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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 12:59:48 -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/20/t1258747290oklm4uczg9pex4p.htm/, Retrieved Fri, 29 Mar 2024 07:24:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58450, Retrieved Fri, 29 Mar 2024 07:24:37 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact134
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2009-11-17 18:05:01] [78d53abea600e0825abda35dbfc51d4c]
- R  D    [Multiple Regression] [Model 5] [2009-11-20 19:59:48] [18c0746232b29e9668aa6bedcb8dd698] [Current]
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Dataseries X:
20.3	18	13.2	15.7	12.6
12.8	23	20.3	13.2	15.7
8	20	12.8	20.3	13.2
0.9	20	8	12.8	20.3
3.6	15	0.9	8	12.8
14.1	17	3.6	0.9	8
21.7	16	14.1	3.6	0.9
24.5	15	21.7	14.1	3.6
18.9	10	24.5	21.7	14.1
13.9	13	18.9	24.5	21.7
11	10	13.9	18.9	24.5
5.8	19	11	13.9	18.9
15.5	21	5.8	11	13.9
22.4	17	15.5	5.8	11
31.7	16	22.4	15.5	5.8
30.3	17	31.7	22.4	15.5
31.4	14	30.3	31.7	22.4
20.2	18	31.4	30.3	31.7
19.7	17	20.2	31.4	30.3
10.8	14	19.7	20.2	31.4
13.2	15	10.8	19.7	20.2
15.1	16	13.2	10.8	19.7
15.6	11	15.1	13.2	10.8
15.5	15	15.6	15.1	13.2
12.7	13	15.5	15.6	15.1
10.9	17	12.7	15.5	15.6
10	16	10.9	12.7	15.5
9.1	9	10	10.9	12.7
10.3	17	9.1	10	10.9
16.9	15	10.3	9.1	10
22	12	16.9	10.3	9.1
27.6	12	22	16.9	10.3
28.9	12	27.6	22	16.9
31	12	28.9	27.6	22
32.9	4	31	28.9	27.6
38.1	7	32.9	31	28.9
28.8	4	38.1	32.9	31
29	3	28.8	38.1	32.9
21.8	3	29	28.8	38.1
28.8	0	21.8	29	28.8
25.6	5	28.8	21.8	29
28.2	3	25.6	28.8	21.8
20.2	4	28.2	25.6	28.8
17.9	3	20.2	28.2	25.6
16.3	10	17.9	20.2	28.2
13.2	4	16.3	17.9	20.2
8.1	1	13.2	16.3	17.9
4.5	1	8.1	13.2	16.3
-0.1	8	4.5	8.1	13.2
0	5	-0.1	4.5	8.1
2.3	4	0	-0.1	4.5
2.8	0	2.3	0	-0.1
2.9	2	2.8	2.3	0
0.1	7	2.9	2.8	2.3
3.5	6	0.1	2.9	2.8
8.6	9	3.5	0.1	2.9
13.8	10	8.6	3.5	0.1




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=58450&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=58450&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58450&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
Y[t] = + 4.44986041010845 -0.00197808566653005X[t] + 0.994282176708847`Yt-1`[t] + 0.147066157402592`Yt-2`[t] -0.382511912582321`Yt-3`[t] + 0.261349548161055M1[t] -0.168476823791646M2[t] -0.471950531563776M3[t] -0.510604588431602M4[t] + 0.146131731851114M5[t] + 0.88606344273659M6[t] + 1.08792158458782M7[t] + 0.0320781246262102M8[t] + 0.147371388525378M9[t] + 0.201264697862938M10[t] -0.309509853446752M11[t] -0.0185575755601159t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  4.44986041010845 -0.00197808566653005X[t] +  0.994282176708847`Yt-1`[t] +  0.147066157402592`Yt-2`[t] -0.382511912582321`Yt-3`[t] +  0.261349548161055M1[t] -0.168476823791646M2[t] -0.471950531563776M3[t] -0.510604588431602M4[t] +  0.146131731851114M5[t] +  0.88606344273659M6[t] +  1.08792158458782M7[t] +  0.0320781246262102M8[t] +  0.147371388525378M9[t] +  0.201264697862938M10[t] -0.309509853446752M11[t] -0.0185575755601159t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58450&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  4.44986041010845 -0.00197808566653005X[t] +  0.994282176708847`Yt-1`[t] +  0.147066157402592`Yt-2`[t] -0.382511912582321`Yt-3`[t] +  0.261349548161055M1[t] -0.168476823791646M2[t] -0.471950531563776M3[t] -0.510604588431602M4[t] +  0.146131731851114M5[t] +  0.88606344273659M6[t] +  1.08792158458782M7[t] +  0.0320781246262102M8[t] +  0.147371388525378M9[t] +  0.201264697862938M10[t] -0.309509853446752M11[t] -0.0185575755601159t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58450&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58450&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
Y[t] = + 4.44986041010845 -0.00197808566653005X[t] + 0.994282176708847`Yt-1`[t] + 0.147066157402592`Yt-2`[t] -0.382511912582321`Yt-3`[t] + 0.261349548161055M1[t] -0.168476823791646M2[t] -0.471950531563776M3[t] -0.510604588431602M4[t] + 0.146131731851114M5[t] + 0.88606344273659M6[t] + 1.08792158458782M7[t] + 0.0320781246262102M8[t] + 0.147371388525378M9[t] + 0.201264697862938M10[t] -0.309509853446752M11[t] -0.0185575755601159t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.449860410108456.1023280.72920.4701240.235062
X-0.001978085666530050.228791-0.00860.9931450.496572
`Yt-1`0.9942821767088470.1466366.780600
`Yt-2`0.1470661574025920.2160180.68080.4999160.249958
`Yt-3`-0.3825119125823210.149498-2.55860.0143980.007199
M10.2613495481610553.4929710.07480.940730.470365
M2-0.1684768237916463.498967-0.04820.9618360.480918
M3-0.4719505315637763.495366-0.1350.8932720.446636
M4-0.5106045884316023.535994-0.14440.8859080.442954
M50.1461317318511143.4986110.04180.9668910.483446
M60.886063442736593.505570.25280.8017490.400875
M71.087921584587823.5099690.310.7582070.379103
M80.03207812462621023.5193040.00910.9927730.496386
M90.1473713885253783.5329180.04170.9669340.483467
M100.2012646978629383.6737790.05480.9565830.478292
M11-0.3095098534467523.792425-0.08160.9353620.467681
t-0.01855757556011590.083933-0.22110.8261390.41307

\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) & 4.44986041010845 & 6.102328 & 0.7292 & 0.470124 & 0.235062 \tabularnewline
X & -0.00197808566653005 & 0.228791 & -0.0086 & 0.993145 & 0.496572 \tabularnewline
`Yt-1` & 0.994282176708847 & 0.146636 & 6.7806 & 0 & 0 \tabularnewline
`Yt-2` & 0.147066157402592 & 0.216018 & 0.6808 & 0.499916 & 0.249958 \tabularnewline
`Yt-3` & -0.382511912582321 & 0.149498 & -2.5586 & 0.014398 & 0.007199 \tabularnewline
M1 & 0.261349548161055 & 3.492971 & 0.0748 & 0.94073 & 0.470365 \tabularnewline
M2 & -0.168476823791646 & 3.498967 & -0.0482 & 0.961836 & 0.480918 \tabularnewline
M3 & -0.471950531563776 & 3.495366 & -0.135 & 0.893272 & 0.446636 \tabularnewline
M4 & -0.510604588431602 & 3.535994 & -0.1444 & 0.885908 & 0.442954 \tabularnewline
M5 & 0.146131731851114 & 3.498611 & 0.0418 & 0.966891 & 0.483446 \tabularnewline
M6 & 0.88606344273659 & 3.50557 & 0.2528 & 0.801749 & 0.400875 \tabularnewline
M7 & 1.08792158458782 & 3.509969 & 0.31 & 0.758207 & 0.379103 \tabularnewline
M8 & 0.0320781246262102 & 3.519304 & 0.0091 & 0.992773 & 0.496386 \tabularnewline
M9 & 0.147371388525378 & 3.532918 & 0.0417 & 0.966934 & 0.483467 \tabularnewline
M10 & 0.201264697862938 & 3.673779 & 0.0548 & 0.956583 & 0.478292 \tabularnewline
M11 & -0.309509853446752 & 3.792425 & -0.0816 & 0.935362 & 0.467681 \tabularnewline
t & -0.0185575755601159 & 0.083933 & -0.2211 & 0.826139 & 0.41307 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58450&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]4.44986041010845[/C][C]6.102328[/C][C]0.7292[/C][C]0.470124[/C][C]0.235062[/C][/ROW]
[ROW][C]X[/C][C]-0.00197808566653005[/C][C]0.228791[/C][C]-0.0086[/C][C]0.993145[/C][C]0.496572[/C][/ROW]
[ROW][C]`Yt-1`[/C][C]0.994282176708847[/C][C]0.146636[/C][C]6.7806[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Yt-2`[/C][C]0.147066157402592[/C][C]0.216018[/C][C]0.6808[/C][C]0.499916[/C][C]0.249958[/C][/ROW]
[ROW][C]`Yt-3`[/C][C]-0.382511912582321[/C][C]0.149498[/C][C]-2.5586[/C][C]0.014398[/C][C]0.007199[/C][/ROW]
[ROW][C]M1[/C][C]0.261349548161055[/C][C]3.492971[/C][C]0.0748[/C][C]0.94073[/C][C]0.470365[/C][/ROW]
[ROW][C]M2[/C][C]-0.168476823791646[/C][C]3.498967[/C][C]-0.0482[/C][C]0.961836[/C][C]0.480918[/C][/ROW]
[ROW][C]M3[/C][C]-0.471950531563776[/C][C]3.495366[/C][C]-0.135[/C][C]0.893272[/C][C]0.446636[/C][/ROW]
[ROW][C]M4[/C][C]-0.510604588431602[/C][C]3.535994[/C][C]-0.1444[/C][C]0.885908[/C][C]0.442954[/C][/ROW]
[ROW][C]M5[/C][C]0.146131731851114[/C][C]3.498611[/C][C]0.0418[/C][C]0.966891[/C][C]0.483446[/C][/ROW]
[ROW][C]M6[/C][C]0.88606344273659[/C][C]3.50557[/C][C]0.2528[/C][C]0.801749[/C][C]0.400875[/C][/ROW]
[ROW][C]M7[/C][C]1.08792158458782[/C][C]3.509969[/C][C]0.31[/C][C]0.758207[/C][C]0.379103[/C][/ROW]
[ROW][C]M8[/C][C]0.0320781246262102[/C][C]3.519304[/C][C]0.0091[/C][C]0.992773[/C][C]0.496386[/C][/ROW]
[ROW][C]M9[/C][C]0.147371388525378[/C][C]3.532918[/C][C]0.0417[/C][C]0.966934[/C][C]0.483467[/C][/ROW]
[ROW][C]M10[/C][C]0.201264697862938[/C][C]3.673779[/C][C]0.0548[/C][C]0.956583[/C][C]0.478292[/C][/ROW]
[ROW][C]M11[/C][C]-0.309509853446752[/C][C]3.792425[/C][C]-0.0816[/C][C]0.935362[/C][C]0.467681[/C][/ROW]
[ROW][C]t[/C][C]-0.0185575755601159[/C][C]0.083933[/C][C]-0.2211[/C][C]0.826139[/C][C]0.41307[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58450&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58450&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)4.449860410108456.1023280.72920.4701240.235062
X-0.001978085666530050.228791-0.00860.9931450.496572
`Yt-1`0.9942821767088470.1466366.780600
`Yt-2`0.1470661574025920.2160180.68080.4999160.249958
`Yt-3`-0.3825119125823210.149498-2.55860.0143980.007199
M10.2613495481610553.4929710.07480.940730.470365
M2-0.1684768237916463.498967-0.04820.9618360.480918
M3-0.4719505315637763.495366-0.1350.8932720.446636
M4-0.5106045884316023.535994-0.14440.8859080.442954
M50.1461317318511143.4986110.04180.9668910.483446
M60.886063442736593.505570.25280.8017490.400875
M71.087921584587823.5099690.310.7582070.379103
M80.03207812462621023.5193040.00910.9927730.496386
M90.1473713885253783.5329180.04170.9669340.483467
M100.2012646978629383.6737790.05480.9565830.478292
M11-0.3095098534467523.792425-0.08160.9353620.467681
t-0.01855757556011590.083933-0.22110.8261390.41307







Multiple Linear Regression - Regression Statistics
Multiple R0.897242589915076
R-squared0.805044265157513
Adjusted R-squared0.727061971220518
F-TEST (value)10.3234237480618
F-TEST (DF numerator)16
F-TEST (DF denominator)40
p-value1.33000888080659e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.18760048847671
Sum Squared Residuals1076.44795312175

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.897242589915076 \tabularnewline
R-squared & 0.805044265157513 \tabularnewline
Adjusted R-squared & 0.727061971220518 \tabularnewline
F-TEST (value) & 10.3234237480618 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 40 \tabularnewline
p-value & 1.33000888080659e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5.18760048847671 \tabularnewline
Sum Squared Residuals & 1076.44795312175 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58450&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.897242589915076[/C][/ROW]
[ROW][C]R-squared[/C][C]0.805044265157513[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.727061971220518[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.3234237480618[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]40[/C][/ROW]
[ROW][C]p-value[/C][C]1.33000888080659e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]5.18760048847671[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1076.44795312175[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58450&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58450&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 R0.897242589915076
R-squared0.805044265157513
Adjusted R-squared0.727061971220518
F-TEST (value)10.3234237480618
F-TEST (DF numerator)16
F-TEST (DF denominator)40
p-value1.33000888080659e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.18760048847671
Sum Squared Residuals1076.44795312175







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
120.315.27086014595215.0291398540479
212.820.3185369022278-7.51853690222775
3814.5457730495929-6.54577304959294
40.95.8971762091086-4.9971762091086
53.61.648763716366011.95123628363399
614.15.842631020308948.25736897969105
721.719.58078573203102.11921426796903
824.526.5763198139180-2.07631981391795
918.926.5682637395198-7.66826373951976
1013.918.5343797318297-4.63437973182968
111111.1449671417302-0.144967141730218
125.89.94143425961047-4.14143425961047
1315.56.496070448436439.00392955156357
1422.416.04467648566086.3553235143392
1531.726.00077397951935.69922602048065
1630.332.4927994388465-2.19279943884652
1731.430.47330046020240.926699539797574
1820.228.5172192398622-8.31721923986218
1919.718.26382696343881.43617303656117
2010.814.6303150298127-3.83031502981268
2113.210.08656160199723.11343839800283
2215.111.38856362961743.71143637038260
2315.616.5155728665759-0.915572866575938
2415.516.6571509990182-1.15715099901824
2512.716.1512313700762-3.45123137007623
2610.912.7049824130811-1.80498241308110
271010.2216872478704-0.221687247870438
289.110.0897825279761-0.989782527976076
2910.310.3734445293143-0.0734445293143178
3016.912.50381462768514.39618537231489
312219.77605192746142.22394807253859
3227.624.28411233691313.31588766308688
3328.928.17428699453160.725713005468367
343128.37494928531522.62505071468475
3532.927.99855370902864.90144629097136
3638.129.98428130985098.11571869014908
3728.834.8794255409795-6.0794255409795
382925.2241668203283.77583317967199
3921.821.74421476306540.0557852369346515
4028.818.120879733829410.6791202661706
4125.624.57376457136621.02623542863384
4228.225.90094078496712.29905921503288
4320.225.5192018332702-5.31920183327018
4417.918.0989315992544-0.198931599254356
4516.313.72391144956262.57608855043742
4613.214.9021073532377-1.70210735323766
478.111.9409062826652-3.8409062826652
484.57.3171334315204-2.81713343152039
49-0.14.40241249455575-4.50241249455575
5000.80763737870233-0.80763737870233
512.31.287550959951931.01244904004807
522.85.29936209023939-2.49936209023940
532.96.73072672275109-3.83072672275109
540.16.73539432717664-6.63539432717664
553.53.96013354379861-0.460133543798610
568.65.810321220101882.78967877989812
5713.812.54697621438881.25302378561116

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 20.3 & 15.2708601459521 & 5.0291398540479 \tabularnewline
2 & 12.8 & 20.3185369022278 & -7.51853690222775 \tabularnewline
3 & 8 & 14.5457730495929 & -6.54577304959294 \tabularnewline
4 & 0.9 & 5.8971762091086 & -4.9971762091086 \tabularnewline
5 & 3.6 & 1.64876371636601 & 1.95123628363399 \tabularnewline
6 & 14.1 & 5.84263102030894 & 8.25736897969105 \tabularnewline
7 & 21.7 & 19.5807857320310 & 2.11921426796903 \tabularnewline
8 & 24.5 & 26.5763198139180 & -2.07631981391795 \tabularnewline
9 & 18.9 & 26.5682637395198 & -7.66826373951976 \tabularnewline
10 & 13.9 & 18.5343797318297 & -4.63437973182968 \tabularnewline
11 & 11 & 11.1449671417302 & -0.144967141730218 \tabularnewline
12 & 5.8 & 9.94143425961047 & -4.14143425961047 \tabularnewline
13 & 15.5 & 6.49607044843643 & 9.00392955156357 \tabularnewline
14 & 22.4 & 16.0446764856608 & 6.3553235143392 \tabularnewline
15 & 31.7 & 26.0007739795193 & 5.69922602048065 \tabularnewline
16 & 30.3 & 32.4927994388465 & -2.19279943884652 \tabularnewline
17 & 31.4 & 30.4733004602024 & 0.926699539797574 \tabularnewline
18 & 20.2 & 28.5172192398622 & -8.31721923986218 \tabularnewline
19 & 19.7 & 18.2638269634388 & 1.43617303656117 \tabularnewline
20 & 10.8 & 14.6303150298127 & -3.83031502981268 \tabularnewline
21 & 13.2 & 10.0865616019972 & 3.11343839800283 \tabularnewline
22 & 15.1 & 11.3885636296174 & 3.71143637038260 \tabularnewline
23 & 15.6 & 16.5155728665759 & -0.915572866575938 \tabularnewline
24 & 15.5 & 16.6571509990182 & -1.15715099901824 \tabularnewline
25 & 12.7 & 16.1512313700762 & -3.45123137007623 \tabularnewline
26 & 10.9 & 12.7049824130811 & -1.80498241308110 \tabularnewline
27 & 10 & 10.2216872478704 & -0.221687247870438 \tabularnewline
28 & 9.1 & 10.0897825279761 & -0.989782527976076 \tabularnewline
29 & 10.3 & 10.3734445293143 & -0.0734445293143178 \tabularnewline
30 & 16.9 & 12.5038146276851 & 4.39618537231489 \tabularnewline
31 & 22 & 19.7760519274614 & 2.22394807253859 \tabularnewline
32 & 27.6 & 24.2841123369131 & 3.31588766308688 \tabularnewline
33 & 28.9 & 28.1742869945316 & 0.725713005468367 \tabularnewline
34 & 31 & 28.3749492853152 & 2.62505071468475 \tabularnewline
35 & 32.9 & 27.9985537090286 & 4.90144629097136 \tabularnewline
36 & 38.1 & 29.9842813098509 & 8.11571869014908 \tabularnewline
37 & 28.8 & 34.8794255409795 & -6.0794255409795 \tabularnewline
38 & 29 & 25.224166820328 & 3.77583317967199 \tabularnewline
39 & 21.8 & 21.7442147630654 & 0.0557852369346515 \tabularnewline
40 & 28.8 & 18.1208797338294 & 10.6791202661706 \tabularnewline
41 & 25.6 & 24.5737645713662 & 1.02623542863384 \tabularnewline
42 & 28.2 & 25.9009407849671 & 2.29905921503288 \tabularnewline
43 & 20.2 & 25.5192018332702 & -5.31920183327018 \tabularnewline
44 & 17.9 & 18.0989315992544 & -0.198931599254356 \tabularnewline
45 & 16.3 & 13.7239114495626 & 2.57608855043742 \tabularnewline
46 & 13.2 & 14.9021073532377 & -1.70210735323766 \tabularnewline
47 & 8.1 & 11.9409062826652 & -3.8409062826652 \tabularnewline
48 & 4.5 & 7.3171334315204 & -2.81713343152039 \tabularnewline
49 & -0.1 & 4.40241249455575 & -4.50241249455575 \tabularnewline
50 & 0 & 0.80763737870233 & -0.80763737870233 \tabularnewline
51 & 2.3 & 1.28755095995193 & 1.01244904004807 \tabularnewline
52 & 2.8 & 5.29936209023939 & -2.49936209023940 \tabularnewline
53 & 2.9 & 6.73072672275109 & -3.83072672275109 \tabularnewline
54 & 0.1 & 6.73539432717664 & -6.63539432717664 \tabularnewline
55 & 3.5 & 3.96013354379861 & -0.460133543798610 \tabularnewline
56 & 8.6 & 5.81032122010188 & 2.78967877989812 \tabularnewline
57 & 13.8 & 12.5469762143888 & 1.25302378561116 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58450&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]20.3[/C][C]15.2708601459521[/C][C]5.0291398540479[/C][/ROW]
[ROW][C]2[/C][C]12.8[/C][C]20.3185369022278[/C][C]-7.51853690222775[/C][/ROW]
[ROW][C]3[/C][C]8[/C][C]14.5457730495929[/C][C]-6.54577304959294[/C][/ROW]
[ROW][C]4[/C][C]0.9[/C][C]5.8971762091086[/C][C]-4.9971762091086[/C][/ROW]
[ROW][C]5[/C][C]3.6[/C][C]1.64876371636601[/C][C]1.95123628363399[/C][/ROW]
[ROW][C]6[/C][C]14.1[/C][C]5.84263102030894[/C][C]8.25736897969105[/C][/ROW]
[ROW][C]7[/C][C]21.7[/C][C]19.5807857320310[/C][C]2.11921426796903[/C][/ROW]
[ROW][C]8[/C][C]24.5[/C][C]26.5763198139180[/C][C]-2.07631981391795[/C][/ROW]
[ROW][C]9[/C][C]18.9[/C][C]26.5682637395198[/C][C]-7.66826373951976[/C][/ROW]
[ROW][C]10[/C][C]13.9[/C][C]18.5343797318297[/C][C]-4.63437973182968[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]11.1449671417302[/C][C]-0.144967141730218[/C][/ROW]
[ROW][C]12[/C][C]5.8[/C][C]9.94143425961047[/C][C]-4.14143425961047[/C][/ROW]
[ROW][C]13[/C][C]15.5[/C][C]6.49607044843643[/C][C]9.00392955156357[/C][/ROW]
[ROW][C]14[/C][C]22.4[/C][C]16.0446764856608[/C][C]6.3553235143392[/C][/ROW]
[ROW][C]15[/C][C]31.7[/C][C]26.0007739795193[/C][C]5.69922602048065[/C][/ROW]
[ROW][C]16[/C][C]30.3[/C][C]32.4927994388465[/C][C]-2.19279943884652[/C][/ROW]
[ROW][C]17[/C][C]31.4[/C][C]30.4733004602024[/C][C]0.926699539797574[/C][/ROW]
[ROW][C]18[/C][C]20.2[/C][C]28.5172192398622[/C][C]-8.31721923986218[/C][/ROW]
[ROW][C]19[/C][C]19.7[/C][C]18.2638269634388[/C][C]1.43617303656117[/C][/ROW]
[ROW][C]20[/C][C]10.8[/C][C]14.6303150298127[/C][C]-3.83031502981268[/C][/ROW]
[ROW][C]21[/C][C]13.2[/C][C]10.0865616019972[/C][C]3.11343839800283[/C][/ROW]
[ROW][C]22[/C][C]15.1[/C][C]11.3885636296174[/C][C]3.71143637038260[/C][/ROW]
[ROW][C]23[/C][C]15.6[/C][C]16.5155728665759[/C][C]-0.915572866575938[/C][/ROW]
[ROW][C]24[/C][C]15.5[/C][C]16.6571509990182[/C][C]-1.15715099901824[/C][/ROW]
[ROW][C]25[/C][C]12.7[/C][C]16.1512313700762[/C][C]-3.45123137007623[/C][/ROW]
[ROW][C]26[/C][C]10.9[/C][C]12.7049824130811[/C][C]-1.80498241308110[/C][/ROW]
[ROW][C]27[/C][C]10[/C][C]10.2216872478704[/C][C]-0.221687247870438[/C][/ROW]
[ROW][C]28[/C][C]9.1[/C][C]10.0897825279761[/C][C]-0.989782527976076[/C][/ROW]
[ROW][C]29[/C][C]10.3[/C][C]10.3734445293143[/C][C]-0.0734445293143178[/C][/ROW]
[ROW][C]30[/C][C]16.9[/C][C]12.5038146276851[/C][C]4.39618537231489[/C][/ROW]
[ROW][C]31[/C][C]22[/C][C]19.7760519274614[/C][C]2.22394807253859[/C][/ROW]
[ROW][C]32[/C][C]27.6[/C][C]24.2841123369131[/C][C]3.31588766308688[/C][/ROW]
[ROW][C]33[/C][C]28.9[/C][C]28.1742869945316[/C][C]0.725713005468367[/C][/ROW]
[ROW][C]34[/C][C]31[/C][C]28.3749492853152[/C][C]2.62505071468475[/C][/ROW]
[ROW][C]35[/C][C]32.9[/C][C]27.9985537090286[/C][C]4.90144629097136[/C][/ROW]
[ROW][C]36[/C][C]38.1[/C][C]29.9842813098509[/C][C]8.11571869014908[/C][/ROW]
[ROW][C]37[/C][C]28.8[/C][C]34.8794255409795[/C][C]-6.0794255409795[/C][/ROW]
[ROW][C]38[/C][C]29[/C][C]25.224166820328[/C][C]3.77583317967199[/C][/ROW]
[ROW][C]39[/C][C]21.8[/C][C]21.7442147630654[/C][C]0.0557852369346515[/C][/ROW]
[ROW][C]40[/C][C]28.8[/C][C]18.1208797338294[/C][C]10.6791202661706[/C][/ROW]
[ROW][C]41[/C][C]25.6[/C][C]24.5737645713662[/C][C]1.02623542863384[/C][/ROW]
[ROW][C]42[/C][C]28.2[/C][C]25.9009407849671[/C][C]2.29905921503288[/C][/ROW]
[ROW][C]43[/C][C]20.2[/C][C]25.5192018332702[/C][C]-5.31920183327018[/C][/ROW]
[ROW][C]44[/C][C]17.9[/C][C]18.0989315992544[/C][C]-0.198931599254356[/C][/ROW]
[ROW][C]45[/C][C]16.3[/C][C]13.7239114495626[/C][C]2.57608855043742[/C][/ROW]
[ROW][C]46[/C][C]13.2[/C][C]14.9021073532377[/C][C]-1.70210735323766[/C][/ROW]
[ROW][C]47[/C][C]8.1[/C][C]11.9409062826652[/C][C]-3.8409062826652[/C][/ROW]
[ROW][C]48[/C][C]4.5[/C][C]7.3171334315204[/C][C]-2.81713343152039[/C][/ROW]
[ROW][C]49[/C][C]-0.1[/C][C]4.40241249455575[/C][C]-4.50241249455575[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.80763737870233[/C][C]-0.80763737870233[/C][/ROW]
[ROW][C]51[/C][C]2.3[/C][C]1.28755095995193[/C][C]1.01244904004807[/C][/ROW]
[ROW][C]52[/C][C]2.8[/C][C]5.29936209023939[/C][C]-2.49936209023940[/C][/ROW]
[ROW][C]53[/C][C]2.9[/C][C]6.73072672275109[/C][C]-3.83072672275109[/C][/ROW]
[ROW][C]54[/C][C]0.1[/C][C]6.73539432717664[/C][C]-6.63539432717664[/C][/ROW]
[ROW][C]55[/C][C]3.5[/C][C]3.96013354379861[/C][C]-0.460133543798610[/C][/ROW]
[ROW][C]56[/C][C]8.6[/C][C]5.81032122010188[/C][C]2.78967877989812[/C][/ROW]
[ROW][C]57[/C][C]13.8[/C][C]12.5469762143888[/C][C]1.25302378561116[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58450&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58450&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
120.315.27086014595215.0291398540479
212.820.3185369022278-7.51853690222775
3814.5457730495929-6.54577304959294
40.95.8971762091086-4.9971762091086
53.61.648763716366011.95123628363399
614.15.842631020308948.25736897969105
721.719.58078573203102.11921426796903
824.526.5763198139180-2.07631981391795
918.926.5682637395198-7.66826373951976
1013.918.5343797318297-4.63437973182968
111111.1449671417302-0.144967141730218
125.89.94143425961047-4.14143425961047
1315.56.496070448436439.00392955156357
1422.416.04467648566086.3553235143392
1531.726.00077397951935.69922602048065
1630.332.4927994388465-2.19279943884652
1731.430.47330046020240.926699539797574
1820.228.5172192398622-8.31721923986218
1919.718.26382696343881.43617303656117
2010.814.6303150298127-3.83031502981268
2113.210.08656160199723.11343839800283
2215.111.38856362961743.71143637038260
2315.616.5155728665759-0.915572866575938
2415.516.6571509990182-1.15715099901824
2512.716.1512313700762-3.45123137007623
2610.912.7049824130811-1.80498241308110
271010.2216872478704-0.221687247870438
289.110.0897825279761-0.989782527976076
2910.310.3734445293143-0.0734445293143178
3016.912.50381462768514.39618537231489
312219.77605192746142.22394807253859
3227.624.28411233691313.31588766308688
3328.928.17428699453160.725713005468367
343128.37494928531522.62505071468475
3532.927.99855370902864.90144629097136
3638.129.98428130985098.11571869014908
3728.834.8794255409795-6.0794255409795
382925.2241668203283.77583317967199
3921.821.74421476306540.0557852369346515
4028.818.120879733829410.6791202661706
4125.624.57376457136621.02623542863384
4228.225.90094078496712.29905921503288
4320.225.5192018332702-5.31920183327018
4417.918.0989315992544-0.198931599254356
4516.313.72391144956262.57608855043742
4613.214.9021073532377-1.70210735323766
478.111.9409062826652-3.8409062826652
484.57.3171334315204-2.81713343152039
49-0.14.40241249455575-4.50241249455575
5000.80763737870233-0.80763737870233
512.31.287550959951931.01244904004807
522.85.29936209023939-2.49936209023940
532.96.73072672275109-3.83072672275109
540.16.73539432717664-6.63539432717664
553.53.96013354379861-0.460133543798610
568.65.810321220101882.78967877989812
5713.812.54697621438881.25302378561116







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.4841799052857820.9683598105715640.515820094714218
210.4067432778179770.8134865556359550.593256722182023
220.311767772809810.623535545619620.68823222719019
230.6262220864991880.7475558270016250.373777913500812
240.6127515219698090.7744969560603810.387248478030191
250.91921241251420.1615751749716010.0807875874858007
260.9002588952211350.1994822095577310.0997411047788653
270.8806730119890750.2386539760218500.119326988010925
280.8797370688777820.2405258622444370.120262931122218
290.949307536460210.1013849270795790.0506924635397893
300.908111990814150.1837760183717020.091888009185851
310.8744642505780520.2510714988438950.125535749421948
320.8069661356717830.3860677286564350.193033864328218
330.7110644636035320.5778710727929370.288935536396468
340.8415181403920140.3169637192159710.158481859607986
350.7639484568767660.4721030862464690.236051543123234
360.854068893989040.291862212021920.14593110601096
370.7532624531800230.4934750936399530.246737546819977

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 & 0.484179905285782 & 0.968359810571564 & 0.515820094714218 \tabularnewline
21 & 0.406743277817977 & 0.813486555635955 & 0.593256722182023 \tabularnewline
22 & 0.31176777280981 & 0.62353554561962 & 0.68823222719019 \tabularnewline
23 & 0.626222086499188 & 0.747555827001625 & 0.373777913500812 \tabularnewline
24 & 0.612751521969809 & 0.774496956060381 & 0.387248478030191 \tabularnewline
25 & 0.9192124125142 & 0.161575174971601 & 0.0807875874858007 \tabularnewline
26 & 0.900258895221135 & 0.199482209557731 & 0.0997411047788653 \tabularnewline
27 & 0.880673011989075 & 0.238653976021850 & 0.119326988010925 \tabularnewline
28 & 0.879737068877782 & 0.240525862244437 & 0.120262931122218 \tabularnewline
29 & 0.94930753646021 & 0.101384927079579 & 0.0506924635397893 \tabularnewline
30 & 0.90811199081415 & 0.183776018371702 & 0.091888009185851 \tabularnewline
31 & 0.874464250578052 & 0.251071498843895 & 0.125535749421948 \tabularnewline
32 & 0.806966135671783 & 0.386067728656435 & 0.193033864328218 \tabularnewline
33 & 0.711064463603532 & 0.577871072792937 & 0.288935536396468 \tabularnewline
34 & 0.841518140392014 & 0.316963719215971 & 0.158481859607986 \tabularnewline
35 & 0.763948456876766 & 0.472103086246469 & 0.236051543123234 \tabularnewline
36 & 0.85406889398904 & 0.29186221202192 & 0.14593110601096 \tabularnewline
37 & 0.753262453180023 & 0.493475093639953 & 0.246737546819977 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58450&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]20[/C][C]0.484179905285782[/C][C]0.968359810571564[/C][C]0.515820094714218[/C][/ROW]
[ROW][C]21[/C][C]0.406743277817977[/C][C]0.813486555635955[/C][C]0.593256722182023[/C][/ROW]
[ROW][C]22[/C][C]0.31176777280981[/C][C]0.62353554561962[/C][C]0.68823222719019[/C][/ROW]
[ROW][C]23[/C][C]0.626222086499188[/C][C]0.747555827001625[/C][C]0.373777913500812[/C][/ROW]
[ROW][C]24[/C][C]0.612751521969809[/C][C]0.774496956060381[/C][C]0.387248478030191[/C][/ROW]
[ROW][C]25[/C][C]0.9192124125142[/C][C]0.161575174971601[/C][C]0.0807875874858007[/C][/ROW]
[ROW][C]26[/C][C]0.900258895221135[/C][C]0.199482209557731[/C][C]0.0997411047788653[/C][/ROW]
[ROW][C]27[/C][C]0.880673011989075[/C][C]0.238653976021850[/C][C]0.119326988010925[/C][/ROW]
[ROW][C]28[/C][C]0.879737068877782[/C][C]0.240525862244437[/C][C]0.120262931122218[/C][/ROW]
[ROW][C]29[/C][C]0.94930753646021[/C][C]0.101384927079579[/C][C]0.0506924635397893[/C][/ROW]
[ROW][C]30[/C][C]0.90811199081415[/C][C]0.183776018371702[/C][C]0.091888009185851[/C][/ROW]
[ROW][C]31[/C][C]0.874464250578052[/C][C]0.251071498843895[/C][C]0.125535749421948[/C][/ROW]
[ROW][C]32[/C][C]0.806966135671783[/C][C]0.386067728656435[/C][C]0.193033864328218[/C][/ROW]
[ROW][C]33[/C][C]0.711064463603532[/C][C]0.577871072792937[/C][C]0.288935536396468[/C][/ROW]
[ROW][C]34[/C][C]0.841518140392014[/C][C]0.316963719215971[/C][C]0.158481859607986[/C][/ROW]
[ROW][C]35[/C][C]0.763948456876766[/C][C]0.472103086246469[/C][C]0.236051543123234[/C][/ROW]
[ROW][C]36[/C][C]0.85406889398904[/C][C]0.29186221202192[/C][C]0.14593110601096[/C][/ROW]
[ROW][C]37[/C][C]0.753262453180023[/C][C]0.493475093639953[/C][C]0.246737546819977[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58450&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58450&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
200.4841799052857820.9683598105715640.515820094714218
210.4067432778179770.8134865556359550.593256722182023
220.311767772809810.623535545619620.68823222719019
230.6262220864991880.7475558270016250.373777913500812
240.6127515219698090.7744969560603810.387248478030191
250.91921241251420.1615751749716010.0807875874858007
260.9002588952211350.1994822095577310.0997411047788653
270.8806730119890750.2386539760218500.119326988010925
280.8797370688777820.2405258622444370.120262931122218
290.949307536460210.1013849270795790.0506924635397893
300.908111990814150.1837760183717020.091888009185851
310.8744642505780520.2510714988438950.125535749421948
320.8069661356717830.3860677286564350.193033864328218
330.7110644636035320.5778710727929370.288935536396468
340.8415181403920140.3169637192159710.158481859607986
350.7639484568767660.4721030862464690.236051543123234
360.854068893989040.291862212021920.14593110601096
370.7532624531800230.4934750936399530.246737546819977







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=58450&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=58450&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58450&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 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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')
}