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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 28 Nov 2011 12:09:00 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/28/t1322500204z2bkusw8y65y0ni.htm/, Retrieved Thu, 28 Mar 2024 16:39:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147885, Retrieved Thu, 28 Mar 2024 16:39:01 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact115
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Classical Decomposition] [HPC Retail Sales] [2008-03-02 16:19:32] [74be16979710d4c4e7c6647856088456]
-  M D  [Classical Decomposition] [Workshop 8, Class...] [2010-11-28 20:55:54] [d946de7cca328fbcf207448a112523ab]
- RMPD      [Multiple Regression] [] [2011-11-28 17:09:00] [e232377fd09030116200e3da7df6eeaf] [Current]
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Dataseries X:
9 676
8 642
9 402
9 610
9 294
9 448
10 319
9 548
9 801
9 596
8 923
9 746
9 829
9 125
9 782
9 441
9 162
9 915
10 444
10 209
9 985
9 842
9 429
10 132
9 849
9 172
10 313
9 819
9 955
10 048
10 082
10 541
10 208
10 233
9 439
9 963
10 158
9 225
10 474
9 757
10 490
10 281
10 444
10 640
10 695
10 786
9 832
9 747
10 411
9 511
10 402
9 701
10 540
10 112
10 915
11 183
10 384
10 834
9 886
10 216
10 943
9 867
10 203
10 837
10 573
10 647
11 502
10 656
10 866
10 835
9 945
10 331




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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147885&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147885&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147885&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' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Monthyly[t] = + 9.22660259487529 -0.000890045065611591births[t] + 0.301839668374023M1[t] -0.578812322578239M2[t] + 0.241982552986256M3[t] -0.039885891944911M4[t] + 0.105125083161294M5[t] + 0.170693808030769M6[t] + 0.690938676845291M7[t] + 0.517222163647724M8[t] + 0.338678844880532M9[t] + 0.348836536284789M10[t] -0.453423046602414M11[t] + 0.0175820464739707t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Monthyly[t] =  +  9.22660259487529 -0.000890045065611591births[t] +  0.301839668374023M1[t] -0.578812322578239M2[t] +  0.241982552986256M3[t] -0.039885891944911M4[t] +  0.105125083161294M5[t] +  0.170693808030769M6[t] +  0.690938676845291M7[t] +  0.517222163647724M8[t] +  0.338678844880532M9[t] +  0.348836536284789M10[t] -0.453423046602414M11[t] +  0.0175820464739707t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147885&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Monthyly[t] =  +  9.22660259487529 -0.000890045065611591births[t] +  0.301839668374023M1[t] -0.578812322578239M2[t] +  0.241982552986256M3[t] -0.039885891944911M4[t] +  0.105125083161294M5[t] +  0.170693808030769M6[t] +  0.690938676845291M7[t] +  0.517222163647724M8[t] +  0.338678844880532M9[t] +  0.348836536284789M10[t] -0.453423046602414M11[t] +  0.0175820464739707t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147885&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147885&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
Monthyly[t] = + 9.22660259487529 -0.000890045065611591births[t] + 0.301839668374023M1[t] -0.578812322578239M2[t] + 0.241982552986256M3[t] -0.039885891944911M4[t] + 0.105125083161294M5[t] + 0.170693808030769M6[t] + 0.690938676845291M7[t] + 0.517222163647724M8[t] + 0.338678844880532M9[t] + 0.348836536284789M10[t] -0.453423046602414M11[t] + 0.0175820464739707t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.226602594875290.13048470.710600
births-0.0008900450656115910.00012-7.425200
M10.3018396683740230.1450292.08120.0418370.020918
M2-0.5788123225782390.144338-4.01010.0001768.8e-05
M30.2419825529862560.1441671.67850.0986330.049316
M4-0.0398858919449110.145353-0.27440.7847460.392373
M50.1051250831612940.1436120.7320.4671120.233556
M60.1706938080307690.144081.18470.2409620.120481
M70.6909386768452910.1436484.80991.1e-056e-06
M80.5172221636477240.1435213.60380.0006520.000326
M90.3386788448805320.144272.34750.0223320.011166
M100.3488365362847890.1446892.41090.01910.00955
M11-0.4534230466024140.145695-3.11210.0028820.001441
t0.01758204647397070.00143512.254500

\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) & 9.22660259487529 & 0.130484 & 70.7106 & 0 & 0 \tabularnewline
births & -0.000890045065611591 & 0.00012 & -7.4252 & 0 & 0 \tabularnewline
M1 & 0.301839668374023 & 0.145029 & 2.0812 & 0.041837 & 0.020918 \tabularnewline
M2 & -0.578812322578239 & 0.144338 & -4.0101 & 0.000176 & 8.8e-05 \tabularnewline
M3 & 0.241982552986256 & 0.144167 & 1.6785 & 0.098633 & 0.049316 \tabularnewline
M4 & -0.039885891944911 & 0.145353 & -0.2744 & 0.784746 & 0.392373 \tabularnewline
M5 & 0.105125083161294 & 0.143612 & 0.732 & 0.467112 & 0.233556 \tabularnewline
M6 & 0.170693808030769 & 0.14408 & 1.1847 & 0.240962 & 0.120481 \tabularnewline
M7 & 0.690938676845291 & 0.143648 & 4.8099 & 1.1e-05 & 6e-06 \tabularnewline
M8 & 0.517222163647724 & 0.143521 & 3.6038 & 0.000652 & 0.000326 \tabularnewline
M9 & 0.338678844880532 & 0.14427 & 2.3475 & 0.022332 & 0.011166 \tabularnewline
M10 & 0.348836536284789 & 0.144689 & 2.4109 & 0.0191 & 0.00955 \tabularnewline
M11 & -0.453423046602414 & 0.145695 & -3.1121 & 0.002882 & 0.001441 \tabularnewline
t & 0.0175820464739707 & 0.001435 & 12.2545 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147885&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]9.22660259487529[/C][C]0.130484[/C][C]70.7106[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]births[/C][C]-0.000890045065611591[/C][C]0.00012[/C][C]-7.4252[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]0.301839668374023[/C][C]0.145029[/C][C]2.0812[/C][C]0.041837[/C][C]0.020918[/C][/ROW]
[ROW][C]M2[/C][C]-0.578812322578239[/C][C]0.144338[/C][C]-4.0101[/C][C]0.000176[/C][C]8.8e-05[/C][/ROW]
[ROW][C]M3[/C][C]0.241982552986256[/C][C]0.144167[/C][C]1.6785[/C][C]0.098633[/C][C]0.049316[/C][/ROW]
[ROW][C]M4[/C][C]-0.039885891944911[/C][C]0.145353[/C][C]-0.2744[/C][C]0.784746[/C][C]0.392373[/C][/ROW]
[ROW][C]M5[/C][C]0.105125083161294[/C][C]0.143612[/C][C]0.732[/C][C]0.467112[/C][C]0.233556[/C][/ROW]
[ROW][C]M6[/C][C]0.170693808030769[/C][C]0.14408[/C][C]1.1847[/C][C]0.240962[/C][C]0.120481[/C][/ROW]
[ROW][C]M7[/C][C]0.690938676845291[/C][C]0.143648[/C][C]4.8099[/C][C]1.1e-05[/C][C]6e-06[/C][/ROW]
[ROW][C]M8[/C][C]0.517222163647724[/C][C]0.143521[/C][C]3.6038[/C][C]0.000652[/C][C]0.000326[/C][/ROW]
[ROW][C]M9[/C][C]0.338678844880532[/C][C]0.14427[/C][C]2.3475[/C][C]0.022332[/C][C]0.011166[/C][/ROW]
[ROW][C]M10[/C][C]0.348836536284789[/C][C]0.144689[/C][C]2.4109[/C][C]0.0191[/C][C]0.00955[/C][/ROW]
[ROW][C]M11[/C][C]-0.453423046602414[/C][C]0.145695[/C][C]-3.1121[/C][C]0.002882[/C][C]0.001441[/C][/ROW]
[ROW][C]t[/C][C]0.0175820464739707[/C][C]0.001435[/C][C]12.2545[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147885&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147885&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)9.226602594875290.13048470.710600
births-0.0008900450656115910.00012-7.425200
M10.3018396683740230.1450292.08120.0418370.020918
M2-0.5788123225782390.144338-4.01010.0001768.8e-05
M30.2419825529862560.1441671.67850.0986330.049316
M4-0.0398858919449110.145353-0.27440.7847460.392373
M50.1051250831612940.1436120.7320.4671120.233556
M60.1706938080307690.144081.18470.2409620.120481
M70.6909386768452910.1436484.80991.1e-056e-06
M80.5172221636477240.1435213.60380.0006520.000326
M90.3386788448805320.144272.34750.0223320.011166
M100.3488365362847890.1446892.41090.01910.00955
M11-0.4534230466024140.145695-3.11210.0028820.001441
t0.01758204647397070.00143512.254500







Multiple Linear Regression - Regression Statistics
Multiple R0.928752699775456
R-squared0.862581577340199
Adjusted R-squared0.831780896399209
F-TEST (value)28.0052762142758
F-TEST (DF numerator)13
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.248129915914169
Sum Squared Residuals3.57097039995121

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.928752699775456 \tabularnewline
R-squared & 0.862581577340199 \tabularnewline
Adjusted R-squared & 0.831780896399209 \tabularnewline
F-TEST (value) & 28.0052762142758 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.248129915914169 \tabularnewline
Sum Squared Residuals & 3.57097039995121 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147885&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.928752699775456[/C][/ROW]
[ROW][C]R-squared[/C][C]0.862581577340199[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.831780896399209[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]28.0052762142758[/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[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.248129915914169[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3.57097039995121[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147885&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147885&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 R0.928752699775456
R-squared0.862581577340199
Adjusted R-squared0.831780896399209
F-TEST (value)28.0052762142758
F-TEST (DF numerator)13
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.248129915914169
Sum Squared Residuals3.57097039995121







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
198.944353845369850.0556461546301535
288.11154543312235-0.111545433122349
399.1635331709076-0.163533170907596
498.714117398803190.285882601196812
599.15796466111663-0.157964661116627
699.10404849235589-0.104048492355888
7109.756691221108280.243308778891724
899.39673643435963-0.396736434359625
999.01059376046667-0.0105937604666708
1099.22079273679527-0.220792736795275
1188.14507046392705-0.145070463927052
1298.773613533616690.226386466383312
1399.01916150801892-0.01916150801892
1498.782683289731190.217316710268811
1599.03630060366284-0.0363006036628396
1699.07551957257919-0.0755195725791954
1799.48643516746501-0.486435167465005
1898.899382004402920.100617995597077
19109.856420145594480.143579854405525
20109.90944626928960.0905537307103969
2199.05781002608179-0.0578100260817863
2299.21282620834247-0.212826208342472
2398.795737284026830.204262715973174
24109.531085761589850.468914238410146
2599.21234516439434-0.212345164394336
2698.951835729335090.0481642706649072
27109.664716297122320.335283702877676
2898.950067095465660.0499329045343378
2998.991613988122660.008386011877338
30109.882035633975820.117964366024179
311010.3896010170335-0.38960101703352
32109.82493586519420.175064134805797
33109.960359599749640.0396404002503588
34109.965848210987580.0341517890124206
3598.997821391058360.0021786089416413
3699.00244286975427-0.00244286975426967
371010.0383508624196-0.0383508624195947
3899.11564789854533-0.115647898545327
39109.732403599246510.267596400753494
4099.21623444722123-0.216234447221229
41109.61646950131970.3835304986803
42109.885639691375970.114360308624031
431010.2783892609698-0.278389260969772
44109.94780596138630.052194038613696
45109.737892210484440.262107789515555
46109.684637847392020.315362152607982
4798.859018237960650.140981762039348
4899.40567716161402-0.405677161614022
491010.0241540185075-0.0241540185075104
5099.07207956746806-0.0720795674680603
511010.0074714016582-0.00747140165818934
5299.47706152858313-0.477061528583127
53109.782951805726770.21704819427323
541010.247041865152-0.247041865151976
551010.0701625927544-0.0701625927543606
561110.56554111405840.43445888594155
571010.2256807835773-0.225680783577298
58109.852900241930310.14709975806969
5999.02194036210527-0.021940362105274
601010.0892756491414-0.0892756491414251
61109.761634601289790.238365398710208
6298.966208081797980.0337919182020179
631010.3955749274025-0.395574927402544
64109.56699995734760.433000042652401
65109.964564876249240.0354351237507648
66109.981852312737420.018147687262577
671110.64873576253960.351264237460403
681010.3555343557118-0.355534355711815
691010.0076636196402-0.00766361964015927
701010.0629947545523-0.0629947545523463
7199.18041226092184-0.180412260921838
721010.1979050242837-0.197905024283741

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9 & 8.94435384536985 & 0.0556461546301535 \tabularnewline
2 & 8 & 8.11154543312235 & -0.111545433122349 \tabularnewline
3 & 9 & 9.1635331709076 & -0.163533170907596 \tabularnewline
4 & 9 & 8.71411739880319 & 0.285882601196812 \tabularnewline
5 & 9 & 9.15796466111663 & -0.157964661116627 \tabularnewline
6 & 9 & 9.10404849235589 & -0.104048492355888 \tabularnewline
7 & 10 & 9.75669122110828 & 0.243308778891724 \tabularnewline
8 & 9 & 9.39673643435963 & -0.396736434359625 \tabularnewline
9 & 9 & 9.01059376046667 & -0.0105937604666708 \tabularnewline
10 & 9 & 9.22079273679527 & -0.220792736795275 \tabularnewline
11 & 8 & 8.14507046392705 & -0.145070463927052 \tabularnewline
12 & 9 & 8.77361353361669 & 0.226386466383312 \tabularnewline
13 & 9 & 9.01916150801892 & -0.01916150801892 \tabularnewline
14 & 9 & 8.78268328973119 & 0.217316710268811 \tabularnewline
15 & 9 & 9.03630060366284 & -0.0363006036628396 \tabularnewline
16 & 9 & 9.07551957257919 & -0.0755195725791954 \tabularnewline
17 & 9 & 9.48643516746501 & -0.486435167465005 \tabularnewline
18 & 9 & 8.89938200440292 & 0.100617995597077 \tabularnewline
19 & 10 & 9.85642014559448 & 0.143579854405525 \tabularnewline
20 & 10 & 9.9094462692896 & 0.0905537307103969 \tabularnewline
21 & 9 & 9.05781002608179 & -0.0578100260817863 \tabularnewline
22 & 9 & 9.21282620834247 & -0.212826208342472 \tabularnewline
23 & 9 & 8.79573728402683 & 0.204262715973174 \tabularnewline
24 & 10 & 9.53108576158985 & 0.468914238410146 \tabularnewline
25 & 9 & 9.21234516439434 & -0.212345164394336 \tabularnewline
26 & 9 & 8.95183572933509 & 0.0481642706649072 \tabularnewline
27 & 10 & 9.66471629712232 & 0.335283702877676 \tabularnewline
28 & 9 & 8.95006709546566 & 0.0499329045343378 \tabularnewline
29 & 9 & 8.99161398812266 & 0.008386011877338 \tabularnewline
30 & 10 & 9.88203563397582 & 0.117964366024179 \tabularnewline
31 & 10 & 10.3896010170335 & -0.38960101703352 \tabularnewline
32 & 10 & 9.8249358651942 & 0.175064134805797 \tabularnewline
33 & 10 & 9.96035959974964 & 0.0396404002503588 \tabularnewline
34 & 10 & 9.96584821098758 & 0.0341517890124206 \tabularnewline
35 & 9 & 8.99782139105836 & 0.0021786089416413 \tabularnewline
36 & 9 & 9.00244286975427 & -0.00244286975426967 \tabularnewline
37 & 10 & 10.0383508624196 & -0.0383508624195947 \tabularnewline
38 & 9 & 9.11564789854533 & -0.115647898545327 \tabularnewline
39 & 10 & 9.73240359924651 & 0.267596400753494 \tabularnewline
40 & 9 & 9.21623444722123 & -0.216234447221229 \tabularnewline
41 & 10 & 9.6164695013197 & 0.3835304986803 \tabularnewline
42 & 10 & 9.88563969137597 & 0.114360308624031 \tabularnewline
43 & 10 & 10.2783892609698 & -0.278389260969772 \tabularnewline
44 & 10 & 9.9478059613863 & 0.052194038613696 \tabularnewline
45 & 10 & 9.73789221048444 & 0.262107789515555 \tabularnewline
46 & 10 & 9.68463784739202 & 0.315362152607982 \tabularnewline
47 & 9 & 8.85901823796065 & 0.140981762039348 \tabularnewline
48 & 9 & 9.40567716161402 & -0.405677161614022 \tabularnewline
49 & 10 & 10.0241540185075 & -0.0241540185075104 \tabularnewline
50 & 9 & 9.07207956746806 & -0.0720795674680603 \tabularnewline
51 & 10 & 10.0074714016582 & -0.00747140165818934 \tabularnewline
52 & 9 & 9.47706152858313 & -0.477061528583127 \tabularnewline
53 & 10 & 9.78295180572677 & 0.21704819427323 \tabularnewline
54 & 10 & 10.247041865152 & -0.247041865151976 \tabularnewline
55 & 10 & 10.0701625927544 & -0.0701625927543606 \tabularnewline
56 & 11 & 10.5655411140584 & 0.43445888594155 \tabularnewline
57 & 10 & 10.2256807835773 & -0.225680783577298 \tabularnewline
58 & 10 & 9.85290024193031 & 0.14709975806969 \tabularnewline
59 & 9 & 9.02194036210527 & -0.021940362105274 \tabularnewline
60 & 10 & 10.0892756491414 & -0.0892756491414251 \tabularnewline
61 & 10 & 9.76163460128979 & 0.238365398710208 \tabularnewline
62 & 9 & 8.96620808179798 & 0.0337919182020179 \tabularnewline
63 & 10 & 10.3955749274025 & -0.395574927402544 \tabularnewline
64 & 10 & 9.5669999573476 & 0.433000042652401 \tabularnewline
65 & 10 & 9.96456487624924 & 0.0354351237507648 \tabularnewline
66 & 10 & 9.98185231273742 & 0.018147687262577 \tabularnewline
67 & 11 & 10.6487357625396 & 0.351264237460403 \tabularnewline
68 & 10 & 10.3555343557118 & -0.355534355711815 \tabularnewline
69 & 10 & 10.0076636196402 & -0.00766361964015927 \tabularnewline
70 & 10 & 10.0629947545523 & -0.0629947545523463 \tabularnewline
71 & 9 & 9.18041226092184 & -0.180412260921838 \tabularnewline
72 & 10 & 10.1979050242837 & -0.197905024283741 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147885&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]9[/C][C]8.94435384536985[/C][C]0.0556461546301535[/C][/ROW]
[ROW][C]2[/C][C]8[/C][C]8.11154543312235[/C][C]-0.111545433122349[/C][/ROW]
[ROW][C]3[/C][C]9[/C][C]9.1635331709076[/C][C]-0.163533170907596[/C][/ROW]
[ROW][C]4[/C][C]9[/C][C]8.71411739880319[/C][C]0.285882601196812[/C][/ROW]
[ROW][C]5[/C][C]9[/C][C]9.15796466111663[/C][C]-0.157964661116627[/C][/ROW]
[ROW][C]6[/C][C]9[/C][C]9.10404849235589[/C][C]-0.104048492355888[/C][/ROW]
[ROW][C]7[/C][C]10[/C][C]9.75669122110828[/C][C]0.243308778891724[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]9.39673643435963[/C][C]-0.396736434359625[/C][/ROW]
[ROW][C]9[/C][C]9[/C][C]9.01059376046667[/C][C]-0.0105937604666708[/C][/ROW]
[ROW][C]10[/C][C]9[/C][C]9.22079273679527[/C][C]-0.220792736795275[/C][/ROW]
[ROW][C]11[/C][C]8[/C][C]8.14507046392705[/C][C]-0.145070463927052[/C][/ROW]
[ROW][C]12[/C][C]9[/C][C]8.77361353361669[/C][C]0.226386466383312[/C][/ROW]
[ROW][C]13[/C][C]9[/C][C]9.01916150801892[/C][C]-0.01916150801892[/C][/ROW]
[ROW][C]14[/C][C]9[/C][C]8.78268328973119[/C][C]0.217316710268811[/C][/ROW]
[ROW][C]15[/C][C]9[/C][C]9.03630060366284[/C][C]-0.0363006036628396[/C][/ROW]
[ROW][C]16[/C][C]9[/C][C]9.07551957257919[/C][C]-0.0755195725791954[/C][/ROW]
[ROW][C]17[/C][C]9[/C][C]9.48643516746501[/C][C]-0.486435167465005[/C][/ROW]
[ROW][C]18[/C][C]9[/C][C]8.89938200440292[/C][C]0.100617995597077[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]9.85642014559448[/C][C]0.143579854405525[/C][/ROW]
[ROW][C]20[/C][C]10[/C][C]9.9094462692896[/C][C]0.0905537307103969[/C][/ROW]
[ROW][C]21[/C][C]9[/C][C]9.05781002608179[/C][C]-0.0578100260817863[/C][/ROW]
[ROW][C]22[/C][C]9[/C][C]9.21282620834247[/C][C]-0.212826208342472[/C][/ROW]
[ROW][C]23[/C][C]9[/C][C]8.79573728402683[/C][C]0.204262715973174[/C][/ROW]
[ROW][C]24[/C][C]10[/C][C]9.53108576158985[/C][C]0.468914238410146[/C][/ROW]
[ROW][C]25[/C][C]9[/C][C]9.21234516439434[/C][C]-0.212345164394336[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]8.95183572933509[/C][C]0.0481642706649072[/C][/ROW]
[ROW][C]27[/C][C]10[/C][C]9.66471629712232[/C][C]0.335283702877676[/C][/ROW]
[ROW][C]28[/C][C]9[/C][C]8.95006709546566[/C][C]0.0499329045343378[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]8.99161398812266[/C][C]0.008386011877338[/C][/ROW]
[ROW][C]30[/C][C]10[/C][C]9.88203563397582[/C][C]0.117964366024179[/C][/ROW]
[ROW][C]31[/C][C]10[/C][C]10.3896010170335[/C][C]-0.38960101703352[/C][/ROW]
[ROW][C]32[/C][C]10[/C][C]9.8249358651942[/C][C]0.175064134805797[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]9.96035959974964[/C][C]0.0396404002503588[/C][/ROW]
[ROW][C]34[/C][C]10[/C][C]9.96584821098758[/C][C]0.0341517890124206[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]8.99782139105836[/C][C]0.0021786089416413[/C][/ROW]
[ROW][C]36[/C][C]9[/C][C]9.00244286975427[/C][C]-0.00244286975426967[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]10.0383508624196[/C][C]-0.0383508624195947[/C][/ROW]
[ROW][C]38[/C][C]9[/C][C]9.11564789854533[/C][C]-0.115647898545327[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]9.73240359924651[/C][C]0.267596400753494[/C][/ROW]
[ROW][C]40[/C][C]9[/C][C]9.21623444722123[/C][C]-0.216234447221229[/C][/ROW]
[ROW][C]41[/C][C]10[/C][C]9.6164695013197[/C][C]0.3835304986803[/C][/ROW]
[ROW][C]42[/C][C]10[/C][C]9.88563969137597[/C][C]0.114360308624031[/C][/ROW]
[ROW][C]43[/C][C]10[/C][C]10.2783892609698[/C][C]-0.278389260969772[/C][/ROW]
[ROW][C]44[/C][C]10[/C][C]9.9478059613863[/C][C]0.052194038613696[/C][/ROW]
[ROW][C]45[/C][C]10[/C][C]9.73789221048444[/C][C]0.262107789515555[/C][/ROW]
[ROW][C]46[/C][C]10[/C][C]9.68463784739202[/C][C]0.315362152607982[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]8.85901823796065[/C][C]0.140981762039348[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]9.40567716161402[/C][C]-0.405677161614022[/C][/ROW]
[ROW][C]49[/C][C]10[/C][C]10.0241540185075[/C][C]-0.0241540185075104[/C][/ROW]
[ROW][C]50[/C][C]9[/C][C]9.07207956746806[/C][C]-0.0720795674680603[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]10.0074714016582[/C][C]-0.00747140165818934[/C][/ROW]
[ROW][C]52[/C][C]9[/C][C]9.47706152858313[/C][C]-0.477061528583127[/C][/ROW]
[ROW][C]53[/C][C]10[/C][C]9.78295180572677[/C][C]0.21704819427323[/C][/ROW]
[ROW][C]54[/C][C]10[/C][C]10.247041865152[/C][C]-0.247041865151976[/C][/ROW]
[ROW][C]55[/C][C]10[/C][C]10.0701625927544[/C][C]-0.0701625927543606[/C][/ROW]
[ROW][C]56[/C][C]11[/C][C]10.5655411140584[/C][C]0.43445888594155[/C][/ROW]
[ROW][C]57[/C][C]10[/C][C]10.2256807835773[/C][C]-0.225680783577298[/C][/ROW]
[ROW][C]58[/C][C]10[/C][C]9.85290024193031[/C][C]0.14709975806969[/C][/ROW]
[ROW][C]59[/C][C]9[/C][C]9.02194036210527[/C][C]-0.021940362105274[/C][/ROW]
[ROW][C]60[/C][C]10[/C][C]10.0892756491414[/C][C]-0.0892756491414251[/C][/ROW]
[ROW][C]61[/C][C]10[/C][C]9.76163460128979[/C][C]0.238365398710208[/C][/ROW]
[ROW][C]62[/C][C]9[/C][C]8.96620808179798[/C][C]0.0337919182020179[/C][/ROW]
[ROW][C]63[/C][C]10[/C][C]10.3955749274025[/C][C]-0.395574927402544[/C][/ROW]
[ROW][C]64[/C][C]10[/C][C]9.5669999573476[/C][C]0.433000042652401[/C][/ROW]
[ROW][C]65[/C][C]10[/C][C]9.96456487624924[/C][C]0.0354351237507648[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]9.98185231273742[/C][C]0.018147687262577[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]10.6487357625396[/C][C]0.351264237460403[/C][/ROW]
[ROW][C]68[/C][C]10[/C][C]10.3555343557118[/C][C]-0.355534355711815[/C][/ROW]
[ROW][C]69[/C][C]10[/C][C]10.0076636196402[/C][C]-0.00766361964015927[/C][/ROW]
[ROW][C]70[/C][C]10[/C][C]10.0629947545523[/C][C]-0.0629947545523463[/C][/ROW]
[ROW][C]71[/C][C]9[/C][C]9.18041226092184[/C][C]-0.180412260921838[/C][/ROW]
[ROW][C]72[/C][C]10[/C][C]10.1979050242837[/C][C]-0.197905024283741[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147885&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147885&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
198.944353845369850.0556461546301535
288.11154543312235-0.111545433122349
399.1635331709076-0.163533170907596
498.714117398803190.285882601196812
599.15796466111663-0.157964661116627
699.10404849235589-0.104048492355888
7109.756691221108280.243308778891724
899.39673643435963-0.396736434359625
999.01059376046667-0.0105937604666708
1099.22079273679527-0.220792736795275
1188.14507046392705-0.145070463927052
1298.773613533616690.226386466383312
1399.01916150801892-0.01916150801892
1498.782683289731190.217316710268811
1599.03630060366284-0.0363006036628396
1699.07551957257919-0.0755195725791954
1799.48643516746501-0.486435167465005
1898.899382004402920.100617995597077
19109.856420145594480.143579854405525
20109.90944626928960.0905537307103969
2199.05781002608179-0.0578100260817863
2299.21282620834247-0.212826208342472
2398.795737284026830.204262715973174
24109.531085761589850.468914238410146
2599.21234516439434-0.212345164394336
2698.951835729335090.0481642706649072
27109.664716297122320.335283702877676
2898.950067095465660.0499329045343378
2998.991613988122660.008386011877338
30109.882035633975820.117964366024179
311010.3896010170335-0.38960101703352
32109.82493586519420.175064134805797
33109.960359599749640.0396404002503588
34109.965848210987580.0341517890124206
3598.997821391058360.0021786089416413
3699.00244286975427-0.00244286975426967
371010.0383508624196-0.0383508624195947
3899.11564789854533-0.115647898545327
39109.732403599246510.267596400753494
4099.21623444722123-0.216234447221229
41109.61646950131970.3835304986803
42109.885639691375970.114360308624031
431010.2783892609698-0.278389260969772
44109.94780596138630.052194038613696
45109.737892210484440.262107789515555
46109.684637847392020.315362152607982
4798.859018237960650.140981762039348
4899.40567716161402-0.405677161614022
491010.0241540185075-0.0241540185075104
5099.07207956746806-0.0720795674680603
511010.0074714016582-0.00747140165818934
5299.47706152858313-0.477061528583127
53109.782951805726770.21704819427323
541010.247041865152-0.247041865151976
551010.0701625927544-0.0701625927543606
561110.56554111405840.43445888594155
571010.2256807835773-0.225680783577298
58109.852900241930310.14709975806969
5999.02194036210527-0.021940362105274
601010.0892756491414-0.0892756491414251
61109.761634601289790.238365398710208
6298.966208081797980.0337919182020179
631010.3955749274025-0.395574927402544
64109.56699995734760.433000042652401
65109.964564876249240.0354351237507648
66109.981852312737420.018147687262577
671110.64873576253960.351264237460403
681010.3555343557118-0.355534355711815
691010.0076636196402-0.00766361964015927
701010.0629947545523-0.0629947545523463
7199.18041226092184-0.180412260921838
721010.1979050242837-0.197905024283741







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.5681397443011580.8637205113976840.431860255698842
180.4664856277003430.9329712554006870.533514372299657
190.3298124593808690.6596249187617380.670187540619131
200.4286229198370770.8572458396741530.571377080162923
210.3119298635945140.6238597271890280.688070136405486
220.2381031637515790.4762063275031580.761896836248421
230.1873027065395350.374605413079070.812697293460465
240.173220406847280.3464408136945590.82677959315272
250.1819735771142520.3639471542285040.818026422885748
260.1265636955991560.2531273911983110.873436304400844
270.1561613450479930.3123226900959870.843838654952006
280.1054501174012420.2109002348024840.894549882598758
290.1745666861150060.3491333722300130.825433313884994
300.1273431210895810.2546862421791610.872656878910419
310.3704190620013260.7408381240026520.629580937998674
320.3378121885918030.6756243771836060.662187811408197
330.2613169685679630.5226339371359270.738683031432037
340.2090995537317970.4181991074635940.790900446268203
350.1560438676931820.3120877353863650.843956132306818
360.1514411234538210.3028822469076410.848558876546179
370.1115754787718450.223150957543690.888424521228155
380.08728706496521550.1745741299304310.912712935034785
390.089729650800740.179459301601480.91027034919926
400.08659695604644050.1731939120928810.91340304395356
410.1364517900506270.2729035801012540.863548209949373
420.1024311615539630.2048623231079270.897568838446037
430.1250923357011180.2501846714022360.874907664298882
440.08594537306403290.1718907461280660.914054626935967
450.08395682112592160.1679136422518430.916043178874078
460.09155460365148170.1831092073029630.908445396348518
470.07472269901879820.1494453980375960.925277300981202
480.1074829870767280.2149659741534560.892517012923272
490.07803801833615990.156076036672320.92196198166384
500.04928440550213410.09856881100426810.950715594497866
510.05037952422741350.1007590484548270.949620475772586
520.2834491663008940.5668983326017870.716550833699106
530.2115980867356240.4231961734712480.788401913264376
540.3115212386378210.6230424772756410.688478761362179
550.2344613340458540.4689226680917080.765538665954146

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.568139744301158 & 0.863720511397684 & 0.431860255698842 \tabularnewline
18 & 0.466485627700343 & 0.932971255400687 & 0.533514372299657 \tabularnewline
19 & 0.329812459380869 & 0.659624918761738 & 0.670187540619131 \tabularnewline
20 & 0.428622919837077 & 0.857245839674153 & 0.571377080162923 \tabularnewline
21 & 0.311929863594514 & 0.623859727189028 & 0.688070136405486 \tabularnewline
22 & 0.238103163751579 & 0.476206327503158 & 0.761896836248421 \tabularnewline
23 & 0.187302706539535 & 0.37460541307907 & 0.812697293460465 \tabularnewline
24 & 0.17322040684728 & 0.346440813694559 & 0.82677959315272 \tabularnewline
25 & 0.181973577114252 & 0.363947154228504 & 0.818026422885748 \tabularnewline
26 & 0.126563695599156 & 0.253127391198311 & 0.873436304400844 \tabularnewline
27 & 0.156161345047993 & 0.312322690095987 & 0.843838654952006 \tabularnewline
28 & 0.105450117401242 & 0.210900234802484 & 0.894549882598758 \tabularnewline
29 & 0.174566686115006 & 0.349133372230013 & 0.825433313884994 \tabularnewline
30 & 0.127343121089581 & 0.254686242179161 & 0.872656878910419 \tabularnewline
31 & 0.370419062001326 & 0.740838124002652 & 0.629580937998674 \tabularnewline
32 & 0.337812188591803 & 0.675624377183606 & 0.662187811408197 \tabularnewline
33 & 0.261316968567963 & 0.522633937135927 & 0.738683031432037 \tabularnewline
34 & 0.209099553731797 & 0.418199107463594 & 0.790900446268203 \tabularnewline
35 & 0.156043867693182 & 0.312087735386365 & 0.843956132306818 \tabularnewline
36 & 0.151441123453821 & 0.302882246907641 & 0.848558876546179 \tabularnewline
37 & 0.111575478771845 & 0.22315095754369 & 0.888424521228155 \tabularnewline
38 & 0.0872870649652155 & 0.174574129930431 & 0.912712935034785 \tabularnewline
39 & 0.08972965080074 & 0.17945930160148 & 0.91027034919926 \tabularnewline
40 & 0.0865969560464405 & 0.173193912092881 & 0.91340304395356 \tabularnewline
41 & 0.136451790050627 & 0.272903580101254 & 0.863548209949373 \tabularnewline
42 & 0.102431161553963 & 0.204862323107927 & 0.897568838446037 \tabularnewline
43 & 0.125092335701118 & 0.250184671402236 & 0.874907664298882 \tabularnewline
44 & 0.0859453730640329 & 0.171890746128066 & 0.914054626935967 \tabularnewline
45 & 0.0839568211259216 & 0.167913642251843 & 0.916043178874078 \tabularnewline
46 & 0.0915546036514817 & 0.183109207302963 & 0.908445396348518 \tabularnewline
47 & 0.0747226990187982 & 0.149445398037596 & 0.925277300981202 \tabularnewline
48 & 0.107482987076728 & 0.214965974153456 & 0.892517012923272 \tabularnewline
49 & 0.0780380183361599 & 0.15607603667232 & 0.92196198166384 \tabularnewline
50 & 0.0492844055021341 & 0.0985688110042681 & 0.950715594497866 \tabularnewline
51 & 0.0503795242274135 & 0.100759048454827 & 0.949620475772586 \tabularnewline
52 & 0.283449166300894 & 0.566898332601787 & 0.716550833699106 \tabularnewline
53 & 0.211598086735624 & 0.423196173471248 & 0.788401913264376 \tabularnewline
54 & 0.311521238637821 & 0.623042477275641 & 0.688478761362179 \tabularnewline
55 & 0.234461334045854 & 0.468922668091708 & 0.765538665954146 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147885&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]0.568139744301158[/C][C]0.863720511397684[/C][C]0.431860255698842[/C][/ROW]
[ROW][C]18[/C][C]0.466485627700343[/C][C]0.932971255400687[/C][C]0.533514372299657[/C][/ROW]
[ROW][C]19[/C][C]0.329812459380869[/C][C]0.659624918761738[/C][C]0.670187540619131[/C][/ROW]
[ROW][C]20[/C][C]0.428622919837077[/C][C]0.857245839674153[/C][C]0.571377080162923[/C][/ROW]
[ROW][C]21[/C][C]0.311929863594514[/C][C]0.623859727189028[/C][C]0.688070136405486[/C][/ROW]
[ROW][C]22[/C][C]0.238103163751579[/C][C]0.476206327503158[/C][C]0.761896836248421[/C][/ROW]
[ROW][C]23[/C][C]0.187302706539535[/C][C]0.37460541307907[/C][C]0.812697293460465[/C][/ROW]
[ROW][C]24[/C][C]0.17322040684728[/C][C]0.346440813694559[/C][C]0.82677959315272[/C][/ROW]
[ROW][C]25[/C][C]0.181973577114252[/C][C]0.363947154228504[/C][C]0.818026422885748[/C][/ROW]
[ROW][C]26[/C][C]0.126563695599156[/C][C]0.253127391198311[/C][C]0.873436304400844[/C][/ROW]
[ROW][C]27[/C][C]0.156161345047993[/C][C]0.312322690095987[/C][C]0.843838654952006[/C][/ROW]
[ROW][C]28[/C][C]0.105450117401242[/C][C]0.210900234802484[/C][C]0.894549882598758[/C][/ROW]
[ROW][C]29[/C][C]0.174566686115006[/C][C]0.349133372230013[/C][C]0.825433313884994[/C][/ROW]
[ROW][C]30[/C][C]0.127343121089581[/C][C]0.254686242179161[/C][C]0.872656878910419[/C][/ROW]
[ROW][C]31[/C][C]0.370419062001326[/C][C]0.740838124002652[/C][C]0.629580937998674[/C][/ROW]
[ROW][C]32[/C][C]0.337812188591803[/C][C]0.675624377183606[/C][C]0.662187811408197[/C][/ROW]
[ROW][C]33[/C][C]0.261316968567963[/C][C]0.522633937135927[/C][C]0.738683031432037[/C][/ROW]
[ROW][C]34[/C][C]0.209099553731797[/C][C]0.418199107463594[/C][C]0.790900446268203[/C][/ROW]
[ROW][C]35[/C][C]0.156043867693182[/C][C]0.312087735386365[/C][C]0.843956132306818[/C][/ROW]
[ROW][C]36[/C][C]0.151441123453821[/C][C]0.302882246907641[/C][C]0.848558876546179[/C][/ROW]
[ROW][C]37[/C][C]0.111575478771845[/C][C]0.22315095754369[/C][C]0.888424521228155[/C][/ROW]
[ROW][C]38[/C][C]0.0872870649652155[/C][C]0.174574129930431[/C][C]0.912712935034785[/C][/ROW]
[ROW][C]39[/C][C]0.08972965080074[/C][C]0.17945930160148[/C][C]0.91027034919926[/C][/ROW]
[ROW][C]40[/C][C]0.0865969560464405[/C][C]0.173193912092881[/C][C]0.91340304395356[/C][/ROW]
[ROW][C]41[/C][C]0.136451790050627[/C][C]0.272903580101254[/C][C]0.863548209949373[/C][/ROW]
[ROW][C]42[/C][C]0.102431161553963[/C][C]0.204862323107927[/C][C]0.897568838446037[/C][/ROW]
[ROW][C]43[/C][C]0.125092335701118[/C][C]0.250184671402236[/C][C]0.874907664298882[/C][/ROW]
[ROW][C]44[/C][C]0.0859453730640329[/C][C]0.171890746128066[/C][C]0.914054626935967[/C][/ROW]
[ROW][C]45[/C][C]0.0839568211259216[/C][C]0.167913642251843[/C][C]0.916043178874078[/C][/ROW]
[ROW][C]46[/C][C]0.0915546036514817[/C][C]0.183109207302963[/C][C]0.908445396348518[/C][/ROW]
[ROW][C]47[/C][C]0.0747226990187982[/C][C]0.149445398037596[/C][C]0.925277300981202[/C][/ROW]
[ROW][C]48[/C][C]0.107482987076728[/C][C]0.214965974153456[/C][C]0.892517012923272[/C][/ROW]
[ROW][C]49[/C][C]0.0780380183361599[/C][C]0.15607603667232[/C][C]0.92196198166384[/C][/ROW]
[ROW][C]50[/C][C]0.0492844055021341[/C][C]0.0985688110042681[/C][C]0.950715594497866[/C][/ROW]
[ROW][C]51[/C][C]0.0503795242274135[/C][C]0.100759048454827[/C][C]0.949620475772586[/C][/ROW]
[ROW][C]52[/C][C]0.283449166300894[/C][C]0.566898332601787[/C][C]0.716550833699106[/C][/ROW]
[ROW][C]53[/C][C]0.211598086735624[/C][C]0.423196173471248[/C][C]0.788401913264376[/C][/ROW]
[ROW][C]54[/C][C]0.311521238637821[/C][C]0.623042477275641[/C][C]0.688478761362179[/C][/ROW]
[ROW][C]55[/C][C]0.234461334045854[/C][C]0.468922668091708[/C][C]0.765538665954146[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147885&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147885&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
170.5681397443011580.8637205113976840.431860255698842
180.4664856277003430.9329712554006870.533514372299657
190.3298124593808690.6596249187617380.670187540619131
200.4286229198370770.8572458396741530.571377080162923
210.3119298635945140.6238597271890280.688070136405486
220.2381031637515790.4762063275031580.761896836248421
230.1873027065395350.374605413079070.812697293460465
240.173220406847280.3464408136945590.82677959315272
250.1819735771142520.3639471542285040.818026422885748
260.1265636955991560.2531273911983110.873436304400844
270.1561613450479930.3123226900959870.843838654952006
280.1054501174012420.2109002348024840.894549882598758
290.1745666861150060.3491333722300130.825433313884994
300.1273431210895810.2546862421791610.872656878910419
310.3704190620013260.7408381240026520.629580937998674
320.3378121885918030.6756243771836060.662187811408197
330.2613169685679630.5226339371359270.738683031432037
340.2090995537317970.4181991074635940.790900446268203
350.1560438676931820.3120877353863650.843956132306818
360.1514411234538210.3028822469076410.848558876546179
370.1115754787718450.223150957543690.888424521228155
380.08728706496521550.1745741299304310.912712935034785
390.089729650800740.179459301601480.91027034919926
400.08659695604644050.1731939120928810.91340304395356
410.1364517900506270.2729035801012540.863548209949373
420.1024311615539630.2048623231079270.897568838446037
430.1250923357011180.2501846714022360.874907664298882
440.08594537306403290.1718907461280660.914054626935967
450.08395682112592160.1679136422518430.916043178874078
460.09155460365148170.1831092073029630.908445396348518
470.07472269901879820.1494453980375960.925277300981202
480.1074829870767280.2149659741534560.892517012923272
490.07803801833615990.156076036672320.92196198166384
500.04928440550213410.09856881100426810.950715594497866
510.05037952422741350.1007590484548270.949620475772586
520.2834491663008940.5668983326017870.716550833699106
530.2115980867356240.4231961734712480.788401913264376
540.3115212386378210.6230424772756410.688478761362179
550.2344613340458540.4689226680917080.765538665954146







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 level10.0256410256410256OK

\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 & 1 & 0.0256410256410256 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147885&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]1[/C][C]0.0256410256410256[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147885&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147885&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 level10.0256410256410256OK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal 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')
}