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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 11 Dec 2015 12:59:26 +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/11/t1449838986zbq6h9497mxiqe2.htm/, Retrieved Thu, 16 May 2024 20:49:29 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=285932, Retrieved Thu, 16 May 2024 20:49:29 +0000

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

-       [Multiple Regression] [Interactie-effect] [2015-12-11 12:59:26] [83aba8bbc702c7812e095afce40a5d1d] [Current]

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Dataseries X:

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6,8	225	0,442	1530
6,3	180	0,435	1134
6,4	190	0,456	1216
6,2	180	0,416	1116
6,9	205	0,449	1414,5
6,4	225	0,431	1440
6,3	185	0,487	1165,5
6,8	235	0,469	1598
6,9	235	0,435	1621,5
6,7	210	0,48	1407
6,9	245	0,516	1690,5
6,9	245	0,493	1690,5
6,3	185	0,374	1165,5
6,1	185	0,424	1128,5
6,2	180	0,441	1116
6,8	220	0,503	1496
6,5	194	0,503	1261
7,6	225	0,425	1710
6,3	210	0,371	1323
7,1	240	0,504	1704
6,8	225	0,4	1530
7,3	263	0,482	1919,9
6,4	210	0,475	1344
6,8	235	0,428	1598
7,2	230	0,559	1656
6,4	190	0,441	1216
6,6	220	0,492	1452
6,8	210	0,402	1428
6,1	180	0,415	1098
6,5	235	0,492	1527,5
6,4	185	0,484	1184
6	175	0,387	1050
6	192	0,436	1152
7,3	263	0,482	1919,9
6,1	180	0,34	1098
6,7	240	0,516	1608
6,4	210	0,475	1344
5,8	160	0,412	928
6,9	230	0,411	1587
7	245	0,407	1715
7,3	228	0,445	1664,4
5,9	155	0,291	914,5
6,2	200	0,449	1240
6,8	235	0,546	1598
7	235	0,48	1645
5,9	105	0,359	619,5
6,1	180	0,528	1098
5,7	185	0,352	1054,5
7,1	245	0,414	1739,5
5,8	180	0,425	1044
7,4	240	0,599	1776
6,8	225	0,482	1530
6,8	215	0,457	1462
7	230	0,435	1610

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'George Udny Yule' @ yule.wessa.net
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 5 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285932&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285932&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285932&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	5 seconds
R Server	'George Udny Yule' @ yule.wessa.net
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Multiple Linear Regression - Estimated Regression Equation
X3[t] = -0.772188 + 0.173308X1[t] + 0.00475541X2[t] -0.000658866`Interactie-effect\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
X3[t] =  -0.772188 +  0.173308X1[t] +  0.00475541X2[t] -0.000658866`Interactie-effect\r`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285932&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]X3[t] =  -0.772188 +  0.173308X1[t] +  0.00475541X2[t] -0.000658866`Interactie-effect\r`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285932&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285932&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
X3[t] = -0.772188 + 0.173308X1[t] + 0.00475541X2[t] -0.000658866`Interactie-effect\r`[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)	-0.7722	0.7818	-9.8780e-01	0.328	0.164
X1	+0.1733	0.127	+1.3650e+00	0.1783	0.08917
X2	+0.004755	0.003508	+1.3560e+00	0.1813	0.09067
`Interactie-effect\r`	-0.0006589	0.0005567	-1.1830e+00	0.2422	0.1211

\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) & -0.7722 &  0.7818 & -9.8780e-01 &  0.328 &  0.164 \tabularnewline
X1 & +0.1733 &  0.127 & +1.3650e+00 &  0.1783 &  0.08917 \tabularnewline
X2 & +0.004755 &  0.003508 & +1.3560e+00 &  0.1813 &  0.09067 \tabularnewline
`Interactie-effect\r` & -0.0006589 &  0.0005567 & -1.1830e+00 &  0.2422 &  0.1211 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285932&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]-0.7722[/C][C] 0.7818[/C][C]-9.8780e-01[/C][C] 0.328[/C][C] 0.164[/C][/ROW]
[ROW][C]X1[/C][C]+0.1733[/C][C] 0.127[/C][C]+1.3650e+00[/C][C] 0.1783[/C][C] 0.08917[/C][/ROW]
[ROW][C]X2[/C][C]+0.004755[/C][C] 0.003508[/C][C]+1.3560e+00[/C][C] 0.1813[/C][C] 0.09067[/C][/ROW]
[ROW][C]`Interactie-effect\r`[/C][C]-0.0006589[/C][C] 0.0005567[/C][C]-1.1830e+00[/C][C] 0.2422[/C][C] 0.1211[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285932&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285932&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)	-0.7722	0.7818	-9.8780e-01	0.328	0.164
X1	+0.1733	0.127	+1.3650e+00	0.1783	0.08917
X2	+0.004755	0.003508	+1.3560e+00	0.1813	0.09067
`Interactie-effect\r`	-0.0006589	0.0005567	-1.1830e+00	0.2422	0.1211

Multiple Linear Regression - Regression Statistics
Multiple R	0.5476
R-squared	0.2998
Adjusted R-squared	0.2578
F-TEST (value)	7.137
F-TEST (DF numerator)	3
F-TEST (DF denominator)	50
p-value	0.0004414
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.04872
Sum Squared Residuals	0.1187

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5476 \tabularnewline
R-squared &  0.2998 \tabularnewline
Adjusted R-squared &  0.2578 \tabularnewline
F-TEST (value) &  7.137 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 50 \tabularnewline
p-value &  0.0004414 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.04872 \tabularnewline
Sum Squared Residuals &  0.1187 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285932&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5476[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.2998[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.2578[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 7.137[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]50[/C][/ROW]
[ROW][C]p-value[/C][C] 0.0004414[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.04872[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 0.1187[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285932&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285932&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	0.5476
R-squared	0.2998
Adjusted R-squared	0.2578
F-TEST (value)	7.137
F-TEST (DF numerator)	3
F-TEST (DF denominator)	50
p-value	0.0004414
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.04872
Sum Squared Residuals	0.1187

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	0.442	0.4682	-0.02621
2	0.435	0.4285	0.006525
3	0.456	0.4393	0.01667
4	0.416	0.423	-0.007004
5	0.449	0.4665	-0.01753
6	0.431	0.4582	-0.02719
7	0.487	0.4315	0.0555
8	0.469	0.471	-0.001963
9	0.435	0.4728	-0.03781
10	0.48	0.4606	0.01941
11	0.516	0.4749	0.0411
12	0.493	0.4749	0.0181
13	0.374	0.4315	-0.0575
14	0.424	0.4212	0.002786
15	0.441	0.423	0.018
16	0.503	0.4668	0.03616
17	0.503	0.446	0.05696
18	0.425	0.4883	-0.06326
19	0.371	0.4466	-0.07561
20	0.504	0.4769	0.02711
21	0.4	0.4682	-0.06821
22	0.482	0.4787	0.00332
23	0.475	0.4501	0.02489
24	0.428	0.471	-0.04296
25	0.559	0.4783	0.0807
26	0.441	0.4393	0.001667
27	0.492	0.4612	0.03084
28	0.402	0.4641	-0.06209
29	0.415	0.4175	-0.002533
30	0.492	0.4654	0.02658
31	0.484	0.4366	0.04736
32	0.387	0.4081	-0.02105
33	0.436	0.4217	0.01431
34	0.482	0.4787	0.00332
35	0.34	0.4175	-0.07753
36	0.516	0.4708	0.04518
37	0.475	0.4501	0.02489
38	0.412	0.3824	0.02956
39	0.411	0.4718	-0.06076
40	0.407	0.4761	-0.06909
41	0.445	0.4806	-0.03558
42	0.291	0.3849	-0.09389
43	0.449	0.4364	0.01259
44	0.546	0.471	0.07504
45	0.48	0.4747	0.005342
46	0.359	0.3415	0.01752
47	0.528	0.4175	0.1105
48	0.352	0.4006	-0.04865
49	0.414	0.4773	-0.06328
50	0.425	0.4011	0.02388
51	0.599	0.4814	0.1176
52	0.482	0.4682	0.01379
53	0.457	0.4655	-0.008461
54	0.435	0.4739	-0.03894

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  0.442 &  0.4682 & -0.02621 \tabularnewline
2 &  0.435 &  0.4285 &  0.006525 \tabularnewline
3 &  0.456 &  0.4393 &  0.01667 \tabularnewline
4 &  0.416 &  0.423 & -0.007004 \tabularnewline
5 &  0.449 &  0.4665 & -0.01753 \tabularnewline
6 &  0.431 &  0.4582 & -0.02719 \tabularnewline
7 &  0.487 &  0.4315 &  0.0555 \tabularnewline
8 &  0.469 &  0.471 & -0.001963 \tabularnewline
9 &  0.435 &  0.4728 & -0.03781 \tabularnewline
10 &  0.48 &  0.4606 &  0.01941 \tabularnewline
11 &  0.516 &  0.4749 &  0.0411 \tabularnewline
12 &  0.493 &  0.4749 &  0.0181 \tabularnewline
13 &  0.374 &  0.4315 & -0.0575 \tabularnewline
14 &  0.424 &  0.4212 &  0.002786 \tabularnewline
15 &  0.441 &  0.423 &  0.018 \tabularnewline
16 &  0.503 &  0.4668 &  0.03616 \tabularnewline
17 &  0.503 &  0.446 &  0.05696 \tabularnewline
18 &  0.425 &  0.4883 & -0.06326 \tabularnewline
19 &  0.371 &  0.4466 & -0.07561 \tabularnewline
20 &  0.504 &  0.4769 &  0.02711 \tabularnewline
21 &  0.4 &  0.4682 & -0.06821 \tabularnewline
22 &  0.482 &  0.4787 &  0.00332 \tabularnewline
23 &  0.475 &  0.4501 &  0.02489 \tabularnewline
24 &  0.428 &  0.471 & -0.04296 \tabularnewline
25 &  0.559 &  0.4783 &  0.0807 \tabularnewline
26 &  0.441 &  0.4393 &  0.001667 \tabularnewline
27 &  0.492 &  0.4612 &  0.03084 \tabularnewline
28 &  0.402 &  0.4641 & -0.06209 \tabularnewline
29 &  0.415 &  0.4175 & -0.002533 \tabularnewline
30 &  0.492 &  0.4654 &  0.02658 \tabularnewline
31 &  0.484 &  0.4366 &  0.04736 \tabularnewline
32 &  0.387 &  0.4081 & -0.02105 \tabularnewline
33 &  0.436 &  0.4217 &  0.01431 \tabularnewline
34 &  0.482 &  0.4787 &  0.00332 \tabularnewline
35 &  0.34 &  0.4175 & -0.07753 \tabularnewline
36 &  0.516 &  0.4708 &  0.04518 \tabularnewline
37 &  0.475 &  0.4501 &  0.02489 \tabularnewline
38 &  0.412 &  0.3824 &  0.02956 \tabularnewline
39 &  0.411 &  0.4718 & -0.06076 \tabularnewline
40 &  0.407 &  0.4761 & -0.06909 \tabularnewline
41 &  0.445 &  0.4806 & -0.03558 \tabularnewline
42 &  0.291 &  0.3849 & -0.09389 \tabularnewline
43 &  0.449 &  0.4364 &  0.01259 \tabularnewline
44 &  0.546 &  0.471 &  0.07504 \tabularnewline
45 &  0.48 &  0.4747 &  0.005342 \tabularnewline
46 &  0.359 &  0.3415 &  0.01752 \tabularnewline
47 &  0.528 &  0.4175 &  0.1105 \tabularnewline
48 &  0.352 &  0.4006 & -0.04865 \tabularnewline
49 &  0.414 &  0.4773 & -0.06328 \tabularnewline
50 &  0.425 &  0.4011 &  0.02388 \tabularnewline
51 &  0.599 &  0.4814 &  0.1176 \tabularnewline
52 &  0.482 &  0.4682 &  0.01379 \tabularnewline
53 &  0.457 &  0.4655 & -0.008461 \tabularnewline
54 &  0.435 &  0.4739 & -0.03894 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285932&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] 0.442[/C][C] 0.4682[/C][C]-0.02621[/C][/ROW]
[ROW][C]2[/C][C] 0.435[/C][C] 0.4285[/C][C] 0.006525[/C][/ROW]
[ROW][C]3[/C][C] 0.456[/C][C] 0.4393[/C][C] 0.01667[/C][/ROW]
[ROW][C]4[/C][C] 0.416[/C][C] 0.423[/C][C]-0.007004[/C][/ROW]
[ROW][C]5[/C][C] 0.449[/C][C] 0.4665[/C][C]-0.01753[/C][/ROW]
[ROW][C]6[/C][C] 0.431[/C][C] 0.4582[/C][C]-0.02719[/C][/ROW]
[ROW][C]7[/C][C] 0.487[/C][C] 0.4315[/C][C] 0.0555[/C][/ROW]
[ROW][C]8[/C][C] 0.469[/C][C] 0.471[/C][C]-0.001963[/C][/ROW]
[ROW][C]9[/C][C] 0.435[/C][C] 0.4728[/C][C]-0.03781[/C][/ROW]
[ROW][C]10[/C][C] 0.48[/C][C] 0.4606[/C][C] 0.01941[/C][/ROW]
[ROW][C]11[/C][C] 0.516[/C][C] 0.4749[/C][C] 0.0411[/C][/ROW]
[ROW][C]12[/C][C] 0.493[/C][C] 0.4749[/C][C] 0.0181[/C][/ROW]
[ROW][C]13[/C][C] 0.374[/C][C] 0.4315[/C][C]-0.0575[/C][/ROW]
[ROW][C]14[/C][C] 0.424[/C][C] 0.4212[/C][C] 0.002786[/C][/ROW]
[ROW][C]15[/C][C] 0.441[/C][C] 0.423[/C][C] 0.018[/C][/ROW]
[ROW][C]16[/C][C] 0.503[/C][C] 0.4668[/C][C] 0.03616[/C][/ROW]
[ROW][C]17[/C][C] 0.503[/C][C] 0.446[/C][C] 0.05696[/C][/ROW]
[ROW][C]18[/C][C] 0.425[/C][C] 0.4883[/C][C]-0.06326[/C][/ROW]
[ROW][C]19[/C][C] 0.371[/C][C] 0.4466[/C][C]-0.07561[/C][/ROW]
[ROW][C]20[/C][C] 0.504[/C][C] 0.4769[/C][C] 0.02711[/C][/ROW]
[ROW][C]21[/C][C] 0.4[/C][C] 0.4682[/C][C]-0.06821[/C][/ROW]
[ROW][C]22[/C][C] 0.482[/C][C] 0.4787[/C][C] 0.00332[/C][/ROW]
[ROW][C]23[/C][C] 0.475[/C][C] 0.4501[/C][C] 0.02489[/C][/ROW]
[ROW][C]24[/C][C] 0.428[/C][C] 0.471[/C][C]-0.04296[/C][/ROW]
[ROW][C]25[/C][C] 0.559[/C][C] 0.4783[/C][C] 0.0807[/C][/ROW]
[ROW][C]26[/C][C] 0.441[/C][C] 0.4393[/C][C] 0.001667[/C][/ROW]
[ROW][C]27[/C][C] 0.492[/C][C] 0.4612[/C][C] 0.03084[/C][/ROW]
[ROW][C]28[/C][C] 0.402[/C][C] 0.4641[/C][C]-0.06209[/C][/ROW]
[ROW][C]29[/C][C] 0.415[/C][C] 0.4175[/C][C]-0.002533[/C][/ROW]
[ROW][C]30[/C][C] 0.492[/C][C] 0.4654[/C][C] 0.02658[/C][/ROW]
[ROW][C]31[/C][C] 0.484[/C][C] 0.4366[/C][C] 0.04736[/C][/ROW]
[ROW][C]32[/C][C] 0.387[/C][C] 0.4081[/C][C]-0.02105[/C][/ROW]
[ROW][C]33[/C][C] 0.436[/C][C] 0.4217[/C][C] 0.01431[/C][/ROW]
[ROW][C]34[/C][C] 0.482[/C][C] 0.4787[/C][C] 0.00332[/C][/ROW]
[ROW][C]35[/C][C] 0.34[/C][C] 0.4175[/C][C]-0.07753[/C][/ROW]
[ROW][C]36[/C][C] 0.516[/C][C] 0.4708[/C][C] 0.04518[/C][/ROW]
[ROW][C]37[/C][C] 0.475[/C][C] 0.4501[/C][C] 0.02489[/C][/ROW]
[ROW][C]38[/C][C] 0.412[/C][C] 0.3824[/C][C] 0.02956[/C][/ROW]
[ROW][C]39[/C][C] 0.411[/C][C] 0.4718[/C][C]-0.06076[/C][/ROW]
[ROW][C]40[/C][C] 0.407[/C][C] 0.4761[/C][C]-0.06909[/C][/ROW]
[ROW][C]41[/C][C] 0.445[/C][C] 0.4806[/C][C]-0.03558[/C][/ROW]
[ROW][C]42[/C][C] 0.291[/C][C] 0.3849[/C][C]-0.09389[/C][/ROW]
[ROW][C]43[/C][C] 0.449[/C][C] 0.4364[/C][C] 0.01259[/C][/ROW]
[ROW][C]44[/C][C] 0.546[/C][C] 0.471[/C][C] 0.07504[/C][/ROW]
[ROW][C]45[/C][C] 0.48[/C][C] 0.4747[/C][C] 0.005342[/C][/ROW]
[ROW][C]46[/C][C] 0.359[/C][C] 0.3415[/C][C] 0.01752[/C][/ROW]
[ROW][C]47[/C][C] 0.528[/C][C] 0.4175[/C][C] 0.1105[/C][/ROW]
[ROW][C]48[/C][C] 0.352[/C][C] 0.4006[/C][C]-0.04865[/C][/ROW]
[ROW][C]49[/C][C] 0.414[/C][C] 0.4773[/C][C]-0.06328[/C][/ROW]
[ROW][C]50[/C][C] 0.425[/C][C] 0.4011[/C][C] 0.02388[/C][/ROW]
[ROW][C]51[/C][C] 0.599[/C][C] 0.4814[/C][C] 0.1176[/C][/ROW]
[ROW][C]52[/C][C] 0.482[/C][C] 0.4682[/C][C] 0.01379[/C][/ROW]
[ROW][C]53[/C][C] 0.457[/C][C] 0.4655[/C][C]-0.008461[/C][/ROW]
[ROW][C]54[/C][C] 0.435[/C][C] 0.4739[/C][C]-0.03894[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285932&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285932&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	0.442	0.4682	-0.02621
2	0.435	0.4285	0.006525
3	0.456	0.4393	0.01667
4	0.416	0.423	-0.007004
5	0.449	0.4665	-0.01753
6	0.431	0.4582	-0.02719
7	0.487	0.4315	0.0555
8	0.469	0.471	-0.001963
9	0.435	0.4728	-0.03781
10	0.48	0.4606	0.01941
11	0.516	0.4749	0.0411
12	0.493	0.4749	0.0181
13	0.374	0.4315	-0.0575
14	0.424	0.4212	0.002786
15	0.441	0.423	0.018
16	0.503	0.4668	0.03616
17	0.503	0.446	0.05696
18	0.425	0.4883	-0.06326
19	0.371	0.4466	-0.07561
20	0.504	0.4769	0.02711
21	0.4	0.4682	-0.06821
22	0.482	0.4787	0.00332
23	0.475	0.4501	0.02489
24	0.428	0.471	-0.04296
25	0.559	0.4783	0.0807
26	0.441	0.4393	0.001667
27	0.492	0.4612	0.03084
28	0.402	0.4641	-0.06209
29	0.415	0.4175	-0.002533
30	0.492	0.4654	0.02658
31	0.484	0.4366	0.04736
32	0.387	0.4081	-0.02105
33	0.436	0.4217	0.01431
34	0.482	0.4787	0.00332
35	0.34	0.4175	-0.07753
36	0.516	0.4708	0.04518
37	0.475	0.4501	0.02489
38	0.412	0.3824	0.02956
39	0.411	0.4718	-0.06076
40	0.407	0.4761	-0.06909
41	0.445	0.4806	-0.03558
42	0.291	0.3849	-0.09389
43	0.449	0.4364	0.01259
44	0.546	0.471	0.07504
45	0.48	0.4747	0.005342
46	0.359	0.3415	0.01752
47	0.528	0.4175	0.1105
48	0.352	0.4006	-0.04865
49	0.414	0.4773	-0.06328
50	0.425	0.4011	0.02388
51	0.599	0.4814	0.1176
52	0.482	0.4682	0.01379
53	0.457	0.4655	-0.008461
54	0.435	0.4739	-0.03894

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.1964	0.3929	0.8036
8	0.1279	0.2558	0.8721
9	0.06822	0.1364	0.9318
10	0.04437	0.08874	0.9556
11	0.08114	0.1623	0.9189
12	0.04475	0.08951	0.9552
13	0.09666	0.1933	0.9033
14	0.05598	0.1119	0.944
15	0.03196	0.06392	0.968
16	0.02921	0.05842	0.9708
17	0.04361	0.08722	0.9564
18	0.05177	0.1035	0.9482
19	0.1366	0.2733	0.8634
20	0.1071	0.2143	0.8929
21	0.141	0.2821	0.859
22	0.0983	0.1966	0.9017
23	0.07427	0.1485	0.9257
24	0.06405	0.1281	0.9359
25	0.1336	0.2672	0.8664
26	0.09265	0.1853	0.9074
27	0.07565	0.1513	0.9243
28	0.09048	0.181	0.9095
29	0.06201	0.124	0.938
30	0.04675	0.09349	0.9533
31	0.04234	0.08469	0.9577
32	0.03085	0.0617	0.9691
33	0.01922	0.03844	0.9808
34	0.01124	0.02249	0.9888
35	0.02493	0.04986	0.9751
36	0.0225	0.04499	0.9775
37	0.01402	0.02804	0.986
38	0.009918	0.01984	0.9901
39	0.01232	0.02464	0.9877
40	0.01576	0.03153	0.9842
41	0.01505	0.03011	0.9849
42	0.04952	0.09903	0.9505
43	0.02794	0.05587	0.9721
44	0.03814	0.07629	0.9619
45	0.01931	0.03862	0.9807
46	0.5291	0.9417	0.4709
47	0.4429	0.8858	0.5571

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 &  0.1964 &  0.3929 &  0.8036 \tabularnewline
8 &  0.1279 &  0.2558 &  0.8721 \tabularnewline
9 &  0.06822 &  0.1364 &  0.9318 \tabularnewline
10 &  0.04437 &  0.08874 &  0.9556 \tabularnewline
11 &  0.08114 &  0.1623 &  0.9189 \tabularnewline
12 &  0.04475 &  0.08951 &  0.9552 \tabularnewline
13 &  0.09666 &  0.1933 &  0.9033 \tabularnewline
14 &  0.05598 &  0.1119 &  0.944 \tabularnewline
15 &  0.03196 &  0.06392 &  0.968 \tabularnewline
16 &  0.02921 &  0.05842 &  0.9708 \tabularnewline
17 &  0.04361 &  0.08722 &  0.9564 \tabularnewline
18 &  0.05177 &  0.1035 &  0.9482 \tabularnewline
19 &  0.1366 &  0.2733 &  0.8634 \tabularnewline
20 &  0.1071 &  0.2143 &  0.8929 \tabularnewline
21 &  0.141 &  0.2821 &  0.859 \tabularnewline
22 &  0.0983 &  0.1966 &  0.9017 \tabularnewline
23 &  0.07427 &  0.1485 &  0.9257 \tabularnewline
24 &  0.06405 &  0.1281 &  0.9359 \tabularnewline
25 &  0.1336 &  0.2672 &  0.8664 \tabularnewline
26 &  0.09265 &  0.1853 &  0.9074 \tabularnewline
27 &  0.07565 &  0.1513 &  0.9243 \tabularnewline
28 &  0.09048 &  0.181 &  0.9095 \tabularnewline
29 &  0.06201 &  0.124 &  0.938 \tabularnewline
30 &  0.04675 &  0.09349 &  0.9533 \tabularnewline
31 &  0.04234 &  0.08469 &  0.9577 \tabularnewline
32 &  0.03085 &  0.0617 &  0.9691 \tabularnewline
33 &  0.01922 &  0.03844 &  0.9808 \tabularnewline
34 &  0.01124 &  0.02249 &  0.9888 \tabularnewline
35 &  0.02493 &  0.04986 &  0.9751 \tabularnewline
36 &  0.0225 &  0.04499 &  0.9775 \tabularnewline
37 &  0.01402 &  0.02804 &  0.986 \tabularnewline
38 &  0.009918 &  0.01984 &  0.9901 \tabularnewline
39 &  0.01232 &  0.02464 &  0.9877 \tabularnewline
40 &  0.01576 &  0.03153 &  0.9842 \tabularnewline
41 &  0.01505 &  0.03011 &  0.9849 \tabularnewline
42 &  0.04952 &  0.09903 &  0.9505 \tabularnewline
43 &  0.02794 &  0.05587 &  0.9721 \tabularnewline
44 &  0.03814 &  0.07629 &  0.9619 \tabularnewline
45 &  0.01931 &  0.03862 &  0.9807 \tabularnewline
46 &  0.5291 &  0.9417 &  0.4709 \tabularnewline
47 &  0.4429 &  0.8858 &  0.5571 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285932&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]7[/C][C] 0.1964[/C][C] 0.3929[/C][C] 0.8036[/C][/ROW]
[ROW][C]8[/C][C] 0.1279[/C][C] 0.2558[/C][C] 0.8721[/C][/ROW]
[ROW][C]9[/C][C] 0.06822[/C][C] 0.1364[/C][C] 0.9318[/C][/ROW]
[ROW][C]10[/C][C] 0.04437[/C][C] 0.08874[/C][C] 0.9556[/C][/ROW]
[ROW][C]11[/C][C] 0.08114[/C][C] 0.1623[/C][C] 0.9189[/C][/ROW]
[ROW][C]12[/C][C] 0.04475[/C][C] 0.08951[/C][C] 0.9552[/C][/ROW]
[ROW][C]13[/C][C] 0.09666[/C][C] 0.1933[/C][C] 0.9033[/C][/ROW]
[ROW][C]14[/C][C] 0.05598[/C][C] 0.1119[/C][C] 0.944[/C][/ROW]
[ROW][C]15[/C][C] 0.03196[/C][C] 0.06392[/C][C] 0.968[/C][/ROW]
[ROW][C]16[/C][C] 0.02921[/C][C] 0.05842[/C][C] 0.9708[/C][/ROW]
[ROW][C]17[/C][C] 0.04361[/C][C] 0.08722[/C][C] 0.9564[/C][/ROW]
[ROW][C]18[/C][C] 0.05177[/C][C] 0.1035[/C][C] 0.9482[/C][/ROW]
[ROW][C]19[/C][C] 0.1366[/C][C] 0.2733[/C][C] 0.8634[/C][/ROW]
[ROW][C]20[/C][C] 0.1071[/C][C] 0.2143[/C][C] 0.8929[/C][/ROW]
[ROW][C]21[/C][C] 0.141[/C][C] 0.2821[/C][C] 0.859[/C][/ROW]
[ROW][C]22[/C][C] 0.0983[/C][C] 0.1966[/C][C] 0.9017[/C][/ROW]
[ROW][C]23[/C][C] 0.07427[/C][C] 0.1485[/C][C] 0.9257[/C][/ROW]
[ROW][C]24[/C][C] 0.06405[/C][C] 0.1281[/C][C] 0.9359[/C][/ROW]
[ROW][C]25[/C][C] 0.1336[/C][C] 0.2672[/C][C] 0.8664[/C][/ROW]
[ROW][C]26[/C][C] 0.09265[/C][C] 0.1853[/C][C] 0.9074[/C][/ROW]
[ROW][C]27[/C][C] 0.07565[/C][C] 0.1513[/C][C] 0.9243[/C][/ROW]
[ROW][C]28[/C][C] 0.09048[/C][C] 0.181[/C][C] 0.9095[/C][/ROW]
[ROW][C]29[/C][C] 0.06201[/C][C] 0.124[/C][C] 0.938[/C][/ROW]
[ROW][C]30[/C][C] 0.04675[/C][C] 0.09349[/C][C] 0.9533[/C][/ROW]
[ROW][C]31[/C][C] 0.04234[/C][C] 0.08469[/C][C] 0.9577[/C][/ROW]
[ROW][C]32[/C][C] 0.03085[/C][C] 0.0617[/C][C] 0.9691[/C][/ROW]
[ROW][C]33[/C][C] 0.01922[/C][C] 0.03844[/C][C] 0.9808[/C][/ROW]
[ROW][C]34[/C][C] 0.01124[/C][C] 0.02249[/C][C] 0.9888[/C][/ROW]
[ROW][C]35[/C][C] 0.02493[/C][C] 0.04986[/C][C] 0.9751[/C][/ROW]
[ROW][C]36[/C][C] 0.0225[/C][C] 0.04499[/C][C] 0.9775[/C][/ROW]
[ROW][C]37[/C][C] 0.01402[/C][C] 0.02804[/C][C] 0.986[/C][/ROW]
[ROW][C]38[/C][C] 0.009918[/C][C] 0.01984[/C][C] 0.9901[/C][/ROW]
[ROW][C]39[/C][C] 0.01232[/C][C] 0.02464[/C][C] 0.9877[/C][/ROW]
[ROW][C]40[/C][C] 0.01576[/C][C] 0.03153[/C][C] 0.9842[/C][/ROW]
[ROW][C]41[/C][C] 0.01505[/C][C] 0.03011[/C][C] 0.9849[/C][/ROW]
[ROW][C]42[/C][C] 0.04952[/C][C] 0.09903[/C][C] 0.9505[/C][/ROW]
[ROW][C]43[/C][C] 0.02794[/C][C] 0.05587[/C][C] 0.9721[/C][/ROW]
[ROW][C]44[/C][C] 0.03814[/C][C] 0.07629[/C][C] 0.9619[/C][/ROW]
[ROW][C]45[/C][C] 0.01931[/C][C] 0.03862[/C][C] 0.9807[/C][/ROW]
[ROW][C]46[/C][C] 0.5291[/C][C] 0.9417[/C][C] 0.4709[/C][/ROW]
[ROW][C]47[/C][C] 0.4429[/C][C] 0.8858[/C][C] 0.5571[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285932&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285932&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
7	0.1964	0.3929	0.8036
8	0.1279	0.2558	0.8721
9	0.06822	0.1364	0.9318
10	0.04437	0.08874	0.9556
11	0.08114	0.1623	0.9189
12	0.04475	0.08951	0.9552
13	0.09666	0.1933	0.9033
14	0.05598	0.1119	0.944
15	0.03196	0.06392	0.968
16	0.02921	0.05842	0.9708
17	0.04361	0.08722	0.9564
18	0.05177	0.1035	0.9482
19	0.1366	0.2733	0.8634
20	0.1071	0.2143	0.8929
21	0.141	0.2821	0.859
22	0.0983	0.1966	0.9017
23	0.07427	0.1485	0.9257
24	0.06405	0.1281	0.9359
25	0.1336	0.2672	0.8664
26	0.09265	0.1853	0.9074
27	0.07565	0.1513	0.9243
28	0.09048	0.181	0.9095
29	0.06201	0.124	0.938
30	0.04675	0.09349	0.9533
31	0.04234	0.08469	0.9577
32	0.03085	0.0617	0.9691
33	0.01922	0.03844	0.9808
34	0.01124	0.02249	0.9888
35	0.02493	0.04986	0.9751
36	0.0225	0.04499	0.9775
37	0.01402	0.02804	0.986
38	0.009918	0.01984	0.9901
39	0.01232	0.02464	0.9877
40	0.01576	0.03153	0.9842
41	0.01505	0.03011	0.9849
42	0.04952	0.09903	0.9505
43	0.02794	0.05587	0.9721
44	0.03814	0.07629	0.9619
45	0.01931	0.03862	0.9807
46	0.5291	0.9417	0.4709
47	0.4429	0.8858	0.5571

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	10	0.243902	NOK
10% type I error level	21	0.512195	NOK

\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 & 10 & 0.243902 & NOK \tabularnewline
10% type I error level & 21 & 0.512195 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285932&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]10[/C][C]0.243902[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]21[/C][C]0.512195[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285932&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285932&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	10	0.243902	NOK
10% type I error level	21	0.512195	NOK

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 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

Parameters (R input):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; 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'){
(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