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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 15 Dec 2015 10:55:32 +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/t14501769640k3lz58ejijfbya.htm/, Retrieved Sat, 18 May 2024 14:23:35 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=286462, Retrieved Sat, 18 May 2024 14:23:35 +0000

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Estimated Impact

126

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 10:55:32] [07325d4e03e5d5deea478d79524d9715] [Current]

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4.031636 1
3.702076 1
3.056176 1
3.280707 1
2.984728 1
3.693712 1
3.226317 0
2.190349 0
2.599515 0
3.080288 0
2.929672 0
2.922548 0
3.234943 0
2.983081 0
3.284389 0
3.806511 0
3.784579 0
2.645654 0
3.092081 0
3.204859 0
3.107225 0
3.466909 0
2.984404 0
3.218072 0
2.82731 1
3.182049 1
2.236319 1
2.033218 1
1.644804 1
1.627971 1
1.677559 1
2.330828 0
2.493615 0
2.257172 0
2.655517 0
2.298655 1
2.600402 1
3.04523 1
2.790583 1
3.227052 1
2.967479 1
2.938817 1
3.277961 1
3.423985 1
3.072646 1
2.754253 1
2.910431 0
3.174369 0
3.068387 1
3.089543 1
2.906654 1
2.931161 1
3.02566 0
2.939551 0
2.691019 0
3.19812 0
3.07639 0
2.863873 0
3.013802 0
3.053364 0
2.864753 0
3.057062 0
2.959365 0
3.252258 0
3.602988 0
3.497704 0
3.296867 0
3.602417 0
3.3001 0
3.40193 0
3.502591 0
3.402348 0
3.498551 0
3.199823 0
2.700064 0
2.801034 1
2.898628 1
2.800854 0
2.399942 0
2.402724 0
2.202331 0
2.102594 0
1.798293 0
1.202484 0
1.400201 0
1.200832 0
1.298083 0
1.099742 0
1.001377 1
0.8361743 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	'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286462&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286462&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286462&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	'Herman Ole Andreas Wold' @ wold.wessa.net

Multiple Linear Regression - Estimated Regression Equation
(1-B12)(1-B)Suicide[t] = -0.056616 -0.097752`(1-B12)(1-B)War`[t] + 0.0760474`(1-B12)(1-B)Suicide(t-1)`[t] -0.102257`(1-B12)(1-B)Suicide(t-2)`[t] + 0.167073`(1-B12)(1-B)Suicide(t-3)`[t] -0.15368`(1-B12)(1-B)Suicide(t-4)`[t] -0.336436`(1-B12)(1-B)Suicide(t-1s)`[t] -0.0558299`(1-B12)(1-B)Suicide(t-2s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
(1-B12)(1-B)Suicide[t] =  -0.056616 -0.097752`(1-B12)(1-B)War`[t] +  0.0760474`(1-B12)(1-B)Suicide(t-1)`[t] -0.102257`(1-B12)(1-B)Suicide(t-2)`[t] +  0.167073`(1-B12)(1-B)Suicide(t-3)`[t] -0.15368`(1-B12)(1-B)Suicide(t-4)`[t] -0.336436`(1-B12)(1-B)Suicide(t-1s)`[t] -0.0558299`(1-B12)(1-B)Suicide(t-2s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286462&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C](1-B12)(1-B)Suicide[t] =  -0.056616 -0.097752`(1-B12)(1-B)War`[t] +  0.0760474`(1-B12)(1-B)Suicide(t-1)`[t] -0.102257`(1-B12)(1-B)Suicide(t-2)`[t] +  0.167073`(1-B12)(1-B)Suicide(t-3)`[t] -0.15368`(1-B12)(1-B)Suicide(t-4)`[t] -0.336436`(1-B12)(1-B)Suicide(t-1s)`[t] -0.0558299`(1-B12)(1-B)Suicide(t-2s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286462&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286462&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)Suicide[t] = -0.056616 -0.097752`(1-B12)(1-B)War`[t] + 0.0760474`(1-B12)(1-B)Suicide(t-1)`[t] -0.102257`(1-B12)(1-B)Suicide(t-2)`[t] + 0.167073`(1-B12)(1-B)Suicide(t-3)`[t] -0.15368`(1-B12)(1-B)Suicide(t-4)`[t] -0.336436`(1-B12)(1-B)Suicide(t-1s)`[t] -0.0558299`(1-B12)(1-B)Suicide(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)	-0.05662	0.0406	-1.3950e+00	0.1707	0.08534
`(1-B12)(1-B)War`	-0.09775	0.08476	-1.1530e+00	0.2555	0.1277
`(1-B12)(1-B)Suicide(t-1)`	+0.07605	0.136	+5.5920e-01	0.5791	0.2895
`(1-B12)(1-B)Suicide(t-2)`	-0.1023	0.1252	-8.1680e-01	0.4188	0.2094
`(1-B12)(1-B)Suicide(t-3)`	+0.1671	0.1328	+1.2580e+00	0.2156	0.1078
`(1-B12)(1-B)Suicide(t-4)`	-0.1537	0.1317	-1.1670e+00	0.2499	0.1249
`(1-B12)(1-B)Suicide(t-1s)`	-0.3364	0.1303	-2.5820e+00	0.01349	0.006745
`(1-B12)(1-B)Suicide(t-2s)`	-0.05583	0.08685	-6.4280e-01	0.5239	0.262

\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) & -0.05662 &  0.0406 & -1.3950e+00 &  0.1707 &  0.08534 \tabularnewline
`(1-B12)(1-B)War` & -0.09775 &  0.08476 & -1.1530e+00 &  0.2555 &  0.1277 \tabularnewline
`(1-B12)(1-B)Suicide(t-1)` & +0.07605 &  0.136 & +5.5920e-01 &  0.5791 &  0.2895 \tabularnewline
`(1-B12)(1-B)Suicide(t-2)` & -0.1023 &  0.1252 & -8.1680e-01 &  0.4188 &  0.2094 \tabularnewline
`(1-B12)(1-B)Suicide(t-3)` & +0.1671 &  0.1328 & +1.2580e+00 &  0.2156 &  0.1078 \tabularnewline
`(1-B12)(1-B)Suicide(t-4)` & -0.1537 &  0.1317 & -1.1670e+00 &  0.2499 &  0.1249 \tabularnewline
`(1-B12)(1-B)Suicide(t-1s)` & -0.3364 &  0.1303 & -2.5820e+00 &  0.01349 &  0.006745 \tabularnewline
`(1-B12)(1-B)Suicide(t-2s)` & -0.05583 &  0.08685 & -6.4280e-01 &  0.5239 &  0.262 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286462&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]-0.05662[/C][C] 0.0406[/C][C]-1.3950e+00[/C][C] 0.1707[/C][C] 0.08534[/C][/ROW]
[ROW][C]`(1-B12)(1-B)War`[/C][C]-0.09775[/C][C] 0.08476[/C][C]-1.1530e+00[/C][C] 0.2555[/C][C] 0.1277[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Suicide(t-1)`[/C][C]+0.07605[/C][C] 0.136[/C][C]+5.5920e-01[/C][C] 0.5791[/C][C] 0.2895[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Suicide(t-2)`[/C][C]-0.1023[/C][C] 0.1252[/C][C]-8.1680e-01[/C][C] 0.4188[/C][C] 0.2094[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Suicide(t-3)`[/C][C]+0.1671[/C][C] 0.1328[/C][C]+1.2580e+00[/C][C] 0.2156[/C][C] 0.1078[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Suicide(t-4)`[/C][C]-0.1537[/C][C] 0.1317[/C][C]-1.1670e+00[/C][C] 0.2499[/C][C] 0.1249[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Suicide(t-1s)`[/C][C]-0.3364[/C][C] 0.1303[/C][C]-2.5820e+00[/C][C] 0.01349[/C][C] 0.006745[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Suicide(t-2s)`[/C][C]-0.05583[/C][C] 0.08685[/C][C]-6.4280e-01[/C][C] 0.5239[/C][C] 0.262[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286462&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286462&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)	-0.05662	0.0406	-1.3950e+00	0.1707	0.08534
`(1-B12)(1-B)War`	-0.09775	0.08476	-1.1530e+00	0.2555	0.1277
`(1-B12)(1-B)Suicide(t-1)`	+0.07605	0.136	+5.5920e-01	0.5791	0.2895
`(1-B12)(1-B)Suicide(t-2)`	-0.1023	0.1252	-8.1680e-01	0.4188	0.2094
`(1-B12)(1-B)Suicide(t-3)`	+0.1671	0.1328	+1.2580e+00	0.2156	0.1078
`(1-B12)(1-B)Suicide(t-4)`	-0.1537	0.1317	-1.1670e+00	0.2499	0.1249
`(1-B12)(1-B)Suicide(t-1s)`	-0.3364	0.1303	-2.5820e+00	0.01349	0.006745
`(1-B12)(1-B)Suicide(t-2s)`	-0.05583	0.08685	-6.4280e-01	0.5239	0.262

Multiple Linear Regression - Regression Statistics
Multiple R	0.5372
R-squared	0.2886
Adjusted R-squared	0.1671
F-TEST (value)	2.376
F-TEST (DF numerator)	7
F-TEST (DF denominator)	41
p-value	0.03903
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.268
Sum Squared Residuals	2.945

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5372 \tabularnewline
R-squared &  0.2886 \tabularnewline
Adjusted R-squared &  0.1671 \tabularnewline
F-TEST (value) &  2.376 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 41 \tabularnewline
p-value &  0.03903 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.268 \tabularnewline
Sum Squared Residuals &  2.945 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286462&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5372[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.2886[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.1671[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 2.376[/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] 0.03903[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.268[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 2.945[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286462&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286462&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	0.5372
R-squared	0.2886
Adjusted R-squared	0.1671
F-TEST (value)	2.376
F-TEST (DF numerator)	7
F-TEST (DF denominator)	41
p-value	0.03903
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.268
Sum Squared Residuals	2.945

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	-0.01183	-0.2849	0.2731
2	0.2896	0.01245	0.2771
3	-0.5072	-0.4539	-0.05337
4	-0.5141	-0.2059	-0.3082
5	-0.08195	0.2137	-0.2956
6	-0.2422	-0.3196	0.07742
7	0.6208	0.2084	0.4124
8	-0.4077	-0.2108	-0.1969
9	-0.4237	-0.2431	-0.1805
10	0.07176	-0.06909	0.1408
11	-0.412	-0.346	-0.06592
12	0.3541	-0.02854	0.3826
13	-0.05745	0.03087	-0.08832
14	-0.5877	-0.2523	-0.3354
15	0.3611	0.1675	0.1936
16	0.2296	0.1254	0.1043
17	0.1059	-0.1046	0.2105
18	-0.006249	0.01314	-0.01939
19	-0.2244	-0.2609	0.03656
20	-0.08263	0.1056	-0.1883
21	0.1712	0.08024	0.09091
22	0.08519	-0.1344	0.2196
23	0.2684	0.05593	0.2125
24	0.2562	-0.2277	0.4839
25	-0.01918	-0.05666	0.03748
26	0.0477	0.129	-0.08133
27	-0.2016	-0.1426	-0.05893
28	-0.1806	-0.1679	-0.01264
29	0.3143	-0.06987	0.3842
30	-0.04927	-0.03963	-0.009643
31	-0.1398	-0.05087	-0.08893
32	0.2848	0.06862	0.2162
33	-0.491	-0.1111	-0.3799
34	-0.4021	-0.1715	-0.2305
35	-0.1919	-0.133	-0.05897
36	-0.2531	-0.2619	0.008744
37	0.00751	0.05946	-0.05195
38	-0.2001	0.01633	-0.2164
39	-0.3028	-0.03775	-0.265
40	0.1019	0.02891	0.07301
41	-0.2016	-0.1642	-0.03741
42	-0.405	-0.08528	-0.3197
43	-0.4956	0.05632	-0.5519
44	0.1015	-0.1934	0.295
45	0.09936	0.1207	-0.02139
46	0.597	0.05051	0.5465
47	-0.2993	0.2191	-0.5184
48	-0.196	-0.1663	-0.02964
49	-0.06743	-0.05564	-0.01178

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -0.01183 & -0.2849 &  0.2731 \tabularnewline
2 &  0.2896 &  0.01245 &  0.2771 \tabularnewline
3 & -0.5072 & -0.4539 & -0.05337 \tabularnewline
4 & -0.5141 & -0.2059 & -0.3082 \tabularnewline
5 & -0.08195 &  0.2137 & -0.2956 \tabularnewline
6 & -0.2422 & -0.3196 &  0.07742 \tabularnewline
7 &  0.6208 &  0.2084 &  0.4124 \tabularnewline
8 & -0.4077 & -0.2108 & -0.1969 \tabularnewline
9 & -0.4237 & -0.2431 & -0.1805 \tabularnewline
10 &  0.07176 & -0.06909 &  0.1408 \tabularnewline
11 & -0.412 & -0.346 & -0.06592 \tabularnewline
12 &  0.3541 & -0.02854 &  0.3826 \tabularnewline
13 & -0.05745 &  0.03087 & -0.08832 \tabularnewline
14 & -0.5877 & -0.2523 & -0.3354 \tabularnewline
15 &  0.3611 &  0.1675 &  0.1936 \tabularnewline
16 &  0.2296 &  0.1254 &  0.1043 \tabularnewline
17 &  0.1059 & -0.1046 &  0.2105 \tabularnewline
18 & -0.006249 &  0.01314 & -0.01939 \tabularnewline
19 & -0.2244 & -0.2609 &  0.03656 \tabularnewline
20 & -0.08263 &  0.1056 & -0.1883 \tabularnewline
21 &  0.1712 &  0.08024 &  0.09091 \tabularnewline
22 &  0.08519 & -0.1344 &  0.2196 \tabularnewline
23 &  0.2684 &  0.05593 &  0.2125 \tabularnewline
24 &  0.2562 & -0.2277 &  0.4839 \tabularnewline
25 & -0.01918 & -0.05666 &  0.03748 \tabularnewline
26 &  0.0477 &  0.129 & -0.08133 \tabularnewline
27 & -0.2016 & -0.1426 & -0.05893 \tabularnewline
28 & -0.1806 & -0.1679 & -0.01264 \tabularnewline
29 &  0.3143 & -0.06987 &  0.3842 \tabularnewline
30 & -0.04927 & -0.03963 & -0.009643 \tabularnewline
31 & -0.1398 & -0.05087 & -0.08893 \tabularnewline
32 &  0.2848 &  0.06862 &  0.2162 \tabularnewline
33 & -0.491 & -0.1111 & -0.3799 \tabularnewline
34 & -0.4021 & -0.1715 & -0.2305 \tabularnewline
35 & -0.1919 & -0.133 & -0.05897 \tabularnewline
36 & -0.2531 & -0.2619 &  0.008744 \tabularnewline
37 &  0.00751 &  0.05946 & -0.05195 \tabularnewline
38 & -0.2001 &  0.01633 & -0.2164 \tabularnewline
39 & -0.3028 & -0.03775 & -0.265 \tabularnewline
40 &  0.1019 &  0.02891 &  0.07301 \tabularnewline
41 & -0.2016 & -0.1642 & -0.03741 \tabularnewline
42 & -0.405 & -0.08528 & -0.3197 \tabularnewline
43 & -0.4956 &  0.05632 & -0.5519 \tabularnewline
44 &  0.1015 & -0.1934 &  0.295 \tabularnewline
45 &  0.09936 &  0.1207 & -0.02139 \tabularnewline
46 &  0.597 &  0.05051 &  0.5465 \tabularnewline
47 & -0.2993 &  0.2191 & -0.5184 \tabularnewline
48 & -0.196 & -0.1663 & -0.02964 \tabularnewline
49 & -0.06743 & -0.05564 & -0.01178 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286462&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]-0.01183[/C][C]-0.2849[/C][C] 0.2731[/C][/ROW]
[ROW][C]2[/C][C] 0.2896[/C][C] 0.01245[/C][C] 0.2771[/C][/ROW]
[ROW][C]3[/C][C]-0.5072[/C][C]-0.4539[/C][C]-0.05337[/C][/ROW]
[ROW][C]4[/C][C]-0.5141[/C][C]-0.2059[/C][C]-0.3082[/C][/ROW]
[ROW][C]5[/C][C]-0.08195[/C][C] 0.2137[/C][C]-0.2956[/C][/ROW]
[ROW][C]6[/C][C]-0.2422[/C][C]-0.3196[/C][C] 0.07742[/C][/ROW]
[ROW][C]7[/C][C] 0.6208[/C][C] 0.2084[/C][C] 0.4124[/C][/ROW]
[ROW][C]8[/C][C]-0.4077[/C][C]-0.2108[/C][C]-0.1969[/C][/ROW]
[ROW][C]9[/C][C]-0.4237[/C][C]-0.2431[/C][C]-0.1805[/C][/ROW]
[ROW][C]10[/C][C] 0.07176[/C][C]-0.06909[/C][C] 0.1408[/C][/ROW]
[ROW][C]11[/C][C]-0.412[/C][C]-0.346[/C][C]-0.06592[/C][/ROW]
[ROW][C]12[/C][C] 0.3541[/C][C]-0.02854[/C][C] 0.3826[/C][/ROW]
[ROW][C]13[/C][C]-0.05745[/C][C] 0.03087[/C][C]-0.08832[/C][/ROW]
[ROW][C]14[/C][C]-0.5877[/C][C]-0.2523[/C][C]-0.3354[/C][/ROW]
[ROW][C]15[/C][C] 0.3611[/C][C] 0.1675[/C][C] 0.1936[/C][/ROW]
[ROW][C]16[/C][C] 0.2296[/C][C] 0.1254[/C][C] 0.1043[/C][/ROW]
[ROW][C]17[/C][C] 0.1059[/C][C]-0.1046[/C][C] 0.2105[/C][/ROW]
[ROW][C]18[/C][C]-0.006249[/C][C] 0.01314[/C][C]-0.01939[/C][/ROW]
[ROW][C]19[/C][C]-0.2244[/C][C]-0.2609[/C][C] 0.03656[/C][/ROW]
[ROW][C]20[/C][C]-0.08263[/C][C] 0.1056[/C][C]-0.1883[/C][/ROW]
[ROW][C]21[/C][C] 0.1712[/C][C] 0.08024[/C][C] 0.09091[/C][/ROW]
[ROW][C]22[/C][C] 0.08519[/C][C]-0.1344[/C][C] 0.2196[/C][/ROW]
[ROW][C]23[/C][C] 0.2684[/C][C] 0.05593[/C][C] 0.2125[/C][/ROW]
[ROW][C]24[/C][C] 0.2562[/C][C]-0.2277[/C][C] 0.4839[/C][/ROW]
[ROW][C]25[/C][C]-0.01918[/C][C]-0.05666[/C][C] 0.03748[/C][/ROW]
[ROW][C]26[/C][C] 0.0477[/C][C] 0.129[/C][C]-0.08133[/C][/ROW]
[ROW][C]27[/C][C]-0.2016[/C][C]-0.1426[/C][C]-0.05893[/C][/ROW]
[ROW][C]28[/C][C]-0.1806[/C][C]-0.1679[/C][C]-0.01264[/C][/ROW]
[ROW][C]29[/C][C] 0.3143[/C][C]-0.06987[/C][C] 0.3842[/C][/ROW]
[ROW][C]30[/C][C]-0.04927[/C][C]-0.03963[/C][C]-0.009643[/C][/ROW]
[ROW][C]31[/C][C]-0.1398[/C][C]-0.05087[/C][C]-0.08893[/C][/ROW]
[ROW][C]32[/C][C] 0.2848[/C][C] 0.06862[/C][C] 0.2162[/C][/ROW]
[ROW][C]33[/C][C]-0.491[/C][C]-0.1111[/C][C]-0.3799[/C][/ROW]
[ROW][C]34[/C][C]-0.4021[/C][C]-0.1715[/C][C]-0.2305[/C][/ROW]
[ROW][C]35[/C][C]-0.1919[/C][C]-0.133[/C][C]-0.05897[/C][/ROW]
[ROW][C]36[/C][C]-0.2531[/C][C]-0.2619[/C][C] 0.008744[/C][/ROW]
[ROW][C]37[/C][C] 0.00751[/C][C] 0.05946[/C][C]-0.05195[/C][/ROW]
[ROW][C]38[/C][C]-0.2001[/C][C] 0.01633[/C][C]-0.2164[/C][/ROW]
[ROW][C]39[/C][C]-0.3028[/C][C]-0.03775[/C][C]-0.265[/C][/ROW]
[ROW][C]40[/C][C] 0.1019[/C][C] 0.02891[/C][C] 0.07301[/C][/ROW]
[ROW][C]41[/C][C]-0.2016[/C][C]-0.1642[/C][C]-0.03741[/C][/ROW]
[ROW][C]42[/C][C]-0.405[/C][C]-0.08528[/C][C]-0.3197[/C][/ROW]
[ROW][C]43[/C][C]-0.4956[/C][C] 0.05632[/C][C]-0.5519[/C][/ROW]
[ROW][C]44[/C][C] 0.1015[/C][C]-0.1934[/C][C] 0.295[/C][/ROW]
[ROW][C]45[/C][C] 0.09936[/C][C] 0.1207[/C][C]-0.02139[/C][/ROW]
[ROW][C]46[/C][C] 0.597[/C][C] 0.05051[/C][C] 0.5465[/C][/ROW]
[ROW][C]47[/C][C]-0.2993[/C][C] 0.2191[/C][C]-0.5184[/C][/ROW]
[ROW][C]48[/C][C]-0.196[/C][C]-0.1663[/C][C]-0.02964[/C][/ROW]
[ROW][C]49[/C][C]-0.06743[/C][C]-0.05564[/C][C]-0.01178[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286462&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286462&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	-0.01183	-0.2849	0.2731
2	0.2896	0.01245	0.2771
3	-0.5072	-0.4539	-0.05337
4	-0.5141	-0.2059	-0.3082
5	-0.08195	0.2137	-0.2956
6	-0.2422	-0.3196	0.07742
7	0.6208	0.2084	0.4124
8	-0.4077	-0.2108	-0.1969
9	-0.4237	-0.2431	-0.1805
10	0.07176	-0.06909	0.1408
11	-0.412	-0.346	-0.06592
12	0.3541	-0.02854	0.3826
13	-0.05745	0.03087	-0.08832
14	-0.5877	-0.2523	-0.3354
15	0.3611	0.1675	0.1936
16	0.2296	0.1254	0.1043
17	0.1059	-0.1046	0.2105
18	-0.006249	0.01314	-0.01939
19	-0.2244	-0.2609	0.03656
20	-0.08263	0.1056	-0.1883
21	0.1712	0.08024	0.09091
22	0.08519	-0.1344	0.2196
23	0.2684	0.05593	0.2125
24	0.2562	-0.2277	0.4839
25	-0.01918	-0.05666	0.03748
26	0.0477	0.129	-0.08133
27	-0.2016	-0.1426	-0.05893
28	-0.1806	-0.1679	-0.01264
29	0.3143	-0.06987	0.3842
30	-0.04927	-0.03963	-0.009643
31	-0.1398	-0.05087	-0.08893
32	0.2848	0.06862	0.2162
33	-0.491	-0.1111	-0.3799
34	-0.4021	-0.1715	-0.2305
35	-0.1919	-0.133	-0.05897
36	-0.2531	-0.2619	0.008744
37	0.00751	0.05946	-0.05195
38	-0.2001	0.01633	-0.2164
39	-0.3028	-0.03775	-0.265
40	0.1019	0.02891	0.07301
41	-0.2016	-0.1642	-0.03741
42	-0.405	-0.08528	-0.3197
43	-0.4956	0.05632	-0.5519
44	0.1015	-0.1934	0.295
45	0.09936	0.1207	-0.02139
46	0.597	0.05051	0.5465
47	-0.2993	0.2191	-0.5184
48	-0.196	-0.1663	-0.02964
49	-0.06743	-0.05564	-0.01178

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.3731	0.7463	0.6269
12	0.3389	0.6778	0.6611
13	0.4473	0.8946	0.5527
14	0.4596	0.9191	0.5404
15	0.4551	0.9102	0.5449
16	0.3395	0.679	0.6605
17	0.3182	0.6364	0.6818
18	0.2402	0.4803	0.7598
19	0.1678	0.3357	0.8322
20	0.3208	0.6417	0.6792
21	0.2462	0.4924	0.7538
22	0.2045	0.4091	0.7955
23	0.1607	0.3213	0.8393
24	0.3467	0.6935	0.6533
25	0.283	0.5659	0.717
26	0.2243	0.4487	0.7757
27	0.1673	0.3346	0.8327
28	0.1136	0.2273	0.8864
29	0.2091	0.4181	0.7909
30	0.1496	0.2992	0.8504
31	0.1023	0.2046	0.8977
32	0.2043	0.4086	0.7957
33	0.1915	0.383	0.8085
34	0.1323	0.2645	0.8677
35	0.08523	0.1705	0.9148
36	0.05591	0.1118	0.9441
37	0.03969	0.07938	0.9603
38	0.02201	0.04402	0.978

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 &  0.3731 &  0.7463 &  0.6269 \tabularnewline
12 &  0.3389 &  0.6778 &  0.6611 \tabularnewline
13 &  0.4473 &  0.8946 &  0.5527 \tabularnewline
14 &  0.4596 &  0.9191 &  0.5404 \tabularnewline
15 &  0.4551 &  0.9102 &  0.5449 \tabularnewline
16 &  0.3395 &  0.679 &  0.6605 \tabularnewline
17 &  0.3182 &  0.6364 &  0.6818 \tabularnewline
18 &  0.2402 &  0.4803 &  0.7598 \tabularnewline
19 &  0.1678 &  0.3357 &  0.8322 \tabularnewline
20 &  0.3208 &  0.6417 &  0.6792 \tabularnewline
21 &  0.2462 &  0.4924 &  0.7538 \tabularnewline
22 &  0.2045 &  0.4091 &  0.7955 \tabularnewline
23 &  0.1607 &  0.3213 &  0.8393 \tabularnewline
24 &  0.3467 &  0.6935 &  0.6533 \tabularnewline
25 &  0.283 &  0.5659 &  0.717 \tabularnewline
26 &  0.2243 &  0.4487 &  0.7757 \tabularnewline
27 &  0.1673 &  0.3346 &  0.8327 \tabularnewline
28 &  0.1136 &  0.2273 &  0.8864 \tabularnewline
29 &  0.2091 &  0.4181 &  0.7909 \tabularnewline
30 &  0.1496 &  0.2992 &  0.8504 \tabularnewline
31 &  0.1023 &  0.2046 &  0.8977 \tabularnewline
32 &  0.2043 &  0.4086 &  0.7957 \tabularnewline
33 &  0.1915 &  0.383 &  0.8085 \tabularnewline
34 &  0.1323 &  0.2645 &  0.8677 \tabularnewline
35 &  0.08523 &  0.1705 &  0.9148 \tabularnewline
36 &  0.05591 &  0.1118 &  0.9441 \tabularnewline
37 &  0.03969 &  0.07938 &  0.9603 \tabularnewline
38 &  0.02201 &  0.04402 &  0.978 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286462&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] 0.3731[/C][C] 0.7463[/C][C] 0.6269[/C][/ROW]
[ROW][C]12[/C][C] 0.3389[/C][C] 0.6778[/C][C] 0.6611[/C][/ROW]
[ROW][C]13[/C][C] 0.4473[/C][C] 0.8946[/C][C] 0.5527[/C][/ROW]
[ROW][C]14[/C][C] 0.4596[/C][C] 0.9191[/C][C] 0.5404[/C][/ROW]
[ROW][C]15[/C][C] 0.4551[/C][C] 0.9102[/C][C] 0.5449[/C][/ROW]
[ROW][C]16[/C][C] 0.3395[/C][C] 0.679[/C][C] 0.6605[/C][/ROW]
[ROW][C]17[/C][C] 0.3182[/C][C] 0.6364[/C][C] 0.6818[/C][/ROW]
[ROW][C]18[/C][C] 0.2402[/C][C] 0.4803[/C][C] 0.7598[/C][/ROW]
[ROW][C]19[/C][C] 0.1678[/C][C] 0.3357[/C][C] 0.8322[/C][/ROW]
[ROW][C]20[/C][C] 0.3208[/C][C] 0.6417[/C][C] 0.6792[/C][/ROW]
[ROW][C]21[/C][C] 0.2462[/C][C] 0.4924[/C][C] 0.7538[/C][/ROW]
[ROW][C]22[/C][C] 0.2045[/C][C] 0.4091[/C][C] 0.7955[/C][/ROW]
[ROW][C]23[/C][C] 0.1607[/C][C] 0.3213[/C][C] 0.8393[/C][/ROW]
[ROW][C]24[/C][C] 0.3467[/C][C] 0.6935[/C][C] 0.6533[/C][/ROW]
[ROW][C]25[/C][C] 0.283[/C][C] 0.5659[/C][C] 0.717[/C][/ROW]
[ROW][C]26[/C][C] 0.2243[/C][C] 0.4487[/C][C] 0.7757[/C][/ROW]
[ROW][C]27[/C][C] 0.1673[/C][C] 0.3346[/C][C] 0.8327[/C][/ROW]
[ROW][C]28[/C][C] 0.1136[/C][C] 0.2273[/C][C] 0.8864[/C][/ROW]
[ROW][C]29[/C][C] 0.2091[/C][C] 0.4181[/C][C] 0.7909[/C][/ROW]
[ROW][C]30[/C][C] 0.1496[/C][C] 0.2992[/C][C] 0.8504[/C][/ROW]
[ROW][C]31[/C][C] 0.1023[/C][C] 0.2046[/C][C] 0.8977[/C][/ROW]
[ROW][C]32[/C][C] 0.2043[/C][C] 0.4086[/C][C] 0.7957[/C][/ROW]
[ROW][C]33[/C][C] 0.1915[/C][C] 0.383[/C][C] 0.8085[/C][/ROW]
[ROW][C]34[/C][C] 0.1323[/C][C] 0.2645[/C][C] 0.8677[/C][/ROW]
[ROW][C]35[/C][C] 0.08523[/C][C] 0.1705[/C][C] 0.9148[/C][/ROW]
[ROW][C]36[/C][C] 0.05591[/C][C] 0.1118[/C][C] 0.9441[/C][/ROW]
[ROW][C]37[/C][C] 0.03969[/C][C] 0.07938[/C][C] 0.9603[/C][/ROW]
[ROW][C]38[/C][C] 0.02201[/C][C] 0.04402[/C][C] 0.978[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286462&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286462&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	0.3731	0.7463	0.6269
12	0.3389	0.6778	0.6611
13	0.4473	0.8946	0.5527
14	0.4596	0.9191	0.5404
15	0.4551	0.9102	0.5449
16	0.3395	0.679	0.6605
17	0.3182	0.6364	0.6818
18	0.2402	0.4803	0.7598
19	0.1678	0.3357	0.8322
20	0.3208	0.6417	0.6792
21	0.2462	0.4924	0.7538
22	0.2045	0.4091	0.7955
23	0.1607	0.3213	0.8393
24	0.3467	0.6935	0.6533
25	0.283	0.5659	0.717
26	0.2243	0.4487	0.7757
27	0.1673	0.3346	0.8327
28	0.1136	0.2273	0.8864
29	0.2091	0.4181	0.7909
30	0.1496	0.2992	0.8504
31	0.1023	0.2046	0.8977
32	0.2043	0.4086	0.7957
33	0.1915	0.383	0.8085
34	0.1323	0.2645	0.8677
35	0.08523	0.1705	0.9148
36	0.05591	0.1118	0.9441
37	0.03969	0.07938	0.9603
38	0.02201	0.04402	0.978

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	1	0.0357143	OK
10% type I error level	2	0.0714286	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 & 1 & 0.0357143 & OK \tabularnewline
10% type I error level & 2 & 0.0714286 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286462&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]1[/C][C]0.0357143[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0714286[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286462&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286462&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	1	0.0357143	OK
10% type I error level	2	0.0714286	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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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