Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 01:35:38 -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/19/t1258619834410my1p3f3c75lu.htm/, Retrieved Thu, 28 Mar 2024 23:53:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57650, Retrieved Thu, 28 Mar 2024 23:53:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact190
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] [model 1] [2009-11-19 08:35:38] [c60887983b0820a525cba943a935572d] [Current]
-    D        [Multiple Regression] [] [2009-11-20 13:39:03] [e149fd9094b67af26551857fa83a9d9d]
-    D          [Multiple Regression] [] [2009-12-15 16:03:21] [e149fd9094b67af26551857fa83a9d9d]
Feedback Forum

Post a new message
Dataseries X:
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	0
101	0
95	0
93	0
84	0
87	0
116	0
120	0
117	1
109	1
105	1
107	1
109	1
109	1
108	1
107	1
99	1
103	1
131	1
137	1
135	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=57650&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=57650&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57650&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
WLH[t] = + 120.617021276596 -7.0785597381342X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
WLH[t] =  +  120.617021276596 -7.0785597381342X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57650&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]WLH[t] =  +  120.617021276596 -7.0785597381342X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57650&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57650&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
WLH[t] = + 120.617021276596 -7.0785597381342X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)120.6170212765962.5696746.938700
X-7.07855973813425.520537-1.28220.2048660.102433

\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) & 120.617021276596 & 2.56967 & 46.9387 & 0 & 0 \tabularnewline
X & -7.0785597381342 & 5.520537 & -1.2822 & 0.204866 & 0.102433 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57650&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]120.617021276596[/C][C]2.56967[/C][C]46.9387[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-7.0785597381342[/C][C]5.520537[/C][C]-1.2822[/C][C]0.204866[/C][C]0.102433[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57650&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57650&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)120.6170212765962.5696746.938700
X-7.07855973813425.520537-1.28220.2048660.102433







Multiple Linear Regression - Regression Statistics
Multiple R0.166027423723804
R-squared0.0275651054283635
Adjusted R-squared0.0107989865564388
F-TEST (value)1.64409578859195
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.204865829975556
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation17.6167715701639
Sum Squared Residuals18000.3371522095

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.166027423723804 \tabularnewline
R-squared & 0.0275651054283635 \tabularnewline
Adjusted R-squared & 0.0107989865564388 \tabularnewline
F-TEST (value) & 1.64409578859195 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0.204865829975556 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 17.6167715701639 \tabularnewline
Sum Squared Residuals & 18000.3371522095 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57650&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.166027423723804[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0275651054283635[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0107989865564388[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.64409578859195[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]0.204865829975556[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]17.6167715701639[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]18000.3371522095[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57650&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57650&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 R0.166027423723804
R-squared0.0275651054283635
Adjusted R-squared0.0107989865564388
F-TEST (value)1.64409578859195
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.204865829975556
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation17.6167715701639
Sum Squared Residuals18000.3371522095







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1149120.61702127659628.3829787234043
2139120.61702127659618.3829787234042
3135120.61702127659614.3829787234043
4130120.6170212765969.38297872340425
5127120.6170212765966.38297872340425
6122120.6170212765961.38297872340425
7117120.617021276596-3.61702127659575
8112120.617021276596-8.61702127659574
9113120.617021276596-7.61702127659574
10149120.61702127659628.3829787234043
11157120.61702127659636.3829787234043
12157120.61702127659636.3829787234043
13147120.61702127659626.3829787234043
14137120.61702127659616.3829787234043
15132120.61702127659611.3829787234043
16125120.6170212765964.38297872340425
17123120.6170212765962.38297872340425
18117120.617021276596-3.61702127659575
19114120.617021276596-6.61702127659574
20111120.617021276596-9.61702127659574
21112120.617021276596-8.61702127659574
22144120.61702127659623.3829787234043
23150120.61702127659629.3829787234043
24149120.61702127659628.3829787234043
25134120.61702127659613.3829787234043
26123120.6170212765962.38297872340425
27116120.617021276596-4.61702127659575
28117120.617021276596-3.61702127659575
29111120.617021276596-9.61702127659574
30105120.617021276596-15.6170212765957
31102120.617021276596-18.6170212765957
3295120.617021276596-25.6170212765957
3393120.617021276596-27.6170212765957
34124120.6170212765963.38297872340425
35130120.6170212765969.38297872340425
36124120.6170212765963.38297872340425
37115120.617021276596-5.61702127659574
38106120.617021276596-14.6170212765957
39105120.617021276596-15.6170212765957
40105120.617021276596-15.6170212765957
41101120.617021276596-19.6170212765957
4295120.617021276596-25.6170212765957
4393120.617021276596-27.6170212765957
4484120.617021276596-36.6170212765957
4587120.617021276596-33.6170212765957
46116120.617021276596-4.61702127659575
47120120.617021276596-0.617021276595746
48117113.5384615384623.46153846153846
49109113.538461538462-4.53846153846154
50105113.538461538462-8.53846153846154
51107113.538461538462-6.53846153846154
52109113.538461538462-4.53846153846154
53109113.538461538462-4.53846153846154
54108113.538461538462-5.53846153846154
55107113.538461538462-6.53846153846154
5699113.538461538462-14.5384615384615
57103113.538461538462-10.5384615384615
58131113.53846153846217.4615384615385
59137113.53846153846223.4615384615385
60135113.53846153846221.4615384615385

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 149 & 120.617021276596 & 28.3829787234043 \tabularnewline
2 & 139 & 120.617021276596 & 18.3829787234042 \tabularnewline
3 & 135 & 120.617021276596 & 14.3829787234043 \tabularnewline
4 & 130 & 120.617021276596 & 9.38297872340425 \tabularnewline
5 & 127 & 120.617021276596 & 6.38297872340425 \tabularnewline
6 & 122 & 120.617021276596 & 1.38297872340425 \tabularnewline
7 & 117 & 120.617021276596 & -3.61702127659575 \tabularnewline
8 & 112 & 120.617021276596 & -8.61702127659574 \tabularnewline
9 & 113 & 120.617021276596 & -7.61702127659574 \tabularnewline
10 & 149 & 120.617021276596 & 28.3829787234043 \tabularnewline
11 & 157 & 120.617021276596 & 36.3829787234043 \tabularnewline
12 & 157 & 120.617021276596 & 36.3829787234043 \tabularnewline
13 & 147 & 120.617021276596 & 26.3829787234043 \tabularnewline
14 & 137 & 120.617021276596 & 16.3829787234043 \tabularnewline
15 & 132 & 120.617021276596 & 11.3829787234043 \tabularnewline
16 & 125 & 120.617021276596 & 4.38297872340425 \tabularnewline
17 & 123 & 120.617021276596 & 2.38297872340425 \tabularnewline
18 & 117 & 120.617021276596 & -3.61702127659575 \tabularnewline
19 & 114 & 120.617021276596 & -6.61702127659574 \tabularnewline
20 & 111 & 120.617021276596 & -9.61702127659574 \tabularnewline
21 & 112 & 120.617021276596 & -8.61702127659574 \tabularnewline
22 & 144 & 120.617021276596 & 23.3829787234043 \tabularnewline
23 & 150 & 120.617021276596 & 29.3829787234043 \tabularnewline
24 & 149 & 120.617021276596 & 28.3829787234043 \tabularnewline
25 & 134 & 120.617021276596 & 13.3829787234043 \tabularnewline
26 & 123 & 120.617021276596 & 2.38297872340425 \tabularnewline
27 & 116 & 120.617021276596 & -4.61702127659575 \tabularnewline
28 & 117 & 120.617021276596 & -3.61702127659575 \tabularnewline
29 & 111 & 120.617021276596 & -9.61702127659574 \tabularnewline
30 & 105 & 120.617021276596 & -15.6170212765957 \tabularnewline
31 & 102 & 120.617021276596 & -18.6170212765957 \tabularnewline
32 & 95 & 120.617021276596 & -25.6170212765957 \tabularnewline
33 & 93 & 120.617021276596 & -27.6170212765957 \tabularnewline
34 & 124 & 120.617021276596 & 3.38297872340425 \tabularnewline
35 & 130 & 120.617021276596 & 9.38297872340425 \tabularnewline
36 & 124 & 120.617021276596 & 3.38297872340425 \tabularnewline
37 & 115 & 120.617021276596 & -5.61702127659574 \tabularnewline
38 & 106 & 120.617021276596 & -14.6170212765957 \tabularnewline
39 & 105 & 120.617021276596 & -15.6170212765957 \tabularnewline
40 & 105 & 120.617021276596 & -15.6170212765957 \tabularnewline
41 & 101 & 120.617021276596 & -19.6170212765957 \tabularnewline
42 & 95 & 120.617021276596 & -25.6170212765957 \tabularnewline
43 & 93 & 120.617021276596 & -27.6170212765957 \tabularnewline
44 & 84 & 120.617021276596 & -36.6170212765957 \tabularnewline
45 & 87 & 120.617021276596 & -33.6170212765957 \tabularnewline
46 & 116 & 120.617021276596 & -4.61702127659575 \tabularnewline
47 & 120 & 120.617021276596 & -0.617021276595746 \tabularnewline
48 & 117 & 113.538461538462 & 3.46153846153846 \tabularnewline
49 & 109 & 113.538461538462 & -4.53846153846154 \tabularnewline
50 & 105 & 113.538461538462 & -8.53846153846154 \tabularnewline
51 & 107 & 113.538461538462 & -6.53846153846154 \tabularnewline
52 & 109 & 113.538461538462 & -4.53846153846154 \tabularnewline
53 & 109 & 113.538461538462 & -4.53846153846154 \tabularnewline
54 & 108 & 113.538461538462 & -5.53846153846154 \tabularnewline
55 & 107 & 113.538461538462 & -6.53846153846154 \tabularnewline
56 & 99 & 113.538461538462 & -14.5384615384615 \tabularnewline
57 & 103 & 113.538461538462 & -10.5384615384615 \tabularnewline
58 & 131 & 113.538461538462 & 17.4615384615385 \tabularnewline
59 & 137 & 113.538461538462 & 23.4615384615385 \tabularnewline
60 & 135 & 113.538461538462 & 21.4615384615385 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57650&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]149[/C][C]120.617021276596[/C][C]28.3829787234043[/C][/ROW]
[ROW][C]2[/C][C]139[/C][C]120.617021276596[/C][C]18.3829787234042[/C][/ROW]
[ROW][C]3[/C][C]135[/C][C]120.617021276596[/C][C]14.3829787234043[/C][/ROW]
[ROW][C]4[/C][C]130[/C][C]120.617021276596[/C][C]9.38297872340425[/C][/ROW]
[ROW][C]5[/C][C]127[/C][C]120.617021276596[/C][C]6.38297872340425[/C][/ROW]
[ROW][C]6[/C][C]122[/C][C]120.617021276596[/C][C]1.38297872340425[/C][/ROW]
[ROW][C]7[/C][C]117[/C][C]120.617021276596[/C][C]-3.61702127659575[/C][/ROW]
[ROW][C]8[/C][C]112[/C][C]120.617021276596[/C][C]-8.61702127659574[/C][/ROW]
[ROW][C]9[/C][C]113[/C][C]120.617021276596[/C][C]-7.61702127659574[/C][/ROW]
[ROW][C]10[/C][C]149[/C][C]120.617021276596[/C][C]28.3829787234043[/C][/ROW]
[ROW][C]11[/C][C]157[/C][C]120.617021276596[/C][C]36.3829787234043[/C][/ROW]
[ROW][C]12[/C][C]157[/C][C]120.617021276596[/C][C]36.3829787234043[/C][/ROW]
[ROW][C]13[/C][C]147[/C][C]120.617021276596[/C][C]26.3829787234043[/C][/ROW]
[ROW][C]14[/C][C]137[/C][C]120.617021276596[/C][C]16.3829787234043[/C][/ROW]
[ROW][C]15[/C][C]132[/C][C]120.617021276596[/C][C]11.3829787234043[/C][/ROW]
[ROW][C]16[/C][C]125[/C][C]120.617021276596[/C][C]4.38297872340425[/C][/ROW]
[ROW][C]17[/C][C]123[/C][C]120.617021276596[/C][C]2.38297872340425[/C][/ROW]
[ROW][C]18[/C][C]117[/C][C]120.617021276596[/C][C]-3.61702127659575[/C][/ROW]
[ROW][C]19[/C][C]114[/C][C]120.617021276596[/C][C]-6.61702127659574[/C][/ROW]
[ROW][C]20[/C][C]111[/C][C]120.617021276596[/C][C]-9.61702127659574[/C][/ROW]
[ROW][C]21[/C][C]112[/C][C]120.617021276596[/C][C]-8.61702127659574[/C][/ROW]
[ROW][C]22[/C][C]144[/C][C]120.617021276596[/C][C]23.3829787234043[/C][/ROW]
[ROW][C]23[/C][C]150[/C][C]120.617021276596[/C][C]29.3829787234043[/C][/ROW]
[ROW][C]24[/C][C]149[/C][C]120.617021276596[/C][C]28.3829787234043[/C][/ROW]
[ROW][C]25[/C][C]134[/C][C]120.617021276596[/C][C]13.3829787234043[/C][/ROW]
[ROW][C]26[/C][C]123[/C][C]120.617021276596[/C][C]2.38297872340425[/C][/ROW]
[ROW][C]27[/C][C]116[/C][C]120.617021276596[/C][C]-4.61702127659575[/C][/ROW]
[ROW][C]28[/C][C]117[/C][C]120.617021276596[/C][C]-3.61702127659575[/C][/ROW]
[ROW][C]29[/C][C]111[/C][C]120.617021276596[/C][C]-9.61702127659574[/C][/ROW]
[ROW][C]30[/C][C]105[/C][C]120.617021276596[/C][C]-15.6170212765957[/C][/ROW]
[ROW][C]31[/C][C]102[/C][C]120.617021276596[/C][C]-18.6170212765957[/C][/ROW]
[ROW][C]32[/C][C]95[/C][C]120.617021276596[/C][C]-25.6170212765957[/C][/ROW]
[ROW][C]33[/C][C]93[/C][C]120.617021276596[/C][C]-27.6170212765957[/C][/ROW]
[ROW][C]34[/C][C]124[/C][C]120.617021276596[/C][C]3.38297872340425[/C][/ROW]
[ROW][C]35[/C][C]130[/C][C]120.617021276596[/C][C]9.38297872340425[/C][/ROW]
[ROW][C]36[/C][C]124[/C][C]120.617021276596[/C][C]3.38297872340425[/C][/ROW]
[ROW][C]37[/C][C]115[/C][C]120.617021276596[/C][C]-5.61702127659574[/C][/ROW]
[ROW][C]38[/C][C]106[/C][C]120.617021276596[/C][C]-14.6170212765957[/C][/ROW]
[ROW][C]39[/C][C]105[/C][C]120.617021276596[/C][C]-15.6170212765957[/C][/ROW]
[ROW][C]40[/C][C]105[/C][C]120.617021276596[/C][C]-15.6170212765957[/C][/ROW]
[ROW][C]41[/C][C]101[/C][C]120.617021276596[/C][C]-19.6170212765957[/C][/ROW]
[ROW][C]42[/C][C]95[/C][C]120.617021276596[/C][C]-25.6170212765957[/C][/ROW]
[ROW][C]43[/C][C]93[/C][C]120.617021276596[/C][C]-27.6170212765957[/C][/ROW]
[ROW][C]44[/C][C]84[/C][C]120.617021276596[/C][C]-36.6170212765957[/C][/ROW]
[ROW][C]45[/C][C]87[/C][C]120.617021276596[/C][C]-33.6170212765957[/C][/ROW]
[ROW][C]46[/C][C]116[/C][C]120.617021276596[/C][C]-4.61702127659575[/C][/ROW]
[ROW][C]47[/C][C]120[/C][C]120.617021276596[/C][C]-0.617021276595746[/C][/ROW]
[ROW][C]48[/C][C]117[/C][C]113.538461538462[/C][C]3.46153846153846[/C][/ROW]
[ROW][C]49[/C][C]109[/C][C]113.538461538462[/C][C]-4.53846153846154[/C][/ROW]
[ROW][C]50[/C][C]105[/C][C]113.538461538462[/C][C]-8.53846153846154[/C][/ROW]
[ROW][C]51[/C][C]107[/C][C]113.538461538462[/C][C]-6.53846153846154[/C][/ROW]
[ROW][C]52[/C][C]109[/C][C]113.538461538462[/C][C]-4.53846153846154[/C][/ROW]
[ROW][C]53[/C][C]109[/C][C]113.538461538462[/C][C]-4.53846153846154[/C][/ROW]
[ROW][C]54[/C][C]108[/C][C]113.538461538462[/C][C]-5.53846153846154[/C][/ROW]
[ROW][C]55[/C][C]107[/C][C]113.538461538462[/C][C]-6.53846153846154[/C][/ROW]
[ROW][C]56[/C][C]99[/C][C]113.538461538462[/C][C]-14.5384615384615[/C][/ROW]
[ROW][C]57[/C][C]103[/C][C]113.538461538462[/C][C]-10.5384615384615[/C][/ROW]
[ROW][C]58[/C][C]131[/C][C]113.538461538462[/C][C]17.4615384615385[/C][/ROW]
[ROW][C]59[/C][C]137[/C][C]113.538461538462[/C][C]23.4615384615385[/C][/ROW]
[ROW][C]60[/C][C]135[/C][C]113.538461538462[/C][C]21.4615384615385[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57650&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57650&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
1149120.61702127659628.3829787234043
2139120.61702127659618.3829787234042
3135120.61702127659614.3829787234043
4130120.6170212765969.38297872340425
5127120.6170212765966.38297872340425
6122120.6170212765961.38297872340425
7117120.617021276596-3.61702127659575
8112120.617021276596-8.61702127659574
9113120.617021276596-7.61702127659574
10149120.61702127659628.3829787234043
11157120.61702127659636.3829787234043
12157120.61702127659636.3829787234043
13147120.61702127659626.3829787234043
14137120.61702127659616.3829787234043
15132120.61702127659611.3829787234043
16125120.6170212765964.38297872340425
17123120.6170212765962.38297872340425
18117120.617021276596-3.61702127659575
19114120.617021276596-6.61702127659574
20111120.617021276596-9.61702127659574
21112120.617021276596-8.61702127659574
22144120.61702127659623.3829787234043
23150120.61702127659629.3829787234043
24149120.61702127659628.3829787234043
25134120.61702127659613.3829787234043
26123120.6170212765962.38297872340425
27116120.617021276596-4.61702127659575
28117120.617021276596-3.61702127659575
29111120.617021276596-9.61702127659574
30105120.617021276596-15.6170212765957
31102120.617021276596-18.6170212765957
3295120.617021276596-25.6170212765957
3393120.617021276596-27.6170212765957
34124120.6170212765963.38297872340425
35130120.6170212765969.38297872340425
36124120.6170212765963.38297872340425
37115120.617021276596-5.61702127659574
38106120.617021276596-14.6170212765957
39105120.617021276596-15.6170212765957
40105120.617021276596-15.6170212765957
41101120.617021276596-19.6170212765957
4295120.617021276596-25.6170212765957
4393120.617021276596-27.6170212765957
4484120.617021276596-36.6170212765957
4587120.617021276596-33.6170212765957
46116120.617021276596-4.61702127659575
47120120.617021276596-0.617021276595746
48117113.5384615384623.46153846153846
49109113.538461538462-4.53846153846154
50105113.538461538462-8.53846153846154
51107113.538461538462-6.53846153846154
52109113.538461538462-4.53846153846154
53109113.538461538462-4.53846153846154
54108113.538461538462-5.53846153846154
55107113.538461538462-6.53846153846154
5699113.538461538462-14.5384615384615
57103113.538461538462-10.5384615384615
58131113.53846153846217.4615384615385
59137113.53846153846223.4615384615385
60135113.53846153846221.4615384615385







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.1886004992866030.3772009985732070.811399500713397
60.1674184236676980.3348368473353970.832581576332302
70.17770557611870.35541115223740.8222944238813
80.2113480752696130.4226961505392260.788651924730387
90.1970394259509120.3940788519018230.802960574049088
100.2725844535800740.5451689071601470.727415546419926
110.4650072535086560.9300145070173130.534992746491344
120.6285624840371690.7428750319256620.371437515962831
130.6432911004899990.7134177990200020.356708899510001
140.590547044805050.8189059103898990.409452955194950
150.5275165555506740.9449668888986520.472483444449326
160.4735730174704850.947146034940970.526426982529515
170.4263514792090910.8527029584181810.573648520790909
180.4101454875469660.8202909750939330.589854512453034
190.4074965126561530.8149930253123060.592503487343847
200.4182553389452820.8365106778905640.581744661054718
210.4080590795121490.8161181590242980.591940920487851
220.4672866190040310.9345732380080630.532713380995969
230.6394279643147460.7211440713705070.360572035685254
240.8136280585642840.3727438828714310.186371941435716
250.8414888489948250.3170223020103490.158511151005175
260.8332369039151890.3335261921696230.166763096084811
270.8233217503793210.3533564992413580.176678249620679
280.8111955473957140.3776089052085720.188804452604286
290.8055425104427920.3889149791144170.194457489557208
300.8169512862540920.3660974274918160.183048713745908
310.8338152716362680.3323694567274630.166184728363732
320.8800580064373110.2398839871253780.119941993562689
330.9181761089437140.1636477821125710.0818238910562855
340.9132093556393650.1735812887212700.086790644360635
350.9382069050744820.1235861898510350.0617930949255175
360.9486333603858220.1027332792283560.0513666396141778
370.9433833391114380.1132333217771240.0566166608885622
380.9310630213150730.1378739573698530.0689369786849267
390.915802079165390.1683958416692210.0841979208346103
400.8968923504169530.2062152991660930.103107649583047
410.8757307896727960.2485384206544070.124269210327204
420.8633165248027510.2733669503944980.136683475197249
430.8556038030046420.2887923939907160.144396196995358
440.9119960430416050.176007913916790.088003956958395
450.9647238751386740.07055224972265290.0352761248613265
460.941184047068570.1176319058628580.058815952931429
470.9039903438117520.1920193123764960.096009656188248
480.8529110174065590.2941779651868820.147088982593441
490.786404952238920.427190095522160.21359504776108
500.7211944901916290.5576110196167420.278805509808371
510.6346189693958890.7307620612082220.365381030604111
520.5273991714783170.9452016570433670.472600828521683
530.4148119281257740.8296238562515490.585188071874226
540.3121449887148660.6242899774297320.687855011285134
550.2293733553247870.4587467106495750.770626644675213

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.188600499286603 & 0.377200998573207 & 0.811399500713397 \tabularnewline
6 & 0.167418423667698 & 0.334836847335397 & 0.832581576332302 \tabularnewline
7 & 0.1777055761187 & 0.3554111522374 & 0.8222944238813 \tabularnewline
8 & 0.211348075269613 & 0.422696150539226 & 0.788651924730387 \tabularnewline
9 & 0.197039425950912 & 0.394078851901823 & 0.802960574049088 \tabularnewline
10 & 0.272584453580074 & 0.545168907160147 & 0.727415546419926 \tabularnewline
11 & 0.465007253508656 & 0.930014507017313 & 0.534992746491344 \tabularnewline
12 & 0.628562484037169 & 0.742875031925662 & 0.371437515962831 \tabularnewline
13 & 0.643291100489999 & 0.713417799020002 & 0.356708899510001 \tabularnewline
14 & 0.59054704480505 & 0.818905910389899 & 0.409452955194950 \tabularnewline
15 & 0.527516555550674 & 0.944966888898652 & 0.472483444449326 \tabularnewline
16 & 0.473573017470485 & 0.94714603494097 & 0.526426982529515 \tabularnewline
17 & 0.426351479209091 & 0.852702958418181 & 0.573648520790909 \tabularnewline
18 & 0.410145487546966 & 0.820290975093933 & 0.589854512453034 \tabularnewline
19 & 0.407496512656153 & 0.814993025312306 & 0.592503487343847 \tabularnewline
20 & 0.418255338945282 & 0.836510677890564 & 0.581744661054718 \tabularnewline
21 & 0.408059079512149 & 0.816118159024298 & 0.591940920487851 \tabularnewline
22 & 0.467286619004031 & 0.934573238008063 & 0.532713380995969 \tabularnewline
23 & 0.639427964314746 & 0.721144071370507 & 0.360572035685254 \tabularnewline
24 & 0.813628058564284 & 0.372743882871431 & 0.186371941435716 \tabularnewline
25 & 0.841488848994825 & 0.317022302010349 & 0.158511151005175 \tabularnewline
26 & 0.833236903915189 & 0.333526192169623 & 0.166763096084811 \tabularnewline
27 & 0.823321750379321 & 0.353356499241358 & 0.176678249620679 \tabularnewline
28 & 0.811195547395714 & 0.377608905208572 & 0.188804452604286 \tabularnewline
29 & 0.805542510442792 & 0.388914979114417 & 0.194457489557208 \tabularnewline
30 & 0.816951286254092 & 0.366097427491816 & 0.183048713745908 \tabularnewline
31 & 0.833815271636268 & 0.332369456727463 & 0.166184728363732 \tabularnewline
32 & 0.880058006437311 & 0.239883987125378 & 0.119941993562689 \tabularnewline
33 & 0.918176108943714 & 0.163647782112571 & 0.0818238910562855 \tabularnewline
34 & 0.913209355639365 & 0.173581288721270 & 0.086790644360635 \tabularnewline
35 & 0.938206905074482 & 0.123586189851035 & 0.0617930949255175 \tabularnewline
36 & 0.948633360385822 & 0.102733279228356 & 0.0513666396141778 \tabularnewline
37 & 0.943383339111438 & 0.113233321777124 & 0.0566166608885622 \tabularnewline
38 & 0.931063021315073 & 0.137873957369853 & 0.0689369786849267 \tabularnewline
39 & 0.91580207916539 & 0.168395841669221 & 0.0841979208346103 \tabularnewline
40 & 0.896892350416953 & 0.206215299166093 & 0.103107649583047 \tabularnewline
41 & 0.875730789672796 & 0.248538420654407 & 0.124269210327204 \tabularnewline
42 & 0.863316524802751 & 0.273366950394498 & 0.136683475197249 \tabularnewline
43 & 0.855603803004642 & 0.288792393990716 & 0.144396196995358 \tabularnewline
44 & 0.911996043041605 & 0.17600791391679 & 0.088003956958395 \tabularnewline
45 & 0.964723875138674 & 0.0705522497226529 & 0.0352761248613265 \tabularnewline
46 & 0.94118404706857 & 0.117631905862858 & 0.058815952931429 \tabularnewline
47 & 0.903990343811752 & 0.192019312376496 & 0.096009656188248 \tabularnewline
48 & 0.852911017406559 & 0.294177965186882 & 0.147088982593441 \tabularnewline
49 & 0.78640495223892 & 0.42719009552216 & 0.21359504776108 \tabularnewline
50 & 0.721194490191629 & 0.557611019616742 & 0.278805509808371 \tabularnewline
51 & 0.634618969395889 & 0.730762061208222 & 0.365381030604111 \tabularnewline
52 & 0.527399171478317 & 0.945201657043367 & 0.472600828521683 \tabularnewline
53 & 0.414811928125774 & 0.829623856251549 & 0.585188071874226 \tabularnewline
54 & 0.312144988714866 & 0.624289977429732 & 0.687855011285134 \tabularnewline
55 & 0.229373355324787 & 0.458746710649575 & 0.770626644675213 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57650&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]0.188600499286603[/C][C]0.377200998573207[/C][C]0.811399500713397[/C][/ROW]
[ROW][C]6[/C][C]0.167418423667698[/C][C]0.334836847335397[/C][C]0.832581576332302[/C][/ROW]
[ROW][C]7[/C][C]0.1777055761187[/C][C]0.3554111522374[/C][C]0.8222944238813[/C][/ROW]
[ROW][C]8[/C][C]0.211348075269613[/C][C]0.422696150539226[/C][C]0.788651924730387[/C][/ROW]
[ROW][C]9[/C][C]0.197039425950912[/C][C]0.394078851901823[/C][C]0.802960574049088[/C][/ROW]
[ROW][C]10[/C][C]0.272584453580074[/C][C]0.545168907160147[/C][C]0.727415546419926[/C][/ROW]
[ROW][C]11[/C][C]0.465007253508656[/C][C]0.930014507017313[/C][C]0.534992746491344[/C][/ROW]
[ROW][C]12[/C][C]0.628562484037169[/C][C]0.742875031925662[/C][C]0.371437515962831[/C][/ROW]
[ROW][C]13[/C][C]0.643291100489999[/C][C]0.713417799020002[/C][C]0.356708899510001[/C][/ROW]
[ROW][C]14[/C][C]0.59054704480505[/C][C]0.818905910389899[/C][C]0.409452955194950[/C][/ROW]
[ROW][C]15[/C][C]0.527516555550674[/C][C]0.944966888898652[/C][C]0.472483444449326[/C][/ROW]
[ROW][C]16[/C][C]0.473573017470485[/C][C]0.94714603494097[/C][C]0.526426982529515[/C][/ROW]
[ROW][C]17[/C][C]0.426351479209091[/C][C]0.852702958418181[/C][C]0.573648520790909[/C][/ROW]
[ROW][C]18[/C][C]0.410145487546966[/C][C]0.820290975093933[/C][C]0.589854512453034[/C][/ROW]
[ROW][C]19[/C][C]0.407496512656153[/C][C]0.814993025312306[/C][C]0.592503487343847[/C][/ROW]
[ROW][C]20[/C][C]0.418255338945282[/C][C]0.836510677890564[/C][C]0.581744661054718[/C][/ROW]
[ROW][C]21[/C][C]0.408059079512149[/C][C]0.816118159024298[/C][C]0.591940920487851[/C][/ROW]
[ROW][C]22[/C][C]0.467286619004031[/C][C]0.934573238008063[/C][C]0.532713380995969[/C][/ROW]
[ROW][C]23[/C][C]0.639427964314746[/C][C]0.721144071370507[/C][C]0.360572035685254[/C][/ROW]
[ROW][C]24[/C][C]0.813628058564284[/C][C]0.372743882871431[/C][C]0.186371941435716[/C][/ROW]
[ROW][C]25[/C][C]0.841488848994825[/C][C]0.317022302010349[/C][C]0.158511151005175[/C][/ROW]
[ROW][C]26[/C][C]0.833236903915189[/C][C]0.333526192169623[/C][C]0.166763096084811[/C][/ROW]
[ROW][C]27[/C][C]0.823321750379321[/C][C]0.353356499241358[/C][C]0.176678249620679[/C][/ROW]
[ROW][C]28[/C][C]0.811195547395714[/C][C]0.377608905208572[/C][C]0.188804452604286[/C][/ROW]
[ROW][C]29[/C][C]0.805542510442792[/C][C]0.388914979114417[/C][C]0.194457489557208[/C][/ROW]
[ROW][C]30[/C][C]0.816951286254092[/C][C]0.366097427491816[/C][C]0.183048713745908[/C][/ROW]
[ROW][C]31[/C][C]0.833815271636268[/C][C]0.332369456727463[/C][C]0.166184728363732[/C][/ROW]
[ROW][C]32[/C][C]0.880058006437311[/C][C]0.239883987125378[/C][C]0.119941993562689[/C][/ROW]
[ROW][C]33[/C][C]0.918176108943714[/C][C]0.163647782112571[/C][C]0.0818238910562855[/C][/ROW]
[ROW][C]34[/C][C]0.913209355639365[/C][C]0.173581288721270[/C][C]0.086790644360635[/C][/ROW]
[ROW][C]35[/C][C]0.938206905074482[/C][C]0.123586189851035[/C][C]0.0617930949255175[/C][/ROW]
[ROW][C]36[/C][C]0.948633360385822[/C][C]0.102733279228356[/C][C]0.0513666396141778[/C][/ROW]
[ROW][C]37[/C][C]0.943383339111438[/C][C]0.113233321777124[/C][C]0.0566166608885622[/C][/ROW]
[ROW][C]38[/C][C]0.931063021315073[/C][C]0.137873957369853[/C][C]0.0689369786849267[/C][/ROW]
[ROW][C]39[/C][C]0.91580207916539[/C][C]0.168395841669221[/C][C]0.0841979208346103[/C][/ROW]
[ROW][C]40[/C][C]0.896892350416953[/C][C]0.206215299166093[/C][C]0.103107649583047[/C][/ROW]
[ROW][C]41[/C][C]0.875730789672796[/C][C]0.248538420654407[/C][C]0.124269210327204[/C][/ROW]
[ROW][C]42[/C][C]0.863316524802751[/C][C]0.273366950394498[/C][C]0.136683475197249[/C][/ROW]
[ROW][C]43[/C][C]0.855603803004642[/C][C]0.288792393990716[/C][C]0.144396196995358[/C][/ROW]
[ROW][C]44[/C][C]0.911996043041605[/C][C]0.17600791391679[/C][C]0.088003956958395[/C][/ROW]
[ROW][C]45[/C][C]0.964723875138674[/C][C]0.0705522497226529[/C][C]0.0352761248613265[/C][/ROW]
[ROW][C]46[/C][C]0.94118404706857[/C][C]0.117631905862858[/C][C]0.058815952931429[/C][/ROW]
[ROW][C]47[/C][C]0.903990343811752[/C][C]0.192019312376496[/C][C]0.096009656188248[/C][/ROW]
[ROW][C]48[/C][C]0.852911017406559[/C][C]0.294177965186882[/C][C]0.147088982593441[/C][/ROW]
[ROW][C]49[/C][C]0.78640495223892[/C][C]0.42719009552216[/C][C]0.21359504776108[/C][/ROW]
[ROW][C]50[/C][C]0.721194490191629[/C][C]0.557611019616742[/C][C]0.278805509808371[/C][/ROW]
[ROW][C]51[/C][C]0.634618969395889[/C][C]0.730762061208222[/C][C]0.365381030604111[/C][/ROW]
[ROW][C]52[/C][C]0.527399171478317[/C][C]0.945201657043367[/C][C]0.472600828521683[/C][/ROW]
[ROW][C]53[/C][C]0.414811928125774[/C][C]0.829623856251549[/C][C]0.585188071874226[/C][/ROW]
[ROW][C]54[/C][C]0.312144988714866[/C][C]0.624289977429732[/C][C]0.687855011285134[/C][/ROW]
[ROW][C]55[/C][C]0.229373355324787[/C][C]0.458746710649575[/C][C]0.770626644675213[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57650&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57650&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
50.1886004992866030.3772009985732070.811399500713397
60.1674184236676980.3348368473353970.832581576332302
70.17770557611870.35541115223740.8222944238813
80.2113480752696130.4226961505392260.788651924730387
90.1970394259509120.3940788519018230.802960574049088
100.2725844535800740.5451689071601470.727415546419926
110.4650072535086560.9300145070173130.534992746491344
120.6285624840371690.7428750319256620.371437515962831
130.6432911004899990.7134177990200020.356708899510001
140.590547044805050.8189059103898990.409452955194950
150.5275165555506740.9449668888986520.472483444449326
160.4735730174704850.947146034940970.526426982529515
170.4263514792090910.8527029584181810.573648520790909
180.4101454875469660.8202909750939330.589854512453034
190.4074965126561530.8149930253123060.592503487343847
200.4182553389452820.8365106778905640.581744661054718
210.4080590795121490.8161181590242980.591940920487851
220.4672866190040310.9345732380080630.532713380995969
230.6394279643147460.7211440713705070.360572035685254
240.8136280585642840.3727438828714310.186371941435716
250.8414888489948250.3170223020103490.158511151005175
260.8332369039151890.3335261921696230.166763096084811
270.8233217503793210.3533564992413580.176678249620679
280.8111955473957140.3776089052085720.188804452604286
290.8055425104427920.3889149791144170.194457489557208
300.8169512862540920.3660974274918160.183048713745908
310.8338152716362680.3323694567274630.166184728363732
320.8800580064373110.2398839871253780.119941993562689
330.9181761089437140.1636477821125710.0818238910562855
340.9132093556393650.1735812887212700.086790644360635
350.9382069050744820.1235861898510350.0617930949255175
360.9486333603858220.1027332792283560.0513666396141778
370.9433833391114380.1132333217771240.0566166608885622
380.9310630213150730.1378739573698530.0689369786849267
390.915802079165390.1683958416692210.0841979208346103
400.8968923504169530.2062152991660930.103107649583047
410.8757307896727960.2485384206544070.124269210327204
420.8633165248027510.2733669503944980.136683475197249
430.8556038030046420.2887923939907160.144396196995358
440.9119960430416050.176007913916790.088003956958395
450.9647238751386740.07055224972265290.0352761248613265
460.941184047068570.1176319058628580.058815952931429
470.9039903438117520.1920193123764960.096009656188248
480.8529110174065590.2941779651868820.147088982593441
490.786404952238920.427190095522160.21359504776108
500.7211944901916290.5576110196167420.278805509808371
510.6346189693958890.7307620612082220.365381030604111
520.5273991714783170.9452016570433670.472600828521683
530.4148119281257740.8296238562515490.585188071874226
540.3121449887148660.6242899774297320.687855011285134
550.2293733553247870.4587467106495750.770626644675213







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 level10.0196078431372549OK

\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 & 1 & 0.0196078431372549 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57650&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]1[/C][C]0.0196078431372549[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57650&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57650&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 level10.0196078431372549OK



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')
}