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Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 11:09:38 -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/t12587406501k6sy44snvvmsi8.htm/, Retrieved Fri, 29 Mar 2024 14:32:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58383, Retrieved Fri, 29 Mar 2024 14:32:54 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact103
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Workshop 7 - mode...] [2009-11-20 18:09:38] [d904c6aa144b8c40108ebe5ec22fe1a0] [Current]
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Dataseries X:
269645	0
267037	0
258113	0
262813	0
267413	0
267366	0
264777	0
258863	0
254844	0
254868	0
277267	0
285351	0
286602	0
283042	0
276687	0
277915	0
277128	0
277103	0
275037	0
270150	0
267140	0
264993	0
287259	0
291186	0
292300	0
288186	0
281477	0
282656	0
280190	0
280408	0
276836	0
275216	0
274352	0
271311	0
289802	0
290726	0
292300	0
278506	0
269826	0
265861	0
269034	0
264176	0
255198	0
253353	0
246057	0
235372	0
258556	0
260993	0
254663	0
250643	0
243422	0
247105	0
248541	0
245039	0
237080	0
237085	0
225554	0
226839	0
247934	0
248333	1
246969	1
245098	1
246263	1
255765	1
264319	1
268347	1
273046	1
273963	1
267430	1
271993	1
292710	1
295881	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58383&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58383&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58383&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 293474.404040404 + 14540.1515151515x[t] -7702.2207792208M1[t] -12230.6226551226M2[t] -17885.1911976912M3[t] -14697.9264069264M4[t] -11813.4949494950M5[t] -12045.0634920635M6[t] -14989.7987012987M7[t] -16747.7005772006M8[t] -21823.7691197691M9[t] -23024.5043290043M10[t] -1199.73953823954M11[t] -466.098124098123t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  293474.404040404 +  14540.1515151515x[t] -7702.2207792208M1[t] -12230.6226551226M2[t] -17885.1911976912M3[t] -14697.9264069264M4[t] -11813.4949494950M5[t] -12045.0634920635M6[t] -14989.7987012987M7[t] -16747.7005772006M8[t] -21823.7691197691M9[t] -23024.5043290043M10[t] -1199.73953823954M11[t] -466.098124098123t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58383&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  293474.404040404 +  14540.1515151515x[t] -7702.2207792208M1[t] -12230.6226551226M2[t] -17885.1911976912M3[t] -14697.9264069264M4[t] -11813.4949494950M5[t] -12045.0634920635M6[t] -14989.7987012987M7[t] -16747.7005772006M8[t] -21823.7691197691M9[t] -23024.5043290043M10[t] -1199.73953823954M11[t] -466.098124098123t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58383&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58383&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] = + 293474.404040404 + 14540.1515151515x[t] -7702.2207792208M1[t] -12230.6226551226M2[t] -17885.1911976912M3[t] -14697.9264069264M4[t] -11813.4949494950M5[t] -12045.0634920635M6[t] -14989.7987012987M7[t] -16747.7005772006M8[t] -21823.7691197691M9[t] -23024.5043290043M10[t] -1199.73953823954M11[t] -466.098124098123t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)293474.4040404047110.32205141.274400
x14540.15151515156151.3152392.36370.0214620.010731
M1-7702.22077922088587.549656-0.89690.3734770.186739
M2-12230.62265512268580.662167-1.42540.159410.079705
M3-17885.19119769128575.301406-2.08570.0414190.020709
M4-14697.92640692648571.470239-1.71470.091730.045865
M5-11813.49494949508569.170716-1.37860.1733110.086656
M6-12045.06349206358568.404072-1.40580.1651320.082566
M7-14989.79870129878569.170716-1.74930.0855340.042767
M8-16747.70057720068571.470239-1.95390.0555420.027771
M9-21823.76911976918575.301406-2.5450.0136120.006806
M10-23024.50432900438580.662167-2.68330.0094840.004742
M11-1199.739538239548587.549656-0.13970.8893760.444688
t-466.098124098123114.622992-4.06640.0001467.3e-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) & 293474.404040404 & 7110.322051 & 41.2744 & 0 & 0 \tabularnewline
x & 14540.1515151515 & 6151.315239 & 2.3637 & 0.021462 & 0.010731 \tabularnewline
M1 & -7702.2207792208 & 8587.549656 & -0.8969 & 0.373477 & 0.186739 \tabularnewline
M2 & -12230.6226551226 & 8580.662167 & -1.4254 & 0.15941 & 0.079705 \tabularnewline
M3 & -17885.1911976912 & 8575.301406 & -2.0857 & 0.041419 & 0.020709 \tabularnewline
M4 & -14697.9264069264 & 8571.470239 & -1.7147 & 0.09173 & 0.045865 \tabularnewline
M5 & -11813.4949494950 & 8569.170716 & -1.3786 & 0.173311 & 0.086656 \tabularnewline
M6 & -12045.0634920635 & 8568.404072 & -1.4058 & 0.165132 & 0.082566 \tabularnewline
M7 & -14989.7987012987 & 8569.170716 & -1.7493 & 0.085534 & 0.042767 \tabularnewline
M8 & -16747.7005772006 & 8571.470239 & -1.9539 & 0.055542 & 0.027771 \tabularnewline
M9 & -21823.7691197691 & 8575.301406 & -2.545 & 0.013612 & 0.006806 \tabularnewline
M10 & -23024.5043290043 & 8580.662167 & -2.6833 & 0.009484 & 0.004742 \tabularnewline
M11 & -1199.73953823954 & 8587.549656 & -0.1397 & 0.889376 & 0.444688 \tabularnewline
t & -466.098124098123 & 114.622992 & -4.0664 & 0.000146 & 7.3e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58383&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]293474.404040404[/C][C]7110.322051[/C][C]41.2744[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]x[/C][C]14540.1515151515[/C][C]6151.315239[/C][C]2.3637[/C][C]0.021462[/C][C]0.010731[/C][/ROW]
[ROW][C]M1[/C][C]-7702.2207792208[/C][C]8587.549656[/C][C]-0.8969[/C][C]0.373477[/C][C]0.186739[/C][/ROW]
[ROW][C]M2[/C][C]-12230.6226551226[/C][C]8580.662167[/C][C]-1.4254[/C][C]0.15941[/C][C]0.079705[/C][/ROW]
[ROW][C]M3[/C][C]-17885.1911976912[/C][C]8575.301406[/C][C]-2.0857[/C][C]0.041419[/C][C]0.020709[/C][/ROW]
[ROW][C]M4[/C][C]-14697.9264069264[/C][C]8571.470239[/C][C]-1.7147[/C][C]0.09173[/C][C]0.045865[/C][/ROW]
[ROW][C]M5[/C][C]-11813.4949494950[/C][C]8569.170716[/C][C]-1.3786[/C][C]0.173311[/C][C]0.086656[/C][/ROW]
[ROW][C]M6[/C][C]-12045.0634920635[/C][C]8568.404072[/C][C]-1.4058[/C][C]0.165132[/C][C]0.082566[/C][/ROW]
[ROW][C]M7[/C][C]-14989.7987012987[/C][C]8569.170716[/C][C]-1.7493[/C][C]0.085534[/C][C]0.042767[/C][/ROW]
[ROW][C]M8[/C][C]-16747.7005772006[/C][C]8571.470239[/C][C]-1.9539[/C][C]0.055542[/C][C]0.027771[/C][/ROW]
[ROW][C]M9[/C][C]-21823.7691197691[/C][C]8575.301406[/C][C]-2.545[/C][C]0.013612[/C][C]0.006806[/C][/ROW]
[ROW][C]M10[/C][C]-23024.5043290043[/C][C]8580.662167[/C][C]-2.6833[/C][C]0.009484[/C][C]0.004742[/C][/ROW]
[ROW][C]M11[/C][C]-1199.73953823954[/C][C]8587.549656[/C][C]-0.1397[/C][C]0.889376[/C][C]0.444688[/C][/ROW]
[ROW][C]t[/C][C]-466.098124098123[/C][C]114.622992[/C][C]-4.0664[/C][C]0.000146[/C][C]7.3e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58383&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58383&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)293474.4040404047110.32205141.274400
x14540.15151515156151.3152392.36370.0214620.010731
M1-7702.22077922088587.549656-0.89690.3734770.186739
M2-12230.62265512268580.662167-1.42540.159410.079705
M3-17885.19119769128575.301406-2.08570.0414190.020709
M4-14697.92640692648571.470239-1.71470.091730.045865
M5-11813.49494949508569.170716-1.37860.1733110.086656
M6-12045.06349206358568.404072-1.40580.1651320.082566
M7-14989.79870129878569.170716-1.74930.0855340.042767
M8-16747.70057720068571.470239-1.95390.0555420.027771
M9-21823.76911976918575.301406-2.5450.0136120.006806
M10-23024.50432900438580.662167-2.68330.0094840.004742
M11-1199.739538239548587.549656-0.13970.8893760.444688
t-466.098124098123114.622992-4.06640.0001467.3e-05







Multiple Linear Regression - Regression Statistics
Multiple R0.604313480269635
R-squared0.365194782435599
Adjusted R-squared0.222910854360819
F-TEST (value)2.56666221812252
F-TEST (DF numerator)13
F-TEST (DF denominator)58
p-value0.00718756475655868
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation14782.3669355914
Sum Squared Residuals12674065588.671

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.604313480269635 \tabularnewline
R-squared & 0.365194782435599 \tabularnewline
Adjusted R-squared & 0.222910854360819 \tabularnewline
F-TEST (value) & 2.56666221812252 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0.00718756475655868 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 14782.3669355914 \tabularnewline
Sum Squared Residuals & 12674065588.671 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58383&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.604313480269635[/C][/ROW]
[ROW][C]R-squared[/C][C]0.365194782435599[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.222910854360819[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.56666221812252[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]0.00718756475655868[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]14782.3669355914[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]12674065588.671[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58383&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58383&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 R0.604313480269635
R-squared0.365194782435599
Adjusted R-squared0.222910854360819
F-TEST (value)2.56666221812252
F-TEST (DF numerator)13
F-TEST (DF denominator)58
p-value0.00718756475655868
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation14782.3669355914
Sum Squared Residuals12674065588.671







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1269645285306.085137085-15661.0851370854
2267037280311.585137085-13274.5851370851
3258113274190.918470418-16077.9184704185
4262813276912.085137085-14099.0851370852
5267413279330.418470418-11917.4184704185
6267366278632.751803752-11266.7518037518
7264777275221.918470418-10444.9184704184
8258863272997.918470418-14134.9184704184
9254844267455.751803752-12611.7518037518
10254868265788.918470418-10920.9184704185
11277267287147.585137085-9880.58513708513
12285351287881.226551227-2530.22655122652
13286602279712.9076479086889.0923520924
14283042274718.4076479088323.59235209234
15276687268597.7409812418089.25901875903
16277915271318.9076479086596.09235209236
17277128273737.2409812413390.75901875902
18277103273039.5743145744063.4256854257
19275037269628.7409812415408.25901875902
20270150267404.7409812412745.25901875902
21267140261862.5743145745277.4256854257
22264993260195.7409812414797.25901875903
23287259281554.4076479085704.59235209236
24291186282288.0490620498897.95093795095
25292300274119.7301587318180.2698412699
26288186269125.2301587319060.7698412698
27281477263004.56349206318472.4365079365
28282656265725.7301587316930.2698412698
29280190268144.06349206312045.9365079365
30280408267446.39682539712961.6031746032
31276836264035.56349206312800.4365079365
32275216261811.56349206313404.4365079365
33274352256269.39682539718082.6031746032
34271311254602.56349206316708.4365079365
35289802275961.2301587313840.7698412698
36290726276694.87157287214031.1284271284
37292300268526.55266955323773.4473304474
38278506263532.05266955314973.9473304473
39269826257411.38600288612414.613997114
40265861260132.5526695535728.44733044733
41269034262550.8860028866483.11399711399
42264176261853.2193362192322.78066378066
43255198258442.386002886-3244.38600288601
44253353256218.386002886-2865.38600288601
45246057250676.219336219-4619.21933621934
46235372249009.386002886-13637.386002886
47258556270368.052669553-11812.0526695527
48260993271101.694083694-10108.6940836941
49254663262933.375180375-8270.37518037515
50250643257938.875180375-7295.87518037519
51243422251818.208513709-8396.20851370851
52247105254539.375180375-7434.37518037519
53248541256957.708513709-8416.70851370853
54245039256260.041847042-11221.0418470419
55237080252849.208513709-15769.2085137085
56237085250625.208513709-13540.2085137085
57225554245083.041847042-19529.0418470419
58226839243416.208513709-16577.2085137085
59247934264774.875180375-16840.8751803752
60248333280048.668109668-31715.6681096681
61246969271880.349206349-24911.3492063492
62245098266885.849206349-21787.8492063492
63246263260765.182539683-14502.1825396825
64255765263486.349206349-7721.3492063492
65264319265904.682539683-1585.68253968255
66268347265207.0158730163139.98412698413
67273046261796.18253968311249.8174603175
68273963259572.18253968314390.8174603175
69267430254030.01587301613399.9841269841
70271993252363.18253968319629.8174603175
71292710273721.84920634918988.1507936508
72295881274455.49062049121425.5093795094

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 269645 & 285306.085137085 & -15661.0851370854 \tabularnewline
2 & 267037 & 280311.585137085 & -13274.5851370851 \tabularnewline
3 & 258113 & 274190.918470418 & -16077.9184704185 \tabularnewline
4 & 262813 & 276912.085137085 & -14099.0851370852 \tabularnewline
5 & 267413 & 279330.418470418 & -11917.4184704185 \tabularnewline
6 & 267366 & 278632.751803752 & -11266.7518037518 \tabularnewline
7 & 264777 & 275221.918470418 & -10444.9184704184 \tabularnewline
8 & 258863 & 272997.918470418 & -14134.9184704184 \tabularnewline
9 & 254844 & 267455.751803752 & -12611.7518037518 \tabularnewline
10 & 254868 & 265788.918470418 & -10920.9184704185 \tabularnewline
11 & 277267 & 287147.585137085 & -9880.58513708513 \tabularnewline
12 & 285351 & 287881.226551227 & -2530.22655122652 \tabularnewline
13 & 286602 & 279712.907647908 & 6889.0923520924 \tabularnewline
14 & 283042 & 274718.407647908 & 8323.59235209234 \tabularnewline
15 & 276687 & 268597.740981241 & 8089.25901875903 \tabularnewline
16 & 277915 & 271318.907647908 & 6596.09235209236 \tabularnewline
17 & 277128 & 273737.240981241 & 3390.75901875902 \tabularnewline
18 & 277103 & 273039.574314574 & 4063.4256854257 \tabularnewline
19 & 275037 & 269628.740981241 & 5408.25901875902 \tabularnewline
20 & 270150 & 267404.740981241 & 2745.25901875902 \tabularnewline
21 & 267140 & 261862.574314574 & 5277.4256854257 \tabularnewline
22 & 264993 & 260195.740981241 & 4797.25901875903 \tabularnewline
23 & 287259 & 281554.407647908 & 5704.59235209236 \tabularnewline
24 & 291186 & 282288.049062049 & 8897.95093795095 \tabularnewline
25 & 292300 & 274119.73015873 & 18180.2698412699 \tabularnewline
26 & 288186 & 269125.23015873 & 19060.7698412698 \tabularnewline
27 & 281477 & 263004.563492063 & 18472.4365079365 \tabularnewline
28 & 282656 & 265725.73015873 & 16930.2698412698 \tabularnewline
29 & 280190 & 268144.063492063 & 12045.9365079365 \tabularnewline
30 & 280408 & 267446.396825397 & 12961.6031746032 \tabularnewline
31 & 276836 & 264035.563492063 & 12800.4365079365 \tabularnewline
32 & 275216 & 261811.563492063 & 13404.4365079365 \tabularnewline
33 & 274352 & 256269.396825397 & 18082.6031746032 \tabularnewline
34 & 271311 & 254602.563492063 & 16708.4365079365 \tabularnewline
35 & 289802 & 275961.23015873 & 13840.7698412698 \tabularnewline
36 & 290726 & 276694.871572872 & 14031.1284271284 \tabularnewline
37 & 292300 & 268526.552669553 & 23773.4473304474 \tabularnewline
38 & 278506 & 263532.052669553 & 14973.9473304473 \tabularnewline
39 & 269826 & 257411.386002886 & 12414.613997114 \tabularnewline
40 & 265861 & 260132.552669553 & 5728.44733044733 \tabularnewline
41 & 269034 & 262550.886002886 & 6483.11399711399 \tabularnewline
42 & 264176 & 261853.219336219 & 2322.78066378066 \tabularnewline
43 & 255198 & 258442.386002886 & -3244.38600288601 \tabularnewline
44 & 253353 & 256218.386002886 & -2865.38600288601 \tabularnewline
45 & 246057 & 250676.219336219 & -4619.21933621934 \tabularnewline
46 & 235372 & 249009.386002886 & -13637.386002886 \tabularnewline
47 & 258556 & 270368.052669553 & -11812.0526695527 \tabularnewline
48 & 260993 & 271101.694083694 & -10108.6940836941 \tabularnewline
49 & 254663 & 262933.375180375 & -8270.37518037515 \tabularnewline
50 & 250643 & 257938.875180375 & -7295.87518037519 \tabularnewline
51 & 243422 & 251818.208513709 & -8396.20851370851 \tabularnewline
52 & 247105 & 254539.375180375 & -7434.37518037519 \tabularnewline
53 & 248541 & 256957.708513709 & -8416.70851370853 \tabularnewline
54 & 245039 & 256260.041847042 & -11221.0418470419 \tabularnewline
55 & 237080 & 252849.208513709 & -15769.2085137085 \tabularnewline
56 & 237085 & 250625.208513709 & -13540.2085137085 \tabularnewline
57 & 225554 & 245083.041847042 & -19529.0418470419 \tabularnewline
58 & 226839 & 243416.208513709 & -16577.2085137085 \tabularnewline
59 & 247934 & 264774.875180375 & -16840.8751803752 \tabularnewline
60 & 248333 & 280048.668109668 & -31715.6681096681 \tabularnewline
61 & 246969 & 271880.349206349 & -24911.3492063492 \tabularnewline
62 & 245098 & 266885.849206349 & -21787.8492063492 \tabularnewline
63 & 246263 & 260765.182539683 & -14502.1825396825 \tabularnewline
64 & 255765 & 263486.349206349 & -7721.3492063492 \tabularnewline
65 & 264319 & 265904.682539683 & -1585.68253968255 \tabularnewline
66 & 268347 & 265207.015873016 & 3139.98412698413 \tabularnewline
67 & 273046 & 261796.182539683 & 11249.8174603175 \tabularnewline
68 & 273963 & 259572.182539683 & 14390.8174603175 \tabularnewline
69 & 267430 & 254030.015873016 & 13399.9841269841 \tabularnewline
70 & 271993 & 252363.182539683 & 19629.8174603175 \tabularnewline
71 & 292710 & 273721.849206349 & 18988.1507936508 \tabularnewline
72 & 295881 & 274455.490620491 & 21425.5093795094 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58383&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]269645[/C][C]285306.085137085[/C][C]-15661.0851370854[/C][/ROW]
[ROW][C]2[/C][C]267037[/C][C]280311.585137085[/C][C]-13274.5851370851[/C][/ROW]
[ROW][C]3[/C][C]258113[/C][C]274190.918470418[/C][C]-16077.9184704185[/C][/ROW]
[ROW][C]4[/C][C]262813[/C][C]276912.085137085[/C][C]-14099.0851370852[/C][/ROW]
[ROW][C]5[/C][C]267413[/C][C]279330.418470418[/C][C]-11917.4184704185[/C][/ROW]
[ROW][C]6[/C][C]267366[/C][C]278632.751803752[/C][C]-11266.7518037518[/C][/ROW]
[ROW][C]7[/C][C]264777[/C][C]275221.918470418[/C][C]-10444.9184704184[/C][/ROW]
[ROW][C]8[/C][C]258863[/C][C]272997.918470418[/C][C]-14134.9184704184[/C][/ROW]
[ROW][C]9[/C][C]254844[/C][C]267455.751803752[/C][C]-12611.7518037518[/C][/ROW]
[ROW][C]10[/C][C]254868[/C][C]265788.918470418[/C][C]-10920.9184704185[/C][/ROW]
[ROW][C]11[/C][C]277267[/C][C]287147.585137085[/C][C]-9880.58513708513[/C][/ROW]
[ROW][C]12[/C][C]285351[/C][C]287881.226551227[/C][C]-2530.22655122652[/C][/ROW]
[ROW][C]13[/C][C]286602[/C][C]279712.907647908[/C][C]6889.0923520924[/C][/ROW]
[ROW][C]14[/C][C]283042[/C][C]274718.407647908[/C][C]8323.59235209234[/C][/ROW]
[ROW][C]15[/C][C]276687[/C][C]268597.740981241[/C][C]8089.25901875903[/C][/ROW]
[ROW][C]16[/C][C]277915[/C][C]271318.907647908[/C][C]6596.09235209236[/C][/ROW]
[ROW][C]17[/C][C]277128[/C][C]273737.240981241[/C][C]3390.75901875902[/C][/ROW]
[ROW][C]18[/C][C]277103[/C][C]273039.574314574[/C][C]4063.4256854257[/C][/ROW]
[ROW][C]19[/C][C]275037[/C][C]269628.740981241[/C][C]5408.25901875902[/C][/ROW]
[ROW][C]20[/C][C]270150[/C][C]267404.740981241[/C][C]2745.25901875902[/C][/ROW]
[ROW][C]21[/C][C]267140[/C][C]261862.574314574[/C][C]5277.4256854257[/C][/ROW]
[ROW][C]22[/C][C]264993[/C][C]260195.740981241[/C][C]4797.25901875903[/C][/ROW]
[ROW][C]23[/C][C]287259[/C][C]281554.407647908[/C][C]5704.59235209236[/C][/ROW]
[ROW][C]24[/C][C]291186[/C][C]282288.049062049[/C][C]8897.95093795095[/C][/ROW]
[ROW][C]25[/C][C]292300[/C][C]274119.73015873[/C][C]18180.2698412699[/C][/ROW]
[ROW][C]26[/C][C]288186[/C][C]269125.23015873[/C][C]19060.7698412698[/C][/ROW]
[ROW][C]27[/C][C]281477[/C][C]263004.563492063[/C][C]18472.4365079365[/C][/ROW]
[ROW][C]28[/C][C]282656[/C][C]265725.73015873[/C][C]16930.2698412698[/C][/ROW]
[ROW][C]29[/C][C]280190[/C][C]268144.063492063[/C][C]12045.9365079365[/C][/ROW]
[ROW][C]30[/C][C]280408[/C][C]267446.396825397[/C][C]12961.6031746032[/C][/ROW]
[ROW][C]31[/C][C]276836[/C][C]264035.563492063[/C][C]12800.4365079365[/C][/ROW]
[ROW][C]32[/C][C]275216[/C][C]261811.563492063[/C][C]13404.4365079365[/C][/ROW]
[ROW][C]33[/C][C]274352[/C][C]256269.396825397[/C][C]18082.6031746032[/C][/ROW]
[ROW][C]34[/C][C]271311[/C][C]254602.563492063[/C][C]16708.4365079365[/C][/ROW]
[ROW][C]35[/C][C]289802[/C][C]275961.23015873[/C][C]13840.7698412698[/C][/ROW]
[ROW][C]36[/C][C]290726[/C][C]276694.871572872[/C][C]14031.1284271284[/C][/ROW]
[ROW][C]37[/C][C]292300[/C][C]268526.552669553[/C][C]23773.4473304474[/C][/ROW]
[ROW][C]38[/C][C]278506[/C][C]263532.052669553[/C][C]14973.9473304473[/C][/ROW]
[ROW][C]39[/C][C]269826[/C][C]257411.386002886[/C][C]12414.613997114[/C][/ROW]
[ROW][C]40[/C][C]265861[/C][C]260132.552669553[/C][C]5728.44733044733[/C][/ROW]
[ROW][C]41[/C][C]269034[/C][C]262550.886002886[/C][C]6483.11399711399[/C][/ROW]
[ROW][C]42[/C][C]264176[/C][C]261853.219336219[/C][C]2322.78066378066[/C][/ROW]
[ROW][C]43[/C][C]255198[/C][C]258442.386002886[/C][C]-3244.38600288601[/C][/ROW]
[ROW][C]44[/C][C]253353[/C][C]256218.386002886[/C][C]-2865.38600288601[/C][/ROW]
[ROW][C]45[/C][C]246057[/C][C]250676.219336219[/C][C]-4619.21933621934[/C][/ROW]
[ROW][C]46[/C][C]235372[/C][C]249009.386002886[/C][C]-13637.386002886[/C][/ROW]
[ROW][C]47[/C][C]258556[/C][C]270368.052669553[/C][C]-11812.0526695527[/C][/ROW]
[ROW][C]48[/C][C]260993[/C][C]271101.694083694[/C][C]-10108.6940836941[/C][/ROW]
[ROW][C]49[/C][C]254663[/C][C]262933.375180375[/C][C]-8270.37518037515[/C][/ROW]
[ROW][C]50[/C][C]250643[/C][C]257938.875180375[/C][C]-7295.87518037519[/C][/ROW]
[ROW][C]51[/C][C]243422[/C][C]251818.208513709[/C][C]-8396.20851370851[/C][/ROW]
[ROW][C]52[/C][C]247105[/C][C]254539.375180375[/C][C]-7434.37518037519[/C][/ROW]
[ROW][C]53[/C][C]248541[/C][C]256957.708513709[/C][C]-8416.70851370853[/C][/ROW]
[ROW][C]54[/C][C]245039[/C][C]256260.041847042[/C][C]-11221.0418470419[/C][/ROW]
[ROW][C]55[/C][C]237080[/C][C]252849.208513709[/C][C]-15769.2085137085[/C][/ROW]
[ROW][C]56[/C][C]237085[/C][C]250625.208513709[/C][C]-13540.2085137085[/C][/ROW]
[ROW][C]57[/C][C]225554[/C][C]245083.041847042[/C][C]-19529.0418470419[/C][/ROW]
[ROW][C]58[/C][C]226839[/C][C]243416.208513709[/C][C]-16577.2085137085[/C][/ROW]
[ROW][C]59[/C][C]247934[/C][C]264774.875180375[/C][C]-16840.8751803752[/C][/ROW]
[ROW][C]60[/C][C]248333[/C][C]280048.668109668[/C][C]-31715.6681096681[/C][/ROW]
[ROW][C]61[/C][C]246969[/C][C]271880.349206349[/C][C]-24911.3492063492[/C][/ROW]
[ROW][C]62[/C][C]245098[/C][C]266885.849206349[/C][C]-21787.8492063492[/C][/ROW]
[ROW][C]63[/C][C]246263[/C][C]260765.182539683[/C][C]-14502.1825396825[/C][/ROW]
[ROW][C]64[/C][C]255765[/C][C]263486.349206349[/C][C]-7721.3492063492[/C][/ROW]
[ROW][C]65[/C][C]264319[/C][C]265904.682539683[/C][C]-1585.68253968255[/C][/ROW]
[ROW][C]66[/C][C]268347[/C][C]265207.015873016[/C][C]3139.98412698413[/C][/ROW]
[ROW][C]67[/C][C]273046[/C][C]261796.182539683[/C][C]11249.8174603175[/C][/ROW]
[ROW][C]68[/C][C]273963[/C][C]259572.182539683[/C][C]14390.8174603175[/C][/ROW]
[ROW][C]69[/C][C]267430[/C][C]254030.015873016[/C][C]13399.9841269841[/C][/ROW]
[ROW][C]70[/C][C]271993[/C][C]252363.182539683[/C][C]19629.8174603175[/C][/ROW]
[ROW][C]71[/C][C]292710[/C][C]273721.849206349[/C][C]18988.1507936508[/C][/ROW]
[ROW][C]72[/C][C]295881[/C][C]274455.490620491[/C][C]21425.5093795094[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58383&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58383&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
1269645285306.085137085-15661.0851370854
2267037280311.585137085-13274.5851370851
3258113274190.918470418-16077.9184704185
4262813276912.085137085-14099.0851370852
5267413279330.418470418-11917.4184704185
6267366278632.751803752-11266.7518037518
7264777275221.918470418-10444.9184704184
8258863272997.918470418-14134.9184704184
9254844267455.751803752-12611.7518037518
10254868265788.918470418-10920.9184704185
11277267287147.585137085-9880.58513708513
12285351287881.226551227-2530.22655122652
13286602279712.9076479086889.0923520924
14283042274718.4076479088323.59235209234
15276687268597.7409812418089.25901875903
16277915271318.9076479086596.09235209236
17277128273737.2409812413390.75901875902
18277103273039.5743145744063.4256854257
19275037269628.7409812415408.25901875902
20270150267404.7409812412745.25901875902
21267140261862.5743145745277.4256854257
22264993260195.7409812414797.25901875903
23287259281554.4076479085704.59235209236
24291186282288.0490620498897.95093795095
25292300274119.7301587318180.2698412699
26288186269125.2301587319060.7698412698
27281477263004.56349206318472.4365079365
28282656265725.7301587316930.2698412698
29280190268144.06349206312045.9365079365
30280408267446.39682539712961.6031746032
31276836264035.56349206312800.4365079365
32275216261811.56349206313404.4365079365
33274352256269.39682539718082.6031746032
34271311254602.56349206316708.4365079365
35289802275961.2301587313840.7698412698
36290726276694.87157287214031.1284271284
37292300268526.55266955323773.4473304474
38278506263532.05266955314973.9473304473
39269826257411.38600288612414.613997114
40265861260132.5526695535728.44733044733
41269034262550.8860028866483.11399711399
42264176261853.2193362192322.78066378066
43255198258442.386002886-3244.38600288601
44253353256218.386002886-2865.38600288601
45246057250676.219336219-4619.21933621934
46235372249009.386002886-13637.386002886
47258556270368.052669553-11812.0526695527
48260993271101.694083694-10108.6940836941
49254663262933.375180375-8270.37518037515
50250643257938.875180375-7295.87518037519
51243422251818.208513709-8396.20851370851
52247105254539.375180375-7434.37518037519
53248541256957.708513709-8416.70851370853
54245039256260.041847042-11221.0418470419
55237080252849.208513709-15769.2085137085
56237085250625.208513709-13540.2085137085
57225554245083.041847042-19529.0418470419
58226839243416.208513709-16577.2085137085
59247934264774.875180375-16840.8751803752
60248333280048.668109668-31715.6681096681
61246969271880.349206349-24911.3492063492
62245098266885.849206349-21787.8492063492
63246263260765.182539683-14502.1825396825
64255765263486.349206349-7721.3492063492
65264319265904.682539683-1585.68253968255
66268347265207.0158730163139.98412698413
67273046261796.18253968311249.8174603175
68273963259572.18253968314390.8174603175
69267430254030.01587301613399.9841269841
70271993252363.18253968319629.8174603175
71292710273721.84920634918988.1507936508
72295881274455.49062049121425.5093795094







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.01056438937950710.02112877875901420.989435610620493
180.004129552823207480.008259105646414960.995870447176793
190.001194970166060710.002389940332121430.99880502983394
200.0002825918667691840.0005651837335383680.99971740813323
215.62148872296459e-050.0001124297744592920.99994378511277
221.54180663869641e-053.08361327739283e-050.999984581933613
234.26377260924846e-068.52754521849692e-060.99999573622739
243.66984776907621e-067.33969553815242e-060.999996330152231
251.19437668518472e-062.38875337036944e-060.999998805623315
264.26474606810519e-078.52949213621037e-070.999999573525393
271.02210862865224e-072.04421725730448e-070.999999897789137
283.3776797790584e-086.7553595581168e-080.999999966223202
294.83771713586087e-089.67543427172173e-080.999999951622829
303.45875257246162e-086.91750514492324e-080.999999965412474
312.74860752128265e-085.4972150425653e-080.999999972513925
327.12427047817952e-091.42485409563590e-080.99999999287573
331.48713334622535e-092.97426669245070e-090.999999998512867
343.29161973301676e-106.58323946603352e-100.999999999670838
351.49480577153677e-102.98961154307353e-100.99999999985052
364.72108591561327e-109.44217183122653e-100.999999999527891
371.54227989749339e-093.08455979498677e-090.99999999845772
381.17587064016743e-072.35174128033486e-070.999999882412936
392.05708651646353e-064.11417303292706e-060.999997942913484
403.90471129930782e-057.80942259861563e-050.999960952887007
410.0001356158962956620.0002712317925913250.999864384103704
420.0005685395836993820.001137079167398760.9994314604163
430.002478905909405870.004957811818811740.997521094090594
440.004596791468463780.009193582936927550.995403208531536
450.01212302288955780.02424604577911560.987876977110442
460.03010700429643940.06021400859287880.96989299570356
470.06201653142994030.1240330628598810.93798346857006
480.1637138633049770.3274277266099530.836286136695023
490.3324464777967110.6648929555934230.667553522203289
500.571050950936480.8578980981270390.428949049063519
510.7503224200083470.4993551599833060.249677579991653
520.8831879778304190.2336240443391620.116812022169581
530.9593975311840960.08120493763180730.0406024688159036
540.9939339993748350.01213200125033070.00606600062516535
550.988039871572950.02392025685410210.0119601284270511

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.0105643893795071 & 0.0211287787590142 & 0.989435610620493 \tabularnewline
18 & 0.00412955282320748 & 0.00825910564641496 & 0.995870447176793 \tabularnewline
19 & 0.00119497016606071 & 0.00238994033212143 & 0.99880502983394 \tabularnewline
20 & 0.000282591866769184 & 0.000565183733538368 & 0.99971740813323 \tabularnewline
21 & 5.62148872296459e-05 & 0.000112429774459292 & 0.99994378511277 \tabularnewline
22 & 1.54180663869641e-05 & 3.08361327739283e-05 & 0.999984581933613 \tabularnewline
23 & 4.26377260924846e-06 & 8.52754521849692e-06 & 0.99999573622739 \tabularnewline
24 & 3.66984776907621e-06 & 7.33969553815242e-06 & 0.999996330152231 \tabularnewline
25 & 1.19437668518472e-06 & 2.38875337036944e-06 & 0.999998805623315 \tabularnewline
26 & 4.26474606810519e-07 & 8.52949213621037e-07 & 0.999999573525393 \tabularnewline
27 & 1.02210862865224e-07 & 2.04421725730448e-07 & 0.999999897789137 \tabularnewline
28 & 3.3776797790584e-08 & 6.7553595581168e-08 & 0.999999966223202 \tabularnewline
29 & 4.83771713586087e-08 & 9.67543427172173e-08 & 0.999999951622829 \tabularnewline
30 & 3.45875257246162e-08 & 6.91750514492324e-08 & 0.999999965412474 \tabularnewline
31 & 2.74860752128265e-08 & 5.4972150425653e-08 & 0.999999972513925 \tabularnewline
32 & 7.12427047817952e-09 & 1.42485409563590e-08 & 0.99999999287573 \tabularnewline
33 & 1.48713334622535e-09 & 2.97426669245070e-09 & 0.999999998512867 \tabularnewline
34 & 3.29161973301676e-10 & 6.58323946603352e-10 & 0.999999999670838 \tabularnewline
35 & 1.49480577153677e-10 & 2.98961154307353e-10 & 0.99999999985052 \tabularnewline
36 & 4.72108591561327e-10 & 9.44217183122653e-10 & 0.999999999527891 \tabularnewline
37 & 1.54227989749339e-09 & 3.08455979498677e-09 & 0.99999999845772 \tabularnewline
38 & 1.17587064016743e-07 & 2.35174128033486e-07 & 0.999999882412936 \tabularnewline
39 & 2.05708651646353e-06 & 4.11417303292706e-06 & 0.999997942913484 \tabularnewline
40 & 3.90471129930782e-05 & 7.80942259861563e-05 & 0.999960952887007 \tabularnewline
41 & 0.000135615896295662 & 0.000271231792591325 & 0.999864384103704 \tabularnewline
42 & 0.000568539583699382 & 0.00113707916739876 & 0.9994314604163 \tabularnewline
43 & 0.00247890590940587 & 0.00495781181881174 & 0.997521094090594 \tabularnewline
44 & 0.00459679146846378 & 0.00919358293692755 & 0.995403208531536 \tabularnewline
45 & 0.0121230228895578 & 0.0242460457791156 & 0.987876977110442 \tabularnewline
46 & 0.0301070042964394 & 0.0602140085928788 & 0.96989299570356 \tabularnewline
47 & 0.0620165314299403 & 0.124033062859881 & 0.93798346857006 \tabularnewline
48 & 0.163713863304977 & 0.327427726609953 & 0.836286136695023 \tabularnewline
49 & 0.332446477796711 & 0.664892955593423 & 0.667553522203289 \tabularnewline
50 & 0.57105095093648 & 0.857898098127039 & 0.428949049063519 \tabularnewline
51 & 0.750322420008347 & 0.499355159983306 & 0.249677579991653 \tabularnewline
52 & 0.883187977830419 & 0.233624044339162 & 0.116812022169581 \tabularnewline
53 & 0.959397531184096 & 0.0812049376318073 & 0.0406024688159036 \tabularnewline
54 & 0.993933999374835 & 0.0121320012503307 & 0.00606600062516535 \tabularnewline
55 & 0.98803987157295 & 0.0239202568541021 & 0.0119601284270511 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58383&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]17[/C][C]0.0105643893795071[/C][C]0.0211287787590142[/C][C]0.989435610620493[/C][/ROW]
[ROW][C]18[/C][C]0.00412955282320748[/C][C]0.00825910564641496[/C][C]0.995870447176793[/C][/ROW]
[ROW][C]19[/C][C]0.00119497016606071[/C][C]0.00238994033212143[/C][C]0.99880502983394[/C][/ROW]
[ROW][C]20[/C][C]0.000282591866769184[/C][C]0.000565183733538368[/C][C]0.99971740813323[/C][/ROW]
[ROW][C]21[/C][C]5.62148872296459e-05[/C][C]0.000112429774459292[/C][C]0.99994378511277[/C][/ROW]
[ROW][C]22[/C][C]1.54180663869641e-05[/C][C]3.08361327739283e-05[/C][C]0.999984581933613[/C][/ROW]
[ROW][C]23[/C][C]4.26377260924846e-06[/C][C]8.52754521849692e-06[/C][C]0.99999573622739[/C][/ROW]
[ROW][C]24[/C][C]3.66984776907621e-06[/C][C]7.33969553815242e-06[/C][C]0.999996330152231[/C][/ROW]
[ROW][C]25[/C][C]1.19437668518472e-06[/C][C]2.38875337036944e-06[/C][C]0.999998805623315[/C][/ROW]
[ROW][C]26[/C][C]4.26474606810519e-07[/C][C]8.52949213621037e-07[/C][C]0.999999573525393[/C][/ROW]
[ROW][C]27[/C][C]1.02210862865224e-07[/C][C]2.04421725730448e-07[/C][C]0.999999897789137[/C][/ROW]
[ROW][C]28[/C][C]3.3776797790584e-08[/C][C]6.7553595581168e-08[/C][C]0.999999966223202[/C][/ROW]
[ROW][C]29[/C][C]4.83771713586087e-08[/C][C]9.67543427172173e-08[/C][C]0.999999951622829[/C][/ROW]
[ROW][C]30[/C][C]3.45875257246162e-08[/C][C]6.91750514492324e-08[/C][C]0.999999965412474[/C][/ROW]
[ROW][C]31[/C][C]2.74860752128265e-08[/C][C]5.4972150425653e-08[/C][C]0.999999972513925[/C][/ROW]
[ROW][C]32[/C][C]7.12427047817952e-09[/C][C]1.42485409563590e-08[/C][C]0.99999999287573[/C][/ROW]
[ROW][C]33[/C][C]1.48713334622535e-09[/C][C]2.97426669245070e-09[/C][C]0.999999998512867[/C][/ROW]
[ROW][C]34[/C][C]3.29161973301676e-10[/C][C]6.58323946603352e-10[/C][C]0.999999999670838[/C][/ROW]
[ROW][C]35[/C][C]1.49480577153677e-10[/C][C]2.98961154307353e-10[/C][C]0.99999999985052[/C][/ROW]
[ROW][C]36[/C][C]4.72108591561327e-10[/C][C]9.44217183122653e-10[/C][C]0.999999999527891[/C][/ROW]
[ROW][C]37[/C][C]1.54227989749339e-09[/C][C]3.08455979498677e-09[/C][C]0.99999999845772[/C][/ROW]
[ROW][C]38[/C][C]1.17587064016743e-07[/C][C]2.35174128033486e-07[/C][C]0.999999882412936[/C][/ROW]
[ROW][C]39[/C][C]2.05708651646353e-06[/C][C]4.11417303292706e-06[/C][C]0.999997942913484[/C][/ROW]
[ROW][C]40[/C][C]3.90471129930782e-05[/C][C]7.80942259861563e-05[/C][C]0.999960952887007[/C][/ROW]
[ROW][C]41[/C][C]0.000135615896295662[/C][C]0.000271231792591325[/C][C]0.999864384103704[/C][/ROW]
[ROW][C]42[/C][C]0.000568539583699382[/C][C]0.00113707916739876[/C][C]0.9994314604163[/C][/ROW]
[ROW][C]43[/C][C]0.00247890590940587[/C][C]0.00495781181881174[/C][C]0.997521094090594[/C][/ROW]
[ROW][C]44[/C][C]0.00459679146846378[/C][C]0.00919358293692755[/C][C]0.995403208531536[/C][/ROW]
[ROW][C]45[/C][C]0.0121230228895578[/C][C]0.0242460457791156[/C][C]0.987876977110442[/C][/ROW]
[ROW][C]46[/C][C]0.0301070042964394[/C][C]0.0602140085928788[/C][C]0.96989299570356[/C][/ROW]
[ROW][C]47[/C][C]0.0620165314299403[/C][C]0.124033062859881[/C][C]0.93798346857006[/C][/ROW]
[ROW][C]48[/C][C]0.163713863304977[/C][C]0.327427726609953[/C][C]0.836286136695023[/C][/ROW]
[ROW][C]49[/C][C]0.332446477796711[/C][C]0.664892955593423[/C][C]0.667553522203289[/C][/ROW]
[ROW][C]50[/C][C]0.57105095093648[/C][C]0.857898098127039[/C][C]0.428949049063519[/C][/ROW]
[ROW][C]51[/C][C]0.750322420008347[/C][C]0.499355159983306[/C][C]0.249677579991653[/C][/ROW]
[ROW][C]52[/C][C]0.883187977830419[/C][C]0.233624044339162[/C][C]0.116812022169581[/C][/ROW]
[ROW][C]53[/C][C]0.959397531184096[/C][C]0.0812049376318073[/C][C]0.0406024688159036[/C][/ROW]
[ROW][C]54[/C][C]0.993933999374835[/C][C]0.0121320012503307[/C][C]0.00606600062516535[/C][/ROW]
[ROW][C]55[/C][C]0.98803987157295[/C][C]0.0239202568541021[/C][C]0.0119601284270511[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58383&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58383&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
170.01056438937950710.02112877875901420.989435610620493
180.004129552823207480.008259105646414960.995870447176793
190.001194970166060710.002389940332121430.99880502983394
200.0002825918667691840.0005651837335383680.99971740813323
215.62148872296459e-050.0001124297744592920.99994378511277
221.54180663869641e-053.08361327739283e-050.999984581933613
234.26377260924846e-068.52754521849692e-060.99999573622739
243.66984776907621e-067.33969553815242e-060.999996330152231
251.19437668518472e-062.38875337036944e-060.999998805623315
264.26474606810519e-078.52949213621037e-070.999999573525393
271.02210862865224e-072.04421725730448e-070.999999897789137
283.3776797790584e-086.7553595581168e-080.999999966223202
294.83771713586087e-089.67543427172173e-080.999999951622829
303.45875257246162e-086.91750514492324e-080.999999965412474
312.74860752128265e-085.4972150425653e-080.999999972513925
327.12427047817952e-091.42485409563590e-080.99999999287573
331.48713334622535e-092.97426669245070e-090.999999998512867
343.29161973301676e-106.58323946603352e-100.999999999670838
351.49480577153677e-102.98961154307353e-100.99999999985052
364.72108591561327e-109.44217183122653e-100.999999999527891
371.54227989749339e-093.08455979498677e-090.99999999845772
381.17587064016743e-072.35174128033486e-070.999999882412936
392.05708651646353e-064.11417303292706e-060.999997942913484
403.90471129930782e-057.80942259861563e-050.999960952887007
410.0001356158962956620.0002712317925913250.999864384103704
420.0005685395836993820.001137079167398760.9994314604163
430.002478905909405870.004957811818811740.997521094090594
440.004596791468463780.009193582936927550.995403208531536
450.01212302288955780.02424604577911560.987876977110442
460.03010700429643940.06021400859287880.96989299570356
470.06201653142994030.1240330628598810.93798346857006
480.1637138633049770.3274277266099530.836286136695023
490.3324464777967110.6648929555934230.667553522203289
500.571050950936480.8578980981270390.428949049063519
510.7503224200083470.4993551599833060.249677579991653
520.8831879778304190.2336240443391620.116812022169581
530.9593975311840960.08120493763180730.0406024688159036
540.9939339993748350.01213200125033070.00606600062516535
550.988039871572950.02392025685410210.0119601284270511







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level270.692307692307692NOK
5% type I error level310.794871794871795NOK
10% type I error level330.846153846153846NOK

\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 & 27 & 0.692307692307692 & NOK \tabularnewline
5% type I error level & 31 & 0.794871794871795 & NOK \tabularnewline
10% type I error level & 33 & 0.846153846153846 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58383&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]27[/C][C]0.692307692307692[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]31[/C][C]0.794871794871795[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]33[/C][C]0.846153846153846[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58383&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58383&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 level270.692307692307692NOK
5% type I error level310.794871794871795NOK
10% type I error level330.846153846153846NOK



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')
}