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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 03 Dec 2015 23:05:23 +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/Dec/03/t1449184033adio6hhi30c69y8.htm/, Retrieved Thu, 16 May 2024 15:11:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=285040, Retrieved Thu, 16 May 2024 15:11:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact93

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2015-12-03 16:40:34] [2e6b1bdc398efa0639617f5108875d85]
-   P     [Multiple Regression] [] [2015-12-03 23:05:23] [417cd1fa2ccbc3df120e1b65b71e6aee] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
221	191
219	189
214	184
210	179
207	175
206	171
217	179
231	191
234	195
233	195
228	193
226	193
227	195
225	193
219	187
215	181
210	176
206	169
215	174
228	185
229	186
222	182
215	178
212	178
211	179
208	178
205	174
201	171
198	168
198	167
210	175
224	187
226	191
222	188
216	185
215	185
215	187
214	188
211	186
207	183
203	179
200	176
209	183
223	198
225	203
216	198
206	192
203	191
203	194
201	194
197	192
192	188
187	182
184	175
194	178
203	181
197	171
191	164
182	159
175	160
163	163
155	159
151	148
156	139
154	129
153	124
167	136
177	146
171	143
169	141
160	135
151	134
139	135
130	134
126	136
130	142
127	142
122	135
129	140
135	146
142	155
156	170
157	167
165	166
170	160
169	156
162	156
148	160
143	156
146	150
175	157
181	158
178	167
166	189
161	197
164	199
173	193
174	188
167	186
156	190
148	186
150	181
174	190
181	189
183	192
178	201
176	200
184	206
193	208
192	202
182	190
163	171
157	163
167	167
205	195
219	208
214	208
198	197
183	189
184	192
192	199
196	202
194	200
185	191
181	190
184	180
206	194
210	196
208	199
197	200
189	199
190	205
191	207
190	211
187	210
184	208
183	201
184	186
203	177
208	168
205	173
195	181
189	185
188	186
190	189
190	186
190	181
193	182
185	176
173	165
176	176
170	174
163	168
170	165
171	162
173	170
171	179
162	178
152	169
142	160
136	151
146	159
179	191
191	195
181	184
170	162
161	152
168	162
180	188
182	202
176	209
164	204
154	193
160	191
189	202
196	204
186	206
171	211
169	214
181	224
198	224
202	222
196	219
183	218
173	213
175	213
198	229
203	225
197	220
191	212
182	204
172	204
158	202
147	195
143	186
146	175
147	170
152	171
177	196
184	202
174	200
162	191
157	186
155	186
159	193
158	193
156	188
157	185
156	182
158	180
173	194
179	204
172	216
169	233
168	241
172	243
180	241
182	233
182	228
181	225
178	219
178	217
196	235
199	237
192	238
187	235
184	234
184	239
188	248
183	248
176	247
168	246
163	240
166	233
189	242
195	239
192	238
189	238
187	238
187	240
190	249
187	251
179	253
168	251
160	246
161	247
177	260
182	260
176	259

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\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 & 6 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285040&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285040&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285040&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
A[t] = + 3.88815 + 0.0106394B[t] + 1.7005`A(t-1)`[t] -0.913115`A(t-2)`[t] -0.276041`A(t-3)`[t] + 0.684715`A(t-4)`[t] -0.245646`A(t-5)`[t] + 0.0637941`A(t-6)`[t] -0.128177`A(t-7)`[t] + 0.119146`A(t-8)`[t] + 0.0712307`A(t-9)`[t] -0.318409`A(t-10)`[t] + 0.247543`A(t-11)`[t] + 0.280746`A(t-12)`[t] -0.787193`A(t-13)`[t] + 0.59172`A(t-14)`[t] + 0.042365`A(t-15)`[t] -0.309746`A(t-16)`[t] + 0.143683`A(t-17)`[t] + 0.0522931`A(t-18)`[t] -0.00821841`A(t-19)`[t] -0.0529618`A(t-20)`[t] -1.35549M1[t] + 5.2455M2[t] + 0.916087M3[t] + 1.47757M4[t] -0.766477M5[t] -2.17444M6[t] -1.31168M7[t] -2.23324M8[t] + 2.31498M9[t] + 3.28001M10[t] + 12.0418M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
A[t] =  +  3.88815 +  0.0106394B[t] +  1.7005`A(t-1)`[t] -0.913115`A(t-2)`[t] -0.276041`A(t-3)`[t] +  0.684715`A(t-4)`[t] -0.245646`A(t-5)`[t] +  0.0637941`A(t-6)`[t] -0.128177`A(t-7)`[t] +  0.119146`A(t-8)`[t] +  0.0712307`A(t-9)`[t] -0.318409`A(t-10)`[t] +  0.247543`A(t-11)`[t] +  0.280746`A(t-12)`[t] -0.787193`A(t-13)`[t] +  0.59172`A(t-14)`[t] +  0.042365`A(t-15)`[t] -0.309746`A(t-16)`[t] +  0.143683`A(t-17)`[t] +  0.0522931`A(t-18)`[t] -0.00821841`A(t-19)`[t] -0.0529618`A(t-20)`[t] -1.35549M1[t] +  5.2455M2[t] +  0.916087M3[t] +  1.47757M4[t] -0.766477M5[t] -2.17444M6[t] -1.31168M7[t] -2.23324M8[t] +  2.31498M9[t] +  3.28001M10[t] +  12.0418M11[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285040&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]A[t] =  +  3.88815 +  0.0106394B[t] +  1.7005`A(t-1)`[t] -0.913115`A(t-2)`[t] -0.276041`A(t-3)`[t] +  0.684715`A(t-4)`[t] -0.245646`A(t-5)`[t] +  0.0637941`A(t-6)`[t] -0.128177`A(t-7)`[t] +  0.119146`A(t-8)`[t] +  0.0712307`A(t-9)`[t] -0.318409`A(t-10)`[t] +  0.247543`A(t-11)`[t] +  0.280746`A(t-12)`[t] -0.787193`A(t-13)`[t] +  0.59172`A(t-14)`[t] +  0.042365`A(t-15)`[t] -0.309746`A(t-16)`[t] +  0.143683`A(t-17)`[t] +  0.0522931`A(t-18)`[t] -0.00821841`A(t-19)`[t] -0.0529618`A(t-20)`[t] -1.35549M1[t] +  5.2455M2[t] +  0.916087M3[t] +  1.47757M4[t] -0.766477M5[t] -2.17444M6[t] -1.31168M7[t] -2.23324M8[t] +  2.31498M9[t] +  3.28001M10[t] +  12.0418M11[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285040&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285040&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
A[t] = + 3.88815 + 0.0106394B[t] + 1.7005`A(t-1)`[t] -0.913115`A(t-2)`[t] -0.276041`A(t-3)`[t] + 0.684715`A(t-4)`[t] -0.245646`A(t-5)`[t] + 0.0637941`A(t-6)`[t] -0.128177`A(t-7)`[t] + 0.119146`A(t-8)`[t] + 0.0712307`A(t-9)`[t] -0.318409`A(t-10)`[t] + 0.247543`A(t-11)`[t] + 0.280746`A(t-12)`[t] -0.787193`A(t-13)`[t] + 0.59172`A(t-14)`[t] + 0.042365`A(t-15)`[t] -0.309746`A(t-16)`[t] + 0.143683`A(t-17)`[t] + 0.0522931`A(t-18)`[t] -0.00821841`A(t-19)`[t] -0.0529618`A(t-20)`[t] -1.35549M1[t] + 5.2455M2[t] + 0.916087M3[t] + 1.47757M4[t] -0.766477M5[t] -2.17444M6[t] -1.31168M7[t] -2.23324M8[t] + 2.31498M9[t] + 3.28001M10[t] + 12.0418M11[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+3.888 3.727+1.0430e+00 0.2981 0.149
B+0.01064 0.01115+9.5470e-01 0.3409 0.1705
`A(t-1)`+1.7 0.07097+2.3960e+01 3.766e-60 1.883e-60
`A(t-2)`-0.9131 0.1394-6.5500e+00 4.938e-10 2.469e-10
`A(t-3)`-0.276 0.154-1.7920e+00 0.07469 0.03735
`A(t-4)`+0.6847 0.1555+4.4040e+00 1.75e-05 8.748e-06
`A(t-5)`-0.2457 0.1612-1.5240e+00 0.1291 0.06455
`A(t-6)`+0.06379 0.1622+3.9330e-01 0.6945 0.3472
`A(t-7)`-0.1282 0.1557-8.2340e-01 0.4113 0.2056
`A(t-8)`+0.1192 0.1423+8.3710e-01 0.4035 0.2018
`A(t-9)`+0.07123 0.1401+5.0850e-01 0.6117 0.3058
`A(t-10)`-0.3184 0.1391-2.2890e+00 0.02317 0.01158
`A(t-11)`+0.2475 0.1393+1.7770e+00 0.07705 0.03852
`A(t-12)`+0.2807 0.1404+2.0000e+00 0.04689 0.02344
`A(t-13)`-0.7872 0.1416-5.5610e+00 8.721e-08 4.361e-08
`A(t-14)`+0.5917 0.1514+3.9080e+00 0.0001279 6.395e-05
`A(t-15)`+0.04236 0.1578+2.6860e-01 0.7885 0.3943
`A(t-16)`-0.3098 0.1576-1.9650e+00 0.05079 0.02539
`A(t-17)`+0.1437 0.1517+9.4700e-01 0.3448 0.1724
`A(t-18)`+0.05229 0.1509+3.4670e-01 0.7292 0.3646
`A(t-19)`-0.008218 0.1372-5.9900e-02 0.9523 0.4761
`A(t-20)`-0.05296 0.07018-7.5470e-01 0.4514 0.2257
M1-1.355 2.347-5.7760e-01 0.5642 0.2821
M2+5.245 2.563+2.0470e+00 0.04204 0.02102
M3+0.9161 2.312+3.9630e-01 0.6923 0.3462
M4+1.478 2.466+5.9930e-01 0.5497 0.2748
M5-0.7665 2.38-3.2200e-01 0.7478 0.3739
M6-2.174 2.467-8.8130e-01 0.3792 0.1896
M7-1.312 2.414-5.4330e-01 0.5875 0.2938
M8-2.233 2.411-9.2640e-01 0.3554 0.1777
M9+2.315 2.277+1.0170e+00 0.3106 0.1553
M10+3.28 2.449+1.3400e+00 0.1819 0.09097
M11+12.04 2.3+5.2350e+00 4.222e-07 2.111e-07

\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) & +3.888 &  3.727 & +1.0430e+00 &  0.2981 &  0.149 \tabularnewline
B & +0.01064 &  0.01115 & +9.5470e-01 &  0.3409 &  0.1705 \tabularnewline
`A(t-1)` & +1.7 &  0.07097 & +2.3960e+01 &  3.766e-60 &  1.883e-60 \tabularnewline
`A(t-2)` & -0.9131 &  0.1394 & -6.5500e+00 &  4.938e-10 &  2.469e-10 \tabularnewline
`A(t-3)` & -0.276 &  0.154 & -1.7920e+00 &  0.07469 &  0.03735 \tabularnewline
`A(t-4)` & +0.6847 &  0.1555 & +4.4040e+00 &  1.75e-05 &  8.748e-06 \tabularnewline
`A(t-5)` & -0.2457 &  0.1612 & -1.5240e+00 &  0.1291 &  0.06455 \tabularnewline
`A(t-6)` & +0.06379 &  0.1622 & +3.9330e-01 &  0.6945 &  0.3472 \tabularnewline
`A(t-7)` & -0.1282 &  0.1557 & -8.2340e-01 &  0.4113 &  0.2056 \tabularnewline
`A(t-8)` & +0.1192 &  0.1423 & +8.3710e-01 &  0.4035 &  0.2018 \tabularnewline
`A(t-9)` & +0.07123 &  0.1401 & +5.0850e-01 &  0.6117 &  0.3058 \tabularnewline
`A(t-10)` & -0.3184 &  0.1391 & -2.2890e+00 &  0.02317 &  0.01158 \tabularnewline
`A(t-11)` & +0.2475 &  0.1393 & +1.7770e+00 &  0.07705 &  0.03852 \tabularnewline
`A(t-12)` & +0.2807 &  0.1404 & +2.0000e+00 &  0.04689 &  0.02344 \tabularnewline
`A(t-13)` & -0.7872 &  0.1416 & -5.5610e+00 &  8.721e-08 &  4.361e-08 \tabularnewline
`A(t-14)` & +0.5917 &  0.1514 & +3.9080e+00 &  0.0001279 &  6.395e-05 \tabularnewline
`A(t-15)` & +0.04236 &  0.1578 & +2.6860e-01 &  0.7885 &  0.3943 \tabularnewline
`A(t-16)` & -0.3098 &  0.1576 & -1.9650e+00 &  0.05079 &  0.02539 \tabularnewline
`A(t-17)` & +0.1437 &  0.1517 & +9.4700e-01 &  0.3448 &  0.1724 \tabularnewline
`A(t-18)` & +0.05229 &  0.1509 & +3.4670e-01 &  0.7292 &  0.3646 \tabularnewline
`A(t-19)` & -0.008218 &  0.1372 & -5.9900e-02 &  0.9523 &  0.4761 \tabularnewline
`A(t-20)` & -0.05296 &  0.07018 & -7.5470e-01 &  0.4514 &  0.2257 \tabularnewline
M1 & -1.355 &  2.347 & -5.7760e-01 &  0.5642 &  0.2821 \tabularnewline
M2 & +5.245 &  2.563 & +2.0470e+00 &  0.04204 &  0.02102 \tabularnewline
M3 & +0.9161 &  2.312 & +3.9630e-01 &  0.6923 &  0.3462 \tabularnewline
M4 & +1.478 &  2.466 & +5.9930e-01 &  0.5497 &  0.2748 \tabularnewline
M5 & -0.7665 &  2.38 & -3.2200e-01 &  0.7478 &  0.3739 \tabularnewline
M6 & -2.174 &  2.467 & -8.8130e-01 &  0.3792 &  0.1896 \tabularnewline
M7 & -1.312 &  2.414 & -5.4330e-01 &  0.5875 &  0.2938 \tabularnewline
M8 & -2.233 &  2.411 & -9.2640e-01 &  0.3554 &  0.1777 \tabularnewline
M9 & +2.315 &  2.277 & +1.0170e+00 &  0.3106 &  0.1553 \tabularnewline
M10 & +3.28 &  2.449 & +1.3400e+00 &  0.1819 &  0.09097 \tabularnewline
M11 & +12.04 &  2.3 & +5.2350e+00 &  4.222e-07 &  2.111e-07 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285040&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]+3.888[/C][C] 3.727[/C][C]+1.0430e+00[/C][C] 0.2981[/C][C] 0.149[/C][/ROW]
[ROW][C]B[/C][C]+0.01064[/C][C] 0.01115[/C][C]+9.5470e-01[/C][C] 0.3409[/C][C] 0.1705[/C][/ROW]
[ROW][C]`A(t-1)`[/C][C]+1.7[/C][C] 0.07097[/C][C]+2.3960e+01[/C][C] 3.766e-60[/C][C] 1.883e-60[/C][/ROW]
[ROW][C]`A(t-2)`[/C][C]-0.9131[/C][C] 0.1394[/C][C]-6.5500e+00[/C][C] 4.938e-10[/C][C] 2.469e-10[/C][/ROW]
[ROW][C]`A(t-3)`[/C][C]-0.276[/C][C] 0.154[/C][C]-1.7920e+00[/C][C] 0.07469[/C][C] 0.03735[/C][/ROW]
[ROW][C]`A(t-4)`[/C][C]+0.6847[/C][C] 0.1555[/C][C]+4.4040e+00[/C][C] 1.75e-05[/C][C] 8.748e-06[/C][/ROW]
[ROW][C]`A(t-5)`[/C][C]-0.2457[/C][C] 0.1612[/C][C]-1.5240e+00[/C][C] 0.1291[/C][C] 0.06455[/C][/ROW]
[ROW][C]`A(t-6)`[/C][C]+0.06379[/C][C] 0.1622[/C][C]+3.9330e-01[/C][C] 0.6945[/C][C] 0.3472[/C][/ROW]
[ROW][C]`A(t-7)`[/C][C]-0.1282[/C][C] 0.1557[/C][C]-8.2340e-01[/C][C] 0.4113[/C][C] 0.2056[/C][/ROW]
[ROW][C]`A(t-8)`[/C][C]+0.1192[/C][C] 0.1423[/C][C]+8.3710e-01[/C][C] 0.4035[/C][C] 0.2018[/C][/ROW]
[ROW][C]`A(t-9)`[/C][C]+0.07123[/C][C] 0.1401[/C][C]+5.0850e-01[/C][C] 0.6117[/C][C] 0.3058[/C][/ROW]
[ROW][C]`A(t-10)`[/C][C]-0.3184[/C][C] 0.1391[/C][C]-2.2890e+00[/C][C] 0.02317[/C][C] 0.01158[/C][/ROW]
[ROW][C]`A(t-11)`[/C][C]+0.2475[/C][C] 0.1393[/C][C]+1.7770e+00[/C][C] 0.07705[/C][C] 0.03852[/C][/ROW]
[ROW][C]`A(t-12)`[/C][C]+0.2807[/C][C] 0.1404[/C][C]+2.0000e+00[/C][C] 0.04689[/C][C] 0.02344[/C][/ROW]
[ROW][C]`A(t-13)`[/C][C]-0.7872[/C][C] 0.1416[/C][C]-5.5610e+00[/C][C] 8.721e-08[/C][C] 4.361e-08[/C][/ROW]
[ROW][C]`A(t-14)`[/C][C]+0.5917[/C][C] 0.1514[/C][C]+3.9080e+00[/C][C] 0.0001279[/C][C] 6.395e-05[/C][/ROW]
[ROW][C]`A(t-15)`[/C][C]+0.04236[/C][C] 0.1578[/C][C]+2.6860e-01[/C][C] 0.7885[/C][C] 0.3943[/C][/ROW]
[ROW][C]`A(t-16)`[/C][C]-0.3098[/C][C] 0.1576[/C][C]-1.9650e+00[/C][C] 0.05079[/C][C] 0.02539[/C][/ROW]
[ROW][C]`A(t-17)`[/C][C]+0.1437[/C][C] 0.1517[/C][C]+9.4700e-01[/C][C] 0.3448[/C][C] 0.1724[/C][/ROW]
[ROW][C]`A(t-18)`[/C][C]+0.05229[/C][C] 0.1509[/C][C]+3.4670e-01[/C][C] 0.7292[/C][C] 0.3646[/C][/ROW]
[ROW][C]`A(t-19)`[/C][C]-0.008218[/C][C] 0.1372[/C][C]-5.9900e-02[/C][C] 0.9523[/C][C] 0.4761[/C][/ROW]
[ROW][C]`A(t-20)`[/C][C]-0.05296[/C][C] 0.07018[/C][C]-7.5470e-01[/C][C] 0.4514[/C][C] 0.2257[/C][/ROW]
[ROW][C]M1[/C][C]-1.355[/C][C] 2.347[/C][C]-5.7760e-01[/C][C] 0.5642[/C][C] 0.2821[/C][/ROW]
[ROW][C]M2[/C][C]+5.245[/C][C] 2.563[/C][C]+2.0470e+00[/C][C] 0.04204[/C][C] 0.02102[/C][/ROW]
[ROW][C]M3[/C][C]+0.9161[/C][C] 2.312[/C][C]+3.9630e-01[/C][C] 0.6923[/C][C] 0.3462[/C][/ROW]
[ROW][C]M4[/C][C]+1.478[/C][C] 2.466[/C][C]+5.9930e-01[/C][C] 0.5497[/C][C] 0.2748[/C][/ROW]
[ROW][C]M5[/C][C]-0.7665[/C][C] 2.38[/C][C]-3.2200e-01[/C][C] 0.7478[/C][C] 0.3739[/C][/ROW]
[ROW][C]M6[/C][C]-2.174[/C][C] 2.467[/C][C]-8.8130e-01[/C][C] 0.3792[/C][C] 0.1896[/C][/ROW]
[ROW][C]M7[/C][C]-1.312[/C][C] 2.414[/C][C]-5.4330e-01[/C][C] 0.5875[/C][C] 0.2938[/C][/ROW]
[ROW][C]M8[/C][C]-2.233[/C][C] 2.411[/C][C]-9.2640e-01[/C][C] 0.3554[/C][C] 0.1777[/C][/ROW]
[ROW][C]M9[/C][C]+2.315[/C][C] 2.277[/C][C]+1.0170e+00[/C][C] 0.3106[/C][C] 0.1553[/C][/ROW]
[ROW][C]M10[/C][C]+3.28[/C][C] 2.449[/C][C]+1.3400e+00[/C][C] 0.1819[/C][C] 0.09097[/C][/ROW]
[ROW][C]M11[/C][C]+12.04[/C][C] 2.3[/C][C]+5.2350e+00[/C][C] 4.222e-07[/C][C] 2.111e-07[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285040&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285040&T=2

As an alternative you can also use a QR Code:  
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)+3.888 3.727+1.0430e+00 0.2981 0.149
B+0.01064 0.01115+9.5470e-01 0.3409 0.1705
`A(t-1)`+1.7 0.07097+2.3960e+01 3.766e-60 1.883e-60
`A(t-2)`-0.9131 0.1394-6.5500e+00 4.938e-10 2.469e-10
`A(t-3)`-0.276 0.154-1.7920e+00 0.07469 0.03735
`A(t-4)`+0.6847 0.1555+4.4040e+00 1.75e-05 8.748e-06
`A(t-5)`-0.2457 0.1612-1.5240e+00 0.1291 0.06455
`A(t-6)`+0.06379 0.1622+3.9330e-01 0.6945 0.3472
`A(t-7)`-0.1282 0.1557-8.2340e-01 0.4113 0.2056
`A(t-8)`+0.1192 0.1423+8.3710e-01 0.4035 0.2018
`A(t-9)`+0.07123 0.1401+5.0850e-01 0.6117 0.3058
`A(t-10)`-0.3184 0.1391-2.2890e+00 0.02317 0.01158
`A(t-11)`+0.2475 0.1393+1.7770e+00 0.07705 0.03852
`A(t-12)`+0.2807 0.1404+2.0000e+00 0.04689 0.02344
`A(t-13)`-0.7872 0.1416-5.5610e+00 8.721e-08 4.361e-08
`A(t-14)`+0.5917 0.1514+3.9080e+00 0.0001279 6.395e-05
`A(t-15)`+0.04236 0.1578+2.6860e-01 0.7885 0.3943
`A(t-16)`-0.3098 0.1576-1.9650e+00 0.05079 0.02539
`A(t-17)`+0.1437 0.1517+9.4700e-01 0.3448 0.1724
`A(t-18)`+0.05229 0.1509+3.4670e-01 0.7292 0.3646
`A(t-19)`-0.008218 0.1372-5.9900e-02 0.9523 0.4761
`A(t-20)`-0.05296 0.07018-7.5470e-01 0.4514 0.2257
M1-1.355 2.347-5.7760e-01 0.5642 0.2821
M2+5.245 2.563+2.0470e+00 0.04204 0.02102
M3+0.9161 2.312+3.9630e-01 0.6923 0.3462
M4+1.478 2.466+5.9930e-01 0.5497 0.2748
M5-0.7665 2.38-3.2200e-01 0.7478 0.3739
M6-2.174 2.467-8.8130e-01 0.3792 0.1896
M7-1.312 2.414-5.4330e-01 0.5875 0.2938
M8-2.233 2.411-9.2640e-01 0.3554 0.1777
M9+2.315 2.277+1.0170e+00 0.3106 0.1553
M10+3.28 2.449+1.3400e+00 0.1819 0.09097
M11+12.04 2.3+5.2350e+00 4.222e-07 2.111e-07






Multiple Linear Regression - Regression Statistics
Multiple R 0.9864
R-squared 0.9729
Adjusted R-squared 0.9685
F-TEST (value) 220.3
F-TEST (DF numerator)32
F-TEST (DF denominator)196
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 3.854
Sum Squared Residuals 2911

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9864 \tabularnewline
R-squared &  0.9729 \tabularnewline
Adjusted R-squared &  0.9685 \tabularnewline
F-TEST (value) &  220.3 \tabularnewline
F-TEST (DF numerator) & 32 \tabularnewline
F-TEST (DF denominator) & 196 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  3.854 \tabularnewline
Sum Squared Residuals &  2911 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285040&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9864[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.9729[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.9685[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 220.3[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]32[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]196[/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] 3.854[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 2911[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285040&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285040&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R 0.9864
R-squared 0.9729
Adjusted R-squared 0.9685
F-TEST (value) 220.3
F-TEST (DF numerator)32
F-TEST (DF denominator)196
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 3.854
Sum Squared Residuals 2911


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 20 ; par5 = 0 ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ; par4 = 20 ; par5 = 0 ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
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 (par5=='') par5 <- 0
par5 <- as.numeric(par5)
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'){
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(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 (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+par4,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1+par4,])
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,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$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$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')
}
}