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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Wed, 30 Jan 2019 11:43:20 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2019/Jan/30/t1548845075d03p6aogoctdzup.htm/, Retrieved Sun, 28 Apr 2024 10:58:55 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=317031, Retrieved Sun, 28 Apr 2024 10:58:55 +0000

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Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [] [2019-01-30 10:43:20] [9e2b27cf675a1d7b8e7da04c0e26bb6a] [Current]

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149 1 0.67 0.67 0 0.5 0.5 2011 1 0
 0.5
 0.5 1 2011 1 1
139 0.89 0.83 0.33 0.5 0.4 1 2011 1 0
 0.5
 0.5 0 2011 1 1
148 0.89 1 0.67 0 0.7 1 2011 1 1
 0.4
 0.3 0.5 2011 1 1
158 0.89 0.83 0 0 0.4 0 2011 1 0
 0.5
 0.4 1 2011 1 1
128 0.89 0.67 0 1 0.7 0 2011 1 1
 0.7
 0.6 0.5 2011 1 1
224 0.78 0 0 0.5 0.6 0.5 2011 1 1
 0.3
 0.2 0.5 2011 1 1
159 0.89 0.83 0.67 0.5 0.4 0.5 2011 1 1
 0.4
 0.4 1 2011 1 0
105 1 0.5 0.67 1 0.5 0 2011 1 0
 0.4
 0.3 0.5 2011 0 0
159 0.89 0.83 0 0.5 0.4 0.5 2011 1 1
 0.7
 0.7 1 2011 1 0
167 0.78 0.33 0.67 0.5 0.5 1 2011 0 1
 0.6
 0.2 1 2011 1 0
165 1 0.5 1 0 0.3 0.5 2011 1 1
 0.6
 0.6 1 2011 1 1
159 0.78 0.67 0 0.5 0.6 1 2011 1 0
 0.2
 0.2 1 2011 1 1
119 0.89 1 0 0.5 0.7 0 2011 1 1
 0.4
 0.2 0 2011 1 1
176 0.89 0.5 0.67 0 1 1 2011 0 1
 0.4
 0.4 0.5 2011 0 0
54 0.89 0.67 0.33 0 0.4 1 2011 1 1
 0.5
 0.2 0.5 2011 1 0
91 0.89 0.17 0.67 0 0.4 1 2011 1 0
 0.3
 0.4 1 2011 1 0
163 0.89 0.83 0.33 0.5 0.7 0.5 2011 1 1
 0.4
 0.2 0.5 2011 1 1
124 0.67 0.67 0.33 0.5 0.6 0.5 2011 1 1
 0.7
 0.3 0.5 2011 1 1
137 1 0.67 0.33 0 0.3 0 2011 1 0
 0.5
 0.2 0 2011 0 1
121 0.78 0.67 0 0 0.5 0.5 2011 1 1
 0.2
 0.7 1 2011 1 0
153 0.78 0.5 0.67 0 0.6 0.5 2011 1 1
 0.3
 0.4 1 2011 1 1
148 0.89 1 0.33 0 0.6 1 2011 1 1
 0.6
 0.4 1 2011 1 1
221 0.78 0.83 0.33 0 0.3 1 2011 0 1
 0.6
 0.5 0.5 2011 1 0
188 0.89 0.83 0.33 0 0.2 1 2011 1 1
 0.2
 0.3 1 2011 0 1
149 0.89 1 0.67 1 0.5 1 2011 1 0
 0.7
 0.7 1 2011 0 1
244 0.33 0.67 0 0 0.4 0.5 2011 0 1
 0.2
 0.3 0.5 2011 0 1
148 1 1 0.33 1 0.2 1 2011 1 1
 1
 0.5 0.5 2011 0 1
92 0.89 0.83 0.67 0 0.4 0 2011 1 1
 0.4
 0.6 1 2011 1 1
150 0.89 1 1 0 0.4 1 2011 0 0
 0.4
 0.4 0.5 2011 1 1
153 0.67 0.83 0.67 0 0.2 0 2011 0 0
 0.2
 0.9 1 2011 0 1
94 0.56 0.67 0.33 0 0.8 0.5 2011 1 1
 0.4
 0.8 1 2011 1 0
156 0.89 0.67 0 0.5 0.3 1 2011 1 0
 0.4
 0.2 0.5 2011 1 1
132 0.89 1 0.67 0.5 0.4 1 2011 1 0
 0.7
 0.2 1 2011 0 1
161 1 0.67 0.67 0 0.2 1 2011 1 0
 0.2
 0.1 1 2011 1 1
105 0.78 1 1 0 0.4 0 2011 1 1
 0.6
 0.5 0.5 2011 0 0
97 0.78 1 1 0.5 0.8 1 2011 1 0
 0.3
 0.4 0.5 2011 1 0
151 0.33 0.5 0.33 0 0.6 0.5 2011 0 0
 0.3
 0.5 1 2011 0 1
131 0.78 0.67 0 0.5 0.3 0 2011 1 0
 0.2
 0.4 0 2011 1 0
166 0.89 0.83 0.67 0.5 0.6 0.5 2011 0 1
 0.5
 0.4 0.5 2011 1 0
157 0.89 1 0.67 0.5 0.3 0 2011 0 0
 0.7
 0.8 1 2011 1 1
111 0.78 1 0.67 0.5 0.6 1 2011 0 0
 0.6
 0.3 0 2011 0 0
145 0.89 1 0.67 0.5 0.5 1 2011 0 0
 0.4
 0.4 1 2011 0 1
162 0.89 1 0.33 0.5 0.3 0 2011 0 0
 0.6
 0.7 0.5 2011 0 1
163 1 1 1 0 0.2 0.5 2011 0 1
 0.4
 0.4 1 2011 0 0
59 0.67 0.83 0.67 0 0.6 0.5 2011 0 0
 0.3
 0.6 1 2011 0 0
187 1 0.83 0.67 0.5 0.6 0.5 2011 0 1
 0.5
 0.4 1 2011 0 1
109 0.89 0.5 0 0 0.6 1 2011 0 1
 0.2
 0.5 1 2011 0 0
90 0.89 0.83 0 0.5 0.5 1 2011 0 1
 0.3
 0.6 1 2011 0 0
105 0.89 0.17 0 0 0.8 1 2011 0 1
 0.5
 0.5 0.5 2011 0 1
83 0.78 0.83 1 0.5 0.6 1 2011 0 1
 0.7
 0.4 1 2011 0 1
116 0.89 1 0.67 1 0.3 1 2011 0 1
 0.4
 0.3 0.5 2011 0 0
42 0.78 1 0 0 0.2 0 2011 0 1
 0.3
 0.4 0.5 2011 0 1
148 0.78 0.67 0.67 1 0.5 0.5 2011 0 1
 0.2
 0.3 0 2011 0 0
155 1 1 0 0 0.4 0 2011 0 0
 0.5
 0.5 0 2011 0 0
125 0.78 1 0 0.5 0.3 1 2011 0 0
 0.4
 0.5 0.5 2011 0 0
116 1 1 0.67 1 0.4 0.5 2011 0 0
 0.6
 0.4 1 2011 0 1
128 0.78 0.83 1 0 0.6 1 2012 1 1
 0.4
 0.3 1 2012 1 1
138 0.67 0.33 0 0 0.4 0.5 2012 1 1
 0.4
 0.3 1 2012 1 1
49 0.33 0.33 0.33 0 1 1 2012 0 0
 0.2
 0.4 0.5 2012 0 1
96 1 1 0.67 0.5 0.8 1 2012 1 1
 0.9
 0.3 1 2012 1 0
164 1 1 0.67 1 0.5 1 2012 1 1
 0.8
 0.4 0.5 2012 1 0
162 0.78 0.83 0 0.5 0.3 1 2012 1 0
 0.8
 0.5 1 2012 1 1
99 0.67 1 1 0.5 0.3 1 2012 1 1
 0.3
 0.3 1 2012 1 1
202 1 0.83 0.67 0 0.4 1 2012 1 1
 0.2
 0.3 0.5 2012 1 1
186 0.89 0.67 0 0.5 0.6 0.5 2012 0 1
 0.4
 0.6 1 2012 1 1
66 0.89 0.83 1 0 0.4 1 2012 1 1
 0.2
 0.4 0 2012 1 0
183 0.78 0.67 0.67 0.5 0.4 0.5 2012 1 1
 0.2
 0.3 1 2012 1 1
214 1 0.83 0.67 0 0.2 0 2012 1 1
 0.1
 0.5 1 2012 1 1
188 0.56 0.67 1 0.5 0.4 1 2012 1 1
 0.4
 0.4 0 2012 1 1
104 0.67 1 0 0.5 0.4 1 2012 1 0
 0.5
 0.3 0.5 2012 1 1
177 0.89 0.83 0.33 0.5 0.4 0.5 2012 0 1
 0.8
 0.2 1 2012 1 1
126 0.89 0.67 0.67 0 0 0 2012 1 1
 0.4
 0.4 1 2012 1 1
76 0.89 0.83 0.33 0.5 0.6 1 2012 1 0
 0.6
 0.4 0.5 2012 1 0
99 0.89 0.83 0.67 0.5 0.4 0.5 2012 1 1
 0.5
 0.4 0 2012 1 0
139 0.78 0.67 0 0 0.2 0 2012 1 0
 0.3
 0.4 1 2012 1 1
162 1 0.33 0 0.5 0.3 0.5 2012 1 0
 0.4
 0.6 0 2012 1 1
108 1 0.83 0.67 0.5 0.6 1 2012 1 1
 0.6
 0.4 1 2012 1 0
159 0.89 1 0.33 0 0.5 0.5 2012 0 1
 0.4
 0.4 1 2012 0 1
74 0.44 0.83 0 0 0.6 1 2012 1 1
 0.3
 0.6 1 2012 1 0
110 0.78 0.83 0 1 0.9 1 2012 0 0
 0.8
 0.4 0 2012 0 0
96 0.89 0.5 0.33 1 0.8 1 2012 0 0
 0.6
 0.5 1 2012 0 1
116 0.67 0.5 0 0 0.4 0 2012 1 0
 0.3
 0.4 0.5 2012 1 1
87 0.78 0.83 0.67 0.5 0.7 0.5 2012 0 0
 0.5
 0.4 1 2012 0 1
97 0.78 1 0.33 0 0.8 1 2012 0 0
 0.4
 0.4 0.5 2012 0 1
127 0.33 0.33 0.67 0 0.3 0.5 2012 0 0
 0.3
 0.5 1 2012 0 0
106 0.89 1 0.33 0 0.8 1 2012 0 1
 0.7
 0.4 0.5 2012 0 0
80 0.89 0.67 0.33 0.5 1 0 2012 0 1
 0.2
 0.5 1 2012 1 1
74 0.89 0.83 1 0 0.5 1 2012 1 1
 0.4
 0.3 1 2012 0 1
91 0.89 1 0.67 0.5 0.3 1 2012 1 1
 0.6
 0.3 1 2012 1 0
133 0.56 0.83 0 0 0.4 0.5 2012 0 1
 0.6
 0.5 1 2012 1 0
74 0.67 0.83 0.67 0.5 0.5 1 2012 0 0
 0.6
 0.4 0 2012 0 0
114 0.67 1 0.33 0.5 0.7 0 2012 1 1
 0.4
 0.5 0.5 2012 0 0
140 0.78 0.83 0 0 0.4 0 2012 0 1
 0.6
 0.7 1 2012 0 0
95 0.78 1 0.33 0.5 0.7 1 2012 0 0
 0.5
 0.7 1 2012 0 0
98 0.78 0.83 0 0 0.7 1 2012 0 0
 0.5
 0.7 0 2012 0 1
121 0.89 0.67 0 0 0.7 1 2012 0 1
 0.6
 0.1 0 2012 0 1
126 1 0.83 0.33 0.5 0.2 1 2012 0 1
 0.8
 0.3 1 2012 0 0
98 0.89 0.83 0.67 1 0.6 0.5 2012 0 0
 0.5
 0.8 1 2012 0 1
95 0.89 0.83 0.67 0.5 0.8 0.5 2012 1 0
 0.6
 0 0 2012 0 0
110 0.78 0.83 0.67 0.5 0.3 1 2012 0 0
 0.4
 0.6 1 2012 0 0
70 1 0.67 0.67 0.5 0.5 1 2012 1 1
 0.3
 0.7 0.5 2012 0 1
102 0.78 0.83 1 0 0.3 1 2012 1 0
 0.3
 0.3 0 2012 0 0
86 0.67 0 0 0 0.4 0.5 2012 1 1
 0.2
 0.4 1 2012 0 0
130 0.78 0.83 0 0 0.1 1 2012 1 1
 0.4
 0.5 1 2012 0 1
96 0.89 1 0 0 0 0 2012 0 0
 0.5
 0.4 0 2012 0 1
102 0.67 0.17 0 0.5 0.6 0.5 2012 0 1
 0.3
 0.4 1 2012 1 1
100 0.22 0.17 0 0.5 0.1 1 2012 1 0
 0.4
 0.3 1 2012 0 0
94 0.44 0.5 1 0 0.7 1 2012 1 0
 0.5
 0.3 1 2012 1 1
52 0.89 0.5 0.67 0 0.5 0 2012 0 1
 0.3
 0.3 1 2012 1 1
98 0.67 1 0 0 0.6 1 2012 0 1
 0.5
 0.9 1 2012 0 0
118 0.89 0.67 0.67 0 0.4 1 2012 1 0
 0.4
 0.3 0.5 2012 0 0
99 0.67 0.83 0.67 0 0.9 1 2012 1 1
 0.4
 0.5 0 2012 1 1
48 0.78 1 0 1 0.3 0.5 2012 1 0
 0.6
 0.6 0.5 2012 0 0
50 0.78 1 0.67 1 0.2 0.5 2012 1 0
 0.3
 0.4 1 2012 0 1
150 0.78 1 0.33 1 0.5 0.5 2012 1 1
 0.4
 0.4 0.5 2012 1 0
154 1 1 1 1 0 0 2012 1 0
 0.3
 0.2 1 2012 1 0
109 0.78 1 1 1 0.5 1 2012 0 1
 1
 0.3 0.5 2012 1 1
68 0.67 1 0 0 0 0 2012 1 1
 0.4
 0.5 1 2012 1 1
194 0.89 0.83 1 0.5 0.6 1 2012 0 0
 0.8
 0.3 0.5 2012 1 1
158 0.89 1 0.67 1 0 0 2012 1 1
 0.3
 0.3 0 2012 0 1
159 1 0.83 0.67 0 0.5 1 2012 1 0
 0.5
 0.4 1 2012 0 0
67 0.78 1 0 0 0.5 0.5 2012 0 1
 0.4
 0.7 0.5 2012 0 0
147 0.67 0.83 0.67 0 0.8 1 2012 0 1
 0.3
 0.6 1 2012 0 1
39 0.89 0.83 1 0 0.4 0.5 2012 1 0
 0.5
 0.5 0 2012 1 1
100 0.67 1 0.67 0 0.5 1 2012 1 0
 0.3
 0.3 1 2012 1 1
111 0.67 0.67 0 0 0.6 1 2012 1 1
 0.3
 0.3 0.5 2012 1 0
138 1 0.83 0 0 0.6 0.5 2012 0 1
 0.4
 0.3 1 2012 0 0
101 0.67 1 0 0 0.7 1 2012 1 1
 0.3
 0.7 1 2012 0 0
131 1 1 0.33 0.5 0.6 1 2012 0 1
 0.6
 0.5 0 2012 1 0
101 0.89 0.83 0.67 1 0.5 0.5 2012 0 0
 0.6
 0.4 1 2012 0 1
114 0.89 1 1 1 0.4 1 2012 0 1
 0.4
 0.7 1 2012 0 0
165 1 1 0 0 0.2 0 2012 0 1
 0.4
 0.5 0.5 2012 0 1
114 0.67 1 0.67 0 0.4 0 2012 0 0
 0.4
 0.2 1 2012 0 1
111 0.44 0.67 0.67 0.5 0.5 0 2012 0 0
 0.3
 0.4 1 2012 0 0
75 0.89 1 0.33 1 0.7 1 2012 0 1
 0.2
 0.6 0 2012 0 1
82 0.56 0.83 0.67 0 0.4 0 2012 0 0
 0.5
 0.5 1 2012 0 1
121 0.78 1 0.67 1 0 0 2012 0 0
 0.4
 0.7 1 2012 0 0
32 1 1 0.67 0 0.4 1 2012 0 1
 0.4
 0.5 1 2012 1 0
150 1 0.83 0.67 0 0.6 0.5 2012 0 0
 0.4
 0.8 0.5 2012 0 1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	13 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time13 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=317031&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

13 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=317031&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317031&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	13 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
LFM[t] = + 457.835 -0.257664Calculation[t] + 0.000592109Graphical_Interpretation[t] -0.0312515Proportionality_and_Ratio[t] + 0.000842536Probability_and_Sampling[t] -0.0314156Algebraic_Reasoning[t] -0.00606398Estimation[t] + 0.0312612year[t] + 0.0253258group[t] -0.226525gender[t] -0.0150551`LFM(t-1s)`[t] -0.0297634`LFM(t-2s)`[t] -0.0229772`LFM(t-3s)`[t] -389.618M1[t] -392.005M2[t] + 62.6812M3[t] + 0.105332M4[t] -386.178M5[t] -391.941M6[t] + 62.9414M7[t] + 0.110614M8[t] -395.217M9[t] -391.977M10[t] + 62.7755M11[t] -0.00840817t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
LFM[t] =  +  457.835 -0.257664Calculation[t] +  0.000592109Graphical_Interpretation[t] -0.0312515Proportionality_and_Ratio[t] +  0.000842536Probability_and_Sampling[t] -0.0314156Algebraic_Reasoning[t] -0.00606398Estimation[t] +  0.0312612year[t] +  0.0253258group[t] -0.226525gender[t] -0.0150551`LFM(t-1s)`[t] -0.0297634`LFM(t-2s)`[t] -0.0229772`LFM(t-3s)`[t] -389.618M1[t] -392.005M2[t] +  62.6812M3[t] +  0.105332M4[t] -386.178M5[t] -391.941M6[t] +  62.9414M7[t] +  0.110614M8[t] -395.217M9[t] -391.977M10[t] +  62.7755M11[t] -0.00840817t  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=317031&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]LFM[t] =  +  457.835 -0.257664Calculation[t] +  0.000592109Graphical_Interpretation[t] -0.0312515Proportionality_and_Ratio[t] +  0.000842536Probability_and_Sampling[t] -0.0314156Algebraic_Reasoning[t] -0.00606398Estimation[t] +  0.0312612year[t] +  0.0253258group[t] -0.226525gender[t] -0.0150551`LFM(t-1s)`[t] -0.0297634`LFM(t-2s)`[t] -0.0229772`LFM(t-3s)`[t] -389.618M1[t] -392.005M2[t] +  62.6812M3[t] +  0.105332M4[t] -386.178M5[t] -391.941M6[t] +  62.9414M7[t] +  0.110614M8[t] -395.217M9[t] -391.977M10[t] +  62.7755M11[t] -0.00840817t  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=317031&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317031&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
LFM[t] = + 457.835 -0.257664Calculation[t] + 0.000592109Graphical_Interpretation[t] -0.0312515Proportionality_and_Ratio[t] + 0.000842536Probability_and_Sampling[t] -0.0314156Algebraic_Reasoning[t] -0.00606398Estimation[t] + 0.0312612year[t] + 0.0253258group[t] -0.226525gender[t] -0.0150551`LFM(t-1s)`[t] -0.0297634`LFM(t-2s)`[t] -0.0229772`LFM(t-3s)`[t] -389.618M1[t] -392.005M2[t] + 62.6812M3[t] + 0.105332M4[t] -386.178M5[t] -391.941M6[t] + 62.9414M7[t] + 0.110614M8[t] -395.217M9[t] -391.977M10[t] + 62.7755M11[t] -0.00840817t + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+457.8	2704	+1.6930e-01	0.8656	0.4328
Calculation	-0.2577	1.344	-1.9180e-01	0.848	0.424
Graphical_Interpretation	+0.0005921	0.04426	+1.3380e-02	0.9893	0.4947
Proportionality_and_Ratio	-0.03125	0.004262	-7.3320e+00	1.541e-12	7.707e-13
Probability_and_Sampling	+0.0008425	0.05007	+1.6830e-02	0.9866	0.4933
Algebraic_Reasoning	-0.03142	0.004232	-7.4240e+00	8.452e-13	4.226e-13
Estimation	-0.006064	0.05357	-1.1320e-01	0.9099	0.455
year	+0.03126	0.004197	+7.4480e+00	7.232e-13	3.616e-13
group	+0.02533	0.05031	+5.0340e-01	0.615	0.3075
gender	-0.2265	1.343	-1.6860e-01	0.8662	0.4331
`LFM(t-1s)`	-0.01506	0.05421	-2.7770e-01	0.7814	0.3907
`LFM(t-2s)`	-0.02976	0.05329	-5.5860e-01	0.5768	0.2884
`LFM(t-3s)`	-0.02298	0.0518	-4.4360e-01	0.6576	0.3288
M1	-389.6	2702	-1.4420e-01	0.8854	0.4427
M2	-392	2701	-1.4510e-01	0.8847	0.4424
M3	+62.68	8.929	+7.0200e+00	1.131e-11	5.653e-12
M4	+0.1053	3.442	+3.0600e-02	0.9756	0.4878
M5	-386.2	2701	-1.4300e-01	0.8864	0.4432
M6	-391.9	2701	-1.4510e-01	0.8847	0.4424
M7	+62.94	8.9	+7.0720e+00	8.152e-12	4.076e-12
M8	+0.1106	3.438	+3.2180e-02	0.9743	0.4872
M9	-395.2	2702	-1.4630e-01	0.8838	0.4419
M10	-392	2701	-1.4510e-01	0.8847	0.4424
M11	+62.77	8.956	+7.0090e+00	1.209e-11	6.045e-12
t	-0.008408	0.006412	-1.3110e+00	0.1906	0.0953

\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) & +457.8 &  2704 & +1.6930e-01 &  0.8656 &  0.4328 \tabularnewline
Calculation & -0.2577 &  1.344 & -1.9180e-01 &  0.848 &  0.424 \tabularnewline
Graphical_Interpretation & +0.0005921 &  0.04426 & +1.3380e-02 &  0.9893 &  0.4947 \tabularnewline
Proportionality_and_Ratio & -0.03125 &  0.004262 & -7.3320e+00 &  1.541e-12 &  7.707e-13 \tabularnewline
Probability_and_Sampling & +0.0008425 &  0.05007 & +1.6830e-02 &  0.9866 &  0.4933 \tabularnewline
Algebraic_Reasoning & -0.03142 &  0.004232 & -7.4240e+00 &  8.452e-13 &  4.226e-13 \tabularnewline
Estimation & -0.006064 &  0.05357 & -1.1320e-01 &  0.9099 &  0.455 \tabularnewline
year & +0.03126 &  0.004197 & +7.4480e+00 &  7.232e-13 &  3.616e-13 \tabularnewline
group & +0.02533 &  0.05031 & +5.0340e-01 &  0.615 &  0.3075 \tabularnewline
gender & -0.2265 &  1.343 & -1.6860e-01 &  0.8662 &  0.4331 \tabularnewline
`LFM(t-1s)` & -0.01506 &  0.05421 & -2.7770e-01 &  0.7814 &  0.3907 \tabularnewline
`LFM(t-2s)` & -0.02976 &  0.05329 & -5.5860e-01 &  0.5768 &  0.2884 \tabularnewline
`LFM(t-3s)` & -0.02298 &  0.0518 & -4.4360e-01 &  0.6576 &  0.3288 \tabularnewline
M1 & -389.6 &  2702 & -1.4420e-01 &  0.8854 &  0.4427 \tabularnewline
M2 & -392 &  2701 & -1.4510e-01 &  0.8847 &  0.4424 \tabularnewline
M3 & +62.68 &  8.929 & +7.0200e+00 &  1.131e-11 &  5.653e-12 \tabularnewline
M4 & +0.1053 &  3.442 & +3.0600e-02 &  0.9756 &  0.4878 \tabularnewline
M5 & -386.2 &  2701 & -1.4300e-01 &  0.8864 &  0.4432 \tabularnewline
M6 & -391.9 &  2701 & -1.4510e-01 &  0.8847 &  0.4424 \tabularnewline
M7 & +62.94 &  8.9 & +7.0720e+00 &  8.152e-12 &  4.076e-12 \tabularnewline
M8 & +0.1106 &  3.438 & +3.2180e-02 &  0.9743 &  0.4872 \tabularnewline
M9 & -395.2 &  2702 & -1.4630e-01 &  0.8838 &  0.4419 \tabularnewline
M10 & -392 &  2701 & -1.4510e-01 &  0.8847 &  0.4424 \tabularnewline
M11 & +62.77 &  8.956 & +7.0090e+00 &  1.209e-11 &  6.045e-12 \tabularnewline
t & -0.008408 &  0.006412 & -1.3110e+00 &  0.1906 &  0.0953 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=317031&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]+457.8[/C][C] 2704[/C][C]+1.6930e-01[/C][C] 0.8656[/C][C] 0.4328[/C][/ROW]
[ROW][C]Calculation[/C][C]-0.2577[/C][C] 1.344[/C][C]-1.9180e-01[/C][C] 0.848[/C][C] 0.424[/C][/ROW]
[ROW][C]Graphical_Interpretation[/C][C]+0.0005921[/C][C] 0.04426[/C][C]+1.3380e-02[/C][C] 0.9893[/C][C] 0.4947[/C][/ROW]
[ROW][C]Proportionality_and_Ratio[/C][C]-0.03125[/C][C] 0.004262[/C][C]-7.3320e+00[/C][C] 1.541e-12[/C][C] 7.707e-13[/C][/ROW]
[ROW][C]Probability_and_Sampling[/C][C]+0.0008425[/C][C] 0.05007[/C][C]+1.6830e-02[/C][C] 0.9866[/C][C] 0.4933[/C][/ROW]
[ROW][C]Algebraic_Reasoning[/C][C]-0.03142[/C][C] 0.004232[/C][C]-7.4240e+00[/C][C] 8.452e-13[/C][C] 4.226e-13[/C][/ROW]
[ROW][C]Estimation[/C][C]-0.006064[/C][C] 0.05357[/C][C]-1.1320e-01[/C][C] 0.9099[/C][C] 0.455[/C][/ROW]
[ROW][C]year[/C][C]+0.03126[/C][C] 0.004197[/C][C]+7.4480e+00[/C][C] 7.232e-13[/C][C] 3.616e-13[/C][/ROW]
[ROW][C]group[/C][C]+0.02533[/C][C] 0.05031[/C][C]+5.0340e-01[/C][C] 0.615[/C][C] 0.3075[/C][/ROW]
[ROW][C]gender[/C][C]-0.2265[/C][C] 1.343[/C][C]-1.6860e-01[/C][C] 0.8662[/C][C] 0.4331[/C][/ROW]
[ROW][C]`LFM(t-1s)`[/C][C]-0.01506[/C][C] 0.05421[/C][C]-2.7770e-01[/C][C] 0.7814[/C][C] 0.3907[/C][/ROW]
[ROW][C]`LFM(t-2s)`[/C][C]-0.02976[/C][C] 0.05329[/C][C]-5.5860e-01[/C][C] 0.5768[/C][C] 0.2884[/C][/ROW]
[ROW][C]`LFM(t-3s)`[/C][C]-0.02298[/C][C] 0.0518[/C][C]-4.4360e-01[/C][C] 0.6576[/C][C] 0.3288[/C][/ROW]
[ROW][C]M1[/C][C]-389.6[/C][C] 2702[/C][C]-1.4420e-01[/C][C] 0.8854[/C][C] 0.4427[/C][/ROW]
[ROW][C]M2[/C][C]-392[/C][C] 2701[/C][C]-1.4510e-01[/C][C] 0.8847[/C][C] 0.4424[/C][/ROW]
[ROW][C]M3[/C][C]+62.68[/C][C] 8.929[/C][C]+7.0200e+00[/C][C] 1.131e-11[/C][C] 5.653e-12[/C][/ROW]
[ROW][C]M4[/C][C]+0.1053[/C][C] 3.442[/C][C]+3.0600e-02[/C][C] 0.9756[/C][C] 0.4878[/C][/ROW]
[ROW][C]M5[/C][C]-386.2[/C][C] 2701[/C][C]-1.4300e-01[/C][C] 0.8864[/C][C] 0.4432[/C][/ROW]
[ROW][C]M6[/C][C]-391.9[/C][C] 2701[/C][C]-1.4510e-01[/C][C] 0.8847[/C][C] 0.4424[/C][/ROW]
[ROW][C]M7[/C][C]+62.94[/C][C] 8.9[/C][C]+7.0720e+00[/C][C] 8.152e-12[/C][C] 4.076e-12[/C][/ROW]
[ROW][C]M8[/C][C]+0.1106[/C][C] 3.438[/C][C]+3.2180e-02[/C][C] 0.9743[/C][C] 0.4872[/C][/ROW]
[ROW][C]M9[/C][C]-395.2[/C][C] 2702[/C][C]-1.4630e-01[/C][C] 0.8838[/C][C] 0.4419[/C][/ROW]
[ROW][C]M10[/C][C]-392[/C][C] 2701[/C][C]-1.4510e-01[/C][C] 0.8847[/C][C] 0.4424[/C][/ROW]
[ROW][C]M11[/C][C]+62.77[/C][C] 8.956[/C][C]+7.0090e+00[/C][C] 1.209e-11[/C][C] 6.045e-12[/C][/ROW]
[ROW][C]t[/C][C]-0.008408[/C][C] 0.006412[/C][C]-1.3110e+00[/C][C] 0.1906[/C][C] 0.0953[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=317031&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317031&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
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+457.8	2704	+1.6930e-01	0.8656	0.4328
Calculation	-0.2577	1.344	-1.9180e-01	0.848	0.424
Graphical_Interpretation	+0.0005921	0.04426	+1.3380e-02	0.9893	0.4947
Proportionality_and_Ratio	-0.03125	0.004262	-7.3320e+00	1.541e-12	7.707e-13
Probability_and_Sampling	+0.0008425	0.05007	+1.6830e-02	0.9866	0.4933
Algebraic_Reasoning	-0.03142	0.004232	-7.4240e+00	8.452e-13	4.226e-13
Estimation	-0.006064	0.05357	-1.1320e-01	0.9099	0.455
year	+0.03126	0.004197	+7.4480e+00	7.232e-13	3.616e-13
group	+0.02533	0.05031	+5.0340e-01	0.615	0.3075
gender	-0.2265	1.343	-1.6860e-01	0.8662	0.4331
`LFM(t-1s)`	-0.01506	0.05421	-2.7770e-01	0.7814	0.3907
`LFM(t-2s)`	-0.02976	0.05329	-5.5860e-01	0.5768	0.2884
`LFM(t-3s)`	-0.02298	0.0518	-4.4360e-01	0.6576	0.3288
M1	-389.6	2702	-1.4420e-01	0.8854	0.4427
M2	-392	2701	-1.4510e-01	0.8847	0.4424
M3	+62.68	8.929	+7.0200e+00	1.131e-11	5.653e-12
M4	+0.1053	3.442	+3.0600e-02	0.9756	0.4878
M5	-386.2	2701	-1.4300e-01	0.8864	0.4432
M6	-391.9	2701	-1.4510e-01	0.8847	0.4424
M7	+62.94	8.9	+7.0720e+00	8.152e-12	4.076e-12
M8	+0.1106	3.438	+3.2180e-02	0.9743	0.4872
M9	-395.2	2702	-1.4630e-01	0.8838	0.4419
M10	-392	2701	-1.4510e-01	0.8847	0.4424
M11	+62.77	8.956	+7.0090e+00	1.209e-11	6.045e-12
t	-0.008408	0.006412	-1.3110e+00	0.1906	0.0953

Multiple Linear Regression - Regression Statistics
Multiple R	0.9525
R-squared	0.9073
Adjusted R-squared	0.901
F-TEST (value)	145.1
F-TEST (DF numerator)	24
F-TEST (DF denominator)	356
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	13.63
Sum Squared Residuals	6.611e+04

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9525 \tabularnewline
R-squared &  0.9073 \tabularnewline
Adjusted R-squared &  0.901 \tabularnewline
F-TEST (value) &  145.1 \tabularnewline
F-TEST (DF numerator) & 24 \tabularnewline
F-TEST (DF denominator) & 356 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  13.63 \tabularnewline
Sum Squared Residuals &  6.611e+04 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=317031&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9525[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.9073[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.901[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 145.1[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]24[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]356[/C][/ROW]
[ROW][C]p-value[/C][C] 0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 13.63[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 6.611e+04[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=317031&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317031&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 R	0.9525
R-squared	0.9073
Adjusted R-squared	0.901
F-TEST (value)	145.1
F-TEST (DF numerator)	24
F-TEST (DF denominator)	356
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	13.63
Sum Squared Residuals	6.611e+04

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=317031&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=317031&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317031&T=4

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 10.823, df1 = 2, df2 = 354, p-value = 2.741e-05
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.99133, df1 = 48, df2 = 308, p-value = 0.4944
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.0642, df1 = 2, df2 = 354, p-value = 0.3461

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 10.823, df1 = 2, df2 = 354, p-value = 2.741e-05
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.99133, df1 = 48, df2 = 308, p-value = 0.4944
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.0642, df1 = 2, df2 = 354, p-value = 0.3461
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=317031&T=5

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 10.823, df1 = 2, df2 = 354, p-value = 2.741e-05
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.99133, df1 = 48, df2 = 308, p-value = 0.4944
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.0642, df1 = 2, df2 = 354, p-value = 0.3461
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=317031&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317031&T=5

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 10.823, df1 = 2, df2 = 354, p-value = 2.741e-05
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.99133, df1 = 48, df2 = 308, p-value = 0.4944
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.0642, df1 = 2, df2 = 354, p-value = 0.3461

Variance Inflation Factors (Multicollinearity)

> vif
              Calculation  Graphical_Interpretation Proportionality_and_Ratio 
             2.823219e+06              7.269652e+00              2.801808e+01 
 Probability_and_Sampling       Algebraic_Reasoning                Estimation 
             9.859236e+00              2.800328e+01              1.014120e+01 
                     year                     group                    gender 
             2.755229e+01              9.503660e+00              2.782353e+06 
              `LFM(t-1s)`               `LFM(t-2s)`               `LFM(t-3s)` 
             1.149021e+01              1.133341e+01              1.137675e+01 
                       M1                        M2                        M3 
             1.151955e+06              1.151804e+06              1.258476e+01 
                       M4                        M5                        M6 
             1.869784e+00              1.151773e+06              1.151630e+06 
                       M7                        M8                        M9 
             1.250389e+01              1.865369e+00              1.151975e+06 
                      M10                       M11                         t 
             1.118877e+06              1.230063e+01              1.020350e+00

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
              Calculation  Graphical_Interpretation Proportionality_and_Ratio 
             2.823219e+06              7.269652e+00              2.801808e+01 
 Probability_and_Sampling       Algebraic_Reasoning                Estimation 
             9.859236e+00              2.800328e+01              1.014120e+01 
                     year                     group                    gender 
             2.755229e+01              9.503660e+00              2.782353e+06 
              `LFM(t-1s)`               `LFM(t-2s)`               `LFM(t-3s)` 
             1.149021e+01              1.133341e+01              1.137675e+01 
                       M1                        M2                        M3 
             1.151955e+06              1.151804e+06              1.258476e+01 
                       M4                        M5                        M6 
             1.869784e+00              1.151773e+06              1.151630e+06 
                       M7                        M8                        M9 
             1.250389e+01              1.865369e+00              1.151975e+06 
                      M10                       M11                         t 
             1.118877e+06              1.230063e+01              1.020350e+00 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=317031&T=6

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
              Calculation  Graphical_Interpretation Proportionality_and_Ratio 
             2.823219e+06              7.269652e+00              2.801808e+01 
 Probability_and_Sampling       Algebraic_Reasoning                Estimation 
             9.859236e+00              2.800328e+01              1.014120e+01 
                     year                     group                    gender 
             2.755229e+01              9.503660e+00              2.782353e+06 
              `LFM(t-1s)`               `LFM(t-2s)`               `LFM(t-3s)` 
             1.149021e+01              1.133341e+01              1.137675e+01 
                       M1                        M2                        M3 
             1.151955e+06              1.151804e+06              1.258476e+01 
                       M4                        M5                        M6 
             1.869784e+00              1.151773e+06              1.151630e+06 
                       M7                        M8                        M9 
             1.250389e+01              1.865369e+00              1.151975e+06 
                      M10                       M11                         t 
             1.118877e+06              1.230063e+01              1.020350e+00 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=317031&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317031&T=6

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Variance Inflation Factors (Multicollinearity)

> vif
              Calculation  Graphical_Interpretation Proportionality_and_Ratio 
             2.823219e+06              7.269652e+00              2.801808e+01 
 Probability_and_Sampling       Algebraic_Reasoning                Estimation 
             9.859236e+00              2.800328e+01              1.014120e+01 
                     year                     group                    gender 
             2.755229e+01              9.503660e+00              2.782353e+06 
              `LFM(t-1s)`               `LFM(t-2s)`               `LFM(t-3s)` 
             1.149021e+01              1.133341e+01              1.137675e+01 
                       M1                        M2                        M3 
             1.151955e+06              1.151804e+06              1.258476e+01 
                       M4                        M5                        M6 
             1.869784e+00              1.151773e+06              1.151630e+06 
                       M7                        M8                        M9 
             1.250389e+01              1.865369e+00              1.151975e+06 
                      M10                       M11                         t 
             1.118877e+06              1.230063e+01              1.020350e+00

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = two.sided ; par2 = 0.95 ; par3 = 0 ;

Parameters (R input):

par1 = 1 ; par2 = Include Seasonal Dummies ; par3 = Linear Trend ; par4 = ; par5 = 3 ; par6 = 12 ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$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, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$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.row.start(a)
a<-table.element(a, mywarning)
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,'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$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
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,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code