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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 01:42:52 -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/t12587066698h6g359c7w6diez.htm/, Retrieved Fri, 29 Mar 2024 09:43:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57983, Retrieved Fri, 29 Mar 2024 09:43:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact171
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 14:03:14] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [SHWWS7model2] [2009-11-20 08:42:52] [db49399df1e4a3dbe31268849cebfd7f] [Current]
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Dataseries X:
161	0
149	0
139	0
135	0
130	0
127	0
122	0
117	0
112	0
113	0
149	0
157	0
157	0
147	0
137	0
132	0
125	0
123	0
117	0
114	0
111	0
112	0
144	0
150	0
149	0
134	0
123	0
116	0
117	0
111	0
105	0
102	0
95	0
93	0
124	0
130	0
124	0
115	0
106	0
105	0
105	1
101	1
95	1
93	1
84	1
87	1
116	1
120	1
117	1
109	1
105	1
107	1
109	1
109	1
108	1
107	1
99	1
103	1
131	1
137	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 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 & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57983&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]8 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=57983&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57983&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 time8 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 145.45625 -16.640625X[t] -0.528124999999965M1[t] -11.328125M2[t] -20.1281250000000M3[t] -23.1281250000000M4[t] -21.6M5[t] -24.6M6[t] -29.4M7[t] -32.2000000000001M8[t] -38.6M9[t] -37.2M10[t] -6.00000000000003M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  145.45625 -16.640625X[t] -0.528124999999965M1[t] -11.328125M2[t] -20.1281250000000M3[t] -23.1281250000000M4[t] -21.6M5[t] -24.6M6[t] -29.4M7[t] -32.2000000000001M8[t] -38.6M9[t] -37.2M10[t] -6.00000000000003M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57983&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  145.45625 -16.640625X[t] -0.528124999999965M1[t] -11.328125M2[t] -20.1281250000000M3[t] -23.1281250000000M4[t] -21.6M5[t] -24.6M6[t] -29.4M7[t] -32.2000000000001M8[t] -38.6M9[t] -37.2M10[t] -6.00000000000003M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57983&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57983&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] = + 145.45625 -16.640625X[t] -0.528124999999965M1[t] -11.328125M2[t] -20.1281250000000M3[t] -23.1281250000000M4[t] -21.6M5[t] -24.6M6[t] -29.4M7[t] -32.2000000000001M8[t] -38.6M9[t] -37.2M10[t] -6.00000000000003M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)145.456255.04309928.842600
X-16.6406253.057828-5.4422e-061e-06
M1-0.5281249999999656.946049-0.0760.9397160.469858
M2-11.3281256.946049-1.63090.1096010.054801
M3-20.12812500000006.946049-2.89780.0056920.002846
M4-23.12812500000006.946049-3.32970.0016980.000849
M5-21.66.919074-3.12180.0030720.001536
M6-24.66.919074-3.55540.0008730.000437
M7-29.46.919074-4.24910.0001015e-05
M8-32.20000000000016.919074-4.65382.7e-051.3e-05
M9-38.66.919074-5.57881e-061e-06
M10-37.26.919074-5.37642e-061e-06
M11-6.000000000000036.919074-0.86720.3902560.195128

\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) & 145.45625 & 5.043099 & 28.8426 & 0 & 0 \tabularnewline
X & -16.640625 & 3.057828 & -5.442 & 2e-06 & 1e-06 \tabularnewline
M1 & -0.528124999999965 & 6.946049 & -0.076 & 0.939716 & 0.469858 \tabularnewline
M2 & -11.328125 & 6.946049 & -1.6309 & 0.109601 & 0.054801 \tabularnewline
M3 & -20.1281250000000 & 6.946049 & -2.8978 & 0.005692 & 0.002846 \tabularnewline
M4 & -23.1281250000000 & 6.946049 & -3.3297 & 0.001698 & 0.000849 \tabularnewline
M5 & -21.6 & 6.919074 & -3.1218 & 0.003072 & 0.001536 \tabularnewline
M6 & -24.6 & 6.919074 & -3.5554 & 0.000873 & 0.000437 \tabularnewline
M7 & -29.4 & 6.919074 & -4.2491 & 0.000101 & 5e-05 \tabularnewline
M8 & -32.2000000000001 & 6.919074 & -4.6538 & 2.7e-05 & 1.3e-05 \tabularnewline
M9 & -38.6 & 6.919074 & -5.5788 & 1e-06 & 1e-06 \tabularnewline
M10 & -37.2 & 6.919074 & -5.3764 & 2e-06 & 1e-06 \tabularnewline
M11 & -6.00000000000003 & 6.919074 & -0.8672 & 0.390256 & 0.195128 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57983&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]145.45625[/C][C]5.043099[/C][C]28.8426[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-16.640625[/C][C]3.057828[/C][C]-5.442[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M1[/C][C]-0.528124999999965[/C][C]6.946049[/C][C]-0.076[/C][C]0.939716[/C][C]0.469858[/C][/ROW]
[ROW][C]M2[/C][C]-11.328125[/C][C]6.946049[/C][C]-1.6309[/C][C]0.109601[/C][C]0.054801[/C][/ROW]
[ROW][C]M3[/C][C]-20.1281250000000[/C][C]6.946049[/C][C]-2.8978[/C][C]0.005692[/C][C]0.002846[/C][/ROW]
[ROW][C]M4[/C][C]-23.1281250000000[/C][C]6.946049[/C][C]-3.3297[/C][C]0.001698[/C][C]0.000849[/C][/ROW]
[ROW][C]M5[/C][C]-21.6[/C][C]6.919074[/C][C]-3.1218[/C][C]0.003072[/C][C]0.001536[/C][/ROW]
[ROW][C]M6[/C][C]-24.6[/C][C]6.919074[/C][C]-3.5554[/C][C]0.000873[/C][C]0.000437[/C][/ROW]
[ROW][C]M7[/C][C]-29.4[/C][C]6.919074[/C][C]-4.2491[/C][C]0.000101[/C][C]5e-05[/C][/ROW]
[ROW][C]M8[/C][C]-32.2000000000001[/C][C]6.919074[/C][C]-4.6538[/C][C]2.7e-05[/C][C]1.3e-05[/C][/ROW]
[ROW][C]M9[/C][C]-38.6[/C][C]6.919074[/C][C]-5.5788[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M10[/C][C]-37.2[/C][C]6.919074[/C][C]-5.3764[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M11[/C][C]-6.00000000000003[/C][C]6.919074[/C][C]-0.8672[/C][C]0.390256[/C][C]0.195128[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57983&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57983&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)145.456255.04309928.842600
X-16.6406253.057828-5.4422e-061e-06
M1-0.5281249999999656.946049-0.0760.9397160.469858
M2-11.3281256.946049-1.63090.1096010.054801
M3-20.12812500000006.946049-2.89780.0056920.002846
M4-23.12812500000006.946049-3.32970.0016980.000849
M5-21.66.919074-3.12180.0030720.001536
M6-24.66.919074-3.55540.0008730.000437
M7-29.46.919074-4.24910.0001015e-05
M8-32.20000000000016.919074-4.65382.7e-051.3e-05
M9-38.66.919074-5.57881e-061e-06
M10-37.26.919074-5.37642e-061e-06
M11-6.000000000000036.919074-0.86720.3902560.195128







Multiple Linear Regression - Regression Statistics
Multiple R0.847811656096568
R-squared0.718784604213205
Adjusted R-squared0.646984928693172
F-TEST (value)10.0109728770662
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value2.53967291641288e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation10.9400171875473
Sum Squared Residuals5625.14687500001

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.847811656096568 \tabularnewline
R-squared & 0.718784604213205 \tabularnewline
Adjusted R-squared & 0.646984928693172 \tabularnewline
F-TEST (value) & 10.0109728770662 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 2.53967291641288e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 10.9400171875473 \tabularnewline
Sum Squared Residuals & 5625.14687500001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57983&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.847811656096568[/C][/ROW]
[ROW][C]R-squared[/C][C]0.718784604213205[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.646984928693172[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.0109728770662[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]2.53967291641288e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]10.9400171875473[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5625.14687500001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57983&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57983&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 R0.847811656096568
R-squared0.718784604213205
Adjusted R-squared0.646984928693172
F-TEST (value)10.0109728770662
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value2.53967291641288e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation10.9400171875473
Sum Squared Residuals5625.14687500001







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1161144.92812500000016.0718750000002
2149134.12812514.87187500
3139125.32812513.671875
4135122.32812512.6718750000000
5130123.856256.14375000000002
6127120.856256.14375000000003
7122116.056255.94374999999993
8117113.256253.74374999999995
9112106.856255.14374999999999
10113108.256254.74375000000006
11149139.456259.54374999999998
12157145.4562511.54375
13157144.92812512.0718750000000
14147134.12812512.871875
15137125.32812511.671875
16132122.3281259.671875
17125123.856251.14374999999999
18123120.856252.14374999999999
19117116.056250.943750000000015
20114113.256250.743750000000012
21111106.856254.14375000000001
22112108.256253.74374999999999
23144139.456254.54375
24150145.456254.54374999999997
25149144.9281254.07187499999995
26134134.128125-0.128124999999997
27123125.328125-2.32812499999999
28116122.328125-6.328125
29117123.85625-6.85625000000001
30111120.85625-9.85625
31105116.05625-11.0562500000000
32102113.25625-11.25625
3395106.85625-11.85625
3493108.25625-15.25625
35124139.45625-15.45625
36130145.45625-15.4562500000000
37124144.928125-20.9281250000001
38115134.128125-19.128125
39106125.328125-19.328125
40105122.328125-17.328125
41105107.215625-2.215625
42101104.215625-3.21562500000001
439599.415625-4.41562499999998
449396.615625-3.61562499999998
458490.215625-6.215625
468791.615625-4.61562500000002
47116122.815625-6.815625
48120128.815625-8.81562500000002
49117128.2875-11.2875000000000
50109117.4875-8.48749999999999
51105108.6875-3.68749999999999
52107105.68751.31250000000001
53109107.2156251.784375
54109104.2156254.78437499999999
5510899.4156258.58437500000002
5610796.61562510.3843750000000
579990.2156258.784375
5810391.61562511.3843750000000
59131122.8156258.18437500000001
60137128.8156258.18437499999999

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 161 & 144.928125000000 & 16.0718750000002 \tabularnewline
2 & 149 & 134.128125 & 14.87187500 \tabularnewline
3 & 139 & 125.328125 & 13.671875 \tabularnewline
4 & 135 & 122.328125 & 12.6718750000000 \tabularnewline
5 & 130 & 123.85625 & 6.14375000000002 \tabularnewline
6 & 127 & 120.85625 & 6.14375000000003 \tabularnewline
7 & 122 & 116.05625 & 5.94374999999993 \tabularnewline
8 & 117 & 113.25625 & 3.74374999999995 \tabularnewline
9 & 112 & 106.85625 & 5.14374999999999 \tabularnewline
10 & 113 & 108.25625 & 4.74375000000006 \tabularnewline
11 & 149 & 139.45625 & 9.54374999999998 \tabularnewline
12 & 157 & 145.45625 & 11.54375 \tabularnewline
13 & 157 & 144.928125 & 12.0718750000000 \tabularnewline
14 & 147 & 134.128125 & 12.871875 \tabularnewline
15 & 137 & 125.328125 & 11.671875 \tabularnewline
16 & 132 & 122.328125 & 9.671875 \tabularnewline
17 & 125 & 123.85625 & 1.14374999999999 \tabularnewline
18 & 123 & 120.85625 & 2.14374999999999 \tabularnewline
19 & 117 & 116.05625 & 0.943750000000015 \tabularnewline
20 & 114 & 113.25625 & 0.743750000000012 \tabularnewline
21 & 111 & 106.85625 & 4.14375000000001 \tabularnewline
22 & 112 & 108.25625 & 3.74374999999999 \tabularnewline
23 & 144 & 139.45625 & 4.54375 \tabularnewline
24 & 150 & 145.45625 & 4.54374999999997 \tabularnewline
25 & 149 & 144.928125 & 4.07187499999995 \tabularnewline
26 & 134 & 134.128125 & -0.128124999999997 \tabularnewline
27 & 123 & 125.328125 & -2.32812499999999 \tabularnewline
28 & 116 & 122.328125 & -6.328125 \tabularnewline
29 & 117 & 123.85625 & -6.85625000000001 \tabularnewline
30 & 111 & 120.85625 & -9.85625 \tabularnewline
31 & 105 & 116.05625 & -11.0562500000000 \tabularnewline
32 & 102 & 113.25625 & -11.25625 \tabularnewline
33 & 95 & 106.85625 & -11.85625 \tabularnewline
34 & 93 & 108.25625 & -15.25625 \tabularnewline
35 & 124 & 139.45625 & -15.45625 \tabularnewline
36 & 130 & 145.45625 & -15.4562500000000 \tabularnewline
37 & 124 & 144.928125 & -20.9281250000001 \tabularnewline
38 & 115 & 134.128125 & -19.128125 \tabularnewline
39 & 106 & 125.328125 & -19.328125 \tabularnewline
40 & 105 & 122.328125 & -17.328125 \tabularnewline
41 & 105 & 107.215625 & -2.215625 \tabularnewline
42 & 101 & 104.215625 & -3.21562500000001 \tabularnewline
43 & 95 & 99.415625 & -4.41562499999998 \tabularnewline
44 & 93 & 96.615625 & -3.61562499999998 \tabularnewline
45 & 84 & 90.215625 & -6.215625 \tabularnewline
46 & 87 & 91.615625 & -4.61562500000002 \tabularnewline
47 & 116 & 122.815625 & -6.815625 \tabularnewline
48 & 120 & 128.815625 & -8.81562500000002 \tabularnewline
49 & 117 & 128.2875 & -11.2875000000000 \tabularnewline
50 & 109 & 117.4875 & -8.48749999999999 \tabularnewline
51 & 105 & 108.6875 & -3.68749999999999 \tabularnewline
52 & 107 & 105.6875 & 1.31250000000001 \tabularnewline
53 & 109 & 107.215625 & 1.784375 \tabularnewline
54 & 109 & 104.215625 & 4.78437499999999 \tabularnewline
55 & 108 & 99.415625 & 8.58437500000002 \tabularnewline
56 & 107 & 96.615625 & 10.3843750000000 \tabularnewline
57 & 99 & 90.215625 & 8.784375 \tabularnewline
58 & 103 & 91.615625 & 11.3843750000000 \tabularnewline
59 & 131 & 122.815625 & 8.18437500000001 \tabularnewline
60 & 137 & 128.815625 & 8.18437499999999 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57983&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]161[/C][C]144.928125000000[/C][C]16.0718750000002[/C][/ROW]
[ROW][C]2[/C][C]149[/C][C]134.128125[/C][C]14.87187500[/C][/ROW]
[ROW][C]3[/C][C]139[/C][C]125.328125[/C][C]13.671875[/C][/ROW]
[ROW][C]4[/C][C]135[/C][C]122.328125[/C][C]12.6718750000000[/C][/ROW]
[ROW][C]5[/C][C]130[/C][C]123.85625[/C][C]6.14375000000002[/C][/ROW]
[ROW][C]6[/C][C]127[/C][C]120.85625[/C][C]6.14375000000003[/C][/ROW]
[ROW][C]7[/C][C]122[/C][C]116.05625[/C][C]5.94374999999993[/C][/ROW]
[ROW][C]8[/C][C]117[/C][C]113.25625[/C][C]3.74374999999995[/C][/ROW]
[ROW][C]9[/C][C]112[/C][C]106.85625[/C][C]5.14374999999999[/C][/ROW]
[ROW][C]10[/C][C]113[/C][C]108.25625[/C][C]4.74375000000006[/C][/ROW]
[ROW][C]11[/C][C]149[/C][C]139.45625[/C][C]9.54374999999998[/C][/ROW]
[ROW][C]12[/C][C]157[/C][C]145.45625[/C][C]11.54375[/C][/ROW]
[ROW][C]13[/C][C]157[/C][C]144.928125[/C][C]12.0718750000000[/C][/ROW]
[ROW][C]14[/C][C]147[/C][C]134.128125[/C][C]12.871875[/C][/ROW]
[ROW][C]15[/C][C]137[/C][C]125.328125[/C][C]11.671875[/C][/ROW]
[ROW][C]16[/C][C]132[/C][C]122.328125[/C][C]9.671875[/C][/ROW]
[ROW][C]17[/C][C]125[/C][C]123.85625[/C][C]1.14374999999999[/C][/ROW]
[ROW][C]18[/C][C]123[/C][C]120.85625[/C][C]2.14374999999999[/C][/ROW]
[ROW][C]19[/C][C]117[/C][C]116.05625[/C][C]0.943750000000015[/C][/ROW]
[ROW][C]20[/C][C]114[/C][C]113.25625[/C][C]0.743750000000012[/C][/ROW]
[ROW][C]21[/C][C]111[/C][C]106.85625[/C][C]4.14375000000001[/C][/ROW]
[ROW][C]22[/C][C]112[/C][C]108.25625[/C][C]3.74374999999999[/C][/ROW]
[ROW][C]23[/C][C]144[/C][C]139.45625[/C][C]4.54375[/C][/ROW]
[ROW][C]24[/C][C]150[/C][C]145.45625[/C][C]4.54374999999997[/C][/ROW]
[ROW][C]25[/C][C]149[/C][C]144.928125[/C][C]4.07187499999995[/C][/ROW]
[ROW][C]26[/C][C]134[/C][C]134.128125[/C][C]-0.128124999999997[/C][/ROW]
[ROW][C]27[/C][C]123[/C][C]125.328125[/C][C]-2.32812499999999[/C][/ROW]
[ROW][C]28[/C][C]116[/C][C]122.328125[/C][C]-6.328125[/C][/ROW]
[ROW][C]29[/C][C]117[/C][C]123.85625[/C][C]-6.85625000000001[/C][/ROW]
[ROW][C]30[/C][C]111[/C][C]120.85625[/C][C]-9.85625[/C][/ROW]
[ROW][C]31[/C][C]105[/C][C]116.05625[/C][C]-11.0562500000000[/C][/ROW]
[ROW][C]32[/C][C]102[/C][C]113.25625[/C][C]-11.25625[/C][/ROW]
[ROW][C]33[/C][C]95[/C][C]106.85625[/C][C]-11.85625[/C][/ROW]
[ROW][C]34[/C][C]93[/C][C]108.25625[/C][C]-15.25625[/C][/ROW]
[ROW][C]35[/C][C]124[/C][C]139.45625[/C][C]-15.45625[/C][/ROW]
[ROW][C]36[/C][C]130[/C][C]145.45625[/C][C]-15.4562500000000[/C][/ROW]
[ROW][C]37[/C][C]124[/C][C]144.928125[/C][C]-20.9281250000001[/C][/ROW]
[ROW][C]38[/C][C]115[/C][C]134.128125[/C][C]-19.128125[/C][/ROW]
[ROW][C]39[/C][C]106[/C][C]125.328125[/C][C]-19.328125[/C][/ROW]
[ROW][C]40[/C][C]105[/C][C]122.328125[/C][C]-17.328125[/C][/ROW]
[ROW][C]41[/C][C]105[/C][C]107.215625[/C][C]-2.215625[/C][/ROW]
[ROW][C]42[/C][C]101[/C][C]104.215625[/C][C]-3.21562500000001[/C][/ROW]
[ROW][C]43[/C][C]95[/C][C]99.415625[/C][C]-4.41562499999998[/C][/ROW]
[ROW][C]44[/C][C]93[/C][C]96.615625[/C][C]-3.61562499999998[/C][/ROW]
[ROW][C]45[/C][C]84[/C][C]90.215625[/C][C]-6.215625[/C][/ROW]
[ROW][C]46[/C][C]87[/C][C]91.615625[/C][C]-4.61562500000002[/C][/ROW]
[ROW][C]47[/C][C]116[/C][C]122.815625[/C][C]-6.815625[/C][/ROW]
[ROW][C]48[/C][C]120[/C][C]128.815625[/C][C]-8.81562500000002[/C][/ROW]
[ROW][C]49[/C][C]117[/C][C]128.2875[/C][C]-11.2875000000000[/C][/ROW]
[ROW][C]50[/C][C]109[/C][C]117.4875[/C][C]-8.48749999999999[/C][/ROW]
[ROW][C]51[/C][C]105[/C][C]108.6875[/C][C]-3.68749999999999[/C][/ROW]
[ROW][C]52[/C][C]107[/C][C]105.6875[/C][C]1.31250000000001[/C][/ROW]
[ROW][C]53[/C][C]109[/C][C]107.215625[/C][C]1.784375[/C][/ROW]
[ROW][C]54[/C][C]109[/C][C]104.215625[/C][C]4.78437499999999[/C][/ROW]
[ROW][C]55[/C][C]108[/C][C]99.415625[/C][C]8.58437500000002[/C][/ROW]
[ROW][C]56[/C][C]107[/C][C]96.615625[/C][C]10.3843750000000[/C][/ROW]
[ROW][C]57[/C][C]99[/C][C]90.215625[/C][C]8.784375[/C][/ROW]
[ROW][C]58[/C][C]103[/C][C]91.615625[/C][C]11.3843750000000[/C][/ROW]
[ROW][C]59[/C][C]131[/C][C]122.815625[/C][C]8.18437500000001[/C][/ROW]
[ROW][C]60[/C][C]137[/C][C]128.815625[/C][C]8.18437499999999[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57983&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57983&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
1161144.92812500000016.0718750000002
2149134.12812514.87187500
3139125.32812513.671875
4135122.32812512.6718750000000
5130123.856256.14375000000002
6127120.856256.14375000000003
7122116.056255.94374999999993
8117113.256253.74374999999995
9112106.856255.14374999999999
10113108.256254.74375000000006
11149139.456259.54374999999998
12157145.4562511.54375
13157144.92812512.0718750000000
14147134.12812512.871875
15137125.32812511.671875
16132122.3281259.671875
17125123.856251.14374999999999
18123120.856252.14374999999999
19117116.056250.943750000000015
20114113.256250.743750000000012
21111106.856254.14375000000001
22112108.256253.74374999999999
23144139.456254.54375
24150145.456254.54374999999997
25149144.9281254.07187499999995
26134134.128125-0.128124999999997
27123125.328125-2.32812499999999
28116122.328125-6.328125
29117123.85625-6.85625000000001
30111120.85625-9.85625
31105116.05625-11.0562500000000
32102113.25625-11.25625
3395106.85625-11.85625
3493108.25625-15.25625
35124139.45625-15.45625
36130145.45625-15.4562500000000
37124144.928125-20.9281250000001
38115134.128125-19.128125
39106125.328125-19.328125
40105122.328125-17.328125
41105107.215625-2.215625
42101104.215625-3.21562500000001
439599.415625-4.41562499999998
449396.615625-3.61562499999998
458490.215625-6.215625
468791.615625-4.61562500000002
47116122.815625-6.815625
48120128.815625-8.81562500000002
49117128.2875-11.2875000000000
50109117.4875-8.48749999999999
51105108.6875-3.68749999999999
52107105.68751.31250000000001
53109107.2156251.784375
54109104.2156254.78437499999999
5510899.4156258.58437500000002
5610796.61562510.3843750000000
579990.2156258.784375
5810391.61562511.3843750000000
59131122.8156258.18437500000001
60137128.8156258.18437499999999







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.02345077719730190.04690155439460390.976549222802698
170.01453690429410070.02907380858820130.9854630957059
180.006657843033257770.01331568606651550.993342156966742
190.003971757727548150.00794351545509630.996028242272452
200.001487256947020100.002974513894040200.99851274305298
210.0004840576764608560.0009681153529217120.999515942323539
220.0001554295096756550.0003108590193513110.999844570490324
230.0001425378521058360.0002850757042116730.999857462147894
240.0002952306956168320.0005904613912336640.999704769304383
250.005458692608177690.01091738521635540.994541307391822
260.07556656813656860.1511331362731370.924433431863431
270.2798075413485090.5596150826970180.720192458651491
280.5138083197776070.9723833604447870.486191680222393
290.53726177193990.92547645612020.4627382280601
300.583235597045360.833528805909280.41676440295464
310.6086090853310460.7827818293379090.391390914668955
320.602160252303320.795679495393360.39783974769668
330.6232099467250540.7535801065498930.376790053274946
340.6675530674872570.6648938650254860.332446932512743
350.7099350508820150.5801298982359710.290064949117985
360.7309476727698120.5381046544603770.269052327230188
370.8072710903549810.3854578192900370.192728909645019
380.818783685343290.3624326293134210.181216314656710
390.7974733812964920.4050532374070150.202526618703507
400.740847025285020.518305949429960.25915297471498
410.6275722982455810.7448554035088370.372427701754419
420.5134974607851190.9730050784297630.486502539214881
430.4308660301285650.861732060257130.569133969871435
440.3509837632312470.7019675264624940.649016236768753

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.0234507771973019 & 0.0469015543946039 & 0.976549222802698 \tabularnewline
17 & 0.0145369042941007 & 0.0290738085882013 & 0.9854630957059 \tabularnewline
18 & 0.00665784303325777 & 0.0133156860665155 & 0.993342156966742 \tabularnewline
19 & 0.00397175772754815 & 0.0079435154550963 & 0.996028242272452 \tabularnewline
20 & 0.00148725694702010 & 0.00297451389404020 & 0.99851274305298 \tabularnewline
21 & 0.000484057676460856 & 0.000968115352921712 & 0.999515942323539 \tabularnewline
22 & 0.000155429509675655 & 0.000310859019351311 & 0.999844570490324 \tabularnewline
23 & 0.000142537852105836 & 0.000285075704211673 & 0.999857462147894 \tabularnewline
24 & 0.000295230695616832 & 0.000590461391233664 & 0.999704769304383 \tabularnewline
25 & 0.00545869260817769 & 0.0109173852163554 & 0.994541307391822 \tabularnewline
26 & 0.0755665681365686 & 0.151133136273137 & 0.924433431863431 \tabularnewline
27 & 0.279807541348509 & 0.559615082697018 & 0.720192458651491 \tabularnewline
28 & 0.513808319777607 & 0.972383360444787 & 0.486191680222393 \tabularnewline
29 & 0.5372617719399 & 0.9254764561202 & 0.4627382280601 \tabularnewline
30 & 0.58323559704536 & 0.83352880590928 & 0.41676440295464 \tabularnewline
31 & 0.608609085331046 & 0.782781829337909 & 0.391390914668955 \tabularnewline
32 & 0.60216025230332 & 0.79567949539336 & 0.39783974769668 \tabularnewline
33 & 0.623209946725054 & 0.753580106549893 & 0.376790053274946 \tabularnewline
34 & 0.667553067487257 & 0.664893865025486 & 0.332446932512743 \tabularnewline
35 & 0.709935050882015 & 0.580129898235971 & 0.290064949117985 \tabularnewline
36 & 0.730947672769812 & 0.538104654460377 & 0.269052327230188 \tabularnewline
37 & 0.807271090354981 & 0.385457819290037 & 0.192728909645019 \tabularnewline
38 & 0.81878368534329 & 0.362432629313421 & 0.181216314656710 \tabularnewline
39 & 0.797473381296492 & 0.405053237407015 & 0.202526618703507 \tabularnewline
40 & 0.74084702528502 & 0.51830594942996 & 0.25915297471498 \tabularnewline
41 & 0.627572298245581 & 0.744855403508837 & 0.372427701754419 \tabularnewline
42 & 0.513497460785119 & 0.973005078429763 & 0.486502539214881 \tabularnewline
43 & 0.430866030128565 & 0.86173206025713 & 0.569133969871435 \tabularnewline
44 & 0.350983763231247 & 0.701967526462494 & 0.649016236768753 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57983&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]16[/C][C]0.0234507771973019[/C][C]0.0469015543946039[/C][C]0.976549222802698[/C][/ROW]
[ROW][C]17[/C][C]0.0145369042941007[/C][C]0.0290738085882013[/C][C]0.9854630957059[/C][/ROW]
[ROW][C]18[/C][C]0.00665784303325777[/C][C]0.0133156860665155[/C][C]0.993342156966742[/C][/ROW]
[ROW][C]19[/C][C]0.00397175772754815[/C][C]0.0079435154550963[/C][C]0.996028242272452[/C][/ROW]
[ROW][C]20[/C][C]0.00148725694702010[/C][C]0.00297451389404020[/C][C]0.99851274305298[/C][/ROW]
[ROW][C]21[/C][C]0.000484057676460856[/C][C]0.000968115352921712[/C][C]0.999515942323539[/C][/ROW]
[ROW][C]22[/C][C]0.000155429509675655[/C][C]0.000310859019351311[/C][C]0.999844570490324[/C][/ROW]
[ROW][C]23[/C][C]0.000142537852105836[/C][C]0.000285075704211673[/C][C]0.999857462147894[/C][/ROW]
[ROW][C]24[/C][C]0.000295230695616832[/C][C]0.000590461391233664[/C][C]0.999704769304383[/C][/ROW]
[ROW][C]25[/C][C]0.00545869260817769[/C][C]0.0109173852163554[/C][C]0.994541307391822[/C][/ROW]
[ROW][C]26[/C][C]0.0755665681365686[/C][C]0.151133136273137[/C][C]0.924433431863431[/C][/ROW]
[ROW][C]27[/C][C]0.279807541348509[/C][C]0.559615082697018[/C][C]0.720192458651491[/C][/ROW]
[ROW][C]28[/C][C]0.513808319777607[/C][C]0.972383360444787[/C][C]0.486191680222393[/C][/ROW]
[ROW][C]29[/C][C]0.5372617719399[/C][C]0.9254764561202[/C][C]0.4627382280601[/C][/ROW]
[ROW][C]30[/C][C]0.58323559704536[/C][C]0.83352880590928[/C][C]0.41676440295464[/C][/ROW]
[ROW][C]31[/C][C]0.608609085331046[/C][C]0.782781829337909[/C][C]0.391390914668955[/C][/ROW]
[ROW][C]32[/C][C]0.60216025230332[/C][C]0.79567949539336[/C][C]0.39783974769668[/C][/ROW]
[ROW][C]33[/C][C]0.623209946725054[/C][C]0.753580106549893[/C][C]0.376790053274946[/C][/ROW]
[ROW][C]34[/C][C]0.667553067487257[/C][C]0.664893865025486[/C][C]0.332446932512743[/C][/ROW]
[ROW][C]35[/C][C]0.709935050882015[/C][C]0.580129898235971[/C][C]0.290064949117985[/C][/ROW]
[ROW][C]36[/C][C]0.730947672769812[/C][C]0.538104654460377[/C][C]0.269052327230188[/C][/ROW]
[ROW][C]37[/C][C]0.807271090354981[/C][C]0.385457819290037[/C][C]0.192728909645019[/C][/ROW]
[ROW][C]38[/C][C]0.81878368534329[/C][C]0.362432629313421[/C][C]0.181216314656710[/C][/ROW]
[ROW][C]39[/C][C]0.797473381296492[/C][C]0.405053237407015[/C][C]0.202526618703507[/C][/ROW]
[ROW][C]40[/C][C]0.74084702528502[/C][C]0.51830594942996[/C][C]0.25915297471498[/C][/ROW]
[ROW][C]41[/C][C]0.627572298245581[/C][C]0.744855403508837[/C][C]0.372427701754419[/C][/ROW]
[ROW][C]42[/C][C]0.513497460785119[/C][C]0.973005078429763[/C][C]0.486502539214881[/C][/ROW]
[ROW][C]43[/C][C]0.430866030128565[/C][C]0.86173206025713[/C][C]0.569133969871435[/C][/ROW]
[ROW][C]44[/C][C]0.350983763231247[/C][C]0.701967526462494[/C][C]0.649016236768753[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57983&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57983&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
160.02345077719730190.04690155439460390.976549222802698
170.01453690429410070.02907380858820130.9854630957059
180.006657843033257770.01331568606651550.993342156966742
190.003971757727548150.00794351545509630.996028242272452
200.001487256947020100.002974513894040200.99851274305298
210.0004840576764608560.0009681153529217120.999515942323539
220.0001554295096756550.0003108590193513110.999844570490324
230.0001425378521058360.0002850757042116730.999857462147894
240.0002952306956168320.0005904613912336640.999704769304383
250.005458692608177690.01091738521635540.994541307391822
260.07556656813656860.1511331362731370.924433431863431
270.2798075413485090.5596150826970180.720192458651491
280.5138083197776070.9723833604447870.486191680222393
290.53726177193990.92547645612020.4627382280601
300.583235597045360.833528805909280.41676440295464
310.6086090853310460.7827818293379090.391390914668955
320.602160252303320.795679495393360.39783974769668
330.6232099467250540.7535801065498930.376790053274946
340.6675530674872570.6648938650254860.332446932512743
350.7099350508820150.5801298982359710.290064949117985
360.7309476727698120.5381046544603770.269052327230188
370.8072710903549810.3854578192900370.192728909645019
380.818783685343290.3624326293134210.181216314656710
390.7974733812964920.4050532374070150.202526618703507
400.740847025285020.518305949429960.25915297471498
410.6275722982455810.7448554035088370.372427701754419
420.5134974607851190.9730050784297630.486502539214881
430.4308660301285650.861732060257130.569133969871435
440.3509837632312470.7019675264624940.649016236768753







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level60.206896551724138NOK
5% type I error level100.344827586206897NOK
10% type I error level100.344827586206897NOK

\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 & 6 & 0.206896551724138 & NOK \tabularnewline
5% type I error level & 10 & 0.344827586206897 & NOK \tabularnewline
10% type I error level & 10 & 0.344827586206897 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57983&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]6[/C][C]0.206896551724138[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]10[/C][C]0.344827586206897[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]10[/C][C]0.344827586206897[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57983&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57983&T=6

As an alternative you can also use a QR Code:  

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Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level60.206896551724138NOK
5% type I error level100.344827586206897NOK
10% type I error level100.344827586206897NOK



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