Free Statistics

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Author's title

Author*The author of this computation has been verified*
R Software Module--
Title produced by softwareMultiple Regression
Date of computationTue, 13 Dec 2011 14:12:37 -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/Dec/13/t1323803571y1g2rq95cpdxwn1.htm/, Retrieved Thu, 31 Oct 2024 23:25:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154651, Retrieved Thu, 31 Oct 2024 23:25:42 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact153
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [Pearson Correlati...] [2011-12-09 14:50:56] [f033824ca1b38a5ddbb2c3414ea3bb75]
- RMPD  [Multiple Regression] [Multiple regression] [2011-12-09 15:28:17] [f033824ca1b38a5ddbb2c3414ea3bb75]
- RM        [Multiple Regression] [] [2011-12-13 19:12:37] [bed8b29df92274246a1a4d59b25859b7] [Current]
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Dataseries X:
12008,00	4,00
9169,00	5,90
8788,00	7,10
8417,00	10,50
8247,00	15,10
8197,00	16,80
8236,00	15,30
8253,00	18,40
7733,00	16,10
8366,00	11,30
8626,00	7,90
8863,00	5,60
10102,00	3,40
8463,00	4,80
9114,00	6,50
8563,00	8,50
8872,00	15,10
8301,00	15,70
8301,00	18,70
8278,00	19,20
7736,00	12,90
7973,00	14,40
8268,00	6,20
9476,00	3,30
11100,00	4,60
8962,00	7,10
9173,00	7,80
8738,00	9,90
8459,00	13,60
8078,00	17,10
8411,00	17,80
8291,00	18,60
7810,00	14,70
8616,00	10,50
8312,00	8,60
9692,00	4,40
9911,00	2,30
8915,00	2,80
9452,00	8,80
9112,00	10,70
8472,00	13,90
8230,00	19,30
8384,00	19,50
8625,00	20,40
8221,00	15,30
8649,00	7,90
8625,00	8,30
10443,00	4,50
10357,00	3,20
8586,00	5,00
8892,00	6,60
8329,00	11,10
8101,00	12,80
7922,00	16,30
8120,00	17,40
7838,00	18,90
7735,00	15,80
8406,00	11,70
8209,00	6,40
9451,00	2,90
10041,00	4,70
9411,00	2,40
10405,00	7,20
8467,00	10,70
8464,00	13,40
8102,00	18,30
7627,00	18,40
7513,00	16,80
7510,00	16,60
8291,00	14,10
8064,00	6,10
9383,00	3,50
9706,00	1,70
8579,00	2,30
9474,00	4,50
8318,00	9,30
8213,00	14,20
8059,00	17,30
9111,00	23,00
7708,00	16,30
7680,00	18,40
8014,00	14,20
8007,00	9,10
8718,00	5,90
9486,00	7,20
9113,00	6,80
9025,00	8,00
8476,00	14,30
7952,00	14,60
7759,00	17,50
7835,00	17,20
7600,00	17,20
7651,00	14,10
8319,00	10,40
8812,00	6,80
8630,00	4,10




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154651&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154651&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154651&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'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Sterftegevallen[t] = + 9702.03898923781 -96.5432003817131Temperatuur[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Sterftegevallen[t] =  +  9702.03898923781 -96.5432003817131Temperatuur[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154651&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Sterftegevallen[t] =  +  9702.03898923781 -96.5432003817131Temperatuur[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154651&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154651&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
Sterftegevallen[t] = + 9702.03898923781 -96.5432003817131Temperatuur[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9702.03898923781136.44724471.104700
Temperatuur-96.543200381713110.99678-8.779200

\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) & 9702.03898923781 & 136.447244 & 71.1047 & 0 & 0 \tabularnewline
Temperatuur & -96.5432003817131 & 10.99678 & -8.7792 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154651&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]9702.03898923781[/C][C]136.447244[/C][C]71.1047[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Temperatuur[/C][C]-96.5432003817131[/C][C]10.99678[/C][C]-8.7792[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154651&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154651&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)9702.03898923781136.44724471.104700
Temperatuur-96.543200381713110.99678-8.779200







Multiple Linear Regression - Regression Statistics
Multiple R0.671217338794935
R-squared0.450532715898954
Adjusted R-squared0.444687319259582
F-TEST (value)77.0747895642021
F-TEST (DF numerator)1
F-TEST (DF denominator)94
p-value7.22755189030977e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation596.998497817071
Sum Squared Residuals33502277.4012089

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.671217338794935 \tabularnewline
R-squared & 0.450532715898954 \tabularnewline
Adjusted R-squared & 0.444687319259582 \tabularnewline
F-TEST (value) & 77.0747895642021 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 94 \tabularnewline
p-value & 7.22755189030977e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 596.998497817071 \tabularnewline
Sum Squared Residuals & 33502277.4012089 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154651&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.671217338794935[/C][/ROW]
[ROW][C]R-squared[/C][C]0.450532715898954[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.444687319259582[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]77.0747895642021[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]94[/C][/ROW]
[ROW][C]p-value[/C][C]7.22755189030977e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]596.998497817071[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]33502277.4012089[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154651&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154651&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 R0.671217338794935
R-squared0.450532715898954
Adjusted R-squared0.444687319259582
F-TEST (value)77.0747895642021
F-TEST (DF numerator)1
F-TEST (DF denominator)94
p-value7.22755189030977e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation596.998497817071
Sum Squared Residuals33502277.4012089







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1120089315.866187710962692.13381228904
291699132.434106985736.5658930142972
387889016.58226652765-228.582266527648
484178688.33538522982-271.335385229823
582478244.236663473942.76333652605727
681978080.11322282503116.88677717497
782368224.928023397611.0719766023999
882537925.64410221429327.35589778571
977338147.69346309223-414.69346309223
1083668611.10082492445-245.100824924452
1186268939.34770622228-313.347706222277
1288639161.39706710022-298.397067100217
13101029373.79210793999728.207892060014
1484639238.63162740559-775.631627405588
1591149074.5081867566839.4918132433245
1685638881.42178599325-318.421785993249
1788728244.23666347394627.763336526057
1883018186.31074324492114.689256755085
1983017896.68114209978404.318857900224
2082787848.40954190892429.590458091081
2177368456.63170431371-720.631704313711
2279738311.81690374114-338.816903741142
2382689103.47114687119-835.471146871189
2494769383.4464279781692.5535720218427
25111009257.940267481931842.05973251807
2689629016.58226652765-54.5822665276476
2791738949.00202626045223.997973739552
2887388746.26130545885-8.26130545885086
2984598389.0514640465169.9485359534875
3080788051.1502627105226.8497372894837
3184117983.57002244332427.429977556683
3282917906.33546213795384.664537862053
3378108282.85394362663-472.853943626628
3486168688.33538522982-72.3353852298231
3583128871.76746595508-559.767465955078
3696929277.24890755827414.751092441727
3799119479.98962835987431.01037164013
3889159431.71802816901-516.718028169014
3994528852.45882587874599.541174121265
4091128669.02674515348442.97325484652
4184728360.088503932111.911496068002
4282307838.75522187075391.244778129252
4383847819.44658179441564.553418205595
4486257732.55770145086892.442298549136
4582218224.9280233976-3.92802339760003
4686498939.34770622228-290.347706222277
4786258900.73042606959-275.730426069592
48104439267.59458752011175.4054124799
49103579393.10074801633963.899251983671
5085869219.32298732925-633.322987329245
5188929064.8538667185-172.853866718504
5283298630.4094650008-301.409465000795
5381018466.28602435188-365.286024351883
5479228128.38482301589-206.384823015887
5581208022.18730259697.8126974039972
5678387877.37250202343-39.3725020234331
5777358176.65642320674-441.656423206744
5884068572.48354477177-166.483544771767
5982099084.16250679485-875.162506794847
6094519422.0637081308428.9362918691574
61100419248.28594744376792.714052556241
6294119470.3353083217-59.3353083216992
63104059006.927946489481398.07205351052
6484678669.02674515348-202.026745153481
6584648408.3601041228655.639895877145
6681027935.29842225246166.701577747539
6776277925.64410221429-298.64410221429
6875138080.11322282503-567.11322282503
6975108099.42186290137-589.421862901373
7082918340.77986385566-49.7798638556558
7180649113.12546690936-1049.12546690936
7293839364.1377879018118.8622120981853
7397069537.9155485889168.084451411102
7485799479.98962835987-900.98962835987
7594749267.5945875201206.405412479898
7683188804.18722568788-486.187225687879
7782138331.12554381748-118.125543817485
7880598031.8416226341727.1583773658262
7991117481.545380458411629.45461954159
8077088128.38482301589-420.384823015887
8176807925.64410221429-245.64410221429
8280148331.12554381748-317.125543817485
8380078823.49586576422-816.495865764221
8487189132.4341069857-414.434106985703
8594869006.92794648948479.072053510524
8691139045.5452266421667.4547733578384
8790258929.6933861841195.3066138158942
8884768321.47122377931154.528776220687
8979528292.5082636648-340.508263664799
9077598012.53298255783-253.532982557831
9178358041.49594267235-206.495942672345
9276008041.49594267235-441.495942672345
9376518340.77986385566-689.779863855656
9483198697.98970526799-378.989705267994
9588129045.54522664216-233.545226642162
9686309306.21186767279-676.211867672787

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12008 & 9315.86618771096 & 2692.13381228904 \tabularnewline
2 & 9169 & 9132.4341069857 & 36.5658930142972 \tabularnewline
3 & 8788 & 9016.58226652765 & -228.582266527648 \tabularnewline
4 & 8417 & 8688.33538522982 & -271.335385229823 \tabularnewline
5 & 8247 & 8244.23666347394 & 2.76333652605727 \tabularnewline
6 & 8197 & 8080.11322282503 & 116.88677717497 \tabularnewline
7 & 8236 & 8224.9280233976 & 11.0719766023999 \tabularnewline
8 & 8253 & 7925.64410221429 & 327.35589778571 \tabularnewline
9 & 7733 & 8147.69346309223 & -414.69346309223 \tabularnewline
10 & 8366 & 8611.10082492445 & -245.100824924452 \tabularnewline
11 & 8626 & 8939.34770622228 & -313.347706222277 \tabularnewline
12 & 8863 & 9161.39706710022 & -298.397067100217 \tabularnewline
13 & 10102 & 9373.79210793999 & 728.207892060014 \tabularnewline
14 & 8463 & 9238.63162740559 & -775.631627405588 \tabularnewline
15 & 9114 & 9074.50818675668 & 39.4918132433245 \tabularnewline
16 & 8563 & 8881.42178599325 & -318.421785993249 \tabularnewline
17 & 8872 & 8244.23666347394 & 627.763336526057 \tabularnewline
18 & 8301 & 8186.31074324492 & 114.689256755085 \tabularnewline
19 & 8301 & 7896.68114209978 & 404.318857900224 \tabularnewline
20 & 8278 & 7848.40954190892 & 429.590458091081 \tabularnewline
21 & 7736 & 8456.63170431371 & -720.631704313711 \tabularnewline
22 & 7973 & 8311.81690374114 & -338.816903741142 \tabularnewline
23 & 8268 & 9103.47114687119 & -835.471146871189 \tabularnewline
24 & 9476 & 9383.44642797816 & 92.5535720218427 \tabularnewline
25 & 11100 & 9257.94026748193 & 1842.05973251807 \tabularnewline
26 & 8962 & 9016.58226652765 & -54.5822665276476 \tabularnewline
27 & 9173 & 8949.00202626045 & 223.997973739552 \tabularnewline
28 & 8738 & 8746.26130545885 & -8.26130545885086 \tabularnewline
29 & 8459 & 8389.05146404651 & 69.9485359534875 \tabularnewline
30 & 8078 & 8051.15026271052 & 26.8497372894837 \tabularnewline
31 & 8411 & 7983.57002244332 & 427.429977556683 \tabularnewline
32 & 8291 & 7906.33546213795 & 384.664537862053 \tabularnewline
33 & 7810 & 8282.85394362663 & -472.853943626628 \tabularnewline
34 & 8616 & 8688.33538522982 & -72.3353852298231 \tabularnewline
35 & 8312 & 8871.76746595508 & -559.767465955078 \tabularnewline
36 & 9692 & 9277.24890755827 & 414.751092441727 \tabularnewline
37 & 9911 & 9479.98962835987 & 431.01037164013 \tabularnewline
38 & 8915 & 9431.71802816901 & -516.718028169014 \tabularnewline
39 & 9452 & 8852.45882587874 & 599.541174121265 \tabularnewline
40 & 9112 & 8669.02674515348 & 442.97325484652 \tabularnewline
41 & 8472 & 8360.088503932 & 111.911496068002 \tabularnewline
42 & 8230 & 7838.75522187075 & 391.244778129252 \tabularnewline
43 & 8384 & 7819.44658179441 & 564.553418205595 \tabularnewline
44 & 8625 & 7732.55770145086 & 892.442298549136 \tabularnewline
45 & 8221 & 8224.9280233976 & -3.92802339760003 \tabularnewline
46 & 8649 & 8939.34770622228 & -290.347706222277 \tabularnewline
47 & 8625 & 8900.73042606959 & -275.730426069592 \tabularnewline
48 & 10443 & 9267.5945875201 & 1175.4054124799 \tabularnewline
49 & 10357 & 9393.10074801633 & 963.899251983671 \tabularnewline
50 & 8586 & 9219.32298732925 & -633.322987329245 \tabularnewline
51 & 8892 & 9064.8538667185 & -172.853866718504 \tabularnewline
52 & 8329 & 8630.4094650008 & -301.409465000795 \tabularnewline
53 & 8101 & 8466.28602435188 & -365.286024351883 \tabularnewline
54 & 7922 & 8128.38482301589 & -206.384823015887 \tabularnewline
55 & 8120 & 8022.187302596 & 97.8126974039972 \tabularnewline
56 & 7838 & 7877.37250202343 & -39.3725020234331 \tabularnewline
57 & 7735 & 8176.65642320674 & -441.656423206744 \tabularnewline
58 & 8406 & 8572.48354477177 & -166.483544771767 \tabularnewline
59 & 8209 & 9084.16250679485 & -875.162506794847 \tabularnewline
60 & 9451 & 9422.06370813084 & 28.9362918691574 \tabularnewline
61 & 10041 & 9248.28594744376 & 792.714052556241 \tabularnewline
62 & 9411 & 9470.3353083217 & -59.3353083216992 \tabularnewline
63 & 10405 & 9006.92794648948 & 1398.07205351052 \tabularnewline
64 & 8467 & 8669.02674515348 & -202.026745153481 \tabularnewline
65 & 8464 & 8408.36010412286 & 55.639895877145 \tabularnewline
66 & 8102 & 7935.29842225246 & 166.701577747539 \tabularnewline
67 & 7627 & 7925.64410221429 & -298.64410221429 \tabularnewline
68 & 7513 & 8080.11322282503 & -567.11322282503 \tabularnewline
69 & 7510 & 8099.42186290137 & -589.421862901373 \tabularnewline
70 & 8291 & 8340.77986385566 & -49.7798638556558 \tabularnewline
71 & 8064 & 9113.12546690936 & -1049.12546690936 \tabularnewline
72 & 9383 & 9364.13778790181 & 18.8622120981853 \tabularnewline
73 & 9706 & 9537.9155485889 & 168.084451411102 \tabularnewline
74 & 8579 & 9479.98962835987 & -900.98962835987 \tabularnewline
75 & 9474 & 9267.5945875201 & 206.405412479898 \tabularnewline
76 & 8318 & 8804.18722568788 & -486.187225687879 \tabularnewline
77 & 8213 & 8331.12554381748 & -118.125543817485 \tabularnewline
78 & 8059 & 8031.84162263417 & 27.1583773658262 \tabularnewline
79 & 9111 & 7481.54538045841 & 1629.45461954159 \tabularnewline
80 & 7708 & 8128.38482301589 & -420.384823015887 \tabularnewline
81 & 7680 & 7925.64410221429 & -245.64410221429 \tabularnewline
82 & 8014 & 8331.12554381748 & -317.125543817485 \tabularnewline
83 & 8007 & 8823.49586576422 & -816.495865764221 \tabularnewline
84 & 8718 & 9132.4341069857 & -414.434106985703 \tabularnewline
85 & 9486 & 9006.92794648948 & 479.072053510524 \tabularnewline
86 & 9113 & 9045.54522664216 & 67.4547733578384 \tabularnewline
87 & 9025 & 8929.69338618411 & 95.3066138158942 \tabularnewline
88 & 8476 & 8321.47122377931 & 154.528776220687 \tabularnewline
89 & 7952 & 8292.5082636648 & -340.508263664799 \tabularnewline
90 & 7759 & 8012.53298255783 & -253.532982557831 \tabularnewline
91 & 7835 & 8041.49594267235 & -206.495942672345 \tabularnewline
92 & 7600 & 8041.49594267235 & -441.495942672345 \tabularnewline
93 & 7651 & 8340.77986385566 & -689.779863855656 \tabularnewline
94 & 8319 & 8697.98970526799 & -378.989705267994 \tabularnewline
95 & 8812 & 9045.54522664216 & -233.545226642162 \tabularnewline
96 & 8630 & 9306.21186767279 & -676.211867672787 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154651&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]12008[/C][C]9315.86618771096[/C][C]2692.13381228904[/C][/ROW]
[ROW][C]2[/C][C]9169[/C][C]9132.4341069857[/C][C]36.5658930142972[/C][/ROW]
[ROW][C]3[/C][C]8788[/C][C]9016.58226652765[/C][C]-228.582266527648[/C][/ROW]
[ROW][C]4[/C][C]8417[/C][C]8688.33538522982[/C][C]-271.335385229823[/C][/ROW]
[ROW][C]5[/C][C]8247[/C][C]8244.23666347394[/C][C]2.76333652605727[/C][/ROW]
[ROW][C]6[/C][C]8197[/C][C]8080.11322282503[/C][C]116.88677717497[/C][/ROW]
[ROW][C]7[/C][C]8236[/C][C]8224.9280233976[/C][C]11.0719766023999[/C][/ROW]
[ROW][C]8[/C][C]8253[/C][C]7925.64410221429[/C][C]327.35589778571[/C][/ROW]
[ROW][C]9[/C][C]7733[/C][C]8147.69346309223[/C][C]-414.69346309223[/C][/ROW]
[ROW][C]10[/C][C]8366[/C][C]8611.10082492445[/C][C]-245.100824924452[/C][/ROW]
[ROW][C]11[/C][C]8626[/C][C]8939.34770622228[/C][C]-313.347706222277[/C][/ROW]
[ROW][C]12[/C][C]8863[/C][C]9161.39706710022[/C][C]-298.397067100217[/C][/ROW]
[ROW][C]13[/C][C]10102[/C][C]9373.79210793999[/C][C]728.207892060014[/C][/ROW]
[ROW][C]14[/C][C]8463[/C][C]9238.63162740559[/C][C]-775.631627405588[/C][/ROW]
[ROW][C]15[/C][C]9114[/C][C]9074.50818675668[/C][C]39.4918132433245[/C][/ROW]
[ROW][C]16[/C][C]8563[/C][C]8881.42178599325[/C][C]-318.421785993249[/C][/ROW]
[ROW][C]17[/C][C]8872[/C][C]8244.23666347394[/C][C]627.763336526057[/C][/ROW]
[ROW][C]18[/C][C]8301[/C][C]8186.31074324492[/C][C]114.689256755085[/C][/ROW]
[ROW][C]19[/C][C]8301[/C][C]7896.68114209978[/C][C]404.318857900224[/C][/ROW]
[ROW][C]20[/C][C]8278[/C][C]7848.40954190892[/C][C]429.590458091081[/C][/ROW]
[ROW][C]21[/C][C]7736[/C][C]8456.63170431371[/C][C]-720.631704313711[/C][/ROW]
[ROW][C]22[/C][C]7973[/C][C]8311.81690374114[/C][C]-338.816903741142[/C][/ROW]
[ROW][C]23[/C][C]8268[/C][C]9103.47114687119[/C][C]-835.471146871189[/C][/ROW]
[ROW][C]24[/C][C]9476[/C][C]9383.44642797816[/C][C]92.5535720218427[/C][/ROW]
[ROW][C]25[/C][C]11100[/C][C]9257.94026748193[/C][C]1842.05973251807[/C][/ROW]
[ROW][C]26[/C][C]8962[/C][C]9016.58226652765[/C][C]-54.5822665276476[/C][/ROW]
[ROW][C]27[/C][C]9173[/C][C]8949.00202626045[/C][C]223.997973739552[/C][/ROW]
[ROW][C]28[/C][C]8738[/C][C]8746.26130545885[/C][C]-8.26130545885086[/C][/ROW]
[ROW][C]29[/C][C]8459[/C][C]8389.05146404651[/C][C]69.9485359534875[/C][/ROW]
[ROW][C]30[/C][C]8078[/C][C]8051.15026271052[/C][C]26.8497372894837[/C][/ROW]
[ROW][C]31[/C][C]8411[/C][C]7983.57002244332[/C][C]427.429977556683[/C][/ROW]
[ROW][C]32[/C][C]8291[/C][C]7906.33546213795[/C][C]384.664537862053[/C][/ROW]
[ROW][C]33[/C][C]7810[/C][C]8282.85394362663[/C][C]-472.853943626628[/C][/ROW]
[ROW][C]34[/C][C]8616[/C][C]8688.33538522982[/C][C]-72.3353852298231[/C][/ROW]
[ROW][C]35[/C][C]8312[/C][C]8871.76746595508[/C][C]-559.767465955078[/C][/ROW]
[ROW][C]36[/C][C]9692[/C][C]9277.24890755827[/C][C]414.751092441727[/C][/ROW]
[ROW][C]37[/C][C]9911[/C][C]9479.98962835987[/C][C]431.01037164013[/C][/ROW]
[ROW][C]38[/C][C]8915[/C][C]9431.71802816901[/C][C]-516.718028169014[/C][/ROW]
[ROW][C]39[/C][C]9452[/C][C]8852.45882587874[/C][C]599.541174121265[/C][/ROW]
[ROW][C]40[/C][C]9112[/C][C]8669.02674515348[/C][C]442.97325484652[/C][/ROW]
[ROW][C]41[/C][C]8472[/C][C]8360.088503932[/C][C]111.911496068002[/C][/ROW]
[ROW][C]42[/C][C]8230[/C][C]7838.75522187075[/C][C]391.244778129252[/C][/ROW]
[ROW][C]43[/C][C]8384[/C][C]7819.44658179441[/C][C]564.553418205595[/C][/ROW]
[ROW][C]44[/C][C]8625[/C][C]7732.55770145086[/C][C]892.442298549136[/C][/ROW]
[ROW][C]45[/C][C]8221[/C][C]8224.9280233976[/C][C]-3.92802339760003[/C][/ROW]
[ROW][C]46[/C][C]8649[/C][C]8939.34770622228[/C][C]-290.347706222277[/C][/ROW]
[ROW][C]47[/C][C]8625[/C][C]8900.73042606959[/C][C]-275.730426069592[/C][/ROW]
[ROW][C]48[/C][C]10443[/C][C]9267.5945875201[/C][C]1175.4054124799[/C][/ROW]
[ROW][C]49[/C][C]10357[/C][C]9393.10074801633[/C][C]963.899251983671[/C][/ROW]
[ROW][C]50[/C][C]8586[/C][C]9219.32298732925[/C][C]-633.322987329245[/C][/ROW]
[ROW][C]51[/C][C]8892[/C][C]9064.8538667185[/C][C]-172.853866718504[/C][/ROW]
[ROW][C]52[/C][C]8329[/C][C]8630.4094650008[/C][C]-301.409465000795[/C][/ROW]
[ROW][C]53[/C][C]8101[/C][C]8466.28602435188[/C][C]-365.286024351883[/C][/ROW]
[ROW][C]54[/C][C]7922[/C][C]8128.38482301589[/C][C]-206.384823015887[/C][/ROW]
[ROW][C]55[/C][C]8120[/C][C]8022.187302596[/C][C]97.8126974039972[/C][/ROW]
[ROW][C]56[/C][C]7838[/C][C]7877.37250202343[/C][C]-39.3725020234331[/C][/ROW]
[ROW][C]57[/C][C]7735[/C][C]8176.65642320674[/C][C]-441.656423206744[/C][/ROW]
[ROW][C]58[/C][C]8406[/C][C]8572.48354477177[/C][C]-166.483544771767[/C][/ROW]
[ROW][C]59[/C][C]8209[/C][C]9084.16250679485[/C][C]-875.162506794847[/C][/ROW]
[ROW][C]60[/C][C]9451[/C][C]9422.06370813084[/C][C]28.9362918691574[/C][/ROW]
[ROW][C]61[/C][C]10041[/C][C]9248.28594744376[/C][C]792.714052556241[/C][/ROW]
[ROW][C]62[/C][C]9411[/C][C]9470.3353083217[/C][C]-59.3353083216992[/C][/ROW]
[ROW][C]63[/C][C]10405[/C][C]9006.92794648948[/C][C]1398.07205351052[/C][/ROW]
[ROW][C]64[/C][C]8467[/C][C]8669.02674515348[/C][C]-202.026745153481[/C][/ROW]
[ROW][C]65[/C][C]8464[/C][C]8408.36010412286[/C][C]55.639895877145[/C][/ROW]
[ROW][C]66[/C][C]8102[/C][C]7935.29842225246[/C][C]166.701577747539[/C][/ROW]
[ROW][C]67[/C][C]7627[/C][C]7925.64410221429[/C][C]-298.64410221429[/C][/ROW]
[ROW][C]68[/C][C]7513[/C][C]8080.11322282503[/C][C]-567.11322282503[/C][/ROW]
[ROW][C]69[/C][C]7510[/C][C]8099.42186290137[/C][C]-589.421862901373[/C][/ROW]
[ROW][C]70[/C][C]8291[/C][C]8340.77986385566[/C][C]-49.7798638556558[/C][/ROW]
[ROW][C]71[/C][C]8064[/C][C]9113.12546690936[/C][C]-1049.12546690936[/C][/ROW]
[ROW][C]72[/C][C]9383[/C][C]9364.13778790181[/C][C]18.8622120981853[/C][/ROW]
[ROW][C]73[/C][C]9706[/C][C]9537.9155485889[/C][C]168.084451411102[/C][/ROW]
[ROW][C]74[/C][C]8579[/C][C]9479.98962835987[/C][C]-900.98962835987[/C][/ROW]
[ROW][C]75[/C][C]9474[/C][C]9267.5945875201[/C][C]206.405412479898[/C][/ROW]
[ROW][C]76[/C][C]8318[/C][C]8804.18722568788[/C][C]-486.187225687879[/C][/ROW]
[ROW][C]77[/C][C]8213[/C][C]8331.12554381748[/C][C]-118.125543817485[/C][/ROW]
[ROW][C]78[/C][C]8059[/C][C]8031.84162263417[/C][C]27.1583773658262[/C][/ROW]
[ROW][C]79[/C][C]9111[/C][C]7481.54538045841[/C][C]1629.45461954159[/C][/ROW]
[ROW][C]80[/C][C]7708[/C][C]8128.38482301589[/C][C]-420.384823015887[/C][/ROW]
[ROW][C]81[/C][C]7680[/C][C]7925.64410221429[/C][C]-245.64410221429[/C][/ROW]
[ROW][C]82[/C][C]8014[/C][C]8331.12554381748[/C][C]-317.125543817485[/C][/ROW]
[ROW][C]83[/C][C]8007[/C][C]8823.49586576422[/C][C]-816.495865764221[/C][/ROW]
[ROW][C]84[/C][C]8718[/C][C]9132.4341069857[/C][C]-414.434106985703[/C][/ROW]
[ROW][C]85[/C][C]9486[/C][C]9006.92794648948[/C][C]479.072053510524[/C][/ROW]
[ROW][C]86[/C][C]9113[/C][C]9045.54522664216[/C][C]67.4547733578384[/C][/ROW]
[ROW][C]87[/C][C]9025[/C][C]8929.69338618411[/C][C]95.3066138158942[/C][/ROW]
[ROW][C]88[/C][C]8476[/C][C]8321.47122377931[/C][C]154.528776220687[/C][/ROW]
[ROW][C]89[/C][C]7952[/C][C]8292.5082636648[/C][C]-340.508263664799[/C][/ROW]
[ROW][C]90[/C][C]7759[/C][C]8012.53298255783[/C][C]-253.532982557831[/C][/ROW]
[ROW][C]91[/C][C]7835[/C][C]8041.49594267235[/C][C]-206.495942672345[/C][/ROW]
[ROW][C]92[/C][C]7600[/C][C]8041.49594267235[/C][C]-441.495942672345[/C][/ROW]
[ROW][C]93[/C][C]7651[/C][C]8340.77986385566[/C][C]-689.779863855656[/C][/ROW]
[ROW][C]94[/C][C]8319[/C][C]8697.98970526799[/C][C]-378.989705267994[/C][/ROW]
[ROW][C]95[/C][C]8812[/C][C]9045.54522664216[/C][C]-233.545226642162[/C][/ROW]
[ROW][C]96[/C][C]8630[/C][C]9306.21186767279[/C][C]-676.211867672787[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154651&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154651&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
1120089315.866187710962692.13381228904
291699132.434106985736.5658930142972
387889016.58226652765-228.582266527648
484178688.33538522982-271.335385229823
582478244.236663473942.76333652605727
681978080.11322282503116.88677717497
782368224.928023397611.0719766023999
882537925.64410221429327.35589778571
977338147.69346309223-414.69346309223
1083668611.10082492445-245.100824924452
1186268939.34770622228-313.347706222277
1288639161.39706710022-298.397067100217
13101029373.79210793999728.207892060014
1484639238.63162740559-775.631627405588
1591149074.5081867566839.4918132433245
1685638881.42178599325-318.421785993249
1788728244.23666347394627.763336526057
1883018186.31074324492114.689256755085
1983017896.68114209978404.318857900224
2082787848.40954190892429.590458091081
2177368456.63170431371-720.631704313711
2279738311.81690374114-338.816903741142
2382689103.47114687119-835.471146871189
2494769383.4464279781692.5535720218427
25111009257.940267481931842.05973251807
2689629016.58226652765-54.5822665276476
2791738949.00202626045223.997973739552
2887388746.26130545885-8.26130545885086
2984598389.0514640465169.9485359534875
3080788051.1502627105226.8497372894837
3184117983.57002244332427.429977556683
3282917906.33546213795384.664537862053
3378108282.85394362663-472.853943626628
3486168688.33538522982-72.3353852298231
3583128871.76746595508-559.767465955078
3696929277.24890755827414.751092441727
3799119479.98962835987431.01037164013
3889159431.71802816901-516.718028169014
3994528852.45882587874599.541174121265
4091128669.02674515348442.97325484652
4184728360.088503932111.911496068002
4282307838.75522187075391.244778129252
4383847819.44658179441564.553418205595
4486257732.55770145086892.442298549136
4582218224.9280233976-3.92802339760003
4686498939.34770622228-290.347706222277
4786258900.73042606959-275.730426069592
48104439267.59458752011175.4054124799
49103579393.10074801633963.899251983671
5085869219.32298732925-633.322987329245
5188929064.8538667185-172.853866718504
5283298630.4094650008-301.409465000795
5381018466.28602435188-365.286024351883
5479228128.38482301589-206.384823015887
5581208022.18730259697.8126974039972
5678387877.37250202343-39.3725020234331
5777358176.65642320674-441.656423206744
5884068572.48354477177-166.483544771767
5982099084.16250679485-875.162506794847
6094519422.0637081308428.9362918691574
61100419248.28594744376792.714052556241
6294119470.3353083217-59.3353083216992
63104059006.927946489481398.07205351052
6484678669.02674515348-202.026745153481
6584648408.3601041228655.639895877145
6681027935.29842225246166.701577747539
6776277925.64410221429-298.64410221429
6875138080.11322282503-567.11322282503
6975108099.42186290137-589.421862901373
7082918340.77986385566-49.7798638556558
7180649113.12546690936-1049.12546690936
7293839364.1377879018118.8622120981853
7397069537.9155485889168.084451411102
7485799479.98962835987-900.98962835987
7594749267.5945875201206.405412479898
7683188804.18722568788-486.187225687879
7782138331.12554381748-118.125543817485
7880598031.8416226341727.1583773658262
7991117481.545380458411629.45461954159
8077088128.38482301589-420.384823015887
8176807925.64410221429-245.64410221429
8280148331.12554381748-317.125543817485
8380078823.49586576422-816.495865764221
8487189132.4341069857-414.434106985703
8594869006.92794648948479.072053510524
8691139045.5452266421667.4547733578384
8790258929.6933861841195.3066138158942
8884768321.47122377931154.528776220687
8979528292.5082636648-340.508263664799
9077598012.53298255783-253.532982557831
9178358041.49594267235-206.495942672345
9276008041.49594267235-441.495942672345
9376518340.77986385566-689.779863855656
9483198697.98970526799-378.989705267994
9588129045.54522664216-233.545226642162
9686309306.21186767279-676.211867672787







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.9969123107414850.006175378517029120.00308768925851456
60.9957688870439490.008462225912102250.00423111295605113
70.9906416766204880.01871664675902380.0093583233795119
80.9881921240988620.02361575180227630.0118078759011381
90.9810185070811190.03796298583776230.0189814929188811
100.9747257485829960.05054850283400790.0252742514170039
110.9762765176587760.04744696468244730.0237234823412237
120.9772666221657980.04546675566840420.0227333778342021
130.9686620111278350.06267597774432970.0313379888721649
140.9855515947262780.02889681054744430.0144484052737222
150.9770748088563980.04585038228720330.0229251911436017
160.9696350779954940.06072984400901160.0303649220045058
170.9703073200357690.05938535992846260.0296926799642313
180.9560160056735460.08796798865290780.0439839943264539
190.9458558424325210.1082883151349580.0541441575674789
200.9332407740848730.1335184518302540.0667592259151271
210.9434983531995870.1130032936008250.0565016468004126
220.9287690281498640.1424619437002720.0712309718501358
230.9488183644990190.1023632710019610.0511816355009806
240.9286690614125140.1426618771749730.0713309385874863
250.994992954627120.01001409074576080.00500704537288041
260.9923629565919720.01527408681605640.00763704340802819
270.9887195008053370.02256099838932510.0112804991946626
280.9832101600406590.03357967991868130.0167898399593406
290.9754875878003890.04902482439922110.0245124121996106
300.9649807065247950.070038586950410.035019293475205
310.958540566735850.08291886652829970.0414594332641498
320.9494468683670230.1011062632659540.0505531316329768
330.9434890065216180.1130219869567630.0565109934783816
340.9248359168361030.1503281663277940.0751640831638969
350.9242693203607560.1514613592784890.0757306796392444
360.9103318929545360.1793362140909280.0896681070454642
370.8963074820550430.2073850358899140.103692517944957
380.8938164730615990.2123670538768020.106183526938401
390.8924626692855150.2150746614289690.107537330714485
400.8788976941723660.2422046116552680.121102305827634
410.8474901523915850.305019695216830.152509847608415
420.8262870147560350.347425970487930.173712985243965
430.8217126385145760.3565747229708480.178287361485424
440.8666432560839420.2667134878321170.133356743916058
450.8337107175451810.3325785649096380.166289282454819
460.8054392352381060.3891215295237890.194560764761894
470.7728583595257510.4542832809484980.227141640474249
480.8874231536289260.2251536927421470.112576846371074
490.9375728119421390.1248543761157230.0624271880578614
500.9391843775565330.1216312448869340.0608156224434671
510.921462463462940.1570750730741190.0785375365370597
520.9033684052618740.1932631894762510.0966315947381257
530.8853714626971060.2292570746057890.114628537302894
540.8572242598402240.2855514803195510.142775740159776
550.8231541167446120.3536917665107760.176845883255388
560.7817425379012320.4365149241975360.218257462098768
570.7576764178377250.4846471643245490.242323582162275
580.7109506709813790.5780986580372420.289049329018621
590.757542653709260.4849146925814790.24245734629074
600.7109032524980780.5781934950038440.289096747501922
610.7805009830073770.4389980339852460.219499016992623
620.7376746075159990.5246507849680020.262325392484001
630.9564945123788770.08701097524224630.0435054876211231
640.9405109068606760.1189781862786480.0594890931393238
650.9216460208443760.1567079583112480.078353979155624
660.8993743233003430.2012513533993150.100625676699657
670.8736970196982740.2526059606034520.126302980301726
680.8677077741766250.2645844516467490.132292225823375
690.8665435134524680.2669129730950650.133456486547532
700.8260328535369110.3479342929261780.173967146463089
710.8777454609115180.2445090781769650.122254539088482
720.8536333899092550.292733220181490.146366610090745
730.8595762155028070.2808475689943870.140423784497193
740.8597035460789760.2805929078420470.140296453921024
750.8647265702410670.2705468595178670.135273429758933
760.8297773957857580.3404452084284830.170222604214242
770.7753392788000130.4493214423999740.224660721199987
780.7104906869571420.5790186260857160.289509313042858
790.9950430552270320.009913889545935840.00495694477296792
800.9911925145444080.01761497091118310.00880748545559155
810.9837887810866380.03242243782672410.016211218913362
820.9711841239735190.0576317520529620.028815876026481
830.9807334874515560.03853302509688750.0192665125484437
840.9708058416741430.05838831665171430.0291941583258571
850.9879514297614910.02409714047701730.0120485702385086
860.9847437444077950.030512511184410.015256255592205
870.98870799729160.02258400541679910.0112920027083995
880.9962992698249620.007401460350077040.00370073017503852
890.9878963631930670.02420727361386680.0121036368069334
900.9662516862723090.06749662745538170.0337483137276909
910.9339993473018960.1320013053962080.0660006526981042

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.996912310741485 & 0.00617537851702912 & 0.00308768925851456 \tabularnewline
6 & 0.995768887043949 & 0.00846222591210225 & 0.00423111295605113 \tabularnewline
7 & 0.990641676620488 & 0.0187166467590238 & 0.0093583233795119 \tabularnewline
8 & 0.988192124098862 & 0.0236157518022763 & 0.0118078759011381 \tabularnewline
9 & 0.981018507081119 & 0.0379629858377623 & 0.0189814929188811 \tabularnewline
10 & 0.974725748582996 & 0.0505485028340079 & 0.0252742514170039 \tabularnewline
11 & 0.976276517658776 & 0.0474469646824473 & 0.0237234823412237 \tabularnewline
12 & 0.977266622165798 & 0.0454667556684042 & 0.0227333778342021 \tabularnewline
13 & 0.968662011127835 & 0.0626759777443297 & 0.0313379888721649 \tabularnewline
14 & 0.985551594726278 & 0.0288968105474443 & 0.0144484052737222 \tabularnewline
15 & 0.977074808856398 & 0.0458503822872033 & 0.0229251911436017 \tabularnewline
16 & 0.969635077995494 & 0.0607298440090116 & 0.0303649220045058 \tabularnewline
17 & 0.970307320035769 & 0.0593853599284626 & 0.0296926799642313 \tabularnewline
18 & 0.956016005673546 & 0.0879679886529078 & 0.0439839943264539 \tabularnewline
19 & 0.945855842432521 & 0.108288315134958 & 0.0541441575674789 \tabularnewline
20 & 0.933240774084873 & 0.133518451830254 & 0.0667592259151271 \tabularnewline
21 & 0.943498353199587 & 0.113003293600825 & 0.0565016468004126 \tabularnewline
22 & 0.928769028149864 & 0.142461943700272 & 0.0712309718501358 \tabularnewline
23 & 0.948818364499019 & 0.102363271001961 & 0.0511816355009806 \tabularnewline
24 & 0.928669061412514 & 0.142661877174973 & 0.0713309385874863 \tabularnewline
25 & 0.99499295462712 & 0.0100140907457608 & 0.00500704537288041 \tabularnewline
26 & 0.992362956591972 & 0.0152740868160564 & 0.00763704340802819 \tabularnewline
27 & 0.988719500805337 & 0.0225609983893251 & 0.0112804991946626 \tabularnewline
28 & 0.983210160040659 & 0.0335796799186813 & 0.0167898399593406 \tabularnewline
29 & 0.975487587800389 & 0.0490248243992211 & 0.0245124121996106 \tabularnewline
30 & 0.964980706524795 & 0.07003858695041 & 0.035019293475205 \tabularnewline
31 & 0.95854056673585 & 0.0829188665282997 & 0.0414594332641498 \tabularnewline
32 & 0.949446868367023 & 0.101106263265954 & 0.0505531316329768 \tabularnewline
33 & 0.943489006521618 & 0.113021986956763 & 0.0565109934783816 \tabularnewline
34 & 0.924835916836103 & 0.150328166327794 & 0.0751640831638969 \tabularnewline
35 & 0.924269320360756 & 0.151461359278489 & 0.0757306796392444 \tabularnewline
36 & 0.910331892954536 & 0.179336214090928 & 0.0896681070454642 \tabularnewline
37 & 0.896307482055043 & 0.207385035889914 & 0.103692517944957 \tabularnewline
38 & 0.893816473061599 & 0.212367053876802 & 0.106183526938401 \tabularnewline
39 & 0.892462669285515 & 0.215074661428969 & 0.107537330714485 \tabularnewline
40 & 0.878897694172366 & 0.242204611655268 & 0.121102305827634 \tabularnewline
41 & 0.847490152391585 & 0.30501969521683 & 0.152509847608415 \tabularnewline
42 & 0.826287014756035 & 0.34742597048793 & 0.173712985243965 \tabularnewline
43 & 0.821712638514576 & 0.356574722970848 & 0.178287361485424 \tabularnewline
44 & 0.866643256083942 & 0.266713487832117 & 0.133356743916058 \tabularnewline
45 & 0.833710717545181 & 0.332578564909638 & 0.166289282454819 \tabularnewline
46 & 0.805439235238106 & 0.389121529523789 & 0.194560764761894 \tabularnewline
47 & 0.772858359525751 & 0.454283280948498 & 0.227141640474249 \tabularnewline
48 & 0.887423153628926 & 0.225153692742147 & 0.112576846371074 \tabularnewline
49 & 0.937572811942139 & 0.124854376115723 & 0.0624271880578614 \tabularnewline
50 & 0.939184377556533 & 0.121631244886934 & 0.0608156224434671 \tabularnewline
51 & 0.92146246346294 & 0.157075073074119 & 0.0785375365370597 \tabularnewline
52 & 0.903368405261874 & 0.193263189476251 & 0.0966315947381257 \tabularnewline
53 & 0.885371462697106 & 0.229257074605789 & 0.114628537302894 \tabularnewline
54 & 0.857224259840224 & 0.285551480319551 & 0.142775740159776 \tabularnewline
55 & 0.823154116744612 & 0.353691766510776 & 0.176845883255388 \tabularnewline
56 & 0.781742537901232 & 0.436514924197536 & 0.218257462098768 \tabularnewline
57 & 0.757676417837725 & 0.484647164324549 & 0.242323582162275 \tabularnewline
58 & 0.710950670981379 & 0.578098658037242 & 0.289049329018621 \tabularnewline
59 & 0.75754265370926 & 0.484914692581479 & 0.24245734629074 \tabularnewline
60 & 0.710903252498078 & 0.578193495003844 & 0.289096747501922 \tabularnewline
61 & 0.780500983007377 & 0.438998033985246 & 0.219499016992623 \tabularnewline
62 & 0.737674607515999 & 0.524650784968002 & 0.262325392484001 \tabularnewline
63 & 0.956494512378877 & 0.0870109752422463 & 0.0435054876211231 \tabularnewline
64 & 0.940510906860676 & 0.118978186278648 & 0.0594890931393238 \tabularnewline
65 & 0.921646020844376 & 0.156707958311248 & 0.078353979155624 \tabularnewline
66 & 0.899374323300343 & 0.201251353399315 & 0.100625676699657 \tabularnewline
67 & 0.873697019698274 & 0.252605960603452 & 0.126302980301726 \tabularnewline
68 & 0.867707774176625 & 0.264584451646749 & 0.132292225823375 \tabularnewline
69 & 0.866543513452468 & 0.266912973095065 & 0.133456486547532 \tabularnewline
70 & 0.826032853536911 & 0.347934292926178 & 0.173967146463089 \tabularnewline
71 & 0.877745460911518 & 0.244509078176965 & 0.122254539088482 \tabularnewline
72 & 0.853633389909255 & 0.29273322018149 & 0.146366610090745 \tabularnewline
73 & 0.859576215502807 & 0.280847568994387 & 0.140423784497193 \tabularnewline
74 & 0.859703546078976 & 0.280592907842047 & 0.140296453921024 \tabularnewline
75 & 0.864726570241067 & 0.270546859517867 & 0.135273429758933 \tabularnewline
76 & 0.829777395785758 & 0.340445208428483 & 0.170222604214242 \tabularnewline
77 & 0.775339278800013 & 0.449321442399974 & 0.224660721199987 \tabularnewline
78 & 0.710490686957142 & 0.579018626085716 & 0.289509313042858 \tabularnewline
79 & 0.995043055227032 & 0.00991388954593584 & 0.00495694477296792 \tabularnewline
80 & 0.991192514544408 & 0.0176149709111831 & 0.00880748545559155 \tabularnewline
81 & 0.983788781086638 & 0.0324224378267241 & 0.016211218913362 \tabularnewline
82 & 0.971184123973519 & 0.057631752052962 & 0.028815876026481 \tabularnewline
83 & 0.980733487451556 & 0.0385330250968875 & 0.0192665125484437 \tabularnewline
84 & 0.970805841674143 & 0.0583883166517143 & 0.0291941583258571 \tabularnewline
85 & 0.987951429761491 & 0.0240971404770173 & 0.0120485702385086 \tabularnewline
86 & 0.984743744407795 & 0.03051251118441 & 0.015256255592205 \tabularnewline
87 & 0.9887079972916 & 0.0225840054167991 & 0.0112920027083995 \tabularnewline
88 & 0.996299269824962 & 0.00740146035007704 & 0.00370073017503852 \tabularnewline
89 & 0.987896363193067 & 0.0242072736138668 & 0.0121036368069334 \tabularnewline
90 & 0.966251686272309 & 0.0674966274553817 & 0.0337483137276909 \tabularnewline
91 & 0.933999347301896 & 0.132001305396208 & 0.0660006526981042 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154651&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]5[/C][C]0.996912310741485[/C][C]0.00617537851702912[/C][C]0.00308768925851456[/C][/ROW]
[ROW][C]6[/C][C]0.995768887043949[/C][C]0.00846222591210225[/C][C]0.00423111295605113[/C][/ROW]
[ROW][C]7[/C][C]0.990641676620488[/C][C]0.0187166467590238[/C][C]0.0093583233795119[/C][/ROW]
[ROW][C]8[/C][C]0.988192124098862[/C][C]0.0236157518022763[/C][C]0.0118078759011381[/C][/ROW]
[ROW][C]9[/C][C]0.981018507081119[/C][C]0.0379629858377623[/C][C]0.0189814929188811[/C][/ROW]
[ROW][C]10[/C][C]0.974725748582996[/C][C]0.0505485028340079[/C][C]0.0252742514170039[/C][/ROW]
[ROW][C]11[/C][C]0.976276517658776[/C][C]0.0474469646824473[/C][C]0.0237234823412237[/C][/ROW]
[ROW][C]12[/C][C]0.977266622165798[/C][C]0.0454667556684042[/C][C]0.0227333778342021[/C][/ROW]
[ROW][C]13[/C][C]0.968662011127835[/C][C]0.0626759777443297[/C][C]0.0313379888721649[/C][/ROW]
[ROW][C]14[/C][C]0.985551594726278[/C][C]0.0288968105474443[/C][C]0.0144484052737222[/C][/ROW]
[ROW][C]15[/C][C]0.977074808856398[/C][C]0.0458503822872033[/C][C]0.0229251911436017[/C][/ROW]
[ROW][C]16[/C][C]0.969635077995494[/C][C]0.0607298440090116[/C][C]0.0303649220045058[/C][/ROW]
[ROW][C]17[/C][C]0.970307320035769[/C][C]0.0593853599284626[/C][C]0.0296926799642313[/C][/ROW]
[ROW][C]18[/C][C]0.956016005673546[/C][C]0.0879679886529078[/C][C]0.0439839943264539[/C][/ROW]
[ROW][C]19[/C][C]0.945855842432521[/C][C]0.108288315134958[/C][C]0.0541441575674789[/C][/ROW]
[ROW][C]20[/C][C]0.933240774084873[/C][C]0.133518451830254[/C][C]0.0667592259151271[/C][/ROW]
[ROW][C]21[/C][C]0.943498353199587[/C][C]0.113003293600825[/C][C]0.0565016468004126[/C][/ROW]
[ROW][C]22[/C][C]0.928769028149864[/C][C]0.142461943700272[/C][C]0.0712309718501358[/C][/ROW]
[ROW][C]23[/C][C]0.948818364499019[/C][C]0.102363271001961[/C][C]0.0511816355009806[/C][/ROW]
[ROW][C]24[/C][C]0.928669061412514[/C][C]0.142661877174973[/C][C]0.0713309385874863[/C][/ROW]
[ROW][C]25[/C][C]0.99499295462712[/C][C]0.0100140907457608[/C][C]0.00500704537288041[/C][/ROW]
[ROW][C]26[/C][C]0.992362956591972[/C][C]0.0152740868160564[/C][C]0.00763704340802819[/C][/ROW]
[ROW][C]27[/C][C]0.988719500805337[/C][C]0.0225609983893251[/C][C]0.0112804991946626[/C][/ROW]
[ROW][C]28[/C][C]0.983210160040659[/C][C]0.0335796799186813[/C][C]0.0167898399593406[/C][/ROW]
[ROW][C]29[/C][C]0.975487587800389[/C][C]0.0490248243992211[/C][C]0.0245124121996106[/C][/ROW]
[ROW][C]30[/C][C]0.964980706524795[/C][C]0.07003858695041[/C][C]0.035019293475205[/C][/ROW]
[ROW][C]31[/C][C]0.95854056673585[/C][C]0.0829188665282997[/C][C]0.0414594332641498[/C][/ROW]
[ROW][C]32[/C][C]0.949446868367023[/C][C]0.101106263265954[/C][C]0.0505531316329768[/C][/ROW]
[ROW][C]33[/C][C]0.943489006521618[/C][C]0.113021986956763[/C][C]0.0565109934783816[/C][/ROW]
[ROW][C]34[/C][C]0.924835916836103[/C][C]0.150328166327794[/C][C]0.0751640831638969[/C][/ROW]
[ROW][C]35[/C][C]0.924269320360756[/C][C]0.151461359278489[/C][C]0.0757306796392444[/C][/ROW]
[ROW][C]36[/C][C]0.910331892954536[/C][C]0.179336214090928[/C][C]0.0896681070454642[/C][/ROW]
[ROW][C]37[/C][C]0.896307482055043[/C][C]0.207385035889914[/C][C]0.103692517944957[/C][/ROW]
[ROW][C]38[/C][C]0.893816473061599[/C][C]0.212367053876802[/C][C]0.106183526938401[/C][/ROW]
[ROW][C]39[/C][C]0.892462669285515[/C][C]0.215074661428969[/C][C]0.107537330714485[/C][/ROW]
[ROW][C]40[/C][C]0.878897694172366[/C][C]0.242204611655268[/C][C]0.121102305827634[/C][/ROW]
[ROW][C]41[/C][C]0.847490152391585[/C][C]0.30501969521683[/C][C]0.152509847608415[/C][/ROW]
[ROW][C]42[/C][C]0.826287014756035[/C][C]0.34742597048793[/C][C]0.173712985243965[/C][/ROW]
[ROW][C]43[/C][C]0.821712638514576[/C][C]0.356574722970848[/C][C]0.178287361485424[/C][/ROW]
[ROW][C]44[/C][C]0.866643256083942[/C][C]0.266713487832117[/C][C]0.133356743916058[/C][/ROW]
[ROW][C]45[/C][C]0.833710717545181[/C][C]0.332578564909638[/C][C]0.166289282454819[/C][/ROW]
[ROW][C]46[/C][C]0.805439235238106[/C][C]0.389121529523789[/C][C]0.194560764761894[/C][/ROW]
[ROW][C]47[/C][C]0.772858359525751[/C][C]0.454283280948498[/C][C]0.227141640474249[/C][/ROW]
[ROW][C]48[/C][C]0.887423153628926[/C][C]0.225153692742147[/C][C]0.112576846371074[/C][/ROW]
[ROW][C]49[/C][C]0.937572811942139[/C][C]0.124854376115723[/C][C]0.0624271880578614[/C][/ROW]
[ROW][C]50[/C][C]0.939184377556533[/C][C]0.121631244886934[/C][C]0.0608156224434671[/C][/ROW]
[ROW][C]51[/C][C]0.92146246346294[/C][C]0.157075073074119[/C][C]0.0785375365370597[/C][/ROW]
[ROW][C]52[/C][C]0.903368405261874[/C][C]0.193263189476251[/C][C]0.0966315947381257[/C][/ROW]
[ROW][C]53[/C][C]0.885371462697106[/C][C]0.229257074605789[/C][C]0.114628537302894[/C][/ROW]
[ROW][C]54[/C][C]0.857224259840224[/C][C]0.285551480319551[/C][C]0.142775740159776[/C][/ROW]
[ROW][C]55[/C][C]0.823154116744612[/C][C]0.353691766510776[/C][C]0.176845883255388[/C][/ROW]
[ROW][C]56[/C][C]0.781742537901232[/C][C]0.436514924197536[/C][C]0.218257462098768[/C][/ROW]
[ROW][C]57[/C][C]0.757676417837725[/C][C]0.484647164324549[/C][C]0.242323582162275[/C][/ROW]
[ROW][C]58[/C][C]0.710950670981379[/C][C]0.578098658037242[/C][C]0.289049329018621[/C][/ROW]
[ROW][C]59[/C][C]0.75754265370926[/C][C]0.484914692581479[/C][C]0.24245734629074[/C][/ROW]
[ROW][C]60[/C][C]0.710903252498078[/C][C]0.578193495003844[/C][C]0.289096747501922[/C][/ROW]
[ROW][C]61[/C][C]0.780500983007377[/C][C]0.438998033985246[/C][C]0.219499016992623[/C][/ROW]
[ROW][C]62[/C][C]0.737674607515999[/C][C]0.524650784968002[/C][C]0.262325392484001[/C][/ROW]
[ROW][C]63[/C][C]0.956494512378877[/C][C]0.0870109752422463[/C][C]0.0435054876211231[/C][/ROW]
[ROW][C]64[/C][C]0.940510906860676[/C][C]0.118978186278648[/C][C]0.0594890931393238[/C][/ROW]
[ROW][C]65[/C][C]0.921646020844376[/C][C]0.156707958311248[/C][C]0.078353979155624[/C][/ROW]
[ROW][C]66[/C][C]0.899374323300343[/C][C]0.201251353399315[/C][C]0.100625676699657[/C][/ROW]
[ROW][C]67[/C][C]0.873697019698274[/C][C]0.252605960603452[/C][C]0.126302980301726[/C][/ROW]
[ROW][C]68[/C][C]0.867707774176625[/C][C]0.264584451646749[/C][C]0.132292225823375[/C][/ROW]
[ROW][C]69[/C][C]0.866543513452468[/C][C]0.266912973095065[/C][C]0.133456486547532[/C][/ROW]
[ROW][C]70[/C][C]0.826032853536911[/C][C]0.347934292926178[/C][C]0.173967146463089[/C][/ROW]
[ROW][C]71[/C][C]0.877745460911518[/C][C]0.244509078176965[/C][C]0.122254539088482[/C][/ROW]
[ROW][C]72[/C][C]0.853633389909255[/C][C]0.29273322018149[/C][C]0.146366610090745[/C][/ROW]
[ROW][C]73[/C][C]0.859576215502807[/C][C]0.280847568994387[/C][C]0.140423784497193[/C][/ROW]
[ROW][C]74[/C][C]0.859703546078976[/C][C]0.280592907842047[/C][C]0.140296453921024[/C][/ROW]
[ROW][C]75[/C][C]0.864726570241067[/C][C]0.270546859517867[/C][C]0.135273429758933[/C][/ROW]
[ROW][C]76[/C][C]0.829777395785758[/C][C]0.340445208428483[/C][C]0.170222604214242[/C][/ROW]
[ROW][C]77[/C][C]0.775339278800013[/C][C]0.449321442399974[/C][C]0.224660721199987[/C][/ROW]
[ROW][C]78[/C][C]0.710490686957142[/C][C]0.579018626085716[/C][C]0.289509313042858[/C][/ROW]
[ROW][C]79[/C][C]0.995043055227032[/C][C]0.00991388954593584[/C][C]0.00495694477296792[/C][/ROW]
[ROW][C]80[/C][C]0.991192514544408[/C][C]0.0176149709111831[/C][C]0.00880748545559155[/C][/ROW]
[ROW][C]81[/C][C]0.983788781086638[/C][C]0.0324224378267241[/C][C]0.016211218913362[/C][/ROW]
[ROW][C]82[/C][C]0.971184123973519[/C][C]0.057631752052962[/C][C]0.028815876026481[/C][/ROW]
[ROW][C]83[/C][C]0.980733487451556[/C][C]0.0385330250968875[/C][C]0.0192665125484437[/C][/ROW]
[ROW][C]84[/C][C]0.970805841674143[/C][C]0.0583883166517143[/C][C]0.0291941583258571[/C][/ROW]
[ROW][C]85[/C][C]0.987951429761491[/C][C]0.0240971404770173[/C][C]0.0120485702385086[/C][/ROW]
[ROW][C]86[/C][C]0.984743744407795[/C][C]0.03051251118441[/C][C]0.015256255592205[/C][/ROW]
[ROW][C]87[/C][C]0.9887079972916[/C][C]0.0225840054167991[/C][C]0.0112920027083995[/C][/ROW]
[ROW][C]88[/C][C]0.996299269824962[/C][C]0.00740146035007704[/C][C]0.00370073017503852[/C][/ROW]
[ROW][C]89[/C][C]0.987896363193067[/C][C]0.0242072736138668[/C][C]0.0121036368069334[/C][/ROW]
[ROW][C]90[/C][C]0.966251686272309[/C][C]0.0674966274553817[/C][C]0.0337483137276909[/C][/ROW]
[ROW][C]91[/C][C]0.933999347301896[/C][C]0.132001305396208[/C][C]0.0660006526981042[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154651&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154651&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
50.9969123107414850.006175378517029120.00308768925851456
60.9957688870439490.008462225912102250.00423111295605113
70.9906416766204880.01871664675902380.0093583233795119
80.9881921240988620.02361575180227630.0118078759011381
90.9810185070811190.03796298583776230.0189814929188811
100.9747257485829960.05054850283400790.0252742514170039
110.9762765176587760.04744696468244730.0237234823412237
120.9772666221657980.04546675566840420.0227333778342021
130.9686620111278350.06267597774432970.0313379888721649
140.9855515947262780.02889681054744430.0144484052737222
150.9770748088563980.04585038228720330.0229251911436017
160.9696350779954940.06072984400901160.0303649220045058
170.9703073200357690.05938535992846260.0296926799642313
180.9560160056735460.08796798865290780.0439839943264539
190.9458558424325210.1082883151349580.0541441575674789
200.9332407740848730.1335184518302540.0667592259151271
210.9434983531995870.1130032936008250.0565016468004126
220.9287690281498640.1424619437002720.0712309718501358
230.9488183644990190.1023632710019610.0511816355009806
240.9286690614125140.1426618771749730.0713309385874863
250.994992954627120.01001409074576080.00500704537288041
260.9923629565919720.01527408681605640.00763704340802819
270.9887195008053370.02256099838932510.0112804991946626
280.9832101600406590.03357967991868130.0167898399593406
290.9754875878003890.04902482439922110.0245124121996106
300.9649807065247950.070038586950410.035019293475205
310.958540566735850.08291886652829970.0414594332641498
320.9494468683670230.1011062632659540.0505531316329768
330.9434890065216180.1130219869567630.0565109934783816
340.9248359168361030.1503281663277940.0751640831638969
350.9242693203607560.1514613592784890.0757306796392444
360.9103318929545360.1793362140909280.0896681070454642
370.8963074820550430.2073850358899140.103692517944957
380.8938164730615990.2123670538768020.106183526938401
390.8924626692855150.2150746614289690.107537330714485
400.8788976941723660.2422046116552680.121102305827634
410.8474901523915850.305019695216830.152509847608415
420.8262870147560350.347425970487930.173712985243965
430.8217126385145760.3565747229708480.178287361485424
440.8666432560839420.2667134878321170.133356743916058
450.8337107175451810.3325785649096380.166289282454819
460.8054392352381060.3891215295237890.194560764761894
470.7728583595257510.4542832809484980.227141640474249
480.8874231536289260.2251536927421470.112576846371074
490.9375728119421390.1248543761157230.0624271880578614
500.9391843775565330.1216312448869340.0608156224434671
510.921462463462940.1570750730741190.0785375365370597
520.9033684052618740.1932631894762510.0966315947381257
530.8853714626971060.2292570746057890.114628537302894
540.8572242598402240.2855514803195510.142775740159776
550.8231541167446120.3536917665107760.176845883255388
560.7817425379012320.4365149241975360.218257462098768
570.7576764178377250.4846471643245490.242323582162275
580.7109506709813790.5780986580372420.289049329018621
590.757542653709260.4849146925814790.24245734629074
600.7109032524980780.5781934950038440.289096747501922
610.7805009830073770.4389980339852460.219499016992623
620.7376746075159990.5246507849680020.262325392484001
630.9564945123788770.08701097524224630.0435054876211231
640.9405109068606760.1189781862786480.0594890931393238
650.9216460208443760.1567079583112480.078353979155624
660.8993743233003430.2012513533993150.100625676699657
670.8736970196982740.2526059606034520.126302980301726
680.8677077741766250.2645844516467490.132292225823375
690.8665435134524680.2669129730950650.133456486547532
700.8260328535369110.3479342929261780.173967146463089
710.8777454609115180.2445090781769650.122254539088482
720.8536333899092550.292733220181490.146366610090745
730.8595762155028070.2808475689943870.140423784497193
740.8597035460789760.2805929078420470.140296453921024
750.8647265702410670.2705468595178670.135273429758933
760.8297773957857580.3404452084284830.170222604214242
770.7753392788000130.4493214423999740.224660721199987
780.7104906869571420.5790186260857160.289509313042858
790.9950430552270320.009913889545935840.00495694477296792
800.9911925145444080.01761497091118310.00880748545559155
810.9837887810866380.03242243782672410.016211218913362
820.9711841239735190.0576317520529620.028815876026481
830.9807334874515560.03853302509688750.0192665125484437
840.9708058416741430.05838831665171430.0291941583258571
850.9879514297614910.02409714047701730.0120485702385086
860.9847437444077950.030512511184410.015256255592205
870.98870799729160.02258400541679910.0112920027083995
880.9962992698249620.007401460350077040.00370073017503852
890.9878963631930670.02420727361386680.0121036368069334
900.9662516862723090.06749662745538170.0337483137276909
910.9339993473018960.1320013053962080.0660006526981042







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level40.0459770114942529NOK
5% type I error level230.264367816091954NOK
10% type I error level340.390804597701149NOK

\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 & 4 & 0.0459770114942529 & NOK \tabularnewline
5% type I error level & 23 & 0.264367816091954 & NOK \tabularnewline
10% type I error level & 34 & 0.390804597701149 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154651&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]4[/C][C]0.0459770114942529[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]23[/C][C]0.264367816091954[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]34[/C][C]0.390804597701149[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154651&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154651&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 level40.0459770114942529NOK
5% type I error level230.264367816091954NOK
10% type I error level340.390804597701149NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')
}