2 = 18, p-value = 0.001918
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.241, df1 = 18, df2 = 2, p-value = 0.5378
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.64159, df1 = 2, df2 = 18, p-value = 0.5381

\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 = 9.0364, df1 = 2, df2 = 18, p-value = 0.001918
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.241, df1 = 18, df2 = 2, p-value = 0.5378
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.64159, df1 = 2, df2 = 18, p-value = 0.5381
\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 = 9.0364, df1 = 2, df2 = 18, p-value = 0.001918
[/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 = 1.241, df1 = 18, df2 = 2, p-value = 0.5378
[/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 = 0.64159, df1 = 2, df2 = 18, p-value = 0.5381
[/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 = 9.0364, df1 = 2, df2 = 18, p-value = 0.001918
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.241, df1 = 18, df2 = 2, p-value = 0.5378
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.64159, df1 = 2, df2 = 18, p-value = 0.5381







Variance Inflation Factors (Multicollinearity)
> vif
        V        SQ        BR        PH        AG        Re        As         C 
 1.446600 13.933951  2.282755  1.350788  6.044902  2.331050 10.417095  2.156168 
        T 
 1.914313 

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
        V        SQ        BR        PH        AG        Re        As         C 
 1.446600 13.933951  2.282755  1.350788  6.044902  2.331050 10.417095  2.156168 
        T 
 1.914313 
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
        V        SQ        BR        PH        AG        Re        As         C 
 1.446600 13.933951  2.282755  1.350788  6.044902  2.331050 10.417095  2.156168 
        T 
 1.914313 
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=9

As an alternative you can also use a QR Code:  

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

Variance Inflation Factors (Multicollinearity)
> vif
        V        SQ        BR        PH        AG        Re        As         C 
 1.446600 13.933951  2.282755  1.350788  6.044902  2.331050 10.417095  2.156168 
        T 
 1.914313 



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):
par6 <- '12'
par5 <- '0'
par4 <- '0'
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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')