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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 24 Nov 2011 15:08:54 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t1322165348w6t2vy2ssbsr01u.htm/, Retrieved Fri, 29 Mar 2024 08:06:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147187, Retrieved Fri, 29 Mar 2024 08:06:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact93
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [month] [2010-12-01 21:21:06] [f1aa04283d83c25edc8ae3bb0d0fb93e]
- R P       [Multiple Regression] [month] [2011-11-24 20:08:54] [cfea828c93f35e07cca4521b1fb38047] [Current]
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Dataseries X:
11	0	8	17	2	6
10	-2	3	23	3	7
9	-4	3	24	1	4
8	-4	7	27	1	3
7	-7	4	31	0	0
6	-9	-4	40	1	6
5	-13	-6	47	-1	3
4	-8	8	43	2	1
3	-13	2	60	2	6
2	-15	-1	64	0	5
1	-15	-2	65	1	7
12	-15	0	65	1	4
11	-10	10	55	3	3
10	-12	3	57	3	6
9	-11	6	57	1	6
8	-11	7	57	1	5
7	-17	-4	65	-2	2
6	-18	-5	69	1	3
5	-19	-7	70	1	-2
4	-22	-10	71	-1	-4
3	-24	-21	71	-4	0
2	-24	-22	73	-2	1
1	-20	-16	68	-1	4
12	-25	-25	65	-5	-3
11	-22	-22	57	-4	-3
10	-17	-22	41	-5	0
9	-9	-19	21	0	6
8	-11	-21	21	-2	-1
7	-13	-31	17	-4	0
6	-11	-28	9	-6	-1
5	-9	-23	11	-2	1
4	-7	-17	6	-2	-4
3	-3	-12	-2	-2	-1
2	-3	-14	0	1	-1
1	-6	-18	5	-2	0
12	-4	-16	3	0	3
11	-8	-22	7	-1	0
10	-1	-9	4	2	8
9	-2	-10	8	3	8
8	-2	-10	9	2	8
7	-1	0	14	3	8
6	1	3	12	4	11
5	2	2	12	5	13
4	2	4	7	5	5
3	-1	-3	15	4	12
2	1	0	14	5	13
1	-1	-1	19	6	9
12	-8	-7	39	4	11
11	1	2	12	6	7
10	2	3	11	6	12
9	-2	-3	17	3	11
8	-2	-5	16	5	10
7	-2	0	25	5	13
6	-2	-3	24	5	14
5	-6	-7	28	3	10
4	-4	-7	25	5	13
3	-5	-7	31	5	12
2	-2	-4	24	6	13
1	-1	-3	24	6	17
12	-5	-6	33	5	15




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147187&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147187&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147187&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 time3 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
werkloosheid[t] = + 1.54451965078874 -0.112572244790881maand[t] -3.93284564582941indicator[t] + 1.00796554976765economie[t] + 0.995091256179223`financiën`[t] + 0.892220731906652spaarvermogen[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
werkloosheid[t] =  +  1.54451965078874 -0.112572244790881maand[t] -3.93284564582941indicator[t] +  1.00796554976765economie[t] +  0.995091256179223`financiën`[t] +  0.892220731906652spaarvermogen[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147187&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]werkloosheid[t] =  +  1.54451965078874 -0.112572244790881maand[t] -3.93284564582941indicator[t] +  1.00796554976765economie[t] +  0.995091256179223`financiën`[t] +  0.892220731906652spaarvermogen[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147187&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147187&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] = + 1.54451965078874 -0.112572244790881maand[t] -3.93284564582941indicator[t] + 1.00796554976765economie[t] + 0.995091256179223`financiën`[t] + 0.892220731906652spaarvermogen[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.544519650788740.5615172.75060.0080780.004039
maand-0.1125722447908810.044449-2.53260.0142610.00713
indicator-3.932845645829410.029754-132.180100
economie1.007965549767650.02211845.572400
`financiën`0.9950912561792230.128567.740300
spaarvermogen0.8922207319066520.05643515.809800

\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) & 1.54451965078874 & 0.561517 & 2.7506 & 0.008078 & 0.004039 \tabularnewline
maand & -0.112572244790881 & 0.044449 & -2.5326 & 0.014261 & 0.00713 \tabularnewline
indicator & -3.93284564582941 & 0.029754 & -132.1801 & 0 & 0 \tabularnewline
economie & 1.00796554976765 & 0.022118 & 45.5724 & 0 & 0 \tabularnewline
`financiën` & 0.995091256179223 & 0.12856 & 7.7403 & 0 & 0 \tabularnewline
spaarvermogen & 0.892220731906652 & 0.056435 & 15.8098 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147187&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]1.54451965078874[/C][C]0.561517[/C][C]2.7506[/C][C]0.008078[/C][C]0.004039[/C][/ROW]
[ROW][C]maand[/C][C]-0.112572244790881[/C][C]0.044449[/C][C]-2.5326[/C][C]0.014261[/C][C]0.00713[/C][/ROW]
[ROW][C]indicator[/C][C]-3.93284564582941[/C][C]0.029754[/C][C]-132.1801[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]economie[/C][C]1.00796554976765[/C][C]0.022118[/C][C]45.5724[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`financiën`[/C][C]0.995091256179223[/C][C]0.12856[/C][C]7.7403[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]spaarvermogen[/C][C]0.892220731906652[/C][C]0.056435[/C][C]15.8098[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147187&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147187&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)1.544519650788740.5615172.75060.0080780.004039
maand-0.1125722447908810.044449-2.53260.0142610.00713
indicator-3.932845645829410.029754-132.180100
economie1.007965549767650.02211845.572400
`financiën`0.9950912561792230.128567.740300
spaarvermogen0.8922207319066520.05643515.809800







Multiple Linear Regression - Regression Statistics
Multiple R0.998822807707077
R-squared0.997647001195849
Adjusted R-squared0.997429130936206
F-TEST (value)4579.08758555642
F-TEST (DF numerator)5
F-TEST (DF denominator)54
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.1714645449262
Sum Squared Residuals74.1057757210335

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.998822807707077 \tabularnewline
R-squared & 0.997647001195849 \tabularnewline
Adjusted R-squared & 0.997429130936206 \tabularnewline
F-TEST (value) & 4579.08758555642 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 54 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.1714645449262 \tabularnewline
Sum Squared Residuals & 74.1057757210335 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147187&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.998822807707077[/C][/ROW]
[ROW][C]R-squared[/C][C]0.997647001195849[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.997429130936206[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4579.08758555642[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]54[/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]1.1714645449262[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]74.1057757210335[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147187&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147187&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 R0.998822807707077
R-squared0.997647001195849
Adjusted R-squared0.997429130936206
F-TEST (value)4579.08758555642
F-TEST (DF numerator)5
F-TEST (DF denominator)54
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.1714645449262
Sum Squared Residuals74.1057757210335







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11715.71345626002861.28654373997139
22320.53920403572592.46079596427406
32423.85062286409720.149377135902814
42727.1028365760521-0.102836576052071
53132.318295657129-1.31829565712903
64038.58125044305671.41874955694335
74747.7424294635515-0.742429463551456
84343.5031234806668-0.503123480666782
96061.6932343155321-1.69323431553206
106463.76519795841370.234802041586304
116565.6493373734295-0.649337373429457
126563.75031158454511.24968841545488
135555.3762728783173-0.376272878317265
145758.9754397621134-1.97543976211336
155756.18888049801930.811119501980662
165756.41719756067120.582802439328785
176563.37728666873681.62271333126324
186970.2922335100337-1.29223351003371
197067.86061664158542.13938335841456
207172.9732051983898-1.97320519838984
217170.44745684648430.552543153515694
227372.43446678577260.565533214227353
236866.5352031977511.46479680224903
246565.663536638226-0.663536638226012
255757.9965598510108-0.996559851010837
264140.12647480619540.873525193804586
272122.12895920599-1.12895920599002
282119.85556400719941.1444359928006
291716.65621026552080.343789734479213
3099.04458462369072-0.0445846236907148
311112.0960998141912-1.09609981419123
3265.929670406395940.070329593604059
33-2-1.9726499875726-0.0273500124274034
340-0.8907350737793520.890735073779352
3554.895458872798140.104541127201865
3632.474248696053330.525751303946665
3778.59865677365676-1.59865677365676
3844.40790126841214-0.407901268412142
3988.440444865444-0.440444865443997
4097.557925854055661.44207414594434
411414.8123992068729-0.812399206872865
421213.7549302612071-1.75493026120707
431211.70622403039340.293775969606582
4476.696961519466380.303038480533619
451516.8027657205393-1.80276572053927
461413.96085531106020.0391446889398368
471918.35736162629480.642638373705178
483938.39545210724990.604547892750064
491210.60540307221681.39459692778315
501112.2541988804792-1.25419888047923
511718.1728659095375-1.17286590953752
521617.3674688352449-1.36746883524489
532525.196531024594-0.196531024593982
542423.17742735198860.822572648011439
552829.4304545410414-1.43045454104141
562526.3441802022519-1.34418020225188
573129.49737736096551.50262263903449
582422.7226213056571.277378694343
592423.47919638201270.520803617987262
603332.16885490333520.831145096664806

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 17 & 15.7134562600286 & 1.28654373997139 \tabularnewline
2 & 23 & 20.5392040357259 & 2.46079596427406 \tabularnewline
3 & 24 & 23.8506228640972 & 0.149377135902814 \tabularnewline
4 & 27 & 27.1028365760521 & -0.102836576052071 \tabularnewline
5 & 31 & 32.318295657129 & -1.31829565712903 \tabularnewline
6 & 40 & 38.5812504430567 & 1.41874955694335 \tabularnewline
7 & 47 & 47.7424294635515 & -0.742429463551456 \tabularnewline
8 & 43 & 43.5031234806668 & -0.503123480666782 \tabularnewline
9 & 60 & 61.6932343155321 & -1.69323431553206 \tabularnewline
10 & 64 & 63.7651979584137 & 0.234802041586304 \tabularnewline
11 & 65 & 65.6493373734295 & -0.649337373429457 \tabularnewline
12 & 65 & 63.7503115845451 & 1.24968841545488 \tabularnewline
13 & 55 & 55.3762728783173 & -0.376272878317265 \tabularnewline
14 & 57 & 58.9754397621134 & -1.97543976211336 \tabularnewline
15 & 57 & 56.1888804980193 & 0.811119501980662 \tabularnewline
16 & 57 & 56.4171975606712 & 0.582802439328785 \tabularnewline
17 & 65 & 63.3772866687368 & 1.62271333126324 \tabularnewline
18 & 69 & 70.2922335100337 & -1.29223351003371 \tabularnewline
19 & 70 & 67.8606166415854 & 2.13938335841456 \tabularnewline
20 & 71 & 72.9732051983898 & -1.97320519838984 \tabularnewline
21 & 71 & 70.4474568464843 & 0.552543153515694 \tabularnewline
22 & 73 & 72.4344667857726 & 0.565533214227353 \tabularnewline
23 & 68 & 66.535203197751 & 1.46479680224903 \tabularnewline
24 & 65 & 65.663536638226 & -0.663536638226012 \tabularnewline
25 & 57 & 57.9965598510108 & -0.996559851010837 \tabularnewline
26 & 41 & 40.1264748061954 & 0.873525193804586 \tabularnewline
27 & 21 & 22.12895920599 & -1.12895920599002 \tabularnewline
28 & 21 & 19.8555640071994 & 1.1444359928006 \tabularnewline
29 & 17 & 16.6562102655208 & 0.343789734479213 \tabularnewline
30 & 9 & 9.04458462369072 & -0.0445846236907148 \tabularnewline
31 & 11 & 12.0960998141912 & -1.09609981419123 \tabularnewline
32 & 6 & 5.92967040639594 & 0.070329593604059 \tabularnewline
33 & -2 & -1.9726499875726 & -0.0273500124274034 \tabularnewline
34 & 0 & -0.890735073779352 & 0.890735073779352 \tabularnewline
35 & 5 & 4.89545887279814 & 0.104541127201865 \tabularnewline
36 & 3 & 2.47424869605333 & 0.525751303946665 \tabularnewline
37 & 7 & 8.59865677365676 & -1.59865677365676 \tabularnewline
38 & 4 & 4.40790126841214 & -0.407901268412142 \tabularnewline
39 & 8 & 8.440444865444 & -0.440444865443997 \tabularnewline
40 & 9 & 7.55792585405566 & 1.44207414594434 \tabularnewline
41 & 14 & 14.8123992068729 & -0.812399206872865 \tabularnewline
42 & 12 & 13.7549302612071 & -1.75493026120707 \tabularnewline
43 & 12 & 11.7062240303934 & 0.293775969606582 \tabularnewline
44 & 7 & 6.69696151946638 & 0.303038480533619 \tabularnewline
45 & 15 & 16.8027657205393 & -1.80276572053927 \tabularnewline
46 & 14 & 13.9608553110602 & 0.0391446889398368 \tabularnewline
47 & 19 & 18.3573616262948 & 0.642638373705178 \tabularnewline
48 & 39 & 38.3954521072499 & 0.604547892750064 \tabularnewline
49 & 12 & 10.6054030722168 & 1.39459692778315 \tabularnewline
50 & 11 & 12.2541988804792 & -1.25419888047923 \tabularnewline
51 & 17 & 18.1728659095375 & -1.17286590953752 \tabularnewline
52 & 16 & 17.3674688352449 & -1.36746883524489 \tabularnewline
53 & 25 & 25.196531024594 & -0.196531024593982 \tabularnewline
54 & 24 & 23.1774273519886 & 0.822572648011439 \tabularnewline
55 & 28 & 29.4304545410414 & -1.43045454104141 \tabularnewline
56 & 25 & 26.3441802022519 & -1.34418020225188 \tabularnewline
57 & 31 & 29.4973773609655 & 1.50262263903449 \tabularnewline
58 & 24 & 22.722621305657 & 1.277378694343 \tabularnewline
59 & 24 & 23.4791963820127 & 0.520803617987262 \tabularnewline
60 & 33 & 32.1688549033352 & 0.831145096664806 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147187&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]17[/C][C]15.7134562600286[/C][C]1.28654373997139[/C][/ROW]
[ROW][C]2[/C][C]23[/C][C]20.5392040357259[/C][C]2.46079596427406[/C][/ROW]
[ROW][C]3[/C][C]24[/C][C]23.8506228640972[/C][C]0.149377135902814[/C][/ROW]
[ROW][C]4[/C][C]27[/C][C]27.1028365760521[/C][C]-0.102836576052071[/C][/ROW]
[ROW][C]5[/C][C]31[/C][C]32.318295657129[/C][C]-1.31829565712903[/C][/ROW]
[ROW][C]6[/C][C]40[/C][C]38.5812504430567[/C][C]1.41874955694335[/C][/ROW]
[ROW][C]7[/C][C]47[/C][C]47.7424294635515[/C][C]-0.742429463551456[/C][/ROW]
[ROW][C]8[/C][C]43[/C][C]43.5031234806668[/C][C]-0.503123480666782[/C][/ROW]
[ROW][C]9[/C][C]60[/C][C]61.6932343155321[/C][C]-1.69323431553206[/C][/ROW]
[ROW][C]10[/C][C]64[/C][C]63.7651979584137[/C][C]0.234802041586304[/C][/ROW]
[ROW][C]11[/C][C]65[/C][C]65.6493373734295[/C][C]-0.649337373429457[/C][/ROW]
[ROW][C]12[/C][C]65[/C][C]63.7503115845451[/C][C]1.24968841545488[/C][/ROW]
[ROW][C]13[/C][C]55[/C][C]55.3762728783173[/C][C]-0.376272878317265[/C][/ROW]
[ROW][C]14[/C][C]57[/C][C]58.9754397621134[/C][C]-1.97543976211336[/C][/ROW]
[ROW][C]15[/C][C]57[/C][C]56.1888804980193[/C][C]0.811119501980662[/C][/ROW]
[ROW][C]16[/C][C]57[/C][C]56.4171975606712[/C][C]0.582802439328785[/C][/ROW]
[ROW][C]17[/C][C]65[/C][C]63.3772866687368[/C][C]1.62271333126324[/C][/ROW]
[ROW][C]18[/C][C]69[/C][C]70.2922335100337[/C][C]-1.29223351003371[/C][/ROW]
[ROW][C]19[/C][C]70[/C][C]67.8606166415854[/C][C]2.13938335841456[/C][/ROW]
[ROW][C]20[/C][C]71[/C][C]72.9732051983898[/C][C]-1.97320519838984[/C][/ROW]
[ROW][C]21[/C][C]71[/C][C]70.4474568464843[/C][C]0.552543153515694[/C][/ROW]
[ROW][C]22[/C][C]73[/C][C]72.4344667857726[/C][C]0.565533214227353[/C][/ROW]
[ROW][C]23[/C][C]68[/C][C]66.535203197751[/C][C]1.46479680224903[/C][/ROW]
[ROW][C]24[/C][C]65[/C][C]65.663536638226[/C][C]-0.663536638226012[/C][/ROW]
[ROW][C]25[/C][C]57[/C][C]57.9965598510108[/C][C]-0.996559851010837[/C][/ROW]
[ROW][C]26[/C][C]41[/C][C]40.1264748061954[/C][C]0.873525193804586[/C][/ROW]
[ROW][C]27[/C][C]21[/C][C]22.12895920599[/C][C]-1.12895920599002[/C][/ROW]
[ROW][C]28[/C][C]21[/C][C]19.8555640071994[/C][C]1.1444359928006[/C][/ROW]
[ROW][C]29[/C][C]17[/C][C]16.6562102655208[/C][C]0.343789734479213[/C][/ROW]
[ROW][C]30[/C][C]9[/C][C]9.04458462369072[/C][C]-0.0445846236907148[/C][/ROW]
[ROW][C]31[/C][C]11[/C][C]12.0960998141912[/C][C]-1.09609981419123[/C][/ROW]
[ROW][C]32[/C][C]6[/C][C]5.92967040639594[/C][C]0.070329593604059[/C][/ROW]
[ROW][C]33[/C][C]-2[/C][C]-1.9726499875726[/C][C]-0.0273500124274034[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]-0.890735073779352[/C][C]0.890735073779352[/C][/ROW]
[ROW][C]35[/C][C]5[/C][C]4.89545887279814[/C][C]0.104541127201865[/C][/ROW]
[ROW][C]36[/C][C]3[/C][C]2.47424869605333[/C][C]0.525751303946665[/C][/ROW]
[ROW][C]37[/C][C]7[/C][C]8.59865677365676[/C][C]-1.59865677365676[/C][/ROW]
[ROW][C]38[/C][C]4[/C][C]4.40790126841214[/C][C]-0.407901268412142[/C][/ROW]
[ROW][C]39[/C][C]8[/C][C]8.440444865444[/C][C]-0.440444865443997[/C][/ROW]
[ROW][C]40[/C][C]9[/C][C]7.55792585405566[/C][C]1.44207414594434[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]14.8123992068729[/C][C]-0.812399206872865[/C][/ROW]
[ROW][C]42[/C][C]12[/C][C]13.7549302612071[/C][C]-1.75493026120707[/C][/ROW]
[ROW][C]43[/C][C]12[/C][C]11.7062240303934[/C][C]0.293775969606582[/C][/ROW]
[ROW][C]44[/C][C]7[/C][C]6.69696151946638[/C][C]0.303038480533619[/C][/ROW]
[ROW][C]45[/C][C]15[/C][C]16.8027657205393[/C][C]-1.80276572053927[/C][/ROW]
[ROW][C]46[/C][C]14[/C][C]13.9608553110602[/C][C]0.0391446889398368[/C][/ROW]
[ROW][C]47[/C][C]19[/C][C]18.3573616262948[/C][C]0.642638373705178[/C][/ROW]
[ROW][C]48[/C][C]39[/C][C]38.3954521072499[/C][C]0.604547892750064[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]10.6054030722168[/C][C]1.39459692778315[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]12.2541988804792[/C][C]-1.25419888047923[/C][/ROW]
[ROW][C]51[/C][C]17[/C][C]18.1728659095375[/C][C]-1.17286590953752[/C][/ROW]
[ROW][C]52[/C][C]16[/C][C]17.3674688352449[/C][C]-1.36746883524489[/C][/ROW]
[ROW][C]53[/C][C]25[/C][C]25.196531024594[/C][C]-0.196531024593982[/C][/ROW]
[ROW][C]54[/C][C]24[/C][C]23.1774273519886[/C][C]0.822572648011439[/C][/ROW]
[ROW][C]55[/C][C]28[/C][C]29.4304545410414[/C][C]-1.43045454104141[/C][/ROW]
[ROW][C]56[/C][C]25[/C][C]26.3441802022519[/C][C]-1.34418020225188[/C][/ROW]
[ROW][C]57[/C][C]31[/C][C]29.4973773609655[/C][C]1.50262263903449[/C][/ROW]
[ROW][C]58[/C][C]24[/C][C]22.722621305657[/C][C]1.277378694343[/C][/ROW]
[ROW][C]59[/C][C]24[/C][C]23.4791963820127[/C][C]0.520803617987262[/C][/ROW]
[ROW][C]60[/C][C]33[/C][C]32.1688549033352[/C][C]0.831145096664806[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147187&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147187&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
11715.71345626002861.28654373997139
22320.53920403572592.46079596427406
32423.85062286409720.149377135902814
42727.1028365760521-0.102836576052071
53132.318295657129-1.31829565712903
64038.58125044305671.41874955694335
74747.7424294635515-0.742429463551456
84343.5031234806668-0.503123480666782
96061.6932343155321-1.69323431553206
106463.76519795841370.234802041586304
116565.6493373734295-0.649337373429457
126563.75031158454511.24968841545488
135555.3762728783173-0.376272878317265
145758.9754397621134-1.97543976211336
155756.18888049801930.811119501980662
165756.41719756067120.582802439328785
176563.37728666873681.62271333126324
186970.2922335100337-1.29223351003371
197067.86061664158542.13938335841456
207172.9732051983898-1.97320519838984
217170.44745684648430.552543153515694
227372.43446678577260.565533214227353
236866.5352031977511.46479680224903
246565.663536638226-0.663536638226012
255757.9965598510108-0.996559851010837
264140.12647480619540.873525193804586
272122.12895920599-1.12895920599002
282119.85556400719941.1444359928006
291716.65621026552080.343789734479213
3099.04458462369072-0.0445846236907148
311112.0960998141912-1.09609981419123
3265.929670406395940.070329593604059
33-2-1.9726499875726-0.0273500124274034
340-0.8907350737793520.890735073779352
3554.895458872798140.104541127201865
3632.474248696053330.525751303946665
3778.59865677365676-1.59865677365676
3844.40790126841214-0.407901268412142
3988.440444865444-0.440444865443997
4097.557925854055661.44207414594434
411414.8123992068729-0.812399206872865
421213.7549302612071-1.75493026120707
431211.70622403039340.293775969606582
4476.696961519466380.303038480533619
451516.8027657205393-1.80276572053927
461413.96085531106020.0391446889398368
471918.35736162629480.642638373705178
483938.39545210724990.604547892750064
491210.60540307221681.39459692778315
501112.2541988804792-1.25419888047923
511718.1728659095375-1.17286590953752
521617.3674688352449-1.36746883524489
532525.196531024594-0.196531024593982
542423.17742735198860.822572648011439
552829.4304545410414-1.43045454104141
562526.3441802022519-1.34418020225188
573129.49737736096551.50262263903449
582422.7226213056571.277378694343
592423.47919638201270.520803617987262
603332.16885490333520.831145096664806







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.06377650211028430.1275530042205690.936223497889716
100.1864880531184190.3729761062368390.81351194688158
110.2584838866503250.5169677733006510.741516113349675
120.3224068604032310.6448137208064610.677593139596769
130.2424320228810710.4848640457621420.757567977118929
140.6360765038594490.7278469922811020.363923496140551
150.5541232601128010.8917534797743990.445876739887199
160.4701777856422810.9403555712845630.529822214357718
170.5580571446386030.8838857107227930.441942855361397
180.4901628731421110.9803257462842220.509837126857889
190.8657179077531790.2685641844936420.134282092246821
200.9292786536577920.1414426926844150.0707213463422076
210.8960303670167520.2079392659664960.103969632983248
220.8520870268346910.2958259463306170.147912973165309
230.8483197401653220.3033605196693560.151680259834678
240.8713557512223420.2572884975553160.128644248777658
250.8814612878914560.2370774242170880.118538712108544
260.8604871054057720.2790257891884560.139512894594228
270.9057781226930880.1884437546138240.0942218773069118
280.8973315221679660.2053369556640680.102668477832034
290.8604199004421590.2791601991156820.139580099557841
300.8437069544180730.3125860911638550.156293045581927
310.8339966080018680.3320067839962640.166003391998132
320.7804402562043840.4391194875912320.219559743795616
330.7377774495837110.5244451008325770.262222550416289
340.6917131333171560.6165737333656880.308286866682844
350.670345802180620.659308395638760.32965419781938
360.6541030593778960.6917938812442080.345896940622104
370.6731799208492170.6536401583015650.326820079150783
380.6005901312389420.7988197375221150.399409868761058
390.5538470689950540.8923058620098910.446152931004946
400.8175104371176590.3649791257646830.182489562882341
410.7879133770379370.4241732459241260.212086622962063
420.8010501142777290.3978997714445420.198949885722271
430.7690290149380030.4619419701239950.230970985061997
440.6977811005281990.6044377989436030.302218899471802
450.6532568488354840.6934863023290320.346743151164516
460.5821732473220120.8356535053559760.417826752677988
470.4837003363281960.9674006726563920.516299663671804
480.3730994297109480.7461988594218960.626900570289052
490.4801239605531870.9602479211063740.519876039446813
500.3946898698722430.7893797397444870.605310130127757
510.3810098741675660.7620197483351310.618990125832434

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.0637765021102843 & 0.127553004220569 & 0.936223497889716 \tabularnewline
10 & 0.186488053118419 & 0.372976106236839 & 0.81351194688158 \tabularnewline
11 & 0.258483886650325 & 0.516967773300651 & 0.741516113349675 \tabularnewline
12 & 0.322406860403231 & 0.644813720806461 & 0.677593139596769 \tabularnewline
13 & 0.242432022881071 & 0.484864045762142 & 0.757567977118929 \tabularnewline
14 & 0.636076503859449 & 0.727846992281102 & 0.363923496140551 \tabularnewline
15 & 0.554123260112801 & 0.891753479774399 & 0.445876739887199 \tabularnewline
16 & 0.470177785642281 & 0.940355571284563 & 0.529822214357718 \tabularnewline
17 & 0.558057144638603 & 0.883885710722793 & 0.441942855361397 \tabularnewline
18 & 0.490162873142111 & 0.980325746284222 & 0.509837126857889 \tabularnewline
19 & 0.865717907753179 & 0.268564184493642 & 0.134282092246821 \tabularnewline
20 & 0.929278653657792 & 0.141442692684415 & 0.0707213463422076 \tabularnewline
21 & 0.896030367016752 & 0.207939265966496 & 0.103969632983248 \tabularnewline
22 & 0.852087026834691 & 0.295825946330617 & 0.147912973165309 \tabularnewline
23 & 0.848319740165322 & 0.303360519669356 & 0.151680259834678 \tabularnewline
24 & 0.871355751222342 & 0.257288497555316 & 0.128644248777658 \tabularnewline
25 & 0.881461287891456 & 0.237077424217088 & 0.118538712108544 \tabularnewline
26 & 0.860487105405772 & 0.279025789188456 & 0.139512894594228 \tabularnewline
27 & 0.905778122693088 & 0.188443754613824 & 0.0942218773069118 \tabularnewline
28 & 0.897331522167966 & 0.205336955664068 & 0.102668477832034 \tabularnewline
29 & 0.860419900442159 & 0.279160199115682 & 0.139580099557841 \tabularnewline
30 & 0.843706954418073 & 0.312586091163855 & 0.156293045581927 \tabularnewline
31 & 0.833996608001868 & 0.332006783996264 & 0.166003391998132 \tabularnewline
32 & 0.780440256204384 & 0.439119487591232 & 0.219559743795616 \tabularnewline
33 & 0.737777449583711 & 0.524445100832577 & 0.262222550416289 \tabularnewline
34 & 0.691713133317156 & 0.616573733365688 & 0.308286866682844 \tabularnewline
35 & 0.67034580218062 & 0.65930839563876 & 0.32965419781938 \tabularnewline
36 & 0.654103059377896 & 0.691793881244208 & 0.345896940622104 \tabularnewline
37 & 0.673179920849217 & 0.653640158301565 & 0.326820079150783 \tabularnewline
38 & 0.600590131238942 & 0.798819737522115 & 0.399409868761058 \tabularnewline
39 & 0.553847068995054 & 0.892305862009891 & 0.446152931004946 \tabularnewline
40 & 0.817510437117659 & 0.364979125764683 & 0.182489562882341 \tabularnewline
41 & 0.787913377037937 & 0.424173245924126 & 0.212086622962063 \tabularnewline
42 & 0.801050114277729 & 0.397899771444542 & 0.198949885722271 \tabularnewline
43 & 0.769029014938003 & 0.461941970123995 & 0.230970985061997 \tabularnewline
44 & 0.697781100528199 & 0.604437798943603 & 0.302218899471802 \tabularnewline
45 & 0.653256848835484 & 0.693486302329032 & 0.346743151164516 \tabularnewline
46 & 0.582173247322012 & 0.835653505355976 & 0.417826752677988 \tabularnewline
47 & 0.483700336328196 & 0.967400672656392 & 0.516299663671804 \tabularnewline
48 & 0.373099429710948 & 0.746198859421896 & 0.626900570289052 \tabularnewline
49 & 0.480123960553187 & 0.960247921106374 & 0.519876039446813 \tabularnewline
50 & 0.394689869872243 & 0.789379739744487 & 0.605310130127757 \tabularnewline
51 & 0.381009874167566 & 0.762019748335131 & 0.618990125832434 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147187&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]9[/C][C]0.0637765021102843[/C][C]0.127553004220569[/C][C]0.936223497889716[/C][/ROW]
[ROW][C]10[/C][C]0.186488053118419[/C][C]0.372976106236839[/C][C]0.81351194688158[/C][/ROW]
[ROW][C]11[/C][C]0.258483886650325[/C][C]0.516967773300651[/C][C]0.741516113349675[/C][/ROW]
[ROW][C]12[/C][C]0.322406860403231[/C][C]0.644813720806461[/C][C]0.677593139596769[/C][/ROW]
[ROW][C]13[/C][C]0.242432022881071[/C][C]0.484864045762142[/C][C]0.757567977118929[/C][/ROW]
[ROW][C]14[/C][C]0.636076503859449[/C][C]0.727846992281102[/C][C]0.363923496140551[/C][/ROW]
[ROW][C]15[/C][C]0.554123260112801[/C][C]0.891753479774399[/C][C]0.445876739887199[/C][/ROW]
[ROW][C]16[/C][C]0.470177785642281[/C][C]0.940355571284563[/C][C]0.529822214357718[/C][/ROW]
[ROW][C]17[/C][C]0.558057144638603[/C][C]0.883885710722793[/C][C]0.441942855361397[/C][/ROW]
[ROW][C]18[/C][C]0.490162873142111[/C][C]0.980325746284222[/C][C]0.509837126857889[/C][/ROW]
[ROW][C]19[/C][C]0.865717907753179[/C][C]0.268564184493642[/C][C]0.134282092246821[/C][/ROW]
[ROW][C]20[/C][C]0.929278653657792[/C][C]0.141442692684415[/C][C]0.0707213463422076[/C][/ROW]
[ROW][C]21[/C][C]0.896030367016752[/C][C]0.207939265966496[/C][C]0.103969632983248[/C][/ROW]
[ROW][C]22[/C][C]0.852087026834691[/C][C]0.295825946330617[/C][C]0.147912973165309[/C][/ROW]
[ROW][C]23[/C][C]0.848319740165322[/C][C]0.303360519669356[/C][C]0.151680259834678[/C][/ROW]
[ROW][C]24[/C][C]0.871355751222342[/C][C]0.257288497555316[/C][C]0.128644248777658[/C][/ROW]
[ROW][C]25[/C][C]0.881461287891456[/C][C]0.237077424217088[/C][C]0.118538712108544[/C][/ROW]
[ROW][C]26[/C][C]0.860487105405772[/C][C]0.279025789188456[/C][C]0.139512894594228[/C][/ROW]
[ROW][C]27[/C][C]0.905778122693088[/C][C]0.188443754613824[/C][C]0.0942218773069118[/C][/ROW]
[ROW][C]28[/C][C]0.897331522167966[/C][C]0.205336955664068[/C][C]0.102668477832034[/C][/ROW]
[ROW][C]29[/C][C]0.860419900442159[/C][C]0.279160199115682[/C][C]0.139580099557841[/C][/ROW]
[ROW][C]30[/C][C]0.843706954418073[/C][C]0.312586091163855[/C][C]0.156293045581927[/C][/ROW]
[ROW][C]31[/C][C]0.833996608001868[/C][C]0.332006783996264[/C][C]0.166003391998132[/C][/ROW]
[ROW][C]32[/C][C]0.780440256204384[/C][C]0.439119487591232[/C][C]0.219559743795616[/C][/ROW]
[ROW][C]33[/C][C]0.737777449583711[/C][C]0.524445100832577[/C][C]0.262222550416289[/C][/ROW]
[ROW][C]34[/C][C]0.691713133317156[/C][C]0.616573733365688[/C][C]0.308286866682844[/C][/ROW]
[ROW][C]35[/C][C]0.67034580218062[/C][C]0.65930839563876[/C][C]0.32965419781938[/C][/ROW]
[ROW][C]36[/C][C]0.654103059377896[/C][C]0.691793881244208[/C][C]0.345896940622104[/C][/ROW]
[ROW][C]37[/C][C]0.673179920849217[/C][C]0.653640158301565[/C][C]0.326820079150783[/C][/ROW]
[ROW][C]38[/C][C]0.600590131238942[/C][C]0.798819737522115[/C][C]0.399409868761058[/C][/ROW]
[ROW][C]39[/C][C]0.553847068995054[/C][C]0.892305862009891[/C][C]0.446152931004946[/C][/ROW]
[ROW][C]40[/C][C]0.817510437117659[/C][C]0.364979125764683[/C][C]0.182489562882341[/C][/ROW]
[ROW][C]41[/C][C]0.787913377037937[/C][C]0.424173245924126[/C][C]0.212086622962063[/C][/ROW]
[ROW][C]42[/C][C]0.801050114277729[/C][C]0.397899771444542[/C][C]0.198949885722271[/C][/ROW]
[ROW][C]43[/C][C]0.769029014938003[/C][C]0.461941970123995[/C][C]0.230970985061997[/C][/ROW]
[ROW][C]44[/C][C]0.697781100528199[/C][C]0.604437798943603[/C][C]0.302218899471802[/C][/ROW]
[ROW][C]45[/C][C]0.653256848835484[/C][C]0.693486302329032[/C][C]0.346743151164516[/C][/ROW]
[ROW][C]46[/C][C]0.582173247322012[/C][C]0.835653505355976[/C][C]0.417826752677988[/C][/ROW]
[ROW][C]47[/C][C]0.483700336328196[/C][C]0.967400672656392[/C][C]0.516299663671804[/C][/ROW]
[ROW][C]48[/C][C]0.373099429710948[/C][C]0.746198859421896[/C][C]0.626900570289052[/C][/ROW]
[ROW][C]49[/C][C]0.480123960553187[/C][C]0.960247921106374[/C][C]0.519876039446813[/C][/ROW]
[ROW][C]50[/C][C]0.394689869872243[/C][C]0.789379739744487[/C][C]0.605310130127757[/C][/ROW]
[ROW][C]51[/C][C]0.381009874167566[/C][C]0.762019748335131[/C][C]0.618990125832434[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147187&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147187&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
90.06377650211028430.1275530042205690.936223497889716
100.1864880531184190.3729761062368390.81351194688158
110.2584838866503250.5169677733006510.741516113349675
120.3224068604032310.6448137208064610.677593139596769
130.2424320228810710.4848640457621420.757567977118929
140.6360765038594490.7278469922811020.363923496140551
150.5541232601128010.8917534797743990.445876739887199
160.4701777856422810.9403555712845630.529822214357718
170.5580571446386030.8838857107227930.441942855361397
180.4901628731421110.9803257462842220.509837126857889
190.8657179077531790.2685641844936420.134282092246821
200.9292786536577920.1414426926844150.0707213463422076
210.8960303670167520.2079392659664960.103969632983248
220.8520870268346910.2958259463306170.147912973165309
230.8483197401653220.3033605196693560.151680259834678
240.8713557512223420.2572884975553160.128644248777658
250.8814612878914560.2370774242170880.118538712108544
260.8604871054057720.2790257891884560.139512894594228
270.9057781226930880.1884437546138240.0942218773069118
280.8973315221679660.2053369556640680.102668477832034
290.8604199004421590.2791601991156820.139580099557841
300.8437069544180730.3125860911638550.156293045581927
310.8339966080018680.3320067839962640.166003391998132
320.7804402562043840.4391194875912320.219559743795616
330.7377774495837110.5244451008325770.262222550416289
340.6917131333171560.6165737333656880.308286866682844
350.670345802180620.659308395638760.32965419781938
360.6541030593778960.6917938812442080.345896940622104
370.6731799208492170.6536401583015650.326820079150783
380.6005901312389420.7988197375221150.399409868761058
390.5538470689950540.8923058620098910.446152931004946
400.8175104371176590.3649791257646830.182489562882341
410.7879133770379370.4241732459241260.212086622962063
420.8010501142777290.3978997714445420.198949885722271
430.7690290149380030.4619419701239950.230970985061997
440.6977811005281990.6044377989436030.302218899471802
450.6532568488354840.6934863023290320.346743151164516
460.5821732473220120.8356535053559760.417826752677988
470.4837003363281960.9674006726563920.516299663671804
480.3730994297109480.7461988594218960.626900570289052
490.4801239605531870.9602479211063740.519876039446813
500.3946898698722430.7893797397444870.605310130127757
510.3810098741675660.7620197483351310.618990125832434







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK

\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=147187&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=147187&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147187&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 level00OK
5% type I error level00OK
10% type I error level00OK



Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; 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')
}