Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 30 Jan 2019 11:47:12 +0100

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2019/Jan/30/t1548845254n1am877toyubszm.htm/, Retrieved Sun, 28 Apr 2024 12:18:46 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=317032, Retrieved Sun, 28 Apr 2024 12:18:46 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

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

-       [Multiple Regression] [] [2019-01-30 10:47:12] [9e2b27cf675a1d7b8e7da04c0e26bb6a] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

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=317032&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=317032&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317032&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] = + 42.9285 -0.0519004Calculation[t] + 0.00235755Graphical_Interpretation[t] -0.0307223Proportionality_and_Ratio[t] + 0.00116168Probability_and_Sampling[t] -0.0306008Algebraic_Reasoning[t] -0.00102689Estimation[t] + 0.0308682year[t] + 0.0193381group[t] -0.021257gender[t] -0.0120594`LFM(t-1s)`[t] + 0.010051`LFM(t-2s)`[t] + 18.1629M1[t] + 19.489M2[t] + 61.9136M3[t] + 0.149741M4[t] + 22.1251M5[t] + 19.4433M6[t] + 62.0821M7[t] + 0.0934455M8[t] + 14.6334M9[t] + 19.4475M10[t] + 61.9705M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
LFM[t] =  +  42.9285 -0.0519004Calculation[t] +  0.00235755Graphical_Interpretation[t] -0.0307223Proportionality_and_Ratio[t] +  0.00116168Probability_and_Sampling[t] -0.0306008Algebraic_Reasoning[t] -0.00102689Estimation[t] +  0.0308682year[t] +  0.0193381group[t] -0.021257gender[t] -0.0120594`LFM(t-1s)`[t] +  0.010051`LFM(t-2s)`[t] +  18.1629M1[t] +  19.489M2[t] +  61.9136M3[t] +  0.149741M4[t] +  22.1251M5[t] +  19.4433M6[t] +  62.0821M7[t] +  0.0934455M8[t] +  14.6334M9[t] +  19.4475M10[t] +  61.9705M11[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=317032&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]LFM[t] =  +  42.9285 -0.0519004Calculation[t] +  0.00235755Graphical_Interpretation[t] -0.0307223Proportionality_and_Ratio[t] +  0.00116168Probability_and_Sampling[t] -0.0306008Algebraic_Reasoning[t] -0.00102689Estimation[t] +  0.0308682year[t] +  0.0193381group[t] -0.021257gender[t] -0.0120594`LFM(t-1s)`[t] +  0.010051`LFM(t-2s)`[t] +  18.1629M1[t] +  19.489M2[t] +  61.9136M3[t] +  0.149741M4[t] +  22.1251M5[t] +  19.4433M6[t] +  62.0821M7[t] +  0.0934455M8[t] +  14.6334M9[t] +  19.4475M10[t] +  61.9705M11[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=317032&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317032&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] = + 42.9285 -0.0519004Calculation[t] + 0.00235755Graphical_Interpretation[t] -0.0307223Proportionality_and_Ratio[t] + 0.00116168Probability_and_Sampling[t] -0.0306008Algebraic_Reasoning[t] -0.00102689Estimation[t] + 0.0308682year[t] + 0.0193381group[t] -0.021257gender[t] -0.0120594`LFM(t-1s)`[t] + 0.010051`LFM(t-2s)`[t] + 18.1629M1[t] + 19.489M2[t] + 61.9136M3[t] + 0.149741M4[t] + 22.1251M5[t] + 19.4433M6[t] + 62.0821M7[t] + 0.0934455M8[t] + 14.6334M9[t] + 19.4475M10[t] + 61.9705M11[t] + 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)	+42.93	2660	+1.6140e-02	0.9871	0.4936
Calculation	-0.0519	1.322	-3.9270e-02	0.9687	0.4843
Graphical_Interpretation	+0.002357	0.04221	+5.5850e-02	0.9555	0.4777
Proportionality_and_Ratio	-0.03072	0.003956	-7.7660e+00	8.029e-14	4.014e-14
Probability_and_Sampling	+0.001162	0.05006	+2.3210e-02	0.9815	0.4907
Algebraic_Reasoning	-0.0306	0.003938	-7.7700e+00	7.828e-14	3.914e-14
Estimation	-0.001027	0.05226	-1.9650e-02	0.9843	0.4922
year	+0.03087	0.00389	+7.9350e+00	2.526e-14	1.263e-14
group	+0.01934	0.04859	+3.9800e-01	0.6909	0.3454
gender	-0.02126	1.321	-1.6090e-02	0.9872	0.4936
`LFM(t-1s)`	-0.01206	0.05366	-2.2470e-01	0.8223	0.4112
`LFM(t-2s)`	+0.01005	0.04955	+2.0280e-01	0.8394	0.4197
M1	+18.16	2658	+6.8340e-03	0.9946	0.4973
M2	+19.49	2657	+7.3340e-03	0.9942	0.4971
M3	+61.91	8.345	+7.4190e+00	8.142e-13	4.071e-13
M4	+0.1497	3.391	+4.4160e-02	0.9648	0.4824
M5	+22.12	2658	+8.3250e-03	0.9934	0.4967
M6	+19.44	2657	+7.3170e-03	0.9942	0.4971
M7	+62.08	8.317	+7.4640e+00	6.054e-13	3.027e-13
M8	+0.09345	3.391	+2.7560e-02	0.978	0.489
M9	+14.63	2658	+5.5060e-03	0.9956	0.4978
M10	+19.45	2657	+7.3190e-03	0.9942	0.4971
M11	+61.97	8.374	+7.4010e+00	9.196e-13	4.598e-13

\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) & +42.93 &  2660 & +1.6140e-02 &  0.9871 &  0.4936 \tabularnewline
Calculation & -0.0519 &  1.322 & -3.9270e-02 &  0.9687 &  0.4843 \tabularnewline
Graphical_Interpretation & +0.002357 &  0.04221 & +5.5850e-02 &  0.9555 &  0.4777 \tabularnewline
Proportionality_and_Ratio & -0.03072 &  0.003956 & -7.7660e+00 &  8.029e-14 &  4.014e-14 \tabularnewline
Probability_and_Sampling & +0.001162 &  0.05006 & +2.3210e-02 &  0.9815 &  0.4907 \tabularnewline
Algebraic_Reasoning & -0.0306 &  0.003938 & -7.7700e+00 &  7.828e-14 &  3.914e-14 \tabularnewline
Estimation & -0.001027 &  0.05226 & -1.9650e-02 &  0.9843 &  0.4922 \tabularnewline
year & +0.03087 &  0.00389 & +7.9350e+00 &  2.526e-14 &  1.263e-14 \tabularnewline
group & +0.01934 &  0.04859 & +3.9800e-01 &  0.6909 &  0.3454 \tabularnewline
gender & -0.02126 &  1.321 & -1.6090e-02 &  0.9872 &  0.4936 \tabularnewline
`LFM(t-1s)` & -0.01206 &  0.05366 & -2.2470e-01 &  0.8223 &  0.4112 \tabularnewline
`LFM(t-2s)` & +0.01005 &  0.04955 & +2.0280e-01 &  0.8394 &  0.4197 \tabularnewline
M1 & +18.16 &  2658 & +6.8340e-03 &  0.9946 &  0.4973 \tabularnewline
M2 & +19.49 &  2657 & +7.3340e-03 &  0.9942 &  0.4971 \tabularnewline
M3 & +61.91 &  8.345 & +7.4190e+00 &  8.142e-13 &  4.071e-13 \tabularnewline
M4 & +0.1497 &  3.391 & +4.4160e-02 &  0.9648 &  0.4824 \tabularnewline
M5 & +22.12 &  2658 & +8.3250e-03 &  0.9934 &  0.4967 \tabularnewline
M6 & +19.44 &  2657 & +7.3170e-03 &  0.9942 &  0.4971 \tabularnewline
M7 & +62.08 &  8.317 & +7.4640e+00 &  6.054e-13 &  3.027e-13 \tabularnewline
M8 & +0.09345 &  3.391 & +2.7560e-02 &  0.978 &  0.489 \tabularnewline
M9 & +14.63 &  2658 & +5.5060e-03 &  0.9956 &  0.4978 \tabularnewline
M10 & +19.45 &  2657 & +7.3190e-03 &  0.9942 &  0.4971 \tabularnewline
M11 & +61.97 &  8.374 & +7.4010e+00 &  9.196e-13 &  4.598e-13 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=317032&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]+42.93[/C][C] 2660[/C][C]+1.6140e-02[/C][C] 0.9871[/C][C] 0.4936[/C][/ROW]
[ROW][C]Calculation[/C][C]-0.0519[/C][C] 1.322[/C][C]-3.9270e-02[/C][C] 0.9687[/C][C] 0.4843[/C][/ROW]
[ROW][C]Graphical_Interpretation[/C][C]+0.002357[/C][C] 0.04221[/C][C]+5.5850e-02[/C][C] 0.9555[/C][C] 0.4777[/C][/ROW]
[ROW][C]Proportionality_and_Ratio[/C][C]-0.03072[/C][C] 0.003956[/C][C]-7.7660e+00[/C][C] 8.029e-14[/C][C] 4.014e-14[/C][/ROW]
[ROW][C]Probability_and_Sampling[/C][C]+0.001162[/C][C] 0.05006[/C][C]+2.3210e-02[/C][C] 0.9815[/C][C] 0.4907[/C][/ROW]
[ROW][C]Algebraic_Reasoning[/C][C]-0.0306[/C][C] 0.003938[/C][C]-7.7700e+00[/C][C] 7.828e-14[/C][C] 3.914e-14[/C][/ROW]
[ROW][C]Estimation[/C][C]-0.001027[/C][C] 0.05226[/C][C]-1.9650e-02[/C][C] 0.9843[/C][C] 0.4922[/C][/ROW]
[ROW][C]year[/C][C]+0.03087[/C][C] 0.00389[/C][C]+7.9350e+00[/C][C] 2.526e-14[/C][C] 1.263e-14[/C][/ROW]
[ROW][C]group[/C][C]+0.01934[/C][C] 0.04859[/C][C]+3.9800e-01[/C][C] 0.6909[/C][C] 0.3454[/C][/ROW]
[ROW][C]gender[/C][C]-0.02126[/C][C] 1.321[/C][C]-1.6090e-02[/C][C] 0.9872[/C][C] 0.4936[/C][/ROW]
[ROW][C]`LFM(t-1s)`[/C][C]-0.01206[/C][C] 0.05366[/C][C]-2.2470e-01[/C][C] 0.8223[/C][C] 0.4112[/C][/ROW]
[ROW][C]`LFM(t-2s)`[/C][C]+0.01005[/C][C] 0.04955[/C][C]+2.0280e-01[/C][C] 0.8394[/C][C] 0.4197[/C][/ROW]
[ROW][C]M1[/C][C]+18.16[/C][C] 2658[/C][C]+6.8340e-03[/C][C] 0.9946[/C][C] 0.4973[/C][/ROW]
[ROW][C]M2[/C][C]+19.49[/C][C] 2657[/C][C]+7.3340e-03[/C][C] 0.9942[/C][C] 0.4971[/C][/ROW]
[ROW][C]M3[/C][C]+61.91[/C][C] 8.345[/C][C]+7.4190e+00[/C][C] 8.142e-13[/C][C] 4.071e-13[/C][/ROW]
[ROW][C]M4[/C][C]+0.1497[/C][C] 3.391[/C][C]+4.4160e-02[/C][C] 0.9648[/C][C] 0.4824[/C][/ROW]
[ROW][C]M5[/C][C]+22.12[/C][C] 2658[/C][C]+8.3250e-03[/C][C] 0.9934[/C][C] 0.4967[/C][/ROW]
[ROW][C]M6[/C][C]+19.44[/C][C] 2657[/C][C]+7.3170e-03[/C][C] 0.9942[/C][C] 0.4971[/C][/ROW]
[ROW][C]M7[/C][C]+62.08[/C][C] 8.317[/C][C]+7.4640e+00[/C][C] 6.054e-13[/C][C] 3.027e-13[/C][/ROW]
[ROW][C]M8[/C][C]+0.09345[/C][C] 3.391[/C][C]+2.7560e-02[/C][C] 0.978[/C][C] 0.489[/C][/ROW]
[ROW][C]M9[/C][C]+14.63[/C][C] 2658[/C][C]+5.5060e-03[/C][C] 0.9956[/C][C] 0.4978[/C][/ROW]
[ROW][C]M10[/C][C]+19.45[/C][C] 2657[/C][C]+7.3190e-03[/C][C] 0.9942[/C][C] 0.4971[/C][/ROW]
[ROW][C]M11[/C][C]+61.97[/C][C] 8.374[/C][C]+7.4010e+00[/C][C] 9.196e-13[/C][C] 4.598e-13[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=317032&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317032&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)	+42.93	2660	+1.6140e-02	0.9871	0.4936
Calculation	-0.0519	1.322	-3.9270e-02	0.9687	0.4843
Graphical_Interpretation	+0.002357	0.04221	+5.5850e-02	0.9555	0.4777
Proportionality_and_Ratio	-0.03072	0.003956	-7.7660e+00	8.029e-14	4.014e-14
Probability_and_Sampling	+0.001162	0.05006	+2.3210e-02	0.9815	0.4907
Algebraic_Reasoning	-0.0306	0.003938	-7.7700e+00	7.828e-14	3.914e-14
Estimation	-0.001027	0.05226	-1.9650e-02	0.9843	0.4922
year	+0.03087	0.00389	+7.9350e+00	2.526e-14	1.263e-14
group	+0.01934	0.04859	+3.9800e-01	0.6909	0.3454
gender	-0.02126	1.321	-1.6090e-02	0.9872	0.4936
`LFM(t-1s)`	-0.01206	0.05366	-2.2470e-01	0.8223	0.4112
`LFM(t-2s)`	+0.01005	0.04955	+2.0280e-01	0.8394	0.4197
M1	+18.16	2658	+6.8340e-03	0.9946	0.4973
M2	+19.49	2657	+7.3340e-03	0.9942	0.4971
M3	+61.91	8.345	+7.4190e+00	8.142e-13	4.071e-13
M4	+0.1497	3.391	+4.4160e-02	0.9648	0.4824
M5	+22.12	2658	+8.3250e-03	0.9934	0.4967
M6	+19.44	2657	+7.3170e-03	0.9942	0.4971
M7	+62.08	8.317	+7.4640e+00	6.054e-13	3.027e-13
M8	+0.09345	3.391	+2.7560e-02	0.978	0.489
M9	+14.63	2658	+5.5060e-03	0.9956	0.4978
M10	+19.45	2657	+7.3190e-03	0.9942	0.4971
M11	+61.97	8.374	+7.4010e+00	9.196e-13	4.598e-13

Multiple Linear Regression - Regression Statistics
Multiple R	0.9524
R-squared	0.9071
Adjusted R-squared	0.9016
F-TEST (value)	164.2
F-TEST (DF numerator)	22
F-TEST (DF denominator)	370
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	13.63
Sum Squared Residuals	6.873e+04

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9524 \tabularnewline
R-squared &  0.9071 \tabularnewline
Adjusted R-squared &  0.9016 \tabularnewline
F-TEST (value) &  164.2 \tabularnewline
F-TEST (DF numerator) & 22 \tabularnewline
F-TEST (DF denominator) & 370 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  13.63 \tabularnewline
Sum Squared Residuals &  6.873e+04 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=317032&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9524[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.9071[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.9016[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 164.2[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]22[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]370[/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] 6.873e+04[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=317032&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317032&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	0.9524
R-squared	0.9071
Adjusted R-squared	0.9016
F-TEST (value)	164.2
F-TEST (DF numerator)	22
F-TEST (DF denominator)	370
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	13.63
Sum Squared Residuals	6.873e+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=317032&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=317032&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317032&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 = 2.9948, df1 = 2, df2 = 368, p-value = 0.05127
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.91179, df1 = 44, df2 = 326, p-value = 0.6343
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.78871, df1 = 2, df2 = 368, p-value = 0.4552

\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 = 2.9948, df1 = 2, df2 = 368, p-value = 0.05127
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.91179, df1 = 44, df2 = 326, p-value = 0.6343
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.78871, df1 = 2, df2 = 368, p-value = 0.4552
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=317032&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 = 2.9948, df1 = 2, df2 = 368, p-value = 0.05127
[/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 = 0.91179, df1 = 44, df2 = 326, p-value = 0.6343
[/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 = 0.78871, df1 = 2, df2 = 368, p-value = 0.4552
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=317032&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317032&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 = 2.9948, df1 = 2, df2 = 368, p-value = 0.05127
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.91179, df1 = 44, df2 = 326, p-value = 0.6343
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.78871, df1 = 2, df2 = 368, p-value = 0.4552

Variance Inflation Factors (Multicollinearity)

> vif
              Calculation  Graphical_Interpretation Proportionality_and_Ratio 
             2.797996e+06              7.148885e+00              2.506373e+01 
 Probability_and_Sampling       Algebraic_Reasoning                Estimation 
             1.003605e+01              2.484110e+01              1.026075e+01 
                     year                     group                    gender 
             2.439874e+01              9.082935e+00              2.796480e+06 
              `LFM(t-1s)`               `LFM(t-2s)`                        M1 
             1.163881e+01              1.062976e+01              1.149434e+06 
                       M2                        M3                        M4 
             1.149152e+06              1.133213e+01              1.871330e+00 
                       M5                        M6                        M7 
             1.149235e+06              1.148991e+06              1.125696e+01 
                       M8                        M9                       M10 
             1.870692e+00              1.149415e+06              1.117296e+06 
                      M11 
             1.109489e+01

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
              Calculation  Graphical_Interpretation Proportionality_and_Ratio 
             2.797996e+06              7.148885e+00              2.506373e+01 
 Probability_and_Sampling       Algebraic_Reasoning                Estimation 
             1.003605e+01              2.484110e+01              1.026075e+01 
                     year                     group                    gender 
             2.439874e+01              9.082935e+00              2.796480e+06 
              `LFM(t-1s)`               `LFM(t-2s)`                        M1 
             1.163881e+01              1.062976e+01              1.149434e+06 
                       M2                        M3                        M4 
             1.149152e+06              1.133213e+01              1.871330e+00 
                       M5                        M6                        M7 
             1.149235e+06              1.148991e+06              1.125696e+01 
                       M8                        M9                       M10 
             1.870692e+00              1.149415e+06              1.117296e+06 
                      M11 
             1.109489e+01 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=317032&T=6

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
              Calculation  Graphical_Interpretation Proportionality_and_Ratio 
             2.797996e+06              7.148885e+00              2.506373e+01 
 Probability_and_Sampling       Algebraic_Reasoning                Estimation 
             1.003605e+01              2.484110e+01              1.026075e+01 
                     year                     group                    gender 
             2.439874e+01              9.082935e+00              2.796480e+06 
              `LFM(t-1s)`               `LFM(t-2s)`                        M1 
             1.163881e+01              1.062976e+01              1.149434e+06 
                       M2                        M3                        M4 
             1.149152e+06              1.133213e+01              1.871330e+00 
                       M5                        M6                        M7 
             1.149235e+06              1.148991e+06              1.125696e+01 
                       M8                        M9                       M10 
             1.870692e+00              1.149415e+06              1.117296e+06 
                      M11 
             1.109489e+01 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=317032&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317032&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 
             2.797996e+06              7.148885e+00              2.506373e+01 
 Probability_and_Sampling       Algebraic_Reasoning                Estimation 
             1.003605e+01              2.484110e+01              1.026075e+01 
                     year                     group                    gender 
             2.439874e+01              9.082935e+00              2.796480e+06 
              `LFM(t-1s)`               `LFM(t-2s)`                        M1 
             1.163881e+01              1.062976e+01              1.149434e+06 
                       M2                        M3                        M4 
             1.149152e+06              1.133213e+01              1.871330e+00 
                       M5                        M6                        M7 
             1.149235e+06              1.148991e+06              1.125696e+01 
                       M8                        M9                       M10 
             1.870692e+00              1.149415e+06              1.117296e+06 
                      M11 
             1.109489e+01

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

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

Parameters (R input):

par1 = 1 ; par2 = Include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = 2 ; 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