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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 04 Dec 2015 09:57:11 +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/04/t1449223635sphs2ss2wxuoaph.htm/, Retrieved Thu, 31 Oct 2024 23:20:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=285049, Retrieved Thu, 31 Oct 2024 23:20:11 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact150
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [multiple regressi...] [2015-12-04 09:57:11] [201263e50fe1652009691bd5c495bb3d] [Current]
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Dataseries X:
21.6 6.5
21.6 6.2
21.6 6.3
19.4 6.4
19.4 6.3
19.4 6.1
15.9 5.7
15.9 5.6
15.9 5.6
21.8 6.2
21.8 6.3
21.8 6.2
17.6 6
17.6 5.9
17.6 6
19 6.1
19 6.1
19 6
16.3 6
16.3 6
16.3 5.9
22.5 6.1
22.5 6.3
22.5 6.5
23.8 7.1
23.8 7.5
23.8 7.6
24.6 7.6
24.6 7.4
24.6 7.1
22.7 6.9
22.7 6.8
22.7 6.8
25.2 7.3
25.2 7.3
25.2 7.3
26.4 7.2
26.4 7.2
26.4 7.4
26 7.7
26 7.8
26 7.9
23.2 7.9
23.2 7.8
23.2 7.7
22.7 7.9
22.7 7.8
22.7 7.6
24 7.5
24 7.4
24 7.7
20.7 8.2
20.7 8.4
20.7 8.4
23.8 8.2
23.8 8
23.8 8
27.1 8.2
27.1 8.2
27.1 8




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285049&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285049&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285049&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'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
M-25[t] = + 1.09921 + 1.27678M_25[t] + 0.758206`M-25(t-1)`[t] -0.00741498`M-25(t-2)`[t] -0.20449`M-25(t-3)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
M-25[t] =  +  1.09921 +  1.27678M_25[t] +  0.758206`M-25(t-1)`[t] -0.00741498`M-25(t-2)`[t] -0.20449`M-25(t-3)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285049&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]M-25[t] =  +  1.09921 +  1.27678M_25[t] +  0.758206`M-25(t-1)`[t] -0.00741498`M-25(t-2)`[t] -0.20449`M-25(t-3)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285049&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285049&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
M-25[t] = + 1.09921 + 1.27678M_25[t] + 0.758206`M-25(t-1)`[t] -0.00741498`M-25(t-2)`[t] -0.20449`M-25(t-3)`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+1.099 1.851+5.9400e-01 0.5551 0.2776
M_25+1.277 0.3982+3.2060e+00 0.002303 0.001151
`M-25(t-1)`+0.7582 0.137+5.5330e+00 1.042e-06 5.21e-07
`M-25(t-2)`-0.007415 0.1726-4.2950e-02 0.9659 0.483
`M-25(t-3)`-0.2045 0.1345-1.5200e+00 0.1345 0.06725

\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) & +1.099 &  1.851 & +5.9400e-01 &  0.5551 &  0.2776 \tabularnewline
M_25 & +1.277 &  0.3982 & +3.2060e+00 &  0.002303 &  0.001151 \tabularnewline
`M-25(t-1)` & +0.7582 &  0.137 & +5.5330e+00 &  1.042e-06 &  5.21e-07 \tabularnewline
`M-25(t-2)` & -0.007415 &  0.1726 & -4.2950e-02 &  0.9659 &  0.483 \tabularnewline
`M-25(t-3)` & -0.2045 &  0.1345 & -1.5200e+00 &  0.1345 &  0.06725 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285049&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]+1.099[/C][C] 1.851[/C][C]+5.9400e-01[/C][C] 0.5551[/C][C] 0.2776[/C][/ROW]
[ROW][C]M_25[/C][C]+1.277[/C][C] 0.3982[/C][C]+3.2060e+00[/C][C] 0.002303[/C][C] 0.001151[/C][/ROW]
[ROW][C]`M-25(t-1)`[/C][C]+0.7582[/C][C] 0.137[/C][C]+5.5330e+00[/C][C] 1.042e-06[/C][C] 5.21e-07[/C][/ROW]
[ROW][C]`M-25(t-2)`[/C][C]-0.007415[/C][C] 0.1726[/C][C]-4.2950e-02[/C][C] 0.9659[/C][C] 0.483[/C][/ROW]
[ROW][C]`M-25(t-3)`[/C][C]-0.2045[/C][C] 0.1345[/C][C]-1.5200e+00[/C][C] 0.1345[/C][C] 0.06725[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285049&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285049&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)+1.099 1.851+5.9400e-01 0.5551 0.2776
M_25+1.277 0.3982+3.2060e+00 0.002303 0.001151
`M-25(t-1)`+0.7582 0.137+5.5330e+00 1.042e-06 5.21e-07
`M-25(t-2)`-0.007415 0.1726-4.2950e-02 0.9659 0.483
`M-25(t-3)`-0.2045 0.1345-1.5200e+00 0.1345 0.06725







Multiple Linear Regression - Regression Statistics
Multiple R 0.8798
R-squared 0.7741
Adjusted R-squared 0.7568
F-TEST (value) 44.55
F-TEST (DF numerator)4
F-TEST (DF denominator)52
p-value 3.331e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.602
Sum Squared Residuals 133.4

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.8798 \tabularnewline
R-squared &  0.7741 \tabularnewline
Adjusted R-squared &  0.7568 \tabularnewline
F-TEST (value) &  44.55 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 52 \tabularnewline
p-value &  3.331e-16 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.602 \tabularnewline
Sum Squared Residuals &  133.4 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285049&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.8798[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.7741[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.7568[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 44.55[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]52[/C][/ROW]
[ROW][C]p-value[/C][C] 3.331e-16[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.602[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 133.4[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285049&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285049&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 0.8798
R-squared 0.7741
Adjusted R-squared 0.7568
F-TEST (value) 44.55
F-TEST (DF numerator)4
F-TEST (DF denominator)52
p-value 3.331e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.602
Sum Squared Residuals 133.4







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 19.4 21.07-1.671
2 19.4 19.27 0.125
3 19.4 19.04 0.364
4 15.9 18.98-3.075
5 15.9 16.19-0.2937
6 15.9 16.22-0.3197
7 21.8 17.7 4.099
8 21.8 22.3-0.5026
9 21.8 22.13-0.3311
10 17.6 20.67-3.069
11 17.6 17.36 0.2429
12 17.6 17.52 0.08404
13 19 18.5 0.4975
14 19 19.56-0.564
15 19 19.43-0.4259
16 16.3 19.14-2.84
17 16.3 17.09-0.7925
18 16.3 16.98-0.6848
19 22.5 17.79 4.708
20 22.5 22.75-0.2485
21 22.5 22.96-0.4579
22 23.8 22.46 1.344
23 23.8 23.95-0.1525
24 23.8 24.07-0.2706
25 24.6 23.8 0.7953
26 24.6 24.16 0.4441
27 24.6 23.77 0.833
28 22.7 23.35-0.648
29 22.7 21.78 0.9202
30 22.7 21.79 0.9062
31 25.2 22.82 2.379
32 25.2 24.72 0.4837
33 25.2 24.7 0.5022
34 26.4 24.06 2.341
35 26.4 24.97 1.431
36 26.4 25.22 1.185
37 26 25.35 0.6472
38 26 25.18 0.8228
39 26 25.31 0.6922
40 23.2 25.39-2.19
41 23.2 23.14 0.06102
42 23.2 23.03 0.1679
43 22.7 23.86-1.16
44 22.7 23.35-0.6532
45 22.7 23.1-0.4016
46 24 23.08 0.9239
47 24 23.93 0.06588
48 24 24.31-0.3075
49 20.7 24.68-3.98
50 20.7 22.43-1.733
51 20.7 22.46-1.758
52 23.8 22.88 0.9227
53 23.8 24.97-1.172
54 23.8 24.95-1.149
55 27.1 24.57 2.529
56 27.1 27.07 0.02711
57 27.1 26.79 0.3069

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  19.4 &  21.07 & -1.671 \tabularnewline
2 &  19.4 &  19.27 &  0.125 \tabularnewline
3 &  19.4 &  19.04 &  0.364 \tabularnewline
4 &  15.9 &  18.98 & -3.075 \tabularnewline
5 &  15.9 &  16.19 & -0.2937 \tabularnewline
6 &  15.9 &  16.22 & -0.3197 \tabularnewline
7 &  21.8 &  17.7 &  4.099 \tabularnewline
8 &  21.8 &  22.3 & -0.5026 \tabularnewline
9 &  21.8 &  22.13 & -0.3311 \tabularnewline
10 &  17.6 &  20.67 & -3.069 \tabularnewline
11 &  17.6 &  17.36 &  0.2429 \tabularnewline
12 &  17.6 &  17.52 &  0.08404 \tabularnewline
13 &  19 &  18.5 &  0.4975 \tabularnewline
14 &  19 &  19.56 & -0.564 \tabularnewline
15 &  19 &  19.43 & -0.4259 \tabularnewline
16 &  16.3 &  19.14 & -2.84 \tabularnewline
17 &  16.3 &  17.09 & -0.7925 \tabularnewline
18 &  16.3 &  16.98 & -0.6848 \tabularnewline
19 &  22.5 &  17.79 &  4.708 \tabularnewline
20 &  22.5 &  22.75 & -0.2485 \tabularnewline
21 &  22.5 &  22.96 & -0.4579 \tabularnewline
22 &  23.8 &  22.46 &  1.344 \tabularnewline
23 &  23.8 &  23.95 & -0.1525 \tabularnewline
24 &  23.8 &  24.07 & -0.2706 \tabularnewline
25 &  24.6 &  23.8 &  0.7953 \tabularnewline
26 &  24.6 &  24.16 &  0.4441 \tabularnewline
27 &  24.6 &  23.77 &  0.833 \tabularnewline
28 &  22.7 &  23.35 & -0.648 \tabularnewline
29 &  22.7 &  21.78 &  0.9202 \tabularnewline
30 &  22.7 &  21.79 &  0.9062 \tabularnewline
31 &  25.2 &  22.82 &  2.379 \tabularnewline
32 &  25.2 &  24.72 &  0.4837 \tabularnewline
33 &  25.2 &  24.7 &  0.5022 \tabularnewline
34 &  26.4 &  24.06 &  2.341 \tabularnewline
35 &  26.4 &  24.97 &  1.431 \tabularnewline
36 &  26.4 &  25.22 &  1.185 \tabularnewline
37 &  26 &  25.35 &  0.6472 \tabularnewline
38 &  26 &  25.18 &  0.8228 \tabularnewline
39 &  26 &  25.31 &  0.6922 \tabularnewline
40 &  23.2 &  25.39 & -2.19 \tabularnewline
41 &  23.2 &  23.14 &  0.06102 \tabularnewline
42 &  23.2 &  23.03 &  0.1679 \tabularnewline
43 &  22.7 &  23.86 & -1.16 \tabularnewline
44 &  22.7 &  23.35 & -0.6532 \tabularnewline
45 &  22.7 &  23.1 & -0.4016 \tabularnewline
46 &  24 &  23.08 &  0.9239 \tabularnewline
47 &  24 &  23.93 &  0.06588 \tabularnewline
48 &  24 &  24.31 & -0.3075 \tabularnewline
49 &  20.7 &  24.68 & -3.98 \tabularnewline
50 &  20.7 &  22.43 & -1.733 \tabularnewline
51 &  20.7 &  22.46 & -1.758 \tabularnewline
52 &  23.8 &  22.88 &  0.9227 \tabularnewline
53 &  23.8 &  24.97 & -1.172 \tabularnewline
54 &  23.8 &  24.95 & -1.149 \tabularnewline
55 &  27.1 &  24.57 &  2.529 \tabularnewline
56 &  27.1 &  27.07 &  0.02711 \tabularnewline
57 &  27.1 &  26.79 &  0.3069 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285049&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] 19.4[/C][C] 21.07[/C][C]-1.671[/C][/ROW]
[ROW][C]2[/C][C] 19.4[/C][C] 19.27[/C][C] 0.125[/C][/ROW]
[ROW][C]3[/C][C] 19.4[/C][C] 19.04[/C][C] 0.364[/C][/ROW]
[ROW][C]4[/C][C] 15.9[/C][C] 18.98[/C][C]-3.075[/C][/ROW]
[ROW][C]5[/C][C] 15.9[/C][C] 16.19[/C][C]-0.2937[/C][/ROW]
[ROW][C]6[/C][C] 15.9[/C][C] 16.22[/C][C]-0.3197[/C][/ROW]
[ROW][C]7[/C][C] 21.8[/C][C] 17.7[/C][C] 4.099[/C][/ROW]
[ROW][C]8[/C][C] 21.8[/C][C] 22.3[/C][C]-0.5026[/C][/ROW]
[ROW][C]9[/C][C] 21.8[/C][C] 22.13[/C][C]-0.3311[/C][/ROW]
[ROW][C]10[/C][C] 17.6[/C][C] 20.67[/C][C]-3.069[/C][/ROW]
[ROW][C]11[/C][C] 17.6[/C][C] 17.36[/C][C] 0.2429[/C][/ROW]
[ROW][C]12[/C][C] 17.6[/C][C] 17.52[/C][C] 0.08404[/C][/ROW]
[ROW][C]13[/C][C] 19[/C][C] 18.5[/C][C] 0.4975[/C][/ROW]
[ROW][C]14[/C][C] 19[/C][C] 19.56[/C][C]-0.564[/C][/ROW]
[ROW][C]15[/C][C] 19[/C][C] 19.43[/C][C]-0.4259[/C][/ROW]
[ROW][C]16[/C][C] 16.3[/C][C] 19.14[/C][C]-2.84[/C][/ROW]
[ROW][C]17[/C][C] 16.3[/C][C] 17.09[/C][C]-0.7925[/C][/ROW]
[ROW][C]18[/C][C] 16.3[/C][C] 16.98[/C][C]-0.6848[/C][/ROW]
[ROW][C]19[/C][C] 22.5[/C][C] 17.79[/C][C] 4.708[/C][/ROW]
[ROW][C]20[/C][C] 22.5[/C][C] 22.75[/C][C]-0.2485[/C][/ROW]
[ROW][C]21[/C][C] 22.5[/C][C] 22.96[/C][C]-0.4579[/C][/ROW]
[ROW][C]22[/C][C] 23.8[/C][C] 22.46[/C][C] 1.344[/C][/ROW]
[ROW][C]23[/C][C] 23.8[/C][C] 23.95[/C][C]-0.1525[/C][/ROW]
[ROW][C]24[/C][C] 23.8[/C][C] 24.07[/C][C]-0.2706[/C][/ROW]
[ROW][C]25[/C][C] 24.6[/C][C] 23.8[/C][C] 0.7953[/C][/ROW]
[ROW][C]26[/C][C] 24.6[/C][C] 24.16[/C][C] 0.4441[/C][/ROW]
[ROW][C]27[/C][C] 24.6[/C][C] 23.77[/C][C] 0.833[/C][/ROW]
[ROW][C]28[/C][C] 22.7[/C][C] 23.35[/C][C]-0.648[/C][/ROW]
[ROW][C]29[/C][C] 22.7[/C][C] 21.78[/C][C] 0.9202[/C][/ROW]
[ROW][C]30[/C][C] 22.7[/C][C] 21.79[/C][C] 0.9062[/C][/ROW]
[ROW][C]31[/C][C] 25.2[/C][C] 22.82[/C][C] 2.379[/C][/ROW]
[ROW][C]32[/C][C] 25.2[/C][C] 24.72[/C][C] 0.4837[/C][/ROW]
[ROW][C]33[/C][C] 25.2[/C][C] 24.7[/C][C] 0.5022[/C][/ROW]
[ROW][C]34[/C][C] 26.4[/C][C] 24.06[/C][C] 2.341[/C][/ROW]
[ROW][C]35[/C][C] 26.4[/C][C] 24.97[/C][C] 1.431[/C][/ROW]
[ROW][C]36[/C][C] 26.4[/C][C] 25.22[/C][C] 1.185[/C][/ROW]
[ROW][C]37[/C][C] 26[/C][C] 25.35[/C][C] 0.6472[/C][/ROW]
[ROW][C]38[/C][C] 26[/C][C] 25.18[/C][C] 0.8228[/C][/ROW]
[ROW][C]39[/C][C] 26[/C][C] 25.31[/C][C] 0.6922[/C][/ROW]
[ROW][C]40[/C][C] 23.2[/C][C] 25.39[/C][C]-2.19[/C][/ROW]
[ROW][C]41[/C][C] 23.2[/C][C] 23.14[/C][C] 0.06102[/C][/ROW]
[ROW][C]42[/C][C] 23.2[/C][C] 23.03[/C][C] 0.1679[/C][/ROW]
[ROW][C]43[/C][C] 22.7[/C][C] 23.86[/C][C]-1.16[/C][/ROW]
[ROW][C]44[/C][C] 22.7[/C][C] 23.35[/C][C]-0.6532[/C][/ROW]
[ROW][C]45[/C][C] 22.7[/C][C] 23.1[/C][C]-0.4016[/C][/ROW]
[ROW][C]46[/C][C] 24[/C][C] 23.08[/C][C] 0.9239[/C][/ROW]
[ROW][C]47[/C][C] 24[/C][C] 23.93[/C][C] 0.06588[/C][/ROW]
[ROW][C]48[/C][C] 24[/C][C] 24.31[/C][C]-0.3075[/C][/ROW]
[ROW][C]49[/C][C] 20.7[/C][C] 24.68[/C][C]-3.98[/C][/ROW]
[ROW][C]50[/C][C] 20.7[/C][C] 22.43[/C][C]-1.733[/C][/ROW]
[ROW][C]51[/C][C] 20.7[/C][C] 22.46[/C][C]-1.758[/C][/ROW]
[ROW][C]52[/C][C] 23.8[/C][C] 22.88[/C][C] 0.9227[/C][/ROW]
[ROW][C]53[/C][C] 23.8[/C][C] 24.97[/C][C]-1.172[/C][/ROW]
[ROW][C]54[/C][C] 23.8[/C][C] 24.95[/C][C]-1.149[/C][/ROW]
[ROW][C]55[/C][C] 27.1[/C][C] 24.57[/C][C] 2.529[/C][/ROW]
[ROW][C]56[/C][C] 27.1[/C][C] 27.07[/C][C] 0.02711[/C][/ROW]
[ROW][C]57[/C][C] 27.1[/C][C] 26.79[/C][C] 0.3069[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285049&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285049&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 19.4 21.07-1.671
2 19.4 19.27 0.125
3 19.4 19.04 0.364
4 15.9 18.98-3.075
5 15.9 16.19-0.2937
6 15.9 16.22-0.3197
7 21.8 17.7 4.099
8 21.8 22.3-0.5026
9 21.8 22.13-0.3311
10 17.6 20.67-3.069
11 17.6 17.36 0.2429
12 17.6 17.52 0.08404
13 19 18.5 0.4975
14 19 19.56-0.564
15 19 19.43-0.4259
16 16.3 19.14-2.84
17 16.3 17.09-0.7925
18 16.3 16.98-0.6848
19 22.5 17.79 4.708
20 22.5 22.75-0.2485
21 22.5 22.96-0.4579
22 23.8 22.46 1.344
23 23.8 23.95-0.1525
24 23.8 24.07-0.2706
25 24.6 23.8 0.7953
26 24.6 24.16 0.4441
27 24.6 23.77 0.833
28 22.7 23.35-0.648
29 22.7 21.78 0.9202
30 22.7 21.79 0.9062
31 25.2 22.82 2.379
32 25.2 24.72 0.4837
33 25.2 24.7 0.5022
34 26.4 24.06 2.341
35 26.4 24.97 1.431
36 26.4 25.22 1.185
37 26 25.35 0.6472
38 26 25.18 0.8228
39 26 25.31 0.6922
40 23.2 25.39-2.19
41 23.2 23.14 0.06102
42 23.2 23.03 0.1679
43 22.7 23.86-1.16
44 22.7 23.35-0.6532
45 22.7 23.1-0.4016
46 24 23.08 0.9239
47 24 23.93 0.06588
48 24 24.31-0.3075
49 20.7 24.68-3.98
50 20.7 22.43-1.733
51 20.7 22.46-1.758
52 23.8 22.88 0.9227
53 23.8 24.97-1.172
54 23.8 24.95-1.149
55 27.1 24.57 2.529
56 27.1 27.07 0.02711
57 27.1 26.79 0.3069







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
8 0.1192 0.2384 0.8808
9 0.1036 0.2071 0.8964
10 0.06611 0.1322 0.9339
11 0.03682 0.07363 0.9632
12 0.01531 0.03063 0.9847
13 0.01794 0.03588 0.9821
14 0.01351 0.02702 0.9865
15 0.006222 0.01244 0.9938
16 0.07796 0.1559 0.922
17 0.1933 0.3866 0.8067
18 0.2515 0.503 0.7485
19 0.5848 0.8305 0.4152
20 0.5376 0.9247 0.4624
21 0.4946 0.9891 0.5054
22 0.419 0.8381 0.581
23 0.4782 0.9564 0.5218
24 0.468 0.936 0.532
25 0.3941 0.7882 0.6059
26 0.3437 0.6874 0.6563
27 0.3989 0.7978 0.6011
28 0.4502 0.9004 0.5498
29 0.4664 0.9328 0.5336
30 0.4667 0.9333 0.5333
31 0.4656 0.9312 0.5344
32 0.3906 0.7811 0.6094
33 0.3208 0.6415 0.6792
34 0.4154 0.8307 0.5846
35 0.4088 0.8175 0.5912
36 0.3493 0.6985 0.6507
37 0.2792 0.5584 0.7208
38 0.2343 0.4685 0.7657
39 0.2115 0.423 0.7885
40 0.3466 0.6932 0.6534
41 0.2968 0.5937 0.7032
42 0.2392 0.4785 0.7608
43 0.2379 0.4757 0.7621
44 0.1881 0.3762 0.8119
45 0.1316 0.2632 0.8684
46 0.08946 0.1789 0.9105
47 0.05049 0.101 0.9495
48 0.02823 0.05647 0.9718
49 0.162 0.3241 0.838

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.1192 &  0.2384 &  0.8808 \tabularnewline
9 &  0.1036 &  0.2071 &  0.8964 \tabularnewline
10 &  0.06611 &  0.1322 &  0.9339 \tabularnewline
11 &  0.03682 &  0.07363 &  0.9632 \tabularnewline
12 &  0.01531 &  0.03063 &  0.9847 \tabularnewline
13 &  0.01794 &  0.03588 &  0.9821 \tabularnewline
14 &  0.01351 &  0.02702 &  0.9865 \tabularnewline
15 &  0.006222 &  0.01244 &  0.9938 \tabularnewline
16 &  0.07796 &  0.1559 &  0.922 \tabularnewline
17 &  0.1933 &  0.3866 &  0.8067 \tabularnewline
18 &  0.2515 &  0.503 &  0.7485 \tabularnewline
19 &  0.5848 &  0.8305 &  0.4152 \tabularnewline
20 &  0.5376 &  0.9247 &  0.4624 \tabularnewline
21 &  0.4946 &  0.9891 &  0.5054 \tabularnewline
22 &  0.419 &  0.8381 &  0.581 \tabularnewline
23 &  0.4782 &  0.9564 &  0.5218 \tabularnewline
24 &  0.468 &  0.936 &  0.532 \tabularnewline
25 &  0.3941 &  0.7882 &  0.6059 \tabularnewline
26 &  0.3437 &  0.6874 &  0.6563 \tabularnewline
27 &  0.3989 &  0.7978 &  0.6011 \tabularnewline
28 &  0.4502 &  0.9004 &  0.5498 \tabularnewline
29 &  0.4664 &  0.9328 &  0.5336 \tabularnewline
30 &  0.4667 &  0.9333 &  0.5333 \tabularnewline
31 &  0.4656 &  0.9312 &  0.5344 \tabularnewline
32 &  0.3906 &  0.7811 &  0.6094 \tabularnewline
33 &  0.3208 &  0.6415 &  0.6792 \tabularnewline
34 &  0.4154 &  0.8307 &  0.5846 \tabularnewline
35 &  0.4088 &  0.8175 &  0.5912 \tabularnewline
36 &  0.3493 &  0.6985 &  0.6507 \tabularnewline
37 &  0.2792 &  0.5584 &  0.7208 \tabularnewline
38 &  0.2343 &  0.4685 &  0.7657 \tabularnewline
39 &  0.2115 &  0.423 &  0.7885 \tabularnewline
40 &  0.3466 &  0.6932 &  0.6534 \tabularnewline
41 &  0.2968 &  0.5937 &  0.7032 \tabularnewline
42 &  0.2392 &  0.4785 &  0.7608 \tabularnewline
43 &  0.2379 &  0.4757 &  0.7621 \tabularnewline
44 &  0.1881 &  0.3762 &  0.8119 \tabularnewline
45 &  0.1316 &  0.2632 &  0.8684 \tabularnewline
46 &  0.08946 &  0.1789 &  0.9105 \tabularnewline
47 &  0.05049 &  0.101 &  0.9495 \tabularnewline
48 &  0.02823 &  0.05647 &  0.9718 \tabularnewline
49 &  0.162 &  0.3241 &  0.838 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285049&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]8[/C][C] 0.1192[/C][C] 0.2384[/C][C] 0.8808[/C][/ROW]
[ROW][C]9[/C][C] 0.1036[/C][C] 0.2071[/C][C] 0.8964[/C][/ROW]
[ROW][C]10[/C][C] 0.06611[/C][C] 0.1322[/C][C] 0.9339[/C][/ROW]
[ROW][C]11[/C][C] 0.03682[/C][C] 0.07363[/C][C] 0.9632[/C][/ROW]
[ROW][C]12[/C][C] 0.01531[/C][C] 0.03063[/C][C] 0.9847[/C][/ROW]
[ROW][C]13[/C][C] 0.01794[/C][C] 0.03588[/C][C] 0.9821[/C][/ROW]
[ROW][C]14[/C][C] 0.01351[/C][C] 0.02702[/C][C] 0.9865[/C][/ROW]
[ROW][C]15[/C][C] 0.006222[/C][C] 0.01244[/C][C] 0.9938[/C][/ROW]
[ROW][C]16[/C][C] 0.07796[/C][C] 0.1559[/C][C] 0.922[/C][/ROW]
[ROW][C]17[/C][C] 0.1933[/C][C] 0.3866[/C][C] 0.8067[/C][/ROW]
[ROW][C]18[/C][C] 0.2515[/C][C] 0.503[/C][C] 0.7485[/C][/ROW]
[ROW][C]19[/C][C] 0.5848[/C][C] 0.8305[/C][C] 0.4152[/C][/ROW]
[ROW][C]20[/C][C] 0.5376[/C][C] 0.9247[/C][C] 0.4624[/C][/ROW]
[ROW][C]21[/C][C] 0.4946[/C][C] 0.9891[/C][C] 0.5054[/C][/ROW]
[ROW][C]22[/C][C] 0.419[/C][C] 0.8381[/C][C] 0.581[/C][/ROW]
[ROW][C]23[/C][C] 0.4782[/C][C] 0.9564[/C][C] 0.5218[/C][/ROW]
[ROW][C]24[/C][C] 0.468[/C][C] 0.936[/C][C] 0.532[/C][/ROW]
[ROW][C]25[/C][C] 0.3941[/C][C] 0.7882[/C][C] 0.6059[/C][/ROW]
[ROW][C]26[/C][C] 0.3437[/C][C] 0.6874[/C][C] 0.6563[/C][/ROW]
[ROW][C]27[/C][C] 0.3989[/C][C] 0.7978[/C][C] 0.6011[/C][/ROW]
[ROW][C]28[/C][C] 0.4502[/C][C] 0.9004[/C][C] 0.5498[/C][/ROW]
[ROW][C]29[/C][C] 0.4664[/C][C] 0.9328[/C][C] 0.5336[/C][/ROW]
[ROW][C]30[/C][C] 0.4667[/C][C] 0.9333[/C][C] 0.5333[/C][/ROW]
[ROW][C]31[/C][C] 0.4656[/C][C] 0.9312[/C][C] 0.5344[/C][/ROW]
[ROW][C]32[/C][C] 0.3906[/C][C] 0.7811[/C][C] 0.6094[/C][/ROW]
[ROW][C]33[/C][C] 0.3208[/C][C] 0.6415[/C][C] 0.6792[/C][/ROW]
[ROW][C]34[/C][C] 0.4154[/C][C] 0.8307[/C][C] 0.5846[/C][/ROW]
[ROW][C]35[/C][C] 0.4088[/C][C] 0.8175[/C][C] 0.5912[/C][/ROW]
[ROW][C]36[/C][C] 0.3493[/C][C] 0.6985[/C][C] 0.6507[/C][/ROW]
[ROW][C]37[/C][C] 0.2792[/C][C] 0.5584[/C][C] 0.7208[/C][/ROW]
[ROW][C]38[/C][C] 0.2343[/C][C] 0.4685[/C][C] 0.7657[/C][/ROW]
[ROW][C]39[/C][C] 0.2115[/C][C] 0.423[/C][C] 0.7885[/C][/ROW]
[ROW][C]40[/C][C] 0.3466[/C][C] 0.6932[/C][C] 0.6534[/C][/ROW]
[ROW][C]41[/C][C] 0.2968[/C][C] 0.5937[/C][C] 0.7032[/C][/ROW]
[ROW][C]42[/C][C] 0.2392[/C][C] 0.4785[/C][C] 0.7608[/C][/ROW]
[ROW][C]43[/C][C] 0.2379[/C][C] 0.4757[/C][C] 0.7621[/C][/ROW]
[ROW][C]44[/C][C] 0.1881[/C][C] 0.3762[/C][C] 0.8119[/C][/ROW]
[ROW][C]45[/C][C] 0.1316[/C][C] 0.2632[/C][C] 0.8684[/C][/ROW]
[ROW][C]46[/C][C] 0.08946[/C][C] 0.1789[/C][C] 0.9105[/C][/ROW]
[ROW][C]47[/C][C] 0.05049[/C][C] 0.101[/C][C] 0.9495[/C][/ROW]
[ROW][C]48[/C][C] 0.02823[/C][C] 0.05647[/C][C] 0.9718[/C][/ROW]
[ROW][C]49[/C][C] 0.162[/C][C] 0.3241[/C][C] 0.838[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285049&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285049&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
8 0.1192 0.2384 0.8808
9 0.1036 0.2071 0.8964
10 0.06611 0.1322 0.9339
11 0.03682 0.07363 0.9632
12 0.01531 0.03063 0.9847
13 0.01794 0.03588 0.9821
14 0.01351 0.02702 0.9865
15 0.006222 0.01244 0.9938
16 0.07796 0.1559 0.922
17 0.1933 0.3866 0.8067
18 0.2515 0.503 0.7485
19 0.5848 0.8305 0.4152
20 0.5376 0.9247 0.4624
21 0.4946 0.9891 0.5054
22 0.419 0.8381 0.581
23 0.4782 0.9564 0.5218
24 0.468 0.936 0.532
25 0.3941 0.7882 0.6059
26 0.3437 0.6874 0.6563
27 0.3989 0.7978 0.6011
28 0.4502 0.9004 0.5498
29 0.4664 0.9328 0.5336
30 0.4667 0.9333 0.5333
31 0.4656 0.9312 0.5344
32 0.3906 0.7811 0.6094
33 0.3208 0.6415 0.6792
34 0.4154 0.8307 0.5846
35 0.4088 0.8175 0.5912
36 0.3493 0.6985 0.6507
37 0.2792 0.5584 0.7208
38 0.2343 0.4685 0.7657
39 0.2115 0.423 0.7885
40 0.3466 0.6932 0.6534
41 0.2968 0.5937 0.7032
42 0.2392 0.4785 0.7608
43 0.2379 0.4757 0.7621
44 0.1881 0.3762 0.8119
45 0.1316 0.2632 0.8684
46 0.08946 0.1789 0.9105
47 0.05049 0.101 0.9495
48 0.02823 0.05647 0.9718
49 0.162 0.3241 0.838







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level40.0952381NOK
10% type I error level60.142857NOK

\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 & 4 & 0.0952381 & NOK \tabularnewline
10% type I error level & 6 & 0.142857 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285049&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]4[/C][C]0.0952381[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]6[/C][C]0.142857[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285049&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285049&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 level0 0OK
5% type I error level40.0952381NOK
10% type I error level60.142857NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 3 ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 3 ; par5 = ;
R code (references can be found in the software module):
par5 <- ''
par4 <- ''
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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')
}
}