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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 09:17:24 -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/t1258734039ovbrseggxn03obv.htm/, Retrieved Thu, 28 Mar 2024 10:27:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58305, Retrieved Thu, 28 Mar 2024 10:27:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact121
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 16:17:24] [fc845972e0ebdb725d2fb9537c0c51aa] [Current]
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Dataseries X:
111.4	0
87.4	0
96.8	0
114.1	0
110.3	0
103.9	0
101.6	0
94.6	0
95.9	0
104.7	0
102.8	0
98.1	0
113.9	0
80.9	0
95.7	0
113.2	0
105.9	0
108.8	0
102.3	0
99	0
100.7	0
115.5	0
100.7	0
109.9	0
114.6	0
85.4	0
100.5	0
114.8	0
116.5	0
112.9	0
102	0
106	0
105.3	0
118.8	0
106.1	0
109.3	0
117.2	0
92.5	0
104.2	0
112.5	0
122.4	0
113.3	0
100	0
110.7	0
112.8	0
109.8	0
117.3	0
109.1	0
115.9	0
96	0
99.8	0
116.8	1
115.7	1
99.4	1
94.3	1
91	1
93.2	1
103.1	1
94.1	1
91.8	1
102.7	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58305&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58305&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58305&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 time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 105.760784313725 -5.55078431372549X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  105.760784313725 -5.55078431372549X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58305&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  105.760784313725 -5.55078431372549X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58305&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58305&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] = + 105.760784313725 -5.55078431372549X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)105.7607843137251.27401283.013900
X-5.550784313725493.146578-1.76410.0828970.041448

\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) & 105.760784313725 & 1.274012 & 83.0139 & 0 & 0 \tabularnewline
X & -5.55078431372549 & 3.146578 & -1.7641 & 0.082897 & 0.041448 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58305&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]105.760784313725[/C][C]1.274012[/C][C]83.0139[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-5.55078431372549[/C][C]3.146578[/C][C]-1.7641[/C][C]0.082897[/C][C]0.041448[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58305&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58305&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)105.7607843137251.27401283.013900
X-5.550784313725493.146578-1.76410.0828970.041448







Multiple Linear Regression - Regression Statistics
Multiple R0.223835121573278
R-squared0.0501021616497242
Adjusted R-squared0.0340021982878552
F-TEST (value)3.11194258791827
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value0.082896958868956
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.09826819862025
Sum Squared Residuals4883.93056862745

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.223835121573278 \tabularnewline
R-squared & 0.0501021616497242 \tabularnewline
Adjusted R-squared & 0.0340021982878552 \tabularnewline
F-TEST (value) & 3.11194258791827 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 0.082896958868956 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9.09826819862025 \tabularnewline
Sum Squared Residuals & 4883.93056862745 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58305&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.223835121573278[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0501021616497242[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0340021982878552[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.11194258791827[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]59[/C][/ROW]
[ROW][C]p-value[/C][C]0.082896958868956[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]9.09826819862025[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4883.93056862745[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58305&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58305&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 R0.223835121573278
R-squared0.0501021616497242
Adjusted R-squared0.0340021982878552
F-TEST (value)3.11194258791827
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value0.082896958868956
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.09826819862025
Sum Squared Residuals4883.93056862745







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1111.4105.7607843137265.63921568627445
287.4105.760784313725-18.3607843137255
396.8105.760784313725-8.9607843137255
4114.1105.7607843137258.3392156862745
5110.3105.7607843137254.53921568627451
6103.9105.760784313725-1.86078431372548
7101.6105.760784313725-4.16078431372549
894.6105.760784313725-11.1607843137255
995.9105.760784313725-9.86078431372549
10104.7105.760784313725-1.06078431372549
11102.8105.760784313725-2.96078431372549
1298.1105.760784313725-7.6607843137255
13113.9105.7607843137258.13921568627451
1480.9105.760784313725-24.8607843137255
1595.7105.760784313725-10.0607843137255
16113.2105.7607843137257.43921568627451
17105.9105.7607843137250.139215686274516
18108.8105.7607843137253.03921568627451
19102.3105.760784313725-3.46078431372549
2099105.760784313725-6.76078431372549
21100.7105.760784313725-5.06078431372549
22115.5105.7607843137259.7392156862745
23100.7105.760784313725-5.06078431372549
24109.9105.7607843137254.13921568627452
25114.6105.7607843137258.8392156862745
2685.4105.760784313725-20.3607843137255
27100.5105.760784313725-5.26078431372549
28114.8105.7607843137259.0392156862745
29116.5105.76078431372510.7392156862745
30112.9105.7607843137257.13921568627452
31102105.760784313725-3.76078431372549
32106105.7607843137250.239215686274511
33105.3105.760784313725-0.460784313725492
34118.8105.76078431372513.0392156862745
35106.1105.7607843137250.339215686274505
36109.3105.7607843137253.53921568627451
37117.2105.76078431372511.4392156862745
3892.5105.760784313725-13.2607843137255
39104.2105.760784313725-1.56078431372549
40112.5105.7607843137256.73921568627451
41122.4105.76078431372516.6392156862745
42113.3105.7607843137257.5392156862745
43100105.760784313725-5.76078431372549
44110.7105.7607843137254.93921568627451
45112.8105.7607843137257.0392156862745
46109.8105.7607843137254.03921568627451
47117.3105.76078431372511.5392156862745
48109.1105.7607843137253.33921568627451
49115.9105.76078431372510.1392156862745
5096105.760784313725-9.7607843137255
5199.8105.760784313725-5.96078431372549
52116.8100.2116.59
53115.7100.2115.49
5499.4100.21-0.809999999999993
5594.3100.21-5.91
5691100.21-9.21
5793.2100.21-7.01
58103.1100.212.89000000000000
5994.1100.21-6.11
6091.8100.21-8.41
61102.7100.212.49000000000000

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 111.4 & 105.760784313726 & 5.63921568627445 \tabularnewline
2 & 87.4 & 105.760784313725 & -18.3607843137255 \tabularnewline
3 & 96.8 & 105.760784313725 & -8.9607843137255 \tabularnewline
4 & 114.1 & 105.760784313725 & 8.3392156862745 \tabularnewline
5 & 110.3 & 105.760784313725 & 4.53921568627451 \tabularnewline
6 & 103.9 & 105.760784313725 & -1.86078431372548 \tabularnewline
7 & 101.6 & 105.760784313725 & -4.16078431372549 \tabularnewline
8 & 94.6 & 105.760784313725 & -11.1607843137255 \tabularnewline
9 & 95.9 & 105.760784313725 & -9.86078431372549 \tabularnewline
10 & 104.7 & 105.760784313725 & -1.06078431372549 \tabularnewline
11 & 102.8 & 105.760784313725 & -2.96078431372549 \tabularnewline
12 & 98.1 & 105.760784313725 & -7.6607843137255 \tabularnewline
13 & 113.9 & 105.760784313725 & 8.13921568627451 \tabularnewline
14 & 80.9 & 105.760784313725 & -24.8607843137255 \tabularnewline
15 & 95.7 & 105.760784313725 & -10.0607843137255 \tabularnewline
16 & 113.2 & 105.760784313725 & 7.43921568627451 \tabularnewline
17 & 105.9 & 105.760784313725 & 0.139215686274516 \tabularnewline
18 & 108.8 & 105.760784313725 & 3.03921568627451 \tabularnewline
19 & 102.3 & 105.760784313725 & -3.46078431372549 \tabularnewline
20 & 99 & 105.760784313725 & -6.76078431372549 \tabularnewline
21 & 100.7 & 105.760784313725 & -5.06078431372549 \tabularnewline
22 & 115.5 & 105.760784313725 & 9.7392156862745 \tabularnewline
23 & 100.7 & 105.760784313725 & -5.06078431372549 \tabularnewline
24 & 109.9 & 105.760784313725 & 4.13921568627452 \tabularnewline
25 & 114.6 & 105.760784313725 & 8.8392156862745 \tabularnewline
26 & 85.4 & 105.760784313725 & -20.3607843137255 \tabularnewline
27 & 100.5 & 105.760784313725 & -5.26078431372549 \tabularnewline
28 & 114.8 & 105.760784313725 & 9.0392156862745 \tabularnewline
29 & 116.5 & 105.760784313725 & 10.7392156862745 \tabularnewline
30 & 112.9 & 105.760784313725 & 7.13921568627452 \tabularnewline
31 & 102 & 105.760784313725 & -3.76078431372549 \tabularnewline
32 & 106 & 105.760784313725 & 0.239215686274511 \tabularnewline
33 & 105.3 & 105.760784313725 & -0.460784313725492 \tabularnewline
34 & 118.8 & 105.760784313725 & 13.0392156862745 \tabularnewline
35 & 106.1 & 105.760784313725 & 0.339215686274505 \tabularnewline
36 & 109.3 & 105.760784313725 & 3.53921568627451 \tabularnewline
37 & 117.2 & 105.760784313725 & 11.4392156862745 \tabularnewline
38 & 92.5 & 105.760784313725 & -13.2607843137255 \tabularnewline
39 & 104.2 & 105.760784313725 & -1.56078431372549 \tabularnewline
40 & 112.5 & 105.760784313725 & 6.73921568627451 \tabularnewline
41 & 122.4 & 105.760784313725 & 16.6392156862745 \tabularnewline
42 & 113.3 & 105.760784313725 & 7.5392156862745 \tabularnewline
43 & 100 & 105.760784313725 & -5.76078431372549 \tabularnewline
44 & 110.7 & 105.760784313725 & 4.93921568627451 \tabularnewline
45 & 112.8 & 105.760784313725 & 7.0392156862745 \tabularnewline
46 & 109.8 & 105.760784313725 & 4.03921568627451 \tabularnewline
47 & 117.3 & 105.760784313725 & 11.5392156862745 \tabularnewline
48 & 109.1 & 105.760784313725 & 3.33921568627451 \tabularnewline
49 & 115.9 & 105.760784313725 & 10.1392156862745 \tabularnewline
50 & 96 & 105.760784313725 & -9.7607843137255 \tabularnewline
51 & 99.8 & 105.760784313725 & -5.96078431372549 \tabularnewline
52 & 116.8 & 100.21 & 16.59 \tabularnewline
53 & 115.7 & 100.21 & 15.49 \tabularnewline
54 & 99.4 & 100.21 & -0.809999999999993 \tabularnewline
55 & 94.3 & 100.21 & -5.91 \tabularnewline
56 & 91 & 100.21 & -9.21 \tabularnewline
57 & 93.2 & 100.21 & -7.01 \tabularnewline
58 & 103.1 & 100.21 & 2.89000000000000 \tabularnewline
59 & 94.1 & 100.21 & -6.11 \tabularnewline
60 & 91.8 & 100.21 & -8.41 \tabularnewline
61 & 102.7 & 100.21 & 2.49000000000000 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58305&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]111.4[/C][C]105.760784313726[/C][C]5.63921568627445[/C][/ROW]
[ROW][C]2[/C][C]87.4[/C][C]105.760784313725[/C][C]-18.3607843137255[/C][/ROW]
[ROW][C]3[/C][C]96.8[/C][C]105.760784313725[/C][C]-8.9607843137255[/C][/ROW]
[ROW][C]4[/C][C]114.1[/C][C]105.760784313725[/C][C]8.3392156862745[/C][/ROW]
[ROW][C]5[/C][C]110.3[/C][C]105.760784313725[/C][C]4.53921568627451[/C][/ROW]
[ROW][C]6[/C][C]103.9[/C][C]105.760784313725[/C][C]-1.86078431372548[/C][/ROW]
[ROW][C]7[/C][C]101.6[/C][C]105.760784313725[/C][C]-4.16078431372549[/C][/ROW]
[ROW][C]8[/C][C]94.6[/C][C]105.760784313725[/C][C]-11.1607843137255[/C][/ROW]
[ROW][C]9[/C][C]95.9[/C][C]105.760784313725[/C][C]-9.86078431372549[/C][/ROW]
[ROW][C]10[/C][C]104.7[/C][C]105.760784313725[/C][C]-1.06078431372549[/C][/ROW]
[ROW][C]11[/C][C]102.8[/C][C]105.760784313725[/C][C]-2.96078431372549[/C][/ROW]
[ROW][C]12[/C][C]98.1[/C][C]105.760784313725[/C][C]-7.6607843137255[/C][/ROW]
[ROW][C]13[/C][C]113.9[/C][C]105.760784313725[/C][C]8.13921568627451[/C][/ROW]
[ROW][C]14[/C][C]80.9[/C][C]105.760784313725[/C][C]-24.8607843137255[/C][/ROW]
[ROW][C]15[/C][C]95.7[/C][C]105.760784313725[/C][C]-10.0607843137255[/C][/ROW]
[ROW][C]16[/C][C]113.2[/C][C]105.760784313725[/C][C]7.43921568627451[/C][/ROW]
[ROW][C]17[/C][C]105.9[/C][C]105.760784313725[/C][C]0.139215686274516[/C][/ROW]
[ROW][C]18[/C][C]108.8[/C][C]105.760784313725[/C][C]3.03921568627451[/C][/ROW]
[ROW][C]19[/C][C]102.3[/C][C]105.760784313725[/C][C]-3.46078431372549[/C][/ROW]
[ROW][C]20[/C][C]99[/C][C]105.760784313725[/C][C]-6.76078431372549[/C][/ROW]
[ROW][C]21[/C][C]100.7[/C][C]105.760784313725[/C][C]-5.06078431372549[/C][/ROW]
[ROW][C]22[/C][C]115.5[/C][C]105.760784313725[/C][C]9.7392156862745[/C][/ROW]
[ROW][C]23[/C][C]100.7[/C][C]105.760784313725[/C][C]-5.06078431372549[/C][/ROW]
[ROW][C]24[/C][C]109.9[/C][C]105.760784313725[/C][C]4.13921568627452[/C][/ROW]
[ROW][C]25[/C][C]114.6[/C][C]105.760784313725[/C][C]8.8392156862745[/C][/ROW]
[ROW][C]26[/C][C]85.4[/C][C]105.760784313725[/C][C]-20.3607843137255[/C][/ROW]
[ROW][C]27[/C][C]100.5[/C][C]105.760784313725[/C][C]-5.26078431372549[/C][/ROW]
[ROW][C]28[/C][C]114.8[/C][C]105.760784313725[/C][C]9.0392156862745[/C][/ROW]
[ROW][C]29[/C][C]116.5[/C][C]105.760784313725[/C][C]10.7392156862745[/C][/ROW]
[ROW][C]30[/C][C]112.9[/C][C]105.760784313725[/C][C]7.13921568627452[/C][/ROW]
[ROW][C]31[/C][C]102[/C][C]105.760784313725[/C][C]-3.76078431372549[/C][/ROW]
[ROW][C]32[/C][C]106[/C][C]105.760784313725[/C][C]0.239215686274511[/C][/ROW]
[ROW][C]33[/C][C]105.3[/C][C]105.760784313725[/C][C]-0.460784313725492[/C][/ROW]
[ROW][C]34[/C][C]118.8[/C][C]105.760784313725[/C][C]13.0392156862745[/C][/ROW]
[ROW][C]35[/C][C]106.1[/C][C]105.760784313725[/C][C]0.339215686274505[/C][/ROW]
[ROW][C]36[/C][C]109.3[/C][C]105.760784313725[/C][C]3.53921568627451[/C][/ROW]
[ROW][C]37[/C][C]117.2[/C][C]105.760784313725[/C][C]11.4392156862745[/C][/ROW]
[ROW][C]38[/C][C]92.5[/C][C]105.760784313725[/C][C]-13.2607843137255[/C][/ROW]
[ROW][C]39[/C][C]104.2[/C][C]105.760784313725[/C][C]-1.56078431372549[/C][/ROW]
[ROW][C]40[/C][C]112.5[/C][C]105.760784313725[/C][C]6.73921568627451[/C][/ROW]
[ROW][C]41[/C][C]122.4[/C][C]105.760784313725[/C][C]16.6392156862745[/C][/ROW]
[ROW][C]42[/C][C]113.3[/C][C]105.760784313725[/C][C]7.5392156862745[/C][/ROW]
[ROW][C]43[/C][C]100[/C][C]105.760784313725[/C][C]-5.76078431372549[/C][/ROW]
[ROW][C]44[/C][C]110.7[/C][C]105.760784313725[/C][C]4.93921568627451[/C][/ROW]
[ROW][C]45[/C][C]112.8[/C][C]105.760784313725[/C][C]7.0392156862745[/C][/ROW]
[ROW][C]46[/C][C]109.8[/C][C]105.760784313725[/C][C]4.03921568627451[/C][/ROW]
[ROW][C]47[/C][C]117.3[/C][C]105.760784313725[/C][C]11.5392156862745[/C][/ROW]
[ROW][C]48[/C][C]109.1[/C][C]105.760784313725[/C][C]3.33921568627451[/C][/ROW]
[ROW][C]49[/C][C]115.9[/C][C]105.760784313725[/C][C]10.1392156862745[/C][/ROW]
[ROW][C]50[/C][C]96[/C][C]105.760784313725[/C][C]-9.7607843137255[/C][/ROW]
[ROW][C]51[/C][C]99.8[/C][C]105.760784313725[/C][C]-5.96078431372549[/C][/ROW]
[ROW][C]52[/C][C]116.8[/C][C]100.21[/C][C]16.59[/C][/ROW]
[ROW][C]53[/C][C]115.7[/C][C]100.21[/C][C]15.49[/C][/ROW]
[ROW][C]54[/C][C]99.4[/C][C]100.21[/C][C]-0.809999999999993[/C][/ROW]
[ROW][C]55[/C][C]94.3[/C][C]100.21[/C][C]-5.91[/C][/ROW]
[ROW][C]56[/C][C]91[/C][C]100.21[/C][C]-9.21[/C][/ROW]
[ROW][C]57[/C][C]93.2[/C][C]100.21[/C][C]-7.01[/C][/ROW]
[ROW][C]58[/C][C]103.1[/C][C]100.21[/C][C]2.89000000000000[/C][/ROW]
[ROW][C]59[/C][C]94.1[/C][C]100.21[/C][C]-6.11[/C][/ROW]
[ROW][C]60[/C][C]91.8[/C][C]100.21[/C][C]-8.41[/C][/ROW]
[ROW][C]61[/C][C]102.7[/C][C]100.21[/C][C]2.49000000000000[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58305&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58305&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
1111.4105.7607843137265.63921568627445
287.4105.760784313725-18.3607843137255
396.8105.760784313725-8.9607843137255
4114.1105.7607843137258.3392156862745
5110.3105.7607843137254.53921568627451
6103.9105.760784313725-1.86078431372548
7101.6105.760784313725-4.16078431372549
894.6105.760784313725-11.1607843137255
995.9105.760784313725-9.86078431372549
10104.7105.760784313725-1.06078431372549
11102.8105.760784313725-2.96078431372549
1298.1105.760784313725-7.6607843137255
13113.9105.7607843137258.13921568627451
1480.9105.760784313725-24.8607843137255
1595.7105.760784313725-10.0607843137255
16113.2105.7607843137257.43921568627451
17105.9105.7607843137250.139215686274516
18108.8105.7607843137253.03921568627451
19102.3105.760784313725-3.46078431372549
2099105.760784313725-6.76078431372549
21100.7105.760784313725-5.06078431372549
22115.5105.7607843137259.7392156862745
23100.7105.760784313725-5.06078431372549
24109.9105.7607843137254.13921568627452
25114.6105.7607843137258.8392156862745
2685.4105.760784313725-20.3607843137255
27100.5105.760784313725-5.26078431372549
28114.8105.7607843137259.0392156862745
29116.5105.76078431372510.7392156862745
30112.9105.7607843137257.13921568627452
31102105.760784313725-3.76078431372549
32106105.7607843137250.239215686274511
33105.3105.760784313725-0.460784313725492
34118.8105.76078431372513.0392156862745
35106.1105.7607843137250.339215686274505
36109.3105.7607843137253.53921568627451
37117.2105.76078431372511.4392156862745
3892.5105.760784313725-13.2607843137255
39104.2105.760784313725-1.56078431372549
40112.5105.7607843137256.73921568627451
41122.4105.76078431372516.6392156862745
42113.3105.7607843137257.5392156862745
43100105.760784313725-5.76078431372549
44110.7105.7607843137254.93921568627451
45112.8105.7607843137257.0392156862745
46109.8105.7607843137254.03921568627451
47117.3105.76078431372511.5392156862745
48109.1105.7607843137253.33921568627451
49115.9105.76078431372510.1392156862745
5096105.760784313725-9.7607843137255
5199.8105.760784313725-5.96078431372549
52116.8100.2116.59
53115.7100.2115.49
5499.4100.21-0.809999999999993
5594.3100.21-5.91
5691100.21-9.21
5793.2100.21-7.01
58103.1100.212.89000000000000
5994.1100.21-6.11
6091.8100.21-8.41
61102.7100.212.49000000000000







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.8979859168170820.2040281663658370.102014083182919
60.8120322247708430.3759355504583150.187967775229157
70.710469566957740.5790608660845210.289530433042260
80.6883084145832840.6233831708334330.311691585416716
90.6351450116399910.7297099767200170.364854988360008
100.5327743405281600.9344513189436810.467225659471840
110.4267871789475960.8535743578951920.573212821052404
120.3553548475994490.7107096951988970.644645152400551
130.4063747858459530.8127495716919060.593625214154047
140.8146790883046750.3706418233906490.185320911695325
150.7959056742792640.4081886514414730.204094325720736
160.8122106269875030.3755787460249930.187789373012497
170.7592831736303260.4814336527393480.240716826369674
180.7158944153578420.5682111692843150.284105584642158
190.6505610807851940.6988778384296110.349438919214806
200.6045080083392140.7909839833215710.395491991660786
210.5453817483844150.909236503231170.454618251615585
220.594405755045330.811188489909340.40559424495467
230.5392265150438850.9215469699122290.460773484956115
240.4933203619544270.9866407239088540.506679638045573
250.5075920194116580.9848159611766840.492407980588342
260.7888810364645370.4222379270709260.211118963535463
270.7608865495792550.478226900841490.239113450420745
280.766834739404390.4663305211912190.233165260595609
290.7896877885144040.4206244229711930.210312211485596
300.7667379211263650.4665241577472690.233262078873635
310.7262533786225520.5474932427548960.273746621377448
320.6658150994534070.6683698010931860.334184900546593
330.6026582338562910.7946835322874190.397341766143709
340.6595659065031090.6808681869937830.340434093496891
350.5917896637604770.8164206724790460.408210336239523
360.5243411869792420.9513176260415160.475658813020758
370.5443761319198630.9112477361602740.455623868080137
380.6747178016379670.6505643967240660.325282198362033
390.6195895576355810.7608208847288370.380410442364419
400.5649429662985660.870114067402870.435057033701434
410.6885105708943590.6229788582112820.311489429105641
420.6458581487122540.7082837025754920.354141851287746
430.6230568041907880.7538863916184240.376943195809212
440.5488727882690060.9022544234619880.451127211730994
450.4900397858057990.9800795716115980.509960214194201
460.4086888506838050.8173777013676090.591311149316196
470.4404878109660490.8809756219320980.559512189033951
480.3670417122768130.7340834245536260.632958287723187
490.4802906690374750.9605813380749490.519709330962525
500.4046726818183990.8093453636367980.595327318181601
510.311350910022440.622701820044880.68864908997756
520.529933645092760.940132709814480.47006635490724
530.911923616671770.1761527666564600.0880763833282299
540.8668365446162790.2663269107674430.133163455383721
550.7768454417729160.4463091164541690.223154558227084
560.710031413986580.5799371720268410.289968586013421

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.897985916817082 & 0.204028166365837 & 0.102014083182919 \tabularnewline
6 & 0.812032224770843 & 0.375935550458315 & 0.187967775229157 \tabularnewline
7 & 0.71046956695774 & 0.579060866084521 & 0.289530433042260 \tabularnewline
8 & 0.688308414583284 & 0.623383170833433 & 0.311691585416716 \tabularnewline
9 & 0.635145011639991 & 0.729709976720017 & 0.364854988360008 \tabularnewline
10 & 0.532774340528160 & 0.934451318943681 & 0.467225659471840 \tabularnewline
11 & 0.426787178947596 & 0.853574357895192 & 0.573212821052404 \tabularnewline
12 & 0.355354847599449 & 0.710709695198897 & 0.644645152400551 \tabularnewline
13 & 0.406374785845953 & 0.812749571691906 & 0.593625214154047 \tabularnewline
14 & 0.814679088304675 & 0.370641823390649 & 0.185320911695325 \tabularnewline
15 & 0.795905674279264 & 0.408188651441473 & 0.204094325720736 \tabularnewline
16 & 0.812210626987503 & 0.375578746024993 & 0.187789373012497 \tabularnewline
17 & 0.759283173630326 & 0.481433652739348 & 0.240716826369674 \tabularnewline
18 & 0.715894415357842 & 0.568211169284315 & 0.284105584642158 \tabularnewline
19 & 0.650561080785194 & 0.698877838429611 & 0.349438919214806 \tabularnewline
20 & 0.604508008339214 & 0.790983983321571 & 0.395491991660786 \tabularnewline
21 & 0.545381748384415 & 0.90923650323117 & 0.454618251615585 \tabularnewline
22 & 0.59440575504533 & 0.81118848990934 & 0.40559424495467 \tabularnewline
23 & 0.539226515043885 & 0.921546969912229 & 0.460773484956115 \tabularnewline
24 & 0.493320361954427 & 0.986640723908854 & 0.506679638045573 \tabularnewline
25 & 0.507592019411658 & 0.984815961176684 & 0.492407980588342 \tabularnewline
26 & 0.788881036464537 & 0.422237927070926 & 0.211118963535463 \tabularnewline
27 & 0.760886549579255 & 0.47822690084149 & 0.239113450420745 \tabularnewline
28 & 0.76683473940439 & 0.466330521191219 & 0.233165260595609 \tabularnewline
29 & 0.789687788514404 & 0.420624422971193 & 0.210312211485596 \tabularnewline
30 & 0.766737921126365 & 0.466524157747269 & 0.233262078873635 \tabularnewline
31 & 0.726253378622552 & 0.547493242754896 & 0.273746621377448 \tabularnewline
32 & 0.665815099453407 & 0.668369801093186 & 0.334184900546593 \tabularnewline
33 & 0.602658233856291 & 0.794683532287419 & 0.397341766143709 \tabularnewline
34 & 0.659565906503109 & 0.680868186993783 & 0.340434093496891 \tabularnewline
35 & 0.591789663760477 & 0.816420672479046 & 0.408210336239523 \tabularnewline
36 & 0.524341186979242 & 0.951317626041516 & 0.475658813020758 \tabularnewline
37 & 0.544376131919863 & 0.911247736160274 & 0.455623868080137 \tabularnewline
38 & 0.674717801637967 & 0.650564396724066 & 0.325282198362033 \tabularnewline
39 & 0.619589557635581 & 0.760820884728837 & 0.380410442364419 \tabularnewline
40 & 0.564942966298566 & 0.87011406740287 & 0.435057033701434 \tabularnewline
41 & 0.688510570894359 & 0.622978858211282 & 0.311489429105641 \tabularnewline
42 & 0.645858148712254 & 0.708283702575492 & 0.354141851287746 \tabularnewline
43 & 0.623056804190788 & 0.753886391618424 & 0.376943195809212 \tabularnewline
44 & 0.548872788269006 & 0.902254423461988 & 0.451127211730994 \tabularnewline
45 & 0.490039785805799 & 0.980079571611598 & 0.509960214194201 \tabularnewline
46 & 0.408688850683805 & 0.817377701367609 & 0.591311149316196 \tabularnewline
47 & 0.440487810966049 & 0.880975621932098 & 0.559512189033951 \tabularnewline
48 & 0.367041712276813 & 0.734083424553626 & 0.632958287723187 \tabularnewline
49 & 0.480290669037475 & 0.960581338074949 & 0.519709330962525 \tabularnewline
50 & 0.404672681818399 & 0.809345363636798 & 0.595327318181601 \tabularnewline
51 & 0.31135091002244 & 0.62270182004488 & 0.68864908997756 \tabularnewline
52 & 0.52993364509276 & 0.94013270981448 & 0.47006635490724 \tabularnewline
53 & 0.91192361667177 & 0.176152766656460 & 0.0880763833282299 \tabularnewline
54 & 0.866836544616279 & 0.266326910767443 & 0.133163455383721 \tabularnewline
55 & 0.776845441772916 & 0.446309116454169 & 0.223154558227084 \tabularnewline
56 & 0.71003141398658 & 0.579937172026841 & 0.289968586013421 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58305&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]0.897985916817082[/C][C]0.204028166365837[/C][C]0.102014083182919[/C][/ROW]
[ROW][C]6[/C][C]0.812032224770843[/C][C]0.375935550458315[/C][C]0.187967775229157[/C][/ROW]
[ROW][C]7[/C][C]0.71046956695774[/C][C]0.579060866084521[/C][C]0.289530433042260[/C][/ROW]
[ROW][C]8[/C][C]0.688308414583284[/C][C]0.623383170833433[/C][C]0.311691585416716[/C][/ROW]
[ROW][C]9[/C][C]0.635145011639991[/C][C]0.729709976720017[/C][C]0.364854988360008[/C][/ROW]
[ROW][C]10[/C][C]0.532774340528160[/C][C]0.934451318943681[/C][C]0.467225659471840[/C][/ROW]
[ROW][C]11[/C][C]0.426787178947596[/C][C]0.853574357895192[/C][C]0.573212821052404[/C][/ROW]
[ROW][C]12[/C][C]0.355354847599449[/C][C]0.710709695198897[/C][C]0.644645152400551[/C][/ROW]
[ROW][C]13[/C][C]0.406374785845953[/C][C]0.812749571691906[/C][C]0.593625214154047[/C][/ROW]
[ROW][C]14[/C][C]0.814679088304675[/C][C]0.370641823390649[/C][C]0.185320911695325[/C][/ROW]
[ROW][C]15[/C][C]0.795905674279264[/C][C]0.408188651441473[/C][C]0.204094325720736[/C][/ROW]
[ROW][C]16[/C][C]0.812210626987503[/C][C]0.375578746024993[/C][C]0.187789373012497[/C][/ROW]
[ROW][C]17[/C][C]0.759283173630326[/C][C]0.481433652739348[/C][C]0.240716826369674[/C][/ROW]
[ROW][C]18[/C][C]0.715894415357842[/C][C]0.568211169284315[/C][C]0.284105584642158[/C][/ROW]
[ROW][C]19[/C][C]0.650561080785194[/C][C]0.698877838429611[/C][C]0.349438919214806[/C][/ROW]
[ROW][C]20[/C][C]0.604508008339214[/C][C]0.790983983321571[/C][C]0.395491991660786[/C][/ROW]
[ROW][C]21[/C][C]0.545381748384415[/C][C]0.90923650323117[/C][C]0.454618251615585[/C][/ROW]
[ROW][C]22[/C][C]0.59440575504533[/C][C]0.81118848990934[/C][C]0.40559424495467[/C][/ROW]
[ROW][C]23[/C][C]0.539226515043885[/C][C]0.921546969912229[/C][C]0.460773484956115[/C][/ROW]
[ROW][C]24[/C][C]0.493320361954427[/C][C]0.986640723908854[/C][C]0.506679638045573[/C][/ROW]
[ROW][C]25[/C][C]0.507592019411658[/C][C]0.984815961176684[/C][C]0.492407980588342[/C][/ROW]
[ROW][C]26[/C][C]0.788881036464537[/C][C]0.422237927070926[/C][C]0.211118963535463[/C][/ROW]
[ROW][C]27[/C][C]0.760886549579255[/C][C]0.47822690084149[/C][C]0.239113450420745[/C][/ROW]
[ROW][C]28[/C][C]0.76683473940439[/C][C]0.466330521191219[/C][C]0.233165260595609[/C][/ROW]
[ROW][C]29[/C][C]0.789687788514404[/C][C]0.420624422971193[/C][C]0.210312211485596[/C][/ROW]
[ROW][C]30[/C][C]0.766737921126365[/C][C]0.466524157747269[/C][C]0.233262078873635[/C][/ROW]
[ROW][C]31[/C][C]0.726253378622552[/C][C]0.547493242754896[/C][C]0.273746621377448[/C][/ROW]
[ROW][C]32[/C][C]0.665815099453407[/C][C]0.668369801093186[/C][C]0.334184900546593[/C][/ROW]
[ROW][C]33[/C][C]0.602658233856291[/C][C]0.794683532287419[/C][C]0.397341766143709[/C][/ROW]
[ROW][C]34[/C][C]0.659565906503109[/C][C]0.680868186993783[/C][C]0.340434093496891[/C][/ROW]
[ROW][C]35[/C][C]0.591789663760477[/C][C]0.816420672479046[/C][C]0.408210336239523[/C][/ROW]
[ROW][C]36[/C][C]0.524341186979242[/C][C]0.951317626041516[/C][C]0.475658813020758[/C][/ROW]
[ROW][C]37[/C][C]0.544376131919863[/C][C]0.911247736160274[/C][C]0.455623868080137[/C][/ROW]
[ROW][C]38[/C][C]0.674717801637967[/C][C]0.650564396724066[/C][C]0.325282198362033[/C][/ROW]
[ROW][C]39[/C][C]0.619589557635581[/C][C]0.760820884728837[/C][C]0.380410442364419[/C][/ROW]
[ROW][C]40[/C][C]0.564942966298566[/C][C]0.87011406740287[/C][C]0.435057033701434[/C][/ROW]
[ROW][C]41[/C][C]0.688510570894359[/C][C]0.622978858211282[/C][C]0.311489429105641[/C][/ROW]
[ROW][C]42[/C][C]0.645858148712254[/C][C]0.708283702575492[/C][C]0.354141851287746[/C][/ROW]
[ROW][C]43[/C][C]0.623056804190788[/C][C]0.753886391618424[/C][C]0.376943195809212[/C][/ROW]
[ROW][C]44[/C][C]0.548872788269006[/C][C]0.902254423461988[/C][C]0.451127211730994[/C][/ROW]
[ROW][C]45[/C][C]0.490039785805799[/C][C]0.980079571611598[/C][C]0.509960214194201[/C][/ROW]
[ROW][C]46[/C][C]0.408688850683805[/C][C]0.817377701367609[/C][C]0.591311149316196[/C][/ROW]
[ROW][C]47[/C][C]0.440487810966049[/C][C]0.880975621932098[/C][C]0.559512189033951[/C][/ROW]
[ROW][C]48[/C][C]0.367041712276813[/C][C]0.734083424553626[/C][C]0.632958287723187[/C][/ROW]
[ROW][C]49[/C][C]0.480290669037475[/C][C]0.960581338074949[/C][C]0.519709330962525[/C][/ROW]
[ROW][C]50[/C][C]0.404672681818399[/C][C]0.809345363636798[/C][C]0.595327318181601[/C][/ROW]
[ROW][C]51[/C][C]0.31135091002244[/C][C]0.62270182004488[/C][C]0.68864908997756[/C][/ROW]
[ROW][C]52[/C][C]0.52993364509276[/C][C]0.94013270981448[/C][C]0.47006635490724[/C][/ROW]
[ROW][C]53[/C][C]0.91192361667177[/C][C]0.176152766656460[/C][C]0.0880763833282299[/C][/ROW]
[ROW][C]54[/C][C]0.866836544616279[/C][C]0.266326910767443[/C][C]0.133163455383721[/C][/ROW]
[ROW][C]55[/C][C]0.776845441772916[/C][C]0.446309116454169[/C][C]0.223154558227084[/C][/ROW]
[ROW][C]56[/C][C]0.71003141398658[/C][C]0.579937172026841[/C][C]0.289968586013421[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58305&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58305&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
50.8979859168170820.2040281663658370.102014083182919
60.8120322247708430.3759355504583150.187967775229157
70.710469566957740.5790608660845210.289530433042260
80.6883084145832840.6233831708334330.311691585416716
90.6351450116399910.7297099767200170.364854988360008
100.5327743405281600.9344513189436810.467225659471840
110.4267871789475960.8535743578951920.573212821052404
120.3553548475994490.7107096951988970.644645152400551
130.4063747858459530.8127495716919060.593625214154047
140.8146790883046750.3706418233906490.185320911695325
150.7959056742792640.4081886514414730.204094325720736
160.8122106269875030.3755787460249930.187789373012497
170.7592831736303260.4814336527393480.240716826369674
180.7158944153578420.5682111692843150.284105584642158
190.6505610807851940.6988778384296110.349438919214806
200.6045080083392140.7909839833215710.395491991660786
210.5453817483844150.909236503231170.454618251615585
220.594405755045330.811188489909340.40559424495467
230.5392265150438850.9215469699122290.460773484956115
240.4933203619544270.9866407239088540.506679638045573
250.5075920194116580.9848159611766840.492407980588342
260.7888810364645370.4222379270709260.211118963535463
270.7608865495792550.478226900841490.239113450420745
280.766834739404390.4663305211912190.233165260595609
290.7896877885144040.4206244229711930.210312211485596
300.7667379211263650.4665241577472690.233262078873635
310.7262533786225520.5474932427548960.273746621377448
320.6658150994534070.6683698010931860.334184900546593
330.6026582338562910.7946835322874190.397341766143709
340.6595659065031090.6808681869937830.340434093496891
350.5917896637604770.8164206724790460.408210336239523
360.5243411869792420.9513176260415160.475658813020758
370.5443761319198630.9112477361602740.455623868080137
380.6747178016379670.6505643967240660.325282198362033
390.6195895576355810.7608208847288370.380410442364419
400.5649429662985660.870114067402870.435057033701434
410.6885105708943590.6229788582112820.311489429105641
420.6458581487122540.7082837025754920.354141851287746
430.6230568041907880.7538863916184240.376943195809212
440.5488727882690060.9022544234619880.451127211730994
450.4900397858057990.9800795716115980.509960214194201
460.4086888506838050.8173777013676090.591311149316196
470.4404878109660490.8809756219320980.559512189033951
480.3670417122768130.7340834245536260.632958287723187
490.4802906690374750.9605813380749490.519709330962525
500.4046726818183990.8093453636367980.595327318181601
510.311350910022440.622701820044880.68864908997756
520.529933645092760.940132709814480.47006635490724
530.911923616671770.1761527666564600.0880763833282299
540.8668365446162790.2663269107674430.133163455383721
550.7768454417729160.4463091164541690.223154558227084
560.710031413986580.5799371720268410.289968586013421







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=58305&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=58305&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58305&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')
}