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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 23 Nov 2010 18:39:30 +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/2010/Nov/23/t1290537507jhfyppf5y4s2lmg.htm/, Retrieved Thu, 18 Apr 2024 22:59:28 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99560, Retrieved Thu, 18 Apr 2024 22:59:28 +0000

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No (this computation is public)

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Estimated Impact

101

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

-     [Multiple Regression] [WS7: Mini-tutorial c] [2010-11-19 13:12:23] [1fd136673b2a4fecb5c545b9b4a05d64]
-         [Multiple Regression] [ws7 mini trend] [2010-11-23 18:39:30] [2953e4eb3235e2fd3d6373a16d27c72f] [Current]

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73	2	71.91	5.11	50	3
28	6	6.06	3.53	48	5
40	5	8.1	4.52	63	11
79	3	79.38	3.72	113	13
75	3	65.34	5.99	128	11
21	3	34.62	3.15	52	7
16	2	26.26	3.17	104	1
81	2	60.92	3.5	40	1
90	2	39.56	3.39	89	11
87	5	65.61	4.15	97	3
99	3	56.49	4.5	29	9
54	3	56.19	3.31	36	5
53	5	80.3	3.09	114	11
6	4	61.2	5.31	49	9
71	5	58.2	4.24	57	7
93	6	75.91	5.06	82	4
82	3	73.66	4.72	34	10
32	4	73.87	4.58	36	13
93	4	87.21	5.3	89	9
24	4	64.29	5.11	69	5
96	5	71.82	4.05	35	8
88	4	89.31	4.62	65	12
83	2	1.41	4.66	70	8
23	6	35.17	4.66	60	5
23	5	34.68	2.76	57	9
20	5	41.08	5.1	127	11
33	3	30.57	4.97	96	8
88	2	68.84	2.87	61	9
42	6	7.17	5.14	127	10
98	2	71.05	4.98	36	1
34	4	23.32	4.55	55	9
59	3	61.39	5.45	75	2
26	6	8.41	4.36	42	3
64	4	65.88	4.78	64	4
13	1	64.06	4.74	83	3
6	2	26.8	5.44	56	1
49	4	12.78	5.78	114	5
3	5	23.84	2.92	33	4
87	6	42.69	4.22	91	2
77	2	54.94	3.93	127	2
70	4	89.99	3.01	45	10
76	4	5.68	3.22	80	6
82	4	72.64	5.12	40	9
12	2	45.92	3.04	115	7
44	3	24.96	5.82	33	1
63	5	18.17	3.11	127	13
35	1	29.12	3.87	45	9
69	1	40.08	3.75	74	11
10	5	1.08	4.82	105	10
36	2	57.52	2.83	60	7

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	17 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 17 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99560&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]17 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99560&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99560&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	17 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation
slaagkans[t] = + 26.8300788181571 -0.39718687633662verzekeraar[t] + 0.525635231419669kost[t] + 0.734765737056835grootte[t] + 0.0308685622071769snelheid[t] + 0.318502498855332maand[t] -0.144918874305115t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
slaagkans[t] =  +  26.8300788181571 -0.39718687633662verzekeraar[t] +  0.525635231419669kost[t] +  0.734765737056835grootte[t] +  0.0308685622071769snelheid[t] +  0.318502498855332maand[t] -0.144918874305115t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99560&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]slaagkans[t] =  +  26.8300788181571 -0.39718687633662verzekeraar[t] +  0.525635231419669kost[t] +  0.734765737056835grootte[t] +  0.0308685622071769snelheid[t] +  0.318502498855332maand[t] -0.144918874305115t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99560&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99560&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
slaagkans[t] = + 26.8300788181571 -0.39718687633662verzekeraar[t] + 0.525635231419669kost[t] + 0.734765737056835grootte[t] + 0.0308685622071769snelheid[t] + 0.318502498855332maand[t] -0.144918874305115t + 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)	26.8300788181571	27.276257	0.9836	0.330794	0.165397
verzekeraar	-0.39718687633662	2.82093	-0.1408	0.888685	0.444343
kost	0.525635231419669	0.165611	3.1739	0.002778	0.001389
grootte	0.734765737056835	4.48739	0.1637	0.870703	0.435351
snelheid	0.0308685622071769	0.135522	0.2278	0.820901	0.41045
maand	0.318502498855332	1.145549	0.278	0.782318	0.391159
t	-0.144918874305115	0.287528	-0.504	0.616822	0.308411

\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) & 26.8300788181571 & 27.276257 & 0.9836 & 0.330794 & 0.165397 \tabularnewline
verzekeraar & -0.39718687633662 & 2.82093 & -0.1408 & 0.888685 & 0.444343 \tabularnewline
kost & 0.525635231419669 & 0.165611 & 3.1739 & 0.002778 & 0.001389 \tabularnewline
grootte & 0.734765737056835 & 4.48739 & 0.1637 & 0.870703 & 0.435351 \tabularnewline
snelheid & 0.0308685622071769 & 0.135522 & 0.2278 & 0.820901 & 0.41045 \tabularnewline
maand & 0.318502498855332 & 1.145549 & 0.278 & 0.782318 & 0.391159 \tabularnewline
t & -0.144918874305115 & 0.287528 & -0.504 & 0.616822 & 0.308411 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99560&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]26.8300788181571[/C][C]27.276257[/C][C]0.9836[/C][C]0.330794[/C][C]0.165397[/C][/ROW]
[ROW][C]verzekeraar[/C][C]-0.39718687633662[/C][C]2.82093[/C][C]-0.1408[/C][C]0.888685[/C][C]0.444343[/C][/ROW]
[ROW][C]kost[/C][C]0.525635231419669[/C][C]0.165611[/C][C]3.1739[/C][C]0.002778[/C][C]0.001389[/C][/ROW]
[ROW][C]grootte[/C][C]0.734765737056835[/C][C]4.48739[/C][C]0.1637[/C][C]0.870703[/C][C]0.435351[/C][/ROW]
[ROW][C]snelheid[/C][C]0.0308685622071769[/C][C]0.135522[/C][C]0.2278[/C][C]0.820901[/C][C]0.41045[/C][/ROW]
[ROW][C]maand[/C][C]0.318502498855332[/C][C]1.145549[/C][C]0.278[/C][C]0.782318[/C][C]0.391159[/C][/ROW]
[ROW][C]t[/C][C]-0.144918874305115[/C][C]0.287528[/C][C]-0.504[/C][C]0.616822[/C][C]0.308411[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99560&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99560&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)	26.8300788181571	27.276257	0.9836	0.330794	0.165397
verzekeraar	-0.39718687633662	2.82093	-0.1408	0.888685	0.444343
kost	0.525635231419669	0.165611	3.1739	0.002778	0.001389
grootte	0.734765737056835	4.48739	0.1637	0.870703	0.435351
snelheid	0.0308685622071769	0.135522	0.2278	0.820901	0.41045
maand	0.318502498855332	1.145549	0.278	0.782318	0.391159
t	-0.144918874305115	0.287528	-0.504	0.616822	0.308411

Multiple Linear Regression - Regression Statistics
Multiple R	0.484479983675251
R-squared	0.234720854581971
Adjusted R-squared	0.127937718012013
F-TEST (value)	2.19810788596003
F-TEST (DF numerator)	6
F-TEST (DF denominator)	43
p-value	0.0615615092501303
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	28.0290263146554
Sum Squared Residuals	33781.9315943488

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.484479983675251 \tabularnewline
R-squared & 0.234720854581971 \tabularnewline
Adjusted R-squared & 0.127937718012013 \tabularnewline
F-TEST (value) & 2.19810788596003 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 43 \tabularnewline
p-value & 0.0615615092501303 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 28.0290263146554 \tabularnewline
Sum Squared Residuals & 33781.9315943488 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99560&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.484479983675251[/C][/ROW]
[ROW][C]R-squared[/C][C]0.234720854581971[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.127937718012013[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.19810788596003[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]43[/C][/ROW]
[ROW][C]p-value[/C][C]0.0615615092501303[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]28.0290263146554[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]33781.9315943488[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99560&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99560&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	0.484479983675251
R-squared	0.234720854581971
Adjusted R-squared	0.127937718012013
F-TEST (value)	2.19810788596003
F-TEST (DF numerator)	6
F-TEST (DF denominator)	43
p-value	0.0615615092501303
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	28.0290263146554
Sum Squared Residuals	33781.9315943488

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	73	69.9428042058524	3.05719579414758
2	28	33.0103958459621	-5.01039584596214
3	40	37.4364212260157	2.56357877398430
4	79	77.1457759184018	1.85422408159817
5	75	71.1148800534806	3.88511994651943
6	21	49.115691453555	-28.1156914535550
7	16	44.6824944773005	-28.6824944773005
8	81	61.0229774359705	19.9770225640295
9	90	54.26725032417	35.73274967583
10	87	64.8809190663154	22.1190809336846
11	99	60.80570140515	38.1942985948501
12	54	58.5707906743502	-4.57079067435021
13	53	74.4616778600394	-21.4616778600394
14	6	63.6620313370442	-57.6620313370442
15	71	60.3667640534394	10.6332359465606
16	93	69.55237271424	23.44762728576
17	82	69.5958388548387	12.4041611451613
18	32	70.0784939205875	-38.0784939205875
19	93	77.8366041656607	15.1633958343393
20	24	63.6131390576111	-39.6131390576111
21	96	66.1561912998012	29.8438087001988
22	88	78.2207028311218	9.77929716887822
23	83	31.5765443127978	51.4234556872022
24	23	46.3241302272365	-23.3241302272365
25	23	46.1041863742642	-23.1041863742642
26	20	53.840489157971	-33.840489157971
27	33	46.9575652833125	-13.9575652833125
28	88	65.0209883655595	22.9790116344405
29	42	34.895143091905	7.10485690809501
30	98	64.1234261375546	33.8765738624454
31	34	40.9141373207600	-6.91413732076005
32	59	60.2264814984457	-1.22648149844571
33	26	29.5407927271432	-3.54079272714323
34	64	61.7047168321768	2.29528316782319
35	13	62.0333120192965	-49.0333120192965
36	6	40.9499173845932	-34.9499173845932
37	49	35.9554257671481	13.0445742328519
38	3	36.3065596303886	-33.3065596303886
39	87	47.7812450604872	39.2187549395128
40	77	56.5622914521314	20.4377085478686
41	70	73.3873270981743	-3.38732709817425
42	76	28.8867923494887	47.1132076505113
43	82	65.0552284797315	16.9447715202685
44	12	51.8095344093154	-39.8095344093154
45	44	37.8505258630149	6.14947413698506
46	63	38.0746297010116	24.9253702989884
47	35	42.0273539798517	-7.02735397985172
48	69	49.0874186551782	19.9125813448218
49	10	28.2786005183774	-18.2786005183774
50	36	55.1853181217762	-19.1853181217762

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 73 & 69.9428042058524 & 3.05719579414758 \tabularnewline
2 & 28 & 33.0103958459621 & -5.01039584596214 \tabularnewline
3 & 40 & 37.4364212260157 & 2.56357877398430 \tabularnewline
4 & 79 & 77.1457759184018 & 1.85422408159817 \tabularnewline
5 & 75 & 71.1148800534806 & 3.88511994651943 \tabularnewline
6 & 21 & 49.115691453555 & -28.1156914535550 \tabularnewline
7 & 16 & 44.6824944773005 & -28.6824944773005 \tabularnewline
8 & 81 & 61.0229774359705 & 19.9770225640295 \tabularnewline
9 & 90 & 54.26725032417 & 35.73274967583 \tabularnewline
10 & 87 & 64.8809190663154 & 22.1190809336846 \tabularnewline
11 & 99 & 60.80570140515 & 38.1942985948501 \tabularnewline
12 & 54 & 58.5707906743502 & -4.57079067435021 \tabularnewline
13 & 53 & 74.4616778600394 & -21.4616778600394 \tabularnewline
14 & 6 & 63.6620313370442 & -57.6620313370442 \tabularnewline
15 & 71 & 60.3667640534394 & 10.6332359465606 \tabularnewline
16 & 93 & 69.55237271424 & 23.44762728576 \tabularnewline
17 & 82 & 69.5958388548387 & 12.4041611451613 \tabularnewline
18 & 32 & 70.0784939205875 & -38.0784939205875 \tabularnewline
19 & 93 & 77.8366041656607 & 15.1633958343393 \tabularnewline
20 & 24 & 63.6131390576111 & -39.6131390576111 \tabularnewline
21 & 96 & 66.1561912998012 & 29.8438087001988 \tabularnewline
22 & 88 & 78.2207028311218 & 9.77929716887822 \tabularnewline
23 & 83 & 31.5765443127978 & 51.4234556872022 \tabularnewline
24 & 23 & 46.3241302272365 & -23.3241302272365 \tabularnewline
25 & 23 & 46.1041863742642 & -23.1041863742642 \tabularnewline
26 & 20 & 53.840489157971 & -33.840489157971 \tabularnewline
27 & 33 & 46.9575652833125 & -13.9575652833125 \tabularnewline
28 & 88 & 65.0209883655595 & 22.9790116344405 \tabularnewline
29 & 42 & 34.895143091905 & 7.10485690809501 \tabularnewline
30 & 98 & 64.1234261375546 & 33.8765738624454 \tabularnewline
31 & 34 & 40.9141373207600 & -6.91413732076005 \tabularnewline
32 & 59 & 60.2264814984457 & -1.22648149844571 \tabularnewline
33 & 26 & 29.5407927271432 & -3.54079272714323 \tabularnewline
34 & 64 & 61.7047168321768 & 2.29528316782319 \tabularnewline
35 & 13 & 62.0333120192965 & -49.0333120192965 \tabularnewline
36 & 6 & 40.9499173845932 & -34.9499173845932 \tabularnewline
37 & 49 & 35.9554257671481 & 13.0445742328519 \tabularnewline
38 & 3 & 36.3065596303886 & -33.3065596303886 \tabularnewline
39 & 87 & 47.7812450604872 & 39.2187549395128 \tabularnewline
40 & 77 & 56.5622914521314 & 20.4377085478686 \tabularnewline
41 & 70 & 73.3873270981743 & -3.38732709817425 \tabularnewline
42 & 76 & 28.8867923494887 & 47.1132076505113 \tabularnewline
43 & 82 & 65.0552284797315 & 16.9447715202685 \tabularnewline
44 & 12 & 51.8095344093154 & -39.8095344093154 \tabularnewline
45 & 44 & 37.8505258630149 & 6.14947413698506 \tabularnewline
46 & 63 & 38.0746297010116 & 24.9253702989884 \tabularnewline
47 & 35 & 42.0273539798517 & -7.02735397985172 \tabularnewline
48 & 69 & 49.0874186551782 & 19.9125813448218 \tabularnewline
49 & 10 & 28.2786005183774 & -18.2786005183774 \tabularnewline
50 & 36 & 55.1853181217762 & -19.1853181217762 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99560&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]73[/C][C]69.9428042058524[/C][C]3.05719579414758[/C][/ROW]
[ROW][C]2[/C][C]28[/C][C]33.0103958459621[/C][C]-5.01039584596214[/C][/ROW]
[ROW][C]3[/C][C]40[/C][C]37.4364212260157[/C][C]2.56357877398430[/C][/ROW]
[ROW][C]4[/C][C]79[/C][C]77.1457759184018[/C][C]1.85422408159817[/C][/ROW]
[ROW][C]5[/C][C]75[/C][C]71.1148800534806[/C][C]3.88511994651943[/C][/ROW]
[ROW][C]6[/C][C]21[/C][C]49.115691453555[/C][C]-28.1156914535550[/C][/ROW]
[ROW][C]7[/C][C]16[/C][C]44.6824944773005[/C][C]-28.6824944773005[/C][/ROW]
[ROW][C]8[/C][C]81[/C][C]61.0229774359705[/C][C]19.9770225640295[/C][/ROW]
[ROW][C]9[/C][C]90[/C][C]54.26725032417[/C][C]35.73274967583[/C][/ROW]
[ROW][C]10[/C][C]87[/C][C]64.8809190663154[/C][C]22.1190809336846[/C][/ROW]
[ROW][C]11[/C][C]99[/C][C]60.80570140515[/C][C]38.1942985948501[/C][/ROW]
[ROW][C]12[/C][C]54[/C][C]58.5707906743502[/C][C]-4.57079067435021[/C][/ROW]
[ROW][C]13[/C][C]53[/C][C]74.4616778600394[/C][C]-21.4616778600394[/C][/ROW]
[ROW][C]14[/C][C]6[/C][C]63.6620313370442[/C][C]-57.6620313370442[/C][/ROW]
[ROW][C]15[/C][C]71[/C][C]60.3667640534394[/C][C]10.6332359465606[/C][/ROW]
[ROW][C]16[/C][C]93[/C][C]69.55237271424[/C][C]23.44762728576[/C][/ROW]
[ROW][C]17[/C][C]82[/C][C]69.5958388548387[/C][C]12.4041611451613[/C][/ROW]
[ROW][C]18[/C][C]32[/C][C]70.0784939205875[/C][C]-38.0784939205875[/C][/ROW]
[ROW][C]19[/C][C]93[/C][C]77.8366041656607[/C][C]15.1633958343393[/C][/ROW]
[ROW][C]20[/C][C]24[/C][C]63.6131390576111[/C][C]-39.6131390576111[/C][/ROW]
[ROW][C]21[/C][C]96[/C][C]66.1561912998012[/C][C]29.8438087001988[/C][/ROW]
[ROW][C]22[/C][C]88[/C][C]78.2207028311218[/C][C]9.77929716887822[/C][/ROW]
[ROW][C]23[/C][C]83[/C][C]31.5765443127978[/C][C]51.4234556872022[/C][/ROW]
[ROW][C]24[/C][C]23[/C][C]46.3241302272365[/C][C]-23.3241302272365[/C][/ROW]
[ROW][C]25[/C][C]23[/C][C]46.1041863742642[/C][C]-23.1041863742642[/C][/ROW]
[ROW][C]26[/C][C]20[/C][C]53.840489157971[/C][C]-33.840489157971[/C][/ROW]
[ROW][C]27[/C][C]33[/C][C]46.9575652833125[/C][C]-13.9575652833125[/C][/ROW]
[ROW][C]28[/C][C]88[/C][C]65.0209883655595[/C][C]22.9790116344405[/C][/ROW]
[ROW][C]29[/C][C]42[/C][C]34.895143091905[/C][C]7.10485690809501[/C][/ROW]
[ROW][C]30[/C][C]98[/C][C]64.1234261375546[/C][C]33.8765738624454[/C][/ROW]
[ROW][C]31[/C][C]34[/C][C]40.9141373207600[/C][C]-6.91413732076005[/C][/ROW]
[ROW][C]32[/C][C]59[/C][C]60.2264814984457[/C][C]-1.22648149844571[/C][/ROW]
[ROW][C]33[/C][C]26[/C][C]29.5407927271432[/C][C]-3.54079272714323[/C][/ROW]
[ROW][C]34[/C][C]64[/C][C]61.7047168321768[/C][C]2.29528316782319[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]62.0333120192965[/C][C]-49.0333120192965[/C][/ROW]
[ROW][C]36[/C][C]6[/C][C]40.9499173845932[/C][C]-34.9499173845932[/C][/ROW]
[ROW][C]37[/C][C]49[/C][C]35.9554257671481[/C][C]13.0445742328519[/C][/ROW]
[ROW][C]38[/C][C]3[/C][C]36.3065596303886[/C][C]-33.3065596303886[/C][/ROW]
[ROW][C]39[/C][C]87[/C][C]47.7812450604872[/C][C]39.2187549395128[/C][/ROW]
[ROW][C]40[/C][C]77[/C][C]56.5622914521314[/C][C]20.4377085478686[/C][/ROW]
[ROW][C]41[/C][C]70[/C][C]73.3873270981743[/C][C]-3.38732709817425[/C][/ROW]
[ROW][C]42[/C][C]76[/C][C]28.8867923494887[/C][C]47.1132076505113[/C][/ROW]
[ROW][C]43[/C][C]82[/C][C]65.0552284797315[/C][C]16.9447715202685[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]51.8095344093154[/C][C]-39.8095344093154[/C][/ROW]
[ROW][C]45[/C][C]44[/C][C]37.8505258630149[/C][C]6.14947413698506[/C][/ROW]
[ROW][C]46[/C][C]63[/C][C]38.0746297010116[/C][C]24.9253702989884[/C][/ROW]
[ROW][C]47[/C][C]35[/C][C]42.0273539798517[/C][C]-7.02735397985172[/C][/ROW]
[ROW][C]48[/C][C]69[/C][C]49.0874186551782[/C][C]19.9125813448218[/C][/ROW]
[ROW][C]49[/C][C]10[/C][C]28.2786005183774[/C][C]-18.2786005183774[/C][/ROW]
[ROW][C]50[/C][C]36[/C][C]55.1853181217762[/C][C]-19.1853181217762[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99560&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99560&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	73	69.9428042058524	3.05719579414758
2	28	33.0103958459621	-5.01039584596214
3	40	37.4364212260157	2.56357877398430
4	79	77.1457759184018	1.85422408159817
5	75	71.1148800534806	3.88511994651943
6	21	49.115691453555	-28.1156914535550
7	16	44.6824944773005	-28.6824944773005
8	81	61.0229774359705	19.9770225640295
9	90	54.26725032417	35.73274967583
10	87	64.8809190663154	22.1190809336846
11	99	60.80570140515	38.1942985948501
12	54	58.5707906743502	-4.57079067435021
13	53	74.4616778600394	-21.4616778600394
14	6	63.6620313370442	-57.6620313370442
15	71	60.3667640534394	10.6332359465606
16	93	69.55237271424	23.44762728576
17	82	69.5958388548387	12.4041611451613
18	32	70.0784939205875	-38.0784939205875
19	93	77.8366041656607	15.1633958343393
20	24	63.6131390576111	-39.6131390576111
21	96	66.1561912998012	29.8438087001988
22	88	78.2207028311218	9.77929716887822
23	83	31.5765443127978	51.4234556872022
24	23	46.3241302272365	-23.3241302272365
25	23	46.1041863742642	-23.1041863742642
26	20	53.840489157971	-33.840489157971
27	33	46.9575652833125	-13.9575652833125
28	88	65.0209883655595	22.9790116344405
29	42	34.895143091905	7.10485690809501
30	98	64.1234261375546	33.8765738624454
31	34	40.9141373207600	-6.91413732076005
32	59	60.2264814984457	-1.22648149844571
33	26	29.5407927271432	-3.54079272714323
34	64	61.7047168321768	2.29528316782319
35	13	62.0333120192965	-49.0333120192965
36	6	40.9499173845932	-34.9499173845932
37	49	35.9554257671481	13.0445742328519
38	3	36.3065596303886	-33.3065596303886
39	87	47.7812450604872	39.2187549395128
40	77	56.5622914521314	20.4377085478686
41	70	73.3873270981743	-3.38732709817425
42	76	28.8867923494887	47.1132076505113
43	82	65.0552284797315	16.9447715202685
44	12	51.8095344093154	-39.8095344093154
45	44	37.8505258630149	6.14947413698506
46	63	38.0746297010116	24.9253702989884
47	35	42.0273539798517	-7.02735397985172
48	69	49.0874186551782	19.9125813448218
49	10	28.2786005183774	-18.2786005183774
50	36	55.1853181217762	-19.1853181217762

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.409373249122097	0.818746498244194	0.590626750877903
11	0.296628931176052	0.593257862352104	0.703371068823948
12	0.318731295663219	0.637462591326437	0.681268704336781
13	0.319200961037059	0.638401922074119	0.680799038962941
14	0.7557064478902	0.488587104219601	0.244293552109800
15	0.692333428276687	0.615333143446626	0.307666571723313
16	0.658917773695881	0.682164452608237	0.341082226304119
17	0.565399285314007	0.869201429371986	0.434600714685993
18	0.608883126274927	0.782233747450147	0.391116873725073
19	0.532422381523346	0.935155236953307	0.467577618476654
20	0.568094044388775	0.86381191122245	0.431905955611225
21	0.57412547363144	0.85174905273712	0.42587452636856
22	0.491072043977387	0.982144087954773	0.508927956022613
23	0.685827839878086	0.628344320243827	0.314172160121914
24	0.65353888426076	0.692922231478481	0.346461115739240
25	0.603272556796338	0.793454886407324	0.396727443203662
26	0.631358118012329	0.737283763975343	0.368641881987671
27	0.557070731653681	0.885858536692639	0.442929268346319
28	0.527289578563128	0.945420842873744	0.472710421436872
29	0.452733039460211	0.905466078920423	0.547266960539789
30	0.549517815371967	0.900964369256067	0.450482184628033
31	0.454379057419053	0.908758114838105	0.545620942580948
32	0.36477232796456	0.72954465592912	0.63522767203544
33	0.271927901566851	0.543855803133703	0.728072098433149
34	0.193468072562519	0.386936145125038	0.806531927437481
35	0.25939847397099	0.51879694794198	0.74060152602901
36	0.2764425356142	0.5528850712284	0.7235574643858
37	0.233818336453229	0.467636672906458	0.766181663546771
38	0.44713614328225	0.8942722865645	0.55286385671775
39	0.374919712280864	0.749839424561727	0.625080287719136
40	0.376014540805878	0.752029081611756	0.623985459194122

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.409373249122097 & 0.818746498244194 & 0.590626750877903 \tabularnewline
11 & 0.296628931176052 & 0.593257862352104 & 0.703371068823948 \tabularnewline
12 & 0.318731295663219 & 0.637462591326437 & 0.681268704336781 \tabularnewline
13 & 0.319200961037059 & 0.638401922074119 & 0.680799038962941 \tabularnewline
14 & 0.7557064478902 & 0.488587104219601 & 0.244293552109800 \tabularnewline
15 & 0.692333428276687 & 0.615333143446626 & 0.307666571723313 \tabularnewline
16 & 0.658917773695881 & 0.682164452608237 & 0.341082226304119 \tabularnewline
17 & 0.565399285314007 & 0.869201429371986 & 0.434600714685993 \tabularnewline
18 & 0.608883126274927 & 0.782233747450147 & 0.391116873725073 \tabularnewline
19 & 0.532422381523346 & 0.935155236953307 & 0.467577618476654 \tabularnewline
20 & 0.568094044388775 & 0.86381191122245 & 0.431905955611225 \tabularnewline
21 & 0.57412547363144 & 0.85174905273712 & 0.42587452636856 \tabularnewline
22 & 0.491072043977387 & 0.982144087954773 & 0.508927956022613 \tabularnewline
23 & 0.685827839878086 & 0.628344320243827 & 0.314172160121914 \tabularnewline
24 & 0.65353888426076 & 0.692922231478481 & 0.346461115739240 \tabularnewline
25 & 0.603272556796338 & 0.793454886407324 & 0.396727443203662 \tabularnewline
26 & 0.631358118012329 & 0.737283763975343 & 0.368641881987671 \tabularnewline
27 & 0.557070731653681 & 0.885858536692639 & 0.442929268346319 \tabularnewline
28 & 0.527289578563128 & 0.945420842873744 & 0.472710421436872 \tabularnewline
29 & 0.452733039460211 & 0.905466078920423 & 0.547266960539789 \tabularnewline
30 & 0.549517815371967 & 0.900964369256067 & 0.450482184628033 \tabularnewline
31 & 0.454379057419053 & 0.908758114838105 & 0.545620942580948 \tabularnewline
32 & 0.36477232796456 & 0.72954465592912 & 0.63522767203544 \tabularnewline
33 & 0.271927901566851 & 0.543855803133703 & 0.728072098433149 \tabularnewline
34 & 0.193468072562519 & 0.386936145125038 & 0.806531927437481 \tabularnewline
35 & 0.25939847397099 & 0.51879694794198 & 0.74060152602901 \tabularnewline
36 & 0.2764425356142 & 0.5528850712284 & 0.7235574643858 \tabularnewline
37 & 0.233818336453229 & 0.467636672906458 & 0.766181663546771 \tabularnewline
38 & 0.44713614328225 & 0.8942722865645 & 0.55286385671775 \tabularnewline
39 & 0.374919712280864 & 0.749839424561727 & 0.625080287719136 \tabularnewline
40 & 0.376014540805878 & 0.752029081611756 & 0.623985459194122 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99560&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]10[/C][C]0.409373249122097[/C][C]0.818746498244194[/C][C]0.590626750877903[/C][/ROW]
[ROW][C]11[/C][C]0.296628931176052[/C][C]0.593257862352104[/C][C]0.703371068823948[/C][/ROW]
[ROW][C]12[/C][C]0.318731295663219[/C][C]0.637462591326437[/C][C]0.681268704336781[/C][/ROW]
[ROW][C]13[/C][C]0.319200961037059[/C][C]0.638401922074119[/C][C]0.680799038962941[/C][/ROW]
[ROW][C]14[/C][C]0.7557064478902[/C][C]0.488587104219601[/C][C]0.244293552109800[/C][/ROW]
[ROW][C]15[/C][C]0.692333428276687[/C][C]0.615333143446626[/C][C]0.307666571723313[/C][/ROW]
[ROW][C]16[/C][C]0.658917773695881[/C][C]0.682164452608237[/C][C]0.341082226304119[/C][/ROW]
[ROW][C]17[/C][C]0.565399285314007[/C][C]0.869201429371986[/C][C]0.434600714685993[/C][/ROW]
[ROW][C]18[/C][C]0.608883126274927[/C][C]0.782233747450147[/C][C]0.391116873725073[/C][/ROW]
[ROW][C]19[/C][C]0.532422381523346[/C][C]0.935155236953307[/C][C]0.467577618476654[/C][/ROW]
[ROW][C]20[/C][C]0.568094044388775[/C][C]0.86381191122245[/C][C]0.431905955611225[/C][/ROW]
[ROW][C]21[/C][C]0.57412547363144[/C][C]0.85174905273712[/C][C]0.42587452636856[/C][/ROW]
[ROW][C]22[/C][C]0.491072043977387[/C][C]0.982144087954773[/C][C]0.508927956022613[/C][/ROW]
[ROW][C]23[/C][C]0.685827839878086[/C][C]0.628344320243827[/C][C]0.314172160121914[/C][/ROW]
[ROW][C]24[/C][C]0.65353888426076[/C][C]0.692922231478481[/C][C]0.346461115739240[/C][/ROW]
[ROW][C]25[/C][C]0.603272556796338[/C][C]0.793454886407324[/C][C]0.396727443203662[/C][/ROW]
[ROW][C]26[/C][C]0.631358118012329[/C][C]0.737283763975343[/C][C]0.368641881987671[/C][/ROW]
[ROW][C]27[/C][C]0.557070731653681[/C][C]0.885858536692639[/C][C]0.442929268346319[/C][/ROW]
[ROW][C]28[/C][C]0.527289578563128[/C][C]0.945420842873744[/C][C]0.472710421436872[/C][/ROW]
[ROW][C]29[/C][C]0.452733039460211[/C][C]0.905466078920423[/C][C]0.547266960539789[/C][/ROW]
[ROW][C]30[/C][C]0.549517815371967[/C][C]0.900964369256067[/C][C]0.450482184628033[/C][/ROW]
[ROW][C]31[/C][C]0.454379057419053[/C][C]0.908758114838105[/C][C]0.545620942580948[/C][/ROW]
[ROW][C]32[/C][C]0.36477232796456[/C][C]0.72954465592912[/C][C]0.63522767203544[/C][/ROW]
[ROW][C]33[/C][C]0.271927901566851[/C][C]0.543855803133703[/C][C]0.728072098433149[/C][/ROW]
[ROW][C]34[/C][C]0.193468072562519[/C][C]0.386936145125038[/C][C]0.806531927437481[/C][/ROW]
[ROW][C]35[/C][C]0.25939847397099[/C][C]0.51879694794198[/C][C]0.74060152602901[/C][/ROW]
[ROW][C]36[/C][C]0.2764425356142[/C][C]0.5528850712284[/C][C]0.7235574643858[/C][/ROW]
[ROW][C]37[/C][C]0.233818336453229[/C][C]0.467636672906458[/C][C]0.766181663546771[/C][/ROW]
[ROW][C]38[/C][C]0.44713614328225[/C][C]0.8942722865645[/C][C]0.55286385671775[/C][/ROW]
[ROW][C]39[/C][C]0.374919712280864[/C][C]0.749839424561727[/C][C]0.625080287719136[/C][/ROW]
[ROW][C]40[/C][C]0.376014540805878[/C][C]0.752029081611756[/C][C]0.623985459194122[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99560&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99560&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
10	0.409373249122097	0.818746498244194	0.590626750877903
11	0.296628931176052	0.593257862352104	0.703371068823948
12	0.318731295663219	0.637462591326437	0.681268704336781
13	0.319200961037059	0.638401922074119	0.680799038962941
14	0.7557064478902	0.488587104219601	0.244293552109800
15	0.692333428276687	0.615333143446626	0.307666571723313
16	0.658917773695881	0.682164452608237	0.341082226304119
17	0.565399285314007	0.869201429371986	0.434600714685993
18	0.608883126274927	0.782233747450147	0.391116873725073
19	0.532422381523346	0.935155236953307	0.467577618476654
20	0.568094044388775	0.86381191122245	0.431905955611225
21	0.57412547363144	0.85174905273712	0.42587452636856
22	0.491072043977387	0.982144087954773	0.508927956022613
23	0.685827839878086	0.628344320243827	0.314172160121914
24	0.65353888426076	0.692922231478481	0.346461115739240
25	0.603272556796338	0.793454886407324	0.396727443203662
26	0.631358118012329	0.737283763975343	0.368641881987671
27	0.557070731653681	0.885858536692639	0.442929268346319
28	0.527289578563128	0.945420842873744	0.472710421436872
29	0.452733039460211	0.905466078920423	0.547266960539789
30	0.549517815371967	0.900964369256067	0.450482184628033
31	0.454379057419053	0.908758114838105	0.545620942580948
32	0.36477232796456	0.72954465592912	0.63522767203544
33	0.271927901566851	0.543855803133703	0.728072098433149
34	0.193468072562519	0.386936145125038	0.806531927437481
35	0.25939847397099	0.51879694794198	0.74060152602901
36	0.2764425356142	0.5528850712284	0.7235574643858
37	0.233818336453229	0.467636672906458	0.766181663546771
38	0.44713614328225	0.8942722865645	0.55286385671775
39	0.374919712280864	0.749839424561727	0.625080287719136
40	0.376014540805878	0.752029081611756	0.623985459194122

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=99560&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=99560&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99560&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 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 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

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

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code