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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, 19 Nov 2009 09:32:12 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t12586485190w3e62h4jkqt1rg.htm/, Retrieved Fri, 29 Mar 2024 02:32:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57813, Retrieved Fri, 29 Mar 2024 02:32:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact144
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:14:11] [b98453cac15ba1066b407e146608df68]
-   PD      [Multiple Regression] [M5] [2009-11-19 16:32:12] [2ecea65fec1cd5f6b1ab182881aa2a91] [Current]
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Dataseries X:
19	2407,6	21
25	2454,62	19
21	2448,05	25
23	2497,84	21
23	2645,64	23
19	2756,76	23
18	2849,27	19
19	2921,44	18
19	2981,85	19
22	3080,58	19
23	3106,22	22
20	3119,31	23
14	3061,26	20
14	3097,31	14
14	3161,69	14
15	3257,16	14
11	3277,01	15
17	3295,32	11
16	3363,99	17
20	3494,17	16
24	3667,03	20
23	3813,06	24
20	3917,96	23
21	3895,51	20
19	3801,06	21
23	3570,12	19
23	3701,61	23
23	3862,27	23
23	3970,1	23
27	4138,52	23
26	4199,75	27
17	4290,89	26
24	4443,91	17
26	4502,64	24
24	4356,98	26
27	4591,27	24
27	4696,96	27
26	4621,4	27
24	4562,84	26
23	4202,52	24
23	4296,49	23
24	4435,23	23
17	4105,18	24
21	4116,68	17
19	3844,49	21
22	3720,98	19
22	3674,4	22
18	3857,62	22
16	3801,06	18
14	3504,37	16
12	3032,6	14
14	3047,03	12
16	2962,34	14
8	2197,82	16
3	2014,45	8
0	1862,83	3
5	1905,41	0
1	1810,99	5
1	1670,07	1
3	1864,44	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57813&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57813&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57813&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Consvertr[t] = + 0.705596169285437 + 0.00366456642861570Aand[t] + 0.459254170458587Y1[t] -0.110442093795957t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Consvertr[t] =  +  0.705596169285437 +  0.00366456642861570Aand[t] +  0.459254170458587Y1[t] -0.110442093795957t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57813&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Consvertr[t] =  +  0.705596169285437 +  0.00366456642861570Aand[t] +  0.459254170458587Y1[t] -0.110442093795957t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57813&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57813&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
Consvertr[t] = + 0.705596169285437 + 0.00366456642861570Aand[t] + 0.459254170458587Y1[t] -0.110442093795957t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.7055961692854371.7399580.40550.6866370.343319
Aand0.003664566428615700.0007954.60922.4e-051.2e-05
Y10.4592541704585870.1080164.25178.1e-054.1e-05
t-0.1104420937959570.027628-3.99750.0001899.5e-05

\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) & 0.705596169285437 & 1.739958 & 0.4055 & 0.686637 & 0.343319 \tabularnewline
Aand & 0.00366456642861570 & 0.000795 & 4.6092 & 2.4e-05 & 1.2e-05 \tabularnewline
Y1 & 0.459254170458587 & 0.108016 & 4.2517 & 8.1e-05 & 4.1e-05 \tabularnewline
t & -0.110442093795957 & 0.027628 & -3.9975 & 0.000189 & 9.5e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57813&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]0.705596169285437[/C][C]1.739958[/C][C]0.4055[/C][C]0.686637[/C][C]0.343319[/C][/ROW]
[ROW][C]Aand[/C][C]0.00366456642861570[/C][C]0.000795[/C][C]4.6092[/C][C]2.4e-05[/C][C]1.2e-05[/C][/ROW]
[ROW][C]Y1[/C][C]0.459254170458587[/C][C]0.108016[/C][C]4.2517[/C][C]8.1e-05[/C][C]4.1e-05[/C][/ROW]
[ROW][C]t[/C][C]-0.110442093795957[/C][C]0.027628[/C][C]-3.9975[/C][C]0.000189[/C][C]9.5e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57813&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57813&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)0.7055961692854371.7399580.40550.6866370.343319
Aand0.003664566428615700.0007954.60922.4e-051.2e-05
Y10.4592541704585870.1080164.25178.1e-054.1e-05
t-0.1104420937959570.027628-3.99750.0001899.5e-05







Multiple Linear Regression - Regression Statistics
Multiple R0.915561174940805
R-squared0.838252265058987
Adjusted R-squared0.829587207830004
F-TEST (value)96.7393801226384
F-TEST (DF numerator)3
F-TEST (DF denominator)56
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.84872942692069
Sum Squared Residuals454.454523477017

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.915561174940805 \tabularnewline
R-squared & 0.838252265058987 \tabularnewline
Adjusted R-squared & 0.829587207830004 \tabularnewline
F-TEST (value) & 96.7393801226384 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 56 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.84872942692069 \tabularnewline
Sum Squared Residuals & 454.454523477017 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57813&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.915561174940805[/C][/ROW]
[ROW][C]R-squared[/C][C]0.838252265058987[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.829587207830004[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]96.7393801226384[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]56[/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]2.84872942692069[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]454.454523477017[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57813&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57813&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 R0.915561174940805
R-squared0.838252265058987
Adjusted R-squared0.829587207830004
F-TEST (value)96.7393801226384
F-TEST (DF numerator)3
F-TEST (DF denominator)56
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.84872942692069
Sum Squared Residuals454.454523477017







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11919.062301788655-0.0623017886549878
22518.20565926741546.79434073258464
32120.82666599493490.173334005065065
42319.06166598178543.93833401821460
52320.4113551470562.58864485294399
61920.7081196748078-1.70811967480784
71819.0996699394888-1.09966993948877
81918.79444543438740.205554565612584
91919.3646339690027-0.364633969002720
102219.6159945187042.38400548129601
112320.97727441951352.02272558048650
122021.3740556707267-1.37405567072671
131419.6731229843739-5.67312298437385
141416.9392634875780-2.93926348757796
151417.0647461804563-3.06474618045629
161517.3041602436003-2.30416024360027
171117.7257139638709-6.72571396387092
181715.84535339954861.15464660045143
191618.7420821051572-2.74208210515717
202018.64943909857981.35056090142018
212421.00947063946872.99052936053127
222323.2711818630779-0.271181863077868
232023.0858986171851-3.08589861718511
242121.5154244956910-0.515424495690969
251921.5181182731708-2.51811827317085
262319.64287286743323.3571271325668
272321.85130129517031.14869870482973
282322.32960844379570.670391556204286
292322.61431654799740.385683452002612
302723.12106073210893.87893926789111
312625.07201672257140.927983277428581
321724.8363090426209-7.83630904262091
332421.15333136960442.84666863039556
342624.47288845537121.52711154462880
352424.7471739565002-0.747173956500248
362724.57679479034752.42320520965251
372726.23142323376770.768576766232311
382625.84408650062550.155913499374474
392425.0597932263112-1.05979322631125
402322.71042621603930.289573783960693
412322.48508925908180.514910740918223
422422.88306911159201.11693088840804
431722.02239103849-5.02239103848998
442118.7393122654132.26068773458701
451919.4684285172465-0.468428517246474
462217.98686748293504.01313251706498
472219.08349239626992.9165076037301
481819.6444721635249-1.64447216352491
491617.4897455106921-1.48974551069210
501415.3735548622730-1.37355486227298
511212.6157719235318-0.615771923531813
521411.63970118238362.36029881761639
531612.13741529866543.86258470133464
54810.1438472197813-2.1438472197813
5535.68740021630138-2.68740021630138
5602.72506570830577-2.72506570830577
5751.392898341664513.60710165833549
5813.23271873797160-2.23271873797160
5910.7688492612207640.231150738779236
6031.370688944154841.62931105584516

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 19 & 19.062301788655 & -0.0623017886549878 \tabularnewline
2 & 25 & 18.2056592674154 & 6.79434073258464 \tabularnewline
3 & 21 & 20.8266659949349 & 0.173334005065065 \tabularnewline
4 & 23 & 19.0616659817854 & 3.93833401821460 \tabularnewline
5 & 23 & 20.411355147056 & 2.58864485294399 \tabularnewline
6 & 19 & 20.7081196748078 & -1.70811967480784 \tabularnewline
7 & 18 & 19.0996699394888 & -1.09966993948877 \tabularnewline
8 & 19 & 18.7944454343874 & 0.205554565612584 \tabularnewline
9 & 19 & 19.3646339690027 & -0.364633969002720 \tabularnewline
10 & 22 & 19.615994518704 & 2.38400548129601 \tabularnewline
11 & 23 & 20.9772744195135 & 2.02272558048650 \tabularnewline
12 & 20 & 21.3740556707267 & -1.37405567072671 \tabularnewline
13 & 14 & 19.6731229843739 & -5.67312298437385 \tabularnewline
14 & 14 & 16.9392634875780 & -2.93926348757796 \tabularnewline
15 & 14 & 17.0647461804563 & -3.06474618045629 \tabularnewline
16 & 15 & 17.3041602436003 & -2.30416024360027 \tabularnewline
17 & 11 & 17.7257139638709 & -6.72571396387092 \tabularnewline
18 & 17 & 15.8453533995486 & 1.15464660045143 \tabularnewline
19 & 16 & 18.7420821051572 & -2.74208210515717 \tabularnewline
20 & 20 & 18.6494390985798 & 1.35056090142018 \tabularnewline
21 & 24 & 21.0094706394687 & 2.99052936053127 \tabularnewline
22 & 23 & 23.2711818630779 & -0.271181863077868 \tabularnewline
23 & 20 & 23.0858986171851 & -3.08589861718511 \tabularnewline
24 & 21 & 21.5154244956910 & -0.515424495690969 \tabularnewline
25 & 19 & 21.5181182731708 & -2.51811827317085 \tabularnewline
26 & 23 & 19.6428728674332 & 3.3571271325668 \tabularnewline
27 & 23 & 21.8513012951703 & 1.14869870482973 \tabularnewline
28 & 23 & 22.3296084437957 & 0.670391556204286 \tabularnewline
29 & 23 & 22.6143165479974 & 0.385683452002612 \tabularnewline
30 & 27 & 23.1210607321089 & 3.87893926789111 \tabularnewline
31 & 26 & 25.0720167225714 & 0.927983277428581 \tabularnewline
32 & 17 & 24.8363090426209 & -7.83630904262091 \tabularnewline
33 & 24 & 21.1533313696044 & 2.84666863039556 \tabularnewline
34 & 26 & 24.4728884553712 & 1.52711154462880 \tabularnewline
35 & 24 & 24.7471739565002 & -0.747173956500248 \tabularnewline
36 & 27 & 24.5767947903475 & 2.42320520965251 \tabularnewline
37 & 27 & 26.2314232337677 & 0.768576766232311 \tabularnewline
38 & 26 & 25.8440865006255 & 0.155913499374474 \tabularnewline
39 & 24 & 25.0597932263112 & -1.05979322631125 \tabularnewline
40 & 23 & 22.7104262160393 & 0.289573783960693 \tabularnewline
41 & 23 & 22.4850892590818 & 0.514910740918223 \tabularnewline
42 & 24 & 22.8830691115920 & 1.11693088840804 \tabularnewline
43 & 17 & 22.02239103849 & -5.02239103848998 \tabularnewline
44 & 21 & 18.739312265413 & 2.26068773458701 \tabularnewline
45 & 19 & 19.4684285172465 & -0.468428517246474 \tabularnewline
46 & 22 & 17.9868674829350 & 4.01313251706498 \tabularnewline
47 & 22 & 19.0834923962699 & 2.9165076037301 \tabularnewline
48 & 18 & 19.6444721635249 & -1.64447216352491 \tabularnewline
49 & 16 & 17.4897455106921 & -1.48974551069210 \tabularnewline
50 & 14 & 15.3735548622730 & -1.37355486227298 \tabularnewline
51 & 12 & 12.6157719235318 & -0.615771923531813 \tabularnewline
52 & 14 & 11.6397011823836 & 2.36029881761639 \tabularnewline
53 & 16 & 12.1374152986654 & 3.86258470133464 \tabularnewline
54 & 8 & 10.1438472197813 & -2.1438472197813 \tabularnewline
55 & 3 & 5.68740021630138 & -2.68740021630138 \tabularnewline
56 & 0 & 2.72506570830577 & -2.72506570830577 \tabularnewline
57 & 5 & 1.39289834166451 & 3.60710165833549 \tabularnewline
58 & 1 & 3.23271873797160 & -2.23271873797160 \tabularnewline
59 & 1 & 0.768849261220764 & 0.231150738779236 \tabularnewline
60 & 3 & 1.37068894415484 & 1.62931105584516 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57813&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]19[/C][C]19.062301788655[/C][C]-0.0623017886549878[/C][/ROW]
[ROW][C]2[/C][C]25[/C][C]18.2056592674154[/C][C]6.79434073258464[/C][/ROW]
[ROW][C]3[/C][C]21[/C][C]20.8266659949349[/C][C]0.173334005065065[/C][/ROW]
[ROW][C]4[/C][C]23[/C][C]19.0616659817854[/C][C]3.93833401821460[/C][/ROW]
[ROW][C]5[/C][C]23[/C][C]20.411355147056[/C][C]2.58864485294399[/C][/ROW]
[ROW][C]6[/C][C]19[/C][C]20.7081196748078[/C][C]-1.70811967480784[/C][/ROW]
[ROW][C]7[/C][C]18[/C][C]19.0996699394888[/C][C]-1.09966993948877[/C][/ROW]
[ROW][C]8[/C][C]19[/C][C]18.7944454343874[/C][C]0.205554565612584[/C][/ROW]
[ROW][C]9[/C][C]19[/C][C]19.3646339690027[/C][C]-0.364633969002720[/C][/ROW]
[ROW][C]10[/C][C]22[/C][C]19.615994518704[/C][C]2.38400548129601[/C][/ROW]
[ROW][C]11[/C][C]23[/C][C]20.9772744195135[/C][C]2.02272558048650[/C][/ROW]
[ROW][C]12[/C][C]20[/C][C]21.3740556707267[/C][C]-1.37405567072671[/C][/ROW]
[ROW][C]13[/C][C]14[/C][C]19.6731229843739[/C][C]-5.67312298437385[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]16.9392634875780[/C][C]-2.93926348757796[/C][/ROW]
[ROW][C]15[/C][C]14[/C][C]17.0647461804563[/C][C]-3.06474618045629[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]17.3041602436003[/C][C]-2.30416024360027[/C][/ROW]
[ROW][C]17[/C][C]11[/C][C]17.7257139638709[/C][C]-6.72571396387092[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]15.8453533995486[/C][C]1.15464660045143[/C][/ROW]
[ROW][C]19[/C][C]16[/C][C]18.7420821051572[/C][C]-2.74208210515717[/C][/ROW]
[ROW][C]20[/C][C]20[/C][C]18.6494390985798[/C][C]1.35056090142018[/C][/ROW]
[ROW][C]21[/C][C]24[/C][C]21.0094706394687[/C][C]2.99052936053127[/C][/ROW]
[ROW][C]22[/C][C]23[/C][C]23.2711818630779[/C][C]-0.271181863077868[/C][/ROW]
[ROW][C]23[/C][C]20[/C][C]23.0858986171851[/C][C]-3.08589861718511[/C][/ROW]
[ROW][C]24[/C][C]21[/C][C]21.5154244956910[/C][C]-0.515424495690969[/C][/ROW]
[ROW][C]25[/C][C]19[/C][C]21.5181182731708[/C][C]-2.51811827317085[/C][/ROW]
[ROW][C]26[/C][C]23[/C][C]19.6428728674332[/C][C]3.3571271325668[/C][/ROW]
[ROW][C]27[/C][C]23[/C][C]21.8513012951703[/C][C]1.14869870482973[/C][/ROW]
[ROW][C]28[/C][C]23[/C][C]22.3296084437957[/C][C]0.670391556204286[/C][/ROW]
[ROW][C]29[/C][C]23[/C][C]22.6143165479974[/C][C]0.385683452002612[/C][/ROW]
[ROW][C]30[/C][C]27[/C][C]23.1210607321089[/C][C]3.87893926789111[/C][/ROW]
[ROW][C]31[/C][C]26[/C][C]25.0720167225714[/C][C]0.927983277428581[/C][/ROW]
[ROW][C]32[/C][C]17[/C][C]24.8363090426209[/C][C]-7.83630904262091[/C][/ROW]
[ROW][C]33[/C][C]24[/C][C]21.1533313696044[/C][C]2.84666863039556[/C][/ROW]
[ROW][C]34[/C][C]26[/C][C]24.4728884553712[/C][C]1.52711154462880[/C][/ROW]
[ROW][C]35[/C][C]24[/C][C]24.7471739565002[/C][C]-0.747173956500248[/C][/ROW]
[ROW][C]36[/C][C]27[/C][C]24.5767947903475[/C][C]2.42320520965251[/C][/ROW]
[ROW][C]37[/C][C]27[/C][C]26.2314232337677[/C][C]0.768576766232311[/C][/ROW]
[ROW][C]38[/C][C]26[/C][C]25.8440865006255[/C][C]0.155913499374474[/C][/ROW]
[ROW][C]39[/C][C]24[/C][C]25.0597932263112[/C][C]-1.05979322631125[/C][/ROW]
[ROW][C]40[/C][C]23[/C][C]22.7104262160393[/C][C]0.289573783960693[/C][/ROW]
[ROW][C]41[/C][C]23[/C][C]22.4850892590818[/C][C]0.514910740918223[/C][/ROW]
[ROW][C]42[/C][C]24[/C][C]22.8830691115920[/C][C]1.11693088840804[/C][/ROW]
[ROW][C]43[/C][C]17[/C][C]22.02239103849[/C][C]-5.02239103848998[/C][/ROW]
[ROW][C]44[/C][C]21[/C][C]18.739312265413[/C][C]2.26068773458701[/C][/ROW]
[ROW][C]45[/C][C]19[/C][C]19.4684285172465[/C][C]-0.468428517246474[/C][/ROW]
[ROW][C]46[/C][C]22[/C][C]17.9868674829350[/C][C]4.01313251706498[/C][/ROW]
[ROW][C]47[/C][C]22[/C][C]19.0834923962699[/C][C]2.9165076037301[/C][/ROW]
[ROW][C]48[/C][C]18[/C][C]19.6444721635249[/C][C]-1.64447216352491[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]17.4897455106921[/C][C]-1.48974551069210[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.3735548622730[/C][C]-1.37355486227298[/C][/ROW]
[ROW][C]51[/C][C]12[/C][C]12.6157719235318[/C][C]-0.615771923531813[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]11.6397011823836[/C][C]2.36029881761639[/C][/ROW]
[ROW][C]53[/C][C]16[/C][C]12.1374152986654[/C][C]3.86258470133464[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]10.1438472197813[/C][C]-2.1438472197813[/C][/ROW]
[ROW][C]55[/C][C]3[/C][C]5.68740021630138[/C][C]-2.68740021630138[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]2.72506570830577[/C][C]-2.72506570830577[/C][/ROW]
[ROW][C]57[/C][C]5[/C][C]1.39289834166451[/C][C]3.60710165833549[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]3.23271873797160[/C][C]-2.23271873797160[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]0.768849261220764[/C][C]0.231150738779236[/C][/ROW]
[ROW][C]60[/C][C]3[/C][C]1.37068894415484[/C][C]1.62931105584516[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57813&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57813&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
11919.062301788655-0.0623017886549878
22518.20565926741546.79434073258464
32120.82666599493490.173334005065065
42319.06166598178543.93833401821460
52320.4113551470562.58864485294399
61920.7081196748078-1.70811967480784
71819.0996699394888-1.09966993948877
81918.79444543438740.205554565612584
91919.3646339690027-0.364633969002720
102219.6159945187042.38400548129601
112320.97727441951352.02272558048650
122021.3740556707267-1.37405567072671
131419.6731229843739-5.67312298437385
141416.9392634875780-2.93926348757796
151417.0647461804563-3.06474618045629
161517.3041602436003-2.30416024360027
171117.7257139638709-6.72571396387092
181715.84535339954861.15464660045143
191618.7420821051572-2.74208210515717
202018.64943909857981.35056090142018
212421.00947063946872.99052936053127
222323.2711818630779-0.271181863077868
232023.0858986171851-3.08589861718511
242121.5154244956910-0.515424495690969
251921.5181182731708-2.51811827317085
262319.64287286743323.3571271325668
272321.85130129517031.14869870482973
282322.32960844379570.670391556204286
292322.61431654799740.385683452002612
302723.12106073210893.87893926789111
312625.07201672257140.927983277428581
321724.8363090426209-7.83630904262091
332421.15333136960442.84666863039556
342624.47288845537121.52711154462880
352424.7471739565002-0.747173956500248
362724.57679479034752.42320520965251
372726.23142323376770.768576766232311
382625.84408650062550.155913499374474
392425.0597932263112-1.05979322631125
402322.71042621603930.289573783960693
412322.48508925908180.514910740918223
422422.88306911159201.11693088840804
431722.02239103849-5.02239103848998
442118.7393122654132.26068773458701
451919.4684285172465-0.468428517246474
462217.98686748293504.01313251706498
472219.08349239626992.9165076037301
481819.6444721635249-1.64447216352491
491617.4897455106921-1.48974551069210
501415.3735548622730-1.37355486227298
511212.6157719235318-0.615771923531813
521411.63970118238362.36029881761639
531612.13741529866543.86258470133464
54810.1438472197813-2.1438472197813
5535.68740021630138-2.68740021630138
5602.72506570830577-2.72506570830577
5751.392898341664513.60710165833549
5813.23271873797160-2.23271873797160
5910.7688492612207640.231150738779236
6031.370688944154841.62931105584516







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.6125066590604280.7749866818791450.387493340939572
80.4509645258906420.9019290517812830.549035474109358
90.3065536622007750.613107324401550.693446337799225
100.4275999164068980.8551998328137950.572400083593102
110.4913528273225830.9827056546451650.508647172677418
120.4382730103742840.8765460207485670.561726989625716
130.6821093365614360.6357813268771290.317890663438564
140.5906134395145660.8187731209708680.409386560485434
150.5034059051703040.9931881896593920.496594094829696
160.4265004720197480.8530009440394960.573499527980252
170.5382079035626950.923584192874610.461792096437305
180.6959068856955070.6081862286089860.304093114304493
190.6980262138282880.6039475723434250.301973786171712
200.7968282117302610.4063435765394780.203171788269739
210.8794940425587630.2410119148824740.120505957441237
220.8344458523670380.3311082952659230.165554147632962
230.8283096921460750.3433806157078490.171690307853925
240.7898198439882130.4203603120235750.210180156011787
250.7811465552059960.4377068895880080.218853444794004
260.8575222885808720.2849554228382560.142477711419128
270.8197523596919070.3604952806161870.180247640308093
280.7692483281733830.4615033436532350.230751671826617
290.7084232833775550.5831534332448910.291576716622445
300.8033490289435210.3933019421129580.196650971056479
310.8217608795904980.3564782408190040.178239120409502
320.975894272398320.04821145520335810.0241057276016790
330.976235665285710.04752866942858010.0237643347142900
340.9661640173171050.06767196536579010.0338359826828951
350.9477197579040890.1045604841918230.0522802420959114
360.9372252243237160.1255495513525680.0627747756762838
370.909767577179540.1804648456409210.0902324228204607
380.8709069691003870.2581860617992260.129093030899613
390.8267808843710450.3464382312579090.173219115628955
400.7705009890920460.4589980218159090.229499010907954
410.7006648677526410.5986702644947180.299335132247359
420.6232539267933310.7534921464133380.376746073206669
430.7796407434318660.4407185131362690.220359256568134
440.7272644057106410.5454711885787190.272735594289359
450.6624419693174130.6751160613651740.337558030682587
460.6966240884396540.6067518231206920.303375911560346
470.805593780918160.3888124381636780.194406219081839
480.7398896429925530.5202207140148940.260110357007447
490.7452109917523180.5095780164953630.254789008247682
500.846696284408060.306607431183880.15330371559194
510.8324507670012890.3350984659974220.167549232998711
520.7911150997136580.4177698005726840.208884900286342
530.6951658145121060.6096683709757870.304834185487894

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.612506659060428 & 0.774986681879145 & 0.387493340939572 \tabularnewline
8 & 0.450964525890642 & 0.901929051781283 & 0.549035474109358 \tabularnewline
9 & 0.306553662200775 & 0.61310732440155 & 0.693446337799225 \tabularnewline
10 & 0.427599916406898 & 0.855199832813795 & 0.572400083593102 \tabularnewline
11 & 0.491352827322583 & 0.982705654645165 & 0.508647172677418 \tabularnewline
12 & 0.438273010374284 & 0.876546020748567 & 0.561726989625716 \tabularnewline
13 & 0.682109336561436 & 0.635781326877129 & 0.317890663438564 \tabularnewline
14 & 0.590613439514566 & 0.818773120970868 & 0.409386560485434 \tabularnewline
15 & 0.503405905170304 & 0.993188189659392 & 0.496594094829696 \tabularnewline
16 & 0.426500472019748 & 0.853000944039496 & 0.573499527980252 \tabularnewline
17 & 0.538207903562695 & 0.92358419287461 & 0.461792096437305 \tabularnewline
18 & 0.695906885695507 & 0.608186228608986 & 0.304093114304493 \tabularnewline
19 & 0.698026213828288 & 0.603947572343425 & 0.301973786171712 \tabularnewline
20 & 0.796828211730261 & 0.406343576539478 & 0.203171788269739 \tabularnewline
21 & 0.879494042558763 & 0.241011914882474 & 0.120505957441237 \tabularnewline
22 & 0.834445852367038 & 0.331108295265923 & 0.165554147632962 \tabularnewline
23 & 0.828309692146075 & 0.343380615707849 & 0.171690307853925 \tabularnewline
24 & 0.789819843988213 & 0.420360312023575 & 0.210180156011787 \tabularnewline
25 & 0.781146555205996 & 0.437706889588008 & 0.218853444794004 \tabularnewline
26 & 0.857522288580872 & 0.284955422838256 & 0.142477711419128 \tabularnewline
27 & 0.819752359691907 & 0.360495280616187 & 0.180247640308093 \tabularnewline
28 & 0.769248328173383 & 0.461503343653235 & 0.230751671826617 \tabularnewline
29 & 0.708423283377555 & 0.583153433244891 & 0.291576716622445 \tabularnewline
30 & 0.803349028943521 & 0.393301942112958 & 0.196650971056479 \tabularnewline
31 & 0.821760879590498 & 0.356478240819004 & 0.178239120409502 \tabularnewline
32 & 0.97589427239832 & 0.0482114552033581 & 0.0241057276016790 \tabularnewline
33 & 0.97623566528571 & 0.0475286694285801 & 0.0237643347142900 \tabularnewline
34 & 0.966164017317105 & 0.0676719653657901 & 0.0338359826828951 \tabularnewline
35 & 0.947719757904089 & 0.104560484191823 & 0.0522802420959114 \tabularnewline
36 & 0.937225224323716 & 0.125549551352568 & 0.0627747756762838 \tabularnewline
37 & 0.90976757717954 & 0.180464845640921 & 0.0902324228204607 \tabularnewline
38 & 0.870906969100387 & 0.258186061799226 & 0.129093030899613 \tabularnewline
39 & 0.826780884371045 & 0.346438231257909 & 0.173219115628955 \tabularnewline
40 & 0.770500989092046 & 0.458998021815909 & 0.229499010907954 \tabularnewline
41 & 0.700664867752641 & 0.598670264494718 & 0.299335132247359 \tabularnewline
42 & 0.623253926793331 & 0.753492146413338 & 0.376746073206669 \tabularnewline
43 & 0.779640743431866 & 0.440718513136269 & 0.220359256568134 \tabularnewline
44 & 0.727264405710641 & 0.545471188578719 & 0.272735594289359 \tabularnewline
45 & 0.662441969317413 & 0.675116061365174 & 0.337558030682587 \tabularnewline
46 & 0.696624088439654 & 0.606751823120692 & 0.303375911560346 \tabularnewline
47 & 0.80559378091816 & 0.388812438163678 & 0.194406219081839 \tabularnewline
48 & 0.739889642992553 & 0.520220714014894 & 0.260110357007447 \tabularnewline
49 & 0.745210991752318 & 0.509578016495363 & 0.254789008247682 \tabularnewline
50 & 0.84669628440806 & 0.30660743118388 & 0.15330371559194 \tabularnewline
51 & 0.832450767001289 & 0.335098465997422 & 0.167549232998711 \tabularnewline
52 & 0.791115099713658 & 0.417769800572684 & 0.208884900286342 \tabularnewline
53 & 0.695165814512106 & 0.609668370975787 & 0.304834185487894 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57813&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]7[/C][C]0.612506659060428[/C][C]0.774986681879145[/C][C]0.387493340939572[/C][/ROW]
[ROW][C]8[/C][C]0.450964525890642[/C][C]0.901929051781283[/C][C]0.549035474109358[/C][/ROW]
[ROW][C]9[/C][C]0.306553662200775[/C][C]0.61310732440155[/C][C]0.693446337799225[/C][/ROW]
[ROW][C]10[/C][C]0.427599916406898[/C][C]0.855199832813795[/C][C]0.572400083593102[/C][/ROW]
[ROW][C]11[/C][C]0.491352827322583[/C][C]0.982705654645165[/C][C]0.508647172677418[/C][/ROW]
[ROW][C]12[/C][C]0.438273010374284[/C][C]0.876546020748567[/C][C]0.561726989625716[/C][/ROW]
[ROW][C]13[/C][C]0.682109336561436[/C][C]0.635781326877129[/C][C]0.317890663438564[/C][/ROW]
[ROW][C]14[/C][C]0.590613439514566[/C][C]0.818773120970868[/C][C]0.409386560485434[/C][/ROW]
[ROW][C]15[/C][C]0.503405905170304[/C][C]0.993188189659392[/C][C]0.496594094829696[/C][/ROW]
[ROW][C]16[/C][C]0.426500472019748[/C][C]0.853000944039496[/C][C]0.573499527980252[/C][/ROW]
[ROW][C]17[/C][C]0.538207903562695[/C][C]0.92358419287461[/C][C]0.461792096437305[/C][/ROW]
[ROW][C]18[/C][C]0.695906885695507[/C][C]0.608186228608986[/C][C]0.304093114304493[/C][/ROW]
[ROW][C]19[/C][C]0.698026213828288[/C][C]0.603947572343425[/C][C]0.301973786171712[/C][/ROW]
[ROW][C]20[/C][C]0.796828211730261[/C][C]0.406343576539478[/C][C]0.203171788269739[/C][/ROW]
[ROW][C]21[/C][C]0.879494042558763[/C][C]0.241011914882474[/C][C]0.120505957441237[/C][/ROW]
[ROW][C]22[/C][C]0.834445852367038[/C][C]0.331108295265923[/C][C]0.165554147632962[/C][/ROW]
[ROW][C]23[/C][C]0.828309692146075[/C][C]0.343380615707849[/C][C]0.171690307853925[/C][/ROW]
[ROW][C]24[/C][C]0.789819843988213[/C][C]0.420360312023575[/C][C]0.210180156011787[/C][/ROW]
[ROW][C]25[/C][C]0.781146555205996[/C][C]0.437706889588008[/C][C]0.218853444794004[/C][/ROW]
[ROW][C]26[/C][C]0.857522288580872[/C][C]0.284955422838256[/C][C]0.142477711419128[/C][/ROW]
[ROW][C]27[/C][C]0.819752359691907[/C][C]0.360495280616187[/C][C]0.180247640308093[/C][/ROW]
[ROW][C]28[/C][C]0.769248328173383[/C][C]0.461503343653235[/C][C]0.230751671826617[/C][/ROW]
[ROW][C]29[/C][C]0.708423283377555[/C][C]0.583153433244891[/C][C]0.291576716622445[/C][/ROW]
[ROW][C]30[/C][C]0.803349028943521[/C][C]0.393301942112958[/C][C]0.196650971056479[/C][/ROW]
[ROW][C]31[/C][C]0.821760879590498[/C][C]0.356478240819004[/C][C]0.178239120409502[/C][/ROW]
[ROW][C]32[/C][C]0.97589427239832[/C][C]0.0482114552033581[/C][C]0.0241057276016790[/C][/ROW]
[ROW][C]33[/C][C]0.97623566528571[/C][C]0.0475286694285801[/C][C]0.0237643347142900[/C][/ROW]
[ROW][C]34[/C][C]0.966164017317105[/C][C]0.0676719653657901[/C][C]0.0338359826828951[/C][/ROW]
[ROW][C]35[/C][C]0.947719757904089[/C][C]0.104560484191823[/C][C]0.0522802420959114[/C][/ROW]
[ROW][C]36[/C][C]0.937225224323716[/C][C]0.125549551352568[/C][C]0.0627747756762838[/C][/ROW]
[ROW][C]37[/C][C]0.90976757717954[/C][C]0.180464845640921[/C][C]0.0902324228204607[/C][/ROW]
[ROW][C]38[/C][C]0.870906969100387[/C][C]0.258186061799226[/C][C]0.129093030899613[/C][/ROW]
[ROW][C]39[/C][C]0.826780884371045[/C][C]0.346438231257909[/C][C]0.173219115628955[/C][/ROW]
[ROW][C]40[/C][C]0.770500989092046[/C][C]0.458998021815909[/C][C]0.229499010907954[/C][/ROW]
[ROW][C]41[/C][C]0.700664867752641[/C][C]0.598670264494718[/C][C]0.299335132247359[/C][/ROW]
[ROW][C]42[/C][C]0.623253926793331[/C][C]0.753492146413338[/C][C]0.376746073206669[/C][/ROW]
[ROW][C]43[/C][C]0.779640743431866[/C][C]0.440718513136269[/C][C]0.220359256568134[/C][/ROW]
[ROW][C]44[/C][C]0.727264405710641[/C][C]0.545471188578719[/C][C]0.272735594289359[/C][/ROW]
[ROW][C]45[/C][C]0.662441969317413[/C][C]0.675116061365174[/C][C]0.337558030682587[/C][/ROW]
[ROW][C]46[/C][C]0.696624088439654[/C][C]0.606751823120692[/C][C]0.303375911560346[/C][/ROW]
[ROW][C]47[/C][C]0.80559378091816[/C][C]0.388812438163678[/C][C]0.194406219081839[/C][/ROW]
[ROW][C]48[/C][C]0.739889642992553[/C][C]0.520220714014894[/C][C]0.260110357007447[/C][/ROW]
[ROW][C]49[/C][C]0.745210991752318[/C][C]0.509578016495363[/C][C]0.254789008247682[/C][/ROW]
[ROW][C]50[/C][C]0.84669628440806[/C][C]0.30660743118388[/C][C]0.15330371559194[/C][/ROW]
[ROW][C]51[/C][C]0.832450767001289[/C][C]0.335098465997422[/C][C]0.167549232998711[/C][/ROW]
[ROW][C]52[/C][C]0.791115099713658[/C][C]0.417769800572684[/C][C]0.208884900286342[/C][/ROW]
[ROW][C]53[/C][C]0.695165814512106[/C][C]0.609668370975787[/C][C]0.304834185487894[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57813&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57813&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
70.6125066590604280.7749866818791450.387493340939572
80.4509645258906420.9019290517812830.549035474109358
90.3065536622007750.613107324401550.693446337799225
100.4275999164068980.8551998328137950.572400083593102
110.4913528273225830.9827056546451650.508647172677418
120.4382730103742840.8765460207485670.561726989625716
130.6821093365614360.6357813268771290.317890663438564
140.5906134395145660.8187731209708680.409386560485434
150.5034059051703040.9931881896593920.496594094829696
160.4265004720197480.8530009440394960.573499527980252
170.5382079035626950.923584192874610.461792096437305
180.6959068856955070.6081862286089860.304093114304493
190.6980262138282880.6039475723434250.301973786171712
200.7968282117302610.4063435765394780.203171788269739
210.8794940425587630.2410119148824740.120505957441237
220.8344458523670380.3311082952659230.165554147632962
230.8283096921460750.3433806157078490.171690307853925
240.7898198439882130.4203603120235750.210180156011787
250.7811465552059960.4377068895880080.218853444794004
260.8575222885808720.2849554228382560.142477711419128
270.8197523596919070.3604952806161870.180247640308093
280.7692483281733830.4615033436532350.230751671826617
290.7084232833775550.5831534332448910.291576716622445
300.8033490289435210.3933019421129580.196650971056479
310.8217608795904980.3564782408190040.178239120409502
320.975894272398320.04821145520335810.0241057276016790
330.976235665285710.04752866942858010.0237643347142900
340.9661640173171050.06767196536579010.0338359826828951
350.9477197579040890.1045604841918230.0522802420959114
360.9372252243237160.1255495513525680.0627747756762838
370.909767577179540.1804648456409210.0902324228204607
380.8709069691003870.2581860617992260.129093030899613
390.8267808843710450.3464382312579090.173219115628955
400.7705009890920460.4589980218159090.229499010907954
410.7006648677526410.5986702644947180.299335132247359
420.6232539267933310.7534921464133380.376746073206669
430.7796407434318660.4407185131362690.220359256568134
440.7272644057106410.5454711885787190.272735594289359
450.6624419693174130.6751160613651740.337558030682587
460.6966240884396540.6067518231206920.303375911560346
470.805593780918160.3888124381636780.194406219081839
480.7398896429925530.5202207140148940.260110357007447
490.7452109917523180.5095780164953630.254789008247682
500.846696284408060.306607431183880.15330371559194
510.8324507670012890.3350984659974220.167549232998711
520.7911150997136580.4177698005726840.208884900286342
530.6951658145121060.6096683709757870.304834185487894







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0425531914893617OK
10% type I error level30.0638297872340425OK

\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 & 2 & 0.0425531914893617 & OK \tabularnewline
10% type I error level & 3 & 0.0638297872340425 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57813&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]2[/C][C]0.0425531914893617[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.0638297872340425[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57813&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57813&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 level20.0425531914893617OK
10% type I error level30.0638297872340425OK



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