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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 computationFri, 20 Nov 2009 07:14:17 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258726571gpca9r73cqpscp3.htm/, Retrieved Fri, 29 Mar 2024 07:42:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58186, Retrieved Fri, 29 Mar 2024 07:42:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact134
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2009-11-20 14:14:17] [dd88bf4749af0c195ad4f54cb428da1c] [Current]
-   PD        [Multiple Regression] [] [2009-11-20 15:11:50] [2f9700e78f159997f527be4a316457f5]
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Dataseries X:
94,6	0
95,9	1
104,7	1
102,8	1
98,1	1
113,9	1
80,9	1
95,7	1
113,2	1
105,9	1
108,8	0
102,3	0
99	1
100,7	1
115,5	0
100,7	1
109,9	1
114,6	0
85,4	1
100,5	1
114,8	0
116,5	0
112,9	1
102	1
106	0
105,3	1
118,8	1
106,1	1
109,3	0
117,2	0
92,5	1
104,2	0
112,5	1
122,4	1
113,3	1
100	1
110,7	1
112,8	1
109,8	1
117,3	1
109,1	1
115,9	1
96	1
99,8	0
116,8	1
115,7	0
99,4	0
94,3	0
91	1
93,2	1
103,1	0
94,1	1
91,8	0
102,7	0
82,6	0
89,1	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58186&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58186&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58186&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 104.905263157895 -0.880938833570407X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  104.905263157895 -0.880938833570407X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58186&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  104.905263157895 -0.880938833570407X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58186&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58186&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
Y[t] = + 104.905263157895 -0.880938833570407X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)104.9052631578952.27719446.067800
X-0.8809388335704072.801516-0.31450.754390.377195

\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) & 104.905263157895 & 2.277194 & 46.0678 & 0 & 0 \tabularnewline
X & -0.880938833570407 & 2.801516 & -0.3145 & 0.75439 & 0.377195 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58186&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]104.905263157895[/C][C]2.277194[/C][C]46.0678[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-0.880938833570407[/C][C]2.801516[/C][C]-0.3145[/C][C]0.75439[/C][C]0.377195[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58186&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58186&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)104.9052631578952.27719446.067800
X-0.8809388335704072.801516-0.31450.754390.377195







Multiple Linear Regression - Regression Statistics
Multiple R0.0427522005347858
R-squared0.00182775065056654
Adjusted R-squared-0.0166569206336824
F-TEST (value)0.0988792617656127
F-TEST (DF numerator)1
F-TEST (DF denominator)54
p-value0.754390470610119
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.9260577212226
Sum Squared Residuals5320.43758179232

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.0427522005347858 \tabularnewline
R-squared & 0.00182775065056654 \tabularnewline
Adjusted R-squared & -0.0166569206336824 \tabularnewline
F-TEST (value) & 0.0988792617656127 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 54 \tabularnewline
p-value & 0.754390470610119 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9.9260577212226 \tabularnewline
Sum Squared Residuals & 5320.43758179232 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58186&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.0427522005347858[/C][/ROW]
[ROW][C]R-squared[/C][C]0.00182775065056654[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0166569206336824[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.0988792617656127[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]54[/C][/ROW]
[ROW][C]p-value[/C][C]0.754390470610119[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]9.9260577212226[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5320.43758179232[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58186&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58186&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 R0.0427522005347858
R-squared0.00182775065056654
Adjusted R-squared-0.0166569206336824
F-TEST (value)0.0988792617656127
F-TEST (DF numerator)1
F-TEST (DF denominator)54
p-value0.754390470610119
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.9260577212226
Sum Squared Residuals5320.43758179232







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
194.6104.905263157895-10.3052631578949
295.9104.024324324324-8.12432432432433
3104.7104.0243243243240.675675675675677
4102.8104.024324324324-1.22432432432433
598.1104.024324324324-5.92432432432433
6113.9104.0243243243249.87567567567568
780.9104.024324324324-23.1243243243243
895.7104.024324324324-8.32432432432432
9113.2104.0243243243249.17567567567568
10105.9104.0243243243241.87567567567568
11108.8104.9052631578953.89473684210527
12102.3104.905263157895-2.60526315789473
1399104.024324324324-5.02432432432433
14100.7104.024324324324-3.32432432432432
15115.5104.90526315789510.5947368421053
16100.7104.024324324324-3.32432432432432
17109.9104.0243243243245.87567567567568
18114.6104.9052631578959.69473684210526
1985.4104.024324324324-18.6243243243243
20100.5104.024324324324-3.52432432432433
21114.8104.9052631578959.89473684210527
22116.5104.90526315789511.5947368421053
23112.9104.0243243243248.87567567567568
24102104.024324324324-2.02432432432433
25106104.9052631578951.09473684210527
26105.3104.0243243243241.27567567567567
27118.8104.02432432432414.7756756756757
28106.1104.0243243243242.07567567567567
29109.3104.9052631578954.39473684210527
30117.2104.90526315789512.2947368421053
3192.5104.024324324324-11.5243243243243
32104.2104.905263157895-0.705263157894728
33112.5104.0243243243248.47567567567567
34122.4104.02432432432418.3756756756757
35113.3104.0243243243249.27567567567567
36100104.024324324324-4.02432432432433
37110.7104.0243243243246.67567567567568
38112.8104.0243243243248.77567567567567
39109.8104.0243243243245.77567567567567
40117.3104.02432432432413.2756756756757
41109.1104.0243243243245.07567567567567
42115.9104.02432432432411.8756756756757
4396104.024324324324-8.02432432432433
4499.8104.905263157895-5.10526315789473
45116.8104.02432432432412.7756756756757
46115.7104.90526315789510.7947368421053
4799.4104.905263157895-5.50526315789472
4894.3104.905263157895-10.6052631578947
4991104.024324324324-13.0243243243243
5093.2104.024324324324-10.8243243243243
51103.1104.905263157895-1.80526315789474
5294.1104.024324324324-9.92432432432433
5391.8104.905263157895-13.1052631578947
54102.7104.905263157895-2.20526315789473
5582.6104.905263157895-22.3052631578947
5689.1104.024324324324-14.9243243243243

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 94.6 & 104.905263157895 & -10.3052631578949 \tabularnewline
2 & 95.9 & 104.024324324324 & -8.12432432432433 \tabularnewline
3 & 104.7 & 104.024324324324 & 0.675675675675677 \tabularnewline
4 & 102.8 & 104.024324324324 & -1.22432432432433 \tabularnewline
5 & 98.1 & 104.024324324324 & -5.92432432432433 \tabularnewline
6 & 113.9 & 104.024324324324 & 9.87567567567568 \tabularnewline
7 & 80.9 & 104.024324324324 & -23.1243243243243 \tabularnewline
8 & 95.7 & 104.024324324324 & -8.32432432432432 \tabularnewline
9 & 113.2 & 104.024324324324 & 9.17567567567568 \tabularnewline
10 & 105.9 & 104.024324324324 & 1.87567567567568 \tabularnewline
11 & 108.8 & 104.905263157895 & 3.89473684210527 \tabularnewline
12 & 102.3 & 104.905263157895 & -2.60526315789473 \tabularnewline
13 & 99 & 104.024324324324 & -5.02432432432433 \tabularnewline
14 & 100.7 & 104.024324324324 & -3.32432432432432 \tabularnewline
15 & 115.5 & 104.905263157895 & 10.5947368421053 \tabularnewline
16 & 100.7 & 104.024324324324 & -3.32432432432432 \tabularnewline
17 & 109.9 & 104.024324324324 & 5.87567567567568 \tabularnewline
18 & 114.6 & 104.905263157895 & 9.69473684210526 \tabularnewline
19 & 85.4 & 104.024324324324 & -18.6243243243243 \tabularnewline
20 & 100.5 & 104.024324324324 & -3.52432432432433 \tabularnewline
21 & 114.8 & 104.905263157895 & 9.89473684210527 \tabularnewline
22 & 116.5 & 104.905263157895 & 11.5947368421053 \tabularnewline
23 & 112.9 & 104.024324324324 & 8.87567567567568 \tabularnewline
24 & 102 & 104.024324324324 & -2.02432432432433 \tabularnewline
25 & 106 & 104.905263157895 & 1.09473684210527 \tabularnewline
26 & 105.3 & 104.024324324324 & 1.27567567567567 \tabularnewline
27 & 118.8 & 104.024324324324 & 14.7756756756757 \tabularnewline
28 & 106.1 & 104.024324324324 & 2.07567567567567 \tabularnewline
29 & 109.3 & 104.905263157895 & 4.39473684210527 \tabularnewline
30 & 117.2 & 104.905263157895 & 12.2947368421053 \tabularnewline
31 & 92.5 & 104.024324324324 & -11.5243243243243 \tabularnewline
32 & 104.2 & 104.905263157895 & -0.705263157894728 \tabularnewline
33 & 112.5 & 104.024324324324 & 8.47567567567567 \tabularnewline
34 & 122.4 & 104.024324324324 & 18.3756756756757 \tabularnewline
35 & 113.3 & 104.024324324324 & 9.27567567567567 \tabularnewline
36 & 100 & 104.024324324324 & -4.02432432432433 \tabularnewline
37 & 110.7 & 104.024324324324 & 6.67567567567568 \tabularnewline
38 & 112.8 & 104.024324324324 & 8.77567567567567 \tabularnewline
39 & 109.8 & 104.024324324324 & 5.77567567567567 \tabularnewline
40 & 117.3 & 104.024324324324 & 13.2756756756757 \tabularnewline
41 & 109.1 & 104.024324324324 & 5.07567567567567 \tabularnewline
42 & 115.9 & 104.024324324324 & 11.8756756756757 \tabularnewline
43 & 96 & 104.024324324324 & -8.02432432432433 \tabularnewline
44 & 99.8 & 104.905263157895 & -5.10526315789473 \tabularnewline
45 & 116.8 & 104.024324324324 & 12.7756756756757 \tabularnewline
46 & 115.7 & 104.905263157895 & 10.7947368421053 \tabularnewline
47 & 99.4 & 104.905263157895 & -5.50526315789472 \tabularnewline
48 & 94.3 & 104.905263157895 & -10.6052631578947 \tabularnewline
49 & 91 & 104.024324324324 & -13.0243243243243 \tabularnewline
50 & 93.2 & 104.024324324324 & -10.8243243243243 \tabularnewline
51 & 103.1 & 104.905263157895 & -1.80526315789474 \tabularnewline
52 & 94.1 & 104.024324324324 & -9.92432432432433 \tabularnewline
53 & 91.8 & 104.905263157895 & -13.1052631578947 \tabularnewline
54 & 102.7 & 104.905263157895 & -2.20526315789473 \tabularnewline
55 & 82.6 & 104.905263157895 & -22.3052631578947 \tabularnewline
56 & 89.1 & 104.024324324324 & -14.9243243243243 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58186&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]94.6[/C][C]104.905263157895[/C][C]-10.3052631578949[/C][/ROW]
[ROW][C]2[/C][C]95.9[/C][C]104.024324324324[/C][C]-8.12432432432433[/C][/ROW]
[ROW][C]3[/C][C]104.7[/C][C]104.024324324324[/C][C]0.675675675675677[/C][/ROW]
[ROW][C]4[/C][C]102.8[/C][C]104.024324324324[/C][C]-1.22432432432433[/C][/ROW]
[ROW][C]5[/C][C]98.1[/C][C]104.024324324324[/C][C]-5.92432432432433[/C][/ROW]
[ROW][C]6[/C][C]113.9[/C][C]104.024324324324[/C][C]9.87567567567568[/C][/ROW]
[ROW][C]7[/C][C]80.9[/C][C]104.024324324324[/C][C]-23.1243243243243[/C][/ROW]
[ROW][C]8[/C][C]95.7[/C][C]104.024324324324[/C][C]-8.32432432432432[/C][/ROW]
[ROW][C]9[/C][C]113.2[/C][C]104.024324324324[/C][C]9.17567567567568[/C][/ROW]
[ROW][C]10[/C][C]105.9[/C][C]104.024324324324[/C][C]1.87567567567568[/C][/ROW]
[ROW][C]11[/C][C]108.8[/C][C]104.905263157895[/C][C]3.89473684210527[/C][/ROW]
[ROW][C]12[/C][C]102.3[/C][C]104.905263157895[/C][C]-2.60526315789473[/C][/ROW]
[ROW][C]13[/C][C]99[/C][C]104.024324324324[/C][C]-5.02432432432433[/C][/ROW]
[ROW][C]14[/C][C]100.7[/C][C]104.024324324324[/C][C]-3.32432432432432[/C][/ROW]
[ROW][C]15[/C][C]115.5[/C][C]104.905263157895[/C][C]10.5947368421053[/C][/ROW]
[ROW][C]16[/C][C]100.7[/C][C]104.024324324324[/C][C]-3.32432432432432[/C][/ROW]
[ROW][C]17[/C][C]109.9[/C][C]104.024324324324[/C][C]5.87567567567568[/C][/ROW]
[ROW][C]18[/C][C]114.6[/C][C]104.905263157895[/C][C]9.69473684210526[/C][/ROW]
[ROW][C]19[/C][C]85.4[/C][C]104.024324324324[/C][C]-18.6243243243243[/C][/ROW]
[ROW][C]20[/C][C]100.5[/C][C]104.024324324324[/C][C]-3.52432432432433[/C][/ROW]
[ROW][C]21[/C][C]114.8[/C][C]104.905263157895[/C][C]9.89473684210527[/C][/ROW]
[ROW][C]22[/C][C]116.5[/C][C]104.905263157895[/C][C]11.5947368421053[/C][/ROW]
[ROW][C]23[/C][C]112.9[/C][C]104.024324324324[/C][C]8.87567567567568[/C][/ROW]
[ROW][C]24[/C][C]102[/C][C]104.024324324324[/C][C]-2.02432432432433[/C][/ROW]
[ROW][C]25[/C][C]106[/C][C]104.905263157895[/C][C]1.09473684210527[/C][/ROW]
[ROW][C]26[/C][C]105.3[/C][C]104.024324324324[/C][C]1.27567567567567[/C][/ROW]
[ROW][C]27[/C][C]118.8[/C][C]104.024324324324[/C][C]14.7756756756757[/C][/ROW]
[ROW][C]28[/C][C]106.1[/C][C]104.024324324324[/C][C]2.07567567567567[/C][/ROW]
[ROW][C]29[/C][C]109.3[/C][C]104.905263157895[/C][C]4.39473684210527[/C][/ROW]
[ROW][C]30[/C][C]117.2[/C][C]104.905263157895[/C][C]12.2947368421053[/C][/ROW]
[ROW][C]31[/C][C]92.5[/C][C]104.024324324324[/C][C]-11.5243243243243[/C][/ROW]
[ROW][C]32[/C][C]104.2[/C][C]104.905263157895[/C][C]-0.705263157894728[/C][/ROW]
[ROW][C]33[/C][C]112.5[/C][C]104.024324324324[/C][C]8.47567567567567[/C][/ROW]
[ROW][C]34[/C][C]122.4[/C][C]104.024324324324[/C][C]18.3756756756757[/C][/ROW]
[ROW][C]35[/C][C]113.3[/C][C]104.024324324324[/C][C]9.27567567567567[/C][/ROW]
[ROW][C]36[/C][C]100[/C][C]104.024324324324[/C][C]-4.02432432432433[/C][/ROW]
[ROW][C]37[/C][C]110.7[/C][C]104.024324324324[/C][C]6.67567567567568[/C][/ROW]
[ROW][C]38[/C][C]112.8[/C][C]104.024324324324[/C][C]8.77567567567567[/C][/ROW]
[ROW][C]39[/C][C]109.8[/C][C]104.024324324324[/C][C]5.77567567567567[/C][/ROW]
[ROW][C]40[/C][C]117.3[/C][C]104.024324324324[/C][C]13.2756756756757[/C][/ROW]
[ROW][C]41[/C][C]109.1[/C][C]104.024324324324[/C][C]5.07567567567567[/C][/ROW]
[ROW][C]42[/C][C]115.9[/C][C]104.024324324324[/C][C]11.8756756756757[/C][/ROW]
[ROW][C]43[/C][C]96[/C][C]104.024324324324[/C][C]-8.02432432432433[/C][/ROW]
[ROW][C]44[/C][C]99.8[/C][C]104.905263157895[/C][C]-5.10526315789473[/C][/ROW]
[ROW][C]45[/C][C]116.8[/C][C]104.024324324324[/C][C]12.7756756756757[/C][/ROW]
[ROW][C]46[/C][C]115.7[/C][C]104.905263157895[/C][C]10.7947368421053[/C][/ROW]
[ROW][C]47[/C][C]99.4[/C][C]104.905263157895[/C][C]-5.50526315789472[/C][/ROW]
[ROW][C]48[/C][C]94.3[/C][C]104.905263157895[/C][C]-10.6052631578947[/C][/ROW]
[ROW][C]49[/C][C]91[/C][C]104.024324324324[/C][C]-13.0243243243243[/C][/ROW]
[ROW][C]50[/C][C]93.2[/C][C]104.024324324324[/C][C]-10.8243243243243[/C][/ROW]
[ROW][C]51[/C][C]103.1[/C][C]104.905263157895[/C][C]-1.80526315789474[/C][/ROW]
[ROW][C]52[/C][C]94.1[/C][C]104.024324324324[/C][C]-9.92432432432433[/C][/ROW]
[ROW][C]53[/C][C]91.8[/C][C]104.905263157895[/C][C]-13.1052631578947[/C][/ROW]
[ROW][C]54[/C][C]102.7[/C][C]104.905263157895[/C][C]-2.20526315789473[/C][/ROW]
[ROW][C]55[/C][C]82.6[/C][C]104.905263157895[/C][C]-22.3052631578947[/C][/ROW]
[ROW][C]56[/C][C]89.1[/C][C]104.024324324324[/C][C]-14.9243243243243[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58186&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58186&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
194.6104.905263157895-10.3052631578949
295.9104.024324324324-8.12432432432433
3104.7104.0243243243240.675675675675677
4102.8104.024324324324-1.22432432432433
598.1104.024324324324-5.92432432432433
6113.9104.0243243243249.87567567567568
780.9104.024324324324-23.1243243243243
895.7104.024324324324-8.32432432432432
9113.2104.0243243243249.17567567567568
10105.9104.0243243243241.87567567567568
11108.8104.9052631578953.89473684210527
12102.3104.905263157895-2.60526315789473
1399104.024324324324-5.02432432432433
14100.7104.024324324324-3.32432432432432
15115.5104.90526315789510.5947368421053
16100.7104.024324324324-3.32432432432432
17109.9104.0243243243245.87567567567568
18114.6104.9052631578959.69473684210526
1985.4104.024324324324-18.6243243243243
20100.5104.024324324324-3.52432432432433
21114.8104.9052631578959.89473684210527
22116.5104.90526315789511.5947368421053
23112.9104.0243243243248.87567567567568
24102104.024324324324-2.02432432432433
25106104.9052631578951.09473684210527
26105.3104.0243243243241.27567567567567
27118.8104.02432432432414.7756756756757
28106.1104.0243243243242.07567567567567
29109.3104.9052631578954.39473684210527
30117.2104.90526315789512.2947368421053
3192.5104.024324324324-11.5243243243243
32104.2104.905263157895-0.705263157894728
33112.5104.0243243243248.47567567567567
34122.4104.02432432432418.3756756756757
35113.3104.0243243243249.27567567567567
36100104.024324324324-4.02432432432433
37110.7104.0243243243246.67567567567568
38112.8104.0243243243248.77567567567567
39109.8104.0243243243245.77567567567567
40117.3104.02432432432413.2756756756757
41109.1104.0243243243245.07567567567567
42115.9104.02432432432411.8756756756757
4396104.024324324324-8.02432432432433
4499.8104.905263157895-5.10526315789473
45116.8104.02432432432412.7756756756757
46115.7104.90526315789510.7947368421053
4799.4104.905263157895-5.50526315789472
4894.3104.905263157895-10.6052631578947
4991104.024324324324-13.0243243243243
5093.2104.024324324324-10.8243243243243
51103.1104.905263157895-1.80526315789474
5294.1104.024324324324-9.92432432432433
5391.8104.905263157895-13.1052631578947
54102.7104.905263157895-2.20526315789473
5582.6104.905263157895-22.3052631578947
5689.1104.024324324324-14.9243243243243







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.07694456099770640.1538891219954130.923055439002294
60.2425847748423800.4851695496847590.75741522515762
70.711941769212520.5761164615749610.288058230787481
80.6151916846429020.7696166307141960.384808315357098
90.674440737930050.65111852413990.32555926206995
100.587547012199930.824905975600140.41245298780007
110.5675976643348450.864804671330310.432402335665155
120.4650743269529160.9301486539058310.534925673047084
130.3758192596425420.7516385192850830.624180740357458
140.2893875101680050.5787750203360090.710612489831995
150.3233999859580050.646799971916010.676600014041995
160.2478379633553040.4956759267106080.752162036644696
170.2243473936052760.4486947872105520.775652606394724
180.2124670080511560.4249340161023130.787532991948844
190.3634153385993630.7268306771987270.636584661400637
200.29419763785150.58839527570300.7058023621485
210.2771575019794270.5543150039588540.722842498020573
220.2822842417870790.5645684835741580.717715758212921
230.2964670086418020.5929340172836050.703532991358198
240.2344326590159440.4688653180318880.765567340984056
250.1843464373962190.3686928747924370.815653562603781
260.1403456021733510.2806912043467030.859654397826649
270.2201415840009480.4402831680018960.779858415999052
280.1695787282532360.3391574565064710.830421271746764
290.1350041212193990.2700082424387990.8649958787806
300.1666158113817450.3332316227634910.833384188618255
310.1929285679936430.3858571359872860.807071432006357
320.1576262444590110.3152524889180210.84237375554099
330.1467961967461380.2935923934922760.853203803253862
340.2896021317873740.5792042635747480.710397868212626
350.2798651265408740.5597302530817480.720134873459126
360.2260394667015030.4520789334030050.773960533298497
370.1923126218911450.384625243782290.807687378108855
380.1813531289168510.3627062578337020.81864687108315
390.1498876747079830.2997753494159670.850112325292017
400.2164754493243520.4329508986487050.783524550675648
410.1920222453020910.3840444906041820.80797775469791
420.3152656816792810.6305313633585620.684734318320719
430.2546100758602850.509220151720570.745389924139715
440.1985599340296650.3971198680593290.801440065970335
450.5340487225699060.9319025548601880.465951277430094
460.8543432656291780.2913134687416430.145656734370822
470.8047576888497770.3904846223004460.195242311150223
480.72568069397060.54863861205880.2743193060294
490.6253542135890770.7492915728218460.374645786410923
500.4941420398363670.9882840796727330.505857960163633
510.4825840366569490.9651680733138970.517415963343051

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0769445609977064 & 0.153889121995413 & 0.923055439002294 \tabularnewline
6 & 0.242584774842380 & 0.485169549684759 & 0.75741522515762 \tabularnewline
7 & 0.71194176921252 & 0.576116461574961 & 0.288058230787481 \tabularnewline
8 & 0.615191684642902 & 0.769616630714196 & 0.384808315357098 \tabularnewline
9 & 0.67444073793005 & 0.6511185241399 & 0.32555926206995 \tabularnewline
10 & 0.58754701219993 & 0.82490597560014 & 0.41245298780007 \tabularnewline
11 & 0.567597664334845 & 0.86480467133031 & 0.432402335665155 \tabularnewline
12 & 0.465074326952916 & 0.930148653905831 & 0.534925673047084 \tabularnewline
13 & 0.375819259642542 & 0.751638519285083 & 0.624180740357458 \tabularnewline
14 & 0.289387510168005 & 0.578775020336009 & 0.710612489831995 \tabularnewline
15 & 0.323399985958005 & 0.64679997191601 & 0.676600014041995 \tabularnewline
16 & 0.247837963355304 & 0.495675926710608 & 0.752162036644696 \tabularnewline
17 & 0.224347393605276 & 0.448694787210552 & 0.775652606394724 \tabularnewline
18 & 0.212467008051156 & 0.424934016102313 & 0.787532991948844 \tabularnewline
19 & 0.363415338599363 & 0.726830677198727 & 0.636584661400637 \tabularnewline
20 & 0.2941976378515 & 0.5883952757030 & 0.7058023621485 \tabularnewline
21 & 0.277157501979427 & 0.554315003958854 & 0.722842498020573 \tabularnewline
22 & 0.282284241787079 & 0.564568483574158 & 0.717715758212921 \tabularnewline
23 & 0.296467008641802 & 0.592934017283605 & 0.703532991358198 \tabularnewline
24 & 0.234432659015944 & 0.468865318031888 & 0.765567340984056 \tabularnewline
25 & 0.184346437396219 & 0.368692874792437 & 0.815653562603781 \tabularnewline
26 & 0.140345602173351 & 0.280691204346703 & 0.859654397826649 \tabularnewline
27 & 0.220141584000948 & 0.440283168001896 & 0.779858415999052 \tabularnewline
28 & 0.169578728253236 & 0.339157456506471 & 0.830421271746764 \tabularnewline
29 & 0.135004121219399 & 0.270008242438799 & 0.8649958787806 \tabularnewline
30 & 0.166615811381745 & 0.333231622763491 & 0.833384188618255 \tabularnewline
31 & 0.192928567993643 & 0.385857135987286 & 0.807071432006357 \tabularnewline
32 & 0.157626244459011 & 0.315252488918021 & 0.84237375554099 \tabularnewline
33 & 0.146796196746138 & 0.293592393492276 & 0.853203803253862 \tabularnewline
34 & 0.289602131787374 & 0.579204263574748 & 0.710397868212626 \tabularnewline
35 & 0.279865126540874 & 0.559730253081748 & 0.720134873459126 \tabularnewline
36 & 0.226039466701503 & 0.452078933403005 & 0.773960533298497 \tabularnewline
37 & 0.192312621891145 & 0.38462524378229 & 0.807687378108855 \tabularnewline
38 & 0.181353128916851 & 0.362706257833702 & 0.81864687108315 \tabularnewline
39 & 0.149887674707983 & 0.299775349415967 & 0.850112325292017 \tabularnewline
40 & 0.216475449324352 & 0.432950898648705 & 0.783524550675648 \tabularnewline
41 & 0.192022245302091 & 0.384044490604182 & 0.80797775469791 \tabularnewline
42 & 0.315265681679281 & 0.630531363358562 & 0.684734318320719 \tabularnewline
43 & 0.254610075860285 & 0.50922015172057 & 0.745389924139715 \tabularnewline
44 & 0.198559934029665 & 0.397119868059329 & 0.801440065970335 \tabularnewline
45 & 0.534048722569906 & 0.931902554860188 & 0.465951277430094 \tabularnewline
46 & 0.854343265629178 & 0.291313468741643 & 0.145656734370822 \tabularnewline
47 & 0.804757688849777 & 0.390484622300446 & 0.195242311150223 \tabularnewline
48 & 0.7256806939706 & 0.5486386120588 & 0.2743193060294 \tabularnewline
49 & 0.625354213589077 & 0.749291572821846 & 0.374645786410923 \tabularnewline
50 & 0.494142039836367 & 0.988284079672733 & 0.505857960163633 \tabularnewline
51 & 0.482584036656949 & 0.965168073313897 & 0.517415963343051 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58186&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]5[/C][C]0.0769445609977064[/C][C]0.153889121995413[/C][C]0.923055439002294[/C][/ROW]
[ROW][C]6[/C][C]0.242584774842380[/C][C]0.485169549684759[/C][C]0.75741522515762[/C][/ROW]
[ROW][C]7[/C][C]0.71194176921252[/C][C]0.576116461574961[/C][C]0.288058230787481[/C][/ROW]
[ROW][C]8[/C][C]0.615191684642902[/C][C]0.769616630714196[/C][C]0.384808315357098[/C][/ROW]
[ROW][C]9[/C][C]0.67444073793005[/C][C]0.6511185241399[/C][C]0.32555926206995[/C][/ROW]
[ROW][C]10[/C][C]0.58754701219993[/C][C]0.82490597560014[/C][C]0.41245298780007[/C][/ROW]
[ROW][C]11[/C][C]0.567597664334845[/C][C]0.86480467133031[/C][C]0.432402335665155[/C][/ROW]
[ROW][C]12[/C][C]0.465074326952916[/C][C]0.930148653905831[/C][C]0.534925673047084[/C][/ROW]
[ROW][C]13[/C][C]0.375819259642542[/C][C]0.751638519285083[/C][C]0.624180740357458[/C][/ROW]
[ROW][C]14[/C][C]0.289387510168005[/C][C]0.578775020336009[/C][C]0.710612489831995[/C][/ROW]
[ROW][C]15[/C][C]0.323399985958005[/C][C]0.64679997191601[/C][C]0.676600014041995[/C][/ROW]
[ROW][C]16[/C][C]0.247837963355304[/C][C]0.495675926710608[/C][C]0.752162036644696[/C][/ROW]
[ROW][C]17[/C][C]0.224347393605276[/C][C]0.448694787210552[/C][C]0.775652606394724[/C][/ROW]
[ROW][C]18[/C][C]0.212467008051156[/C][C]0.424934016102313[/C][C]0.787532991948844[/C][/ROW]
[ROW][C]19[/C][C]0.363415338599363[/C][C]0.726830677198727[/C][C]0.636584661400637[/C][/ROW]
[ROW][C]20[/C][C]0.2941976378515[/C][C]0.5883952757030[/C][C]0.7058023621485[/C][/ROW]
[ROW][C]21[/C][C]0.277157501979427[/C][C]0.554315003958854[/C][C]0.722842498020573[/C][/ROW]
[ROW][C]22[/C][C]0.282284241787079[/C][C]0.564568483574158[/C][C]0.717715758212921[/C][/ROW]
[ROW][C]23[/C][C]0.296467008641802[/C][C]0.592934017283605[/C][C]0.703532991358198[/C][/ROW]
[ROW][C]24[/C][C]0.234432659015944[/C][C]0.468865318031888[/C][C]0.765567340984056[/C][/ROW]
[ROW][C]25[/C][C]0.184346437396219[/C][C]0.368692874792437[/C][C]0.815653562603781[/C][/ROW]
[ROW][C]26[/C][C]0.140345602173351[/C][C]0.280691204346703[/C][C]0.859654397826649[/C][/ROW]
[ROW][C]27[/C][C]0.220141584000948[/C][C]0.440283168001896[/C][C]0.779858415999052[/C][/ROW]
[ROW][C]28[/C][C]0.169578728253236[/C][C]0.339157456506471[/C][C]0.830421271746764[/C][/ROW]
[ROW][C]29[/C][C]0.135004121219399[/C][C]0.270008242438799[/C][C]0.8649958787806[/C][/ROW]
[ROW][C]30[/C][C]0.166615811381745[/C][C]0.333231622763491[/C][C]0.833384188618255[/C][/ROW]
[ROW][C]31[/C][C]0.192928567993643[/C][C]0.385857135987286[/C][C]0.807071432006357[/C][/ROW]
[ROW][C]32[/C][C]0.157626244459011[/C][C]0.315252488918021[/C][C]0.84237375554099[/C][/ROW]
[ROW][C]33[/C][C]0.146796196746138[/C][C]0.293592393492276[/C][C]0.853203803253862[/C][/ROW]
[ROW][C]34[/C][C]0.289602131787374[/C][C]0.579204263574748[/C][C]0.710397868212626[/C][/ROW]
[ROW][C]35[/C][C]0.279865126540874[/C][C]0.559730253081748[/C][C]0.720134873459126[/C][/ROW]
[ROW][C]36[/C][C]0.226039466701503[/C][C]0.452078933403005[/C][C]0.773960533298497[/C][/ROW]
[ROW][C]37[/C][C]0.192312621891145[/C][C]0.38462524378229[/C][C]0.807687378108855[/C][/ROW]
[ROW][C]38[/C][C]0.181353128916851[/C][C]0.362706257833702[/C][C]0.81864687108315[/C][/ROW]
[ROW][C]39[/C][C]0.149887674707983[/C][C]0.299775349415967[/C][C]0.850112325292017[/C][/ROW]
[ROW][C]40[/C][C]0.216475449324352[/C][C]0.432950898648705[/C][C]0.783524550675648[/C][/ROW]
[ROW][C]41[/C][C]0.192022245302091[/C][C]0.384044490604182[/C][C]0.80797775469791[/C][/ROW]
[ROW][C]42[/C][C]0.315265681679281[/C][C]0.630531363358562[/C][C]0.684734318320719[/C][/ROW]
[ROW][C]43[/C][C]0.254610075860285[/C][C]0.50922015172057[/C][C]0.745389924139715[/C][/ROW]
[ROW][C]44[/C][C]0.198559934029665[/C][C]0.397119868059329[/C][C]0.801440065970335[/C][/ROW]
[ROW][C]45[/C][C]0.534048722569906[/C][C]0.931902554860188[/C][C]0.465951277430094[/C][/ROW]
[ROW][C]46[/C][C]0.854343265629178[/C][C]0.291313468741643[/C][C]0.145656734370822[/C][/ROW]
[ROW][C]47[/C][C]0.804757688849777[/C][C]0.390484622300446[/C][C]0.195242311150223[/C][/ROW]
[ROW][C]48[/C][C]0.7256806939706[/C][C]0.5486386120588[/C][C]0.2743193060294[/C][/ROW]
[ROW][C]49[/C][C]0.625354213589077[/C][C]0.749291572821846[/C][C]0.374645786410923[/C][/ROW]
[ROW][C]50[/C][C]0.494142039836367[/C][C]0.988284079672733[/C][C]0.505857960163633[/C][/ROW]
[ROW][C]51[/C][C]0.482584036656949[/C][C]0.965168073313897[/C][C]0.517415963343051[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58186&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58186&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
50.07694456099770640.1538891219954130.923055439002294
60.2425847748423800.4851695496847590.75741522515762
70.711941769212520.5761164615749610.288058230787481
80.6151916846429020.7696166307141960.384808315357098
90.674440737930050.65111852413990.32555926206995
100.587547012199930.824905975600140.41245298780007
110.5675976643348450.864804671330310.432402335665155
120.4650743269529160.9301486539058310.534925673047084
130.3758192596425420.7516385192850830.624180740357458
140.2893875101680050.5787750203360090.710612489831995
150.3233999859580050.646799971916010.676600014041995
160.2478379633553040.4956759267106080.752162036644696
170.2243473936052760.4486947872105520.775652606394724
180.2124670080511560.4249340161023130.787532991948844
190.3634153385993630.7268306771987270.636584661400637
200.29419763785150.58839527570300.7058023621485
210.2771575019794270.5543150039588540.722842498020573
220.2822842417870790.5645684835741580.717715758212921
230.2964670086418020.5929340172836050.703532991358198
240.2344326590159440.4688653180318880.765567340984056
250.1843464373962190.3686928747924370.815653562603781
260.1403456021733510.2806912043467030.859654397826649
270.2201415840009480.4402831680018960.779858415999052
280.1695787282532360.3391574565064710.830421271746764
290.1350041212193990.2700082424387990.8649958787806
300.1666158113817450.3332316227634910.833384188618255
310.1929285679936430.3858571359872860.807071432006357
320.1576262444590110.3152524889180210.84237375554099
330.1467961967461380.2935923934922760.853203803253862
340.2896021317873740.5792042635747480.710397868212626
350.2798651265408740.5597302530817480.720134873459126
360.2260394667015030.4520789334030050.773960533298497
370.1923126218911450.384625243782290.807687378108855
380.1813531289168510.3627062578337020.81864687108315
390.1498876747079830.2997753494159670.850112325292017
400.2164754493243520.4329508986487050.783524550675648
410.1920222453020910.3840444906041820.80797775469791
420.3152656816792810.6305313633585620.684734318320719
430.2546100758602850.509220151720570.745389924139715
440.1985599340296650.3971198680593290.801440065970335
450.5340487225699060.9319025548601880.465951277430094
460.8543432656291780.2913134687416430.145656734370822
470.8047576888497770.3904846223004460.195242311150223
480.72568069397060.54863861205880.2743193060294
490.6253542135890770.7492915728218460.374645786410923
500.4941420398363670.9882840796727330.505857960163633
510.4825840366569490.9651680733138970.517415963343051







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 level00OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58186&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58186&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58186&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 level00OK



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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$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')
}