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Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 12 Dec 2014 14:00:40 +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/12/t1418392901tvgsd5qoeqcyran.htm/, Retrieved Sun, 06 Apr 2025 02:54:19 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266716, Retrieved Sun, 06 Apr 2025 02:54:19 +0000

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No (this computation is public)

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Estimated Impact

146

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

-       [Multiple Regression] [] [2014-12-12 14:00:40] [4897fbbb7461c8caec7645a3718e7cbe] [Current]

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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	'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266716&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266716&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266716&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	'Herman Ole Andreas Wold' @ wold.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Ex[t] = + 2.81236 + 0.035641PRH[t] + 0.00929074CH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ex[t] =  +  2.81236 +  0.035641PRH[t] +  0.00929074CH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266716&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ex[t] =  +  2.81236 +  0.035641PRH[t] +  0.00929074CH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266716&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266716&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] = + 2.81236 + 0.035641PRH[t] + 0.00929074CH[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)	2.81236	0.732332	3.84	0.000302538	0.000151269
PRH	0.035641	0.0125882	2.831	0.00633058	0.00316529
CH	0.00929074	0.015632	0.5943	0.554556	0.277278

\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) & 2.81236 & 0.732332 & 3.84 & 0.000302538 & 0.000151269 \tabularnewline
PRH & 0.035641 & 0.0125882 & 2.831 & 0.00633058 & 0.00316529 \tabularnewline
CH & 0.00929074 & 0.015632 & 0.5943 & 0.554556 & 0.277278 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266716&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]2.81236[/C][C]0.732332[/C][C]3.84[/C][C]0.000302538[/C][C]0.000151269[/C][/ROW]
[ROW][C]PRH[/C][C]0.035641[/C][C]0.0125882[/C][C]2.831[/C][C]0.00633058[/C][C]0.00316529[/C][/ROW]
[ROW][C]CH[/C][C]0.00929074[/C][C]0.015632[/C][C]0.5943[/C][C]0.554556[/C][C]0.277278[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266716&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266716&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)	2.81236	0.732332	3.84	0.000302538	0.000151269
PRH	0.035641	0.0125882	2.831	0.00633058	0.00316529
CH	0.00929074	0.015632	0.5943	0.554556	0.277278

Multiple Linear Regression - Regression Statistics
Multiple R	0.393727
R-squared	0.155021
Adjusted R-squared	0.126377
F-TEST (value)	5.4121
F-TEST (DF numerator)	2
F-TEST (DF denominator)	59
p-value	0.00694959
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.90699
Sum Squared Residuals	214.56

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.393727 \tabularnewline
R-squared & 0.155021 \tabularnewline
Adjusted R-squared & 0.126377 \tabularnewline
F-TEST (value) & 5.4121 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 0.00694959 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.90699 \tabularnewline
Sum Squared Residuals & 214.56 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266716&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.393727[/C][/ROW]
[ROW][C]R-squared[/C][C]0.155021[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.126377[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.4121[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]59[/C][/ROW]
[ROW][C]p-value[/C][C]0.00694959[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.90699[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]214.56[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266716&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266716&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	0.393727
R-squared	0.155021
Adjusted R-squared	0.126377
F-TEST (value)	5.4121
F-TEST (DF numerator)	2
F-TEST (DF denominator)	59
p-value	0.00694959
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.90699
Sum Squared Residuals	214.56

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	7.5	4.08566	3.41434
2	6.5	4.49966	2.00034
3	1	5.02787	-4.02787
4	1	4.22382	-3.22382
5	5.5	5.68342	-0.183422
6	8.5	4.63582	3.86418
7	6.5	5.38108	1.11892
8	4.5	6.26281	-1.76281
9	2	4.51199	-2.51199
10	5	4.39882	0.601176
11	0.5	4.68075	-4.18075
12	5	5.99626	-0.996263
13	2.5	3.73734	-1.23734
14	5	4.02703	0.972965
15	5.5	5.35016	0.149841
16	3.5	4.42061	-0.920608
17	4	4.05018	-0.0501822
18	6.5	7.03642	-0.536417
19	4.5	4.46858	0.0314164
20	5.5	4.98918	0.510817
21	4	4.81859	-0.818588
22	7.5	7.44048	0.0595153
23	4	4.09191	-0.091911
24	5.5	3.97738	1.52262
25	2.5	4.05947	-1.55947
26	5.5	4.31521	1.18479
27	3.5	4.19747	-0.697471
28	4.5	4.73513	-0.23513
29	4.5	3.74495	0.755049
30	6	4.74458	1.25542
31	5	4.83733	0.162672
32	6.5	4.95354	1.54646
33	5	3.94478	1.05522
34	6	3.94798	2.05202
35	4.5	5.34255	-0.842549
36	5	4.29663	0.703375
37	5	3.76826	1.23174
38	6.5	3.8566	2.6434
39	7	4.94121	2.05879
40	4.5	3.95727	0.542725
41	8.5	4.36943	4.13057
42	3.5	4.70237	-1.20237
43	6	4.68107	1.31893
44	1.5	3.82552	-2.32552
45	3.5	4.33227	-0.832266
46	7.5	5.46044	2.03956
47	5	6.18392	-1.18392
48	6.5	4.4174	2.0826
49	6.5	4.89307	1.60693
50	6.5	4.41756	2.08244
51	7	4.41268	2.58732
52	1.5	4.4267	-2.9267
53	4	4.38497	-0.384967
54	4.5	4.2919	0.2081
55	0	3.47232	-3.47232
56	3.5	4.53987	-1.03987
57	4.5	4.81586	-0.315862
58	0	3.55289	-3.55289
59	3	3.73886	-0.738863
60	3.5	3.39951	0.100488
61	3	4.44071	-1.44071
62	1	3.30188	-2.30188

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 7.5 & 4.08566 & 3.41434 \tabularnewline
2 & 6.5 & 4.49966 & 2.00034 \tabularnewline
3 & 1 & 5.02787 & -4.02787 \tabularnewline
4 & 1 & 4.22382 & -3.22382 \tabularnewline
5 & 5.5 & 5.68342 & -0.183422 \tabularnewline
6 & 8.5 & 4.63582 & 3.86418 \tabularnewline
7 & 6.5 & 5.38108 & 1.11892 \tabularnewline
8 & 4.5 & 6.26281 & -1.76281 \tabularnewline
9 & 2 & 4.51199 & -2.51199 \tabularnewline
10 & 5 & 4.39882 & 0.601176 \tabularnewline
11 & 0.5 & 4.68075 & -4.18075 \tabularnewline
12 & 5 & 5.99626 & -0.996263 \tabularnewline
13 & 2.5 & 3.73734 & -1.23734 \tabularnewline
14 & 5 & 4.02703 & 0.972965 \tabularnewline
15 & 5.5 & 5.35016 & 0.149841 \tabularnewline
16 & 3.5 & 4.42061 & -0.920608 \tabularnewline
17 & 4 & 4.05018 & -0.0501822 \tabularnewline
18 & 6.5 & 7.03642 & -0.536417 \tabularnewline
19 & 4.5 & 4.46858 & 0.0314164 \tabularnewline
20 & 5.5 & 4.98918 & 0.510817 \tabularnewline
21 & 4 & 4.81859 & -0.818588 \tabularnewline
22 & 7.5 & 7.44048 & 0.0595153 \tabularnewline
23 & 4 & 4.09191 & -0.091911 \tabularnewline
24 & 5.5 & 3.97738 & 1.52262 \tabularnewline
25 & 2.5 & 4.05947 & -1.55947 \tabularnewline
26 & 5.5 & 4.31521 & 1.18479 \tabularnewline
27 & 3.5 & 4.19747 & -0.697471 \tabularnewline
28 & 4.5 & 4.73513 & -0.23513 \tabularnewline
29 & 4.5 & 3.74495 & 0.755049 \tabularnewline
30 & 6 & 4.74458 & 1.25542 \tabularnewline
31 & 5 & 4.83733 & 0.162672 \tabularnewline
32 & 6.5 & 4.95354 & 1.54646 \tabularnewline
33 & 5 & 3.94478 & 1.05522 \tabularnewline
34 & 6 & 3.94798 & 2.05202 \tabularnewline
35 & 4.5 & 5.34255 & -0.842549 \tabularnewline
36 & 5 & 4.29663 & 0.703375 \tabularnewline
37 & 5 & 3.76826 & 1.23174 \tabularnewline
38 & 6.5 & 3.8566 & 2.6434 \tabularnewline
39 & 7 & 4.94121 & 2.05879 \tabularnewline
40 & 4.5 & 3.95727 & 0.542725 \tabularnewline
41 & 8.5 & 4.36943 & 4.13057 \tabularnewline
42 & 3.5 & 4.70237 & -1.20237 \tabularnewline
43 & 6 & 4.68107 & 1.31893 \tabularnewline
44 & 1.5 & 3.82552 & -2.32552 \tabularnewline
45 & 3.5 & 4.33227 & -0.832266 \tabularnewline
46 & 7.5 & 5.46044 & 2.03956 \tabularnewline
47 & 5 & 6.18392 & -1.18392 \tabularnewline
48 & 6.5 & 4.4174 & 2.0826 \tabularnewline
49 & 6.5 & 4.89307 & 1.60693 \tabularnewline
50 & 6.5 & 4.41756 & 2.08244 \tabularnewline
51 & 7 & 4.41268 & 2.58732 \tabularnewline
52 & 1.5 & 4.4267 & -2.9267 \tabularnewline
53 & 4 & 4.38497 & -0.384967 \tabularnewline
54 & 4.5 & 4.2919 & 0.2081 \tabularnewline
55 & 0 & 3.47232 & -3.47232 \tabularnewline
56 & 3.5 & 4.53987 & -1.03987 \tabularnewline
57 & 4.5 & 4.81586 & -0.315862 \tabularnewline
58 & 0 & 3.55289 & -3.55289 \tabularnewline
59 & 3 & 3.73886 & -0.738863 \tabularnewline
60 & 3.5 & 3.39951 & 0.100488 \tabularnewline
61 & 3 & 4.44071 & -1.44071 \tabularnewline
62 & 1 & 3.30188 & -2.30188 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266716&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]7.5[/C][C]4.08566[/C][C]3.41434[/C][/ROW]
[ROW][C]2[/C][C]6.5[/C][C]4.49966[/C][C]2.00034[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]5.02787[/C][C]-4.02787[/C][/ROW]
[ROW][C]4[/C][C]1[/C][C]4.22382[/C][C]-3.22382[/C][/ROW]
[ROW][C]5[/C][C]5.5[/C][C]5.68342[/C][C]-0.183422[/C][/ROW]
[ROW][C]6[/C][C]8.5[/C][C]4.63582[/C][C]3.86418[/C][/ROW]
[ROW][C]7[/C][C]6.5[/C][C]5.38108[/C][C]1.11892[/C][/ROW]
[ROW][C]8[/C][C]4.5[/C][C]6.26281[/C][C]-1.76281[/C][/ROW]
[ROW][C]9[/C][C]2[/C][C]4.51199[/C][C]-2.51199[/C][/ROW]
[ROW][C]10[/C][C]5[/C][C]4.39882[/C][C]0.601176[/C][/ROW]
[ROW][C]11[/C][C]0.5[/C][C]4.68075[/C][C]-4.18075[/C][/ROW]
[ROW][C]12[/C][C]5[/C][C]5.99626[/C][C]-0.996263[/C][/ROW]
[ROW][C]13[/C][C]2.5[/C][C]3.73734[/C][C]-1.23734[/C][/ROW]
[ROW][C]14[/C][C]5[/C][C]4.02703[/C][C]0.972965[/C][/ROW]
[ROW][C]15[/C][C]5.5[/C][C]5.35016[/C][C]0.149841[/C][/ROW]
[ROW][C]16[/C][C]3.5[/C][C]4.42061[/C][C]-0.920608[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]4.05018[/C][C]-0.0501822[/C][/ROW]
[ROW][C]18[/C][C]6.5[/C][C]7.03642[/C][C]-0.536417[/C][/ROW]
[ROW][C]19[/C][C]4.5[/C][C]4.46858[/C][C]0.0314164[/C][/ROW]
[ROW][C]20[/C][C]5.5[/C][C]4.98918[/C][C]0.510817[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]4.81859[/C][C]-0.818588[/C][/ROW]
[ROW][C]22[/C][C]7.5[/C][C]7.44048[/C][C]0.0595153[/C][/ROW]
[ROW][C]23[/C][C]4[/C][C]4.09191[/C][C]-0.091911[/C][/ROW]
[ROW][C]24[/C][C]5.5[/C][C]3.97738[/C][C]1.52262[/C][/ROW]
[ROW][C]25[/C][C]2.5[/C][C]4.05947[/C][C]-1.55947[/C][/ROW]
[ROW][C]26[/C][C]5.5[/C][C]4.31521[/C][C]1.18479[/C][/ROW]
[ROW][C]27[/C][C]3.5[/C][C]4.19747[/C][C]-0.697471[/C][/ROW]
[ROW][C]28[/C][C]4.5[/C][C]4.73513[/C][C]-0.23513[/C][/ROW]
[ROW][C]29[/C][C]4.5[/C][C]3.74495[/C][C]0.755049[/C][/ROW]
[ROW][C]30[/C][C]6[/C][C]4.74458[/C][C]1.25542[/C][/ROW]
[ROW][C]31[/C][C]5[/C][C]4.83733[/C][C]0.162672[/C][/ROW]
[ROW][C]32[/C][C]6.5[/C][C]4.95354[/C][C]1.54646[/C][/ROW]
[ROW][C]33[/C][C]5[/C][C]3.94478[/C][C]1.05522[/C][/ROW]
[ROW][C]34[/C][C]6[/C][C]3.94798[/C][C]2.05202[/C][/ROW]
[ROW][C]35[/C][C]4.5[/C][C]5.34255[/C][C]-0.842549[/C][/ROW]
[ROW][C]36[/C][C]5[/C][C]4.29663[/C][C]0.703375[/C][/ROW]
[ROW][C]37[/C][C]5[/C][C]3.76826[/C][C]1.23174[/C][/ROW]
[ROW][C]38[/C][C]6.5[/C][C]3.8566[/C][C]2.6434[/C][/ROW]
[ROW][C]39[/C][C]7[/C][C]4.94121[/C][C]2.05879[/C][/ROW]
[ROW][C]40[/C][C]4.5[/C][C]3.95727[/C][C]0.542725[/C][/ROW]
[ROW][C]41[/C][C]8.5[/C][C]4.36943[/C][C]4.13057[/C][/ROW]
[ROW][C]42[/C][C]3.5[/C][C]4.70237[/C][C]-1.20237[/C][/ROW]
[ROW][C]43[/C][C]6[/C][C]4.68107[/C][C]1.31893[/C][/ROW]
[ROW][C]44[/C][C]1.5[/C][C]3.82552[/C][C]-2.32552[/C][/ROW]
[ROW][C]45[/C][C]3.5[/C][C]4.33227[/C][C]-0.832266[/C][/ROW]
[ROW][C]46[/C][C]7.5[/C][C]5.46044[/C][C]2.03956[/C][/ROW]
[ROW][C]47[/C][C]5[/C][C]6.18392[/C][C]-1.18392[/C][/ROW]
[ROW][C]48[/C][C]6.5[/C][C]4.4174[/C][C]2.0826[/C][/ROW]
[ROW][C]49[/C][C]6.5[/C][C]4.89307[/C][C]1.60693[/C][/ROW]
[ROW][C]50[/C][C]6.5[/C][C]4.41756[/C][C]2.08244[/C][/ROW]
[ROW][C]51[/C][C]7[/C][C]4.41268[/C][C]2.58732[/C][/ROW]
[ROW][C]52[/C][C]1.5[/C][C]4.4267[/C][C]-2.9267[/C][/ROW]
[ROW][C]53[/C][C]4[/C][C]4.38497[/C][C]-0.384967[/C][/ROW]
[ROW][C]54[/C][C]4.5[/C][C]4.2919[/C][C]0.2081[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]3.47232[/C][C]-3.47232[/C][/ROW]
[ROW][C]56[/C][C]3.5[/C][C]4.53987[/C][C]-1.03987[/C][/ROW]
[ROW][C]57[/C][C]4.5[/C][C]4.81586[/C][C]-0.315862[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]3.55289[/C][C]-3.55289[/C][/ROW]
[ROW][C]59[/C][C]3[/C][C]3.73886[/C][C]-0.738863[/C][/ROW]
[ROW][C]60[/C][C]3.5[/C][C]3.39951[/C][C]0.100488[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]4.44071[/C][C]-1.44071[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]3.30188[/C][C]-2.30188[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266716&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266716&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	7.5	4.08566	3.41434
2	6.5	4.49966	2.00034
3	1	5.02787	-4.02787
4	1	4.22382	-3.22382
5	5.5	5.68342	-0.183422
6	8.5	4.63582	3.86418
7	6.5	5.38108	1.11892
8	4.5	6.26281	-1.76281
9	2	4.51199	-2.51199
10	5	4.39882	0.601176
11	0.5	4.68075	-4.18075
12	5	5.99626	-0.996263
13	2.5	3.73734	-1.23734
14	5	4.02703	0.972965
15	5.5	5.35016	0.149841
16	3.5	4.42061	-0.920608
17	4	4.05018	-0.0501822
18	6.5	7.03642	-0.536417
19	4.5	4.46858	0.0314164
20	5.5	4.98918	0.510817
21	4	4.81859	-0.818588
22	7.5	7.44048	0.0595153
23	4	4.09191	-0.091911
24	5.5	3.97738	1.52262
25	2.5	4.05947	-1.55947
26	5.5	4.31521	1.18479
27	3.5	4.19747	-0.697471
28	4.5	4.73513	-0.23513
29	4.5	3.74495	0.755049
30	6	4.74458	1.25542
31	5	4.83733	0.162672
32	6.5	4.95354	1.54646
33	5	3.94478	1.05522
34	6	3.94798	2.05202
35	4.5	5.34255	-0.842549
36	5	4.29663	0.703375
37	5	3.76826	1.23174
38	6.5	3.8566	2.6434
39	7	4.94121	2.05879
40	4.5	3.95727	0.542725
41	8.5	4.36943	4.13057
42	3.5	4.70237	-1.20237
43	6	4.68107	1.31893
44	1.5	3.82552	-2.32552
45	3.5	4.33227	-0.832266
46	7.5	5.46044	2.03956
47	5	6.18392	-1.18392
48	6.5	4.4174	2.0826
49	6.5	4.89307	1.60693
50	6.5	4.41756	2.08244
51	7	4.41268	2.58732
52	1.5	4.4267	-2.9267
53	4	4.38497	-0.384967
54	4.5	4.2919	0.2081
55	0	3.47232	-3.47232
56	3.5	4.53987	-1.03987
57	4.5	4.81586	-0.315862
58	0	3.55289	-3.55289
59	3	3.73886	-0.738863
60	3.5	3.39951	0.100488
61	3	4.44071	-1.44071
62	1	3.30188	-2.30188

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.997046	0.00590731	0.00295366
7	0.992704	0.014592	0.00729601
8	0.985833	0.0283339	0.014167
9	0.985724	0.0285517	0.0142759
10	0.973133	0.0537348	0.0268674
11	0.997202	0.00559694	0.00279847
12	0.994827	0.0103463	0.00517316
13	0.990956	0.0180873	0.00904367
14	0.987033	0.0259343	0.0129672
15	0.978411	0.0431787	0.0215893
16	0.966159	0.067682	0.033841
17	0.947062	0.105877	0.0529383
18	0.951293	0.0974138	0.0487069
19	0.927115	0.14577	0.0728852
20	0.898687	0.202625	0.101313
21	0.866123	0.267753	0.133877
22	0.837548	0.324903	0.162452
23	0.784113	0.431774	0.215887
24	0.767328	0.465345	0.232672
25	0.741155	0.51769	0.258845
26	0.70427	0.59146	0.29573
27	0.641782	0.716436	0.358218
28	0.572145	0.855709	0.427855
29	0.512168	0.975664	0.487832
30	0.471781	0.943562	0.528219
31	0.398554	0.797109	0.601446
32	0.364925	0.729849	0.635075
33	0.317579	0.635158	0.682421
34	0.329691	0.659382	0.670309
35	0.292873	0.585745	0.707127
36	0.236822	0.473644	0.763178
37	0.210292	0.420584	0.789708
38	0.283609	0.567217	0.716391
39	0.275542	0.551084	0.724458
40	0.227481	0.454963	0.772519
41	0.532821	0.934358	0.467179
42	0.480322	0.960645	0.519678
43	0.444414	0.888828	0.555586
44	0.451276	0.902552	0.548724
45	0.377645	0.755289	0.622355
46	0.389903	0.779807	0.610097
47	0.473163	0.946326	0.526837
48	0.484583	0.969167	0.515417
49	0.416591	0.833182	0.583409
50	0.520788	0.958424	0.479212
51	0.781684	0.436632	0.218316
52	0.814049	0.371902	0.185951
53	0.722405	0.55519	0.277595
54	0.713841	0.572319	0.286159
55	0.73181	0.536379	0.26819
56	0.587662	0.824677	0.412338

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.997046 & 0.00590731 & 0.00295366 \tabularnewline
7 & 0.992704 & 0.014592 & 0.00729601 \tabularnewline
8 & 0.985833 & 0.0283339 & 0.014167 \tabularnewline
9 & 0.985724 & 0.0285517 & 0.0142759 \tabularnewline
10 & 0.973133 & 0.0537348 & 0.0268674 \tabularnewline
11 & 0.997202 & 0.00559694 & 0.00279847 \tabularnewline
12 & 0.994827 & 0.0103463 & 0.00517316 \tabularnewline
13 & 0.990956 & 0.0180873 & 0.00904367 \tabularnewline
14 & 0.987033 & 0.0259343 & 0.0129672 \tabularnewline
15 & 0.978411 & 0.0431787 & 0.0215893 \tabularnewline
16 & 0.966159 & 0.067682 & 0.033841 \tabularnewline
17 & 0.947062 & 0.105877 & 0.0529383 \tabularnewline
18 & 0.951293 & 0.0974138 & 0.0487069 \tabularnewline
19 & 0.927115 & 0.14577 & 0.0728852 \tabularnewline
20 & 0.898687 & 0.202625 & 0.101313 \tabularnewline
21 & 0.866123 & 0.267753 & 0.133877 \tabularnewline
22 & 0.837548 & 0.324903 & 0.162452 \tabularnewline
23 & 0.784113 & 0.431774 & 0.215887 \tabularnewline
24 & 0.767328 & 0.465345 & 0.232672 \tabularnewline
25 & 0.741155 & 0.51769 & 0.258845 \tabularnewline
26 & 0.70427 & 0.59146 & 0.29573 \tabularnewline
27 & 0.641782 & 0.716436 & 0.358218 \tabularnewline
28 & 0.572145 & 0.855709 & 0.427855 \tabularnewline
29 & 0.512168 & 0.975664 & 0.487832 \tabularnewline
30 & 0.471781 & 0.943562 & 0.528219 \tabularnewline
31 & 0.398554 & 0.797109 & 0.601446 \tabularnewline
32 & 0.364925 & 0.729849 & 0.635075 \tabularnewline
33 & 0.317579 & 0.635158 & 0.682421 \tabularnewline
34 & 0.329691 & 0.659382 & 0.670309 \tabularnewline
35 & 0.292873 & 0.585745 & 0.707127 \tabularnewline
36 & 0.236822 & 0.473644 & 0.763178 \tabularnewline
37 & 0.210292 & 0.420584 & 0.789708 \tabularnewline
38 & 0.283609 & 0.567217 & 0.716391 \tabularnewline
39 & 0.275542 & 0.551084 & 0.724458 \tabularnewline
40 & 0.227481 & 0.454963 & 0.772519 \tabularnewline
41 & 0.532821 & 0.934358 & 0.467179 \tabularnewline
42 & 0.480322 & 0.960645 & 0.519678 \tabularnewline
43 & 0.444414 & 0.888828 & 0.555586 \tabularnewline
44 & 0.451276 & 0.902552 & 0.548724 \tabularnewline
45 & 0.377645 & 0.755289 & 0.622355 \tabularnewline
46 & 0.389903 & 0.779807 & 0.610097 \tabularnewline
47 & 0.473163 & 0.946326 & 0.526837 \tabularnewline
48 & 0.484583 & 0.969167 & 0.515417 \tabularnewline
49 & 0.416591 & 0.833182 & 0.583409 \tabularnewline
50 & 0.520788 & 0.958424 & 0.479212 \tabularnewline
51 & 0.781684 & 0.436632 & 0.218316 \tabularnewline
52 & 0.814049 & 0.371902 & 0.185951 \tabularnewline
53 & 0.722405 & 0.55519 & 0.277595 \tabularnewline
54 & 0.713841 & 0.572319 & 0.286159 \tabularnewline
55 & 0.73181 & 0.536379 & 0.26819 \tabularnewline
56 & 0.587662 & 0.824677 & 0.412338 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266716&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]6[/C][C]0.997046[/C][C]0.00590731[/C][C]0.00295366[/C][/ROW]
[ROW][C]7[/C][C]0.992704[/C][C]0.014592[/C][C]0.00729601[/C][/ROW]
[ROW][C]8[/C][C]0.985833[/C][C]0.0283339[/C][C]0.014167[/C][/ROW]
[ROW][C]9[/C][C]0.985724[/C][C]0.0285517[/C][C]0.0142759[/C][/ROW]
[ROW][C]10[/C][C]0.973133[/C][C]0.0537348[/C][C]0.0268674[/C][/ROW]
[ROW][C]11[/C][C]0.997202[/C][C]0.00559694[/C][C]0.00279847[/C][/ROW]
[ROW][C]12[/C][C]0.994827[/C][C]0.0103463[/C][C]0.00517316[/C][/ROW]
[ROW][C]13[/C][C]0.990956[/C][C]0.0180873[/C][C]0.00904367[/C][/ROW]
[ROW][C]14[/C][C]0.987033[/C][C]0.0259343[/C][C]0.0129672[/C][/ROW]
[ROW][C]15[/C][C]0.978411[/C][C]0.0431787[/C][C]0.0215893[/C][/ROW]
[ROW][C]16[/C][C]0.966159[/C][C]0.067682[/C][C]0.033841[/C][/ROW]
[ROW][C]17[/C][C]0.947062[/C][C]0.105877[/C][C]0.0529383[/C][/ROW]
[ROW][C]18[/C][C]0.951293[/C][C]0.0974138[/C][C]0.0487069[/C][/ROW]
[ROW][C]19[/C][C]0.927115[/C][C]0.14577[/C][C]0.0728852[/C][/ROW]
[ROW][C]20[/C][C]0.898687[/C][C]0.202625[/C][C]0.101313[/C][/ROW]
[ROW][C]21[/C][C]0.866123[/C][C]0.267753[/C][C]0.133877[/C][/ROW]
[ROW][C]22[/C][C]0.837548[/C][C]0.324903[/C][C]0.162452[/C][/ROW]
[ROW][C]23[/C][C]0.784113[/C][C]0.431774[/C][C]0.215887[/C][/ROW]
[ROW][C]24[/C][C]0.767328[/C][C]0.465345[/C][C]0.232672[/C][/ROW]
[ROW][C]25[/C][C]0.741155[/C][C]0.51769[/C][C]0.258845[/C][/ROW]
[ROW][C]26[/C][C]0.70427[/C][C]0.59146[/C][C]0.29573[/C][/ROW]
[ROW][C]27[/C][C]0.641782[/C][C]0.716436[/C][C]0.358218[/C][/ROW]
[ROW][C]28[/C][C]0.572145[/C][C]0.855709[/C][C]0.427855[/C][/ROW]
[ROW][C]29[/C][C]0.512168[/C][C]0.975664[/C][C]0.487832[/C][/ROW]
[ROW][C]30[/C][C]0.471781[/C][C]0.943562[/C][C]0.528219[/C][/ROW]
[ROW][C]31[/C][C]0.398554[/C][C]0.797109[/C][C]0.601446[/C][/ROW]
[ROW][C]32[/C][C]0.364925[/C][C]0.729849[/C][C]0.635075[/C][/ROW]
[ROW][C]33[/C][C]0.317579[/C][C]0.635158[/C][C]0.682421[/C][/ROW]
[ROW][C]34[/C][C]0.329691[/C][C]0.659382[/C][C]0.670309[/C][/ROW]
[ROW][C]35[/C][C]0.292873[/C][C]0.585745[/C][C]0.707127[/C][/ROW]
[ROW][C]36[/C][C]0.236822[/C][C]0.473644[/C][C]0.763178[/C][/ROW]
[ROW][C]37[/C][C]0.210292[/C][C]0.420584[/C][C]0.789708[/C][/ROW]
[ROW][C]38[/C][C]0.283609[/C][C]0.567217[/C][C]0.716391[/C][/ROW]
[ROW][C]39[/C][C]0.275542[/C][C]0.551084[/C][C]0.724458[/C][/ROW]
[ROW][C]40[/C][C]0.227481[/C][C]0.454963[/C][C]0.772519[/C][/ROW]
[ROW][C]41[/C][C]0.532821[/C][C]0.934358[/C][C]0.467179[/C][/ROW]
[ROW][C]42[/C][C]0.480322[/C][C]0.960645[/C][C]0.519678[/C][/ROW]
[ROW][C]43[/C][C]0.444414[/C][C]0.888828[/C][C]0.555586[/C][/ROW]
[ROW][C]44[/C][C]0.451276[/C][C]0.902552[/C][C]0.548724[/C][/ROW]
[ROW][C]45[/C][C]0.377645[/C][C]0.755289[/C][C]0.622355[/C][/ROW]
[ROW][C]46[/C][C]0.389903[/C][C]0.779807[/C][C]0.610097[/C][/ROW]
[ROW][C]47[/C][C]0.473163[/C][C]0.946326[/C][C]0.526837[/C][/ROW]
[ROW][C]48[/C][C]0.484583[/C][C]0.969167[/C][C]0.515417[/C][/ROW]
[ROW][C]49[/C][C]0.416591[/C][C]0.833182[/C][C]0.583409[/C][/ROW]
[ROW][C]50[/C][C]0.520788[/C][C]0.958424[/C][C]0.479212[/C][/ROW]
[ROW][C]51[/C][C]0.781684[/C][C]0.436632[/C][C]0.218316[/C][/ROW]
[ROW][C]52[/C][C]0.814049[/C][C]0.371902[/C][C]0.185951[/C][/ROW]
[ROW][C]53[/C][C]0.722405[/C][C]0.55519[/C][C]0.277595[/C][/ROW]
[ROW][C]54[/C][C]0.713841[/C][C]0.572319[/C][C]0.286159[/C][/ROW]
[ROW][C]55[/C][C]0.73181[/C][C]0.536379[/C][C]0.26819[/C][/ROW]
[ROW][C]56[/C][C]0.587662[/C][C]0.824677[/C][C]0.412338[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266716&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266716&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
6	0.997046	0.00590731	0.00295366
7	0.992704	0.014592	0.00729601
8	0.985833	0.0283339	0.014167
9	0.985724	0.0285517	0.0142759
10	0.973133	0.0537348	0.0268674
11	0.997202	0.00559694	0.00279847
12	0.994827	0.0103463	0.00517316
13	0.990956	0.0180873	0.00904367
14	0.987033	0.0259343	0.0129672
15	0.978411	0.0431787	0.0215893
16	0.966159	0.067682	0.033841
17	0.947062	0.105877	0.0529383
18	0.951293	0.0974138	0.0487069
19	0.927115	0.14577	0.0728852
20	0.898687	0.202625	0.101313
21	0.866123	0.267753	0.133877
22	0.837548	0.324903	0.162452
23	0.784113	0.431774	0.215887
24	0.767328	0.465345	0.232672
25	0.741155	0.51769	0.258845
26	0.70427	0.59146	0.29573
27	0.641782	0.716436	0.358218
28	0.572145	0.855709	0.427855
29	0.512168	0.975664	0.487832
30	0.471781	0.943562	0.528219
31	0.398554	0.797109	0.601446
32	0.364925	0.729849	0.635075
33	0.317579	0.635158	0.682421
34	0.329691	0.659382	0.670309
35	0.292873	0.585745	0.707127
36	0.236822	0.473644	0.763178
37	0.210292	0.420584	0.789708
38	0.283609	0.567217	0.716391
39	0.275542	0.551084	0.724458
40	0.227481	0.454963	0.772519
41	0.532821	0.934358	0.467179
42	0.480322	0.960645	0.519678
43	0.444414	0.888828	0.555586
44	0.451276	0.902552	0.548724
45	0.377645	0.755289	0.622355
46	0.389903	0.779807	0.610097
47	0.473163	0.946326	0.526837
48	0.484583	0.969167	0.515417
49	0.416591	0.833182	0.583409
50	0.520788	0.958424	0.479212
51	0.781684	0.436632	0.218316
52	0.814049	0.371902	0.185951
53	0.722405	0.55519	0.277595
54	0.713841	0.572319	0.286159
55	0.73181	0.536379	0.26819
56	0.587662	0.824677	0.412338

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	2	0.0392157	NOK
5% type I error level	9	0.176471	NOK
10% type I error level	12	0.235294	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 & 0.0392157 & NOK \tabularnewline
5% type I error level & 9 & 0.176471 & NOK \tabularnewline
10% type I error level & 12 & 0.235294 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266716&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]0.0392157[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]9[/C][C]0.176471[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]12[/C][C]0.235294[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266716&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266716&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	0.0392157	NOK
5% type I error level	9	0.176471	NOK
10% type I error level	12	0.235294	NOK

Figure 1

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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