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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 15 Dec 2015 10:29:59 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Dec/15/t1450175427tw52d5g2ju0raoz.htm/, Retrieved Sat, 18 May 2024 16:17:31 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=286452, Retrieved Sat, 18 May 2024 16:17:31 +0000

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Estimated Impact

118

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

-       [Multiple Regression] [Multiple regressi...] [2015-12-15 10:29:59] [07325d4e03e5d5deea478d79524d9715] [Current]

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0.5215052 1
0.4248284 1
0.4250311 1
0.4771938 1
0.8280212 1
0.6156186 1
0.366627 0
0.4308883 0
0.2810287 0
0.4646245 0
0.2693951 0
0.5779049 0
0.5661151 0
0.5077584 0
0.7507175 0
0.6808395 0
0.7661091 0
0.4561473 0
0.4977496 0
0.4193273 0
0.6095514 0
0.457337 0
0.5705478 0
0.3478996 0
0.3874993 1
0.5824285 1
0.2391033 1
0.2367445 1
0.2626158 1
0.4240934 1
0.365275 1
0.3750758 0
0.4090056 0
0.3891676 0
0.240261 0
0.1589496 1
0.4393373 1
0.5094681 1
0.3743465 1
0.4339828 1
0.4130557 1
0.3288928 1
0.5186648 1
0.5486504 1
0.5469111 1
0.4963494 1
0.5308929 0
0.5957761 0
0.5570584 1
0.5731325 1
0.5005416 1
0.5431269 1
0.5593657 0
0.6911693 0
0.4403485 0
0.5676662 0
0.5969114 0
0.4735537 0
0.5923935 0
0.5975556 0
0.6334127 0
0.6057115 0
0.7046107 0
0.4805263 0
0.702686 0
0.7009017 0
0.6030854 0
0.6980919 0
0.597656 0
0.8023421 0
0.6017109 0
0.5993127 0
0.6025625 0
0.7016625 0
0.4995714 0
0.4980918 1
0.497569 1
0.600183 0
0.3339542 0
0.274437 0
0.3209428 0
0.5406671 0
0.4050209 0
0.2885961 0
0.3275942 0
0.3132606 0
0.2575562 0
0.2138386 0
0.1861856 1
0.1592713 1

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	'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 & 4 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286452&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]4 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=286452&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286452&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	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

Multiple Linear Regression - Estimated Regression Equation
(1-B12)(1-B)Homicide[t] = -0.0197106 -0.0212351`(1-B12)(1-B)War`[t] -0.633904`(1-B12)(1-B)Homicide(t-1)`[t] -0.452629`(1-B12)(1-B)Homicide(t-2)`[t] -0.219614`(1-B12)(1-B)Homicide(t-3)`[t] -0.0499623`(1-B12)(1-B)Homicide(t-4)`[t] -0.415628`(1-B12)(1-B)Homicide(t-1s)`[t] -0.115983`(1-B12)(1-B)Homicide(t-2s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
(1-B12)(1-B)Homicide[t] =  -0.0197106 -0.0212351`(1-B12)(1-B)War`[t] -0.633904`(1-B12)(1-B)Homicide(t-1)`[t] -0.452629`(1-B12)(1-B)Homicide(t-2)`[t] -0.219614`(1-B12)(1-B)Homicide(t-3)`[t] -0.0499623`(1-B12)(1-B)Homicide(t-4)`[t] -0.415628`(1-B12)(1-B)Homicide(t-1s)`[t] -0.115983`(1-B12)(1-B)Homicide(t-2s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286452&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C](1-B12)(1-B)Homicide[t] =  -0.0197106 -0.0212351`(1-B12)(1-B)War`[t] -0.633904`(1-B12)(1-B)Homicide(t-1)`[t] -0.452629`(1-B12)(1-B)Homicide(t-2)`[t] -0.219614`(1-B12)(1-B)Homicide(t-3)`[t] -0.0499623`(1-B12)(1-B)Homicide(t-4)`[t] -0.415628`(1-B12)(1-B)Homicide(t-1s)`[t] -0.115983`(1-B12)(1-B)Homicide(t-2s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286452&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286452&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
(1-B12)(1-B)Homicide[t] = -0.0197106 -0.0212351`(1-B12)(1-B)War`[t] -0.633904`(1-B12)(1-B)Homicide(t-1)`[t] -0.452629`(1-B12)(1-B)Homicide(t-2)`[t] -0.219614`(1-B12)(1-B)Homicide(t-3)`[t] -0.0499623`(1-B12)(1-B)Homicide(t-4)`[t] -0.415628`(1-B12)(1-B)Homicide(t-1s)`[t] -0.115983`(1-B12)(1-B)Homicide(t-2s)`[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)	-0.01971	0.01765	-1.1170e+00	0.2706	0.1353
`(1-B12)(1-B)War`	-0.02124	0.03394	-6.2570e-01	0.535	0.2675
`(1-B12)(1-B)Homicide(t-1)`	-0.6339	0.1506	-4.2090e+00	0.0001364	6.819e-05
`(1-B12)(1-B)Homicide(t-2)`	-0.4526	0.1812	-2.4990e+00	0.01657	0.008286
`(1-B12)(1-B)Homicide(t-3)`	-0.2196	0.1684	-1.3040e+00	0.1995	0.09974
`(1-B12)(1-B)Homicide(t-4)`	-0.04996	0.1374	-3.6350e-01	0.7181	0.359
`(1-B12)(1-B)Homicide(t-1s)`	-0.4156	0.1284	-3.2360e+00	0.002399	0.001199
`(1-B12)(1-B)Homicide(t-2s)`	-0.116	0.1103	-1.0520e+00	0.299	0.1495

\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) & -0.01971 &  0.01765 & -1.1170e+00 &  0.2706 &  0.1353 \tabularnewline
`(1-B12)(1-B)War` & -0.02124 &  0.03394 & -6.2570e-01 &  0.535 &  0.2675 \tabularnewline
`(1-B12)(1-B)Homicide(t-1)` & -0.6339 &  0.1506 & -4.2090e+00 &  0.0001364 &  6.819e-05 \tabularnewline
`(1-B12)(1-B)Homicide(t-2)` & -0.4526 &  0.1812 & -2.4990e+00 &  0.01657 &  0.008286 \tabularnewline
`(1-B12)(1-B)Homicide(t-3)` & -0.2196 &  0.1684 & -1.3040e+00 &  0.1995 &  0.09974 \tabularnewline
`(1-B12)(1-B)Homicide(t-4)` & -0.04996 &  0.1374 & -3.6350e-01 &  0.7181 &  0.359 \tabularnewline
`(1-B12)(1-B)Homicide(t-1s)` & -0.4156 &  0.1284 & -3.2360e+00 &  0.002399 &  0.001199 \tabularnewline
`(1-B12)(1-B)Homicide(t-2s)` & -0.116 &  0.1103 & -1.0520e+00 &  0.299 &  0.1495 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286452&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]-0.01971[/C][C] 0.01765[/C][C]-1.1170e+00[/C][C] 0.2706[/C][C] 0.1353[/C][/ROW]
[ROW][C]`(1-B12)(1-B)War`[/C][C]-0.02124[/C][C] 0.03394[/C][C]-6.2570e-01[/C][C] 0.535[/C][C] 0.2675[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Homicide(t-1)`[/C][C]-0.6339[/C][C] 0.1506[/C][C]-4.2090e+00[/C][C] 0.0001364[/C][C] 6.819e-05[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Homicide(t-2)`[/C][C]-0.4526[/C][C] 0.1812[/C][C]-2.4990e+00[/C][C] 0.01657[/C][C] 0.008286[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Homicide(t-3)`[/C][C]-0.2196[/C][C] 0.1684[/C][C]-1.3040e+00[/C][C] 0.1995[/C][C] 0.09974[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Homicide(t-4)`[/C][C]-0.04996[/C][C] 0.1374[/C][C]-3.6350e-01[/C][C] 0.7181[/C][C] 0.359[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Homicide(t-1s)`[/C][C]-0.4156[/C][C] 0.1284[/C][C]-3.2360e+00[/C][C] 0.002399[/C][C] 0.001199[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Homicide(t-2s)`[/C][C]-0.116[/C][C] 0.1103[/C][C]-1.0520e+00[/C][C] 0.299[/C][C] 0.1495[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286452&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286452&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)	-0.01971	0.01765	-1.1170e+00	0.2706	0.1353
`(1-B12)(1-B)War`	-0.02124	0.03394	-6.2570e-01	0.535	0.2675
`(1-B12)(1-B)Homicide(t-1)`	-0.6339	0.1506	-4.2090e+00	0.0001364	6.819e-05
`(1-B12)(1-B)Homicide(t-2)`	-0.4526	0.1812	-2.4990e+00	0.01657	0.008286
`(1-B12)(1-B)Homicide(t-3)`	-0.2196	0.1684	-1.3040e+00	0.1995	0.09974
`(1-B12)(1-B)Homicide(t-4)`	-0.04996	0.1374	-3.6350e-01	0.7181	0.359
`(1-B12)(1-B)Homicide(t-1s)`	-0.4156	0.1284	-3.2360e+00	0.002399	0.001199
`(1-B12)(1-B)Homicide(t-2s)`	-0.116	0.1103	-1.0520e+00	0.299	0.1495

Multiple Linear Regression - Regression Statistics
Multiple R	0.7488
R-squared	0.5606
Adjusted R-squared	0.4856
F-TEST (value)	7.474
F-TEST (DF numerator)	7
F-TEST (DF denominator)	41
p-value	8.622e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.1195
Sum Squared Residuals	0.5858

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.7488 \tabularnewline
R-squared &  0.5606 \tabularnewline
Adjusted R-squared &  0.4856 \tabularnewline
F-TEST (value) &  7.474 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 41 \tabularnewline
p-value &  8.622e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.1195 \tabularnewline
Sum Squared Residuals &  0.5858 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286452&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.7488[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.5606[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.4856[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 7.474[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]41[/C][/ROW]
[ROW][C]p-value[/C][C] 8.622e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.1195[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 0.5858[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286452&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286452&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	0.7488
R-squared	0.5606
Adjusted R-squared	0.4856
F-TEST (value)	7.474
F-TEST (DF numerator)	7
F-TEST (DF denominator)	41
p-value	8.622e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.1195
Sum Squared Residuals	0.5858

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	-0.2456	-0.2422	-0.003417
2	0.2486	0.1412	0.1074
3	0.02018	-0.1003	0.1205
4	-0.03567	-0.06322	0.02756
5	-0.03072	-0.06463	0.0339
6	0.1835	0.09346	0.08999
7	0.1462	-0.09117	0.2374
8	-0.3191	-0.3142	-0.004943
9	-0.05406	0.1001	-0.1542
10	0.06253	0.09918	-0.03665
11	-0.01705	-0.005704	-0.01135
12	0.03717	0.03818	-0.001019
13	0.216	0.0008321	0.2151
14	-0.4406	-0.2645	-0.1761
15	0.09733	0.1359	-0.03857
16	0.03098	0.1017	-0.0707
17	-0.0728	-2.064e-05	-0.07278
18	0.0843	-0.05403	0.1383
19	-0.05972	-0.129	0.0693
20	0.07457	0.1204	-0.04579
21	-0.04378	-0.01789	-0.02589
22	0.1715	-0.06695	0.2384
23	-0.2667	-0.1221	-0.1446
24	0.2059	0.04634	0.1596
25	-0.1336	-0.1263	-0.0073
26	0.153	0.1761	-0.02305
27	-0.03231	-0.1309	0.09862
28	-0.1297	-0.05817	-0.07151
29	0.328	0.08401	0.244
30	-0.3195	-0.2258	-0.09365
31	-0.00756	0.07228	-0.07984
32	-0.03261	0.07014	-0.1027
33	0.1268	0.08262	0.04419
34	-0.301	-0.1462	-0.1548
35	0.2226	0.2128	0.009794
36	-0.2227	-0.1407	-0.08198
37	0.1044	0.1322	-0.02777
38	-0.1684	-0.03144	-0.137
39	-0.1545	0.07972	-0.2342
40	0.1469	0.193	-0.04603
41	0.01504	-0.139	0.1541
42	0.06499	0.0696	-0.004616
43	-0.114	-0.08219	-0.03183
44	0.03575	0.01742	0.01833
45	-0.1134	-0.05341	-0.06003
46	0.1464	0.163	-0.01663
47	-0.04224	-0.1037	0.06143
48	-0.02713	0.01137	-0.0385
49	-0.1295	-0.05901	-0.07052

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -0.2456 & -0.2422 & -0.003417 \tabularnewline
2 &  0.2486 &  0.1412 &  0.1074 \tabularnewline
3 &  0.02018 & -0.1003 &  0.1205 \tabularnewline
4 & -0.03567 & -0.06322 &  0.02756 \tabularnewline
5 & -0.03072 & -0.06463 &  0.0339 \tabularnewline
6 &  0.1835 &  0.09346 &  0.08999 \tabularnewline
7 &  0.1462 & -0.09117 &  0.2374 \tabularnewline
8 & -0.3191 & -0.3142 & -0.004943 \tabularnewline
9 & -0.05406 &  0.1001 & -0.1542 \tabularnewline
10 &  0.06253 &  0.09918 & -0.03665 \tabularnewline
11 & -0.01705 & -0.005704 & -0.01135 \tabularnewline
12 &  0.03717 &  0.03818 & -0.001019 \tabularnewline
13 &  0.216 &  0.0008321 &  0.2151 \tabularnewline
14 & -0.4406 & -0.2645 & -0.1761 \tabularnewline
15 &  0.09733 &  0.1359 & -0.03857 \tabularnewline
16 &  0.03098 &  0.1017 & -0.0707 \tabularnewline
17 & -0.0728 & -2.064e-05 & -0.07278 \tabularnewline
18 &  0.0843 & -0.05403 &  0.1383 \tabularnewline
19 & -0.05972 & -0.129 &  0.0693 \tabularnewline
20 &  0.07457 &  0.1204 & -0.04579 \tabularnewline
21 & -0.04378 & -0.01789 & -0.02589 \tabularnewline
22 &  0.1715 & -0.06695 &  0.2384 \tabularnewline
23 & -0.2667 & -0.1221 & -0.1446 \tabularnewline
24 &  0.2059 &  0.04634 &  0.1596 \tabularnewline
25 & -0.1336 & -0.1263 & -0.0073 \tabularnewline
26 &  0.153 &  0.1761 & -0.02305 \tabularnewline
27 & -0.03231 & -0.1309 &  0.09862 \tabularnewline
28 & -0.1297 & -0.05817 & -0.07151 \tabularnewline
29 &  0.328 &  0.08401 &  0.244 \tabularnewline
30 & -0.3195 & -0.2258 & -0.09365 \tabularnewline
31 & -0.00756 &  0.07228 & -0.07984 \tabularnewline
32 & -0.03261 &  0.07014 & -0.1027 \tabularnewline
33 &  0.1268 &  0.08262 &  0.04419 \tabularnewline
34 & -0.301 & -0.1462 & -0.1548 \tabularnewline
35 &  0.2226 &  0.2128 &  0.009794 \tabularnewline
36 & -0.2227 & -0.1407 & -0.08198 \tabularnewline
37 &  0.1044 &  0.1322 & -0.02777 \tabularnewline
38 & -0.1684 & -0.03144 & -0.137 \tabularnewline
39 & -0.1545 &  0.07972 & -0.2342 \tabularnewline
40 &  0.1469 &  0.193 & -0.04603 \tabularnewline
41 &  0.01504 & -0.139 &  0.1541 \tabularnewline
42 &  0.06499 &  0.0696 & -0.004616 \tabularnewline
43 & -0.114 & -0.08219 & -0.03183 \tabularnewline
44 &  0.03575 &  0.01742 &  0.01833 \tabularnewline
45 & -0.1134 & -0.05341 & -0.06003 \tabularnewline
46 &  0.1464 &  0.163 & -0.01663 \tabularnewline
47 & -0.04224 & -0.1037 &  0.06143 \tabularnewline
48 & -0.02713 &  0.01137 & -0.0385 \tabularnewline
49 & -0.1295 & -0.05901 & -0.07052 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286452&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]-0.2456[/C][C]-0.2422[/C][C]-0.003417[/C][/ROW]
[ROW][C]2[/C][C] 0.2486[/C][C] 0.1412[/C][C] 0.1074[/C][/ROW]
[ROW][C]3[/C][C] 0.02018[/C][C]-0.1003[/C][C] 0.1205[/C][/ROW]
[ROW][C]4[/C][C]-0.03567[/C][C]-0.06322[/C][C] 0.02756[/C][/ROW]
[ROW][C]5[/C][C]-0.03072[/C][C]-0.06463[/C][C] 0.0339[/C][/ROW]
[ROW][C]6[/C][C] 0.1835[/C][C] 0.09346[/C][C] 0.08999[/C][/ROW]
[ROW][C]7[/C][C] 0.1462[/C][C]-0.09117[/C][C] 0.2374[/C][/ROW]
[ROW][C]8[/C][C]-0.3191[/C][C]-0.3142[/C][C]-0.004943[/C][/ROW]
[ROW][C]9[/C][C]-0.05406[/C][C] 0.1001[/C][C]-0.1542[/C][/ROW]
[ROW][C]10[/C][C] 0.06253[/C][C] 0.09918[/C][C]-0.03665[/C][/ROW]
[ROW][C]11[/C][C]-0.01705[/C][C]-0.005704[/C][C]-0.01135[/C][/ROW]
[ROW][C]12[/C][C] 0.03717[/C][C] 0.03818[/C][C]-0.001019[/C][/ROW]
[ROW][C]13[/C][C] 0.216[/C][C] 0.0008321[/C][C] 0.2151[/C][/ROW]
[ROW][C]14[/C][C]-0.4406[/C][C]-0.2645[/C][C]-0.1761[/C][/ROW]
[ROW][C]15[/C][C] 0.09733[/C][C] 0.1359[/C][C]-0.03857[/C][/ROW]
[ROW][C]16[/C][C] 0.03098[/C][C] 0.1017[/C][C]-0.0707[/C][/ROW]
[ROW][C]17[/C][C]-0.0728[/C][C]-2.064e-05[/C][C]-0.07278[/C][/ROW]
[ROW][C]18[/C][C] 0.0843[/C][C]-0.05403[/C][C] 0.1383[/C][/ROW]
[ROW][C]19[/C][C]-0.05972[/C][C]-0.129[/C][C] 0.0693[/C][/ROW]
[ROW][C]20[/C][C] 0.07457[/C][C] 0.1204[/C][C]-0.04579[/C][/ROW]
[ROW][C]21[/C][C]-0.04378[/C][C]-0.01789[/C][C]-0.02589[/C][/ROW]
[ROW][C]22[/C][C] 0.1715[/C][C]-0.06695[/C][C] 0.2384[/C][/ROW]
[ROW][C]23[/C][C]-0.2667[/C][C]-0.1221[/C][C]-0.1446[/C][/ROW]
[ROW][C]24[/C][C] 0.2059[/C][C] 0.04634[/C][C] 0.1596[/C][/ROW]
[ROW][C]25[/C][C]-0.1336[/C][C]-0.1263[/C][C]-0.0073[/C][/ROW]
[ROW][C]26[/C][C] 0.153[/C][C] 0.1761[/C][C]-0.02305[/C][/ROW]
[ROW][C]27[/C][C]-0.03231[/C][C]-0.1309[/C][C] 0.09862[/C][/ROW]
[ROW][C]28[/C][C]-0.1297[/C][C]-0.05817[/C][C]-0.07151[/C][/ROW]
[ROW][C]29[/C][C] 0.328[/C][C] 0.08401[/C][C] 0.244[/C][/ROW]
[ROW][C]30[/C][C]-0.3195[/C][C]-0.2258[/C][C]-0.09365[/C][/ROW]
[ROW][C]31[/C][C]-0.00756[/C][C] 0.07228[/C][C]-0.07984[/C][/ROW]
[ROW][C]32[/C][C]-0.03261[/C][C] 0.07014[/C][C]-0.1027[/C][/ROW]
[ROW][C]33[/C][C] 0.1268[/C][C] 0.08262[/C][C] 0.04419[/C][/ROW]
[ROW][C]34[/C][C]-0.301[/C][C]-0.1462[/C][C]-0.1548[/C][/ROW]
[ROW][C]35[/C][C] 0.2226[/C][C] 0.2128[/C][C] 0.009794[/C][/ROW]
[ROW][C]36[/C][C]-0.2227[/C][C]-0.1407[/C][C]-0.08198[/C][/ROW]
[ROW][C]37[/C][C] 0.1044[/C][C] 0.1322[/C][C]-0.02777[/C][/ROW]
[ROW][C]38[/C][C]-0.1684[/C][C]-0.03144[/C][C]-0.137[/C][/ROW]
[ROW][C]39[/C][C]-0.1545[/C][C] 0.07972[/C][C]-0.2342[/C][/ROW]
[ROW][C]40[/C][C] 0.1469[/C][C] 0.193[/C][C]-0.04603[/C][/ROW]
[ROW][C]41[/C][C] 0.01504[/C][C]-0.139[/C][C] 0.1541[/C][/ROW]
[ROW][C]42[/C][C] 0.06499[/C][C] 0.0696[/C][C]-0.004616[/C][/ROW]
[ROW][C]43[/C][C]-0.114[/C][C]-0.08219[/C][C]-0.03183[/C][/ROW]
[ROW][C]44[/C][C] 0.03575[/C][C] 0.01742[/C][C] 0.01833[/C][/ROW]
[ROW][C]45[/C][C]-0.1134[/C][C]-0.05341[/C][C]-0.06003[/C][/ROW]
[ROW][C]46[/C][C] 0.1464[/C][C] 0.163[/C][C]-0.01663[/C][/ROW]
[ROW][C]47[/C][C]-0.04224[/C][C]-0.1037[/C][C] 0.06143[/C][/ROW]
[ROW][C]48[/C][C]-0.02713[/C][C] 0.01137[/C][C]-0.0385[/C][/ROW]
[ROW][C]49[/C][C]-0.1295[/C][C]-0.05901[/C][C]-0.07052[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286452&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286452&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	-0.2456	-0.2422	-0.003417
2	0.2486	0.1412	0.1074
3	0.02018	-0.1003	0.1205
4	-0.03567	-0.06322	0.02756
5	-0.03072	-0.06463	0.0339
6	0.1835	0.09346	0.08999
7	0.1462	-0.09117	0.2374
8	-0.3191	-0.3142	-0.004943
9	-0.05406	0.1001	-0.1542
10	0.06253	0.09918	-0.03665
11	-0.01705	-0.005704	-0.01135
12	0.03717	0.03818	-0.001019
13	0.216	0.0008321	0.2151
14	-0.4406	-0.2645	-0.1761
15	0.09733	0.1359	-0.03857
16	0.03098	0.1017	-0.0707
17	-0.0728	-2.064e-05	-0.07278
18	0.0843	-0.05403	0.1383
19	-0.05972	-0.129	0.0693
20	0.07457	0.1204	-0.04579
21	-0.04378	-0.01789	-0.02589
22	0.1715	-0.06695	0.2384
23	-0.2667	-0.1221	-0.1446
24	0.2059	0.04634	0.1596
25	-0.1336	-0.1263	-0.0073
26	0.153	0.1761	-0.02305
27	-0.03231	-0.1309	0.09862
28	-0.1297	-0.05817	-0.07151
29	0.328	0.08401	0.244
30	-0.3195	-0.2258	-0.09365
31	-0.00756	0.07228	-0.07984
32	-0.03261	0.07014	-0.1027
33	0.1268	0.08262	0.04419
34	-0.301	-0.1462	-0.1548
35	0.2226	0.2128	0.009794
36	-0.2227	-0.1407	-0.08198
37	0.1044	0.1322	-0.02777
38	-0.1684	-0.03144	-0.137
39	-0.1545	0.07972	-0.2342
40	0.1469	0.193	-0.04603
41	0.01504	-0.139	0.1541
42	0.06499	0.0696	-0.004616
43	-0.114	-0.08219	-0.03183
44	0.03575	0.01742	0.01833
45	-0.1134	-0.05341	-0.06003
46	0.1464	0.163	-0.01663
47	-0.04224	-0.1037	0.06143
48	-0.02713	0.01137	-0.0385
49	-0.1295	-0.05901	-0.07052

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.5902	0.8196	0.4098
12	0.4322	0.8645	0.5678
13	0.3927	0.7855	0.6073
14	0.7783	0.4434	0.2217
15	0.7348	0.5305	0.2652
16	0.7534	0.4931	0.2466
17	0.6897	0.6206	0.3103
18	0.6804	0.6393	0.3196
19	0.6073	0.7854	0.3927
20	0.5504	0.8992	0.4496
21	0.4765	0.953	0.5235
22	0.7023	0.5954	0.2977
23	0.7343	0.5314	0.2657
24	0.7781	0.4438	0.2219
25	0.6972	0.6055	0.3028
26	0.6156	0.7688	0.3844
27	0.5991	0.8018	0.4009
28	0.5195	0.961	0.4805
29	0.8668	0.2663	0.1332
30	0.8331	0.3339	0.1669
31	0.7708	0.4584	0.2292
32	0.7238	0.5524	0.2762
33	0.6236	0.7527	0.3764
34	0.6098	0.7804	0.3902
35	0.6051	0.7898	0.3949
36	0.7915	0.4171	0.2085
37	0.674	0.652	0.326
38	0.5565	0.8871	0.4435

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 &  0.5902 &  0.8196 &  0.4098 \tabularnewline
12 &  0.4322 &  0.8645 &  0.5678 \tabularnewline
13 &  0.3927 &  0.7855 &  0.6073 \tabularnewline
14 &  0.7783 &  0.4434 &  0.2217 \tabularnewline
15 &  0.7348 &  0.5305 &  0.2652 \tabularnewline
16 &  0.7534 &  0.4931 &  0.2466 \tabularnewline
17 &  0.6897 &  0.6206 &  0.3103 \tabularnewline
18 &  0.6804 &  0.6393 &  0.3196 \tabularnewline
19 &  0.6073 &  0.7854 &  0.3927 \tabularnewline
20 &  0.5504 &  0.8992 &  0.4496 \tabularnewline
21 &  0.4765 &  0.953 &  0.5235 \tabularnewline
22 &  0.7023 &  0.5954 &  0.2977 \tabularnewline
23 &  0.7343 &  0.5314 &  0.2657 \tabularnewline
24 &  0.7781 &  0.4438 &  0.2219 \tabularnewline
25 &  0.6972 &  0.6055 &  0.3028 \tabularnewline
26 &  0.6156 &  0.7688 &  0.3844 \tabularnewline
27 &  0.5991 &  0.8018 &  0.4009 \tabularnewline
28 &  0.5195 &  0.961 &  0.4805 \tabularnewline
29 &  0.8668 &  0.2663 &  0.1332 \tabularnewline
30 &  0.8331 &  0.3339 &  0.1669 \tabularnewline
31 &  0.7708 &  0.4584 &  0.2292 \tabularnewline
32 &  0.7238 &  0.5524 &  0.2762 \tabularnewline
33 &  0.6236 &  0.7527 &  0.3764 \tabularnewline
34 &  0.6098 &  0.7804 &  0.3902 \tabularnewline
35 &  0.6051 &  0.7898 &  0.3949 \tabularnewline
36 &  0.7915 &  0.4171 &  0.2085 \tabularnewline
37 &  0.674 &  0.652 &  0.326 \tabularnewline
38 &  0.5565 &  0.8871 &  0.4435 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286452&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C] 0.5902[/C][C] 0.8196[/C][C] 0.4098[/C][/ROW]
[ROW][C]12[/C][C] 0.4322[/C][C] 0.8645[/C][C] 0.5678[/C][/ROW]
[ROW][C]13[/C][C] 0.3927[/C][C] 0.7855[/C][C] 0.6073[/C][/ROW]
[ROW][C]14[/C][C] 0.7783[/C][C] 0.4434[/C][C] 0.2217[/C][/ROW]
[ROW][C]15[/C][C] 0.7348[/C][C] 0.5305[/C][C] 0.2652[/C][/ROW]
[ROW][C]16[/C][C] 0.7534[/C][C] 0.4931[/C][C] 0.2466[/C][/ROW]
[ROW][C]17[/C][C] 0.6897[/C][C] 0.6206[/C][C] 0.3103[/C][/ROW]
[ROW][C]18[/C][C] 0.6804[/C][C] 0.6393[/C][C] 0.3196[/C][/ROW]
[ROW][C]19[/C][C] 0.6073[/C][C] 0.7854[/C][C] 0.3927[/C][/ROW]
[ROW][C]20[/C][C] 0.5504[/C][C] 0.8992[/C][C] 0.4496[/C][/ROW]
[ROW][C]21[/C][C] 0.4765[/C][C] 0.953[/C][C] 0.5235[/C][/ROW]
[ROW][C]22[/C][C] 0.7023[/C][C] 0.5954[/C][C] 0.2977[/C][/ROW]
[ROW][C]23[/C][C] 0.7343[/C][C] 0.5314[/C][C] 0.2657[/C][/ROW]
[ROW][C]24[/C][C] 0.7781[/C][C] 0.4438[/C][C] 0.2219[/C][/ROW]
[ROW][C]25[/C][C] 0.6972[/C][C] 0.6055[/C][C] 0.3028[/C][/ROW]
[ROW][C]26[/C][C] 0.6156[/C][C] 0.7688[/C][C] 0.3844[/C][/ROW]
[ROW][C]27[/C][C] 0.5991[/C][C] 0.8018[/C][C] 0.4009[/C][/ROW]
[ROW][C]28[/C][C] 0.5195[/C][C] 0.961[/C][C] 0.4805[/C][/ROW]
[ROW][C]29[/C][C] 0.8668[/C][C] 0.2663[/C][C] 0.1332[/C][/ROW]
[ROW][C]30[/C][C] 0.8331[/C][C] 0.3339[/C][C] 0.1669[/C][/ROW]
[ROW][C]31[/C][C] 0.7708[/C][C] 0.4584[/C][C] 0.2292[/C][/ROW]
[ROW][C]32[/C][C] 0.7238[/C][C] 0.5524[/C][C] 0.2762[/C][/ROW]
[ROW][C]33[/C][C] 0.6236[/C][C] 0.7527[/C][C] 0.3764[/C][/ROW]
[ROW][C]34[/C][C] 0.6098[/C][C] 0.7804[/C][C] 0.3902[/C][/ROW]
[ROW][C]35[/C][C] 0.6051[/C][C] 0.7898[/C][C] 0.3949[/C][/ROW]
[ROW][C]36[/C][C] 0.7915[/C][C] 0.4171[/C][C] 0.2085[/C][/ROW]
[ROW][C]37[/C][C] 0.674[/C][C] 0.652[/C][C] 0.326[/C][/ROW]
[ROW][C]38[/C][C] 0.5565[/C][C] 0.8871[/C][C] 0.4435[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286452&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286452&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.5902	0.8196	0.4098
12	0.4322	0.8645	0.5678
13	0.3927	0.7855	0.6073
14	0.7783	0.4434	0.2217
15	0.7348	0.5305	0.2652
16	0.7534	0.4931	0.2466
17	0.6897	0.6206	0.3103
18	0.6804	0.6393	0.3196
19	0.6073	0.7854	0.3927
20	0.5504	0.8992	0.4496
21	0.4765	0.953	0.5235
22	0.7023	0.5954	0.2977
23	0.7343	0.5314	0.2657
24	0.7781	0.4438	0.2219
25	0.6972	0.6055	0.3028
26	0.6156	0.7688	0.3844
27	0.5991	0.8018	0.4009
28	0.5195	0.961	0.4805
29	0.8668	0.2663	0.1332
30	0.8331	0.3339	0.1669
31	0.7708	0.4584	0.2292
32	0.7238	0.5524	0.2762
33	0.6236	0.7527	0.3764
34	0.6098	0.7804	0.3902
35	0.6051	0.7898	0.3949
36	0.7915	0.4171	0.2085
37	0.674	0.652	0.326
38	0.5565	0.8871	0.4435

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286452&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286452&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286452&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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PDF link

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s=12) ; par4 = 4 ; par5 = 2 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s=12) ; par4 = 4 ; par5 = 2 ;

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'){
(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=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(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 - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,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*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
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'
}
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$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')
}
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code