Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 28 Dec 2010 20:33:13 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/28/t1293568298933hdiqdvmoopjd.htm/, Retrieved Sun, 05 May 2024 04:01:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116544, Retrieved Sun, 05 May 2024 04:01:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact126
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-    D  [Multiple Regression] [multiple regression] [2010-12-01 14:19:57] [f1aa04283d83c25edc8ae3bb0d0fb93e]
-    D      [Multiple Regression] [multiple regression] [2010-12-28 20:33:13] [cfea828c93f35e07cca4521b1fb38047] [Current]
-   P         [Multiple Regression] [] [2010-12-29 20:40:53] [99820e5c3330fe494c612533a1ea567a]
Feedback Forum

Post a new message
Dataseries X:
-2	3	16	0	6
0	8	17	2	6
-2	3	23	3	7
-4	3	24	1	4
-4	7	27	1	3
-7	4	31	0	0
-9	-4	40	1	6
-13	-6	47	-1	3
-8	8	43	2	1
-13	2	60	2	6
-15	-1	64	0	5
-15	-2	65	1	7
-15	0	65	1	4
-10	10	55	3	3
-12	3	57	3	6
-11	6	57	1	6
-11	7	57	1	5
-17	-4	65	-2	2
-18	-5	69	1	3
-19	-7	70	1	-2
-22	-10	71	-1	-4
-24	-21	71	-4	0
-24	-22	73	-2	1
-20	-16	68	-1	4
-25	-25	65	-5	-3
-22	-22	57	-4	-3
-17	-22	41	-5	0
-9	-19	21	0	6
-11	-21	21	-2	-1
-13	-31	17	-4	0
-11	-28	9	-6	-1
-9	-23	11	-2	1
-7	-17	6	-2	-4
-3	-12	-2	-2	-1
-3	-14	0	1	-1
-6	-18	5	-2	0
-4	-16	3	0	3
-8	-22	7	-1	0
-1	-9	4	2	8
-2	-10	8	3	8
-2	-10	9	2	8
-1	0	14	3	8
1	3	12	4	11
2	2	12	5	13
2	4	7	5	5
-1	-3	15	4	12
1	0	14	5	13
-1	-1	19	6	9
-8	-7	39	4	11
1	2	12	6	7
2	3	11	6	12
-2	-3	17	3	11
-2	-5	16	5	10
-2	0	25	5	13
-2	-3	24	5	14
-6	-7	28	3	10
-4	-7	25	5	13
-5	-7	31	5	12
-2	-4	24	6	13
-1	-3	24	6	17




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116544&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116544&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116544&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Multiple Linear Regression - Estimated Regression Equation
werkloosheid[t] = + 0.595725392460535 -3.94502235891269indicator[t] + 0.9968486769921economie[t] + 1.06507538235466finaciën[t] + 0.880345457337176spaarvermogen[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
werkloosheid[t] =  +  0.595725392460535 -3.94502235891269indicator[t] +  0.9968486769921economie[t] +  1.06507538235466finaciën[t] +  0.880345457337176spaarvermogen[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116544&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]werkloosheid[t] =  +  0.595725392460535 -3.94502235891269indicator[t] +  0.9968486769921economie[t] +  1.06507538235466finaciën[t] +  0.880345457337176spaarvermogen[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116544&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116544&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
werkloosheid[t] = + 0.595725392460535 -3.94502235891269indicator[t] + 0.9968486769921economie[t] + 1.06507538235466finaciën[t] + 0.880345457337176spaarvermogen[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.5957253924605350.4562241.30580.1970650.098532
indicator-3.945022358912690.030602-128.913900
economie0.99684867699210.02231244.677500
finaciën1.065075382354660.127338.364700
spaarvermogen0.8803454573371760.05947214.802700

\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) & 0.595725392460535 & 0.456224 & 1.3058 & 0.197065 & 0.098532 \tabularnewline
indicator & -3.94502235891269 & 0.030602 & -128.9139 & 0 & 0 \tabularnewline
economie & 0.9968486769921 & 0.022312 & 44.6775 & 0 & 0 \tabularnewline
finaciën & 1.06507538235466 & 0.12733 & 8.3647 & 0 & 0 \tabularnewline
spaarvermogen & 0.880345457337176 & 0.059472 & 14.8027 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116544&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]0.595725392460535[/C][C]0.456224[/C][C]1.3058[/C][C]0.197065[/C][C]0.098532[/C][/ROW]
[ROW][C]indicator[/C][C]-3.94502235891269[/C][C]0.030602[/C][C]-128.9139[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]economie[/C][C]0.9968486769921[/C][C]0.022312[/C][C]44.6775[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]finaciën[/C][C]1.06507538235466[/C][C]0.12733[/C][C]8.3647[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]spaarvermogen[/C][C]0.880345457337176[/C][C]0.059472[/C][C]14.8027[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116544&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116544&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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.5957253924605350.4562241.30580.1970650.098532
indicator-3.945022358912690.030602-128.913900
economie0.99684867699210.02231244.677500
finaciën1.065075382354660.127338.364700
spaarvermogen0.8803454573371760.05947214.802700







Multiple Linear Regression - Regression Statistics
Multiple R0.998682644366743
R-squared0.99736702415935
Adjusted R-squared0.997175535007303
F-TEST (value)5208.47793985419
F-TEST (DF numerator)4
F-TEST (DF denominator)55
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.23256411359052
Sum Squared Residuals83.5567861761156

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.998682644366743 \tabularnewline
R-squared & 0.99736702415935 \tabularnewline
Adjusted R-squared & 0.997175535007303 \tabularnewline
F-TEST (value) & 5208.47793985419 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 55 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.23256411359052 \tabularnewline
Sum Squared Residuals & 83.5567861761156 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116544&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.998682644366743[/C][/ROW]
[ROW][C]R-squared[/C][C]0.99736702415935[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.997175535007303[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5208.47793985419[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]55[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.23256411359052[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]83.5567861761156[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116544&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116544&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 R0.998682644366743
R-squared0.99736702415935
Adjusted R-squared0.997175535007303
F-TEST (value)5208.47793985419
F-TEST (DF numerator)4
F-TEST (DF denominator)55
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.23256411359052
Sum Squared Residuals83.5567861761156







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11616.7583888852853-0.758388885285256
21715.98273831712971.01726168287032
32320.83396048968642.16603951031357
42423.95281807079090.0471819292090656
52727.0598673214222-0.0598673214221609
63132.1982766128177-1.19827661281773
74038.4606800410841.53931995891598
84747.4758849860297-0.475884986029705
94343.1411899017453-0.141189901745319
106061.286936921042-1.28693692104202
116463.17593938584460.82406061415541
126565.0048570058815-0.00485700588150748
136564.35751798785420.642482012145821
145555.8506982705839-0.850698270583903
155759.4038386214761-2.40383862147611
165756.31921152883040.68078847116961
175756.43571474848530.564285251514685
186563.30425093597281.6957490640272
196970.3279962222946-1.32799622229456
207067.87759394053722.12240605946283
217172.8312733069152-1.83127330691524
227170.08213826011220.917861739887782
237372.09578580516660.904214194833377
246866.00290018583471.99709981416533
256566.3336541566903-1.33365415669031
265758.5542084932832-1.55420849328322
274140.40505768837670.59494231162334
282122.4428745038479-1.44287450384786
292120.04665290161950.953347098380532
301716.71840554215170.281594457848317
3198.808410633256110.191589366743887
321111.9236017444842-0.923601744484248
3365.61292180192560.387078198074401
34-2-2.541887876753110.541887876753113
350-1.340359083673321.34035908367332
3654.192432595369520.807567404630483
3733.0672723682492-0.0672723682492024
3879.16015798758115-2.16015798758115
3944.74202408185106-0.742024081851058
4088.7552731461263-0.755273146126308
4197.690197763771641.30980223622836
421414.7787375571346-0.778737557134618
431213.5853506246517-1.58535062465174
441211.46924588577600.530754114224026
4576.420179581062770.579820418937234
461516.3746487378617-1.37464873786169
471413.42057089070450.57942910929554
481917.85746048454371.14253951545631
493939.1220650849449-0.122065084944909
501211.19727088302030.80272911697973
511112.6508244877856-1.65082448778556
521718.3742502570825-1.37425025708253
531617.6303582104705-1.63035821047049
542525.2556379674425-0.255637967442515
552423.14543739380340.854562606196608
562829.2865995274277-1.28659952742770
572526.1677419463232-1.16774194632318
583129.23241884789871.76758115210131
592422.33331864182881.66668135817122
602422.90652678925691.09347321074310

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 16 & 16.7583888852853 & -0.758388885285256 \tabularnewline
2 & 17 & 15.9827383171297 & 1.01726168287032 \tabularnewline
3 & 23 & 20.8339604896864 & 2.16603951031357 \tabularnewline
4 & 24 & 23.9528180707909 & 0.0471819292090656 \tabularnewline
5 & 27 & 27.0598673214222 & -0.0598673214221609 \tabularnewline
6 & 31 & 32.1982766128177 & -1.19827661281773 \tabularnewline
7 & 40 & 38.460680041084 & 1.53931995891598 \tabularnewline
8 & 47 & 47.4758849860297 & -0.475884986029705 \tabularnewline
9 & 43 & 43.1411899017453 & -0.141189901745319 \tabularnewline
10 & 60 & 61.286936921042 & -1.28693692104202 \tabularnewline
11 & 64 & 63.1759393858446 & 0.82406061415541 \tabularnewline
12 & 65 & 65.0048570058815 & -0.00485700588150748 \tabularnewline
13 & 65 & 64.3575179878542 & 0.642482012145821 \tabularnewline
14 & 55 & 55.8506982705839 & -0.850698270583903 \tabularnewline
15 & 57 & 59.4038386214761 & -2.40383862147611 \tabularnewline
16 & 57 & 56.3192115288304 & 0.68078847116961 \tabularnewline
17 & 57 & 56.4357147484853 & 0.564285251514685 \tabularnewline
18 & 65 & 63.3042509359728 & 1.6957490640272 \tabularnewline
19 & 69 & 70.3279962222946 & -1.32799622229456 \tabularnewline
20 & 70 & 67.8775939405372 & 2.12240605946283 \tabularnewline
21 & 71 & 72.8312733069152 & -1.83127330691524 \tabularnewline
22 & 71 & 70.0821382601122 & 0.917861739887782 \tabularnewline
23 & 73 & 72.0957858051666 & 0.904214194833377 \tabularnewline
24 & 68 & 66.0029001858347 & 1.99709981416533 \tabularnewline
25 & 65 & 66.3336541566903 & -1.33365415669031 \tabularnewline
26 & 57 & 58.5542084932832 & -1.55420849328322 \tabularnewline
27 & 41 & 40.4050576883767 & 0.59494231162334 \tabularnewline
28 & 21 & 22.4428745038479 & -1.44287450384786 \tabularnewline
29 & 21 & 20.0466529016195 & 0.953347098380532 \tabularnewline
30 & 17 & 16.7184055421517 & 0.281594457848317 \tabularnewline
31 & 9 & 8.80841063325611 & 0.191589366743887 \tabularnewline
32 & 11 & 11.9236017444842 & -0.923601744484248 \tabularnewline
33 & 6 & 5.6129218019256 & 0.387078198074401 \tabularnewline
34 & -2 & -2.54188787675311 & 0.541887876753113 \tabularnewline
35 & 0 & -1.34035908367332 & 1.34035908367332 \tabularnewline
36 & 5 & 4.19243259536952 & 0.807567404630483 \tabularnewline
37 & 3 & 3.0672723682492 & -0.0672723682492024 \tabularnewline
38 & 7 & 9.16015798758115 & -2.16015798758115 \tabularnewline
39 & 4 & 4.74202408185106 & -0.742024081851058 \tabularnewline
40 & 8 & 8.7552731461263 & -0.755273146126308 \tabularnewline
41 & 9 & 7.69019776377164 & 1.30980223622836 \tabularnewline
42 & 14 & 14.7787375571346 & -0.778737557134618 \tabularnewline
43 & 12 & 13.5853506246517 & -1.58535062465174 \tabularnewline
44 & 12 & 11.4692458857760 & 0.530754114224026 \tabularnewline
45 & 7 & 6.42017958106277 & 0.579820418937234 \tabularnewline
46 & 15 & 16.3746487378617 & -1.37464873786169 \tabularnewline
47 & 14 & 13.4205708907045 & 0.57942910929554 \tabularnewline
48 & 19 & 17.8574604845437 & 1.14253951545631 \tabularnewline
49 & 39 & 39.1220650849449 & -0.122065084944909 \tabularnewline
50 & 12 & 11.1972708830203 & 0.80272911697973 \tabularnewline
51 & 11 & 12.6508244877856 & -1.65082448778556 \tabularnewline
52 & 17 & 18.3742502570825 & -1.37425025708253 \tabularnewline
53 & 16 & 17.6303582104705 & -1.63035821047049 \tabularnewline
54 & 25 & 25.2556379674425 & -0.255637967442515 \tabularnewline
55 & 24 & 23.1454373938034 & 0.854562606196608 \tabularnewline
56 & 28 & 29.2865995274277 & -1.28659952742770 \tabularnewline
57 & 25 & 26.1677419463232 & -1.16774194632318 \tabularnewline
58 & 31 & 29.2324188478987 & 1.76758115210131 \tabularnewline
59 & 24 & 22.3333186418288 & 1.66668135817122 \tabularnewline
60 & 24 & 22.9065267892569 & 1.09347321074310 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116544&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]16[/C][C]16.7583888852853[/C][C]-0.758388885285256[/C][/ROW]
[ROW][C]2[/C][C]17[/C][C]15.9827383171297[/C][C]1.01726168287032[/C][/ROW]
[ROW][C]3[/C][C]23[/C][C]20.8339604896864[/C][C]2.16603951031357[/C][/ROW]
[ROW][C]4[/C][C]24[/C][C]23.9528180707909[/C][C]0.0471819292090656[/C][/ROW]
[ROW][C]5[/C][C]27[/C][C]27.0598673214222[/C][C]-0.0598673214221609[/C][/ROW]
[ROW][C]6[/C][C]31[/C][C]32.1982766128177[/C][C]-1.19827661281773[/C][/ROW]
[ROW][C]7[/C][C]40[/C][C]38.460680041084[/C][C]1.53931995891598[/C][/ROW]
[ROW][C]8[/C][C]47[/C][C]47.4758849860297[/C][C]-0.475884986029705[/C][/ROW]
[ROW][C]9[/C][C]43[/C][C]43.1411899017453[/C][C]-0.141189901745319[/C][/ROW]
[ROW][C]10[/C][C]60[/C][C]61.286936921042[/C][C]-1.28693692104202[/C][/ROW]
[ROW][C]11[/C][C]64[/C][C]63.1759393858446[/C][C]0.82406061415541[/C][/ROW]
[ROW][C]12[/C][C]65[/C][C]65.0048570058815[/C][C]-0.00485700588150748[/C][/ROW]
[ROW][C]13[/C][C]65[/C][C]64.3575179878542[/C][C]0.642482012145821[/C][/ROW]
[ROW][C]14[/C][C]55[/C][C]55.8506982705839[/C][C]-0.850698270583903[/C][/ROW]
[ROW][C]15[/C][C]57[/C][C]59.4038386214761[/C][C]-2.40383862147611[/C][/ROW]
[ROW][C]16[/C][C]57[/C][C]56.3192115288304[/C][C]0.68078847116961[/C][/ROW]
[ROW][C]17[/C][C]57[/C][C]56.4357147484853[/C][C]0.564285251514685[/C][/ROW]
[ROW][C]18[/C][C]65[/C][C]63.3042509359728[/C][C]1.6957490640272[/C][/ROW]
[ROW][C]19[/C][C]69[/C][C]70.3279962222946[/C][C]-1.32799622229456[/C][/ROW]
[ROW][C]20[/C][C]70[/C][C]67.8775939405372[/C][C]2.12240605946283[/C][/ROW]
[ROW][C]21[/C][C]71[/C][C]72.8312733069152[/C][C]-1.83127330691524[/C][/ROW]
[ROW][C]22[/C][C]71[/C][C]70.0821382601122[/C][C]0.917861739887782[/C][/ROW]
[ROW][C]23[/C][C]73[/C][C]72.0957858051666[/C][C]0.904214194833377[/C][/ROW]
[ROW][C]24[/C][C]68[/C][C]66.0029001858347[/C][C]1.99709981416533[/C][/ROW]
[ROW][C]25[/C][C]65[/C][C]66.3336541566903[/C][C]-1.33365415669031[/C][/ROW]
[ROW][C]26[/C][C]57[/C][C]58.5542084932832[/C][C]-1.55420849328322[/C][/ROW]
[ROW][C]27[/C][C]41[/C][C]40.4050576883767[/C][C]0.59494231162334[/C][/ROW]
[ROW][C]28[/C][C]21[/C][C]22.4428745038479[/C][C]-1.44287450384786[/C][/ROW]
[ROW][C]29[/C][C]21[/C][C]20.0466529016195[/C][C]0.953347098380532[/C][/ROW]
[ROW][C]30[/C][C]17[/C][C]16.7184055421517[/C][C]0.281594457848317[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]8.80841063325611[/C][C]0.191589366743887[/C][/ROW]
[ROW][C]32[/C][C]11[/C][C]11.9236017444842[/C][C]-0.923601744484248[/C][/ROW]
[ROW][C]33[/C][C]6[/C][C]5.6129218019256[/C][C]0.387078198074401[/C][/ROW]
[ROW][C]34[/C][C]-2[/C][C]-2.54188787675311[/C][C]0.541887876753113[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]-1.34035908367332[/C][C]1.34035908367332[/C][/ROW]
[ROW][C]36[/C][C]5[/C][C]4.19243259536952[/C][C]0.807567404630483[/C][/ROW]
[ROW][C]37[/C][C]3[/C][C]3.0672723682492[/C][C]-0.0672723682492024[/C][/ROW]
[ROW][C]38[/C][C]7[/C][C]9.16015798758115[/C][C]-2.16015798758115[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]4.74202408185106[/C][C]-0.742024081851058[/C][/ROW]
[ROW][C]40[/C][C]8[/C][C]8.7552731461263[/C][C]-0.755273146126308[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]7.69019776377164[/C][C]1.30980223622836[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]14.7787375571346[/C][C]-0.778737557134618[/C][/ROW]
[ROW][C]43[/C][C]12[/C][C]13.5853506246517[/C][C]-1.58535062465174[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]11.4692458857760[/C][C]0.530754114224026[/C][/ROW]
[ROW][C]45[/C][C]7[/C][C]6.42017958106277[/C][C]0.579820418937234[/C][/ROW]
[ROW][C]46[/C][C]15[/C][C]16.3746487378617[/C][C]-1.37464873786169[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]13.4205708907045[/C][C]0.57942910929554[/C][/ROW]
[ROW][C]48[/C][C]19[/C][C]17.8574604845437[/C][C]1.14253951545631[/C][/ROW]
[ROW][C]49[/C][C]39[/C][C]39.1220650849449[/C][C]-0.122065084944909[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]11.1972708830203[/C][C]0.80272911697973[/C][/ROW]
[ROW][C]51[/C][C]11[/C][C]12.6508244877856[/C][C]-1.65082448778556[/C][/ROW]
[ROW][C]52[/C][C]17[/C][C]18.3742502570825[/C][C]-1.37425025708253[/C][/ROW]
[ROW][C]53[/C][C]16[/C][C]17.6303582104705[/C][C]-1.63035821047049[/C][/ROW]
[ROW][C]54[/C][C]25[/C][C]25.2556379674425[/C][C]-0.255637967442515[/C][/ROW]
[ROW][C]55[/C][C]24[/C][C]23.1454373938034[/C][C]0.854562606196608[/C][/ROW]
[ROW][C]56[/C][C]28[/C][C]29.2865995274277[/C][C]-1.28659952742770[/C][/ROW]
[ROW][C]57[/C][C]25[/C][C]26.1677419463232[/C][C]-1.16774194632318[/C][/ROW]
[ROW][C]58[/C][C]31[/C][C]29.2324188478987[/C][C]1.76758115210131[/C][/ROW]
[ROW][C]59[/C][C]24[/C][C]22.3333186418288[/C][C]1.66668135817122[/C][/ROW]
[ROW][C]60[/C][C]24[/C][C]22.9065267892569[/C][C]1.09347321074310[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116544&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116544&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 IndexActualsInterpolationForecastResidualsPrediction Error
11616.7583888852853-0.758388885285256
21715.98273831712971.01726168287032
32320.83396048968642.16603951031357
42423.95281807079090.0471819292090656
52727.0598673214222-0.0598673214221609
63132.1982766128177-1.19827661281773
74038.4606800410841.53931995891598
84747.4758849860297-0.475884986029705
94343.1411899017453-0.141189901745319
106061.286936921042-1.28693692104202
116463.17593938584460.82406061415541
126565.0048570058815-0.00485700588150748
136564.35751798785420.642482012145821
145555.8506982705839-0.850698270583903
155759.4038386214761-2.40383862147611
165756.31921152883040.68078847116961
175756.43571474848530.564285251514685
186563.30425093597281.6957490640272
196970.3279962222946-1.32799622229456
207067.87759394053722.12240605946283
217172.8312733069152-1.83127330691524
227170.08213826011220.917861739887782
237372.09578580516660.904214194833377
246866.00290018583471.99709981416533
256566.3336541566903-1.33365415669031
265758.5542084932832-1.55420849328322
274140.40505768837670.59494231162334
282122.4428745038479-1.44287450384786
292120.04665290161950.953347098380532
301716.71840554215170.281594457848317
3198.808410633256110.191589366743887
321111.9236017444842-0.923601744484248
3365.61292180192560.387078198074401
34-2-2.541887876753110.541887876753113
350-1.340359083673321.34035908367332
3654.192432595369520.807567404630483
3733.0672723682492-0.0672723682492024
3879.16015798758115-2.16015798758115
3944.74202408185106-0.742024081851058
4088.7552731461263-0.755273146126308
4197.690197763771641.30980223622836
421414.7787375571346-0.778737557134618
431213.5853506246517-1.58535062465174
441211.46924588577600.530754114224026
4576.420179581062770.579820418937234
461516.3746487378617-1.37464873786169
471413.42057089070450.57942910929554
481917.85746048454371.14253951545631
493939.1220650849449-0.122065084944909
501211.19727088302030.80272911697973
511112.6508244877856-1.65082448778556
521718.3742502570825-1.37425025708253
531617.6303582104705-1.63035821047049
542525.2556379674425-0.255637967442515
552423.14543739380340.854562606196608
562829.2865995274277-1.28659952742770
572526.1677419463232-1.16774194632318
583129.23241884789871.76758115210131
592422.33331864182881.66668135817122
602422.90652678925691.09347321074310







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.001395819786603490.002791639573206980.998604180213396
90.01107303671838710.02214607343677410.988926963281613
100.04285647072468010.08571294144936030.95714352927532
110.2981100465808890.5962200931617780.701889953419111
120.1966508139468220.3933016278936440.803349186053178
130.1562523854089220.3125047708178440.843747614591078
140.1112562925223220.2225125850446450.888743707477678
150.4677750683052520.9355501366105040.532224931694748
160.4448203697837690.8896407395675380.555179630216231
170.3856480760418680.7712961520837360.614351923958132
180.4786675316130380.9573350632260750.521332468386962
190.4403076150470620.8806152300941230.559692384952938
200.6508621677530170.6982756644939670.349137832246983
210.7485994957866150.5028010084267710.251400504213385
220.6918113956095130.6163772087809740.308188604390487
230.626574058639830.746851882720340.37342594136017
240.7121690238832110.5756619522335780.287830976116789
250.7423811582220350.5152376835559310.257618841777965
260.764993307649480.4700133847010410.235006692350520
270.7248429819271960.5503140361456080.275157018072804
280.7942320707965320.4115358584069350.205767929203468
290.7778245473328060.4443509053343870.222175452667194
300.7181407991875050.5637184016249910.281859200812495
310.6846780798367910.6306438403264180.315321920163209
320.6389447498035390.7221105003929230.361055250196461
330.584218990219320.831562019561360.41578100978068
340.5697259353987020.8605481292025960.430274064601298
350.5597578084934220.8804843830131560.440242191506578
360.6599109761052420.6801780477895150.340089023894758
370.632606053228220.734787893543560.36739394677178
380.6742084276912860.6515831446174280.325791572308714
390.6091834989504960.7816330020990070.390816501049504
400.5888022607801420.8223954784397160.411197739219858
410.7115728822725850.5768542354548290.288427117727415
420.6677855896970170.6644288206059670.332214410302983
430.6331604477485240.7336791045029530.366839552251476
440.5853213447279070.8293573105441870.414678655272093
450.56890564152720.86218871694560.4310943584728
460.4857825939865190.9715651879730390.514217406013481
470.4738996805054320.9477993610108630.526100319494568
480.4053676803769040.8107353607538090.594632319623096
490.3370566681966850.674113336393370.662943331803315
500.432612821320770.865225642641540.56738717867923
510.3615429247542750.723085849508550.638457075245725
520.3496800751587600.6993601503175190.65031992484124

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.00139581978660349 & 0.00279163957320698 & 0.998604180213396 \tabularnewline
9 & 0.0110730367183871 & 0.0221460734367741 & 0.988926963281613 \tabularnewline
10 & 0.0428564707246801 & 0.0857129414493603 & 0.95714352927532 \tabularnewline
11 & 0.298110046580889 & 0.596220093161778 & 0.701889953419111 \tabularnewline
12 & 0.196650813946822 & 0.393301627893644 & 0.803349186053178 \tabularnewline
13 & 0.156252385408922 & 0.312504770817844 & 0.843747614591078 \tabularnewline
14 & 0.111256292522322 & 0.222512585044645 & 0.888743707477678 \tabularnewline
15 & 0.467775068305252 & 0.935550136610504 & 0.532224931694748 \tabularnewline
16 & 0.444820369783769 & 0.889640739567538 & 0.555179630216231 \tabularnewline
17 & 0.385648076041868 & 0.771296152083736 & 0.614351923958132 \tabularnewline
18 & 0.478667531613038 & 0.957335063226075 & 0.521332468386962 \tabularnewline
19 & 0.440307615047062 & 0.880615230094123 & 0.559692384952938 \tabularnewline
20 & 0.650862167753017 & 0.698275664493967 & 0.349137832246983 \tabularnewline
21 & 0.748599495786615 & 0.502801008426771 & 0.251400504213385 \tabularnewline
22 & 0.691811395609513 & 0.616377208780974 & 0.308188604390487 \tabularnewline
23 & 0.62657405863983 & 0.74685188272034 & 0.37342594136017 \tabularnewline
24 & 0.712169023883211 & 0.575661952233578 & 0.287830976116789 \tabularnewline
25 & 0.742381158222035 & 0.515237683555931 & 0.257618841777965 \tabularnewline
26 & 0.76499330764948 & 0.470013384701041 & 0.235006692350520 \tabularnewline
27 & 0.724842981927196 & 0.550314036145608 & 0.275157018072804 \tabularnewline
28 & 0.794232070796532 & 0.411535858406935 & 0.205767929203468 \tabularnewline
29 & 0.777824547332806 & 0.444350905334387 & 0.222175452667194 \tabularnewline
30 & 0.718140799187505 & 0.563718401624991 & 0.281859200812495 \tabularnewline
31 & 0.684678079836791 & 0.630643840326418 & 0.315321920163209 \tabularnewline
32 & 0.638944749803539 & 0.722110500392923 & 0.361055250196461 \tabularnewline
33 & 0.58421899021932 & 0.83156201956136 & 0.41578100978068 \tabularnewline
34 & 0.569725935398702 & 0.860548129202596 & 0.430274064601298 \tabularnewline
35 & 0.559757808493422 & 0.880484383013156 & 0.440242191506578 \tabularnewline
36 & 0.659910976105242 & 0.680178047789515 & 0.340089023894758 \tabularnewline
37 & 0.63260605322822 & 0.73478789354356 & 0.36739394677178 \tabularnewline
38 & 0.674208427691286 & 0.651583144617428 & 0.325791572308714 \tabularnewline
39 & 0.609183498950496 & 0.781633002099007 & 0.390816501049504 \tabularnewline
40 & 0.588802260780142 & 0.822395478439716 & 0.411197739219858 \tabularnewline
41 & 0.711572882272585 & 0.576854235454829 & 0.288427117727415 \tabularnewline
42 & 0.667785589697017 & 0.664428820605967 & 0.332214410302983 \tabularnewline
43 & 0.633160447748524 & 0.733679104502953 & 0.366839552251476 \tabularnewline
44 & 0.585321344727907 & 0.829357310544187 & 0.414678655272093 \tabularnewline
45 & 0.5689056415272 & 0.8621887169456 & 0.4310943584728 \tabularnewline
46 & 0.485782593986519 & 0.971565187973039 & 0.514217406013481 \tabularnewline
47 & 0.473899680505432 & 0.947799361010863 & 0.526100319494568 \tabularnewline
48 & 0.405367680376904 & 0.810735360753809 & 0.594632319623096 \tabularnewline
49 & 0.337056668196685 & 0.67411333639337 & 0.662943331803315 \tabularnewline
50 & 0.43261282132077 & 0.86522564264154 & 0.56738717867923 \tabularnewline
51 & 0.361542924754275 & 0.72308584950855 & 0.638457075245725 \tabularnewline
52 & 0.349680075158760 & 0.699360150317519 & 0.65031992484124 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116544&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]0.00139581978660349[/C][C]0.00279163957320698[/C][C]0.998604180213396[/C][/ROW]
[ROW][C]9[/C][C]0.0110730367183871[/C][C]0.0221460734367741[/C][C]0.988926963281613[/C][/ROW]
[ROW][C]10[/C][C]0.0428564707246801[/C][C]0.0857129414493603[/C][C]0.95714352927532[/C][/ROW]
[ROW][C]11[/C][C]0.298110046580889[/C][C]0.596220093161778[/C][C]0.701889953419111[/C][/ROW]
[ROW][C]12[/C][C]0.196650813946822[/C][C]0.393301627893644[/C][C]0.803349186053178[/C][/ROW]
[ROW][C]13[/C][C]0.156252385408922[/C][C]0.312504770817844[/C][C]0.843747614591078[/C][/ROW]
[ROW][C]14[/C][C]0.111256292522322[/C][C]0.222512585044645[/C][C]0.888743707477678[/C][/ROW]
[ROW][C]15[/C][C]0.467775068305252[/C][C]0.935550136610504[/C][C]0.532224931694748[/C][/ROW]
[ROW][C]16[/C][C]0.444820369783769[/C][C]0.889640739567538[/C][C]0.555179630216231[/C][/ROW]
[ROW][C]17[/C][C]0.385648076041868[/C][C]0.771296152083736[/C][C]0.614351923958132[/C][/ROW]
[ROW][C]18[/C][C]0.478667531613038[/C][C]0.957335063226075[/C][C]0.521332468386962[/C][/ROW]
[ROW][C]19[/C][C]0.440307615047062[/C][C]0.880615230094123[/C][C]0.559692384952938[/C][/ROW]
[ROW][C]20[/C][C]0.650862167753017[/C][C]0.698275664493967[/C][C]0.349137832246983[/C][/ROW]
[ROW][C]21[/C][C]0.748599495786615[/C][C]0.502801008426771[/C][C]0.251400504213385[/C][/ROW]
[ROW][C]22[/C][C]0.691811395609513[/C][C]0.616377208780974[/C][C]0.308188604390487[/C][/ROW]
[ROW][C]23[/C][C]0.62657405863983[/C][C]0.74685188272034[/C][C]0.37342594136017[/C][/ROW]
[ROW][C]24[/C][C]0.712169023883211[/C][C]0.575661952233578[/C][C]0.287830976116789[/C][/ROW]
[ROW][C]25[/C][C]0.742381158222035[/C][C]0.515237683555931[/C][C]0.257618841777965[/C][/ROW]
[ROW][C]26[/C][C]0.76499330764948[/C][C]0.470013384701041[/C][C]0.235006692350520[/C][/ROW]
[ROW][C]27[/C][C]0.724842981927196[/C][C]0.550314036145608[/C][C]0.275157018072804[/C][/ROW]
[ROW][C]28[/C][C]0.794232070796532[/C][C]0.411535858406935[/C][C]0.205767929203468[/C][/ROW]
[ROW][C]29[/C][C]0.777824547332806[/C][C]0.444350905334387[/C][C]0.222175452667194[/C][/ROW]
[ROW][C]30[/C][C]0.718140799187505[/C][C]0.563718401624991[/C][C]0.281859200812495[/C][/ROW]
[ROW][C]31[/C][C]0.684678079836791[/C][C]0.630643840326418[/C][C]0.315321920163209[/C][/ROW]
[ROW][C]32[/C][C]0.638944749803539[/C][C]0.722110500392923[/C][C]0.361055250196461[/C][/ROW]
[ROW][C]33[/C][C]0.58421899021932[/C][C]0.83156201956136[/C][C]0.41578100978068[/C][/ROW]
[ROW][C]34[/C][C]0.569725935398702[/C][C]0.860548129202596[/C][C]0.430274064601298[/C][/ROW]
[ROW][C]35[/C][C]0.559757808493422[/C][C]0.880484383013156[/C][C]0.440242191506578[/C][/ROW]
[ROW][C]36[/C][C]0.659910976105242[/C][C]0.680178047789515[/C][C]0.340089023894758[/C][/ROW]
[ROW][C]37[/C][C]0.63260605322822[/C][C]0.73478789354356[/C][C]0.36739394677178[/C][/ROW]
[ROW][C]38[/C][C]0.674208427691286[/C][C]0.651583144617428[/C][C]0.325791572308714[/C][/ROW]
[ROW][C]39[/C][C]0.609183498950496[/C][C]0.781633002099007[/C][C]0.390816501049504[/C][/ROW]
[ROW][C]40[/C][C]0.588802260780142[/C][C]0.822395478439716[/C][C]0.411197739219858[/C][/ROW]
[ROW][C]41[/C][C]0.711572882272585[/C][C]0.576854235454829[/C][C]0.288427117727415[/C][/ROW]
[ROW][C]42[/C][C]0.667785589697017[/C][C]0.664428820605967[/C][C]0.332214410302983[/C][/ROW]
[ROW][C]43[/C][C]0.633160447748524[/C][C]0.733679104502953[/C][C]0.366839552251476[/C][/ROW]
[ROW][C]44[/C][C]0.585321344727907[/C][C]0.829357310544187[/C][C]0.414678655272093[/C][/ROW]
[ROW][C]45[/C][C]0.5689056415272[/C][C]0.8621887169456[/C][C]0.4310943584728[/C][/ROW]
[ROW][C]46[/C][C]0.485782593986519[/C][C]0.971565187973039[/C][C]0.514217406013481[/C][/ROW]
[ROW][C]47[/C][C]0.473899680505432[/C][C]0.947799361010863[/C][C]0.526100319494568[/C][/ROW]
[ROW][C]48[/C][C]0.405367680376904[/C][C]0.810735360753809[/C][C]0.594632319623096[/C][/ROW]
[ROW][C]49[/C][C]0.337056668196685[/C][C]0.67411333639337[/C][C]0.662943331803315[/C][/ROW]
[ROW][C]50[/C][C]0.43261282132077[/C][C]0.86522564264154[/C][C]0.56738717867923[/C][/ROW]
[ROW][C]51[/C][C]0.361542924754275[/C][C]0.72308584950855[/C][C]0.638457075245725[/C][/ROW]
[ROW][C]52[/C][C]0.349680075158760[/C][C]0.699360150317519[/C][C]0.65031992484124[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116544&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116544&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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.001395819786603490.002791639573206980.998604180213396
90.01107303671838710.02214607343677410.988926963281613
100.04285647072468010.08571294144936030.95714352927532
110.2981100465808890.5962200931617780.701889953419111
120.1966508139468220.3933016278936440.803349186053178
130.1562523854089220.3125047708178440.843747614591078
140.1112562925223220.2225125850446450.888743707477678
150.4677750683052520.9355501366105040.532224931694748
160.4448203697837690.8896407395675380.555179630216231
170.3856480760418680.7712961520837360.614351923958132
180.4786675316130380.9573350632260750.521332468386962
190.4403076150470620.8806152300941230.559692384952938
200.6508621677530170.6982756644939670.349137832246983
210.7485994957866150.5028010084267710.251400504213385
220.6918113956095130.6163772087809740.308188604390487
230.626574058639830.746851882720340.37342594136017
240.7121690238832110.5756619522335780.287830976116789
250.7423811582220350.5152376835559310.257618841777965
260.764993307649480.4700133847010410.235006692350520
270.7248429819271960.5503140361456080.275157018072804
280.7942320707965320.4115358584069350.205767929203468
290.7778245473328060.4443509053343870.222175452667194
300.7181407991875050.5637184016249910.281859200812495
310.6846780798367910.6306438403264180.315321920163209
320.6389447498035390.7221105003929230.361055250196461
330.584218990219320.831562019561360.41578100978068
340.5697259353987020.8605481292025960.430274064601298
350.5597578084934220.8804843830131560.440242191506578
360.6599109761052420.6801780477895150.340089023894758
370.632606053228220.734787893543560.36739394677178
380.6742084276912860.6515831446174280.325791572308714
390.6091834989504960.7816330020990070.390816501049504
400.5888022607801420.8223954784397160.411197739219858
410.7115728822725850.5768542354548290.288427117727415
420.6677855896970170.6644288206059670.332214410302983
430.6331604477485240.7336791045029530.366839552251476
440.5853213447279070.8293573105441870.414678655272093
450.56890564152720.86218871694560.4310943584728
460.4857825939865190.9715651879730390.514217406013481
470.4738996805054320.9477993610108630.526100319494568
480.4053676803769040.8107353607538090.594632319623096
490.3370566681966850.674113336393370.662943331803315
500.432612821320770.865225642641540.56738717867923
510.3615429247542750.723085849508550.638457075245725
520.3496800751587600.6993601503175190.65031992484124







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.0222222222222222NOK
5% type I error level20.0444444444444444OK
10% type I error level30.0666666666666667OK

\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 & 1 & 0.0222222222222222 & NOK \tabularnewline
5% type I error level & 2 & 0.0444444444444444 & OK \tabularnewline
10% type I error level & 3 & 0.0666666666666667 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116544&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]1[/C][C]0.0222222222222222[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]2[/C][C]0.0444444444444444[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.0666666666666667[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116544&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116544&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 testsOK/NOK
1% type I error level10.0222222222222222NOK
5% type I error level20.0444444444444444OK
10% type I error level30.0666666666666667OK



Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$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')
}