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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 13 Dec 2010 14:30:19 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/13/t1292250547trc6dzx10y1cn0i.htm/, Retrieved Mon, 06 May 2024 13:00:43 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108948, Retrieved Mon, 06 May 2024 13:00:43 +0000

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Estimated Impact

178

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

-     [Multiple Regression] [Workshop 5] [2010-12-07 19:07:21] [29e492448d11757ae0fad5ef6e7f8e86]
-    D  [Multiple Regression] [Multiple Regression] [2010-12-08 17:52:32] [6a528ed37664d761abf4790b0717b23b]
-   PD      [Multiple Regression] [Paper MR] [2010-12-13 14:30:19] [fd751bc40fbbb4c72222c10190589d42] [Current]

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2
1
-8
-1
1
-1
2
2
1
-1
-2
-2
-1
-8
-4
-6
-3
-3
-7
-9
-11
-13
-11
-9
-17
-22
-25
-20
-24
-24
-22
-19
-18
-17
-11
-11
-12
-10
-15
-15
-15
-13
-8
-13
-9
-7
-4
-4
-2
0

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108948&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108948&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108948&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	6 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 1.41071428571429 -0.818452380952382M1[t] -2.35476190476191M2[t] -8.8732142857143M3[t] -6.10952380952381M4[t] -5.59583333333334M5[t] -5.33214285714286M6[t] -3.56845238095238M7[t] -4.30476190476191M8[t] -3.54107142857143M9[t] -3.52738095238095M10[t] -0.763690476190479M11[t] -0.263690476190476t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  1.41071428571429 -0.818452380952382M1[t] -2.35476190476191M2[t] -8.8732142857143M3[t] -6.10952380952381M4[t] -5.59583333333334M5[t] -5.33214285714286M6[t] -3.56845238095238M7[t] -4.30476190476191M8[t] -3.54107142857143M9[t] -3.52738095238095M10[t] -0.763690476190479M11[t] -0.263690476190476t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108948&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  1.41071428571429 -0.818452380952382M1[t] -2.35476190476191M2[t] -8.8732142857143M3[t] -6.10952380952381M4[t] -5.59583333333334M5[t] -5.33214285714286M6[t] -3.56845238095238M7[t] -4.30476190476191M8[t] -3.54107142857143M9[t] -3.52738095238095M10[t] -0.763690476190479M11[t] -0.263690476190476t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108948&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108948&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
Y[t] = + 1.41071428571429 -0.818452380952382M1[t] -2.35476190476191M2[t] -8.8732142857143M3[t] -6.10952380952381M4[t] -5.59583333333334M5[t] -5.33214285714286M6[t] -3.56845238095238M7[t] -4.30476190476191M8[t] -3.54107142857143M9[t] -3.52738095238095M10[t] -0.763690476190479M11[t] -0.263690476190476t + 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)	1.41071428571429	4.374293	0.3225	0.748888	0.374444
M1	-0.818452380952382	5.051552	-0.162	0.872172	0.436086
M2	-2.35476190476191	5.046566	-0.4666	0.643517	0.321759
M3	-8.8732142857143	5.352691	-1.6577	0.105832	0.052916
M4	-6.10952380952381	5.343799	-1.1433	0.260261	0.13013
M5	-5.59583333333334	5.335941	-1.0487	0.301116	0.150558
M6	-5.33214285714286	5.329121	-1.0006	0.323535	0.161768
M7	-3.56845238095238	5.323344	-0.6703	0.506804	0.253402
M8	-4.30476190476191	5.318612	-0.8094	0.423473	0.211736
M9	-3.54107142857143	5.314929	-0.6662	0.509384	0.254692
M10	-3.52738095238095	5.312297	-0.664	0.510805	0.255403
M11	-0.763690476190479	5.310717	-0.1438	0.886437	0.443219
t	-0.263690476190476	0.074799	-3.5253	0.001146	0.000573

\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) & 1.41071428571429 & 4.374293 & 0.3225 & 0.748888 & 0.374444 \tabularnewline
M1 & -0.818452380952382 & 5.051552 & -0.162 & 0.872172 & 0.436086 \tabularnewline
M2 & -2.35476190476191 & 5.046566 & -0.4666 & 0.643517 & 0.321759 \tabularnewline
M3 & -8.8732142857143 & 5.352691 & -1.6577 & 0.105832 & 0.052916 \tabularnewline
M4 & -6.10952380952381 & 5.343799 & -1.1433 & 0.260261 & 0.13013 \tabularnewline
M5 & -5.59583333333334 & 5.335941 & -1.0487 & 0.301116 & 0.150558 \tabularnewline
M6 & -5.33214285714286 & 5.329121 & -1.0006 & 0.323535 & 0.161768 \tabularnewline
M7 & -3.56845238095238 & 5.323344 & -0.6703 & 0.506804 & 0.253402 \tabularnewline
M8 & -4.30476190476191 & 5.318612 & -0.8094 & 0.423473 & 0.211736 \tabularnewline
M9 & -3.54107142857143 & 5.314929 & -0.6662 & 0.509384 & 0.254692 \tabularnewline
M10 & -3.52738095238095 & 5.312297 & -0.664 & 0.510805 & 0.255403 \tabularnewline
M11 & -0.763690476190479 & 5.310717 & -0.1438 & 0.886437 & 0.443219 \tabularnewline
t & -0.263690476190476 & 0.074799 & -3.5253 & 0.001146 & 0.000573 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108948&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]1.41071428571429[/C][C]4.374293[/C][C]0.3225[/C][C]0.748888[/C][C]0.374444[/C][/ROW]
[ROW][C]M1[/C][C]-0.818452380952382[/C][C]5.051552[/C][C]-0.162[/C][C]0.872172[/C][C]0.436086[/C][/ROW]
[ROW][C]M2[/C][C]-2.35476190476191[/C][C]5.046566[/C][C]-0.4666[/C][C]0.643517[/C][C]0.321759[/C][/ROW]
[ROW][C]M3[/C][C]-8.8732142857143[/C][C]5.352691[/C][C]-1.6577[/C][C]0.105832[/C][C]0.052916[/C][/ROW]
[ROW][C]M4[/C][C]-6.10952380952381[/C][C]5.343799[/C][C]-1.1433[/C][C]0.260261[/C][C]0.13013[/C][/ROW]
[ROW][C]M5[/C][C]-5.59583333333334[/C][C]5.335941[/C][C]-1.0487[/C][C]0.301116[/C][C]0.150558[/C][/ROW]
[ROW][C]M6[/C][C]-5.33214285714286[/C][C]5.329121[/C][C]-1.0006[/C][C]0.323535[/C][C]0.161768[/C][/ROW]
[ROW][C]M7[/C][C]-3.56845238095238[/C][C]5.323344[/C][C]-0.6703[/C][C]0.506804[/C][C]0.253402[/C][/ROW]
[ROW][C]M8[/C][C]-4.30476190476191[/C][C]5.318612[/C][C]-0.8094[/C][C]0.423473[/C][C]0.211736[/C][/ROW]
[ROW][C]M9[/C][C]-3.54107142857143[/C][C]5.314929[/C][C]-0.6662[/C][C]0.509384[/C][C]0.254692[/C][/ROW]
[ROW][C]M10[/C][C]-3.52738095238095[/C][C]5.312297[/C][C]-0.664[/C][C]0.510805[/C][C]0.255403[/C][/ROW]
[ROW][C]M11[/C][C]-0.763690476190479[/C][C]5.310717[/C][C]-0.1438[/C][C]0.886437[/C][C]0.443219[/C][/ROW]
[ROW][C]t[/C][C]-0.263690476190476[/C][C]0.074799[/C][C]-3.5253[/C][C]0.001146[/C][C]0.000573[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108948&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108948&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)	1.41071428571429	4.374293	0.3225	0.748888	0.374444
M1	-0.818452380952382	5.051552	-0.162	0.872172	0.436086
M2	-2.35476190476191	5.046566	-0.4666	0.643517	0.321759
M3	-8.8732142857143	5.352691	-1.6577	0.105832	0.052916
M4	-6.10952380952381	5.343799	-1.1433	0.260261	0.13013
M5	-5.59583333333334	5.335941	-1.0487	0.301116	0.150558
M6	-5.33214285714286	5.329121	-1.0006	0.323535	0.161768
M7	-3.56845238095238	5.323344	-0.6703	0.506804	0.253402
M8	-4.30476190476191	5.318612	-0.8094	0.423473	0.211736
M9	-3.54107142857143	5.314929	-0.6662	0.509384	0.254692
M10	-3.52738095238095	5.312297	-0.664	0.510805	0.255403
M11	-0.763690476190479	5.310717	-0.1438	0.886437	0.443219
t	-0.263690476190476	0.074799	-3.5253	0.001146	0.000573

Multiple Linear Regression - Regression Statistics
Multiple R	0.545345495731066
R-squared	0.297401709714163
Adjusted R-squared	0.0695319939457829
F-TEST (value)	1.30513924902798
F-TEST (DF numerator)	12
F-TEST (DF denominator)	37
p-value	0.25689643837216
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	7.50974270672674
Sum Squared Residuals	2086.66071428571

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.545345495731066 \tabularnewline
R-squared & 0.297401709714163 \tabularnewline
Adjusted R-squared & 0.0695319939457829 \tabularnewline
F-TEST (value) & 1.30513924902798 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 37 \tabularnewline
p-value & 0.25689643837216 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 7.50974270672674 \tabularnewline
Sum Squared Residuals & 2086.66071428571 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108948&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.545345495731066[/C][/ROW]
[ROW][C]R-squared[/C][C]0.297401709714163[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0695319939457829[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.30513924902798[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]37[/C][/ROW]
[ROW][C]p-value[/C][C]0.25689643837216[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]7.50974270672674[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2086.66071428571[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108948&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108948&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	0.545345495731066
R-squared	0.297401709714163
Adjusted R-squared	0.0695319939457829
F-TEST (value)	1.30513924902798
F-TEST (DF numerator)	12
F-TEST (DF denominator)	37
p-value	0.25689643837216
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	7.50974270672674
Sum Squared Residuals	2086.66071428571

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	2	0.328571428571431	1.67142857142857
2	1	-1.47142857142857	2.47142857142857
3	-8	-8.25357142857143	0.253571428571429
4	-1	-5.75357142857143	4.75357142857143
5	1	-5.50357142857142	6.50357142857142
6	-1	-5.50357142857143	4.50357142857143
7	2	-4.00357142857143	6.00357142857143
8	2	-5.00357142857143	7.00357142857143
9	1	-4.50357142857143	5.50357142857143
10	-1	-4.75357142857143	3.75357142857143
11	-2	-2.25357142857143	0.253571428571429
12	-2	-1.75357142857143	-0.246428571428573
13	-1	-2.83571428571429	1.83571428571429
14	-8	-4.63571428571429	-3.36428571428571
15	-4	-11.4178571428571	7.41785714285714
16	-6	-8.91785714285714	2.91785714285714
17	-3	-8.66785714285714	5.66785714285714
18	-3	-8.66785714285714	5.66785714285714
19	-7	-7.16785714285714	0.167857142857144
20	-9	-8.16785714285714	-0.832142857142856
21	-11	-7.66785714285714	-3.33214285714286
22	-13	-7.91785714285714	-5.08214285714286
23	-11	-5.41785714285714	-5.58214285714286
24	-9	-4.91785714285715	-4.08214285714285
25	-17	-6	-11
26	-22	-7.8	-14.2
27	-25	-14.5821428571429	-10.4178571428571
28	-20	-12.0821428571429	-7.91785714285714
29	-24	-11.8321428571429	-12.1678571428571
30	-24	-11.8321428571429	-12.1678571428571
31	-22	-10.3321428571429	-11.6678571428571
32	-19	-11.3321428571429	-7.66785714285714
33	-18	-10.8321428571429	-7.16785714285714
34	-17	-11.0821428571429	-5.91785714285714
35	-11	-8.58214285714286	-2.41785714285714
36	-11	-8.08214285714286	-2.91785714285714
37	-12	-9.16428571428571	-2.83571428571428
38	-10	-10.9642857142857	0.964285714285713
39	-15	-17.7464285714286	2.74642857142857
40	-15	-15.2464285714286	0.246428571428571
41	-15	-14.9964285714286	-0.00357142857142653
42	-13	-14.9964285714286	1.99642857142857
43	-8	-13.4964285714286	5.49642857142857
44	-13	-14.4964285714286	1.49642857142857
45	-9	-13.9964285714286	4.99642857142857
46	-7	-14.2464285714286	7.24642857142857
47	-4	-11.7464285714286	7.74642857142857
48	-4	-11.2464285714286	7.24642857142857
49	-2	-12.3285714285714	10.3285714285714
50	0	-14.1285714285714	14.1285714285714

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 0.328571428571431 & 1.67142857142857 \tabularnewline
2 & 1 & -1.47142857142857 & 2.47142857142857 \tabularnewline
3 & -8 & -8.25357142857143 & 0.253571428571429 \tabularnewline
4 & -1 & -5.75357142857143 & 4.75357142857143 \tabularnewline
5 & 1 & -5.50357142857142 & 6.50357142857142 \tabularnewline
6 & -1 & -5.50357142857143 & 4.50357142857143 \tabularnewline
7 & 2 & -4.00357142857143 & 6.00357142857143 \tabularnewline
8 & 2 & -5.00357142857143 & 7.00357142857143 \tabularnewline
9 & 1 & -4.50357142857143 & 5.50357142857143 \tabularnewline
10 & -1 & -4.75357142857143 & 3.75357142857143 \tabularnewline
11 & -2 & -2.25357142857143 & 0.253571428571429 \tabularnewline
12 & -2 & -1.75357142857143 & -0.246428571428573 \tabularnewline
13 & -1 & -2.83571428571429 & 1.83571428571429 \tabularnewline
14 & -8 & -4.63571428571429 & -3.36428571428571 \tabularnewline
15 & -4 & -11.4178571428571 & 7.41785714285714 \tabularnewline
16 & -6 & -8.91785714285714 & 2.91785714285714 \tabularnewline
17 & -3 & -8.66785714285714 & 5.66785714285714 \tabularnewline
18 & -3 & -8.66785714285714 & 5.66785714285714 \tabularnewline
19 & -7 & -7.16785714285714 & 0.167857142857144 \tabularnewline
20 & -9 & -8.16785714285714 & -0.832142857142856 \tabularnewline
21 & -11 & -7.66785714285714 & -3.33214285714286 \tabularnewline
22 & -13 & -7.91785714285714 & -5.08214285714286 \tabularnewline
23 & -11 & -5.41785714285714 & -5.58214285714286 \tabularnewline
24 & -9 & -4.91785714285715 & -4.08214285714285 \tabularnewline
25 & -17 & -6 & -11 \tabularnewline
26 & -22 & -7.8 & -14.2 \tabularnewline
27 & -25 & -14.5821428571429 & -10.4178571428571 \tabularnewline
28 & -20 & -12.0821428571429 & -7.91785714285714 \tabularnewline
29 & -24 & -11.8321428571429 & -12.1678571428571 \tabularnewline
30 & -24 & -11.8321428571429 & -12.1678571428571 \tabularnewline
31 & -22 & -10.3321428571429 & -11.6678571428571 \tabularnewline
32 & -19 & -11.3321428571429 & -7.66785714285714 \tabularnewline
33 & -18 & -10.8321428571429 & -7.16785714285714 \tabularnewline
34 & -17 & -11.0821428571429 & -5.91785714285714 \tabularnewline
35 & -11 & -8.58214285714286 & -2.41785714285714 \tabularnewline
36 & -11 & -8.08214285714286 & -2.91785714285714 \tabularnewline
37 & -12 & -9.16428571428571 & -2.83571428571428 \tabularnewline
38 & -10 & -10.9642857142857 & 0.964285714285713 \tabularnewline
39 & -15 & -17.7464285714286 & 2.74642857142857 \tabularnewline
40 & -15 & -15.2464285714286 & 0.246428571428571 \tabularnewline
41 & -15 & -14.9964285714286 & -0.00357142857142653 \tabularnewline
42 & -13 & -14.9964285714286 & 1.99642857142857 \tabularnewline
43 & -8 & -13.4964285714286 & 5.49642857142857 \tabularnewline
44 & -13 & -14.4964285714286 & 1.49642857142857 \tabularnewline
45 & -9 & -13.9964285714286 & 4.99642857142857 \tabularnewline
46 & -7 & -14.2464285714286 & 7.24642857142857 \tabularnewline
47 & -4 & -11.7464285714286 & 7.74642857142857 \tabularnewline
48 & -4 & -11.2464285714286 & 7.24642857142857 \tabularnewline
49 & -2 & -12.3285714285714 & 10.3285714285714 \tabularnewline
50 & 0 & -14.1285714285714 & 14.1285714285714 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108948&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]2[/C][C]0.328571428571431[/C][C]1.67142857142857[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]-1.47142857142857[/C][C]2.47142857142857[/C][/ROW]
[ROW][C]3[/C][C]-8[/C][C]-8.25357142857143[/C][C]0.253571428571429[/C][/ROW]
[ROW][C]4[/C][C]-1[/C][C]-5.75357142857143[/C][C]4.75357142857143[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]-5.50357142857142[/C][C]6.50357142857142[/C][/ROW]
[ROW][C]6[/C][C]-1[/C][C]-5.50357142857143[/C][C]4.50357142857143[/C][/ROW]
[ROW][C]7[/C][C]2[/C][C]-4.00357142857143[/C][C]6.00357142857143[/C][/ROW]
[ROW][C]8[/C][C]2[/C][C]-5.00357142857143[/C][C]7.00357142857143[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]-4.50357142857143[/C][C]5.50357142857143[/C][/ROW]
[ROW][C]10[/C][C]-1[/C][C]-4.75357142857143[/C][C]3.75357142857143[/C][/ROW]
[ROW][C]11[/C][C]-2[/C][C]-2.25357142857143[/C][C]0.253571428571429[/C][/ROW]
[ROW][C]12[/C][C]-2[/C][C]-1.75357142857143[/C][C]-0.246428571428573[/C][/ROW]
[ROW][C]13[/C][C]-1[/C][C]-2.83571428571429[/C][C]1.83571428571429[/C][/ROW]
[ROW][C]14[/C][C]-8[/C][C]-4.63571428571429[/C][C]-3.36428571428571[/C][/ROW]
[ROW][C]15[/C][C]-4[/C][C]-11.4178571428571[/C][C]7.41785714285714[/C][/ROW]
[ROW][C]16[/C][C]-6[/C][C]-8.91785714285714[/C][C]2.91785714285714[/C][/ROW]
[ROW][C]17[/C][C]-3[/C][C]-8.66785714285714[/C][C]5.66785714285714[/C][/ROW]
[ROW][C]18[/C][C]-3[/C][C]-8.66785714285714[/C][C]5.66785714285714[/C][/ROW]
[ROW][C]19[/C][C]-7[/C][C]-7.16785714285714[/C][C]0.167857142857144[/C][/ROW]
[ROW][C]20[/C][C]-9[/C][C]-8.16785714285714[/C][C]-0.832142857142856[/C][/ROW]
[ROW][C]21[/C][C]-11[/C][C]-7.66785714285714[/C][C]-3.33214285714286[/C][/ROW]
[ROW][C]22[/C][C]-13[/C][C]-7.91785714285714[/C][C]-5.08214285714286[/C][/ROW]
[ROW][C]23[/C][C]-11[/C][C]-5.41785714285714[/C][C]-5.58214285714286[/C][/ROW]
[ROW][C]24[/C][C]-9[/C][C]-4.91785714285715[/C][C]-4.08214285714285[/C][/ROW]
[ROW][C]25[/C][C]-17[/C][C]-6[/C][C]-11[/C][/ROW]
[ROW][C]26[/C][C]-22[/C][C]-7.8[/C][C]-14.2[/C][/ROW]
[ROW][C]27[/C][C]-25[/C][C]-14.5821428571429[/C][C]-10.4178571428571[/C][/ROW]
[ROW][C]28[/C][C]-20[/C][C]-12.0821428571429[/C][C]-7.91785714285714[/C][/ROW]
[ROW][C]29[/C][C]-24[/C][C]-11.8321428571429[/C][C]-12.1678571428571[/C][/ROW]
[ROW][C]30[/C][C]-24[/C][C]-11.8321428571429[/C][C]-12.1678571428571[/C][/ROW]
[ROW][C]31[/C][C]-22[/C][C]-10.3321428571429[/C][C]-11.6678571428571[/C][/ROW]
[ROW][C]32[/C][C]-19[/C][C]-11.3321428571429[/C][C]-7.66785714285714[/C][/ROW]
[ROW][C]33[/C][C]-18[/C][C]-10.8321428571429[/C][C]-7.16785714285714[/C][/ROW]
[ROW][C]34[/C][C]-17[/C][C]-11.0821428571429[/C][C]-5.91785714285714[/C][/ROW]
[ROW][C]35[/C][C]-11[/C][C]-8.58214285714286[/C][C]-2.41785714285714[/C][/ROW]
[ROW][C]36[/C][C]-11[/C][C]-8.08214285714286[/C][C]-2.91785714285714[/C][/ROW]
[ROW][C]37[/C][C]-12[/C][C]-9.16428571428571[/C][C]-2.83571428571428[/C][/ROW]
[ROW][C]38[/C][C]-10[/C][C]-10.9642857142857[/C][C]0.964285714285713[/C][/ROW]
[ROW][C]39[/C][C]-15[/C][C]-17.7464285714286[/C][C]2.74642857142857[/C][/ROW]
[ROW][C]40[/C][C]-15[/C][C]-15.2464285714286[/C][C]0.246428571428571[/C][/ROW]
[ROW][C]41[/C][C]-15[/C][C]-14.9964285714286[/C][C]-0.00357142857142653[/C][/ROW]
[ROW][C]42[/C][C]-13[/C][C]-14.9964285714286[/C][C]1.99642857142857[/C][/ROW]
[ROW][C]43[/C][C]-8[/C][C]-13.4964285714286[/C][C]5.49642857142857[/C][/ROW]
[ROW][C]44[/C][C]-13[/C][C]-14.4964285714286[/C][C]1.49642857142857[/C][/ROW]
[ROW][C]45[/C][C]-9[/C][C]-13.9964285714286[/C][C]4.99642857142857[/C][/ROW]
[ROW][C]46[/C][C]-7[/C][C]-14.2464285714286[/C][C]7.24642857142857[/C][/ROW]
[ROW][C]47[/C][C]-4[/C][C]-11.7464285714286[/C][C]7.74642857142857[/C][/ROW]
[ROW][C]48[/C][C]-4[/C][C]-11.2464285714286[/C][C]7.24642857142857[/C][/ROW]
[ROW][C]49[/C][C]-2[/C][C]-12.3285714285714[/C][C]10.3285714285714[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]-14.1285714285714[/C][C]14.1285714285714[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108948&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108948&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	2	0.328571428571431	1.67142857142857
2	1	-1.47142857142857	2.47142857142857
3	-8	-8.25357142857143	0.253571428571429
4	-1	-5.75357142857143	4.75357142857143
5	1	-5.50357142857142	6.50357142857142
6	-1	-5.50357142857143	4.50357142857143
7	2	-4.00357142857143	6.00357142857143
8	2	-5.00357142857143	7.00357142857143
9	1	-4.50357142857143	5.50357142857143
10	-1	-4.75357142857143	3.75357142857143
11	-2	-2.25357142857143	0.253571428571429
12	-2	-1.75357142857143	-0.246428571428573
13	-1	-2.83571428571429	1.83571428571429
14	-8	-4.63571428571429	-3.36428571428571
15	-4	-11.4178571428571	7.41785714285714
16	-6	-8.91785714285714	2.91785714285714
17	-3	-8.66785714285714	5.66785714285714
18	-3	-8.66785714285714	5.66785714285714
19	-7	-7.16785714285714	0.167857142857144
20	-9	-8.16785714285714	-0.832142857142856
21	-11	-7.66785714285714	-3.33214285714286
22	-13	-7.91785714285714	-5.08214285714286
23	-11	-5.41785714285714	-5.58214285714286
24	-9	-4.91785714285715	-4.08214285714285
25	-17	-6	-11
26	-22	-7.8	-14.2
27	-25	-14.5821428571429	-10.4178571428571
28	-20	-12.0821428571429	-7.91785714285714
29	-24	-11.8321428571429	-12.1678571428571
30	-24	-11.8321428571429	-12.1678571428571
31	-22	-10.3321428571429	-11.6678571428571
32	-19	-11.3321428571429	-7.66785714285714
33	-18	-10.8321428571429	-7.16785714285714
34	-17	-11.0821428571429	-5.91785714285714
35	-11	-8.58214285714286	-2.41785714285714
36	-11	-8.08214285714286	-2.91785714285714
37	-12	-9.16428571428571	-2.83571428571428
38	-10	-10.9642857142857	0.964285714285713
39	-15	-17.7464285714286	2.74642857142857
40	-15	-15.2464285714286	0.246428571428571
41	-15	-14.9964285714286	-0.00357142857142653
42	-13	-14.9964285714286	1.99642857142857
43	-8	-13.4964285714286	5.49642857142857
44	-13	-14.4964285714286	1.49642857142857
45	-9	-13.9964285714286	4.99642857142857
46	-7	-14.2464285714286	7.24642857142857
47	-4	-11.7464285714286	7.74642857142857
48	-4	-11.2464285714286	7.24642857142857
49	-2	-12.3285714285714	10.3285714285714
50	0	-14.1285714285714	14.1285714285714

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.154979056687285	0.309958113374571	0.845020943312715
17	0.0960554407884942	0.192110881576988	0.903944559211506
18	0.0860617341484417	0.172123468296883	0.913938265851558
19	0.124215382977149	0.248430765954299	0.87578461702285
20	0.270625499793064	0.541250999586127	0.729374500206936
21	0.469283175465743	0.938566350931486	0.530716824534257
22	0.629543995244992	0.740912009510015	0.370456004755007
23	0.697478875202713	0.605042249594574	0.302521124797287
24	0.968541570407875	0.0629168591842495	0.0314584295921247
25	0.987843532197245	0.0243129356055102	0.0121564678027551
26	0.992863457447548	0.0142730851049049	0.00713654255245246
27	0.98981362968816	0.0203727406236802	0.0101863703118401
28	0.99216182673301	0.0156763465339789	0.00783817326698943
29	0.989965460147412	0.0200690797051759	0.010034539852588
30	0.984605619490446	0.0307887610191088	0.0153943805095544
31	0.996790596883396	0.00641880623320735	0.00320940311660368
32	0.996280488280457	0.00743902343908557	0.00371951171954278
33	0.98523135347134	0.0295372930573208	0.0147686465286604
34	0.961301295853014	0.0773974082939718	0.0386987041469859

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.154979056687285 & 0.309958113374571 & 0.845020943312715 \tabularnewline
17 & 0.0960554407884942 & 0.192110881576988 & 0.903944559211506 \tabularnewline
18 & 0.0860617341484417 & 0.172123468296883 & 0.913938265851558 \tabularnewline
19 & 0.124215382977149 & 0.248430765954299 & 0.87578461702285 \tabularnewline
20 & 0.270625499793064 & 0.541250999586127 & 0.729374500206936 \tabularnewline
21 & 0.469283175465743 & 0.938566350931486 & 0.530716824534257 \tabularnewline
22 & 0.629543995244992 & 0.740912009510015 & 0.370456004755007 \tabularnewline
23 & 0.697478875202713 & 0.605042249594574 & 0.302521124797287 \tabularnewline
24 & 0.968541570407875 & 0.0629168591842495 & 0.0314584295921247 \tabularnewline
25 & 0.987843532197245 & 0.0243129356055102 & 0.0121564678027551 \tabularnewline
26 & 0.992863457447548 & 0.0142730851049049 & 0.00713654255245246 \tabularnewline
27 & 0.98981362968816 & 0.0203727406236802 & 0.0101863703118401 \tabularnewline
28 & 0.99216182673301 & 0.0156763465339789 & 0.00783817326698943 \tabularnewline
29 & 0.989965460147412 & 0.0200690797051759 & 0.010034539852588 \tabularnewline
30 & 0.984605619490446 & 0.0307887610191088 & 0.0153943805095544 \tabularnewline
31 & 0.996790596883396 & 0.00641880623320735 & 0.00320940311660368 \tabularnewline
32 & 0.996280488280457 & 0.00743902343908557 & 0.00371951171954278 \tabularnewline
33 & 0.98523135347134 & 0.0295372930573208 & 0.0147686465286604 \tabularnewline
34 & 0.961301295853014 & 0.0773974082939718 & 0.0386987041469859 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108948&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]16[/C][C]0.154979056687285[/C][C]0.309958113374571[/C][C]0.845020943312715[/C][/ROW]
[ROW][C]17[/C][C]0.0960554407884942[/C][C]0.192110881576988[/C][C]0.903944559211506[/C][/ROW]
[ROW][C]18[/C][C]0.0860617341484417[/C][C]0.172123468296883[/C][C]0.913938265851558[/C][/ROW]
[ROW][C]19[/C][C]0.124215382977149[/C][C]0.248430765954299[/C][C]0.87578461702285[/C][/ROW]
[ROW][C]20[/C][C]0.270625499793064[/C][C]0.541250999586127[/C][C]0.729374500206936[/C][/ROW]
[ROW][C]21[/C][C]0.469283175465743[/C][C]0.938566350931486[/C][C]0.530716824534257[/C][/ROW]
[ROW][C]22[/C][C]0.629543995244992[/C][C]0.740912009510015[/C][C]0.370456004755007[/C][/ROW]
[ROW][C]23[/C][C]0.697478875202713[/C][C]0.605042249594574[/C][C]0.302521124797287[/C][/ROW]
[ROW][C]24[/C][C]0.968541570407875[/C][C]0.0629168591842495[/C][C]0.0314584295921247[/C][/ROW]
[ROW][C]25[/C][C]0.987843532197245[/C][C]0.0243129356055102[/C][C]0.0121564678027551[/C][/ROW]
[ROW][C]26[/C][C]0.992863457447548[/C][C]0.0142730851049049[/C][C]0.00713654255245246[/C][/ROW]
[ROW][C]27[/C][C]0.98981362968816[/C][C]0.0203727406236802[/C][C]0.0101863703118401[/C][/ROW]
[ROW][C]28[/C][C]0.99216182673301[/C][C]0.0156763465339789[/C][C]0.00783817326698943[/C][/ROW]
[ROW][C]29[/C][C]0.989965460147412[/C][C]0.0200690797051759[/C][C]0.010034539852588[/C][/ROW]
[ROW][C]30[/C][C]0.984605619490446[/C][C]0.0307887610191088[/C][C]0.0153943805095544[/C][/ROW]
[ROW][C]31[/C][C]0.996790596883396[/C][C]0.00641880623320735[/C][C]0.00320940311660368[/C][/ROW]
[ROW][C]32[/C][C]0.996280488280457[/C][C]0.00743902343908557[/C][C]0.00371951171954278[/C][/ROW]
[ROW][C]33[/C][C]0.98523135347134[/C][C]0.0295372930573208[/C][C]0.0147686465286604[/C][/ROW]
[ROW][C]34[/C][C]0.961301295853014[/C][C]0.0773974082939718[/C][C]0.0386987041469859[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108948&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108948&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
16	0.154979056687285	0.309958113374571	0.845020943312715
17	0.0960554407884942	0.192110881576988	0.903944559211506
18	0.0860617341484417	0.172123468296883	0.913938265851558
19	0.124215382977149	0.248430765954299	0.87578461702285
20	0.270625499793064	0.541250999586127	0.729374500206936
21	0.469283175465743	0.938566350931486	0.530716824534257
22	0.629543995244992	0.740912009510015	0.370456004755007
23	0.697478875202713	0.605042249594574	0.302521124797287
24	0.968541570407875	0.0629168591842495	0.0314584295921247
25	0.987843532197245	0.0243129356055102	0.0121564678027551
26	0.992863457447548	0.0142730851049049	0.00713654255245246
27	0.98981362968816	0.0203727406236802	0.0101863703118401
28	0.99216182673301	0.0156763465339789	0.00783817326698943
29	0.989965460147412	0.0200690797051759	0.010034539852588
30	0.984605619490446	0.0307887610191088	0.0153943805095544
31	0.996790596883396	0.00641880623320735	0.00320940311660368
32	0.996280488280457	0.00743902343908557	0.00371951171954278
33	0.98523135347134	0.0295372930573208	0.0147686465286604
34	0.961301295853014	0.0773974082939718	0.0386987041469859

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	2	0.105263157894737	NOK
5% type I error level	9	0.473684210526316	NOK
10% type I error level	11	0.578947368421053	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 & 2 & 0.105263157894737 & NOK \tabularnewline
5% type I error level & 9 & 0.473684210526316 & NOK \tabularnewline
10% type I error level & 11 & 0.578947368421053 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108948&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]2[/C][C]0.105263157894737[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]9[/C][C]0.473684210526316[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]11[/C][C]0.578947368421053[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108948&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108948&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	2	0.105263157894737	NOK
5% type I error level	9	0.473684210526316	NOK
10% type I error level	11	0.578947368421053	NOK

Figure 1

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Figure 9

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Figure 10

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Parameters (Session):

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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