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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Module--
Title produced by softwareMultiple Regression
Date of computationThu, 15 Jan 2015 22:21:07 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Jan/15/t1421360713i53rglx40xyl35q.htm/, Retrieved Wed, 15 May 2024 05:24:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=273032, Retrieved Wed, 15 May 2024 05:24:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact58

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Variability] [] [2011-10-13 20:33:03] [db02340d173e1867f482a5214ce3fc15]
- RM D    [Multiple Regression] [] [2015-01-15 22:21:07] [c4557137b9b718365486b3b7af9cd43b] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
149 1 0.5 0.67 0.67 0 0.5 1 0 2011
139 0.89 0.5 0.83 0.33 0.5 1 1 1 2011
148 0.89 0.4 1 0.67 0 1 1 0 2011
158 0.89 0.5 0.83 0 0 0 1 1 2011
128 0.89 0.7 0.67 0 1 1 1 1 2011
224 0.78 0.3 0 0 0.5 0.5 1 1 2011
159 0.89 0.4 0.83 0.67 0.5 0 1 0 2011
105 1 0.4 0.5 0.67 1 1 1 1 2011
159 0.89 0.7 0.83 0 0.5 0 1 1 2011
167 0.78 0.6 0.33 0.67 0.5 0.5 1 1 2011
165 1 0.6 0.5 1 0 0.5 1 1 2011
159 0.78 0.2 0.67 0 0.5 0.5 1 1 2011
119 0.89 0.4 1 0 0.5 0.5 1 1 2011
176 0.89 0.4 0.5 0.67 0 1 1 0 2011
54 0.89 0.5 0.67 0.33 0 0 1 0 2011
91 0.89 0.3 0.17 0.67 0 0.5 0 0 2011
163 0.89 0.4 0.83 0.33 0.5 0.5 1 1 2011
124 0.67 0.7 0.67 0.33 0.5 1 1 0 2011
137 1 0.5 0.67 0.33 0 1 0 1 2011
121 0.78 0.2 0.67 0 0 1 1 0 2011
153 0.78 0.3 0.5 0.67 0 0.5 1 1 2011
148 0.89 0.6 1 0.33 0 1 1 1 2011
221 0.78 0.6 0.83 0.33 0 1 1 0 2011
188 0.89 0.2 0.83 0.33 0 1 1 1 2011
149 0.89 0.7 1 0.67 1 0 1 1 2011
244 0.33 0.2 0.67 0 0 0 1 1 2011
148 1 1 1 0.33 1 1 0 1 2011
92 0.89 0.4 0.83 0.67 0 0.5 0 0 2011
150 0.89 0.4 1 1 0 1 1 1 2011
153 0.67 0.2 0.83 0.67 0 0.5 1 0 2011
94 0.56 0.4 0.67 0.33 0 1 1 0 2011
156 0.89 0.4 0.67 0 0.5 1 1 0 2011
132 0.89 0.7 1 0.67 0.5 0.5 1 1 2011
161 1 0.2 0.67 0.67 0 0.5 1 1 2011
105 0.78 0.6 1 1 0 0.5 1 1 2011
97 0.78 0.3 1 1 0.5 0.5 1 1 2011
151 0.33 0.3 0.5 0.33 0 0 1 0 2011
131 0.78 0.2 0.67 0 0.5 0 0 1 2011
166 0.89 0.5 0.83 0.67 0.5 0.5 1 1 2011
157 0.89 0.7 1 0.67 0.5 1 1 0 2011
111 0.78 0.6 1 0.67 0.5 0.5 1 1 2011
145 0.89 0.4 1 0.67 0.5 1 1 1 2011
162 0.89 0.6 1 0.33 0.5 1 1 1 2011
163 1 0.4 1 1 0 1 1 1 2011
59 0.67 0.3 0.83 0.67 0 1 0 1 2011
187 1 0.5 0.83 0.67 0.5 0.5 1 0 2011
109 0.89 0.2 0.5 0 0 1 1 1 2011
90 0.89 0.3 0.83 0 0.5 1 0 1 2011
105 0.89 0.5 0.17 0 0 1 1 0 2011
83 0.78 0.7 0.83 1 0.5 1 0 1 2011
116 0.89 0.4 1 0.67 1 0.5 0 1 2011
42 0.78 0.3 1 0 0 0.5 0 1 2011
148 0.78 0.2 0.67 0.67 1 1 1 1 2011
155 1 0.5 1 0 0 0.5 0 1 2011
125 0.78 0.4 1 0 0.5 0 1 1 2011
116 1 0.6 1 0.67 1 1 1 1 2011
128 0.78 0.4 0.83 1 0 1 0 0 2011
138 0.67 0.4 0.33 0 0 0.5 1 1 2011
49 0.33 0.2 0.33 0.33 0 0 0 0 2011
96 1 0.9 1 0.67 0.5 1 0 1 2011
164 1 0.8 1 0.67 1 0.5 1 1 2011
162 0.78 0.8 0.83 0 0.5 1 1 0 2011
99 0.67 0.3 1 1 0.5 1 1 0 2011
202 1 0.2 0.83 0.67 0 0.5 1 1 2011
186 0.89 0.4 0.67 0 0.5 1 1 0 2011
66 0.89 0.2 0.83 1 0 1 0 1 2011
183 0.78 0.2 0.67 0.67 0.5 1 1 0 2011
214 1 0.1 0.83 0.67 0 1 1 1 2011
188 0.56 0.4 0.67 1 0.5 0 1 1 2011
104 0.67 0.5 1 0 0.5 0.5 0 0 2011
177 0.89 0.8 0.83 0.33 0.5 1 1 0 2011
126 0.89 0.4 0.67 0.67 0 0.5 1 0 2011
76 0.89 0.6 0.83 0.33 0.5 0.5 0 0 2011
99 0.89 0.5 0.83 0.67 0.5 1 0 1 2011
139 0.78 0.3 0.67 0 0 0 1 0 2011
162 1 0.4 0.33 0 0.5 0 1 0 2011
108 1 0.6 0.83 0.67 0.5 0.5 0 1 2011
159 0.89 0.4 1 0.33 0 0.5 1 0 2011
74 0.44 0.3 0.83 0 0 0 0 0 2011
110 0.78 0.8 0.83 0 1 1 1 1 2011
96 0.89 0.6 0.5 0.33 1 1 0 0 2011
116 0.67 0.3 0.5 0 0 0 0 0 2011
87 0.78 0.5 0.83 0.67 0.5 1 0 0 2011
97 0.78 0.4 1 0.33 0 1 0 1 2011
127 0.33 0.3 0.33 0.67 0 0 0 0 2011
106 0.89 0.7 1 0.33 0 0.5 0 1 2011
80 0.89 0.2 0.67 0.33 0.5 0.5 0 1 2011
74 0.89 0.4 0.83 1 0 1 0 0 2011
91 0.89 0.6 1 0.67 0.5 0.5 0 0 2011
133 0.56 0.6 0.83 0 0 1 0 0 2011
74 0.67 0.6 0.83 0.67 0.5 0.5 0 1 2011
114 0.67 0.4 1 0.33 0.5 1 0 1 2011
140 0.78 0.6 0.83 0 0 1 0 1 2011
95 0.78 0.5 1 0.33 0.5 1 0 0 2011
98 0.78 0.5 0.83 0 0 1 0 1 2011
121 0.89 0.6 0.67 0 0 1 0 0 2011
126 1 0.8 0.83 0.33 0.5 1 0 1 2011
98 0.89 0.5 0.83 0.67 1 0.5 0 1 2011
95 0.89 0.6 0.83 0.67 0.5 1 0 1 2011
110 0.78 0.4 0.83 0.67 0.5 1 0 1 2011
70 1 0.3 0.67 0.67 0.5 1 0 1 2011
102 0.78 0.3 0.83 1 0 0.5 0 0 2011
86 0.67 0.2 0 0 0 0 0 1 2011
130 0.78 0.4 0.83 0 0 0.5 0 1 2011
96 0.89 0.5 1 0 0 0.5 0 1 2011
102 0.67 0.3 0.17 0 0.5 0 0 0 2011
100 0.22 0.4 0.17 0 0.5 0 0 0 2011
94 0.44 0.5 0.5 1 0 0 0 0 2011
52 0.89 0.3 0.5 0.67 0 1 0 0 2011
98 0.67 0.5 1 0 0 0.5 0 0 2011
118 0.89 0.4 0.67 0.67 0 0.5 0 0 2011
99 0.67 0.4 0.83 0.67 0 1 0 1 2011
48 0.78 0.6 1 0 1 1 1 1 2012
50 0.78 0.3 1 0.67 1 1 1 1 2012
150 0.78 0.4 1 0.33 1 0.5 1 1 2012
154 1 0.3 1 1 1 1 1 1 2012
109 0.78 1 1 1 1 1 0 0 2012
68 0.67 0.4 1 0 0 0.5 0 1 2012
194 0.89 0.8 0.83 1 0.5 1 1 1 2012
158 0.89 0.3 1 0.67 1 1 1 0 2012
159 1 0.5 0.83 0.67 0 1 1 1 2012
67 0.78 0.4 1 0 0 0.5 1 0 2012
147 0.67 0.3 0.83 0.67 0 1 1 0 2012
39 0.89 0.5 0.83 1 0 1 1 1 2012
100 0.67 0.3 1 0.67 0 1 1 1 2012
111 0.67 0.3 0.67 0 0 1 1 1 2012
138 1 0.4 0.83 0 0 1 1 1 2012
101 0.67 0.3 1 0 0 0.5 1 1 2012
131 1 0.6 1 0.33 0.5 0.5 0 1 2012
101 0.89 0.6 0.83 0.67 1 1 1 1 2012
114 0.89 0.4 1 1 1 1 1 1 2012
165 1 0.4 1 0 0 0 1 0 2012
114 0.67 0.4 1 0.67 0 0.5 1 1 2012
111 0.44 0.3 0.67 0.67 0.5 1 1 1 2012
75 0.89 0.2 1 0.33 1 0 1 1 2012
82 0.56 0.5 0.83 0.67 0 1 1 1 2012
121 0.78 0.4 1 0.67 1 1 1 1 2012
32 1 0.4 1 0.67 0 0 1 1 2012
150 1 0.4 0.83 0.67 0 1 1 0 2012
117 0.89 0.3 0.67 0.67 0.5 0.5 1 1 2012
71 0.67 0.4 0.83 0.67 1 0.5 0 1 2012
165 0.89 0.2 1 0.33 0.5 1 1 1 2012
154 0.33 0 0 0 0 0 1 1 2012
126 0.89 0.4 1 0.67 0.5 1 1 1 2012
149 0.78 0.6 1 0 1 1 1 0 2012
145 1 0.4 0.67 0.67 0 0.5 1 0 2012
120 0.44 0.4 1 0 0 0.5 1 1 2012
109 0.67 0.4 0.83 0 0.5 0 1 0 2012
132 0.33 0.2 0.17 0 0.5 0 1 0 2012
172 0.89 0.4 0.83 1 1 1 1 1 2012
169 0.89 0.3 0.83 0 0 0.5 1 0 2012
114 1 0.6 0.83 0.67 1 0 1 1 2012
156 0.89 0.6 0.83 1 0 1 1 1 2012
172 0.89 0.4 0.83 0 0 1 1 0 2012
68 1 0.5 1 0.67 1 0.5 0 1 2012
89 0.89 0.4 0.83 0 0.5 1 0 1 2012
167 1 0.6 1 1 1 1 1 1 2012
113 0.78 0.6 0.83 0.67 0.5 1 1 0 2012
115 0.78 0.9 1 0.67 0.5 1 0 0 2012
78 0.67 0.4 0.83 0.67 0.5 0 0 0 2012
118 0.89 0.8 1 1 0.5 1 0 0 2012
87 0.67 0.5 0.83 1 0 1 0 1 2012
173 0.78 0.4 0.83 1 0 0 1 0 2012
2 0.89 0.4 1 0.67 1 0.5 1 1 2012
162 0.89 0.7 1 1 1 0.5 0 0 2012
49 0.78 0.4 1 0.33 1 1 0 1 2012
122 1 0.8 1 0.67 0.5 1 0 0 2012
96 1 0.4 1 1 1 0.5 0 1 2012
100 1 0.3 1 0.67 0 0.5 0 0 2012
82 0.67 0.5 1 0.67 0.5 1 0 0 2012
100 0.89 0.8 1 0.67 1 1 0 1 2012
115 1 0.4 0.83 0.33 0 0.5 0 0 2012
141 1 1 1 1 0.5 0 0 1 2012
165 0.89 0.5 1 0.67 1 1 1 1 2012
165 0.89 0.5 1 0.67 1 1 1 1 2012
110 0.89 0.3 1 0.33 0 1 0 1 2012
118 0.89 0.3 0.83 0.33 0.5 1 1 1 2012
158 0.89 0.3 0.5 0 0 1 1 0 2012
146 1 0.4 0.67 0.33 0.5 0.5 0 1 2012
49 0.67 0.5 1 0.33 0 1 1 0 2012
90 1 0.5 0.67 0.67 0.5 1 0 0 2012
121 0.89 0.4 1 0 0 0 0 0 2012
155 0.89 0.7 1 1 0.5 0 1 1 2012
104 0.89 0.5 0.5 0.33 0 0.5 0 0 2012
147 0.89 0.4 0.67 0.33 1 0 0 1 2012
110 1 0.7 0.67 1 0 1 0 0 2012
108 1 0.7 0.67 1 0 1 0 0 2012
113 1 0.7 0.67 1 0 1 0 0 2012
115 0.89 0.7 0.67 1 0 1 0 0 2012
61 0.89 0.7 0.67 0 0 0 0 1 2012
60 0.89 0.7 1 0.67 0.5 1 0 1 2012
109 0.33 0.1 0.67 0.33 0.5 0 0 1 2012
68 0.67 0.2 0.67 0.67 0.5 1 0 1 2012
111 0.56 0.3 0.33 0.33 0 1 0 0 2012
77 0.44 0.6 0.83 0.33 0 0.5 0 0 2012
73 1 0.8 1 1 1 1 0 1 2012
151 0.89 0.8 1 0.33 0.5 0.5 1 0 2012
89 0.33 0 0.17 0 0 0 0 0 2012
78 0.67 0.3 0.67 0.33 0 1 0 0 2012
110 0.67 0.6 0.83 0.33 0.5 1 0 0 2012
220 1 0.5 0.83 0.67 0 1 1 1 2012
65 0.78 0.7 1 0.33 0 0.5 0 1 2012
141 0.67 0.3 0.83 0 0.5 1 1 0 2012
117 1 0.3 1 0.67 0 0 0 0 2012
122 0.78 0.4 1 0.67 0 0.5 1 1 2012
63 0.89 0.4 0.83 1 0 1 0 0 2012
44 0.89 0.1 0.83 0 0 1 1 1 2012
52 0.89 0.5 1 0.67 0 1 0 1 2012
131 0 0 0 0 0 0 0 0 2012
101 0.67 0.4 1 0.33 0.5 0 0 1 2012
42 1 0.6 0.83 0.67 1 0.5 0 1 2012
152 1 0.4 1 0.33 0.5 1 1 1 2012
107 0.67 0.1 0.33 0 0.5 1 1 0 2012
77 0.89 0.3 0.83 0 0 1 0 0 2012
154 0.89 0.7 0.83 0.67 0 1 1 0 2012
103 0.56 0.3 0.17 0 0 1 1 1 2012
96 0.67 0.5 0.83 0.33 0.5 0 0 1 2012
175 1 0.3 0.83 0.67 1 1 1 1 2012
57 1 0.6 0.67 0.67 0.5 1 0 1 2012
112 1 0.9 1 1 0 1 0 0 2012
143 0.67 0.4 0.83 0 0.5 1 1 0 2012
49 0.44 0.3 1 0 0.5 0.5 0 0 2012
110 0.89 0.9 1 0.67 1 1 1 1 2012
131 0.44 0.5 1 0 0.5 0 1 1 2012
167 0.56 0.3 1 1 0.5 0.5 1 0 2012
56 0.89 0.6 0.83 0.67 0 0.5 0 0 2012
137 0.67 0.2 1 0.33 0 0.5 1 0 2012
86 0.89 0.4 0.83 1 0.5 1 0 1 2012
121 1 0.5 0.83 0.67 0.5 0.5 1 1 2012
149 0.78 0.4 0.83 0.67 0 0.5 1 0 2012
168 0.44 0 0 0 0 0 1 0 2012
140 0.89 0.2 1 0.33 0.5 1 1 0 2012
88 0.89 0.5 1 0.67 0.5 1 0 1 2012
168 0.89 0.3 1 0.67 0 0.5 1 1 2012
94 0.44 0 0 0 0 0 1 1 2012
51 1 0.5 0.83 1 0 1 1 1 2012
48 0.89 0.6 0.83 0.33 0 1 0 0 2012
145 0.67 0.3 0.83 0 0.5 0.5 1 1 2012
66 0.33 0 0 0 0 0 1 1 2012
85 0.78 0.3 0.67 0 0.5 0 0 1 2012
109 0.89 0.5 1 0.67 0.5 1 1 0 2012
63 0.78 0.4 0.67 0 0 1 0 0 2012
102 0.78 0.5 0.83 0.67 0 0.5 0 1 2012
162 0.89 0.7 1 1 1 0.5 0 0 2012
86 0.78 0.8 1 0.67 0.5 1 0 1 2012
114 0.78 0.6 1 0.33 0.5 1 0 1 2012
164 0.67 0.4 0.83 0.33 0 0.5 1 0 2012
119 0.89 0.5 0.83 0.33 0.5 0 1 1 2012
126 0.89 0.5 1 0 0.5 1 1 0 2012
132 0.78 0.3 1 0.33 0 1 1 1 2012
142 1 0.6 1 0 0.5 1 1 1 2012
83 1 0.3 0.67 0.67 0 0.5 1 0 2012
94 0.78 0.6 0.83 1 0.5 0.5 0 1 2012
81 0.78 0.3 0.33 0.33 0 1 0 0 2012
166 0.89 0.7 1 0.67 1 1 1 1 2012
110 0.89 0.7 1 1 0 1 0 0 2012
64 0.67 0.6 0.67 1 0.5 1 0 1 2012
93 1 0.5 1 0.33 0.5 0 1 0 2012
104 0.67 0.5 0.83 0.33 0 0.5 0 0 2012
105 0.56 0.4 0.67 0 0 1 0 1 2012
49 0.78 0.4 1 0.33 1 1 0 1 2012
88 1 0.7 1 1 0 1 0 0 2012
95 0.67 0.2 0.17 0 0.5 0 0 1 2012
102 0.78 0.5 0.83 0.67 0 0.5 0 1 2012
99 0.56 0.4 0.83 0.67 0.5 0 0 0 2012
63 1 0.2 1 0.67 1 1 0 1 2012
76 0.89 0.5 0.67 0.67 0 0 0 0 2012
109 0.44 0.4 0.5 0 0 1 0 0 2012
117 1 0.7 0.67 1 1 1 0 1 2012
57 0.89 0.6 0.83 0.67 1 0 0 1 2012
120 0.78 0.4 0.83 0 0 0 0 0 2012
73 0.89 0.5 1 0.67 1 1 0 1 2012
91 0.11 0 0.17 0 0 0 0 0 2012
108 0.89 0.7 1 0.67 0.5 1 0 0 2012
105 0.89 0.4 0.67 0.67 0 1 0 1 2012
117 1 0.5 0.67 1 0 1 1 0 2012
119 0.89 0.6 0.83 0.67 0 0.5 0 0 2012
31 1 0.8 0.5 0.67 0.5 0.5 0 1 2012

Tables (Output of Computation)





\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
R Engine error message & 
Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 
  0 (non-NA) cases
Calls: lm -> lm.fit
In addition: Warning messages:
1: In model.matrix.default(mt, mf, contrasts) :
  the response appeared on the right-hand side and was dropped
2: In model.matrix.default(mt, mf, contrasts) :
  problem with term 1 in model.matrix: no columns are assigned
Execution halted

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=273032&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[ROW][C]R Engine error message[/C][C]
Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 
  0 (non-NA) cases
Calls: lm -> lm.fit
In addition: Warning messages:
1: In model.matrix.default(mt, mf, contrasts) :
  the response appeared on the right-hand side and was dropped
2: In model.matrix.default(mt, mf, contrasts) :
  problem with term 1 in model.matrix: no columns are assigned
Execution halted

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=273032&T=0


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
Parameters (R input):
par1 = ; par2 = ; par3 = ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$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$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1/numgqtests,6))
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')
}