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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 24 Jan 2020 16:10:34 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2020/Jan/24/t1579878647ngglnfmt7g0dlwl.htm/, Retrieved Thu, 28 Mar 2024 19:26:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319036, Retrieved Thu, 28 Mar 2024 19:26:38 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact119
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [multi] [2020-01-24 15:10:34] [a2a17d9df12946011e58b1b0ef216b29] [Current]
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Dataseries X:
102750 42.6
95276 42.9
112053 43.3
98841 43.6
123102 43.9
118152 44.2
101752 44.3
148219 45.1
124966 45.2
134741 45.6
132168 45.9
100950 46.2
96418 46.6
86891 47.2
89796 47.8
119663 48
130539 48.6
120851 49
145422 49.4
150583 50
127054 50.6
137473 51.1
127094 51.5
132080 51.9
188311 52.1
107487 52.5
84669 52.7
149184 52.9
121026 53.2
81073 53.6
132947 54.2
141294 54.3
155077 54.6
145154 54.9
127094 55.3
151414 55.5
167858 55.6
127070 55.8
154692 55.9
170905 56.1
127751 56.5
173795 56.8
190181 57.1
198417 57.4
183018 57.6
171608 57.9
188087 58
197042 58.2
208788 58.5
178111 59.1
236455 59.5
233219 60
188106 60.3
238876 60.7
205148 61
214727 61.2
213428 61.4
195128 61.6
206047 61.9
201773 62.1
192772 62.5
198230 62.9
181172 63.4
189079 63.9
179073 64.5
197421 65.2
195244 65.7
219826 66
211793 66.5
203394 67.1
209578 67.4
214769 67.7
226177 68.3
191449 69.1
200989 69.8
216707 70.6
192882 71.5
199736 72.3
202349 73.1
204137 73.8
215588 74.6
229454 75.2
175048 75.9
212799 76.7
181727 77.8
211607 78.9
185853 80.1
158277 81
180695 81.8
175959 82.7
139550 82.7
155810 83.3
138305 84
147014 84.8
135994 85.5
166455 86.3
177737 87
167021 87.9
132134 88.5
169834 89.1
130599 89.8
156836 90.6
119749 91.6
148996 92.3
147491 93.2
147216 93.4
153455 93.7
112004 94
158512 94.3
104139 94.6
102536 94.5
93017 94.9
91988 95.8
123616 97
134498 97.5
149812 97.7
110334 97.9
136639 98.2
102712 98
112951 97.6
107897 97.8
73242 97.9
72800 97.9
78767 98.6
114791 99.2
109351 99.5
122520 99.9
137338 100.2
132061 100.7
130607 101
118570 101.2
95873 101.3
103116 101.9
98619 102.4
104178 102.6
123468 103.1
99651 103.4
120264 103.7
122795 104.1
108524 104.5
105760 105
117191 105.3
122882 105.3
93275 105.3
99842 105.5
83803 106
61132 106.4
118563 106.9
106993 107.3
118108 107.6
99017 107.8
99852 108
112720 108.3
113636 108.7
118220 109
128854 109.3
123898 109.6
100823 109.3
115107 108.8
90624 108.6
132001 108.9
157969 109.5
169333 109.5
144907 109.7
169346 110.2
144666 110.3
158829 110.4
127286 110.5
120578 111.2
129293 111.6
122371 112.1
115176 112.7
142168 113.1
153260 113.5
173906 113.8
178446 114.4
155962 115
168257 115.3
149456 115.4
136105 115.4
141507 115.7
152084 116
145138 116.5
146548 117.1
173098 117.5
165471 118
152271 118.5
163201 119
157823 119.8
166167 120.2
154253 120.3
170299 120.5
166388 121.1
141051 121.6
160254 122.3
164995 123.1
195971 123.8
182635 124.1
189829 124.4
209476 124.6
189848 125
183746 125.6
192682 125.9
169677 126.1
201823 127.4
172643 128
202931 128.7
175863 128.9
222061 129.2
199797 129.9
214638 130.4
200106 131.6
166077 132.7
160586 133.5
158330 133.8
141749 133.8
170795 134.6
153286 134.8
163426 135
172562 135.2
197474 135.6
189822 136
188511 136.2
207437 136.6
192128 137.2
175716 137.4
159108 137.8
175801 137.9
186723 138.1
154970 138.6
172446 139.3
185965 139.5
195525 139.7
193156 140.2
212705 140.5
201357 140.9
189971 141.3
216523 141.8
193233 142
191996 141.9
211974 142.6
175907 143.1
206109 143.6
220275 144
211342 144.2
222528 144.4
229523 144.4
204153 144.8
206735 145.1
223416 145.7
228292 145.8
203121 145.8
205957 146.2
176918 146.7
219839 147.2
217213 147.4
216618 147.5
248057 148
245642 148.4
242485 149
260423 149.4
221030 149.5
229157 149.7
220858 149.7
212270 150.3
195944 150.9
239741 151.4
212013 151.9
240514 152.2
241982 152.5
245447 152.5
240839 152.9
244875 153.2
226375 153.7
231567 153.6
235746 153.5
238990 154.4
198120 154.9
201663 155.7
238198 156.3
261641 156.6
253014 156.7
275225 157
250957 157.3
260375 157.8
250694 158.3
216953 158.6
247816 158.6
224135 159.1
211073 159.6
245623 160
250947 160.2
278223 160.1
254232 160.3
266293 160.5
280897 160.8
274565 161.2
280555 161.6
252757 161.5
250131 161.3
271208 161.6
230593 161.9
263407 162.2
289968 162.5
282846 162.8
271314 163
289718 163.2
300227 163.4
259951 163.6
263149 164
267953 164
252378 163.9
280356 164.3
234298 164.5
271574 165
262378 166.2
289457 166.2
278274 166.2
288932 166.7
283813 167.1
267600 167.9
267574 168.2
254862 168.3
248974 168.3
256840 168.8
250914 169.8
279334 171.2
286549 171.3
302266 171.5
298205 172.4
300843 172.8
312955 172.8
275962 173.7
299561 174
260975 174.1
274836 174
284112 175.1
247331 175.8
298120 176.2
306008 176.9
306813 177.7
288550 178
301636 177.5
293215 177.5
270713 178.3
311803 177.7
281316 177.4
281450 176.7
295494 177.1
246411 177.8
267037 178.8
296134 179.8
296505 179.8
270677 179.9
290855 180.1
296068 180.7
272653 181
315720 181.3
286298 181.3
284170 180.9
273338 181.7
250262 183.1
294768 184.2
318088 183.8
319111 183.5
312982 183.7
335511 183.9
319674 184.6
316796 185.2
329992 185
291352 184.5
314131 184.3
309876 185.2
288494 186.2
329991 187.4
311663 188
317854 189.1
344729 189.7
324108 189.4
333756 189.5
297013 189.9
313249 190.9
329660 191
320586 190.3
325786 190.7
293425 191.8
324180 193.3
315528 194.6
319982 194.4
327865 194.5
312106 195.4
329039 196.4
277589 198.8
300884 199.2
314028 197.6
314259 196.8
303472 198.3
290744 198.7
313340 199.8
294281 201.5
325796 202.5
329839 202.9
322588 203.5
336528 203.9
316381 202.9
308602 201.8
299010 201.5
293645 201.8
320108 202.4
252869 203.5
324248 205.4
304775 206.7
320208 207.9
321260 208.4
310320 208.3
319197 207.9
297503 208.5
316184 208.9
303411 210.2
300841 210




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R ServerBig 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 time4 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319036&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]4 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319036&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319036&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 Outputview raw output of R engine
Computing time4 seconds
R ServerBig Analytics Cloud Computing Center







Multiple Linear Regression - Estimated Regression Equation
barrels_purchased[t] = + 461011 -8115.59cpi[t] + 7537.62M1[t] -16386.8M2[t] + 2249.84M3[t] + 9045.05M4[t] + 15645.9M5[t] + 18499.3M6[t] + 20494.1M7[t] + 25074.1M8[t] + 13383.9M9[t] + 16831.8M10[t] + 5014.51M11[t] + 3647.52t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
barrels_purchased[t] =  +  461011 -8115.59cpi[t] +  7537.62M1[t] -16386.8M2[t] +  2249.84M3[t] +  9045.05M4[t] +  15645.9M5[t] +  18499.3M6[t] +  20494.1M7[t] +  25074.1M8[t] +  13383.9M9[t] +  16831.8M10[t] +  5014.51M11[t] +  3647.52t  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319036&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]barrels_purchased[t] =  +  461011 -8115.59cpi[t] +  7537.62M1[t] -16386.8M2[t] +  2249.84M3[t] +  9045.05M4[t] +  15645.9M5[t] +  18499.3M6[t] +  20494.1M7[t] +  25074.1M8[t] +  13383.9M9[t] +  16831.8M10[t] +  5014.51M11[t] +  3647.52t  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319036&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319036&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
barrels_purchased[t] = + 461011 -8115.59cpi[t] + 7537.62M1[t] -16386.8M2[t] + 2249.84M3[t] + 9045.05M4[t] + 15645.9M5[t] + 18499.3M6[t] + 20494.1M7[t] + 25074.1M8[t] + 13383.9M9[t] + 16831.8M10[t] + 5014.51M11[t] + 3647.52t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+4.61e+05 1.726e+04+2.6700e+01 2.007e-91 1.004e-91
cpi-8116 368.5-2.2020e+01 2.671e-71 1.336e-71
M1+7538 6298+1.1970e+00 0.2321 0.116
M2-1.639e+04 6298-2.6020e+00 0.009614 0.004807
M3+2250 6300+3.5710e-01 0.7212 0.3606
M4+9045 6301+1.4350e+00 0.1519 0.07596
M5+1.565e+04 6301+2.4830e+00 0.01343 0.006716
M6+1.85e+04 6302+2.9360e+00 0.003518 0.001759
M7+2.049e+04 6300+3.2530e+00 0.001238 0.0006192
M8+2.507e+04 6300+3.9800e+00 8.174e-05 4.087e-05
M9+1.338e+04 6302+2.1240e+00 0.0343 0.01715
M10+1.683e+04 6301+2.6710e+00 0.007862 0.003931
M11+5014 6298+7.9610e-01 0.4264 0.2132
t+3648 143.9+2.5340e+01 1.195e-85 5.975e-86

\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) & +4.61e+05 &  1.726e+04 & +2.6700e+01 &  2.007e-91 &  1.004e-91 \tabularnewline
cpi & -8116 &  368.5 & -2.2020e+01 &  2.671e-71 &  1.336e-71 \tabularnewline
M1 & +7538 &  6298 & +1.1970e+00 &  0.2321 &  0.116 \tabularnewline
M2 & -1.639e+04 &  6298 & -2.6020e+00 &  0.009614 &  0.004807 \tabularnewline
M3 & +2250 &  6300 & +3.5710e-01 &  0.7212 &  0.3606 \tabularnewline
M4 & +9045 &  6301 & +1.4350e+00 &  0.1519 &  0.07596 \tabularnewline
M5 & +1.565e+04 &  6301 & +2.4830e+00 &  0.01343 &  0.006716 \tabularnewline
M6 & +1.85e+04 &  6302 & +2.9360e+00 &  0.003518 &  0.001759 \tabularnewline
M7 & +2.049e+04 &  6300 & +3.2530e+00 &  0.001238 &  0.0006192 \tabularnewline
M8 & +2.507e+04 &  6300 & +3.9800e+00 &  8.174e-05 &  4.087e-05 \tabularnewline
M9 & +1.338e+04 &  6302 & +2.1240e+00 &  0.0343 &  0.01715 \tabularnewline
M10 & +1.683e+04 &  6301 & +2.6710e+00 &  0.007862 &  0.003931 \tabularnewline
M11 & +5014 &  6298 & +7.9610e-01 &  0.4264 &  0.2132 \tabularnewline
t & +3648 &  143.9 & +2.5340e+01 &  1.195e-85 &  5.975e-86 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319036&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]+4.61e+05[/C][C] 1.726e+04[/C][C]+2.6700e+01[/C][C] 2.007e-91[/C][C] 1.004e-91[/C][/ROW]
[ROW][C]cpi[/C][C]-8116[/C][C] 368.5[/C][C]-2.2020e+01[/C][C] 2.671e-71[/C][C] 1.336e-71[/C][/ROW]
[ROW][C]M1[/C][C]+7538[/C][C] 6298[/C][C]+1.1970e+00[/C][C] 0.2321[/C][C] 0.116[/C][/ROW]
[ROW][C]M2[/C][C]-1.639e+04[/C][C] 6298[/C][C]-2.6020e+00[/C][C] 0.009614[/C][C] 0.004807[/C][/ROW]
[ROW][C]M3[/C][C]+2250[/C][C] 6300[/C][C]+3.5710e-01[/C][C] 0.7212[/C][C] 0.3606[/C][/ROW]
[ROW][C]M4[/C][C]+9045[/C][C] 6301[/C][C]+1.4350e+00[/C][C] 0.1519[/C][C] 0.07596[/C][/ROW]
[ROW][C]M5[/C][C]+1.565e+04[/C][C] 6301[/C][C]+2.4830e+00[/C][C] 0.01343[/C][C] 0.006716[/C][/ROW]
[ROW][C]M6[/C][C]+1.85e+04[/C][C] 6302[/C][C]+2.9360e+00[/C][C] 0.003518[/C][C] 0.001759[/C][/ROW]
[ROW][C]M7[/C][C]+2.049e+04[/C][C] 6300[/C][C]+3.2530e+00[/C][C] 0.001238[/C][C] 0.0006192[/C][/ROW]
[ROW][C]M8[/C][C]+2.507e+04[/C][C] 6300[/C][C]+3.9800e+00[/C][C] 8.174e-05[/C][C] 4.087e-05[/C][/ROW]
[ROW][C]M9[/C][C]+1.338e+04[/C][C] 6302[/C][C]+2.1240e+00[/C][C] 0.0343[/C][C] 0.01715[/C][/ROW]
[ROW][C]M10[/C][C]+1.683e+04[/C][C] 6301[/C][C]+2.6710e+00[/C][C] 0.007862[/C][C] 0.003931[/C][/ROW]
[ROW][C]M11[/C][C]+5014[/C][C] 6298[/C][C]+7.9610e-01[/C][C] 0.4264[/C][C] 0.2132[/C][/ROW]
[ROW][C]t[/C][C]+3648[/C][C] 143.9[/C][C]+2.5340e+01[/C][C] 1.195e-85[/C][C] 5.975e-86[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319036&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319036&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+4.61e+05 1.726e+04+2.6700e+01 2.007e-91 1.004e-91
cpi-8116 368.5-2.2020e+01 2.671e-71 1.336e-71
M1+7538 6298+1.1970e+00 0.2321 0.116
M2-1.639e+04 6298-2.6020e+00 0.009614 0.004807
M3+2250 6300+3.5710e-01 0.7212 0.3606
M4+9045 6301+1.4350e+00 0.1519 0.07596
M5+1.565e+04 6301+2.4830e+00 0.01343 0.006716
M6+1.85e+04 6302+2.9360e+00 0.003518 0.001759
M7+2.049e+04 6300+3.2530e+00 0.001238 0.0006192
M8+2.507e+04 6300+3.9800e+00 8.174e-05 4.087e-05
M9+1.338e+04 6302+2.1240e+00 0.0343 0.01715
M10+1.683e+04 6301+2.6710e+00 0.007862 0.003931
M11+5014 6298+7.9610e-01 0.4264 0.2132
t+3648 143.9+2.5340e+01 1.195e-85 5.975e-86







Multiple Linear Regression - Regression Statistics
Multiple R 0.9312
R-squared 0.8672
Adjusted R-squared 0.8629
F-TEST (value) 203.9
F-TEST (DF numerator)13
F-TEST (DF denominator)406
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 2.634e+04
Sum Squared Residuals 2.817e+11

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9312 \tabularnewline
R-squared &  0.8672 \tabularnewline
Adjusted R-squared &  0.8629 \tabularnewline
F-TEST (value) &  203.9 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 406 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.634e+04 \tabularnewline
Sum Squared Residuals &  2.817e+11 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319036&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9312[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.8672[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.8629[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 203.9[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]406[/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] 2.634e+04[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 2.817e+11[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319036&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319036&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 0.9312
R-squared 0.8672
Adjusted R-squared 0.8629
F-TEST (value) 203.9
F-TEST (DF numerator)13
F-TEST (DF denominator)406
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 2.634e+04
Sum Squared Residuals 2.817e+11







Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute

\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=319036&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=319036&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319036&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
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute







Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 14.391, df1 = 2, df2 = 404, p-value = 9.178e-07
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.091, df1 = 26, df2 = 380, p-value = 0.3482
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 3.1221, df1 = 2, df2 = 404, p-value = 0.04513

\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 = 14.391, df1 = 2, df2 = 404, p-value = 9.178e-07
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.091, df1 = 26, df2 = 380, p-value = 0.3482
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 3.1221, df1 = 2, df2 = 404, p-value = 0.04513
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=319036&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 = 14.391, df1 = 2, df2 = 404, p-value = 9.178e-07
[/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 = 1.091, df1 = 26, df2 = 380, p-value = 0.3482
[/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 = 3.1221, df1 = 2, df2 = 404, p-value = 0.04513
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319036&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319036&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 = 14.391, df1 = 2, df2 = 404, p-value = 9.178e-07
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.091, df1 = 26, df2 = 380, p-value = 0.3482
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 3.1221, df1 = 2, df2 = 404, p-value = 0.04513







Variance Inflation Factors (Multicollinearity)
> vif
       cpi         M1         M2         M3         M4         M5         M6 
184.313785   1.833981   1.834152   1.834923   1.835689   1.835834   1.836092 
        M7         M8         M9        M10        M11          t 
  1.835386   1.835392   1.836357   1.835805   1.834183 184.303778 

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
       cpi         M1         M2         M3         M4         M5         M6 
184.313785   1.833981   1.834152   1.834923   1.835689   1.835834   1.836092 
        M7         M8         M9        M10        M11          t 
  1.835386   1.835392   1.836357   1.835805   1.834183 184.303778 
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=319036&T=6

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
       cpi         M1         M2         M3         M4         M5         M6 
184.313785   1.833981   1.834152   1.834923   1.835689   1.835834   1.836092 
        M7         M8         M9        M10        M11          t 
  1.835386   1.835392   1.836357   1.835805   1.834183 184.303778 
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319036&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319036&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
       cpi         M1         M2         M3         M4         M5         M6 
184.313785   1.833981   1.834152   1.834923   1.835689   1.835834   1.836092 
        M7         M8         M9        M10        M11          t 
  1.835386   1.835392   1.836357   1.835805   1.834183 184.303778 



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;
Parameters (R input):
par1 = 1 ; par2 = Include Seasonal Dummies ; par3 = Linear Trend ; par4 = 0 ; par5 = 0 ; 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')