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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 computationWed, 29 Jan 2020 13:25:51 +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/29/t1580300762x0oarisp9brz24u.htm/, Retrieved Wed, 21 Apr 2021 06:58:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319062, Retrieved Wed, 21 Apr 2021 06:58:17 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact41
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [OIl case] [2020-01-29 12:25:51] [29047c3e33fc217d37990ea636800623] [Current]
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Dataseries X:
102750 2.75 42.6
95276 2.73 42.9
112053 2.82 43.3
98841 2.83 43.6
123102 2.9 43.9
118152 3.05 44.2
101752 3.15 44.3
148219 3.26 45.1
124966 3.38 45.2
134741 3.54 45.6
132168 3.81 45.9
100950 5.27 46.2
96418 6.71 46.6
86891 9.09 47.2
89796 11.08 47.8
119663 11.91 48
130539 11.81 48.6
120851 11.81 49
145422 12.09 49.4
150583 11.95 50
127054 11.67 50.6
137473 11.6 51.1
127094 11.71 51.5
132080 11.62 51.9
188311 11.64 52.1
107487 11.66 52.5
84669 11.67 52.7
149184 11.69 52.9
121026 11.58 53.2
81073 11.4 53.6
132947 11.44 54.2
141294 11.38 54.3
155077 11.31 54.6
145154 11.45 54.9
127094 11.73 55.3
151414 12.11 55.5
167858 12.23 55.6
127070 12.39 55.8
154692 12.34 55.9
170905 12.42 56.1
127751 12.37 56.5
173795 12.37 56.8
190181 12.39 57.1
198417 12.43 57.4
183018 12.48 57.6
171608 12.45 57.9
188087 12.58 58
197042 12.59 58.2
208788 12.54 58.5
178111 13.01 59.1
236455 13.31 59.5
233219 13.45 60
188106 13.28 60.3
238876 13.38 60.7
205148 13.36 61
214727 13.4 61.2
213428 13.49 61.4
195128 13.47 61.6
206047 13.62 61.9
201773 13.57 62.1
192772 13.59 62.5
198230 13.48 62.9
181172 13.47 63.4
189079 13.47 63.9
179073 13.36 64.5
197421 13.37 65.2
195244 13.4 65.7
219826 13.41 66
211793 13.37 66.5
203394 13.42 67.1
209578 13.41 67.4
214769 13.46 67.7
226177 13.64 68.3
191449 13.93 69.1
200989 14.46 69.8
216707 14.92 70.6
192882 16.27 71.5
199736 17.36 72.3
202349 19.07 73.1
204137 21.1 73.8
215588 22.39 74.6
229454 23.13 75.2
175048 23.27 75.9
212799 24.57 76.7
181727 26.32 77.8
211607 28.57 78.9
185853 30.44 80.1
158277 31.4 81
180695 31.84 81.8
175959 31.86 82.7
139550 32.3 82.7
155810 32.93 83.3
138305 32.73 84
147014 33.1 84.8
135994 33.23 85.5
166455 33.94 86.3
177737 34.27 87
167021 35.96 87.9
132134 36.25 88.5
169834 36.92 89.1
130599 36.16 89.8
156836 36.59 90.6
119749 35.05 91.6
148996 34.53 92.3
147491 34.07 93.2
147216 33.65 93.4
153455 33.84 93.7
112004 33.99 94
158512 35.41 94.3
104139 35.53 94.6
102536 34.71 94.5
93017 33.2 94.9
91988 32.25 95.8
123616 32.92 97
134498 33.27 97.5
149812 32.91 97.7
110334 32.39 97.9
136639 32.44 98.2
102712 32.84 98
112951 32.44 97.6
107897 32.5 97.8
73242 31.12 97.9
72800 30.28 97.9
78767 28.76 98.6
114791 28.59 99.2
109351 28.83 99.5
122520 28.93 99.9
137338 29.31 100.2
132061 29.27 100.7
130607 29.36 101
118570 29.05 101.2
95873 29 101.3
103116 27.65 101.9
98619 27.64 102.4
104178 27.8 102.6
123468 27.84 103.1
99651 27.85 103.4
120264 27.76 103.7
122795 28.05 104.1
108524 27.66 104.5
105760 27.39 105
117191 27.56 105.3
122882 27.55 105.3
93275 27.3 105.3
99842 27.38 105.5
83803 26.91 106
61132 26.05 106.4
118563 26.52 106.9
106993 26.79 107.3
118108 26.52 107.6
99017 25.91 107.8
99852 25.76 108
112720 25.42 108.3
113636 25.65 108.7
118220 25.69 109
128854 26.04 109.3
123898 25.8 109.6
100823 23.13 109.3
115107 18.1 108.8
90624 12.78 108.6
132001 12.24 108.9
157969 12.04 109.5
169333 11.03 109.5
144907 10.09 109.7
169346 11.08 110.2
144666 11.79 110.3
158829 12.23 110.4
127286 12.4 110.5
120578 13.86 111.2
129293 15.47 111.6
122371 15.87 112.1
115176 16.57 112.7
142168 16.92 113.1
153260 17.31 113.5
173906 17.77 113.8
178446 18.07 114.4
155962 17.49 115
168257 17.21 115.3
149456 17.12 115.4
136105 16.46 115.4
141507 22.4 115.7
152084 15.2 116
145138 14.24 116.5
146548 14.21 117.1
173098 14.69 117.5
165471 14.68 118
152271 14.02 118.5
163201 13.38 119
157823 13.08 119.8
166167 11.92 120.2
154253 11.52 120.3
170299 12.34 120.5
166388 13.91 121.1
141051 14.84 121.6
160254 15.54 122.3
164995 17.33 123.1
195971 17.97 123.8
182635 17.27 124.1
189829 16.93 124.4
209476 15.95 124.6
189848 16.14 125
183746 16.61 125.6
192682 17.08 125.9
169677 17.72 126.1
201823 18.85 127.4
172643 18.79 128
202931 17.75 128.7
175863 16.02 128.9
222061 14.61 129.2
199797 13.83 129.9
214638 13.92 130.4
200106 19.57 131.6
166077 25.63 132.7
160586 30.08 133.5
158330 29.51 133.8
141749 25.75 133.8
170795 22.98 134.6
153286 18.39 134.8
163426 16.75 135
172562 16.39 135.2
197474 16.57 135.6
189822 16.4 136
188511 16.15 136.2
207437 16.8 136.6
192128 17.14 137.2
175716 17.97 137.4
159108 18.06 137.8
175801 16.6 137.9
186723 14.87 138.1
154970 14.42 138.6
172446 14.48 139.3
185965 15.5 139.5
195525 16.74 139.7
193156 18.27 140.2
212705 18.2 140.5
201357 18.03 140.9
189971 17.86 141.3
216523 18.22 141.8
193233 17.63 142
191996 16.22 141.9
211974 15.5 142.6
175907 15.71 143.1
206109 16.49 143.6
220275 16.69 144
211342 16.71 144.2
222528 16.07 144.4
229523 14.96 144.4
204153 14.51 144.8
206735 14.37 145.1
223416 14.59 145.7
228292 13.72 145.8
203121 12.2 145.8
205957 11.64 146.2
176918 12.09 146.7
219839 11.76 147.2
217213 12.85 147.4
216618 14.05 147.5
248057 15.18 148
245642 16.09 148.4
242485 15.97 149
260423 15 149.4
221030 14.8 149.5
229157 15.31 149.7
220858 14.7 149.7
212270 15.06 150.3
195944 15.53 150.9
239741 15.78 151.4
212013 16.76 151.9
240514 17.4 152.2
241982 16.78 152.5
245447 15.51 152.5
240839 15.22 152.9
244875 15.44 153.2
226375 15.25 153.7
231567 15.1 153.6
235746 15.82 153.5
238990 16.43 154.4
198120 16.1 154.9
201663 17.31 155.7
238198 19.27 156.3
261641 18.9 156.6
253014 17.96 156.7
275225 18.16 157
250957 18.65 157.3
260375 19.97 157.8
250694 21.41 158.3
216953 21.38 158.6
247816 21.63 158.6
224135 21.86 159.1
211073 20.48 159.6
245623 18.76 160
250947 17.13 160.2
278223 17.06 160.1
254232 16.85 160.3
266293 16.41 160.5
280897 16.95 160.8
274565 16.73 161.2
280555 17.71 161.6
252757 17.25 161.5
250131 16.05 161.3
271208 14.31 161.6
230593 13.02 161.9
263407 11.88 162.2
289968 11.77 162.5
282846 11.8 162.8
271314 11.12 163
289718 10.78 163.2
300227 10.55 163.4
259951 10.99 163.6
263149 11.66 164
267953 10.79 164
252378 9.38 163.9
280356 9.21 164.3
234298 9.48 164.5
271574 10.5 165
262378 12.88 166.2
289457 14.6 166.2
278274 14.52 166.2
288932 16.11 166.7
283813 17.88 167.1
267600 19.69 167.9
267574 20.76 168.2
254862 21.05 168.3
248974 22.79 168.3
256840 23.31 168.8
250914 25.14 169.8
279334 26.41 171.2
286549 24.41 171.3
302266 24.28 171.5
298205 26.78 172.4
300843 27.73 172.8
312955 26.59 172.8
275962 29.03 173.7
299561 28.57 174
260975 28.34 174.1
274836 26.4 174
284112 23.19 175.1
247331 23.85 175.8
298120 22.75 176.2
306008 21.66 176.9
306813 22.65 177.7
288550 23.09 178
301636 22.33 177.5
293215 22.14 177.5
270713 23.02 178.3
311803 19.88 177.7
281316 17 177.4
281450 15.46 176.7
295494 16.29 177.1
246411 16.58 177.8
267037 19.27 178.8
296134 22.53 179.8
296505 23.75 179.8
270677 23.35 179.9
290855 23.73 180.1
296068 24.58 180.7
272653 25.49 181
315720 26.25 181.3
286298 24.19 181.3
284170 24.15 180.9
273338 27.76 181.7
250262 30.37 183.1
294768 30.39 184.2
318088 26.01 183.8
319111 24.05 183.5
312982 25.5 183.7
335511 26.75 183.9
319674 27.56 184.6
316796 26.43 185.2
329992 26.28 185
291352 26.54 184.5
314131 27.17 184.3
309876 28.57 185.2
288494 29.17 186.2
329991 30.66 187.4
311663 31 188
317854 33.14 189.1
344729 33.74 189.7
324108 33.38 189.4
333756 36.54 189.5
297013 37.52 189.9
313249 41.84 190.9
329660 41.19 191
320586 36.46 190.3
325786 35.27 190.7
293425 36.93 191.8
324180 41.28 193.3
315528 44.78 194.6
319982 43.04 194.4
327865 44.41 194.5
312106 49.07 195.4
329039 52.85 196.4
277589 57.42 198.8
300884 56.21 199.2
314028 52.16 197.6
314259 49.79 196.8
303472 51.8 198.3
290744 53.86 198.7
313340 52.32 199.8
294281 56.65 201.5
325796 62.04 202.5
329839 62.12 202.9
322588 64.93 203.5
336528 66.13 203.9
316381 62.4 202.9
308602 55.47 201.8
299010 52.22 201.5
293645 53.84 201.8
320108 52.23 202.4
252869 50.71 203.5
324248 53 205.4
304775 57.28 206.7
320208 59.36 207.9
321260 60.95 208.4
310320 65.56 208.3
319197 68.21 207.9
297503 68.51 208.5
316184 72.49 208.9
303411 79.65 210.2
300841 82.76 210


 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 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 time4 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319062&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=319062&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319062&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 4 seconds R Server Big Analytics Cloud Computing Center

 Multiple Linear Regression - Estimated Regression Equation barrels_purchased[t] = + 7323.02 -381.068unit_price[t] + 108.332cpi[t] + 0.528378barrels_purchased(t-1)[t] + 0.424374barrels_purchased(t-1s)[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
barrels_purchased[t] =  +  7323.02 -381.068unit_price[t] +  108.332cpi[t] +  0.528378barrels_purchased(t-1)[t] +  0.424374barrels_purchased(t-1s)[t]  + e[t] \tabularnewline
\tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319062&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]barrels_purchased[t] =  +  7323.02 -381.068unit_price[t] +  108.332cpi[t] +  0.528378barrels_purchased(t-1)[t] +  0.424374barrels_purchased(t-1s)[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319062&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319062&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] = + 7323.02 -381.068unit_price[t] + 108.332cpi[t] + 0.528378barrels_purchased(t-1)[t] + 0.424374barrels_purchased(t-1s)[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) +7323 3140 +2.3320e+00 0.0202 0.0101 unit_price -381.1 88.32 -4.3150e+00 2.014e-05 1.007e-05 cpi +108.3 36.9 +2.9360e+00 0.003519 0.00176 barrels_purchased(t-1) +0.5284 0.03731 +1.4160e+01 3.248e-37 1.624e-37 barrels_purchased(t-1s) +0.4244 0.03832 +1.1070e+01 4.679e-25 2.339e-25

\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) & +7323 &  3140 & +2.3320e+00 &  0.0202 &  0.0101 \tabularnewline
unit_price & -381.1 &  88.32 & -4.3150e+00 &  2.014e-05 &  1.007e-05 \tabularnewline
cpi & +108.3 &  36.9 & +2.9360e+00 &  0.003519 &  0.00176 \tabularnewline
barrels_purchased(t-1) & +0.5284 &  0.03731 & +1.4160e+01 &  3.248e-37 &  1.624e-37 \tabularnewline
barrels_purchased(t-1s) & +0.4244 &  0.03832 & +1.1070e+01 &  4.679e-25 &  2.339e-25 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319062&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]+7323[/C][C] 3140[/C][C]+2.3320e+00[/C][C] 0.0202[/C][C] 0.0101[/C][/ROW]
[ROW][C]unit_price[/C][C]-381.1[/C][C] 88.32[/C][C]-4.3150e+00[/C][C] 2.014e-05[/C][C] 1.007e-05[/C][/ROW]
[ROW][C]cpi[/C][C]+108.3[/C][C] 36.9[/C][C]+2.9360e+00[/C][C] 0.003519[/C][C] 0.00176[/C][/ROW]
[ROW][C]barrels_purchased(t-1)[/C][C]+0.5284[/C][C] 0.03731[/C][C]+1.4160e+01[/C][C] 3.248e-37[/C][C] 1.624e-37[/C][/ROW]
[ROW][C]barrels_purchased(t-1s)[/C][C]+0.4244[/C][C] 0.03832[/C][C]+1.1070e+01[/C][C] 4.679e-25[/C][C] 2.339e-25[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319062&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319062&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) +7323 3140 +2.3320e+00 0.0202 0.0101 unit_price -381.1 88.32 -4.3150e+00 2.014e-05 1.007e-05 cpi +108.3 36.9 +2.9360e+00 0.003519 0.00176 barrels_purchased(t-1) +0.5284 0.03731 +1.4160e+01 3.248e-37 1.624e-37 barrels_purchased(t-1s) +0.4244 0.03832 +1.1070e+01 4.679e-25 2.339e-25

 Multiple Linear Regression - Regression Statistics Multiple R 0.9619 R-squared 0.9252 Adjusted R-squared 0.9245 F-TEST (value) 1243 F-TEST (DF numerator) 4 F-TEST (DF denominator) 402 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.932e+04 Sum Squared Residuals 1.501e+11

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9619 \tabularnewline
R-squared &  0.9252 \tabularnewline
F-TEST (value) &  1243 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 402 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.932e+04 \tabularnewline
Sum Squared Residuals &  1.501e+11 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319062&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9619[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.9252[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1243[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]402[/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] 1.932e+04[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1.501e+11[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319062&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319062&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 0.9619 R-squared 0.9252 Adjusted R-squared 0.9245 F-TEST (value) 1243 F-TEST (DF numerator) 4 F-TEST (DF denominator) 402 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.932e+04 Sum Squared Residuals 1.501e+11

 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
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=319062&T=4

[TABLE]
[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=319062&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319062&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 = 1.7122, df1 = 2, df2 = 400, p-value = 0.1818  Ramsey RESET F-Test for powers (2 and 3) of regressors > reset_test_regressors RESET test data: mylm RESET = 7.8458, df1 = 8, df2 = 394, p-value = 8.46e-10  Ramsey RESET F-Test for powers (2 and 3) of principal components > reset_test_principal_components RESET test data: mylm RESET = 2.846, df1 = 2, df2 = 400, p-value = 0.05925 

\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 = 1.7122, df1 = 2, df2 = 400, p-value = 0.1818
\tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
RESET test
data:  mylm
RESET = 7.8458, df1 = 8, df2 = 394, p-value = 8.46e-10
\tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
RESET test
data:  mylm
RESET = 2.846, df1 = 2, df2 = 400, p-value = 0.05925
\tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319062&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 = 1.7122, df1 = 2, df2 = 400, p-value = 0.1818
[/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 = 7.8458, df1 = 8, df2 = 394, p-value = 8.46e-10
[/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 = 2.846, df1 = 2, df2 = 400, p-value = 0.05925
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319062&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319062&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 = 1.7122, df1 = 2, df2 = 400, p-value = 0.1818  Ramsey RESET F-Test for powers (2 and 3) of regressors > reset_test_regressors RESET test data: mylm RESET = 7.8458, df1 = 8, df2 = 394, p-value = 8.46e-10  Ramsey RESET F-Test for powers (2 and 3) of principal components > reset_test_principal_components RESET test data: mylm RESET = 2.846, df1 = 2, df2 = 400, p-value = 0.05925 

 Variance Inflation Factors (Multicollinearity) > vif unit_price cpi barrels_purchased(t-1) 1.426005 3.101454 7.498729 barrels_purchased(t-1s) 7.785503 

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
unit_price                       cpi  barrels_purchased(t-1)
1.426005                  3.101454                  7.498729
barrels_purchased(t-1s)
7.785503
\tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319062&T=6

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
unit_price                       cpi  barrels_purchased(t-1)
1.426005                  3.101454                  7.498729
barrels_purchased(t-1s)
7.785503
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319062&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319062&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 unit_price cpi barrels_purchased(t-1) 1.426005 3.101454 7.498729 barrels_purchased(t-1s) 7.785503 

library(lattice)library(lmtest)library(car)library(MASS)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt testmywarning <- ''par6 <- as.numeric(par6)if(is.na(par6)) {par6 <- 12mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'}par1 <- as.numeric(par1)if(is.na(par1)) {par1 <- 1mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'}if (par4=='') par4 <- 0par4 <- as.numeric(par4)if (!is.numeric(par4)) par4 <- 0if (par5=='') par5 <- 0par5 <- as.numeric(par5)if (!is.numeric(par5)) par5 <- 0x <- 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 <- x1if (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 + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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$resid))bitmap(file='test5.png')dum <- cbind(lag(myerror,k=1),myerror)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)print(z)plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')lines(lowess(z))abline(lm(z))grid()dev.off()bitmap(file='test6.png')acf(mysum$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-STATH0: 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$resid)myra <-table.start()a <- table.row.start(a)a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)a <- table.row.end(a)a <- table.row.start(a)a <- table.element(a,'Description',1,TRUE)a <- table.element(a,'Link',1,TRUE)a <- table.row.end(a)a <- table.row.start(a)a <-table.element(a,'Histogram',1,header=TRUE)a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$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$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)a <- table.row.end(a)a<-table.end(a)table.save(a,file='mytable7.tab')if(n < 200) {a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))a<-table.element(a,formatC(signif(x[i]-mysum$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')