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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 10 Dec 2014 15:09:31 +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/2014/Dec/10/t1418224902vlikc4ik4skpdot.htm/, Retrieved Thu, 31 Oct 2024 23:34:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=265361, Retrieved Thu, 31 Oct 2024 23:34:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact137
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-10 15:09:31] [20f13859937b25726fc27e6925bc2ae4] [Current]
- RM      [Multiple Regression] [] [2014-12-12 13:22:41] [93cb0d178904cf975da218b7c929e42d]
- RMPD    [Skewness and Kurtosis Test] [] [2014-12-17 13:02:38] [93cb0d178904cf975da218b7c929e42d]
- RMPD    [Central Tendency] [] [2014-12-17 13:21:46] [93cb0d178904cf975da218b7c929e42d]
- RMPD    [Two-Way ANOVA] [] [2014-12-17 13:33:51] [93cb0d178904cf975da218b7c929e42d]
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Dataseries X:
7.5 12 13 13
6.5 11 11 11
1.0 13 14 10
1.0 11 15 9
5.5 10 14 8
8.5 7 11 26
6.5 10 13 10
4.5 15 16 10
2.0 12 14 8
5.0 12 14 13
0.5 10 15 11
5.0 14 13 12
2.5 6 14 24
5.0 12 11 21
5.5 14 12 5
3.5 11 14 14
4.0 12 12 9
6.5 13 15 17
4.5 11 14 18
5.5 7 12 23
4.0 11 12 9
7.5 7 12 14
4.0 12 14 10
5.5 13 16 8
2.5 9 12 10
5.5 11 12 19
3.5 12 14 11
4.5 12 15 12
4.5 5 14 11
6.0 13 13 10
5.0 6 16 14
6.5 6 15 11
5.0 12 13 13
6.0 11 16 15
4.5 6 16 15
5.0 11 15 14
5.0 12 13 12
6.5 13 12 13
7.0 14 14 7
4.5 12 14 8
8.5 14 10 20
3.5 11 16 16
6.0 10 14 11
1.5 7 14 26
3.5 7 15 15
7.5 10 16 20
5.0 12 15 15
6.5 5 13 17
6.5 10 12 19
6.5 12 12 13
7.0 11 14 8
1.5 12 15 9
4.0 11 11 12
4.5 12 14 9
0.0 10 16 14
3.5 9 13 14
4.5 7 11 13
0.0 9 12 16
3.0 10 12 14
3.5 12 14 11
3.0 14 12 11
1.0 9 13 14
5.5 12 14 15




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265361&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265361&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265361&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'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Ex[t] = + 6.27376 + 0.0984574Conf[t] -0.26679Stress[t] + 0.0662546Dep[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ex[t] =  +  6.27376 +  0.0984574Conf[t] -0.26679Stress[t] +  0.0662546Dep[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265361&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ex[t] =  +  6.27376 +  0.0984574Conf[t] -0.26679Stress[t] +  0.0662546Dep[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265361&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265361&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
Ex[t] = + 6.27376 + 0.0984574Conf[t] -0.26679Stress[t] + 0.0662546Dep[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.273763.106632.0190.04798790.023994
Conf0.09845740.1146350.85890.3938850.196942
Stress-0.266790.166765-1.60.1149860.0574931
Dep0.06625460.06307611.050.2978220.148911

\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) & 6.27376 & 3.10663 & 2.019 & 0.0479879 & 0.023994 \tabularnewline
Conf & 0.0984574 & 0.114635 & 0.8589 & 0.393885 & 0.196942 \tabularnewline
Stress & -0.26679 & 0.166765 & -1.6 & 0.114986 & 0.0574931 \tabularnewline
Dep & 0.0662546 & 0.0630761 & 1.05 & 0.297822 & 0.148911 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265361&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]6.27376[/C][C]3.10663[/C][C]2.019[/C][C]0.0479879[/C][C]0.023994[/C][/ROW]
[ROW][C]Conf[/C][C]0.0984574[/C][C]0.114635[/C][C]0.8589[/C][C]0.393885[/C][C]0.196942[/C][/ROW]
[ROW][C]Stress[/C][C]-0.26679[/C][C]0.166765[/C][C]-1.6[/C][C]0.114986[/C][C]0.0574931[/C][/ROW]
[ROW][C]Dep[/C][C]0.0662546[/C][C]0.0630761[/C][C]1.05[/C][C]0.297822[/C][C]0.148911[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265361&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265361&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)6.273763.106632.0190.04798790.023994
Conf0.09845740.1146350.85890.3938850.196942
Stress-0.266790.166765-1.60.1149860.0574931
Dep0.06625460.06307611.050.2978220.148911







Multiple Linear Regression - Regression Statistics
Multiple R0.273022
R-squared0.0745412
Adjusted R-squared0.027484
F-TEST (value)1.58405
F-TEST (DF numerator)3
F-TEST (DF denominator)59
p-value0.202798
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.99906
Sum Squared Residuals235.779

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.273022 \tabularnewline
R-squared & 0.0745412 \tabularnewline
Adjusted R-squared & 0.027484 \tabularnewline
F-TEST (value) & 1.58405 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 0.202798 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.99906 \tabularnewline
Sum Squared Residuals & 235.779 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265361&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.273022[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0745412[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.027484[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.58405[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]59[/C][/ROW]
[ROW][C]p-value[/C][C]0.202798[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.99906[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]235.779[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265361&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265361&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 R0.273022
R-squared0.0745412
Adjusted R-squared0.027484
F-TEST (value)1.58405
F-TEST (DF numerator)3
F-TEST (DF denominator)59
p-value0.202798
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.99906
Sum Squared Residuals235.779







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
17.54.848292.65171
26.55.15091.3491
314.48119-3.48119
413.95123-2.95123
55.54.053311.44669
68.55.750892.74911
76.54.452612.04739
84.54.144530.355473
924.25023-2.25023
1054.58150.418501
110.53.98529-3.48529
1254.978950.0210505
132.54.71956-2.21956
1455.91191-0.911906
155.54.781960.718043
163.54.5493-1.0493
1744.85006-0.850061
186.54.678181.82182
194.54.81431-0.314315
205.55.285340.214661
2144.7516-0.751604
227.54.689052.81095
2344.38274-0.382735
245.53.81511.6849
252.54.62094-2.12094
265.55.414150.0858503
273.54.44899-0.94899
284.54.248450.251546
294.53.759790.740212
3064.747981.25202
3153.523431.47657
326.53.591462.90854
3354.848290.151711
3464.081971.91803
354.53.589680.910316
3654.282510.717494
3754.782030.217965
386.55.213541.28646
3974.380892.61911
404.54.250230.249774
418.56.309362.19064
423.54.14823-0.648225
4364.252081.74792
441.54.95052-3.45052
453.53.95493-0.454931
467.54.314793.18521
4754.447220.552782
486.54.424112.07589
496.55.315691.18431
506.55.115081.38492
5174.151772.84823
521.54.04969-2.54969
5345.21716-1.21716
544.54.316480.183519
5503.91726-3.91726
563.54.61917-1.11917
574.54.88958-0.389583
5805.01847-5.01847
5934.98442-1.98442
603.54.44899-0.94899
6135.17949-2.17949
6214.61917-3.61917
635.54.714010.785992

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 7.5 & 4.84829 & 2.65171 \tabularnewline
2 & 6.5 & 5.1509 & 1.3491 \tabularnewline
3 & 1 & 4.48119 & -3.48119 \tabularnewline
4 & 1 & 3.95123 & -2.95123 \tabularnewline
5 & 5.5 & 4.05331 & 1.44669 \tabularnewline
6 & 8.5 & 5.75089 & 2.74911 \tabularnewline
7 & 6.5 & 4.45261 & 2.04739 \tabularnewline
8 & 4.5 & 4.14453 & 0.355473 \tabularnewline
9 & 2 & 4.25023 & -2.25023 \tabularnewline
10 & 5 & 4.5815 & 0.418501 \tabularnewline
11 & 0.5 & 3.98529 & -3.48529 \tabularnewline
12 & 5 & 4.97895 & 0.0210505 \tabularnewline
13 & 2.5 & 4.71956 & -2.21956 \tabularnewline
14 & 5 & 5.91191 & -0.911906 \tabularnewline
15 & 5.5 & 4.78196 & 0.718043 \tabularnewline
16 & 3.5 & 4.5493 & -1.0493 \tabularnewline
17 & 4 & 4.85006 & -0.850061 \tabularnewline
18 & 6.5 & 4.67818 & 1.82182 \tabularnewline
19 & 4.5 & 4.81431 & -0.314315 \tabularnewline
20 & 5.5 & 5.28534 & 0.214661 \tabularnewline
21 & 4 & 4.7516 & -0.751604 \tabularnewline
22 & 7.5 & 4.68905 & 2.81095 \tabularnewline
23 & 4 & 4.38274 & -0.382735 \tabularnewline
24 & 5.5 & 3.8151 & 1.6849 \tabularnewline
25 & 2.5 & 4.62094 & -2.12094 \tabularnewline
26 & 5.5 & 5.41415 & 0.0858503 \tabularnewline
27 & 3.5 & 4.44899 & -0.94899 \tabularnewline
28 & 4.5 & 4.24845 & 0.251546 \tabularnewline
29 & 4.5 & 3.75979 & 0.740212 \tabularnewline
30 & 6 & 4.74798 & 1.25202 \tabularnewline
31 & 5 & 3.52343 & 1.47657 \tabularnewline
32 & 6.5 & 3.59146 & 2.90854 \tabularnewline
33 & 5 & 4.84829 & 0.151711 \tabularnewline
34 & 6 & 4.08197 & 1.91803 \tabularnewline
35 & 4.5 & 3.58968 & 0.910316 \tabularnewline
36 & 5 & 4.28251 & 0.717494 \tabularnewline
37 & 5 & 4.78203 & 0.217965 \tabularnewline
38 & 6.5 & 5.21354 & 1.28646 \tabularnewline
39 & 7 & 4.38089 & 2.61911 \tabularnewline
40 & 4.5 & 4.25023 & 0.249774 \tabularnewline
41 & 8.5 & 6.30936 & 2.19064 \tabularnewline
42 & 3.5 & 4.14823 & -0.648225 \tabularnewline
43 & 6 & 4.25208 & 1.74792 \tabularnewline
44 & 1.5 & 4.95052 & -3.45052 \tabularnewline
45 & 3.5 & 3.95493 & -0.454931 \tabularnewline
46 & 7.5 & 4.31479 & 3.18521 \tabularnewline
47 & 5 & 4.44722 & 0.552782 \tabularnewline
48 & 6.5 & 4.42411 & 2.07589 \tabularnewline
49 & 6.5 & 5.31569 & 1.18431 \tabularnewline
50 & 6.5 & 5.11508 & 1.38492 \tabularnewline
51 & 7 & 4.15177 & 2.84823 \tabularnewline
52 & 1.5 & 4.04969 & -2.54969 \tabularnewline
53 & 4 & 5.21716 & -1.21716 \tabularnewline
54 & 4.5 & 4.31648 & 0.183519 \tabularnewline
55 & 0 & 3.91726 & -3.91726 \tabularnewline
56 & 3.5 & 4.61917 & -1.11917 \tabularnewline
57 & 4.5 & 4.88958 & -0.389583 \tabularnewline
58 & 0 & 5.01847 & -5.01847 \tabularnewline
59 & 3 & 4.98442 & -1.98442 \tabularnewline
60 & 3.5 & 4.44899 & -0.94899 \tabularnewline
61 & 3 & 5.17949 & -2.17949 \tabularnewline
62 & 1 & 4.61917 & -3.61917 \tabularnewline
63 & 5.5 & 4.71401 & 0.785992 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265361&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]7.5[/C][C]4.84829[/C][C]2.65171[/C][/ROW]
[ROW][C]2[/C][C]6.5[/C][C]5.1509[/C][C]1.3491[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]4.48119[/C][C]-3.48119[/C][/ROW]
[ROW][C]4[/C][C]1[/C][C]3.95123[/C][C]-2.95123[/C][/ROW]
[ROW][C]5[/C][C]5.5[/C][C]4.05331[/C][C]1.44669[/C][/ROW]
[ROW][C]6[/C][C]8.5[/C][C]5.75089[/C][C]2.74911[/C][/ROW]
[ROW][C]7[/C][C]6.5[/C][C]4.45261[/C][C]2.04739[/C][/ROW]
[ROW][C]8[/C][C]4.5[/C][C]4.14453[/C][C]0.355473[/C][/ROW]
[ROW][C]9[/C][C]2[/C][C]4.25023[/C][C]-2.25023[/C][/ROW]
[ROW][C]10[/C][C]5[/C][C]4.5815[/C][C]0.418501[/C][/ROW]
[ROW][C]11[/C][C]0.5[/C][C]3.98529[/C][C]-3.48529[/C][/ROW]
[ROW][C]12[/C][C]5[/C][C]4.97895[/C][C]0.0210505[/C][/ROW]
[ROW][C]13[/C][C]2.5[/C][C]4.71956[/C][C]-2.21956[/C][/ROW]
[ROW][C]14[/C][C]5[/C][C]5.91191[/C][C]-0.911906[/C][/ROW]
[ROW][C]15[/C][C]5.5[/C][C]4.78196[/C][C]0.718043[/C][/ROW]
[ROW][C]16[/C][C]3.5[/C][C]4.5493[/C][C]-1.0493[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]4.85006[/C][C]-0.850061[/C][/ROW]
[ROW][C]18[/C][C]6.5[/C][C]4.67818[/C][C]1.82182[/C][/ROW]
[ROW][C]19[/C][C]4.5[/C][C]4.81431[/C][C]-0.314315[/C][/ROW]
[ROW][C]20[/C][C]5.5[/C][C]5.28534[/C][C]0.214661[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]4.7516[/C][C]-0.751604[/C][/ROW]
[ROW][C]22[/C][C]7.5[/C][C]4.68905[/C][C]2.81095[/C][/ROW]
[ROW][C]23[/C][C]4[/C][C]4.38274[/C][C]-0.382735[/C][/ROW]
[ROW][C]24[/C][C]5.5[/C][C]3.8151[/C][C]1.6849[/C][/ROW]
[ROW][C]25[/C][C]2.5[/C][C]4.62094[/C][C]-2.12094[/C][/ROW]
[ROW][C]26[/C][C]5.5[/C][C]5.41415[/C][C]0.0858503[/C][/ROW]
[ROW][C]27[/C][C]3.5[/C][C]4.44899[/C][C]-0.94899[/C][/ROW]
[ROW][C]28[/C][C]4.5[/C][C]4.24845[/C][C]0.251546[/C][/ROW]
[ROW][C]29[/C][C]4.5[/C][C]3.75979[/C][C]0.740212[/C][/ROW]
[ROW][C]30[/C][C]6[/C][C]4.74798[/C][C]1.25202[/C][/ROW]
[ROW][C]31[/C][C]5[/C][C]3.52343[/C][C]1.47657[/C][/ROW]
[ROW][C]32[/C][C]6.5[/C][C]3.59146[/C][C]2.90854[/C][/ROW]
[ROW][C]33[/C][C]5[/C][C]4.84829[/C][C]0.151711[/C][/ROW]
[ROW][C]34[/C][C]6[/C][C]4.08197[/C][C]1.91803[/C][/ROW]
[ROW][C]35[/C][C]4.5[/C][C]3.58968[/C][C]0.910316[/C][/ROW]
[ROW][C]36[/C][C]5[/C][C]4.28251[/C][C]0.717494[/C][/ROW]
[ROW][C]37[/C][C]5[/C][C]4.78203[/C][C]0.217965[/C][/ROW]
[ROW][C]38[/C][C]6.5[/C][C]5.21354[/C][C]1.28646[/C][/ROW]
[ROW][C]39[/C][C]7[/C][C]4.38089[/C][C]2.61911[/C][/ROW]
[ROW][C]40[/C][C]4.5[/C][C]4.25023[/C][C]0.249774[/C][/ROW]
[ROW][C]41[/C][C]8.5[/C][C]6.30936[/C][C]2.19064[/C][/ROW]
[ROW][C]42[/C][C]3.5[/C][C]4.14823[/C][C]-0.648225[/C][/ROW]
[ROW][C]43[/C][C]6[/C][C]4.25208[/C][C]1.74792[/C][/ROW]
[ROW][C]44[/C][C]1.5[/C][C]4.95052[/C][C]-3.45052[/C][/ROW]
[ROW][C]45[/C][C]3.5[/C][C]3.95493[/C][C]-0.454931[/C][/ROW]
[ROW][C]46[/C][C]7.5[/C][C]4.31479[/C][C]3.18521[/C][/ROW]
[ROW][C]47[/C][C]5[/C][C]4.44722[/C][C]0.552782[/C][/ROW]
[ROW][C]48[/C][C]6.5[/C][C]4.42411[/C][C]2.07589[/C][/ROW]
[ROW][C]49[/C][C]6.5[/C][C]5.31569[/C][C]1.18431[/C][/ROW]
[ROW][C]50[/C][C]6.5[/C][C]5.11508[/C][C]1.38492[/C][/ROW]
[ROW][C]51[/C][C]7[/C][C]4.15177[/C][C]2.84823[/C][/ROW]
[ROW][C]52[/C][C]1.5[/C][C]4.04969[/C][C]-2.54969[/C][/ROW]
[ROW][C]53[/C][C]4[/C][C]5.21716[/C][C]-1.21716[/C][/ROW]
[ROW][C]54[/C][C]4.5[/C][C]4.31648[/C][C]0.183519[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]3.91726[/C][C]-3.91726[/C][/ROW]
[ROW][C]56[/C][C]3.5[/C][C]4.61917[/C][C]-1.11917[/C][/ROW]
[ROW][C]57[/C][C]4.5[/C][C]4.88958[/C][C]-0.389583[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]5.01847[/C][C]-5.01847[/C][/ROW]
[ROW][C]59[/C][C]3[/C][C]4.98442[/C][C]-1.98442[/C][/ROW]
[ROW][C]60[/C][C]3.5[/C][C]4.44899[/C][C]-0.94899[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]5.17949[/C][C]-2.17949[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]4.61917[/C][C]-3.61917[/C][/ROW]
[ROW][C]63[/C][C]5.5[/C][C]4.71401[/C][C]0.785992[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265361&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265361&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
17.54.848292.65171
26.55.15091.3491
314.48119-3.48119
413.95123-2.95123
55.54.053311.44669
68.55.750892.74911
76.54.452612.04739
84.54.144530.355473
924.25023-2.25023
1054.58150.418501
110.53.98529-3.48529
1254.978950.0210505
132.54.71956-2.21956
1455.91191-0.911906
155.54.781960.718043
163.54.5493-1.0493
1744.85006-0.850061
186.54.678181.82182
194.54.81431-0.314315
205.55.285340.214661
2144.7516-0.751604
227.54.689052.81095
2344.38274-0.382735
245.53.81511.6849
252.54.62094-2.12094
265.55.414150.0858503
273.54.44899-0.94899
284.54.248450.251546
294.53.759790.740212
3064.747981.25202
3153.523431.47657
326.53.591462.90854
3354.848290.151711
3464.081971.91803
354.53.589680.910316
3654.282510.717494
3754.782030.217965
386.55.213541.28646
3974.380892.61911
404.54.250230.249774
418.56.309362.19064
423.54.14823-0.648225
4364.252081.74792
441.54.95052-3.45052
453.53.95493-0.454931
467.54.314793.18521
4754.447220.552782
486.54.424112.07589
496.55.315691.18431
506.55.115081.38492
5174.151772.84823
521.54.04969-2.54969
5345.21716-1.21716
544.54.316480.183519
5503.91726-3.91726
563.54.61917-1.11917
574.54.88958-0.389583
5805.01847-5.01847
5934.98442-1.98442
603.54.44899-0.94899
6135.17949-2.17949
6214.61917-3.61917
635.54.714010.785992







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.8200770.3598460.179923
80.8754720.2490560.124528
90.8577590.2844810.142241
100.777540.444920.22246
110.8145820.3708360.185418
120.7454560.5090890.254544
130.7084950.5830110.291505
140.7604090.4791820.239591
150.6837990.6324010.316201
160.6037510.7924980.396249
170.5614790.8770430.438521
180.6112230.7775540.388777
190.5253690.9492630.474631
200.443230.886460.55677
210.3757660.7515320.624234
220.4650530.9301070.534947
230.388140.7762810.61186
240.4309760.8619510.569024
250.4417950.8835910.558205
260.370530.741060.62947
270.313190.626380.68681
280.254030.5080610.74597
290.2135230.4270450.786477
300.1774380.3548750.822562
310.1641060.3282120.835894
320.2203830.4407650.779617
330.167550.33510.83245
340.1596760.3193530.840324
350.13270.26540.8673
360.0995390.1990780.900461
370.06975550.1395110.930244
380.05427530.1085510.945725
390.0675020.1350040.932498
400.04600650.09201290.953994
410.0532410.1064820.946759
420.0360990.07219810.963901
430.03277620.06555250.967224
440.06970980.139420.93029
450.04647870.09295730.953521
460.08140950.1628190.918591
470.06641930.1328390.933581
480.1182140.2364280.881786
490.2198770.4397540.780123
500.2689810.5379610.731019
510.3878160.7756310.612184
520.3909580.7819150.609042
530.2936420.5872840.706358
540.2006180.4012360.799382
550.2615790.5231580.738421
560.1594290.3188570.840571

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.820077 & 0.359846 & 0.179923 \tabularnewline
8 & 0.875472 & 0.249056 & 0.124528 \tabularnewline
9 & 0.857759 & 0.284481 & 0.142241 \tabularnewline
10 & 0.77754 & 0.44492 & 0.22246 \tabularnewline
11 & 0.814582 & 0.370836 & 0.185418 \tabularnewline
12 & 0.745456 & 0.509089 & 0.254544 \tabularnewline
13 & 0.708495 & 0.583011 & 0.291505 \tabularnewline
14 & 0.760409 & 0.479182 & 0.239591 \tabularnewline
15 & 0.683799 & 0.632401 & 0.316201 \tabularnewline
16 & 0.603751 & 0.792498 & 0.396249 \tabularnewline
17 & 0.561479 & 0.877043 & 0.438521 \tabularnewline
18 & 0.611223 & 0.777554 & 0.388777 \tabularnewline
19 & 0.525369 & 0.949263 & 0.474631 \tabularnewline
20 & 0.44323 & 0.88646 & 0.55677 \tabularnewline
21 & 0.375766 & 0.751532 & 0.624234 \tabularnewline
22 & 0.465053 & 0.930107 & 0.534947 \tabularnewline
23 & 0.38814 & 0.776281 & 0.61186 \tabularnewline
24 & 0.430976 & 0.861951 & 0.569024 \tabularnewline
25 & 0.441795 & 0.883591 & 0.558205 \tabularnewline
26 & 0.37053 & 0.74106 & 0.62947 \tabularnewline
27 & 0.31319 & 0.62638 & 0.68681 \tabularnewline
28 & 0.25403 & 0.508061 & 0.74597 \tabularnewline
29 & 0.213523 & 0.427045 & 0.786477 \tabularnewline
30 & 0.177438 & 0.354875 & 0.822562 \tabularnewline
31 & 0.164106 & 0.328212 & 0.835894 \tabularnewline
32 & 0.220383 & 0.440765 & 0.779617 \tabularnewline
33 & 0.16755 & 0.3351 & 0.83245 \tabularnewline
34 & 0.159676 & 0.319353 & 0.840324 \tabularnewline
35 & 0.1327 & 0.2654 & 0.8673 \tabularnewline
36 & 0.099539 & 0.199078 & 0.900461 \tabularnewline
37 & 0.0697555 & 0.139511 & 0.930244 \tabularnewline
38 & 0.0542753 & 0.108551 & 0.945725 \tabularnewline
39 & 0.067502 & 0.135004 & 0.932498 \tabularnewline
40 & 0.0460065 & 0.0920129 & 0.953994 \tabularnewline
41 & 0.053241 & 0.106482 & 0.946759 \tabularnewline
42 & 0.036099 & 0.0721981 & 0.963901 \tabularnewline
43 & 0.0327762 & 0.0655525 & 0.967224 \tabularnewline
44 & 0.0697098 & 0.13942 & 0.93029 \tabularnewline
45 & 0.0464787 & 0.0929573 & 0.953521 \tabularnewline
46 & 0.0814095 & 0.162819 & 0.918591 \tabularnewline
47 & 0.0664193 & 0.132839 & 0.933581 \tabularnewline
48 & 0.118214 & 0.236428 & 0.881786 \tabularnewline
49 & 0.219877 & 0.439754 & 0.780123 \tabularnewline
50 & 0.268981 & 0.537961 & 0.731019 \tabularnewline
51 & 0.387816 & 0.775631 & 0.612184 \tabularnewline
52 & 0.390958 & 0.781915 & 0.609042 \tabularnewline
53 & 0.293642 & 0.587284 & 0.706358 \tabularnewline
54 & 0.200618 & 0.401236 & 0.799382 \tabularnewline
55 & 0.261579 & 0.523158 & 0.738421 \tabularnewline
56 & 0.159429 & 0.318857 & 0.840571 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265361&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]7[/C][C]0.820077[/C][C]0.359846[/C][C]0.179923[/C][/ROW]
[ROW][C]8[/C][C]0.875472[/C][C]0.249056[/C][C]0.124528[/C][/ROW]
[ROW][C]9[/C][C]0.857759[/C][C]0.284481[/C][C]0.142241[/C][/ROW]
[ROW][C]10[/C][C]0.77754[/C][C]0.44492[/C][C]0.22246[/C][/ROW]
[ROW][C]11[/C][C]0.814582[/C][C]0.370836[/C][C]0.185418[/C][/ROW]
[ROW][C]12[/C][C]0.745456[/C][C]0.509089[/C][C]0.254544[/C][/ROW]
[ROW][C]13[/C][C]0.708495[/C][C]0.583011[/C][C]0.291505[/C][/ROW]
[ROW][C]14[/C][C]0.760409[/C][C]0.479182[/C][C]0.239591[/C][/ROW]
[ROW][C]15[/C][C]0.683799[/C][C]0.632401[/C][C]0.316201[/C][/ROW]
[ROW][C]16[/C][C]0.603751[/C][C]0.792498[/C][C]0.396249[/C][/ROW]
[ROW][C]17[/C][C]0.561479[/C][C]0.877043[/C][C]0.438521[/C][/ROW]
[ROW][C]18[/C][C]0.611223[/C][C]0.777554[/C][C]0.388777[/C][/ROW]
[ROW][C]19[/C][C]0.525369[/C][C]0.949263[/C][C]0.474631[/C][/ROW]
[ROW][C]20[/C][C]0.44323[/C][C]0.88646[/C][C]0.55677[/C][/ROW]
[ROW][C]21[/C][C]0.375766[/C][C]0.751532[/C][C]0.624234[/C][/ROW]
[ROW][C]22[/C][C]0.465053[/C][C]0.930107[/C][C]0.534947[/C][/ROW]
[ROW][C]23[/C][C]0.38814[/C][C]0.776281[/C][C]0.61186[/C][/ROW]
[ROW][C]24[/C][C]0.430976[/C][C]0.861951[/C][C]0.569024[/C][/ROW]
[ROW][C]25[/C][C]0.441795[/C][C]0.883591[/C][C]0.558205[/C][/ROW]
[ROW][C]26[/C][C]0.37053[/C][C]0.74106[/C][C]0.62947[/C][/ROW]
[ROW][C]27[/C][C]0.31319[/C][C]0.62638[/C][C]0.68681[/C][/ROW]
[ROW][C]28[/C][C]0.25403[/C][C]0.508061[/C][C]0.74597[/C][/ROW]
[ROW][C]29[/C][C]0.213523[/C][C]0.427045[/C][C]0.786477[/C][/ROW]
[ROW][C]30[/C][C]0.177438[/C][C]0.354875[/C][C]0.822562[/C][/ROW]
[ROW][C]31[/C][C]0.164106[/C][C]0.328212[/C][C]0.835894[/C][/ROW]
[ROW][C]32[/C][C]0.220383[/C][C]0.440765[/C][C]0.779617[/C][/ROW]
[ROW][C]33[/C][C]0.16755[/C][C]0.3351[/C][C]0.83245[/C][/ROW]
[ROW][C]34[/C][C]0.159676[/C][C]0.319353[/C][C]0.840324[/C][/ROW]
[ROW][C]35[/C][C]0.1327[/C][C]0.2654[/C][C]0.8673[/C][/ROW]
[ROW][C]36[/C][C]0.099539[/C][C]0.199078[/C][C]0.900461[/C][/ROW]
[ROW][C]37[/C][C]0.0697555[/C][C]0.139511[/C][C]0.930244[/C][/ROW]
[ROW][C]38[/C][C]0.0542753[/C][C]0.108551[/C][C]0.945725[/C][/ROW]
[ROW][C]39[/C][C]0.067502[/C][C]0.135004[/C][C]0.932498[/C][/ROW]
[ROW][C]40[/C][C]0.0460065[/C][C]0.0920129[/C][C]0.953994[/C][/ROW]
[ROW][C]41[/C][C]0.053241[/C][C]0.106482[/C][C]0.946759[/C][/ROW]
[ROW][C]42[/C][C]0.036099[/C][C]0.0721981[/C][C]0.963901[/C][/ROW]
[ROW][C]43[/C][C]0.0327762[/C][C]0.0655525[/C][C]0.967224[/C][/ROW]
[ROW][C]44[/C][C]0.0697098[/C][C]0.13942[/C][C]0.93029[/C][/ROW]
[ROW][C]45[/C][C]0.0464787[/C][C]0.0929573[/C][C]0.953521[/C][/ROW]
[ROW][C]46[/C][C]0.0814095[/C][C]0.162819[/C][C]0.918591[/C][/ROW]
[ROW][C]47[/C][C]0.0664193[/C][C]0.132839[/C][C]0.933581[/C][/ROW]
[ROW][C]48[/C][C]0.118214[/C][C]0.236428[/C][C]0.881786[/C][/ROW]
[ROW][C]49[/C][C]0.219877[/C][C]0.439754[/C][C]0.780123[/C][/ROW]
[ROW][C]50[/C][C]0.268981[/C][C]0.537961[/C][C]0.731019[/C][/ROW]
[ROW][C]51[/C][C]0.387816[/C][C]0.775631[/C][C]0.612184[/C][/ROW]
[ROW][C]52[/C][C]0.390958[/C][C]0.781915[/C][C]0.609042[/C][/ROW]
[ROW][C]53[/C][C]0.293642[/C][C]0.587284[/C][C]0.706358[/C][/ROW]
[ROW][C]54[/C][C]0.200618[/C][C]0.401236[/C][C]0.799382[/C][/ROW]
[ROW][C]55[/C][C]0.261579[/C][C]0.523158[/C][C]0.738421[/C][/ROW]
[ROW][C]56[/C][C]0.159429[/C][C]0.318857[/C][C]0.840571[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265361&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265361&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
70.8200770.3598460.179923
80.8754720.2490560.124528
90.8577590.2844810.142241
100.777540.444920.22246
110.8145820.3708360.185418
120.7454560.5090890.254544
130.7084950.5830110.291505
140.7604090.4791820.239591
150.6837990.6324010.316201
160.6037510.7924980.396249
170.5614790.8770430.438521
180.6112230.7775540.388777
190.5253690.9492630.474631
200.443230.886460.55677
210.3757660.7515320.624234
220.4650530.9301070.534947
230.388140.7762810.61186
240.4309760.8619510.569024
250.4417950.8835910.558205
260.370530.741060.62947
270.313190.626380.68681
280.254030.5080610.74597
290.2135230.4270450.786477
300.1774380.3548750.822562
310.1641060.3282120.835894
320.2203830.4407650.779617
330.167550.33510.83245
340.1596760.3193530.840324
350.13270.26540.8673
360.0995390.1990780.900461
370.06975550.1395110.930244
380.05427530.1085510.945725
390.0675020.1350040.932498
400.04600650.09201290.953994
410.0532410.1064820.946759
420.0360990.07219810.963901
430.03277620.06555250.967224
440.06970980.139420.93029
450.04647870.09295730.953521
460.08140950.1628190.918591
470.06641930.1328390.933581
480.1182140.2364280.881786
490.2198770.4397540.780123
500.2689810.5379610.731019
510.3878160.7756310.612184
520.3909580.7819150.609042
530.2936420.5872840.706358
540.2006180.4012360.799382
550.2615790.5231580.738421
560.1594290.3188570.840571







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level40.08OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 4 & 0.08 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265361&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.08[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265361&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265361&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 level00OK
5% type I error level00OK
10% type I error level40.08OK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$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$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1/numgqtests,6))
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')
}