Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 10 Dec 2014 16:10:02 +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/2014/Dec/10/t1418227860znjj66imcsifjkb.htm/, Retrieved Sun, 19 May 2024 13:03:46 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=265462, Retrieved Sun, 19 May 2024 13:03:46 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

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

-       [Multiple Regression] [multiple regressi...] [2014-12-10 16:10:02] [76a64141a2f0df75ca87242b7cc8f1c2] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

62 0 72 11 5.0
56 1 61 6 3.0
57 1 68 7 7.5
51 0 61 10 7.0
56 1 64 9 6.0
30 1 65 7 6.0
61 1 69 4 1.0
47 1 63 4 6.0
56 1 75 4 5.0
50 1 63 8 1.0
67 1 73 4 6.5
41 0 75 7 0.0
45 0 63 4 3.5
48 1 63 4 7.5
44 1 62 9 3.5
37 0 64 4 6.0
56 0 60 10 3.5
66 1 56 4 7.5
38 1 59 5 6.5
34 0 68 4 3.5
49 0 66 4 4.0
55 0 73 4 7.5
49 0 72 4 4.5
59 1 71 6 0.0
40 0 59 10 3.5
58 1 64 7 5.5
60 1 66 4 5.0
63 0 78 4 4.5
56 0 68 7 2.5
54 0 73 4 7.5
52 1 62 8 7.0
34 1 65 11 0.0
69 1 68 6 4.5
32 0 65 14 3.0
48 1 60 5 1.5
67 0 71 4 3.5
58 1 65 8 2.5
57 1 68 9 5.5
42 1 64 4 8.0
64 1 74 4 1.0
58 1 69 5 5.0
66 0 76 4 4.5
26 1 68 5 3.0
61 1 72 4 3.0
52 1 67 4 8.0
51 0 63 7 2.5
55 0 59 10 7.0
50 0 73 4 0.0
60 0 66 5 1.0
56 0 62 4 3.5
63 0 69 4 5.5
61 1 66 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	'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265462&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265462&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265462&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	'Gwilym Jenkins' @ jenkins.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Ex[t] = + 12.2315 + 0.0267947AMS.I[t] + 0.186487gender[t] -0.125751AMS.E[t] -0.171995AMS.A[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ex[t] =  +  12.2315 +  0.0267947AMS.I[t] +  0.186487gender[t] -0.125751AMS.E[t] -0.171995AMS.A[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265462&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ex[t] =  +  12.2315 +  0.0267947AMS.I[t] +  0.186487gender[t] -0.125751AMS.E[t] -0.171995AMS.A[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265462&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265462&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] = + 12.2315 + 0.0267947AMS.I[t] + 0.186487gender[t] -0.125751AMS.E[t] -0.171995AMS.A[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)	12.2315	5.10841	2.394	0.020689	0.0103445
AMS.I	0.0267947	0.0334426	0.8012	0.427039	0.21352
gender	0.186487	0.666235	0.2799	0.780773	0.390387
AMS.E	-0.125751	0.0714208	-1.761	0.0847939	0.0423969
AMS.A	-0.171995	0.139303	-1.235	0.223086	0.111543

\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) & 12.2315 & 5.10841 & 2.394 & 0.020689 & 0.0103445 \tabularnewline
AMS.I & 0.0267947 & 0.0334426 & 0.8012 & 0.427039 & 0.21352 \tabularnewline
gender & 0.186487 & 0.666235 & 0.2799 & 0.780773 & 0.390387 \tabularnewline
AMS.E & -0.125751 & 0.0714208 & -1.761 & 0.0847939 & 0.0423969 \tabularnewline
AMS.A & -0.171995 & 0.139303 & -1.235 & 0.223086 & 0.111543 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265462&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]12.2315[/C][C]5.10841[/C][C]2.394[/C][C]0.020689[/C][C]0.0103445[/C][/ROW]
[ROW][C]AMS.I[/C][C]0.0267947[/C][C]0.0334426[/C][C]0.8012[/C][C]0.427039[/C][C]0.21352[/C][/ROW]
[ROW][C]gender[/C][C]0.186487[/C][C]0.666235[/C][C]0.2799[/C][C]0.780773[/C][C]0.390387[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.125751[/C][C]0.0714208[/C][C]-1.761[/C][C]0.0847939[/C][C]0.0423969[/C][/ROW]
[ROW][C]AMS.A[/C][C]-0.171995[/C][C]0.139303[/C][C]-1.235[/C][C]0.223086[/C][C]0.111543[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265462&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265462&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)	12.2315	5.10841	2.394	0.020689	0.0103445
AMS.I	0.0267947	0.0334426	0.8012	0.427039	0.21352
gender	0.186487	0.666235	0.2799	0.780773	0.390387
AMS.E	-0.125751	0.0714208	-1.761	0.0847939	0.0423969
AMS.A	-0.171995	0.139303	-1.235	0.223086	0.111543

Multiple Linear Regression - Regression Statistics
Multiple R	0.297734
R-squared	0.0886456
Adjusted R-squared	0.0110835
F-TEST (value)	1.1429
F-TEST (DF numerator)	4
F-TEST (DF denominator)	47
p-value	0.348056
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.31315
Sum Squared Residuals	251.481

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.297734 \tabularnewline
R-squared & 0.0886456 \tabularnewline
Adjusted R-squared & 0.0110835 \tabularnewline
F-TEST (value) & 1.1429 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 0.348056 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.31315 \tabularnewline
Sum Squared Residuals & 251.481 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265462&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.297734[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0886456[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0110835[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.1429[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]0.348056[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.31315[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]251.481[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265462&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265462&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	0.297734
R-squared	0.0886456
Adjusted R-squared	0.0110835
F-TEST (value)	1.1429
F-TEST (DF numerator)	4
F-TEST (DF denominator)	47
p-value	0.348056
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.31315
Sum Squared Residuals	251.481

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	5	2.94676	2.05324
2	3	5.21571	-2.21571
3	7.5	4.19026	3.30974
4	7	4.20727	2.79273
5	6	4.32248	1.67752
6	6	3.84405	2.15595
7	1	4.68767	-3.68767
8	6	5.06705	0.932953
9	5	3.79919	1.20081
10	1	4.45945	-3.45945
11	6.5	4.34543	2.15457
12	0	2.6948	-2.6948
13	3.5	4.82697	-1.32697
14	7.5	5.09384	2.40616
15	3.5	4.25244	-0.752441
16	6	4.48686	1.51314
17	3.5	4.467	-0.966997
18	7.5	6.4564	1.0436
19	6.5	5.1569	1.3431
20	3.5	3.90347	-0.403473
21	4	4.5569	-0.556896
22	7.5	3.83741	3.66259
23	4.5	3.80239	0.69761
24	0	4.03859	-4.03859
25	3.5	4.16403	-0.664033
26	5.5	4.72005	0.779946
27	5	5.03812	-0.0381249
28	4.5	3.42301	1.07699
29	2.5	3.97697	-1.47697
30	7.5	3.81061	3.68939
31	7	4.63879	2.36121
32	0	3.26325	-3.26325
33	4.5	4.68379	-0.183786
34	3	2.50719	0.492809
35	1.5	5.2991	-3.7991
36	3.5	4.41045	-0.910446
37	2.5	4.42231	-1.92231
38	5.5	3.84627	1.65373
39	8	4.80732	3.19268
40	1	4.1393	-3.1393
41	5	4.43529	0.564712
42	4.5	3.7549	0.745104
43	3	3.70361	-0.703609
44	3	4.31041	-1.31041
45	8	4.69802	3.30198
46	2.5	4.47175	-1.97175
47	7	4.56595	2.43405
48	0	3.70343	-3.70343
49	1	4.67964	-3.67964
50	3.5	5.24746	-1.74746
51	5.5	4.55477	0.945231
52	5.5	5.06492	0.43508

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 5 & 2.94676 & 2.05324 \tabularnewline
2 & 3 & 5.21571 & -2.21571 \tabularnewline
3 & 7.5 & 4.19026 & 3.30974 \tabularnewline
4 & 7 & 4.20727 & 2.79273 \tabularnewline
5 & 6 & 4.32248 & 1.67752 \tabularnewline
6 & 6 & 3.84405 & 2.15595 \tabularnewline
7 & 1 & 4.68767 & -3.68767 \tabularnewline
8 & 6 & 5.06705 & 0.932953 \tabularnewline
9 & 5 & 3.79919 & 1.20081 \tabularnewline
10 & 1 & 4.45945 & -3.45945 \tabularnewline
11 & 6.5 & 4.34543 & 2.15457 \tabularnewline
12 & 0 & 2.6948 & -2.6948 \tabularnewline
13 & 3.5 & 4.82697 & -1.32697 \tabularnewline
14 & 7.5 & 5.09384 & 2.40616 \tabularnewline
15 & 3.5 & 4.25244 & -0.752441 \tabularnewline
16 & 6 & 4.48686 & 1.51314 \tabularnewline
17 & 3.5 & 4.467 & -0.966997 \tabularnewline
18 & 7.5 & 6.4564 & 1.0436 \tabularnewline
19 & 6.5 & 5.1569 & 1.3431 \tabularnewline
20 & 3.5 & 3.90347 & -0.403473 \tabularnewline
21 & 4 & 4.5569 & -0.556896 \tabularnewline
22 & 7.5 & 3.83741 & 3.66259 \tabularnewline
23 & 4.5 & 3.80239 & 0.69761 \tabularnewline
24 & 0 & 4.03859 & -4.03859 \tabularnewline
25 & 3.5 & 4.16403 & -0.664033 \tabularnewline
26 & 5.5 & 4.72005 & 0.779946 \tabularnewline
27 & 5 & 5.03812 & -0.0381249 \tabularnewline
28 & 4.5 & 3.42301 & 1.07699 \tabularnewline
29 & 2.5 & 3.97697 & -1.47697 \tabularnewline
30 & 7.5 & 3.81061 & 3.68939 \tabularnewline
31 & 7 & 4.63879 & 2.36121 \tabularnewline
32 & 0 & 3.26325 & -3.26325 \tabularnewline
33 & 4.5 & 4.68379 & -0.183786 \tabularnewline
34 & 3 & 2.50719 & 0.492809 \tabularnewline
35 & 1.5 & 5.2991 & -3.7991 \tabularnewline
36 & 3.5 & 4.41045 & -0.910446 \tabularnewline
37 & 2.5 & 4.42231 & -1.92231 \tabularnewline
38 & 5.5 & 3.84627 & 1.65373 \tabularnewline
39 & 8 & 4.80732 & 3.19268 \tabularnewline
40 & 1 & 4.1393 & -3.1393 \tabularnewline
41 & 5 & 4.43529 & 0.564712 \tabularnewline
42 & 4.5 & 3.7549 & 0.745104 \tabularnewline
43 & 3 & 3.70361 & -0.703609 \tabularnewline
44 & 3 & 4.31041 & -1.31041 \tabularnewline
45 & 8 & 4.69802 & 3.30198 \tabularnewline
46 & 2.5 & 4.47175 & -1.97175 \tabularnewline
47 & 7 & 4.56595 & 2.43405 \tabularnewline
48 & 0 & 3.70343 & -3.70343 \tabularnewline
49 & 1 & 4.67964 & -3.67964 \tabularnewline
50 & 3.5 & 5.24746 & -1.74746 \tabularnewline
51 & 5.5 & 4.55477 & 0.945231 \tabularnewline
52 & 5.5 & 5.06492 & 0.43508 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265462&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]2.94676[/C][C]2.05324[/C][/ROW]
[ROW][C]2[/C][C]3[/C][C]5.21571[/C][C]-2.21571[/C][/ROW]
[ROW][C]3[/C][C]7.5[/C][C]4.19026[/C][C]3.30974[/C][/ROW]
[ROW][C]4[/C][C]7[/C][C]4.20727[/C][C]2.79273[/C][/ROW]
[ROW][C]5[/C][C]6[/C][C]4.32248[/C][C]1.67752[/C][/ROW]
[ROW][C]6[/C][C]6[/C][C]3.84405[/C][C]2.15595[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]4.68767[/C][C]-3.68767[/C][/ROW]
[ROW][C]8[/C][C]6[/C][C]5.06705[/C][C]0.932953[/C][/ROW]
[ROW][C]9[/C][C]5[/C][C]3.79919[/C][C]1.20081[/C][/ROW]
[ROW][C]10[/C][C]1[/C][C]4.45945[/C][C]-3.45945[/C][/ROW]
[ROW][C]11[/C][C]6.5[/C][C]4.34543[/C][C]2.15457[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]2.6948[/C][C]-2.6948[/C][/ROW]
[ROW][C]13[/C][C]3.5[/C][C]4.82697[/C][C]-1.32697[/C][/ROW]
[ROW][C]14[/C][C]7.5[/C][C]5.09384[/C][C]2.40616[/C][/ROW]
[ROW][C]15[/C][C]3.5[/C][C]4.25244[/C][C]-0.752441[/C][/ROW]
[ROW][C]16[/C][C]6[/C][C]4.48686[/C][C]1.51314[/C][/ROW]
[ROW][C]17[/C][C]3.5[/C][C]4.467[/C][C]-0.966997[/C][/ROW]
[ROW][C]18[/C][C]7.5[/C][C]6.4564[/C][C]1.0436[/C][/ROW]
[ROW][C]19[/C][C]6.5[/C][C]5.1569[/C][C]1.3431[/C][/ROW]
[ROW][C]20[/C][C]3.5[/C][C]3.90347[/C][C]-0.403473[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]4.5569[/C][C]-0.556896[/C][/ROW]
[ROW][C]22[/C][C]7.5[/C][C]3.83741[/C][C]3.66259[/C][/ROW]
[ROW][C]23[/C][C]4.5[/C][C]3.80239[/C][C]0.69761[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]4.03859[/C][C]-4.03859[/C][/ROW]
[ROW][C]25[/C][C]3.5[/C][C]4.16403[/C][C]-0.664033[/C][/ROW]
[ROW][C]26[/C][C]5.5[/C][C]4.72005[/C][C]0.779946[/C][/ROW]
[ROW][C]27[/C][C]5[/C][C]5.03812[/C][C]-0.0381249[/C][/ROW]
[ROW][C]28[/C][C]4.5[/C][C]3.42301[/C][C]1.07699[/C][/ROW]
[ROW][C]29[/C][C]2.5[/C][C]3.97697[/C][C]-1.47697[/C][/ROW]
[ROW][C]30[/C][C]7.5[/C][C]3.81061[/C][C]3.68939[/C][/ROW]
[ROW][C]31[/C][C]7[/C][C]4.63879[/C][C]2.36121[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]3.26325[/C][C]-3.26325[/C][/ROW]
[ROW][C]33[/C][C]4.5[/C][C]4.68379[/C][C]-0.183786[/C][/ROW]
[ROW][C]34[/C][C]3[/C][C]2.50719[/C][C]0.492809[/C][/ROW]
[ROW][C]35[/C][C]1.5[/C][C]5.2991[/C][C]-3.7991[/C][/ROW]
[ROW][C]36[/C][C]3.5[/C][C]4.41045[/C][C]-0.910446[/C][/ROW]
[ROW][C]37[/C][C]2.5[/C][C]4.42231[/C][C]-1.92231[/C][/ROW]
[ROW][C]38[/C][C]5.5[/C][C]3.84627[/C][C]1.65373[/C][/ROW]
[ROW][C]39[/C][C]8[/C][C]4.80732[/C][C]3.19268[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]4.1393[/C][C]-3.1393[/C][/ROW]
[ROW][C]41[/C][C]5[/C][C]4.43529[/C][C]0.564712[/C][/ROW]
[ROW][C]42[/C][C]4.5[/C][C]3.7549[/C][C]0.745104[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]3.70361[/C][C]-0.703609[/C][/ROW]
[ROW][C]44[/C][C]3[/C][C]4.31041[/C][C]-1.31041[/C][/ROW]
[ROW][C]45[/C][C]8[/C][C]4.69802[/C][C]3.30198[/C][/ROW]
[ROW][C]46[/C][C]2.5[/C][C]4.47175[/C][C]-1.97175[/C][/ROW]
[ROW][C]47[/C][C]7[/C][C]4.56595[/C][C]2.43405[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]3.70343[/C][C]-3.70343[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]4.67964[/C][C]-3.67964[/C][/ROW]
[ROW][C]50[/C][C]3.5[/C][C]5.24746[/C][C]-1.74746[/C][/ROW]
[ROW][C]51[/C][C]5.5[/C][C]4.55477[/C][C]0.945231[/C][/ROW]
[ROW][C]52[/C][C]5.5[/C][C]5.06492[/C][C]0.43508[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265462&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265462&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	2.94676	2.05324
2	3	5.21571	-2.21571
3	7.5	4.19026	3.30974
4	7	4.20727	2.79273
5	6	4.32248	1.67752
6	6	3.84405	2.15595
7	1	4.68767	-3.68767
8	6	5.06705	0.932953
9	5	3.79919	1.20081
10	1	4.45945	-3.45945
11	6.5	4.34543	2.15457
12	0	2.6948	-2.6948
13	3.5	4.82697	-1.32697
14	7.5	5.09384	2.40616
15	3.5	4.25244	-0.752441
16	6	4.48686	1.51314
17	3.5	4.467	-0.966997
18	7.5	6.4564	1.0436
19	6.5	5.1569	1.3431
20	3.5	3.90347	-0.403473
21	4	4.5569	-0.556896
22	7.5	3.83741	3.66259
23	4.5	3.80239	0.69761
24	0	4.03859	-4.03859
25	3.5	4.16403	-0.664033
26	5.5	4.72005	0.779946
27	5	5.03812	-0.0381249
28	4.5	3.42301	1.07699
29	2.5	3.97697	-1.47697
30	7.5	3.81061	3.68939
31	7	4.63879	2.36121
32	0	3.26325	-3.26325
33	4.5	4.68379	-0.183786
34	3	2.50719	0.492809
35	1.5	5.2991	-3.7991
36	3.5	4.41045	-0.910446
37	2.5	4.42231	-1.92231
38	5.5	3.84627	1.65373
39	8	4.80732	3.19268
40	1	4.1393	-3.1393
41	5	4.43529	0.564712
42	4.5	3.7549	0.745104
43	3	3.70361	-0.703609
44	3	4.31041	-1.31041
45	8	4.69802	3.30198
46	2.5	4.47175	-1.97175
47	7	4.56595	2.43405
48	0	3.70343	-3.70343
49	1	4.67964	-3.67964
50	3.5	5.24746	-1.74746
51	5.5	4.55477	0.945231
52	5.5	5.06492	0.43508

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.687758	0.624485	0.312242
9	0.592288	0.815423	0.407712
10	0.807318	0.385364	0.192682
11	0.805494	0.389011	0.194506
12	0.902192	0.195617	0.0978084
13	0.855663	0.288674	0.144337
14	0.860887	0.278225	0.139113
15	0.817933	0.364133	0.182067
16	0.781132	0.437735	0.218868
17	0.720933	0.558133	0.279067
18	0.651241	0.697518	0.348759
19	0.584549	0.830903	0.415451
20	0.49397	0.98794	0.50603
21	0.405927	0.811854	0.594073
22	0.503133	0.993734	0.496867
23	0.42072	0.841441	0.57928
24	0.593615	0.812769	0.406385
25	0.513355	0.97329	0.486645
26	0.433195	0.866389	0.566805
27	0.348934	0.697868	0.651066
28	0.284173	0.568345	0.715827
29	0.239601	0.479202	0.760399
30	0.368894	0.737789	0.631106
31	0.358683	0.717365	0.641317
32	0.423179	0.846357	0.576821
33	0.337593	0.675185	0.662407
34	0.262668	0.525337	0.737332
35	0.438177	0.876355	0.561823
36	0.364296	0.728593	0.635704
37	0.406921	0.813843	0.593079
38	0.32263	0.64526	0.67737
39	0.36588	0.731761	0.63412
40	0.49021	0.98042	0.50979
41	0.378952	0.757904	0.621048
42	0.386792	0.773585	0.613208
43	0.259784	0.519569	0.740216
44	0.228115	0.45623	0.771885

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.687758 & 0.624485 & 0.312242 \tabularnewline
9 & 0.592288 & 0.815423 & 0.407712 \tabularnewline
10 & 0.807318 & 0.385364 & 0.192682 \tabularnewline
11 & 0.805494 & 0.389011 & 0.194506 \tabularnewline
12 & 0.902192 & 0.195617 & 0.0978084 \tabularnewline
13 & 0.855663 & 0.288674 & 0.144337 \tabularnewline
14 & 0.860887 & 0.278225 & 0.139113 \tabularnewline
15 & 0.817933 & 0.364133 & 0.182067 \tabularnewline
16 & 0.781132 & 0.437735 & 0.218868 \tabularnewline
17 & 0.720933 & 0.558133 & 0.279067 \tabularnewline
18 & 0.651241 & 0.697518 & 0.348759 \tabularnewline
19 & 0.584549 & 0.830903 & 0.415451 \tabularnewline
20 & 0.49397 & 0.98794 & 0.50603 \tabularnewline
21 & 0.405927 & 0.811854 & 0.594073 \tabularnewline
22 & 0.503133 & 0.993734 & 0.496867 \tabularnewline
23 & 0.42072 & 0.841441 & 0.57928 \tabularnewline
24 & 0.593615 & 0.812769 & 0.406385 \tabularnewline
25 & 0.513355 & 0.97329 & 0.486645 \tabularnewline
26 & 0.433195 & 0.866389 & 0.566805 \tabularnewline
27 & 0.348934 & 0.697868 & 0.651066 \tabularnewline
28 & 0.284173 & 0.568345 & 0.715827 \tabularnewline
29 & 0.239601 & 0.479202 & 0.760399 \tabularnewline
30 & 0.368894 & 0.737789 & 0.631106 \tabularnewline
31 & 0.358683 & 0.717365 & 0.641317 \tabularnewline
32 & 0.423179 & 0.846357 & 0.576821 \tabularnewline
33 & 0.337593 & 0.675185 & 0.662407 \tabularnewline
34 & 0.262668 & 0.525337 & 0.737332 \tabularnewline
35 & 0.438177 & 0.876355 & 0.561823 \tabularnewline
36 & 0.364296 & 0.728593 & 0.635704 \tabularnewline
37 & 0.406921 & 0.813843 & 0.593079 \tabularnewline
38 & 0.32263 & 0.64526 & 0.67737 \tabularnewline
39 & 0.36588 & 0.731761 & 0.63412 \tabularnewline
40 & 0.49021 & 0.98042 & 0.50979 \tabularnewline
41 & 0.378952 & 0.757904 & 0.621048 \tabularnewline
42 & 0.386792 & 0.773585 & 0.613208 \tabularnewline
43 & 0.259784 & 0.519569 & 0.740216 \tabularnewline
44 & 0.228115 & 0.45623 & 0.771885 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265462&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]8[/C][C]0.687758[/C][C]0.624485[/C][C]0.312242[/C][/ROW]
[ROW][C]9[/C][C]0.592288[/C][C]0.815423[/C][C]0.407712[/C][/ROW]
[ROW][C]10[/C][C]0.807318[/C][C]0.385364[/C][C]0.192682[/C][/ROW]
[ROW][C]11[/C][C]0.805494[/C][C]0.389011[/C][C]0.194506[/C][/ROW]
[ROW][C]12[/C][C]0.902192[/C][C]0.195617[/C][C]0.0978084[/C][/ROW]
[ROW][C]13[/C][C]0.855663[/C][C]0.288674[/C][C]0.144337[/C][/ROW]
[ROW][C]14[/C][C]0.860887[/C][C]0.278225[/C][C]0.139113[/C][/ROW]
[ROW][C]15[/C][C]0.817933[/C][C]0.364133[/C][C]0.182067[/C][/ROW]
[ROW][C]16[/C][C]0.781132[/C][C]0.437735[/C][C]0.218868[/C][/ROW]
[ROW][C]17[/C][C]0.720933[/C][C]0.558133[/C][C]0.279067[/C][/ROW]
[ROW][C]18[/C][C]0.651241[/C][C]0.697518[/C][C]0.348759[/C][/ROW]
[ROW][C]19[/C][C]0.584549[/C][C]0.830903[/C][C]0.415451[/C][/ROW]
[ROW][C]20[/C][C]0.49397[/C][C]0.98794[/C][C]0.50603[/C][/ROW]
[ROW][C]21[/C][C]0.405927[/C][C]0.811854[/C][C]0.594073[/C][/ROW]
[ROW][C]22[/C][C]0.503133[/C][C]0.993734[/C][C]0.496867[/C][/ROW]
[ROW][C]23[/C][C]0.42072[/C][C]0.841441[/C][C]0.57928[/C][/ROW]
[ROW][C]24[/C][C]0.593615[/C][C]0.812769[/C][C]0.406385[/C][/ROW]
[ROW][C]25[/C][C]0.513355[/C][C]0.97329[/C][C]0.486645[/C][/ROW]
[ROW][C]26[/C][C]0.433195[/C][C]0.866389[/C][C]0.566805[/C][/ROW]
[ROW][C]27[/C][C]0.348934[/C][C]0.697868[/C][C]0.651066[/C][/ROW]
[ROW][C]28[/C][C]0.284173[/C][C]0.568345[/C][C]0.715827[/C][/ROW]
[ROW][C]29[/C][C]0.239601[/C][C]0.479202[/C][C]0.760399[/C][/ROW]
[ROW][C]30[/C][C]0.368894[/C][C]0.737789[/C][C]0.631106[/C][/ROW]
[ROW][C]31[/C][C]0.358683[/C][C]0.717365[/C][C]0.641317[/C][/ROW]
[ROW][C]32[/C][C]0.423179[/C][C]0.846357[/C][C]0.576821[/C][/ROW]
[ROW][C]33[/C][C]0.337593[/C][C]0.675185[/C][C]0.662407[/C][/ROW]
[ROW][C]34[/C][C]0.262668[/C][C]0.525337[/C][C]0.737332[/C][/ROW]
[ROW][C]35[/C][C]0.438177[/C][C]0.876355[/C][C]0.561823[/C][/ROW]
[ROW][C]36[/C][C]0.364296[/C][C]0.728593[/C][C]0.635704[/C][/ROW]
[ROW][C]37[/C][C]0.406921[/C][C]0.813843[/C][C]0.593079[/C][/ROW]
[ROW][C]38[/C][C]0.32263[/C][C]0.64526[/C][C]0.67737[/C][/ROW]
[ROW][C]39[/C][C]0.36588[/C][C]0.731761[/C][C]0.63412[/C][/ROW]
[ROW][C]40[/C][C]0.49021[/C][C]0.98042[/C][C]0.50979[/C][/ROW]
[ROW][C]41[/C][C]0.378952[/C][C]0.757904[/C][C]0.621048[/C][/ROW]
[ROW][C]42[/C][C]0.386792[/C][C]0.773585[/C][C]0.613208[/C][/ROW]
[ROW][C]43[/C][C]0.259784[/C][C]0.519569[/C][C]0.740216[/C][/ROW]
[ROW][C]44[/C][C]0.228115[/C][C]0.45623[/C][C]0.771885[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265462&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265462&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
8	0.687758	0.624485	0.312242
9	0.592288	0.815423	0.407712
10	0.807318	0.385364	0.192682
11	0.805494	0.389011	0.194506
12	0.902192	0.195617	0.0978084
13	0.855663	0.288674	0.144337
14	0.860887	0.278225	0.139113
15	0.817933	0.364133	0.182067
16	0.781132	0.437735	0.218868
17	0.720933	0.558133	0.279067
18	0.651241	0.697518	0.348759
19	0.584549	0.830903	0.415451
20	0.49397	0.98794	0.50603
21	0.405927	0.811854	0.594073
22	0.503133	0.993734	0.496867
23	0.42072	0.841441	0.57928
24	0.593615	0.812769	0.406385
25	0.513355	0.97329	0.486645
26	0.433195	0.866389	0.566805
27	0.348934	0.697868	0.651066
28	0.284173	0.568345	0.715827
29	0.239601	0.479202	0.760399
30	0.368894	0.737789	0.631106
31	0.358683	0.717365	0.641317
32	0.423179	0.846357	0.576821
33	0.337593	0.675185	0.662407
34	0.262668	0.525337	0.737332
35	0.438177	0.876355	0.561823
36	0.364296	0.728593	0.635704
37	0.406921	0.813843	0.593079
38	0.32263	0.64526	0.67737
39	0.36588	0.731761	0.63412
40	0.49021	0.98042	0.50979
41	0.378952	0.757904	0.621048
42	0.386792	0.773585	0.613208
43	0.259784	0.519569	0.740216
44	0.228115	0.45623	0.771885

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=265462&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=265462&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265462&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

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 5 ; 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