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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 19 Dec 2010 13:30:08 +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/2010/Dec/19/t1292765303f97rco2hrhj45c6.htm/, Retrieved Fri, 05 Dec 2025 05:15:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112368, Retrieved Fri, 05 Dec 2025 05:15:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact527

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Classical Decomposition] [HPC Retail Sales] [2008-03-02 16:19:32] [74be16979710d4c4e7c6647856088456]
-  M D  [Classical Decomposition] [] [2010-11-26 09:51:05] [7789b9488494790f41ddb7f073cada1b]
- RMPD    [Multiple Regression] [] [2010-12-17 11:54:43] [7789b9488494790f41ddb7f073cada1b]
-   PD        [Multiple Regression] [] [2010-12-19 13:30:08] [c05c5ae4ce2db58f67fd725429d7f25c] [Current]
-               [Multiple Regression] [] [2010-12-20 20:46:17] [504b6ff240ec7a3fcbc007044ae7a0bb]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
101.76
102.37
102.38
102.86
102.87
102.92
102.95
103.02
104.08
104.16
104.24
104.33
104.73
104.86
105.03
105.62
105.63
105.63
105.94
106.61
107.69
107.78
107.93
108.48
108.14
108.48
108.48
108.89
108.93
109.21
109.47
109.80
111.73
111.85
112.12
112.15
112.17
112.67
112.80
113.44
113.53
114.53
114.51
115.05
116.67
117.07
116.92
117.00
117.02
117.35
117.36
117.82
117.88
118.24
118.50
118.80
119.76
120.09

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 6 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112368&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112368&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112368&T=0

As an alternative you can also use a QR Code:  
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 time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
cultuurbestedingen[t] = + 110.49 -1.72600000000004M1[t] -1.344M2[t] -1.28M3[t] -0.764000000000003M4[t] -0.722000000000002M5[t] -0.384000000000005M6[t] -0.216000000000002M7[t] + 0.165999999999995M8[t] + 1.496M9[t] + 1.7M10[t] -0.1875M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
cultuurbestedingen[t] =  +  110.49 -1.72600000000004M1[t] -1.344M2[t] -1.28M3[t] -0.764000000000003M4[t] -0.722000000000002M5[t] -0.384000000000005M6[t] -0.216000000000002M7[t] +  0.165999999999995M8[t] +  1.496M9[t] +  1.7M10[t] -0.1875M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112368&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]cultuurbestedingen[t] =  +  110.49 -1.72600000000004M1[t] -1.344M2[t] -1.28M3[t] -0.764000000000003M4[t] -0.722000000000002M5[t] -0.384000000000005M6[t] -0.216000000000002M7[t] +  0.165999999999995M8[t] +  1.496M9[t] +  1.7M10[t] -0.1875M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112368&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112368&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
cultuurbestedingen[t] = + 110.49 -1.72600000000004M1[t] -1.344M2[t] -1.28M3[t] -0.764000000000003M4[t] -0.722000000000002M5[t] -0.384000000000005M6[t] -0.216000000000002M7[t] + 0.165999999999995M8[t] + 1.496M9[t] + 1.7M10[t] -0.1875M11[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)110.493.04983336.228200
M1-1.726000000000044.09178-0.42180.675120.33756
M2-1.3444.09178-0.32850.7440520.372026
M3-1.284.09178-0.31280.755830.377915
M4-0.7640000000000034.09178-0.18670.8527050.426352
M5-0.7220000000000024.09178-0.17650.8607140.430357
M6-0.3840000000000054.09178-0.09380.9256390.462819
M7-0.2160000000000024.09178-0.05280.9581290.479064
M80.1659999999999954.091780.04060.9678150.483907
M91.4964.091780.36560.7163310.358166
M101.74.091780.41550.6797320.339866
M11-0.18754.313115-0.04350.9655130.482757

\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) & 110.49 & 3.049833 & 36.2282 & 0 & 0 \tabularnewline
M1 & -1.72600000000004 & 4.09178 & -0.4218 & 0.67512 & 0.33756 \tabularnewline
M2 & -1.344 & 4.09178 & -0.3285 & 0.744052 & 0.372026 \tabularnewline
M3 & -1.28 & 4.09178 & -0.3128 & 0.75583 & 0.377915 \tabularnewline
M4 & -0.764000000000003 & 4.09178 & -0.1867 & 0.852705 & 0.426352 \tabularnewline
M5 & -0.722000000000002 & 4.09178 & -0.1765 & 0.860714 & 0.430357 \tabularnewline
M6 & -0.384000000000005 & 4.09178 & -0.0938 & 0.925639 & 0.462819 \tabularnewline
M7 & -0.216000000000002 & 4.09178 & -0.0528 & 0.958129 & 0.479064 \tabularnewline
M8 & 0.165999999999995 & 4.09178 & 0.0406 & 0.967815 & 0.483907 \tabularnewline
M9 & 1.496 & 4.09178 & 0.3656 & 0.716331 & 0.358166 \tabularnewline
M10 & 1.7 & 4.09178 & 0.4155 & 0.679732 & 0.339866 \tabularnewline
M11 & -0.1875 & 4.313115 & -0.0435 & 0.965513 & 0.482757 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112368&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]110.49[/C][C]3.049833[/C][C]36.2282[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-1.72600000000004[/C][C]4.09178[/C][C]-0.4218[/C][C]0.67512[/C][C]0.33756[/C][/ROW]
[ROW][C]M2[/C][C]-1.344[/C][C]4.09178[/C][C]-0.3285[/C][C]0.744052[/C][C]0.372026[/C][/ROW]
[ROW][C]M3[/C][C]-1.28[/C][C]4.09178[/C][C]-0.3128[/C][C]0.75583[/C][C]0.377915[/C][/ROW]
[ROW][C]M4[/C][C]-0.764000000000003[/C][C]4.09178[/C][C]-0.1867[/C][C]0.852705[/C][C]0.426352[/C][/ROW]
[ROW][C]M5[/C][C]-0.722000000000002[/C][C]4.09178[/C][C]-0.1765[/C][C]0.860714[/C][C]0.430357[/C][/ROW]
[ROW][C]M6[/C][C]-0.384000000000005[/C][C]4.09178[/C][C]-0.0938[/C][C]0.925639[/C][C]0.462819[/C][/ROW]
[ROW][C]M7[/C][C]-0.216000000000002[/C][C]4.09178[/C][C]-0.0528[/C][C]0.958129[/C][C]0.479064[/C][/ROW]
[ROW][C]M8[/C][C]0.165999999999995[/C][C]4.09178[/C][C]0.0406[/C][C]0.967815[/C][C]0.483907[/C][/ROW]
[ROW][C]M9[/C][C]1.496[/C][C]4.09178[/C][C]0.3656[/C][C]0.716331[/C][C]0.358166[/C][/ROW]
[ROW][C]M10[/C][C]1.7[/C][C]4.09178[/C][C]0.4155[/C][C]0.679732[/C][C]0.339866[/C][/ROW]
[ROW][C]M11[/C][C]-0.1875[/C][C]4.313115[/C][C]-0.0435[/C][C]0.965513[/C][C]0.482757[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112368&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112368&T=2

As an alternative you can also use a QR Code:  
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)110.493.04983336.228200
M1-1.726000000000044.09178-0.42180.675120.33756
M2-1.3444.09178-0.32850.7440520.372026
M3-1.284.09178-0.31280.755830.377915
M4-0.7640000000000034.09178-0.18670.8527050.426352
M5-0.7220000000000024.09178-0.17650.8607140.430357
M6-0.3840000000000054.09178-0.09380.9256390.462819
M7-0.2160000000000024.09178-0.05280.9581290.479064
M80.1659999999999954.091780.04060.9678150.483907
M91.4964.091780.36560.7163310.358166
M101.74.091780.41550.6797320.339866
M11-0.18754.313115-0.04350.9655130.482757






Multiple Linear Regression - Regression Statistics
Multiple R0.184116543409472
R-squared0.0338989015570519
Adjusted R-squared-0.197125274157566
F-TEST (value)0.146733134972536
F-TEST (DF numerator)11
F-TEST (DF denominator)46
p-value0.999225903166059
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.09966569825223
Sum Squared Residuals1711.472395

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.184116543409472 \tabularnewline
R-squared & 0.0338989015570519 \tabularnewline
Adjusted R-squared & -0.197125274157566 \tabularnewline
F-TEST (value) & 0.146733134972536 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 46 \tabularnewline
p-value & 0.999225903166059 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 6.09966569825223 \tabularnewline
Sum Squared Residuals & 1711.472395 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112368&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.184116543409472[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0338989015570519[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.197125274157566[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.146733134972536[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]46[/C][/ROW]
[ROW][C]p-value[/C][C]0.999225903166059[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]6.09966569825223[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1711.472395[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112368&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112368&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.184116543409472
R-squared0.0338989015570519
Adjusted R-squared-0.197125274157566
F-TEST (value)0.146733134972536
F-TEST (DF numerator)11
F-TEST (DF denominator)46
p-value0.999225903166059
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.09966569825223
Sum Squared Residuals1711.472395






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1101.76108.764-7.00400000000017
2102.37109.146-6.776
3102.38109.21-6.83
4102.86109.726-6.866
5102.87109.768-6.898
6102.92110.106-7.186
7102.95110.274-7.324
8103.02110.656-7.636
9104.08111.986-7.906
10104.16112.19-8.03
11104.24110.3025-6.06250000000001
12104.33110.49-6.16
13104.73108.764-4.03399999999995
14104.86109.146-4.286
15105.03109.21-4.18
16105.62109.726-4.106
17105.63109.768-4.138
18105.63110.106-4.476
19105.94110.274-4.334
20106.61110.656-4.046
21107.69111.986-4.296
22107.78112.19-4.41
23107.93110.3025-2.37249999999999
24108.48110.49-2.01
25108.14108.764-0.623999999999959
26108.48109.146-0.665999999999996
27108.48109.21-0.729999999999996
28108.89109.726-0.835999999999999
29108.93109.768-0.837999999999993
30109.21110.106-0.896000000000003
31109.47110.274-0.804
32109.8110.656-0.856
33111.73111.986-0.255999999999997
34111.85112.19-0.340000000000002
35112.12110.30251.8175
36112.15110.491.66
37112.17108.7643.40600000000004
38112.67109.1463.524
39112.8109.213.59
40113.44109.7263.714
41113.53109.7683.762
42114.53110.1064.424
43114.51110.2744.236
44115.05110.6564.394
45116.67111.9864.684
46117.07112.194.87999999999999
47116.92110.30256.6175
48117110.496.51
49117.02108.7648.25600000000004
50117.35109.1468.204
51117.36109.218.15
52117.82109.7268.094
53117.88109.7688.112
54118.24110.1068.134
55118.5110.2748.226
56118.8110.6568.144
57119.76111.9867.774
58120.09112.197.9

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 101.76 & 108.764 & -7.00400000000017 \tabularnewline
2 & 102.37 & 109.146 & -6.776 \tabularnewline
3 & 102.38 & 109.21 & -6.83 \tabularnewline
4 & 102.86 & 109.726 & -6.866 \tabularnewline
5 & 102.87 & 109.768 & -6.898 \tabularnewline
6 & 102.92 & 110.106 & -7.186 \tabularnewline
7 & 102.95 & 110.274 & -7.324 \tabularnewline
8 & 103.02 & 110.656 & -7.636 \tabularnewline
9 & 104.08 & 111.986 & -7.906 \tabularnewline
10 & 104.16 & 112.19 & -8.03 \tabularnewline
11 & 104.24 & 110.3025 & -6.06250000000001 \tabularnewline
12 & 104.33 & 110.49 & -6.16 \tabularnewline
13 & 104.73 & 108.764 & -4.03399999999995 \tabularnewline
14 & 104.86 & 109.146 & -4.286 \tabularnewline
15 & 105.03 & 109.21 & -4.18 \tabularnewline
16 & 105.62 & 109.726 & -4.106 \tabularnewline
17 & 105.63 & 109.768 & -4.138 \tabularnewline
18 & 105.63 & 110.106 & -4.476 \tabularnewline
19 & 105.94 & 110.274 & -4.334 \tabularnewline
20 & 106.61 & 110.656 & -4.046 \tabularnewline
21 & 107.69 & 111.986 & -4.296 \tabularnewline
22 & 107.78 & 112.19 & -4.41 \tabularnewline
23 & 107.93 & 110.3025 & -2.37249999999999 \tabularnewline
24 & 108.48 & 110.49 & -2.01 \tabularnewline
25 & 108.14 & 108.764 & -0.623999999999959 \tabularnewline
26 & 108.48 & 109.146 & -0.665999999999996 \tabularnewline
27 & 108.48 & 109.21 & -0.729999999999996 \tabularnewline
28 & 108.89 & 109.726 & -0.835999999999999 \tabularnewline
29 & 108.93 & 109.768 & -0.837999999999993 \tabularnewline
30 & 109.21 & 110.106 & -0.896000000000003 \tabularnewline
31 & 109.47 & 110.274 & -0.804 \tabularnewline
32 & 109.8 & 110.656 & -0.856 \tabularnewline
33 & 111.73 & 111.986 & -0.255999999999997 \tabularnewline
34 & 111.85 & 112.19 & -0.340000000000002 \tabularnewline
35 & 112.12 & 110.3025 & 1.8175 \tabularnewline
36 & 112.15 & 110.49 & 1.66 \tabularnewline
37 & 112.17 & 108.764 & 3.40600000000004 \tabularnewline
38 & 112.67 & 109.146 & 3.524 \tabularnewline
39 & 112.8 & 109.21 & 3.59 \tabularnewline
40 & 113.44 & 109.726 & 3.714 \tabularnewline
41 & 113.53 & 109.768 & 3.762 \tabularnewline
42 & 114.53 & 110.106 & 4.424 \tabularnewline
43 & 114.51 & 110.274 & 4.236 \tabularnewline
44 & 115.05 & 110.656 & 4.394 \tabularnewline
45 & 116.67 & 111.986 & 4.684 \tabularnewline
46 & 117.07 & 112.19 & 4.87999999999999 \tabularnewline
47 & 116.92 & 110.3025 & 6.6175 \tabularnewline
48 & 117 & 110.49 & 6.51 \tabularnewline
49 & 117.02 & 108.764 & 8.25600000000004 \tabularnewline
50 & 117.35 & 109.146 & 8.204 \tabularnewline
51 & 117.36 & 109.21 & 8.15 \tabularnewline
52 & 117.82 & 109.726 & 8.094 \tabularnewline
53 & 117.88 & 109.768 & 8.112 \tabularnewline
54 & 118.24 & 110.106 & 8.134 \tabularnewline
55 & 118.5 & 110.274 & 8.226 \tabularnewline
56 & 118.8 & 110.656 & 8.144 \tabularnewline
57 & 119.76 & 111.986 & 7.774 \tabularnewline
58 & 120.09 & 112.19 & 7.9 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112368&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]101.76[/C][C]108.764[/C][C]-7.00400000000017[/C][/ROW]
[ROW][C]2[/C][C]102.37[/C][C]109.146[/C][C]-6.776[/C][/ROW]
[ROW][C]3[/C][C]102.38[/C][C]109.21[/C][C]-6.83[/C][/ROW]
[ROW][C]4[/C][C]102.86[/C][C]109.726[/C][C]-6.866[/C][/ROW]
[ROW][C]5[/C][C]102.87[/C][C]109.768[/C][C]-6.898[/C][/ROW]
[ROW][C]6[/C][C]102.92[/C][C]110.106[/C][C]-7.186[/C][/ROW]
[ROW][C]7[/C][C]102.95[/C][C]110.274[/C][C]-7.324[/C][/ROW]
[ROW][C]8[/C][C]103.02[/C][C]110.656[/C][C]-7.636[/C][/ROW]
[ROW][C]9[/C][C]104.08[/C][C]111.986[/C][C]-7.906[/C][/ROW]
[ROW][C]10[/C][C]104.16[/C][C]112.19[/C][C]-8.03[/C][/ROW]
[ROW][C]11[/C][C]104.24[/C][C]110.3025[/C][C]-6.06250000000001[/C][/ROW]
[ROW][C]12[/C][C]104.33[/C][C]110.49[/C][C]-6.16[/C][/ROW]
[ROW][C]13[/C][C]104.73[/C][C]108.764[/C][C]-4.03399999999995[/C][/ROW]
[ROW][C]14[/C][C]104.86[/C][C]109.146[/C][C]-4.286[/C][/ROW]
[ROW][C]15[/C][C]105.03[/C][C]109.21[/C][C]-4.18[/C][/ROW]
[ROW][C]16[/C][C]105.62[/C][C]109.726[/C][C]-4.106[/C][/ROW]
[ROW][C]17[/C][C]105.63[/C][C]109.768[/C][C]-4.138[/C][/ROW]
[ROW][C]18[/C][C]105.63[/C][C]110.106[/C][C]-4.476[/C][/ROW]
[ROW][C]19[/C][C]105.94[/C][C]110.274[/C][C]-4.334[/C][/ROW]
[ROW][C]20[/C][C]106.61[/C][C]110.656[/C][C]-4.046[/C][/ROW]
[ROW][C]21[/C][C]107.69[/C][C]111.986[/C][C]-4.296[/C][/ROW]
[ROW][C]22[/C][C]107.78[/C][C]112.19[/C][C]-4.41[/C][/ROW]
[ROW][C]23[/C][C]107.93[/C][C]110.3025[/C][C]-2.37249999999999[/C][/ROW]
[ROW][C]24[/C][C]108.48[/C][C]110.49[/C][C]-2.01[/C][/ROW]
[ROW][C]25[/C][C]108.14[/C][C]108.764[/C][C]-0.623999999999959[/C][/ROW]
[ROW][C]26[/C][C]108.48[/C][C]109.146[/C][C]-0.665999999999996[/C][/ROW]
[ROW][C]27[/C][C]108.48[/C][C]109.21[/C][C]-0.729999999999996[/C][/ROW]
[ROW][C]28[/C][C]108.89[/C][C]109.726[/C][C]-0.835999999999999[/C][/ROW]
[ROW][C]29[/C][C]108.93[/C][C]109.768[/C][C]-0.837999999999993[/C][/ROW]
[ROW][C]30[/C][C]109.21[/C][C]110.106[/C][C]-0.896000000000003[/C][/ROW]
[ROW][C]31[/C][C]109.47[/C][C]110.274[/C][C]-0.804[/C][/ROW]
[ROW][C]32[/C][C]109.8[/C][C]110.656[/C][C]-0.856[/C][/ROW]
[ROW][C]33[/C][C]111.73[/C][C]111.986[/C][C]-0.255999999999997[/C][/ROW]
[ROW][C]34[/C][C]111.85[/C][C]112.19[/C][C]-0.340000000000002[/C][/ROW]
[ROW][C]35[/C][C]112.12[/C][C]110.3025[/C][C]1.8175[/C][/ROW]
[ROW][C]36[/C][C]112.15[/C][C]110.49[/C][C]1.66[/C][/ROW]
[ROW][C]37[/C][C]112.17[/C][C]108.764[/C][C]3.40600000000004[/C][/ROW]
[ROW][C]38[/C][C]112.67[/C][C]109.146[/C][C]3.524[/C][/ROW]
[ROW][C]39[/C][C]112.8[/C][C]109.21[/C][C]3.59[/C][/ROW]
[ROW][C]40[/C][C]113.44[/C][C]109.726[/C][C]3.714[/C][/ROW]
[ROW][C]41[/C][C]113.53[/C][C]109.768[/C][C]3.762[/C][/ROW]
[ROW][C]42[/C][C]114.53[/C][C]110.106[/C][C]4.424[/C][/ROW]
[ROW][C]43[/C][C]114.51[/C][C]110.274[/C][C]4.236[/C][/ROW]
[ROW][C]44[/C][C]115.05[/C][C]110.656[/C][C]4.394[/C][/ROW]
[ROW][C]45[/C][C]116.67[/C][C]111.986[/C][C]4.684[/C][/ROW]
[ROW][C]46[/C][C]117.07[/C][C]112.19[/C][C]4.87999999999999[/C][/ROW]
[ROW][C]47[/C][C]116.92[/C][C]110.3025[/C][C]6.6175[/C][/ROW]
[ROW][C]48[/C][C]117[/C][C]110.49[/C][C]6.51[/C][/ROW]
[ROW][C]49[/C][C]117.02[/C][C]108.764[/C][C]8.25600000000004[/C][/ROW]
[ROW][C]50[/C][C]117.35[/C][C]109.146[/C][C]8.204[/C][/ROW]
[ROW][C]51[/C][C]117.36[/C][C]109.21[/C][C]8.15[/C][/ROW]
[ROW][C]52[/C][C]117.82[/C][C]109.726[/C][C]8.094[/C][/ROW]
[ROW][C]53[/C][C]117.88[/C][C]109.768[/C][C]8.112[/C][/ROW]
[ROW][C]54[/C][C]118.24[/C][C]110.106[/C][C]8.134[/C][/ROW]
[ROW][C]55[/C][C]118.5[/C][C]110.274[/C][C]8.226[/C][/ROW]
[ROW][C]56[/C][C]118.8[/C][C]110.656[/C][C]8.144[/C][/ROW]
[ROW][C]57[/C][C]119.76[/C][C]111.986[/C][C]7.774[/C][/ROW]
[ROW][C]58[/C][C]120.09[/C][C]112.19[/C][C]7.9[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112368&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112368&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1101.76108.764-7.00400000000017
2102.37109.146-6.776
3102.38109.21-6.83
4102.86109.726-6.866
5102.87109.768-6.898
6102.92110.106-7.186
7102.95110.274-7.324
8103.02110.656-7.636
9104.08111.986-7.906
10104.16112.19-8.03
11104.24110.3025-6.06250000000001
12104.33110.49-6.16
13104.73108.764-4.03399999999995
14104.86109.146-4.286
15105.03109.21-4.18
16105.62109.726-4.106
17105.63109.768-4.138
18105.63110.106-4.476
19105.94110.274-4.334
20106.61110.656-4.046
21107.69111.986-4.296
22107.78112.19-4.41
23107.93110.3025-2.37249999999999
24108.48110.49-2.01
25108.14108.764-0.623999999999959
26108.48109.146-0.665999999999996
27108.48109.21-0.729999999999996
28108.89109.726-0.835999999999999
29108.93109.768-0.837999999999993
30109.21110.106-0.896000000000003
31109.47110.274-0.804
32109.8110.656-0.856
33111.73111.986-0.255999999999997
34111.85112.19-0.340000000000002
35112.12110.30251.8175
36112.15110.491.66
37112.17108.7643.40600000000004
38112.67109.1463.524
39112.8109.213.59
40113.44109.7263.714
41113.53109.7683.762
42114.53110.1064.424
43114.51110.2744.236
44115.05110.6564.394
45116.67111.9864.684
46117.07112.194.87999999999999
47116.92110.30256.6175
48117110.496.51
49117.02108.7648.25600000000004
50117.35109.1468.204
51117.36109.218.15
52117.82109.7268.094
53117.88109.7688.112
54118.24110.1068.134
55118.5110.2748.226
56118.8110.6568.144
57119.76111.9867.774
58120.09112.197.9






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.06458939293160110.1291787858632020.935410607068399
160.03598932640423920.07197865280847840.96401067359576
170.02138792061312350.04277584122624690.978612079386877
180.01361703943109040.02723407886218090.98638296056891
190.01010665553411590.02021331106823190.989893344465884
200.009631595843881780.01926319168776360.990368404156118
210.009964935208358970.01992987041671790.99003506479164
220.01125668687352890.02251337374705790.98874331312647
230.01108648416042240.02217296832084480.988913515839578
240.01214885168465920.02429770336931850.98785114831534
250.02093770492932350.0418754098586470.979062295070677
260.03255524120183830.06511048240367670.967444758798162
270.04716856738526670.09433713477053330.952831432614733
280.06593066240026280.1318613248005260.934069337599737
290.09233658435789470.1846731687157890.907663415642105
300.1406241889910640.2812483779821270.859375811008936
310.210200834755610.420401669511220.78979916524439
320.3121240052418560.6242480104837130.687875994758144
330.4443002680015950.888600536003190.555699731998405
340.6133912004543070.7732175990913860.386608799545693
350.6628928037448510.6742143925102970.337107196255149
360.7015942297724690.5968115404550620.298405770227531
370.7664522043236380.4670955913527230.233547795676362
380.8106911255053550.3786177489892890.189308874494645
390.8397690481917850.320461903616430.160230951808215
400.8562063127194280.2875873745611440.143793687280572
410.866150001404470.2676999971910610.133849998595531
420.8532614021259330.2934771957481340.146738597874067
430.8380730308799960.3238539382400070.161926969120004

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.0645893929316011 & 0.129178785863202 & 0.935410607068399 \tabularnewline
16 & 0.0359893264042392 & 0.0719786528084784 & 0.96401067359576 \tabularnewline
17 & 0.0213879206131235 & 0.0427758412262469 & 0.978612079386877 \tabularnewline
18 & 0.0136170394310904 & 0.0272340788621809 & 0.98638296056891 \tabularnewline
19 & 0.0101066555341159 & 0.0202133110682319 & 0.989893344465884 \tabularnewline
20 & 0.00963159584388178 & 0.0192631916877636 & 0.990368404156118 \tabularnewline
21 & 0.00996493520835897 & 0.0199298704167179 & 0.99003506479164 \tabularnewline
22 & 0.0112566868735289 & 0.0225133737470579 & 0.98874331312647 \tabularnewline
23 & 0.0110864841604224 & 0.0221729683208448 & 0.988913515839578 \tabularnewline
24 & 0.0121488516846592 & 0.0242977033693185 & 0.98785114831534 \tabularnewline
25 & 0.0209377049293235 & 0.041875409858647 & 0.979062295070677 \tabularnewline
26 & 0.0325552412018383 & 0.0651104824036767 & 0.967444758798162 \tabularnewline
27 & 0.0471685673852667 & 0.0943371347705333 & 0.952831432614733 \tabularnewline
28 & 0.0659306624002628 & 0.131861324800526 & 0.934069337599737 \tabularnewline
29 & 0.0923365843578947 & 0.184673168715789 & 0.907663415642105 \tabularnewline
30 & 0.140624188991064 & 0.281248377982127 & 0.859375811008936 \tabularnewline
31 & 0.21020083475561 & 0.42040166951122 & 0.78979916524439 \tabularnewline
32 & 0.312124005241856 & 0.624248010483713 & 0.687875994758144 \tabularnewline
33 & 0.444300268001595 & 0.88860053600319 & 0.555699731998405 \tabularnewline
34 & 0.613391200454307 & 0.773217599091386 & 0.386608799545693 \tabularnewline
35 & 0.662892803744851 & 0.674214392510297 & 0.337107196255149 \tabularnewline
36 & 0.701594229772469 & 0.596811540455062 & 0.298405770227531 \tabularnewline
37 & 0.766452204323638 & 0.467095591352723 & 0.233547795676362 \tabularnewline
38 & 0.810691125505355 & 0.378617748989289 & 0.189308874494645 \tabularnewline
39 & 0.839769048191785 & 0.32046190361643 & 0.160230951808215 \tabularnewline
40 & 0.856206312719428 & 0.287587374561144 & 0.143793687280572 \tabularnewline
41 & 0.86615000140447 & 0.267699997191061 & 0.133849998595531 \tabularnewline
42 & 0.853261402125933 & 0.293477195748134 & 0.146738597874067 \tabularnewline
43 & 0.838073030879996 & 0.323853938240007 & 0.161926969120004 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112368&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]15[/C][C]0.0645893929316011[/C][C]0.129178785863202[/C][C]0.935410607068399[/C][/ROW]
[ROW][C]16[/C][C]0.0359893264042392[/C][C]0.0719786528084784[/C][C]0.96401067359576[/C][/ROW]
[ROW][C]17[/C][C]0.0213879206131235[/C][C]0.0427758412262469[/C][C]0.978612079386877[/C][/ROW]
[ROW][C]18[/C][C]0.0136170394310904[/C][C]0.0272340788621809[/C][C]0.98638296056891[/C][/ROW]
[ROW][C]19[/C][C]0.0101066555341159[/C][C]0.0202133110682319[/C][C]0.989893344465884[/C][/ROW]
[ROW][C]20[/C][C]0.00963159584388178[/C][C]0.0192631916877636[/C][C]0.990368404156118[/C][/ROW]
[ROW][C]21[/C][C]0.00996493520835897[/C][C]0.0199298704167179[/C][C]0.99003506479164[/C][/ROW]
[ROW][C]22[/C][C]0.0112566868735289[/C][C]0.0225133737470579[/C][C]0.98874331312647[/C][/ROW]
[ROW][C]23[/C][C]0.0110864841604224[/C][C]0.0221729683208448[/C][C]0.988913515839578[/C][/ROW]
[ROW][C]24[/C][C]0.0121488516846592[/C][C]0.0242977033693185[/C][C]0.98785114831534[/C][/ROW]
[ROW][C]25[/C][C]0.0209377049293235[/C][C]0.041875409858647[/C][C]0.979062295070677[/C][/ROW]
[ROW][C]26[/C][C]0.0325552412018383[/C][C]0.0651104824036767[/C][C]0.967444758798162[/C][/ROW]
[ROW][C]27[/C][C]0.0471685673852667[/C][C]0.0943371347705333[/C][C]0.952831432614733[/C][/ROW]
[ROW][C]28[/C][C]0.0659306624002628[/C][C]0.131861324800526[/C][C]0.934069337599737[/C][/ROW]
[ROW][C]29[/C][C]0.0923365843578947[/C][C]0.184673168715789[/C][C]0.907663415642105[/C][/ROW]
[ROW][C]30[/C][C]0.140624188991064[/C][C]0.281248377982127[/C][C]0.859375811008936[/C][/ROW]
[ROW][C]31[/C][C]0.21020083475561[/C][C]0.42040166951122[/C][C]0.78979916524439[/C][/ROW]
[ROW][C]32[/C][C]0.312124005241856[/C][C]0.624248010483713[/C][C]0.687875994758144[/C][/ROW]
[ROW][C]33[/C][C]0.444300268001595[/C][C]0.88860053600319[/C][C]0.555699731998405[/C][/ROW]
[ROW][C]34[/C][C]0.613391200454307[/C][C]0.773217599091386[/C][C]0.386608799545693[/C][/ROW]
[ROW][C]35[/C][C]0.662892803744851[/C][C]0.674214392510297[/C][C]0.337107196255149[/C][/ROW]
[ROW][C]36[/C][C]0.701594229772469[/C][C]0.596811540455062[/C][C]0.298405770227531[/C][/ROW]
[ROW][C]37[/C][C]0.766452204323638[/C][C]0.467095591352723[/C][C]0.233547795676362[/C][/ROW]
[ROW][C]38[/C][C]0.810691125505355[/C][C]0.378617748989289[/C][C]0.189308874494645[/C][/ROW]
[ROW][C]39[/C][C]0.839769048191785[/C][C]0.32046190361643[/C][C]0.160230951808215[/C][/ROW]
[ROW][C]40[/C][C]0.856206312719428[/C][C]0.287587374561144[/C][C]0.143793687280572[/C][/ROW]
[ROW][C]41[/C][C]0.86615000140447[/C][C]0.267699997191061[/C][C]0.133849998595531[/C][/ROW]
[ROW][C]42[/C][C]0.853261402125933[/C][C]0.293477195748134[/C][C]0.146738597874067[/C][/ROW]
[ROW][C]43[/C][C]0.838073030879996[/C][C]0.323853938240007[/C][C]0.161926969120004[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112368&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112368&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.06458939293160110.1291787858632020.935410607068399
160.03598932640423920.07197865280847840.96401067359576
170.02138792061312350.04277584122624690.978612079386877
180.01361703943109040.02723407886218090.98638296056891
190.01010665553411590.02021331106823190.989893344465884
200.009631595843881780.01926319168776360.990368404156118
210.009964935208358970.01992987041671790.99003506479164
220.01125668687352890.02251337374705790.98874331312647
230.01108648416042240.02217296832084480.988913515839578
240.01214885168465920.02429770336931850.98785114831534
250.02093770492932350.0418754098586470.979062295070677
260.03255524120183830.06511048240367670.967444758798162
270.04716856738526670.09433713477053330.952831432614733
280.06593066240026280.1318613248005260.934069337599737
290.09233658435789470.1846731687157890.907663415642105
300.1406241889910640.2812483779821270.859375811008936
310.210200834755610.420401669511220.78979916524439
320.3121240052418560.6242480104837130.687875994758144
330.4443002680015950.888600536003190.555699731998405
340.6133912004543070.7732175990913860.386608799545693
350.6628928037448510.6742143925102970.337107196255149
360.7015942297724690.5968115404550620.298405770227531
370.7664522043236380.4670955913527230.233547795676362
380.8106911255053550.3786177489892890.189308874494645
390.8397690481917850.320461903616430.160230951808215
400.8562063127194280.2875873745611440.143793687280572
410.866150001404470.2676999971910610.133849998595531
420.8532614021259330.2934771957481340.146738597874067
430.8380730308799960.3238539382400070.161926969120004






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level90.310344827586207NOK
10% type I error level120.413793103448276NOK

\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 & 9 & 0.310344827586207 & NOK \tabularnewline
10% type I error level & 12 & 0.413793103448276 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112368&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]9[/C][C]0.310344827586207[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]12[/C][C]0.413793103448276[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112368&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112368&T=6

As an alternative you can also use a QR Code:  
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 level00OK
5% type I error level90.310344827586207NOK
10% type I error level120.413793103448276NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
PNG link  
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Input Parameters & R Code

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