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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 02:41:55 -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/t12587101935m1tohhs72pitcj.htm/, Retrieved Fri, 29 Mar 2024 01:20:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57997, Retrieved Fri, 29 Mar 2024 01:20:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact156
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]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:06:21] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [SHWWS7model3] [2009-11-20 09:41:55] [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 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=57997&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=57997&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57997&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] = + 161.73125 + 3.70312500000001X[t] -3.91874999999995M1[t] -14.040625M2[t] -22.1625000000000M3[t] -24.4843750000000M4[t] -26.3468750000000M5[t] -28.66875M6[t] -32.790625M7[t] -34.9125000000001M8[t] -40.634375M9[t] -38.55625M10[t] -6.67812500000001M11[t] -0.678125t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  161.73125 +  3.70312500000001X[t] -3.91874999999995M1[t] -14.040625M2[t] -22.1625000000000M3[t] -24.4843750000000M4[t] -26.3468750000000M5[t] -28.66875M6[t] -32.790625M7[t] -34.9125000000001M8[t] -40.634375M9[t] -38.55625M10[t] -6.67812500000001M11[t] -0.678125t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57997&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  161.73125 +  3.70312500000001X[t] -3.91874999999995M1[t] -14.040625M2[t] -22.1625000000000M3[t] -24.4843750000000M4[t] -26.3468750000000M5[t] -28.66875M6[t] -32.790625M7[t] -34.9125000000001M8[t] -40.634375M9[t] -38.55625M10[t] -6.67812500000001M11[t] -0.678125t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57997&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57997&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] = + 161.73125 + 3.70312500000001X[t] -3.91874999999995M1[t] -14.040625M2[t] -22.1625000000000M3[t] -24.4843750000000M4[t] -26.3468750000000M5[t] -28.66875M6[t] -32.790625M7[t] -34.9125000000001M8[t] -40.634375M9[t] -38.55625M10[t] -6.67812500000001M11[t] -0.678125t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)161.731254.4966735.966900
X3.703125000000013.8942310.95090.3466120.173306
M1-3.918749999999955.134656-0.76320.4492430.224621
M2-14.0406255.124802-2.73970.0087220.004361
M3-22.16250000000005.117124-4.3318e-054e-05
M4-24.48437500000005.111633-4.78991.8e-059e-06
M5-26.34687500000005.141215-5.12466e-063e-06
M6-28.668755.126993-5.59171e-061e-06
M7-32.7906255.114928-6.410800
M8-34.91250000000015.105036-6.838800
M9-40.6343755.097328-7.971700
M10-38.556255.091816-7.572200
M11-6.678125000000015.088506-1.31240.1958980.097949
t-0.6781250.105988-6.398200

\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) & 161.73125 & 4.49667 & 35.9669 & 0 & 0 \tabularnewline
X & 3.70312500000001 & 3.894231 & 0.9509 & 0.346612 & 0.173306 \tabularnewline
M1 & -3.91874999999995 & 5.134656 & -0.7632 & 0.449243 & 0.224621 \tabularnewline
M2 & -14.040625 & 5.124802 & -2.7397 & 0.008722 & 0.004361 \tabularnewline
M3 & -22.1625000000000 & 5.117124 & -4.331 & 8e-05 & 4e-05 \tabularnewline
M4 & -24.4843750000000 & 5.111633 & -4.7899 & 1.8e-05 & 9e-06 \tabularnewline
M5 & -26.3468750000000 & 5.141215 & -5.1246 & 6e-06 & 3e-06 \tabularnewline
M6 & -28.66875 & 5.126993 & -5.5917 & 1e-06 & 1e-06 \tabularnewline
M7 & -32.790625 & 5.114928 & -6.4108 & 0 & 0 \tabularnewline
M8 & -34.9125000000001 & 5.105036 & -6.8388 & 0 & 0 \tabularnewline
M9 & -40.634375 & 5.097328 & -7.9717 & 0 & 0 \tabularnewline
M10 & -38.55625 & 5.091816 & -7.5722 & 0 & 0 \tabularnewline
M11 & -6.67812500000001 & 5.088506 & -1.3124 & 0.195898 & 0.097949 \tabularnewline
t & -0.678125 & 0.105988 & -6.3982 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57997&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]161.73125[/C][C]4.49667[/C][C]35.9669[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]3.70312500000001[/C][C]3.894231[/C][C]0.9509[/C][C]0.346612[/C][C]0.173306[/C][/ROW]
[ROW][C]M1[/C][C]-3.91874999999995[/C][C]5.134656[/C][C]-0.7632[/C][C]0.449243[/C][C]0.224621[/C][/ROW]
[ROW][C]M2[/C][C]-14.040625[/C][C]5.124802[/C][C]-2.7397[/C][C]0.008722[/C][C]0.004361[/C][/ROW]
[ROW][C]M3[/C][C]-22.1625000000000[/C][C]5.117124[/C][C]-4.331[/C][C]8e-05[/C][C]4e-05[/C][/ROW]
[ROW][C]M4[/C][C]-24.4843750000000[/C][C]5.111633[/C][C]-4.7899[/C][C]1.8e-05[/C][C]9e-06[/C][/ROW]
[ROW][C]M5[/C][C]-26.3468750000000[/C][C]5.141215[/C][C]-5.1246[/C][C]6e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]M6[/C][C]-28.66875[/C][C]5.126993[/C][C]-5.5917[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M7[/C][C]-32.790625[/C][C]5.114928[/C][C]-6.4108[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M8[/C][C]-34.9125000000001[/C][C]5.105036[/C][C]-6.8388[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]-40.634375[/C][C]5.097328[/C][C]-7.9717[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M10[/C][C]-38.55625[/C][C]5.091816[/C][C]-7.5722[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M11[/C][C]-6.67812500000001[/C][C]5.088506[/C][C]-1.3124[/C][C]0.195898[/C][C]0.097949[/C][/ROW]
[ROW][C]t[/C][C]-0.678125[/C][C]0.105988[/C][C]-6.3982[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57997&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57997&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)161.731254.4966735.966900
X3.703125000000013.8942310.95090.3466120.173306
M1-3.918749999999955.134656-0.76320.4492430.224621
M2-14.0406255.124802-2.73970.0087220.004361
M3-22.16250000000005.117124-4.3318e-054e-05
M4-24.48437500000005.111633-4.78991.8e-059e-06
M5-26.34687500000005.141215-5.12466e-063e-06
M6-28.668755.126993-5.59171e-061e-06
M7-32.7906255.114928-6.410800
M8-34.91250000000015.105036-6.838800
M9-40.6343755.097328-7.971700
M10-38.556255.091816-7.572200
M11-6.678125000000015.088506-1.31240.1958980.097949
t-0.6781250.105988-6.398200







Multiple Linear Regression - Regression Statistics
Multiple R0.922606451501705
R-squared0.851202664352567
Adjusted R-squared0.809151243408728
F-TEST (value)20.2419477213234
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value8.77076189453874e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.0438886132106
Sum Squared Residuals2976.390625

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.922606451501705 \tabularnewline
R-squared & 0.851202664352567 \tabularnewline
Adjusted R-squared & 0.809151243408728 \tabularnewline
F-TEST (value) & 20.2419477213234 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 46 \tabularnewline
p-value & 8.77076189453874e-15 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 8.0438886132106 \tabularnewline
Sum Squared Residuals & 2976.390625 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57997&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.922606451501705[/C][/ROW]
[ROW][C]R-squared[/C][C]0.851202664352567[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.809151243408728[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]20.2419477213234[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]46[/C][/ROW]
[ROW][C]p-value[/C][C]8.77076189453874e-15[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]8.0438886132106[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2976.390625[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57997&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57997&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 R0.922606451501705
R-squared0.851202664352567
Adjusted R-squared0.809151243408728
F-TEST (value)20.2419477213234
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value8.77076189453874e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.0438886132106
Sum Squared Residuals2976.390625







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1161157.1343750000003.86562500000017
2149146.3343752.665625
3139137.5343751.46562499999998
4135134.5343750.465624999999968
5130131.99375-1.99374999999998
6127128.99375-1.99374999999997
7122124.19375-2.19375000000008
8117121.39375-4.39375000000006
9112114.99375-2.99375000000002
10113116.39375-3.39374999999994
11149147.593751.40624999999998
12157153.593753.40625000000001
13157148.9968758.00312499999996
14147138.1968758.803125
15137129.3968757.603125
16132126.3968755.603125
17125123.856251.14374999999999
18123120.856252.14374999999999
19117116.056250.94375000000002
20114113.256250.743750000000013
21111106.856254.14375000000001
22112108.256253.74374999999999
23144139.456254.54375
24150145.456254.54374999999999
25149140.8593758.14062499999996
26134130.0593753.940625
27123121.2593751.74062500000001
28116118.259375-2.25937499999999
29117115.718751.28124999999999
30111112.71875-1.71875000000000
31105107.91875-2.91874999999997
32102105.11875-3.11874999999999
339598.71875-3.71874999999999
3493100.11875-7.11875
35124131.31875-7.31874999999998
36130137.31875-7.31875
37124132.721875-8.72187500000004
38115121.921875-6.921875
39106113.121875-7.12187499999998
40105110.121875-5.12187499999999
41105111.284375-6.28437500000001
42101108.284375-7.28437500000001
4395103.484375-8.48437499999998
4493100.684375-7.68437499999999
458494.284375-10.284375
468795.684375-8.68437500000002
47116126.884375-10.884375
48120132.884375-12.884375
49117128.2875-11.2875000000000
50109117.4875-8.4875
51105108.6875-3.68749999999999
52107105.68751.31250000000001
53109103.1468755.853125
54109100.1468758.853125
5510895.34687512.6531250000000
5610792.54687514.4531250000000
579986.14687512.853125
5810387.54687515.4531250000000
59131118.74687512.253125
60137124.74687512.253125

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 161 & 157.134375000000 & 3.86562500000017 \tabularnewline
2 & 149 & 146.334375 & 2.665625 \tabularnewline
3 & 139 & 137.534375 & 1.46562499999998 \tabularnewline
4 & 135 & 134.534375 & 0.465624999999968 \tabularnewline
5 & 130 & 131.99375 & -1.99374999999998 \tabularnewline
6 & 127 & 128.99375 & -1.99374999999997 \tabularnewline
7 & 122 & 124.19375 & -2.19375000000008 \tabularnewline
8 & 117 & 121.39375 & -4.39375000000006 \tabularnewline
9 & 112 & 114.99375 & -2.99375000000002 \tabularnewline
10 & 113 & 116.39375 & -3.39374999999994 \tabularnewline
11 & 149 & 147.59375 & 1.40624999999998 \tabularnewline
12 & 157 & 153.59375 & 3.40625000000001 \tabularnewline
13 & 157 & 148.996875 & 8.00312499999996 \tabularnewline
14 & 147 & 138.196875 & 8.803125 \tabularnewline
15 & 137 & 129.396875 & 7.603125 \tabularnewline
16 & 132 & 126.396875 & 5.603125 \tabularnewline
17 & 125 & 123.85625 & 1.14374999999999 \tabularnewline
18 & 123 & 120.85625 & 2.14374999999999 \tabularnewline
19 & 117 & 116.05625 & 0.94375000000002 \tabularnewline
20 & 114 & 113.25625 & 0.743750000000013 \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 & 4.54374999999999 \tabularnewline
25 & 149 & 140.859375 & 8.14062499999996 \tabularnewline
26 & 134 & 130.059375 & 3.940625 \tabularnewline
27 & 123 & 121.259375 & 1.74062500000001 \tabularnewline
28 & 116 & 118.259375 & -2.25937499999999 \tabularnewline
29 & 117 & 115.71875 & 1.28124999999999 \tabularnewline
30 & 111 & 112.71875 & -1.71875000000000 \tabularnewline
31 & 105 & 107.91875 & -2.91874999999997 \tabularnewline
32 & 102 & 105.11875 & -3.11874999999999 \tabularnewline
33 & 95 & 98.71875 & -3.71874999999999 \tabularnewline
34 & 93 & 100.11875 & -7.11875 \tabularnewline
35 & 124 & 131.31875 & -7.31874999999998 \tabularnewline
36 & 130 & 137.31875 & -7.31875 \tabularnewline
37 & 124 & 132.721875 & -8.72187500000004 \tabularnewline
38 & 115 & 121.921875 & -6.921875 \tabularnewline
39 & 106 & 113.121875 & -7.12187499999998 \tabularnewline
40 & 105 & 110.121875 & -5.12187499999999 \tabularnewline
41 & 105 & 111.284375 & -6.28437500000001 \tabularnewline
42 & 101 & 108.284375 & -7.28437500000001 \tabularnewline
43 & 95 & 103.484375 & -8.48437499999998 \tabularnewline
44 & 93 & 100.684375 & -7.68437499999999 \tabularnewline
45 & 84 & 94.284375 & -10.284375 \tabularnewline
46 & 87 & 95.684375 & -8.68437500000002 \tabularnewline
47 & 116 & 126.884375 & -10.884375 \tabularnewline
48 & 120 & 132.884375 & -12.884375 \tabularnewline
49 & 117 & 128.2875 & -11.2875000000000 \tabularnewline
50 & 109 & 117.4875 & -8.4875 \tabularnewline
51 & 105 & 108.6875 & -3.68749999999999 \tabularnewline
52 & 107 & 105.6875 & 1.31250000000001 \tabularnewline
53 & 109 & 103.146875 & 5.853125 \tabularnewline
54 & 109 & 100.146875 & 8.853125 \tabularnewline
55 & 108 & 95.346875 & 12.6531250000000 \tabularnewline
56 & 107 & 92.546875 & 14.4531250000000 \tabularnewline
57 & 99 & 86.146875 & 12.853125 \tabularnewline
58 & 103 & 87.546875 & 15.4531250000000 \tabularnewline
59 & 131 & 118.746875 & 12.253125 \tabularnewline
60 & 137 & 124.746875 & 12.253125 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57997&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]157.134375000000[/C][C]3.86562500000017[/C][/ROW]
[ROW][C]2[/C][C]149[/C][C]146.334375[/C][C]2.665625[/C][/ROW]
[ROW][C]3[/C][C]139[/C][C]137.534375[/C][C]1.46562499999998[/C][/ROW]
[ROW][C]4[/C][C]135[/C][C]134.534375[/C][C]0.465624999999968[/C][/ROW]
[ROW][C]5[/C][C]130[/C][C]131.99375[/C][C]-1.99374999999998[/C][/ROW]
[ROW][C]6[/C][C]127[/C][C]128.99375[/C][C]-1.99374999999997[/C][/ROW]
[ROW][C]7[/C][C]122[/C][C]124.19375[/C][C]-2.19375000000008[/C][/ROW]
[ROW][C]8[/C][C]117[/C][C]121.39375[/C][C]-4.39375000000006[/C][/ROW]
[ROW][C]9[/C][C]112[/C][C]114.99375[/C][C]-2.99375000000002[/C][/ROW]
[ROW][C]10[/C][C]113[/C][C]116.39375[/C][C]-3.39374999999994[/C][/ROW]
[ROW][C]11[/C][C]149[/C][C]147.59375[/C][C]1.40624999999998[/C][/ROW]
[ROW][C]12[/C][C]157[/C][C]153.59375[/C][C]3.40625000000001[/C][/ROW]
[ROW][C]13[/C][C]157[/C][C]148.996875[/C][C]8.00312499999996[/C][/ROW]
[ROW][C]14[/C][C]147[/C][C]138.196875[/C][C]8.803125[/C][/ROW]
[ROW][C]15[/C][C]137[/C][C]129.396875[/C][C]7.603125[/C][/ROW]
[ROW][C]16[/C][C]132[/C][C]126.396875[/C][C]5.603125[/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]0.94375000000002[/C][/ROW]
[ROW][C]20[/C][C]114[/C][C]113.25625[/C][C]0.743750000000013[/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]4.54374999999999[/C][/ROW]
[ROW][C]25[/C][C]149[/C][C]140.859375[/C][C]8.14062499999996[/C][/ROW]
[ROW][C]26[/C][C]134[/C][C]130.059375[/C][C]3.940625[/C][/ROW]
[ROW][C]27[/C][C]123[/C][C]121.259375[/C][C]1.74062500000001[/C][/ROW]
[ROW][C]28[/C][C]116[/C][C]118.259375[/C][C]-2.25937499999999[/C][/ROW]
[ROW][C]29[/C][C]117[/C][C]115.71875[/C][C]1.28124999999999[/C][/ROW]
[ROW][C]30[/C][C]111[/C][C]112.71875[/C][C]-1.71875000000000[/C][/ROW]
[ROW][C]31[/C][C]105[/C][C]107.91875[/C][C]-2.91874999999997[/C][/ROW]
[ROW][C]32[/C][C]102[/C][C]105.11875[/C][C]-3.11874999999999[/C][/ROW]
[ROW][C]33[/C][C]95[/C][C]98.71875[/C][C]-3.71874999999999[/C][/ROW]
[ROW][C]34[/C][C]93[/C][C]100.11875[/C][C]-7.11875[/C][/ROW]
[ROW][C]35[/C][C]124[/C][C]131.31875[/C][C]-7.31874999999998[/C][/ROW]
[ROW][C]36[/C][C]130[/C][C]137.31875[/C][C]-7.31875[/C][/ROW]
[ROW][C]37[/C][C]124[/C][C]132.721875[/C][C]-8.72187500000004[/C][/ROW]
[ROW][C]38[/C][C]115[/C][C]121.921875[/C][C]-6.921875[/C][/ROW]
[ROW][C]39[/C][C]106[/C][C]113.121875[/C][C]-7.12187499999998[/C][/ROW]
[ROW][C]40[/C][C]105[/C][C]110.121875[/C][C]-5.12187499999999[/C][/ROW]
[ROW][C]41[/C][C]105[/C][C]111.284375[/C][C]-6.28437500000001[/C][/ROW]
[ROW][C]42[/C][C]101[/C][C]108.284375[/C][C]-7.28437500000001[/C][/ROW]
[ROW][C]43[/C][C]95[/C][C]103.484375[/C][C]-8.48437499999998[/C][/ROW]
[ROW][C]44[/C][C]93[/C][C]100.684375[/C][C]-7.68437499999999[/C][/ROW]
[ROW][C]45[/C][C]84[/C][C]94.284375[/C][C]-10.284375[/C][/ROW]
[ROW][C]46[/C][C]87[/C][C]95.684375[/C][C]-8.68437500000002[/C][/ROW]
[ROW][C]47[/C][C]116[/C][C]126.884375[/C][C]-10.884375[/C][/ROW]
[ROW][C]48[/C][C]120[/C][C]132.884375[/C][C]-12.884375[/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]-8.4875[/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]103.146875[/C][C]5.853125[/C][/ROW]
[ROW][C]54[/C][C]109[/C][C]100.146875[/C][C]8.853125[/C][/ROW]
[ROW][C]55[/C][C]108[/C][C]95.346875[/C][C]12.6531250000000[/C][/ROW]
[ROW][C]56[/C][C]107[/C][C]92.546875[/C][C]14.4531250000000[/C][/ROW]
[ROW][C]57[/C][C]99[/C][C]86.146875[/C][C]12.853125[/C][/ROW]
[ROW][C]58[/C][C]103[/C][C]87.546875[/C][C]15.4531250000000[/C][/ROW]
[ROW][C]59[/C][C]131[/C][C]118.746875[/C][C]12.253125[/C][/ROW]
[ROW][C]60[/C][C]137[/C][C]124.746875[/C][C]12.253125[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57997&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57997&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
1161157.1343750000003.86562500000017
2149146.3343752.665625
3139137.5343751.46562499999998
4135134.5343750.465624999999968
5130131.99375-1.99374999999998
6127128.99375-1.99374999999997
7122124.19375-2.19375000000008
8117121.39375-4.39375000000006
9112114.99375-2.99375000000002
10113116.39375-3.39374999999994
11149147.593751.40624999999998
12157153.593753.40625000000001
13157148.9968758.00312499999996
14147138.1968758.803125
15137129.3968757.603125
16132126.3968755.603125
17125123.856251.14374999999999
18123120.856252.14374999999999
19117116.056250.94375000000002
20114113.256250.743750000000013
21111106.856254.14375000000001
22112108.256253.74374999999999
23144139.456254.54375
24150145.456254.54374999999999
25149140.8593758.14062499999996
26134130.0593753.940625
27123121.2593751.74062500000001
28116118.259375-2.25937499999999
29117115.718751.28124999999999
30111112.71875-1.71875000000000
31105107.91875-2.91874999999997
32102105.11875-3.11874999999999
339598.71875-3.71874999999999
3493100.11875-7.11875
35124131.31875-7.31874999999998
36130137.31875-7.31875
37124132.721875-8.72187500000004
38115121.921875-6.921875
39106113.121875-7.12187499999998
40105110.121875-5.12187499999999
41105111.284375-6.28437500000001
42101108.284375-7.28437500000001
4395103.484375-8.48437499999998
4493100.684375-7.68437499999999
458494.284375-10.284375
468795.684375-8.68437500000002
47116126.884375-10.884375
48120132.884375-12.884375
49117128.2875-11.2875000000000
50109117.4875-8.4875
51105108.6875-3.68749999999999
52107105.68751.31250000000001
53109103.1468755.853125
54109100.1468758.853125
5510895.34687512.6531250000000
5610792.54687514.4531250000000
579986.14687512.853125
5810387.54687515.4531250000000
59131118.74687512.253125
60137124.74687512.253125







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.002359997541421470.004719995082842940.997640002458579
180.0002649287122507510.0005298574245015010.99973507128775
194.75498500139823e-059.50997000279646e-050.999952450149986
204.8299956388852e-069.6599912777704e-060.999995170004361
212.14177406416343e-064.28354812832686e-060.999997858225936
226.71133508482571e-071.34226701696514e-060.999999328866491
232.16464806705663e-074.32929613411326e-070.999999783535193
244.19736007423164e-078.39472014846329e-070.999999580263993
256.41118728715735e-061.28223745743147e-050.999993588812713
260.0007020976250817050.001404195250163410.999297902374918
270.02779057049736030.05558114099472050.97220942950264
280.3883481844788390.7766963689576770.611651815521161
290.5072733602500590.9854532794998810.492726639749941
300.5729163052315270.8541673895369450.427083694768473
310.57863588383640.84272823232720.4213641161636
320.5281547637128390.9436904725743210.471845236287161
330.6025877692052070.7948244615895860.397412230794793
340.6456312346408110.7087375307183780.354368765359189
350.7394455755226620.5211088489546760.260554424477338
360.8250876191533240.3498247616933530.174912380846677
370.903196565511680.1936068689766420.0968034344883208
380.9191795138121770.1616409723756470.0808204861878233
390.8819766676663890.2360466646672220.118023332333611
400.7989434260617650.402113147876470.201056573938235
410.9439092761868140.1121814476263710.0560907238131857
420.9932962518281410.01340749634371760.00670374817185878
430.9883084327921820.02338313441563640.0116915672078182

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.00235999754142147 & 0.00471999508284294 & 0.997640002458579 \tabularnewline
18 & 0.000264928712250751 & 0.000529857424501501 & 0.99973507128775 \tabularnewline
19 & 4.75498500139823e-05 & 9.50997000279646e-05 & 0.999952450149986 \tabularnewline
20 & 4.8299956388852e-06 & 9.6599912777704e-06 & 0.999995170004361 \tabularnewline
21 & 2.14177406416343e-06 & 4.28354812832686e-06 & 0.999997858225936 \tabularnewline
22 & 6.71133508482571e-07 & 1.34226701696514e-06 & 0.999999328866491 \tabularnewline
23 & 2.16464806705663e-07 & 4.32929613411326e-07 & 0.999999783535193 \tabularnewline
24 & 4.19736007423164e-07 & 8.39472014846329e-07 & 0.999999580263993 \tabularnewline
25 & 6.41118728715735e-06 & 1.28223745743147e-05 & 0.999993588812713 \tabularnewline
26 & 0.000702097625081705 & 0.00140419525016341 & 0.999297902374918 \tabularnewline
27 & 0.0277905704973603 & 0.0555811409947205 & 0.97220942950264 \tabularnewline
28 & 0.388348184478839 & 0.776696368957677 & 0.611651815521161 \tabularnewline
29 & 0.507273360250059 & 0.985453279499881 & 0.492726639749941 \tabularnewline
30 & 0.572916305231527 & 0.854167389536945 & 0.427083694768473 \tabularnewline
31 & 0.5786358838364 & 0.8427282323272 & 0.4213641161636 \tabularnewline
32 & 0.528154763712839 & 0.943690472574321 & 0.471845236287161 \tabularnewline
33 & 0.602587769205207 & 0.794824461589586 & 0.397412230794793 \tabularnewline
34 & 0.645631234640811 & 0.708737530718378 & 0.354368765359189 \tabularnewline
35 & 0.739445575522662 & 0.521108848954676 & 0.260554424477338 \tabularnewline
36 & 0.825087619153324 & 0.349824761693353 & 0.174912380846677 \tabularnewline
37 & 0.90319656551168 & 0.193606868976642 & 0.0968034344883208 \tabularnewline
38 & 0.919179513812177 & 0.161640972375647 & 0.0808204861878233 \tabularnewline
39 & 0.881976667666389 & 0.236046664667222 & 0.118023332333611 \tabularnewline
40 & 0.798943426061765 & 0.40211314787647 & 0.201056573938235 \tabularnewline
41 & 0.943909276186814 & 0.112181447626371 & 0.0560907238131857 \tabularnewline
42 & 0.993296251828141 & 0.0134074963437176 & 0.00670374817185878 \tabularnewline
43 & 0.988308432792182 & 0.0233831344156364 & 0.0116915672078182 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57997&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]17[/C][C]0.00235999754142147[/C][C]0.00471999508284294[/C][C]0.997640002458579[/C][/ROW]
[ROW][C]18[/C][C]0.000264928712250751[/C][C]0.000529857424501501[/C][C]0.99973507128775[/C][/ROW]
[ROW][C]19[/C][C]4.75498500139823e-05[/C][C]9.50997000279646e-05[/C][C]0.999952450149986[/C][/ROW]
[ROW][C]20[/C][C]4.8299956388852e-06[/C][C]9.6599912777704e-06[/C][C]0.999995170004361[/C][/ROW]
[ROW][C]21[/C][C]2.14177406416343e-06[/C][C]4.28354812832686e-06[/C][C]0.999997858225936[/C][/ROW]
[ROW][C]22[/C][C]6.71133508482571e-07[/C][C]1.34226701696514e-06[/C][C]0.999999328866491[/C][/ROW]
[ROW][C]23[/C][C]2.16464806705663e-07[/C][C]4.32929613411326e-07[/C][C]0.999999783535193[/C][/ROW]
[ROW][C]24[/C][C]4.19736007423164e-07[/C][C]8.39472014846329e-07[/C][C]0.999999580263993[/C][/ROW]
[ROW][C]25[/C][C]6.41118728715735e-06[/C][C]1.28223745743147e-05[/C][C]0.999993588812713[/C][/ROW]
[ROW][C]26[/C][C]0.000702097625081705[/C][C]0.00140419525016341[/C][C]0.999297902374918[/C][/ROW]
[ROW][C]27[/C][C]0.0277905704973603[/C][C]0.0555811409947205[/C][C]0.97220942950264[/C][/ROW]
[ROW][C]28[/C][C]0.388348184478839[/C][C]0.776696368957677[/C][C]0.611651815521161[/C][/ROW]
[ROW][C]29[/C][C]0.507273360250059[/C][C]0.985453279499881[/C][C]0.492726639749941[/C][/ROW]
[ROW][C]30[/C][C]0.572916305231527[/C][C]0.854167389536945[/C][C]0.427083694768473[/C][/ROW]
[ROW][C]31[/C][C]0.5786358838364[/C][C]0.8427282323272[/C][C]0.4213641161636[/C][/ROW]
[ROW][C]32[/C][C]0.528154763712839[/C][C]0.943690472574321[/C][C]0.471845236287161[/C][/ROW]
[ROW][C]33[/C][C]0.602587769205207[/C][C]0.794824461589586[/C][C]0.397412230794793[/C][/ROW]
[ROW][C]34[/C][C]0.645631234640811[/C][C]0.708737530718378[/C][C]0.354368765359189[/C][/ROW]
[ROW][C]35[/C][C]0.739445575522662[/C][C]0.521108848954676[/C][C]0.260554424477338[/C][/ROW]
[ROW][C]36[/C][C]0.825087619153324[/C][C]0.349824761693353[/C][C]0.174912380846677[/C][/ROW]
[ROW][C]37[/C][C]0.90319656551168[/C][C]0.193606868976642[/C][C]0.0968034344883208[/C][/ROW]
[ROW][C]38[/C][C]0.919179513812177[/C][C]0.161640972375647[/C][C]0.0808204861878233[/C][/ROW]
[ROW][C]39[/C][C]0.881976667666389[/C][C]0.236046664667222[/C][C]0.118023332333611[/C][/ROW]
[ROW][C]40[/C][C]0.798943426061765[/C][C]0.40211314787647[/C][C]0.201056573938235[/C][/ROW]
[ROW][C]41[/C][C]0.943909276186814[/C][C]0.112181447626371[/C][C]0.0560907238131857[/C][/ROW]
[ROW][C]42[/C][C]0.993296251828141[/C][C]0.0134074963437176[/C][C]0.00670374817185878[/C][/ROW]
[ROW][C]43[/C][C]0.988308432792182[/C][C]0.0233831344156364[/C][C]0.0116915672078182[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57997&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57997&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
170.002359997541421470.004719995082842940.997640002458579
180.0002649287122507510.0005298574245015010.99973507128775
194.75498500139823e-059.50997000279646e-050.999952450149986
204.8299956388852e-069.6599912777704e-060.999995170004361
212.14177406416343e-064.28354812832686e-060.999997858225936
226.71133508482571e-071.34226701696514e-060.999999328866491
232.16464806705663e-074.32929613411326e-070.999999783535193
244.19736007423164e-078.39472014846329e-070.999999580263993
256.41118728715735e-061.28223745743147e-050.999993588812713
260.0007020976250817050.001404195250163410.999297902374918
270.02779057049736030.05558114099472050.97220942950264
280.3883481844788390.7766963689576770.611651815521161
290.5072733602500590.9854532794998810.492726639749941
300.5729163052315270.8541673895369450.427083694768473
310.57863588383640.84272823232720.4213641161636
320.5281547637128390.9436904725743210.471845236287161
330.6025877692052070.7948244615895860.397412230794793
340.6456312346408110.7087375307183780.354368765359189
350.7394455755226620.5211088489546760.260554424477338
360.8250876191533240.3498247616933530.174912380846677
370.903196565511680.1936068689766420.0968034344883208
380.9191795138121770.1616409723756470.0808204861878233
390.8819766676663890.2360466646672220.118023332333611
400.7989434260617650.402113147876470.201056573938235
410.9439092761868140.1121814476263710.0560907238131857
420.9932962518281410.01340749634371760.00670374817185878
430.9883084327921820.02338313441563640.0116915672078182







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level100.370370370370370NOK
5% type I error level120.444444444444444NOK
10% type I error level130.481481481481481NOK

\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 & 10 & 0.370370370370370 & NOK \tabularnewline
5% type I error level & 12 & 0.444444444444444 & NOK \tabularnewline
10% type I error level & 13 & 0.481481481481481 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57997&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]10[/C][C]0.370370370370370[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]12[/C][C]0.444444444444444[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]13[/C][C]0.481481481481481[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57997&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57997&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 level100.370370370370370NOK
5% type I error level120.444444444444444NOK
10% type I error level130.481481481481481NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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')
}