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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 computationTue, 01 Dec 2015 11:58:35 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Dec/01/t1448971166s87tbjilvvhv2ge.htm/, Retrieved Thu, 31 Oct 2024 23:47:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=284686, Retrieved Thu, 31 Oct 2024 23:47:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsAnderlecht PC
Estimated Impact171
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Pearson Correlation] [] [2015-09-24 13:12:15] [32b17a345b130fdf5cc88718ed94a974]
- RM D    [Multiple Regression] [Multiple Regressi...] [2015-12-01 11:58:35] [7f15ad2b324a02cb27046274e327e025] [Current]
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Dataseries X:
23867 0.74
24107 0.76
24041 0.75
24415 0.69
24496 0.59
24022 0.71
24367 0.44
23869 0.72
24495 0.7
23818 0.71
24081 0.7
24132 0.57
23651 0.72
23622 0.58
23726 0.63
23942 0.78
24573 0.48
23085 0.58
22612 0.73
22960 0.68
22921 0.66
23510 0.74
22729 0.69
23047 0.63
22850 0.78
23426 0.59
22812 0.69
22446 0.78
23567 0.41
23185 0.68
22777 0.64
23508 0.55
23193 0.81
23006 0.81
22332 0.77
22347 0.77
23061 0.45
22887 0.57
22890 0.69
22701 0.74
22467 0.76
22357 0.83
22443 0.78
22824 0.68
22906 0.57
23059 0.78
23055 0.76
22564 0.67
18570 0.69
20329 0.59
19279 0.77
19541 0.54
19517 0.63




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284686&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284686&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284686&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Toeschouwers[t] = + 23733.3 -1171.09`%_winst_thuis`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Toeschouwers[t] =  +  23733.3 -1171.09`%_winst_thuis`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284686&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Toeschouwers[t] =  +  23733.3 -1171.09`%_winst_thuis`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284686&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284686&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
Toeschouwers[t] = + 23733.3 -1171.09`%_winst_thuis`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+2.373e+04 1245+1.9070e+01 7.345e-25 3.673e-25
`%_winst_thuis`-1171 1825-6.4170e-01 0.5239 0.262

\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) & +2.373e+04 &  1245 & +1.9070e+01 &  7.345e-25 &  3.673e-25 \tabularnewline
`%_winst_thuis` & -1171 &  1825 & -6.4170e-01 &  0.5239 &  0.262 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284686&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]+2.373e+04[/C][C] 1245[/C][C]+1.9070e+01[/C][C] 7.345e-25[/C][C] 3.673e-25[/C][/ROW]
[ROW][C]`%_winst_thuis`[/C][C]-1171[/C][C] 1825[/C][C]-6.4170e-01[/C][C] 0.5239[/C][C] 0.262[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284686&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284686&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)+2.373e+04 1245+1.9070e+01 7.345e-25 3.673e-25
`%_winst_thuis`-1171 1825-6.4170e-01 0.5239 0.262







Multiple Linear Regression - Regression Statistics
Multiple R 0.0895
R-squared 0.00801
Adjusted R-squared-0.01144
F-TEST (value) 0.4118
F-TEST (DF numerator)1
F-TEST (DF denominator)51
p-value 0.5239
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1320
Sum Squared Residuals 8.882e+07

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.0895 \tabularnewline
R-squared &  0.00801 \tabularnewline
Adjusted R-squared & -0.01144 \tabularnewline
F-TEST (value) &  0.4118 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 51 \tabularnewline
p-value &  0.5239 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1320 \tabularnewline
Sum Squared Residuals &  8.882e+07 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284686&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.0895[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.00801[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.01144[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 0.4118[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]51[/C][/ROW]
[ROW][C]p-value[/C][C] 0.5239[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1320[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 8.882e+07[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284686&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284686&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 0.0895
R-squared 0.00801
Adjusted R-squared-0.01144
F-TEST (value) 0.4118
F-TEST (DF numerator)1
F-TEST (DF denominator)51
p-value 0.5239
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1320
Sum Squared Residuals 8.882e+07







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 2.387e+04 2.287e+04 1000
2 2.411e+04 2.284e+04 1264
3 2.404e+04 2.286e+04 1186
4 2.442e+04 2.293e+04 1490
5 2.45e+04 2.304e+04 1454
6 2.402e+04 2.29e+04 1120
7 2.437e+04 2.322e+04 1149
8 2.387e+04 2.289e+04 978.9
9 2.45e+04 2.291e+04 1581
10 2.382e+04 2.29e+04 916.2
11 2.408e+04 2.291e+04 1167
12 2.413e+04 2.307e+04 1066
13 2.365e+04 2.289e+04 760.9
14 2.362e+04 2.305e+04 567.9
15 2.373e+04 2.3e+04 730.5
16 2.394e+04 2.282e+04 1122
17 2.457e+04 2.317e+04 1402
18 2.308e+04 2.305e+04 30.93
19 2.261e+04 2.288e+04-266.4
20 2.296e+04 2.294e+04 23.04
21 2.292e+04 2.296e+04-39.39
22 2.351e+04 2.287e+04 643.3
23 2.273e+04 2.293e+04-196.3
24 2.305e+04 2.3e+04 51.48
25 2.285e+04 2.282e+04 30.14
26 2.343e+04 2.304e+04 383.6
27 2.281e+04 2.293e+04-113.3
28 2.245e+04 2.282e+04-373.9
29 2.357e+04 2.325e+04 313.8
30 2.318e+04 2.294e+04 248
31 2.278e+04 2.298e+04-206.8
32 2.351e+04 2.309e+04 418.8
33 2.319e+04 2.278e+04 408.3
34 2.301e+04 2.278e+04 221.3
35 2.233e+04 2.283e+04-499.6
36 2.235e+04 2.283e+04-484.6
37 2.306e+04 2.321e+04-145.3
38 2.289e+04 2.307e+04-178.8
39 2.289e+04 2.293e+04-35.25
40 2.27e+04 2.287e+04-165.7
41 2.247e+04 2.284e+04-376.3
42 2.236e+04 2.276e+04-404.3
43 2.244e+04 2.282e+04-376.9
44 2.282e+04 2.294e+04-113
45 2.291e+04 2.307e+04-159.8
46 2.306e+04 2.282e+04 239.1
47 2.306e+04 2.284e+04 211.7
48 2.256e+04 2.295e+04-384.7
49 1.857e+04 2.293e+04-4355
50 2.033e+04 2.304e+04-2713
51 1.928e+04 2.283e+04-3553
52 1.954e+04 2.31e+04-3560
53 1.952e+04 2.3e+04-3479

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  2.387e+04 &  2.287e+04 &  1000 \tabularnewline
2 &  2.411e+04 &  2.284e+04 &  1264 \tabularnewline
3 &  2.404e+04 &  2.286e+04 &  1186 \tabularnewline
4 &  2.442e+04 &  2.293e+04 &  1490 \tabularnewline
5 &  2.45e+04 &  2.304e+04 &  1454 \tabularnewline
6 &  2.402e+04 &  2.29e+04 &  1120 \tabularnewline
7 &  2.437e+04 &  2.322e+04 &  1149 \tabularnewline
8 &  2.387e+04 &  2.289e+04 &  978.9 \tabularnewline
9 &  2.45e+04 &  2.291e+04 &  1581 \tabularnewline
10 &  2.382e+04 &  2.29e+04 &  916.2 \tabularnewline
11 &  2.408e+04 &  2.291e+04 &  1167 \tabularnewline
12 &  2.413e+04 &  2.307e+04 &  1066 \tabularnewline
13 &  2.365e+04 &  2.289e+04 &  760.9 \tabularnewline
14 &  2.362e+04 &  2.305e+04 &  567.9 \tabularnewline
15 &  2.373e+04 &  2.3e+04 &  730.5 \tabularnewline
16 &  2.394e+04 &  2.282e+04 &  1122 \tabularnewline
17 &  2.457e+04 &  2.317e+04 &  1402 \tabularnewline
18 &  2.308e+04 &  2.305e+04 &  30.93 \tabularnewline
19 &  2.261e+04 &  2.288e+04 & -266.4 \tabularnewline
20 &  2.296e+04 &  2.294e+04 &  23.04 \tabularnewline
21 &  2.292e+04 &  2.296e+04 & -39.39 \tabularnewline
22 &  2.351e+04 &  2.287e+04 &  643.3 \tabularnewline
23 &  2.273e+04 &  2.293e+04 & -196.3 \tabularnewline
24 &  2.305e+04 &  2.3e+04 &  51.48 \tabularnewline
25 &  2.285e+04 &  2.282e+04 &  30.14 \tabularnewline
26 &  2.343e+04 &  2.304e+04 &  383.6 \tabularnewline
27 &  2.281e+04 &  2.293e+04 & -113.3 \tabularnewline
28 &  2.245e+04 &  2.282e+04 & -373.9 \tabularnewline
29 &  2.357e+04 &  2.325e+04 &  313.8 \tabularnewline
30 &  2.318e+04 &  2.294e+04 &  248 \tabularnewline
31 &  2.278e+04 &  2.298e+04 & -206.8 \tabularnewline
32 &  2.351e+04 &  2.309e+04 &  418.8 \tabularnewline
33 &  2.319e+04 &  2.278e+04 &  408.3 \tabularnewline
34 &  2.301e+04 &  2.278e+04 &  221.3 \tabularnewline
35 &  2.233e+04 &  2.283e+04 & -499.6 \tabularnewline
36 &  2.235e+04 &  2.283e+04 & -484.6 \tabularnewline
37 &  2.306e+04 &  2.321e+04 & -145.3 \tabularnewline
38 &  2.289e+04 &  2.307e+04 & -178.8 \tabularnewline
39 &  2.289e+04 &  2.293e+04 & -35.25 \tabularnewline
40 &  2.27e+04 &  2.287e+04 & -165.7 \tabularnewline
41 &  2.247e+04 &  2.284e+04 & -376.3 \tabularnewline
42 &  2.236e+04 &  2.276e+04 & -404.3 \tabularnewline
43 &  2.244e+04 &  2.282e+04 & -376.9 \tabularnewline
44 &  2.282e+04 &  2.294e+04 & -113 \tabularnewline
45 &  2.291e+04 &  2.307e+04 & -159.8 \tabularnewline
46 &  2.306e+04 &  2.282e+04 &  239.1 \tabularnewline
47 &  2.306e+04 &  2.284e+04 &  211.7 \tabularnewline
48 &  2.256e+04 &  2.295e+04 & -384.7 \tabularnewline
49 &  1.857e+04 &  2.293e+04 & -4355 \tabularnewline
50 &  2.033e+04 &  2.304e+04 & -2713 \tabularnewline
51 &  1.928e+04 &  2.283e+04 & -3553 \tabularnewline
52 &  1.954e+04 &  2.31e+04 & -3560 \tabularnewline
53 &  1.952e+04 &  2.3e+04 & -3479 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284686&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 2.387e+04[/C][C] 2.287e+04[/C][C] 1000[/C][/ROW]
[ROW][C]2[/C][C] 2.411e+04[/C][C] 2.284e+04[/C][C] 1264[/C][/ROW]
[ROW][C]3[/C][C] 2.404e+04[/C][C] 2.286e+04[/C][C] 1186[/C][/ROW]
[ROW][C]4[/C][C] 2.442e+04[/C][C] 2.293e+04[/C][C] 1490[/C][/ROW]
[ROW][C]5[/C][C] 2.45e+04[/C][C] 2.304e+04[/C][C] 1454[/C][/ROW]
[ROW][C]6[/C][C] 2.402e+04[/C][C] 2.29e+04[/C][C] 1120[/C][/ROW]
[ROW][C]7[/C][C] 2.437e+04[/C][C] 2.322e+04[/C][C] 1149[/C][/ROW]
[ROW][C]8[/C][C] 2.387e+04[/C][C] 2.289e+04[/C][C] 978.9[/C][/ROW]
[ROW][C]9[/C][C] 2.45e+04[/C][C] 2.291e+04[/C][C] 1581[/C][/ROW]
[ROW][C]10[/C][C] 2.382e+04[/C][C] 2.29e+04[/C][C] 916.2[/C][/ROW]
[ROW][C]11[/C][C] 2.408e+04[/C][C] 2.291e+04[/C][C] 1167[/C][/ROW]
[ROW][C]12[/C][C] 2.413e+04[/C][C] 2.307e+04[/C][C] 1066[/C][/ROW]
[ROW][C]13[/C][C] 2.365e+04[/C][C] 2.289e+04[/C][C] 760.9[/C][/ROW]
[ROW][C]14[/C][C] 2.362e+04[/C][C] 2.305e+04[/C][C] 567.9[/C][/ROW]
[ROW][C]15[/C][C] 2.373e+04[/C][C] 2.3e+04[/C][C] 730.5[/C][/ROW]
[ROW][C]16[/C][C] 2.394e+04[/C][C] 2.282e+04[/C][C] 1122[/C][/ROW]
[ROW][C]17[/C][C] 2.457e+04[/C][C] 2.317e+04[/C][C] 1402[/C][/ROW]
[ROW][C]18[/C][C] 2.308e+04[/C][C] 2.305e+04[/C][C] 30.93[/C][/ROW]
[ROW][C]19[/C][C] 2.261e+04[/C][C] 2.288e+04[/C][C]-266.4[/C][/ROW]
[ROW][C]20[/C][C] 2.296e+04[/C][C] 2.294e+04[/C][C] 23.04[/C][/ROW]
[ROW][C]21[/C][C] 2.292e+04[/C][C] 2.296e+04[/C][C]-39.39[/C][/ROW]
[ROW][C]22[/C][C] 2.351e+04[/C][C] 2.287e+04[/C][C] 643.3[/C][/ROW]
[ROW][C]23[/C][C] 2.273e+04[/C][C] 2.293e+04[/C][C]-196.3[/C][/ROW]
[ROW][C]24[/C][C] 2.305e+04[/C][C] 2.3e+04[/C][C] 51.48[/C][/ROW]
[ROW][C]25[/C][C] 2.285e+04[/C][C] 2.282e+04[/C][C] 30.14[/C][/ROW]
[ROW][C]26[/C][C] 2.343e+04[/C][C] 2.304e+04[/C][C] 383.6[/C][/ROW]
[ROW][C]27[/C][C] 2.281e+04[/C][C] 2.293e+04[/C][C]-113.3[/C][/ROW]
[ROW][C]28[/C][C] 2.245e+04[/C][C] 2.282e+04[/C][C]-373.9[/C][/ROW]
[ROW][C]29[/C][C] 2.357e+04[/C][C] 2.325e+04[/C][C] 313.8[/C][/ROW]
[ROW][C]30[/C][C] 2.318e+04[/C][C] 2.294e+04[/C][C] 248[/C][/ROW]
[ROW][C]31[/C][C] 2.278e+04[/C][C] 2.298e+04[/C][C]-206.8[/C][/ROW]
[ROW][C]32[/C][C] 2.351e+04[/C][C] 2.309e+04[/C][C] 418.8[/C][/ROW]
[ROW][C]33[/C][C] 2.319e+04[/C][C] 2.278e+04[/C][C] 408.3[/C][/ROW]
[ROW][C]34[/C][C] 2.301e+04[/C][C] 2.278e+04[/C][C] 221.3[/C][/ROW]
[ROW][C]35[/C][C] 2.233e+04[/C][C] 2.283e+04[/C][C]-499.6[/C][/ROW]
[ROW][C]36[/C][C] 2.235e+04[/C][C] 2.283e+04[/C][C]-484.6[/C][/ROW]
[ROW][C]37[/C][C] 2.306e+04[/C][C] 2.321e+04[/C][C]-145.3[/C][/ROW]
[ROW][C]38[/C][C] 2.289e+04[/C][C] 2.307e+04[/C][C]-178.8[/C][/ROW]
[ROW][C]39[/C][C] 2.289e+04[/C][C] 2.293e+04[/C][C]-35.25[/C][/ROW]
[ROW][C]40[/C][C] 2.27e+04[/C][C] 2.287e+04[/C][C]-165.7[/C][/ROW]
[ROW][C]41[/C][C] 2.247e+04[/C][C] 2.284e+04[/C][C]-376.3[/C][/ROW]
[ROW][C]42[/C][C] 2.236e+04[/C][C] 2.276e+04[/C][C]-404.3[/C][/ROW]
[ROW][C]43[/C][C] 2.244e+04[/C][C] 2.282e+04[/C][C]-376.9[/C][/ROW]
[ROW][C]44[/C][C] 2.282e+04[/C][C] 2.294e+04[/C][C]-113[/C][/ROW]
[ROW][C]45[/C][C] 2.291e+04[/C][C] 2.307e+04[/C][C]-159.8[/C][/ROW]
[ROW][C]46[/C][C] 2.306e+04[/C][C] 2.282e+04[/C][C] 239.1[/C][/ROW]
[ROW][C]47[/C][C] 2.306e+04[/C][C] 2.284e+04[/C][C] 211.7[/C][/ROW]
[ROW][C]48[/C][C] 2.256e+04[/C][C] 2.295e+04[/C][C]-384.7[/C][/ROW]
[ROW][C]49[/C][C] 1.857e+04[/C][C] 2.293e+04[/C][C]-4355[/C][/ROW]
[ROW][C]50[/C][C] 2.033e+04[/C][C] 2.304e+04[/C][C]-2713[/C][/ROW]
[ROW][C]51[/C][C] 1.928e+04[/C][C] 2.283e+04[/C][C]-3553[/C][/ROW]
[ROW][C]52[/C][C] 1.954e+04[/C][C] 2.31e+04[/C][C]-3560[/C][/ROW]
[ROW][C]53[/C][C] 1.952e+04[/C][C] 2.3e+04[/C][C]-3479[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284686&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284686&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 2.387e+04 2.287e+04 1000
2 2.411e+04 2.284e+04 1264
3 2.404e+04 2.286e+04 1186
4 2.442e+04 2.293e+04 1490
5 2.45e+04 2.304e+04 1454
6 2.402e+04 2.29e+04 1120
7 2.437e+04 2.322e+04 1149
8 2.387e+04 2.289e+04 978.9
9 2.45e+04 2.291e+04 1581
10 2.382e+04 2.29e+04 916.2
11 2.408e+04 2.291e+04 1167
12 2.413e+04 2.307e+04 1066
13 2.365e+04 2.289e+04 760.9
14 2.362e+04 2.305e+04 567.9
15 2.373e+04 2.3e+04 730.5
16 2.394e+04 2.282e+04 1122
17 2.457e+04 2.317e+04 1402
18 2.308e+04 2.305e+04 30.93
19 2.261e+04 2.288e+04-266.4
20 2.296e+04 2.294e+04 23.04
21 2.292e+04 2.296e+04-39.39
22 2.351e+04 2.287e+04 643.3
23 2.273e+04 2.293e+04-196.3
24 2.305e+04 2.3e+04 51.48
25 2.285e+04 2.282e+04 30.14
26 2.343e+04 2.304e+04 383.6
27 2.281e+04 2.293e+04-113.3
28 2.245e+04 2.282e+04-373.9
29 2.357e+04 2.325e+04 313.8
30 2.318e+04 2.294e+04 248
31 2.278e+04 2.298e+04-206.8
32 2.351e+04 2.309e+04 418.8
33 2.319e+04 2.278e+04 408.3
34 2.301e+04 2.278e+04 221.3
35 2.233e+04 2.283e+04-499.6
36 2.235e+04 2.283e+04-484.6
37 2.306e+04 2.321e+04-145.3
38 2.289e+04 2.307e+04-178.8
39 2.289e+04 2.293e+04-35.25
40 2.27e+04 2.287e+04-165.7
41 2.247e+04 2.284e+04-376.3
42 2.236e+04 2.276e+04-404.3
43 2.244e+04 2.282e+04-376.9
44 2.282e+04 2.294e+04-113
45 2.291e+04 2.307e+04-159.8
46 2.306e+04 2.282e+04 239.1
47 2.306e+04 2.284e+04 211.7
48 2.256e+04 2.295e+04-384.7
49 1.857e+04 2.293e+04-4355
50 2.033e+04 2.304e+04-2713
51 1.928e+04 2.283e+04-3553
52 1.954e+04 2.31e+04-3560
53 1.952e+04 2.3e+04-3479







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
5 0.00306 0.006119 0.9969
6 0.0004884 0.0009767 0.9995
7 0.0002167 0.0004333 0.9998
8 5.996e-05 0.0001199 0.9999
9 4.08e-05 8.16e-05 1
10 1.649e-05 3.298e-05 1
11 3.172e-06 6.344e-06 1
12 7.551e-07 1.51e-06 1
13 6.16e-07 1.232e-06 1
14 1.224e-06 2.447e-06 1
15 6.151e-07 1.23e-06 1
16 1.656e-07 3.312e-07 1
17 9.664e-08 1.933e-07 1
18 2.213e-06 4.427e-06 1
19 3.363e-05 6.725e-05 1
20 5.175e-05 0.0001035 0.9999
21 7.141e-05 0.0001428 0.9999
22 3.535e-05 7.07e-05 1
23 5.414e-05 0.0001083 0.9999
24 4.598e-05 9.197e-05 1
25 3.26e-05 6.521e-05 1
26 1.964e-05 3.927e-05 1
27 1.715e-05 3.429e-05 1
28 1.742e-05 3.485e-05 1
29 1.535e-05 3.071e-05 1
30 8.977e-06 1.795e-05 1
31 7.947e-06 1.589e-05 1
32 7.027e-06 1.405e-05 1
33 3.392e-06 6.785e-06 1
34 1.665e-06 3.331e-06 1
35 1.628e-06 3.257e-06 1
36 1.331e-06 2.661e-06 1
37 2.892e-06 5.784e-06 1
38 5.336e-06 1.067e-05 1
39 4.548e-06 9.097e-06 1
40 2.909e-06 5.818e-06 1
41 1.822e-06 3.645e-06 1
42 8.693e-07 1.739e-06 1
43 4.476e-07 8.951e-07 1
44 5.457e-07 1.091e-06 1
45 9.052e-06 1.81e-05 1
46 1.346e-05 2.693e-05 1
47 0.0004692 0.0009385 0.9995
48 0.4162 0.8325 0.5838

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 &  0.00306 &  0.006119 &  0.9969 \tabularnewline
6 &  0.0004884 &  0.0009767 &  0.9995 \tabularnewline
7 &  0.0002167 &  0.0004333 &  0.9998 \tabularnewline
8 &  5.996e-05 &  0.0001199 &  0.9999 \tabularnewline
9 &  4.08e-05 &  8.16e-05 &  1 \tabularnewline
10 &  1.649e-05 &  3.298e-05 &  1 \tabularnewline
11 &  3.172e-06 &  6.344e-06 &  1 \tabularnewline
12 &  7.551e-07 &  1.51e-06 &  1 \tabularnewline
13 &  6.16e-07 &  1.232e-06 &  1 \tabularnewline
14 &  1.224e-06 &  2.447e-06 &  1 \tabularnewline
15 &  6.151e-07 &  1.23e-06 &  1 \tabularnewline
16 &  1.656e-07 &  3.312e-07 &  1 \tabularnewline
17 &  9.664e-08 &  1.933e-07 &  1 \tabularnewline
18 &  2.213e-06 &  4.427e-06 &  1 \tabularnewline
19 &  3.363e-05 &  6.725e-05 &  1 \tabularnewline
20 &  5.175e-05 &  0.0001035 &  0.9999 \tabularnewline
21 &  7.141e-05 &  0.0001428 &  0.9999 \tabularnewline
22 &  3.535e-05 &  7.07e-05 &  1 \tabularnewline
23 &  5.414e-05 &  0.0001083 &  0.9999 \tabularnewline
24 &  4.598e-05 &  9.197e-05 &  1 \tabularnewline
25 &  3.26e-05 &  6.521e-05 &  1 \tabularnewline
26 &  1.964e-05 &  3.927e-05 &  1 \tabularnewline
27 &  1.715e-05 &  3.429e-05 &  1 \tabularnewline
28 &  1.742e-05 &  3.485e-05 &  1 \tabularnewline
29 &  1.535e-05 &  3.071e-05 &  1 \tabularnewline
30 &  8.977e-06 &  1.795e-05 &  1 \tabularnewline
31 &  7.947e-06 &  1.589e-05 &  1 \tabularnewline
32 &  7.027e-06 &  1.405e-05 &  1 \tabularnewline
33 &  3.392e-06 &  6.785e-06 &  1 \tabularnewline
34 &  1.665e-06 &  3.331e-06 &  1 \tabularnewline
35 &  1.628e-06 &  3.257e-06 &  1 \tabularnewline
36 &  1.331e-06 &  2.661e-06 &  1 \tabularnewline
37 &  2.892e-06 &  5.784e-06 &  1 \tabularnewline
38 &  5.336e-06 &  1.067e-05 &  1 \tabularnewline
39 &  4.548e-06 &  9.097e-06 &  1 \tabularnewline
40 &  2.909e-06 &  5.818e-06 &  1 \tabularnewline
41 &  1.822e-06 &  3.645e-06 &  1 \tabularnewline
42 &  8.693e-07 &  1.739e-06 &  1 \tabularnewline
43 &  4.476e-07 &  8.951e-07 &  1 \tabularnewline
44 &  5.457e-07 &  1.091e-06 &  1 \tabularnewline
45 &  9.052e-06 &  1.81e-05 &  1 \tabularnewline
46 &  1.346e-05 &  2.693e-05 &  1 \tabularnewline
47 &  0.0004692 &  0.0009385 &  0.9995 \tabularnewline
48 &  0.4162 &  0.8325 &  0.5838 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284686&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]5[/C][C] 0.00306[/C][C] 0.006119[/C][C] 0.9969[/C][/ROW]
[ROW][C]6[/C][C] 0.0004884[/C][C] 0.0009767[/C][C] 0.9995[/C][/ROW]
[ROW][C]7[/C][C] 0.0002167[/C][C] 0.0004333[/C][C] 0.9998[/C][/ROW]
[ROW][C]8[/C][C] 5.996e-05[/C][C] 0.0001199[/C][C] 0.9999[/C][/ROW]
[ROW][C]9[/C][C] 4.08e-05[/C][C] 8.16e-05[/C][C] 1[/C][/ROW]
[ROW][C]10[/C][C] 1.649e-05[/C][C] 3.298e-05[/C][C] 1[/C][/ROW]
[ROW][C]11[/C][C] 3.172e-06[/C][C] 6.344e-06[/C][C] 1[/C][/ROW]
[ROW][C]12[/C][C] 7.551e-07[/C][C] 1.51e-06[/C][C] 1[/C][/ROW]
[ROW][C]13[/C][C] 6.16e-07[/C][C] 1.232e-06[/C][C] 1[/C][/ROW]
[ROW][C]14[/C][C] 1.224e-06[/C][C] 2.447e-06[/C][C] 1[/C][/ROW]
[ROW][C]15[/C][C] 6.151e-07[/C][C] 1.23e-06[/C][C] 1[/C][/ROW]
[ROW][C]16[/C][C] 1.656e-07[/C][C] 3.312e-07[/C][C] 1[/C][/ROW]
[ROW][C]17[/C][C] 9.664e-08[/C][C] 1.933e-07[/C][C] 1[/C][/ROW]
[ROW][C]18[/C][C] 2.213e-06[/C][C] 4.427e-06[/C][C] 1[/C][/ROW]
[ROW][C]19[/C][C] 3.363e-05[/C][C] 6.725e-05[/C][C] 1[/C][/ROW]
[ROW][C]20[/C][C] 5.175e-05[/C][C] 0.0001035[/C][C] 0.9999[/C][/ROW]
[ROW][C]21[/C][C] 7.141e-05[/C][C] 0.0001428[/C][C] 0.9999[/C][/ROW]
[ROW][C]22[/C][C] 3.535e-05[/C][C] 7.07e-05[/C][C] 1[/C][/ROW]
[ROW][C]23[/C][C] 5.414e-05[/C][C] 0.0001083[/C][C] 0.9999[/C][/ROW]
[ROW][C]24[/C][C] 4.598e-05[/C][C] 9.197e-05[/C][C] 1[/C][/ROW]
[ROW][C]25[/C][C] 3.26e-05[/C][C] 6.521e-05[/C][C] 1[/C][/ROW]
[ROW][C]26[/C][C] 1.964e-05[/C][C] 3.927e-05[/C][C] 1[/C][/ROW]
[ROW][C]27[/C][C] 1.715e-05[/C][C] 3.429e-05[/C][C] 1[/C][/ROW]
[ROW][C]28[/C][C] 1.742e-05[/C][C] 3.485e-05[/C][C] 1[/C][/ROW]
[ROW][C]29[/C][C] 1.535e-05[/C][C] 3.071e-05[/C][C] 1[/C][/ROW]
[ROW][C]30[/C][C] 8.977e-06[/C][C] 1.795e-05[/C][C] 1[/C][/ROW]
[ROW][C]31[/C][C] 7.947e-06[/C][C] 1.589e-05[/C][C] 1[/C][/ROW]
[ROW][C]32[/C][C] 7.027e-06[/C][C] 1.405e-05[/C][C] 1[/C][/ROW]
[ROW][C]33[/C][C] 3.392e-06[/C][C] 6.785e-06[/C][C] 1[/C][/ROW]
[ROW][C]34[/C][C] 1.665e-06[/C][C] 3.331e-06[/C][C] 1[/C][/ROW]
[ROW][C]35[/C][C] 1.628e-06[/C][C] 3.257e-06[/C][C] 1[/C][/ROW]
[ROW][C]36[/C][C] 1.331e-06[/C][C] 2.661e-06[/C][C] 1[/C][/ROW]
[ROW][C]37[/C][C] 2.892e-06[/C][C] 5.784e-06[/C][C] 1[/C][/ROW]
[ROW][C]38[/C][C] 5.336e-06[/C][C] 1.067e-05[/C][C] 1[/C][/ROW]
[ROW][C]39[/C][C] 4.548e-06[/C][C] 9.097e-06[/C][C] 1[/C][/ROW]
[ROW][C]40[/C][C] 2.909e-06[/C][C] 5.818e-06[/C][C] 1[/C][/ROW]
[ROW][C]41[/C][C] 1.822e-06[/C][C] 3.645e-06[/C][C] 1[/C][/ROW]
[ROW][C]42[/C][C] 8.693e-07[/C][C] 1.739e-06[/C][C] 1[/C][/ROW]
[ROW][C]43[/C][C] 4.476e-07[/C][C] 8.951e-07[/C][C] 1[/C][/ROW]
[ROW][C]44[/C][C] 5.457e-07[/C][C] 1.091e-06[/C][C] 1[/C][/ROW]
[ROW][C]45[/C][C] 9.052e-06[/C][C] 1.81e-05[/C][C] 1[/C][/ROW]
[ROW][C]46[/C][C] 1.346e-05[/C][C] 2.693e-05[/C][C] 1[/C][/ROW]
[ROW][C]47[/C][C] 0.0004692[/C][C] 0.0009385[/C][C] 0.9995[/C][/ROW]
[ROW][C]48[/C][C] 0.4162[/C][C] 0.8325[/C][C] 0.5838[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284686&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284686&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
5 0.00306 0.006119 0.9969
6 0.0004884 0.0009767 0.9995
7 0.0002167 0.0004333 0.9998
8 5.996e-05 0.0001199 0.9999
9 4.08e-05 8.16e-05 1
10 1.649e-05 3.298e-05 1
11 3.172e-06 6.344e-06 1
12 7.551e-07 1.51e-06 1
13 6.16e-07 1.232e-06 1
14 1.224e-06 2.447e-06 1
15 6.151e-07 1.23e-06 1
16 1.656e-07 3.312e-07 1
17 9.664e-08 1.933e-07 1
18 2.213e-06 4.427e-06 1
19 3.363e-05 6.725e-05 1
20 5.175e-05 0.0001035 0.9999
21 7.141e-05 0.0001428 0.9999
22 3.535e-05 7.07e-05 1
23 5.414e-05 0.0001083 0.9999
24 4.598e-05 9.197e-05 1
25 3.26e-05 6.521e-05 1
26 1.964e-05 3.927e-05 1
27 1.715e-05 3.429e-05 1
28 1.742e-05 3.485e-05 1
29 1.535e-05 3.071e-05 1
30 8.977e-06 1.795e-05 1
31 7.947e-06 1.589e-05 1
32 7.027e-06 1.405e-05 1
33 3.392e-06 6.785e-06 1
34 1.665e-06 3.331e-06 1
35 1.628e-06 3.257e-06 1
36 1.331e-06 2.661e-06 1
37 2.892e-06 5.784e-06 1
38 5.336e-06 1.067e-05 1
39 4.548e-06 9.097e-06 1
40 2.909e-06 5.818e-06 1
41 1.822e-06 3.645e-06 1
42 8.693e-07 1.739e-06 1
43 4.476e-07 8.951e-07 1
44 5.457e-07 1.091e-06 1
45 9.052e-06 1.81e-05 1
46 1.346e-05 2.693e-05 1
47 0.0004692 0.0009385 0.9995
48 0.4162 0.8325 0.5838







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level43 0.9773NOK
5% type I error level430.977273NOK
10% type I error level430.977273NOK

\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 & 43 &  0.9773 & NOK \tabularnewline
5% type I error level & 43 & 0.977273 & NOK \tabularnewline
10% type I error level & 43 & 0.977273 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284686&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]43[/C][C] 0.9773[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]43[/C][C]0.977273[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]43[/C][C]0.977273[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284686&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284686&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level43 0.9773NOK
5% type I error level430.977273NOK
10% type I error level430.977273NOK



Parameters (Session):
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
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 (par5=='') par5 <- 0
par5 <- as.numeric(par5)
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'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if(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 (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1+par4,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1+par4,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,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$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$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')
}
}