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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 15 Dec 2014 14:36:11 +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/2014/Dec/15/t1418654205si4n3y28futjz1o.htm/, Retrieved Thu, 16 May 2024 07:24:57 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268509, Retrieved Thu, 16 May 2024 07:24:57 +0000

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Estimated Impact

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

-     [Cronbach Alpha] [Intrinsic Motivat...] [2010-10-12 11:42:57] [b98453cac15ba1066b407e146608df68]
- RM D  [Multiple Regression] [MR nieuw] [2014-12-10 15:58:18] [837635e05a8b7d15572545de61d5b5ed]
-   PD      [Multiple Regression] [vernieuwde] [2014-12-15 14:36:11] [76a64141a2f0df75ca87242b7cc8f1c2] [Current]

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Dataseries X:

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Boxplots

62 72 11 0 0 0 0 5.0
56 61 6 1 48 41 6 3.0
57 68 7 1 46 49 7 7.5
51 61 10 0 0 0 0 7.0
56 64 9 1 47 41 9 6.0
30 65 7 1 24 42 7 6.0
61 69 4 1 50 49 4 1.0
47 63 4 1 41 40 4 6.0
56 75 4 1 48 52 4 5.0
50 63 8 1 42 44 8 1.0
67 73 4 1 57 52 4 6.5
41 75 7 0 0 0 0 0.0
45 63 4 0 0 0 0 3.5
48 63 4 1 40 43 4 7.5
44 62 9 1 36 41 9 3.5
37 64 4 0 0 0 0 6.0
56 60 10 0 0 0 0 3.5
66 56 4 1 55 39 4 7.5
38 59 5 1 32 40 5 6.5
34 68 4 0 0 0 0 3.5
49 66 4 0 0 0 0 4.0
55 73 4 0 0 0 0 7.5
49 72 4 0 0 0 0 4.5
59 71 6 1 50 46 6 0.0
40 59 10 0 0 0 0 3.5
58 64 7 1 49 44 7 5.5
60 66 4 1 51 47 4 5.0
63 78 4 0 0 0 0 4.5
56 68 7 0 0 0 0 2.5
54 73 4 0 0 0 0 7.5
52 62 8 1 43 41 8 7.0
34 65 11 1 28 42 11 0.0
69 68 6 1 62 46 6 4.5
32 65 14 0 0 0 0 3.0
48 60 5 1 37 42 5 1.5
67 71 4 0 0 0 0 3.5
58 65 8 1 49 43 8 2.5
57 68 9 1 47 46 9 5.5
42 64 4 1 36 45 4 8.0
64 74 4 1 54 52 4 1.0
58 69 5 1 48 47 5 5.0
66 76 4 0 0 0 0 4.5
26 68 5 1 23 48 5 3.0
61 72 4 1 52 51 4 3.0
52 67 4 1 46 44 4 8.0
51 63 7 0 0 0 0 2.5
55 59 10 0 0 0 0 7.0
50 73 4 0 0 0 0 0.0
60 66 5 0 0 0 0 1.0
56 62 4 0 0 0 0 3.5
63 69 4 0 0 0 0 5.5
61 66 4 1 50 44 4 5.5

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	'Sir Maurice George Kendall' @ kendall.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 & 5 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268509&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268509&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268509&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	'Sir Maurice George Kendall' @ kendall.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Ex[t] = + 6.15699 + 0.0425008AMS.I[t] -0.062475AMS.E[t] -0.0144101AMS.A[t] + 11.0779gender[t] -0.0315752AMS_I_NIEUW_Gender[t] -0.154091AMS_E_NIEUW_Gender[t] -0.414509AMS_A_NIEUW_Gender[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ex[t] =  +  6.15699 +  0.0425008AMS.I[t] -0.062475AMS.E[t] -0.0144101AMS.A[t] +  11.0779gender[t] -0.0315752AMS_I_NIEUW_Gender[t] -0.154091AMS_E_NIEUW_Gender[t] -0.414509AMS_A_NIEUW_Gender[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268509&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ex[t] =  +  6.15699 +  0.0425008AMS.I[t] -0.062475AMS.E[t] -0.0144101AMS.A[t] +  11.0779gender[t] -0.0315752AMS_I_NIEUW_Gender[t] -0.154091AMS_E_NIEUW_Gender[t] -0.414509AMS_A_NIEUW_Gender[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268509&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268509&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
Ex[t] = + 6.15699 + 0.0425008AMS.I[t] -0.062475AMS.E[t] -0.0144101AMS.A[t] + 11.0779gender[t] -0.0315752AMS_I_NIEUW_Gender[t] -0.154091AMS_E_NIEUW_Gender[t] -0.414509AMS_A_NIEUW_Gender[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)	6.15699	6.92578	0.889	0.37884	0.18942
AMS.I	0.0425008	0.0520684	0.8162	0.418755	0.209378
AMS.E	-0.062475	0.0934354	-0.6686	0.507216	0.253608
AMS.A	-0.0144101	0.181714	-0.0793	0.937152	0.468576
gender	11.0779	8.47831	1.307	0.198131	0.0990657
AMS_I_NIEUW_Gender	-0.0315752	0.0784647	-0.4024	0.689329	0.344664
AMS_E_NIEUW_Gender	-0.154091	0.165279	-0.9323	0.356264	0.178132
AMS_A_NIEUW_Gender	-0.414509	0.305931	-1.355	0.182362	0.0911809

\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) & 6.15699 & 6.92578 & 0.889 & 0.37884 & 0.18942 \tabularnewline
AMS.I & 0.0425008 & 0.0520684 & 0.8162 & 0.418755 & 0.209378 \tabularnewline
AMS.E & -0.062475 & 0.0934354 & -0.6686 & 0.507216 & 0.253608 \tabularnewline
AMS.A & -0.0144101 & 0.181714 & -0.0793 & 0.937152 & 0.468576 \tabularnewline
gender & 11.0779 & 8.47831 & 1.307 & 0.198131 & 0.0990657 \tabularnewline
AMS_I_NIEUW_Gender & -0.0315752 & 0.0784647 & -0.4024 & 0.689329 & 0.344664 \tabularnewline
AMS_E_NIEUW_Gender & -0.154091 & 0.165279 & -0.9323 & 0.356264 & 0.178132 \tabularnewline
AMS_A_NIEUW_Gender & -0.414509 & 0.305931 & -1.355 & 0.182362 & 0.0911809 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268509&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]6.15699[/C][C]6.92578[/C][C]0.889[/C][C]0.37884[/C][C]0.18942[/C][/ROW]
[ROW][C]AMS.I[/C][C]0.0425008[/C][C]0.0520684[/C][C]0.8162[/C][C]0.418755[/C][C]0.209378[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.062475[/C][C]0.0934354[/C][C]-0.6686[/C][C]0.507216[/C][C]0.253608[/C][/ROW]
[ROW][C]AMS.A[/C][C]-0.0144101[/C][C]0.181714[/C][C]-0.0793[/C][C]0.937152[/C][C]0.468576[/C][/ROW]
[ROW][C]gender[/C][C]11.0779[/C][C]8.47831[/C][C]1.307[/C][C]0.198131[/C][C]0.0990657[/C][/ROW]
[ROW][C]AMS_I_NIEUW_Gender[/C][C]-0.0315752[/C][C]0.0784647[/C][C]-0.4024[/C][C]0.689329[/C][C]0.344664[/C][/ROW]
[ROW][C]AMS_E_NIEUW_Gender[/C][C]-0.154091[/C][C]0.165279[/C][C]-0.9323[/C][C]0.356264[/C][C]0.178132[/C][/ROW]
[ROW][C]AMS_A_NIEUW_Gender[/C][C]-0.414509[/C][C]0.305931[/C][C]-1.355[/C][C]0.182362[/C][C]0.0911809[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268509&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268509&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)	6.15699	6.92578	0.889	0.37884	0.18942
AMS.I	0.0425008	0.0520684	0.8162	0.418755	0.209378
AMS.E	-0.062475	0.0934354	-0.6686	0.507216	0.253608
AMS.A	-0.0144101	0.181714	-0.0793	0.937152	0.468576
gender	11.0779	8.47831	1.307	0.198131	0.0990657
AMS_I_NIEUW_Gender	-0.0315752	0.0784647	-0.4024	0.689329	0.344664
AMS_E_NIEUW_Gender	-0.154091	0.165279	-0.9323	0.356264	0.178132
AMS_A_NIEUW_Gender	-0.414509	0.305931	-1.355	0.182362	0.0911809

Multiple Linear Regression - Regression Statistics
Multiple R	0.362626
R-squared	0.131498
Adjusted R-squared	-0.0066729
F-TEST (value)	0.951705
F-TEST (DF numerator)	7
F-TEST (DF denominator)	44
p-value	0.477629
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.33382
Sum Squared Residuals	239.656

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.362626 \tabularnewline
R-squared & 0.131498 \tabularnewline
Adjusted R-squared & -0.0066729 \tabularnewline
F-TEST (value) & 0.951705 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 44 \tabularnewline
p-value & 0.477629 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.33382 \tabularnewline
Sum Squared Residuals & 239.656 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268509&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.362626[/C][/ROW]
[ROW][C]R-squared[/C][C]0.131498[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0066729[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.951705[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]44[/C][/ROW]
[ROW][C]p-value[/C][C]0.477629[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.33382[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]239.656[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268509&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268509&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	0.362626
R-squared	0.131498
Adjusted R-squared	-0.0066729
F-TEST (value)	0.951705
F-TEST (DF numerator)	7
F-TEST (DF denominator)	44
p-value	0.477629
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.33382
Sum Squared Residuals	239.656

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	5	4.13533	0.864671
2	3	5.39711	-2.39711
3	7.5	3.40378	4.09622
4	7	4.36946	2.63054
5	6	3.9545	2.0455
6	6	4.21698	1.78302
7	1	4.67177	-3.67177
8	6	6.1226	-0.122604
9	5	3.68529	1.31471
10	1	3.88649	-2.88649
11	6.5	3.99357	2.50643
12	0	3.11303	-3.11303
13	3.5	4.07596	-0.575962
14	7.5	5.73441	1.76559
15	3.5	3.91676	-0.416765
16	6	3.67348	2.32652
17	3.5	4.64443	-1.14443
18	7.5	7.07948	0.420518
19	6.5	5.84525	0.654746
20	3.5	3.29608	0.203922
21	4	4.05854	-0.0585398
22	7.5	3.87622	3.62378
23	4.5	3.68369	0.81631
24	0	4.06625	-4.06625
25	3.5	4.0269	-0.526897
26	5.5	4.37191	1.12809
27	5	5.0933	-0.0933003
28	4.5	3.90385	0.596149
29	2.5	4.18786	-1.68786
30	7.5	3.83372	3.66628
31	7	4.46466	2.53534
32	0	2.545	-2.545
33	4.5	4.29978	0.200218
34	3	3.2544	-0.2544
35	1.5	5.74173	-4.24173
36	3.5	4.51118	-1.01118
37	2.5	4.03461	-1.53461
38	5.5	2.97664	2.52336
39	8	5.23505	2.76495
40	1	3.89832	-2.89832
41	5	4.48668	0.51332
42	4.5	4.1563	0.343697
43	3	3.82442	-0.824419
44	3	4.11301	-1.11301
45	8	5.31097	2.68903
46	2.5	4.28774	-1.78774
47	7	4.66441	2.33559
48	0	3.66372	-3.66372
49	1	4.51164	-3.51164
50	3.5	4.60595	-1.10595
51	5.5	4.46613	1.03387
52	5.5	5.62965	-0.129649

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 5 & 4.13533 & 0.864671 \tabularnewline
2 & 3 & 5.39711 & -2.39711 \tabularnewline
3 & 7.5 & 3.40378 & 4.09622 \tabularnewline
4 & 7 & 4.36946 & 2.63054 \tabularnewline
5 & 6 & 3.9545 & 2.0455 \tabularnewline
6 & 6 & 4.21698 & 1.78302 \tabularnewline
7 & 1 & 4.67177 & -3.67177 \tabularnewline
8 & 6 & 6.1226 & -0.122604 \tabularnewline
9 & 5 & 3.68529 & 1.31471 \tabularnewline
10 & 1 & 3.88649 & -2.88649 \tabularnewline
11 & 6.5 & 3.99357 & 2.50643 \tabularnewline
12 & 0 & 3.11303 & -3.11303 \tabularnewline
13 & 3.5 & 4.07596 & -0.575962 \tabularnewline
14 & 7.5 & 5.73441 & 1.76559 \tabularnewline
15 & 3.5 & 3.91676 & -0.416765 \tabularnewline
16 & 6 & 3.67348 & 2.32652 \tabularnewline
17 & 3.5 & 4.64443 & -1.14443 \tabularnewline
18 & 7.5 & 7.07948 & 0.420518 \tabularnewline
19 & 6.5 & 5.84525 & 0.654746 \tabularnewline
20 & 3.5 & 3.29608 & 0.203922 \tabularnewline
21 & 4 & 4.05854 & -0.0585398 \tabularnewline
22 & 7.5 & 3.87622 & 3.62378 \tabularnewline
23 & 4.5 & 3.68369 & 0.81631 \tabularnewline
24 & 0 & 4.06625 & -4.06625 \tabularnewline
25 & 3.5 & 4.0269 & -0.526897 \tabularnewline
26 & 5.5 & 4.37191 & 1.12809 \tabularnewline
27 & 5 & 5.0933 & -0.0933003 \tabularnewline
28 & 4.5 & 3.90385 & 0.596149 \tabularnewline
29 & 2.5 & 4.18786 & -1.68786 \tabularnewline
30 & 7.5 & 3.83372 & 3.66628 \tabularnewline
31 & 7 & 4.46466 & 2.53534 \tabularnewline
32 & 0 & 2.545 & -2.545 \tabularnewline
33 & 4.5 & 4.29978 & 0.200218 \tabularnewline
34 & 3 & 3.2544 & -0.2544 \tabularnewline
35 & 1.5 & 5.74173 & -4.24173 \tabularnewline
36 & 3.5 & 4.51118 & -1.01118 \tabularnewline
37 & 2.5 & 4.03461 & -1.53461 \tabularnewline
38 & 5.5 & 2.97664 & 2.52336 \tabularnewline
39 & 8 & 5.23505 & 2.76495 \tabularnewline
40 & 1 & 3.89832 & -2.89832 \tabularnewline
41 & 5 & 4.48668 & 0.51332 \tabularnewline
42 & 4.5 & 4.1563 & 0.343697 \tabularnewline
43 & 3 & 3.82442 & -0.824419 \tabularnewline
44 & 3 & 4.11301 & -1.11301 \tabularnewline
45 & 8 & 5.31097 & 2.68903 \tabularnewline
46 & 2.5 & 4.28774 & -1.78774 \tabularnewline
47 & 7 & 4.66441 & 2.33559 \tabularnewline
48 & 0 & 3.66372 & -3.66372 \tabularnewline
49 & 1 & 4.51164 & -3.51164 \tabularnewline
50 & 3.5 & 4.60595 & -1.10595 \tabularnewline
51 & 5.5 & 4.46613 & 1.03387 \tabularnewline
52 & 5.5 & 5.62965 & -0.129649 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268509&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]5[/C][C]4.13533[/C][C]0.864671[/C][/ROW]
[ROW][C]2[/C][C]3[/C][C]5.39711[/C][C]-2.39711[/C][/ROW]
[ROW][C]3[/C][C]7.5[/C][C]3.40378[/C][C]4.09622[/C][/ROW]
[ROW][C]4[/C][C]7[/C][C]4.36946[/C][C]2.63054[/C][/ROW]
[ROW][C]5[/C][C]6[/C][C]3.9545[/C][C]2.0455[/C][/ROW]
[ROW][C]6[/C][C]6[/C][C]4.21698[/C][C]1.78302[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]4.67177[/C][C]-3.67177[/C][/ROW]
[ROW][C]8[/C][C]6[/C][C]6.1226[/C][C]-0.122604[/C][/ROW]
[ROW][C]9[/C][C]5[/C][C]3.68529[/C][C]1.31471[/C][/ROW]
[ROW][C]10[/C][C]1[/C][C]3.88649[/C][C]-2.88649[/C][/ROW]
[ROW][C]11[/C][C]6.5[/C][C]3.99357[/C][C]2.50643[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]3.11303[/C][C]-3.11303[/C][/ROW]
[ROW][C]13[/C][C]3.5[/C][C]4.07596[/C][C]-0.575962[/C][/ROW]
[ROW][C]14[/C][C]7.5[/C][C]5.73441[/C][C]1.76559[/C][/ROW]
[ROW][C]15[/C][C]3.5[/C][C]3.91676[/C][C]-0.416765[/C][/ROW]
[ROW][C]16[/C][C]6[/C][C]3.67348[/C][C]2.32652[/C][/ROW]
[ROW][C]17[/C][C]3.5[/C][C]4.64443[/C][C]-1.14443[/C][/ROW]
[ROW][C]18[/C][C]7.5[/C][C]7.07948[/C][C]0.420518[/C][/ROW]
[ROW][C]19[/C][C]6.5[/C][C]5.84525[/C][C]0.654746[/C][/ROW]
[ROW][C]20[/C][C]3.5[/C][C]3.29608[/C][C]0.203922[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]4.05854[/C][C]-0.0585398[/C][/ROW]
[ROW][C]22[/C][C]7.5[/C][C]3.87622[/C][C]3.62378[/C][/ROW]
[ROW][C]23[/C][C]4.5[/C][C]3.68369[/C][C]0.81631[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]4.06625[/C][C]-4.06625[/C][/ROW]
[ROW][C]25[/C][C]3.5[/C][C]4.0269[/C][C]-0.526897[/C][/ROW]
[ROW][C]26[/C][C]5.5[/C][C]4.37191[/C][C]1.12809[/C][/ROW]
[ROW][C]27[/C][C]5[/C][C]5.0933[/C][C]-0.0933003[/C][/ROW]
[ROW][C]28[/C][C]4.5[/C][C]3.90385[/C][C]0.596149[/C][/ROW]
[ROW][C]29[/C][C]2.5[/C][C]4.18786[/C][C]-1.68786[/C][/ROW]
[ROW][C]30[/C][C]7.5[/C][C]3.83372[/C][C]3.66628[/C][/ROW]
[ROW][C]31[/C][C]7[/C][C]4.46466[/C][C]2.53534[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]2.545[/C][C]-2.545[/C][/ROW]
[ROW][C]33[/C][C]4.5[/C][C]4.29978[/C][C]0.200218[/C][/ROW]
[ROW][C]34[/C][C]3[/C][C]3.2544[/C][C]-0.2544[/C][/ROW]
[ROW][C]35[/C][C]1.5[/C][C]5.74173[/C][C]-4.24173[/C][/ROW]
[ROW][C]36[/C][C]3.5[/C][C]4.51118[/C][C]-1.01118[/C][/ROW]
[ROW][C]37[/C][C]2.5[/C][C]4.03461[/C][C]-1.53461[/C][/ROW]
[ROW][C]38[/C][C]5.5[/C][C]2.97664[/C][C]2.52336[/C][/ROW]
[ROW][C]39[/C][C]8[/C][C]5.23505[/C][C]2.76495[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]3.89832[/C][C]-2.89832[/C][/ROW]
[ROW][C]41[/C][C]5[/C][C]4.48668[/C][C]0.51332[/C][/ROW]
[ROW][C]42[/C][C]4.5[/C][C]4.1563[/C][C]0.343697[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]3.82442[/C][C]-0.824419[/C][/ROW]
[ROW][C]44[/C][C]3[/C][C]4.11301[/C][C]-1.11301[/C][/ROW]
[ROW][C]45[/C][C]8[/C][C]5.31097[/C][C]2.68903[/C][/ROW]
[ROW][C]46[/C][C]2.5[/C][C]4.28774[/C][C]-1.78774[/C][/ROW]
[ROW][C]47[/C][C]7[/C][C]4.66441[/C][C]2.33559[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]3.66372[/C][C]-3.66372[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]4.51164[/C][C]-3.51164[/C][/ROW]
[ROW][C]50[/C][C]3.5[/C][C]4.60595[/C][C]-1.10595[/C][/ROW]
[ROW][C]51[/C][C]5.5[/C][C]4.46613[/C][C]1.03387[/C][/ROW]
[ROW][C]52[/C][C]5.5[/C][C]5.62965[/C][C]-0.129649[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268509&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268509&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	5	4.13533	0.864671
2	3	5.39711	-2.39711
3	7.5	3.40378	4.09622
4	7	4.36946	2.63054
5	6	3.9545	2.0455
6	6	4.21698	1.78302
7	1	4.67177	-3.67177
8	6	6.1226	-0.122604
9	5	3.68529	1.31471
10	1	3.88649	-2.88649
11	6.5	3.99357	2.50643
12	0	3.11303	-3.11303
13	3.5	4.07596	-0.575962
14	7.5	5.73441	1.76559
15	3.5	3.91676	-0.416765
16	6	3.67348	2.32652
17	3.5	4.64443	-1.14443
18	7.5	7.07948	0.420518
19	6.5	5.84525	0.654746
20	3.5	3.29608	0.203922
21	4	4.05854	-0.0585398
22	7.5	3.87622	3.62378
23	4.5	3.68369	0.81631
24	0	4.06625	-4.06625
25	3.5	4.0269	-0.526897
26	5.5	4.37191	1.12809
27	5	5.0933	-0.0933003
28	4.5	3.90385	0.596149
29	2.5	4.18786	-1.68786
30	7.5	3.83372	3.66628
31	7	4.46466	2.53534
32	0	2.545	-2.545
33	4.5	4.29978	0.200218
34	3	3.2544	-0.2544
35	1.5	5.74173	-4.24173
36	3.5	4.51118	-1.01118
37	2.5	4.03461	-1.53461
38	5.5	2.97664	2.52336
39	8	5.23505	2.76495
40	1	3.89832	-2.89832
41	5	4.48668	0.51332
42	4.5	4.1563	0.343697
43	3	3.82442	-0.824419
44	3	4.11301	-1.11301
45	8	5.31097	2.68903
46	2.5	4.28774	-1.78774
47	7	4.66441	2.33559
48	0	3.66372	-3.66372
49	1	4.51164	-3.51164
50	3.5	4.60595	-1.10595
51	5.5	4.46613	1.03387
52	5.5	5.62965	-0.129649

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.890517	0.218965	0.109483
12	0.917511	0.164978	0.082489
13	0.930214	0.139572	0.0697858
14	0.904017	0.191965	0.0959826
15	0.851806	0.296387	0.148194
16	0.852474	0.295053	0.147526
17	0.831502	0.336996	0.168498
18	0.776793	0.446414	0.223207
19	0.691609	0.616781	0.308391
20	0.597697	0.804606	0.402303
21	0.497538	0.995075	0.502462
22	0.552497	0.895006	0.447503
23	0.468175	0.93635	0.531825
24	0.617342	0.765316	0.382658
25	0.523732	0.952536	0.476268
26	0.450789	0.901577	0.549211
27	0.35808	0.716161	0.64192
28	0.279925	0.559851	0.720075
29	0.254319	0.508637	0.745681
30	0.399389	0.798777	0.600611
31	0.401325	0.80265	0.598675
32	0.40241	0.804821	0.59759
33	0.317606	0.635212	0.682394
34	0.265309	0.530619	0.734691
35	0.680007	0.639985	0.319993
36	0.609095	0.78181	0.390905
37	0.736712	0.526576	0.263288
38	0.664978	0.670044	0.335022
39	0.59083	0.81834	0.40917
40	0.493154	0.986308	0.506846
41	0.524867	0.950265	0.475133

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.890517 & 0.218965 & 0.109483 \tabularnewline
12 & 0.917511 & 0.164978 & 0.082489 \tabularnewline
13 & 0.930214 & 0.139572 & 0.0697858 \tabularnewline
14 & 0.904017 & 0.191965 & 0.0959826 \tabularnewline
15 & 0.851806 & 0.296387 & 0.148194 \tabularnewline
16 & 0.852474 & 0.295053 & 0.147526 \tabularnewline
17 & 0.831502 & 0.336996 & 0.168498 \tabularnewline
18 & 0.776793 & 0.446414 & 0.223207 \tabularnewline
19 & 0.691609 & 0.616781 & 0.308391 \tabularnewline
20 & 0.597697 & 0.804606 & 0.402303 \tabularnewline
21 & 0.497538 & 0.995075 & 0.502462 \tabularnewline
22 & 0.552497 & 0.895006 & 0.447503 \tabularnewline
23 & 0.468175 & 0.93635 & 0.531825 \tabularnewline
24 & 0.617342 & 0.765316 & 0.382658 \tabularnewline
25 & 0.523732 & 0.952536 & 0.476268 \tabularnewline
26 & 0.450789 & 0.901577 & 0.549211 \tabularnewline
27 & 0.35808 & 0.716161 & 0.64192 \tabularnewline
28 & 0.279925 & 0.559851 & 0.720075 \tabularnewline
29 & 0.254319 & 0.508637 & 0.745681 \tabularnewline
30 & 0.399389 & 0.798777 & 0.600611 \tabularnewline
31 & 0.401325 & 0.80265 & 0.598675 \tabularnewline
32 & 0.40241 & 0.804821 & 0.59759 \tabularnewline
33 & 0.317606 & 0.635212 & 0.682394 \tabularnewline
34 & 0.265309 & 0.530619 & 0.734691 \tabularnewline
35 & 0.680007 & 0.639985 & 0.319993 \tabularnewline
36 & 0.609095 & 0.78181 & 0.390905 \tabularnewline
37 & 0.736712 & 0.526576 & 0.263288 \tabularnewline
38 & 0.664978 & 0.670044 & 0.335022 \tabularnewline
39 & 0.59083 & 0.81834 & 0.40917 \tabularnewline
40 & 0.493154 & 0.986308 & 0.506846 \tabularnewline
41 & 0.524867 & 0.950265 & 0.475133 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268509&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]0.890517[/C][C]0.218965[/C][C]0.109483[/C][/ROW]
[ROW][C]12[/C][C]0.917511[/C][C]0.164978[/C][C]0.082489[/C][/ROW]
[ROW][C]13[/C][C]0.930214[/C][C]0.139572[/C][C]0.0697858[/C][/ROW]
[ROW][C]14[/C][C]0.904017[/C][C]0.191965[/C][C]0.0959826[/C][/ROW]
[ROW][C]15[/C][C]0.851806[/C][C]0.296387[/C][C]0.148194[/C][/ROW]
[ROW][C]16[/C][C]0.852474[/C][C]0.295053[/C][C]0.147526[/C][/ROW]
[ROW][C]17[/C][C]0.831502[/C][C]0.336996[/C][C]0.168498[/C][/ROW]
[ROW][C]18[/C][C]0.776793[/C][C]0.446414[/C][C]0.223207[/C][/ROW]
[ROW][C]19[/C][C]0.691609[/C][C]0.616781[/C][C]0.308391[/C][/ROW]
[ROW][C]20[/C][C]0.597697[/C][C]0.804606[/C][C]0.402303[/C][/ROW]
[ROW][C]21[/C][C]0.497538[/C][C]0.995075[/C][C]0.502462[/C][/ROW]
[ROW][C]22[/C][C]0.552497[/C][C]0.895006[/C][C]0.447503[/C][/ROW]
[ROW][C]23[/C][C]0.468175[/C][C]0.93635[/C][C]0.531825[/C][/ROW]
[ROW][C]24[/C][C]0.617342[/C][C]0.765316[/C][C]0.382658[/C][/ROW]
[ROW][C]25[/C][C]0.523732[/C][C]0.952536[/C][C]0.476268[/C][/ROW]
[ROW][C]26[/C][C]0.450789[/C][C]0.901577[/C][C]0.549211[/C][/ROW]
[ROW][C]27[/C][C]0.35808[/C][C]0.716161[/C][C]0.64192[/C][/ROW]
[ROW][C]28[/C][C]0.279925[/C][C]0.559851[/C][C]0.720075[/C][/ROW]
[ROW][C]29[/C][C]0.254319[/C][C]0.508637[/C][C]0.745681[/C][/ROW]
[ROW][C]30[/C][C]0.399389[/C][C]0.798777[/C][C]0.600611[/C][/ROW]
[ROW][C]31[/C][C]0.401325[/C][C]0.80265[/C][C]0.598675[/C][/ROW]
[ROW][C]32[/C][C]0.40241[/C][C]0.804821[/C][C]0.59759[/C][/ROW]
[ROW][C]33[/C][C]0.317606[/C][C]0.635212[/C][C]0.682394[/C][/ROW]
[ROW][C]34[/C][C]0.265309[/C][C]0.530619[/C][C]0.734691[/C][/ROW]
[ROW][C]35[/C][C]0.680007[/C][C]0.639985[/C][C]0.319993[/C][/ROW]
[ROW][C]36[/C][C]0.609095[/C][C]0.78181[/C][C]0.390905[/C][/ROW]
[ROW][C]37[/C][C]0.736712[/C][C]0.526576[/C][C]0.263288[/C][/ROW]
[ROW][C]38[/C][C]0.664978[/C][C]0.670044[/C][C]0.335022[/C][/ROW]
[ROW][C]39[/C][C]0.59083[/C][C]0.81834[/C][C]0.40917[/C][/ROW]
[ROW][C]40[/C][C]0.493154[/C][C]0.986308[/C][C]0.506846[/C][/ROW]
[ROW][C]41[/C][C]0.524867[/C][C]0.950265[/C][C]0.475133[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268509&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268509&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	0.890517	0.218965	0.109483
12	0.917511	0.164978	0.082489
13	0.930214	0.139572	0.0697858
14	0.904017	0.191965	0.0959826
15	0.851806	0.296387	0.148194
16	0.852474	0.295053	0.147526
17	0.831502	0.336996	0.168498
18	0.776793	0.446414	0.223207
19	0.691609	0.616781	0.308391
20	0.597697	0.804606	0.402303
21	0.497538	0.995075	0.502462
22	0.552497	0.895006	0.447503
23	0.468175	0.93635	0.531825
24	0.617342	0.765316	0.382658
25	0.523732	0.952536	0.476268
26	0.450789	0.901577	0.549211
27	0.35808	0.716161	0.64192
28	0.279925	0.559851	0.720075
29	0.254319	0.508637	0.745681
30	0.399389	0.798777	0.600611
31	0.401325	0.80265	0.598675
32	0.40241	0.804821	0.59759
33	0.317606	0.635212	0.682394
34	0.265309	0.530619	0.734691
35	0.680007	0.639985	0.319993
36	0.609095	0.78181	0.390905
37	0.736712	0.526576	0.263288
38	0.664978	0.670044	0.335022
39	0.59083	0.81834	0.40917
40	0.493154	0.986308	0.506846
41	0.524867	0.950265	0.475133

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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268509&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268509&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268509&T=6

As an alternative you can also use a QR Code:

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

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

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 8 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 8 ; par2 = Do not include Seasonal Dummies ; par3 = No 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$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$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1/numgqtests,6))
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