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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 20 Dec 2012 13:43:05 -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/2012/Dec/20/t1356029022atqrisznj1r1f7n.htm/, Retrieved Thu, 28 Mar 2024 10:07:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203004, Retrieved Thu, 28 Mar 2024 10:07:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact156
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Regression met li...] [2012-12-20 18:43:05] [12383fa010e7b5252e187b5f14cfe683] [Current]
- R       [Multiple Regression] [Paper 5.9] [2012-12-21 17:20:37] [fe055a25191a04e375a94ef97fddf389]
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Dataseries X:
1	0	0	1
0	0	0	0
0	0	0	0
0	0	0	0
0	0	0	0
0	0	0	1
0	0	0	0
1	0	0	0
0	0	0	1
0	0	0	0
1	0	0	0
0	0	0	0
0	0	1	0
1	0	0	0
0	0	1	1
1	0	1	1
1	1	1	0
1	0	0	0
0	0	0	1
1	1	1	1
0	0	0	0
0	0	1	1
0	0	0	1
0	0	0	1
1	0	1	1
0	0	1	0
0	0	0	1
0	0	1	0
0	0	0	1
0	0	0	0
0	0	0	0
0	0	0	0
0	0	0	0
1	0	0	1
0	0	0	0
0	0	0	0
1	0	1	0
0	0	1	1
0	0	0	1
1	0	0	0
0	1	1	1
0	0	1	1
0	0	0	1
1	0	0	0
0	0	0	0
0	0	0	1
0	0	0	0
0	0	0	1
0	0	0	1
0	0	0	0
1	0	1	0
1	1	1	0
0	0	0	1
0	1	1	0
0	0	0	0
1	0	1	1
0	0	1	1
0	0	0	1
0	0	0	1
1	1	1	1
1	0	0	1
0	0	1	0
0	0	0	0
1	0	0	1
0	0	0	0
0	0	0	0
1	1	1	0
0	0	0	0
0	0	0	1
0	0	1	0
0	0	0	0
0	0	0	1
0	0	1	1
0	0	1	0
0	0	0	1
1	0	0	1
0	0	0	1
0	0	1	1
1	1	1	1
1	0	0	0
0	0	0	0
0	0	1	1
0	0	0	0
0	1	1	0
0	0	0	1
0	0	0	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ yule.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 & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203004&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203004&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203004&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 time10 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Outcome[t] = + 0.339186258749768 + 0.0323353633518997Treatment[t] -0.162233051977261CA[t] + 0.137637176966352Used[t] + 0.00205627503425921t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Outcome[t] =  +  0.339186258749768 +  0.0323353633518997Treatment[t] -0.162233051977261CA[t] +  0.137637176966352Used[t] +  0.00205627503425921t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203004&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Outcome[t] =  +  0.339186258749768 +  0.0323353633518997Treatment[t] -0.162233051977261CA[t] +  0.137637176966352Used[t] +  0.00205627503425921t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203004&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203004&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
Outcome[t] = + 0.339186258749768 + 0.0323353633518997Treatment[t] -0.162233051977261CA[t] + 0.137637176966352Used[t] + 0.00205627503425921t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.3391862587497680.1201552.82290.0059860.002993
Treatment0.03233536335189970.1311510.24650.805880.40294
CA-0.1622330519772610.213142-0.76110.4487780.224389
Used0.1376371769663520.1347161.02170.3099720.154986
t0.002056275034259210.0022440.91620.3622770.181138

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 0.339186258749768 & 0.120155 & 2.8229 & 0.005986 & 0.002993 \tabularnewline
Treatment & 0.0323353633518997 & 0.131151 & 0.2465 & 0.80588 & 0.40294 \tabularnewline
CA & -0.162233051977261 & 0.213142 & -0.7611 & 0.448778 & 0.224389 \tabularnewline
Used & 0.137637176966352 & 0.134716 & 1.0217 & 0.309972 & 0.154986 \tabularnewline
t & 0.00205627503425921 & 0.002244 & 0.9162 & 0.362277 & 0.181138 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203004&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]0.339186258749768[/C][C]0.120155[/C][C]2.8229[/C][C]0.005986[/C][C]0.002993[/C][/ROW]
[ROW][C]Treatment[/C][C]0.0323353633518997[/C][C]0.131151[/C][C]0.2465[/C][C]0.80588[/C][C]0.40294[/C][/ROW]
[ROW][C]CA[/C][C]-0.162233051977261[/C][C]0.213142[/C][C]-0.7611[/C][C]0.448778[/C][C]0.224389[/C][/ROW]
[ROW][C]Used[/C][C]0.137637176966352[/C][C]0.134716[/C][C]1.0217[/C][C]0.309972[/C][C]0.154986[/C][/ROW]
[ROW][C]t[/C][C]0.00205627503425921[/C][C]0.002244[/C][C]0.9162[/C][C]0.362277[/C][C]0.181138[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203004&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203004&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.3391862587497680.1201552.82290.0059860.002993
Treatment0.03233536335189970.1311510.24650.805880.40294
CA-0.1622330519772610.213142-0.76110.4487780.224389
Used0.1376371769663520.1347161.02170.3099720.154986
t0.002056275034259210.0022440.91620.3622770.181138







Multiple Linear Regression - Regression Statistics
Multiple R0.158606585660115
R-squared0.0251560490147595
Adjusted R-squared-0.0229843930092029
F-TEST (value)0.522555422366869
F-TEST (DF numerator)4
F-TEST (DF denominator)81
p-value0.719397945760993
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.507440033471968
Sum Squared Residuals20.8571263931726

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.158606585660115 \tabularnewline
R-squared & 0.0251560490147595 \tabularnewline
Adjusted R-squared & -0.0229843930092029 \tabularnewline
F-TEST (value) & 0.522555422366869 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 81 \tabularnewline
p-value & 0.719397945760993 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.507440033471968 \tabularnewline
Sum Squared Residuals & 20.8571263931726 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203004&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.158606585660115[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0251560490147595[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0229843930092029[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.522555422366869[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]81[/C][/ROW]
[ROW][C]p-value[/C][C]0.719397945760993[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.507440033471968[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]20.8571263931726[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203004&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203004&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 R0.158606585660115
R-squared0.0251560490147595
Adjusted R-squared-0.0229843930092029
F-TEST (value)0.522555422366869
F-TEST (DF numerator)4
F-TEST (DF denominator)81
p-value0.719397945760993
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.507440033471968
Sum Squared Residuals20.8571263931726







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.3735778971359280.626422102864072
200.343298808818287-0.343298808818287
300.345355083852546-0.345355083852546
400.347411358886806-0.347411358886806
500.349467633921065-0.349467633921065
610.3515239089553240.648476091044676
700.353580183989583-0.353580183989583
800.387971822375742-0.387971822375742
910.3576927340581020.642307265941898
1000.359749009092361-0.359749009092361
1100.39414064747852-0.39414064747852
1200.363861559160879-0.363861559160879
1300.50355501116149-0.50355501116149
1400.400309472581297-0.400309472581297
1510.5076675612300080.492332438769992
1610.5420591996161670.457940800383833
1700.381882422673166-0.381882422673166
1800.408534572718334-0.408534572718334
1910.3782554844006940.621744515599306
2010.3880512477759440.611948752224056
2100.382368034469212-0.382368034469212
2210.5220614864698230.477938513530177
2310.386480584537730.61351941546227
2410.388536859571990.61146314042801
2510.56056567492450.4394343250755
2600.53028658660686-0.53028658660686
2710.3947056846747670.605294315325233
2800.534399136675378-0.534399136675378
2910.3988182347432860.601181765256714
3000.400874509777545-0.400874509777545
3100.402930784811804-0.402930784811804
3200.404987059846063-0.404987059846063
3300.407043334880323-0.407043334880323
3410.4414349732664820.558565026733518
3500.411155884948841-0.411155884948841
3600.4132121599831-0.4132121599831
3700.585240975335611-0.585240975335611
3810.554961887017970.44503811298203
3910.4193809850858780.580619014914122
4000.453772623472037-0.453772623472037
4110.3988976601434870.601102339856513
4210.5631869871550070.436813012844993
4310.4276060852229150.572393914777085
4400.461997723609073-0.461997723609073
4500.431718635291433-0.431718635291433
4610.4337749103256920.566225089674308
4700.435831185359951-0.435831185359951
4810.4378874603942110.562112539605789
4910.439943735428470.56005626457153
5000.442000010462729-0.442000010462729
5100.61402882581524-0.61402882581524
5200.453852048872238-0.453852048872238
5310.4481688355655070.551831164434493
5400.425629235588857-0.425629235588857
5500.452281385634025-0.452281385634025
5610.6243102009865360.375689799013464
5710.5940311126688950.405968887331105
5810.4584502107368030.541549789263197
5910.4605064857710620.539493514228938
6010.4703022491463120.529697750853688
6110.496954399191480.50304560080852
6200.604312487840191-0.604312487840191
6300.468731585908099-0.468731585908099
6410.5031232242942580.496876775705742
6500.472844135976617-0.472844135976617
6600.474900411010876-0.474900411010876
6700.484696174386126-0.484696174386126
6800.479012961079395-0.479012961079395
6910.4810692361136540.518930763886346
7000.620762688114265-0.620762688114265
7100.485181786182173-0.485181786182173
7210.4872380612164320.512761938783568
7310.6269315132170430.373068486782957
7400.628987788251302-0.628987788251302
7510.4934068863192090.506593113680791
7610.5277985247053680.472201475294632
7710.4975194363877280.502480563612272
7810.6372128883883390.362787111611661
7910.5093714747972370.490628525202763
8000.536023624842405-0.536023624842405
8100.505744536524765-0.505744536524765
8210.6454379885253750.354562011474625
8300.509857086593283-0.509857086593283
8400.487317486616633-0.487317486616633
8510.5139696366618010.486030363338199
8600.516025911696061-0.516025911696061

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 0.373577897135928 & 0.626422102864072 \tabularnewline
2 & 0 & 0.343298808818287 & -0.343298808818287 \tabularnewline
3 & 0 & 0.345355083852546 & -0.345355083852546 \tabularnewline
4 & 0 & 0.347411358886806 & -0.347411358886806 \tabularnewline
5 & 0 & 0.349467633921065 & -0.349467633921065 \tabularnewline
6 & 1 & 0.351523908955324 & 0.648476091044676 \tabularnewline
7 & 0 & 0.353580183989583 & -0.353580183989583 \tabularnewline
8 & 0 & 0.387971822375742 & -0.387971822375742 \tabularnewline
9 & 1 & 0.357692734058102 & 0.642307265941898 \tabularnewline
10 & 0 & 0.359749009092361 & -0.359749009092361 \tabularnewline
11 & 0 & 0.39414064747852 & -0.39414064747852 \tabularnewline
12 & 0 & 0.363861559160879 & -0.363861559160879 \tabularnewline
13 & 0 & 0.50355501116149 & -0.50355501116149 \tabularnewline
14 & 0 & 0.400309472581297 & -0.400309472581297 \tabularnewline
15 & 1 & 0.507667561230008 & 0.492332438769992 \tabularnewline
16 & 1 & 0.542059199616167 & 0.457940800383833 \tabularnewline
17 & 0 & 0.381882422673166 & -0.381882422673166 \tabularnewline
18 & 0 & 0.408534572718334 & -0.408534572718334 \tabularnewline
19 & 1 & 0.378255484400694 & 0.621744515599306 \tabularnewline
20 & 1 & 0.388051247775944 & 0.611948752224056 \tabularnewline
21 & 0 & 0.382368034469212 & -0.382368034469212 \tabularnewline
22 & 1 & 0.522061486469823 & 0.477938513530177 \tabularnewline
23 & 1 & 0.38648058453773 & 0.61351941546227 \tabularnewline
24 & 1 & 0.38853685957199 & 0.61146314042801 \tabularnewline
25 & 1 & 0.5605656749245 & 0.4394343250755 \tabularnewline
26 & 0 & 0.53028658660686 & -0.53028658660686 \tabularnewline
27 & 1 & 0.394705684674767 & 0.605294315325233 \tabularnewline
28 & 0 & 0.534399136675378 & -0.534399136675378 \tabularnewline
29 & 1 & 0.398818234743286 & 0.601181765256714 \tabularnewline
30 & 0 & 0.400874509777545 & -0.400874509777545 \tabularnewline
31 & 0 & 0.402930784811804 & -0.402930784811804 \tabularnewline
32 & 0 & 0.404987059846063 & -0.404987059846063 \tabularnewline
33 & 0 & 0.407043334880323 & -0.407043334880323 \tabularnewline
34 & 1 & 0.441434973266482 & 0.558565026733518 \tabularnewline
35 & 0 & 0.411155884948841 & -0.411155884948841 \tabularnewline
36 & 0 & 0.4132121599831 & -0.4132121599831 \tabularnewline
37 & 0 & 0.585240975335611 & -0.585240975335611 \tabularnewline
38 & 1 & 0.55496188701797 & 0.44503811298203 \tabularnewline
39 & 1 & 0.419380985085878 & 0.580619014914122 \tabularnewline
40 & 0 & 0.453772623472037 & -0.453772623472037 \tabularnewline
41 & 1 & 0.398897660143487 & 0.601102339856513 \tabularnewline
42 & 1 & 0.563186987155007 & 0.436813012844993 \tabularnewline
43 & 1 & 0.427606085222915 & 0.572393914777085 \tabularnewline
44 & 0 & 0.461997723609073 & -0.461997723609073 \tabularnewline
45 & 0 & 0.431718635291433 & -0.431718635291433 \tabularnewline
46 & 1 & 0.433774910325692 & 0.566225089674308 \tabularnewline
47 & 0 & 0.435831185359951 & -0.435831185359951 \tabularnewline
48 & 1 & 0.437887460394211 & 0.562112539605789 \tabularnewline
49 & 1 & 0.43994373542847 & 0.56005626457153 \tabularnewline
50 & 0 & 0.442000010462729 & -0.442000010462729 \tabularnewline
51 & 0 & 0.61402882581524 & -0.61402882581524 \tabularnewline
52 & 0 & 0.453852048872238 & -0.453852048872238 \tabularnewline
53 & 1 & 0.448168835565507 & 0.551831164434493 \tabularnewline
54 & 0 & 0.425629235588857 & -0.425629235588857 \tabularnewline
55 & 0 & 0.452281385634025 & -0.452281385634025 \tabularnewline
56 & 1 & 0.624310200986536 & 0.375689799013464 \tabularnewline
57 & 1 & 0.594031112668895 & 0.405968887331105 \tabularnewline
58 & 1 & 0.458450210736803 & 0.541549789263197 \tabularnewline
59 & 1 & 0.460506485771062 & 0.539493514228938 \tabularnewline
60 & 1 & 0.470302249146312 & 0.529697750853688 \tabularnewline
61 & 1 & 0.49695439919148 & 0.50304560080852 \tabularnewline
62 & 0 & 0.604312487840191 & -0.604312487840191 \tabularnewline
63 & 0 & 0.468731585908099 & -0.468731585908099 \tabularnewline
64 & 1 & 0.503123224294258 & 0.496876775705742 \tabularnewline
65 & 0 & 0.472844135976617 & -0.472844135976617 \tabularnewline
66 & 0 & 0.474900411010876 & -0.474900411010876 \tabularnewline
67 & 0 & 0.484696174386126 & -0.484696174386126 \tabularnewline
68 & 0 & 0.479012961079395 & -0.479012961079395 \tabularnewline
69 & 1 & 0.481069236113654 & 0.518930763886346 \tabularnewline
70 & 0 & 0.620762688114265 & -0.620762688114265 \tabularnewline
71 & 0 & 0.485181786182173 & -0.485181786182173 \tabularnewline
72 & 1 & 0.487238061216432 & 0.512761938783568 \tabularnewline
73 & 1 & 0.626931513217043 & 0.373068486782957 \tabularnewline
74 & 0 & 0.628987788251302 & -0.628987788251302 \tabularnewline
75 & 1 & 0.493406886319209 & 0.506593113680791 \tabularnewline
76 & 1 & 0.527798524705368 & 0.472201475294632 \tabularnewline
77 & 1 & 0.497519436387728 & 0.502480563612272 \tabularnewline
78 & 1 & 0.637212888388339 & 0.362787111611661 \tabularnewline
79 & 1 & 0.509371474797237 & 0.490628525202763 \tabularnewline
80 & 0 & 0.536023624842405 & -0.536023624842405 \tabularnewline
81 & 0 & 0.505744536524765 & -0.505744536524765 \tabularnewline
82 & 1 & 0.645437988525375 & 0.354562011474625 \tabularnewline
83 & 0 & 0.509857086593283 & -0.509857086593283 \tabularnewline
84 & 0 & 0.487317486616633 & -0.487317486616633 \tabularnewline
85 & 1 & 0.513969636661801 & 0.486030363338199 \tabularnewline
86 & 0 & 0.516025911696061 & -0.516025911696061 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203004&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]1[/C][C]0.373577897135928[/C][C]0.626422102864072[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.343298808818287[/C][C]-0.343298808818287[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.345355083852546[/C][C]-0.345355083852546[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.347411358886806[/C][C]-0.347411358886806[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.349467633921065[/C][C]-0.349467633921065[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]0.351523908955324[/C][C]0.648476091044676[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.353580183989583[/C][C]-0.353580183989583[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.387971822375742[/C][C]-0.387971822375742[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]0.357692734058102[/C][C]0.642307265941898[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.359749009092361[/C][C]-0.359749009092361[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.39414064747852[/C][C]-0.39414064747852[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.363861559160879[/C][C]-0.363861559160879[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.50355501116149[/C][C]-0.50355501116149[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.400309472581297[/C][C]-0.400309472581297[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]0.507667561230008[/C][C]0.492332438769992[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]0.542059199616167[/C][C]0.457940800383833[/C][/ROW]
[ROW][C]17[/C][C]0[/C][C]0.381882422673166[/C][C]-0.381882422673166[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.408534572718334[/C][C]-0.408534572718334[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]0.378255484400694[/C][C]0.621744515599306[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.388051247775944[/C][C]0.611948752224056[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.382368034469212[/C][C]-0.382368034469212[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]0.522061486469823[/C][C]0.477938513530177[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]0.38648058453773[/C][C]0.61351941546227[/C][/ROW]
[ROW][C]24[/C][C]1[/C][C]0.38853685957199[/C][C]0.61146314042801[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]0.5605656749245[/C][C]0.4394343250755[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.53028658660686[/C][C]-0.53028658660686[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]0.394705684674767[/C][C]0.605294315325233[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.534399136675378[/C][C]-0.534399136675378[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]0.398818234743286[/C][C]0.601181765256714[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.400874509777545[/C][C]-0.400874509777545[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.402930784811804[/C][C]-0.402930784811804[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.404987059846063[/C][C]-0.404987059846063[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.407043334880323[/C][C]-0.407043334880323[/C][/ROW]
[ROW][C]34[/C][C]1[/C][C]0.441434973266482[/C][C]0.558565026733518[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.411155884948841[/C][C]-0.411155884948841[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.4132121599831[/C][C]-0.4132121599831[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.585240975335611[/C][C]-0.585240975335611[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]0.55496188701797[/C][C]0.44503811298203[/C][/ROW]
[ROW][C]39[/C][C]1[/C][C]0.419380985085878[/C][C]0.580619014914122[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.453772623472037[/C][C]-0.453772623472037[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.398897660143487[/C][C]0.601102339856513[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]0.563186987155007[/C][C]0.436813012844993[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]0.427606085222915[/C][C]0.572393914777085[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.461997723609073[/C][C]-0.461997723609073[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.431718635291433[/C][C]-0.431718635291433[/C][/ROW]
[ROW][C]46[/C][C]1[/C][C]0.433774910325692[/C][C]0.566225089674308[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.435831185359951[/C][C]-0.435831185359951[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]0.437887460394211[/C][C]0.562112539605789[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]0.43994373542847[/C][C]0.56005626457153[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.442000010462729[/C][C]-0.442000010462729[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.61402882581524[/C][C]-0.61402882581524[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0.453852048872238[/C][C]-0.453852048872238[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]0.448168835565507[/C][C]0.551831164434493[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.425629235588857[/C][C]-0.425629235588857[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.452281385634025[/C][C]-0.452281385634025[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]0.624310200986536[/C][C]0.375689799013464[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]0.594031112668895[/C][C]0.405968887331105[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]0.458450210736803[/C][C]0.541549789263197[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]0.460506485771062[/C][C]0.539493514228938[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.470302249146312[/C][C]0.529697750853688[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]0.49695439919148[/C][C]0.50304560080852[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.604312487840191[/C][C]-0.604312487840191[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.468731585908099[/C][C]-0.468731585908099[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]0.503123224294258[/C][C]0.496876775705742[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.472844135976617[/C][C]-0.472844135976617[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.474900411010876[/C][C]-0.474900411010876[/C][/ROW]
[ROW][C]67[/C][C]0[/C][C]0.484696174386126[/C][C]-0.484696174386126[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.479012961079395[/C][C]-0.479012961079395[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]0.481069236113654[/C][C]0.518930763886346[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.620762688114265[/C][C]-0.620762688114265[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0.485181786182173[/C][C]-0.485181786182173[/C][/ROW]
[ROW][C]72[/C][C]1[/C][C]0.487238061216432[/C][C]0.512761938783568[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]0.626931513217043[/C][C]0.373068486782957[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.628987788251302[/C][C]-0.628987788251302[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]0.493406886319209[/C][C]0.506593113680791[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]0.527798524705368[/C][C]0.472201475294632[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]0.497519436387728[/C][C]0.502480563612272[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]0.637212888388339[/C][C]0.362787111611661[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.509371474797237[/C][C]0.490628525202763[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0.536023624842405[/C][C]-0.536023624842405[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0.505744536524765[/C][C]-0.505744536524765[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]0.645437988525375[/C][C]0.354562011474625[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0.509857086593283[/C][C]-0.509857086593283[/C][/ROW]
[ROW][C]84[/C][C]0[/C][C]0.487317486616633[/C][C]-0.487317486616633[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]0.513969636661801[/C][C]0.486030363338199[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]0.516025911696061[/C][C]-0.516025911696061[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203004&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203004&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
110.3735778971359280.626422102864072
200.343298808818287-0.343298808818287
300.345355083852546-0.345355083852546
400.347411358886806-0.347411358886806
500.349467633921065-0.349467633921065
610.3515239089553240.648476091044676
700.353580183989583-0.353580183989583
800.387971822375742-0.387971822375742
910.3576927340581020.642307265941898
1000.359749009092361-0.359749009092361
1100.39414064747852-0.39414064747852
1200.363861559160879-0.363861559160879
1300.50355501116149-0.50355501116149
1400.400309472581297-0.400309472581297
1510.5076675612300080.492332438769992
1610.5420591996161670.457940800383833
1700.381882422673166-0.381882422673166
1800.408534572718334-0.408534572718334
1910.3782554844006940.621744515599306
2010.3880512477759440.611948752224056
2100.382368034469212-0.382368034469212
2210.5220614864698230.477938513530177
2310.386480584537730.61351941546227
2410.388536859571990.61146314042801
2510.56056567492450.4394343250755
2600.53028658660686-0.53028658660686
2710.3947056846747670.605294315325233
2800.534399136675378-0.534399136675378
2910.3988182347432860.601181765256714
3000.400874509777545-0.400874509777545
3100.402930784811804-0.402930784811804
3200.404987059846063-0.404987059846063
3300.407043334880323-0.407043334880323
3410.4414349732664820.558565026733518
3500.411155884948841-0.411155884948841
3600.4132121599831-0.4132121599831
3700.585240975335611-0.585240975335611
3810.554961887017970.44503811298203
3910.4193809850858780.580619014914122
4000.453772623472037-0.453772623472037
4110.3988976601434870.601102339856513
4210.5631869871550070.436813012844993
4310.4276060852229150.572393914777085
4400.461997723609073-0.461997723609073
4500.431718635291433-0.431718635291433
4610.4337749103256920.566225089674308
4700.435831185359951-0.435831185359951
4810.4378874603942110.562112539605789
4910.439943735428470.56005626457153
5000.442000010462729-0.442000010462729
5100.61402882581524-0.61402882581524
5200.453852048872238-0.453852048872238
5310.4481688355655070.551831164434493
5400.425629235588857-0.425629235588857
5500.452281385634025-0.452281385634025
5610.6243102009865360.375689799013464
5710.5940311126688950.405968887331105
5810.4584502107368030.541549789263197
5910.4605064857710620.539493514228938
6010.4703022491463120.529697750853688
6110.496954399191480.50304560080852
6200.604312487840191-0.604312487840191
6300.468731585908099-0.468731585908099
6410.5031232242942580.496876775705742
6500.472844135976617-0.472844135976617
6600.474900411010876-0.474900411010876
6700.484696174386126-0.484696174386126
6800.479012961079395-0.479012961079395
6910.4810692361136540.518930763886346
7000.620762688114265-0.620762688114265
7100.485181786182173-0.485181786182173
7210.4872380612164320.512761938783568
7310.6269315132170430.373068486782957
7400.628987788251302-0.628987788251302
7510.4934068863192090.506593113680791
7610.5277985247053680.472201475294632
7710.4975194363877280.502480563612272
7810.6372128883883390.362787111611661
7910.5093714747972370.490628525202763
8000.536023624842405-0.536023624842405
8100.505744536524765-0.505744536524765
8210.6454379885253750.354562011474625
8300.509857086593283-0.509857086593283
8400.487317486616633-0.487317486616633
8510.5139696366618010.486030363338199
8600.516025911696061-0.516025911696061







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.7953116180708780.4093767638582450.204688381929122
90.8649875235403780.2700249529192440.135012476459622
100.8048199368026760.3903601263946480.195180063197324
110.7475523947080060.5048952105839870.252447605291994
120.655747144841440.688505710317120.34425285515856
130.5623110382956270.8753779234087470.437688961704373
140.4715686796259840.9431373592519670.528431320374016
150.5596209737400330.8807580525199350.440379026259967
160.5070946242209930.9858107515580130.492905375779007
170.4257782439400280.8515564878800560.574221756059972
180.3552805122974320.7105610245948650.644719487702568
190.4935093366750970.9870186733501930.506490663324903
200.5430272102250690.9139455795498620.456972789774931
210.4993827835901310.9987655671802620.500617216409869
220.4502385863099510.9004771726199030.549761413690049
230.4684529785604070.9369059571208150.531547021439593
240.4519009961307080.9038019922614150.548099003869292
250.3966462134972520.7932924269945050.603353786502748
260.4963430890238830.9926861780477660.503656910976117
270.4748646251989050.9497292503978110.525135374801095
280.5295378283602980.9409243432794040.470462171639702
290.5067622113562210.9864755772875570.493237788643779
300.5264053834790760.9471892330418470.473594616520924
310.5236347446874790.9527305106250410.476365255312521
320.5089972124084440.9820055751831130.491002787591556
330.4886220468652720.9772440937305440.511377953134728
340.4825722011311720.9651444022623440.517427798868828
350.4658705897772790.9317411795545570.534129410222721
360.4490107682735560.8980215365471130.550989231726444
370.4801939109856950.9603878219713910.519806089014305
380.4564115870179350.9128231740358710.543588412982065
390.46088964162020.92177928324040.5391103583798
400.4558853730262110.9117707460524220.544114626973789
410.4489856089874390.8979712179748770.551014391012561
420.4217540592630160.8435081185260310.578245940736984
430.4241718781243220.8483437562486450.575828121875678
440.4304038815577650.860807763115530.569596118442235
450.4222332100989050.8444664201978110.577766789901095
460.4222417503225760.8444835006451520.577758249677424
470.4154970201476290.8309940402952580.584502979852371
480.4140064525664880.8280129051329760.585993547433512
490.418221467996560.836442935993120.58177853200344
500.4071212764341570.8142425528683130.592878723565843
510.4770227884249770.9540455768499550.522977211575023
520.4900191792510850.9800383585021690.509980820748915
530.4917327490816080.9834654981632170.508267250918392
540.4652518767032690.9305037534065390.534748123296731
550.4590230110796520.9180460221593050.540976988920348
560.4096071259925460.8192142519850920.590392874007454
570.3751748352874240.7503496705748470.624825164712576
580.3781708051443730.7563416102887470.621829194855627
590.4048663448844960.8097326897689920.595133655115504
600.4186300515584780.8372601031169550.581369948441522
610.3937938621182630.7875877242365260.606206137881737
620.3917528048792970.7835056097585950.608247195120703
630.3561228952597130.7122457905194250.643877104740287
640.3342327248423430.6684654496846860.665767275157657
650.2994642804298050.5989285608596090.700535719570195
660.2728733976681170.5457467953362340.727126602331883
670.2627049648303120.5254099296606250.737295035169688
680.2698384607375810.5396769214751620.730161539262419
690.2403745320292240.4807490640584480.759625467970776
700.3010958234380510.6021916468761030.698904176561949
710.3736823685496360.7473647370992730.626317631450364
720.3007692735975770.6015385471951530.699230726402423
730.225836348143130.4516726962862610.77416365185687
740.516740426779490.9665191464410210.48325957322051
750.4079148459435950.815829691887190.592085154056405
760.3192138800372230.6384277600744460.680786119962777
770.3352110189647730.6704220379295450.664788981035227
780.2108069890378980.4216139780757960.789193010962102

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.795311618070878 & 0.409376763858245 & 0.204688381929122 \tabularnewline
9 & 0.864987523540378 & 0.270024952919244 & 0.135012476459622 \tabularnewline
10 & 0.804819936802676 & 0.390360126394648 & 0.195180063197324 \tabularnewline
11 & 0.747552394708006 & 0.504895210583987 & 0.252447605291994 \tabularnewline
12 & 0.65574714484144 & 0.68850571031712 & 0.34425285515856 \tabularnewline
13 & 0.562311038295627 & 0.875377923408747 & 0.437688961704373 \tabularnewline
14 & 0.471568679625984 & 0.943137359251967 & 0.528431320374016 \tabularnewline
15 & 0.559620973740033 & 0.880758052519935 & 0.440379026259967 \tabularnewline
16 & 0.507094624220993 & 0.985810751558013 & 0.492905375779007 \tabularnewline
17 & 0.425778243940028 & 0.851556487880056 & 0.574221756059972 \tabularnewline
18 & 0.355280512297432 & 0.710561024594865 & 0.644719487702568 \tabularnewline
19 & 0.493509336675097 & 0.987018673350193 & 0.506490663324903 \tabularnewline
20 & 0.543027210225069 & 0.913945579549862 & 0.456972789774931 \tabularnewline
21 & 0.499382783590131 & 0.998765567180262 & 0.500617216409869 \tabularnewline
22 & 0.450238586309951 & 0.900477172619903 & 0.549761413690049 \tabularnewline
23 & 0.468452978560407 & 0.936905957120815 & 0.531547021439593 \tabularnewline
24 & 0.451900996130708 & 0.903801992261415 & 0.548099003869292 \tabularnewline
25 & 0.396646213497252 & 0.793292426994505 & 0.603353786502748 \tabularnewline
26 & 0.496343089023883 & 0.992686178047766 & 0.503656910976117 \tabularnewline
27 & 0.474864625198905 & 0.949729250397811 & 0.525135374801095 \tabularnewline
28 & 0.529537828360298 & 0.940924343279404 & 0.470462171639702 \tabularnewline
29 & 0.506762211356221 & 0.986475577287557 & 0.493237788643779 \tabularnewline
30 & 0.526405383479076 & 0.947189233041847 & 0.473594616520924 \tabularnewline
31 & 0.523634744687479 & 0.952730510625041 & 0.476365255312521 \tabularnewline
32 & 0.508997212408444 & 0.982005575183113 & 0.491002787591556 \tabularnewline
33 & 0.488622046865272 & 0.977244093730544 & 0.511377953134728 \tabularnewline
34 & 0.482572201131172 & 0.965144402262344 & 0.517427798868828 \tabularnewline
35 & 0.465870589777279 & 0.931741179554557 & 0.534129410222721 \tabularnewline
36 & 0.449010768273556 & 0.898021536547113 & 0.550989231726444 \tabularnewline
37 & 0.480193910985695 & 0.960387821971391 & 0.519806089014305 \tabularnewline
38 & 0.456411587017935 & 0.912823174035871 & 0.543588412982065 \tabularnewline
39 & 0.4608896416202 & 0.9217792832404 & 0.5391103583798 \tabularnewline
40 & 0.455885373026211 & 0.911770746052422 & 0.544114626973789 \tabularnewline
41 & 0.448985608987439 & 0.897971217974877 & 0.551014391012561 \tabularnewline
42 & 0.421754059263016 & 0.843508118526031 & 0.578245940736984 \tabularnewline
43 & 0.424171878124322 & 0.848343756248645 & 0.575828121875678 \tabularnewline
44 & 0.430403881557765 & 0.86080776311553 & 0.569596118442235 \tabularnewline
45 & 0.422233210098905 & 0.844466420197811 & 0.577766789901095 \tabularnewline
46 & 0.422241750322576 & 0.844483500645152 & 0.577758249677424 \tabularnewline
47 & 0.415497020147629 & 0.830994040295258 & 0.584502979852371 \tabularnewline
48 & 0.414006452566488 & 0.828012905132976 & 0.585993547433512 \tabularnewline
49 & 0.41822146799656 & 0.83644293599312 & 0.58177853200344 \tabularnewline
50 & 0.407121276434157 & 0.814242552868313 & 0.592878723565843 \tabularnewline
51 & 0.477022788424977 & 0.954045576849955 & 0.522977211575023 \tabularnewline
52 & 0.490019179251085 & 0.980038358502169 & 0.509980820748915 \tabularnewline
53 & 0.491732749081608 & 0.983465498163217 & 0.508267250918392 \tabularnewline
54 & 0.465251876703269 & 0.930503753406539 & 0.534748123296731 \tabularnewline
55 & 0.459023011079652 & 0.918046022159305 & 0.540976988920348 \tabularnewline
56 & 0.409607125992546 & 0.819214251985092 & 0.590392874007454 \tabularnewline
57 & 0.375174835287424 & 0.750349670574847 & 0.624825164712576 \tabularnewline
58 & 0.378170805144373 & 0.756341610288747 & 0.621829194855627 \tabularnewline
59 & 0.404866344884496 & 0.809732689768992 & 0.595133655115504 \tabularnewline
60 & 0.418630051558478 & 0.837260103116955 & 0.581369948441522 \tabularnewline
61 & 0.393793862118263 & 0.787587724236526 & 0.606206137881737 \tabularnewline
62 & 0.391752804879297 & 0.783505609758595 & 0.608247195120703 \tabularnewline
63 & 0.356122895259713 & 0.712245790519425 & 0.643877104740287 \tabularnewline
64 & 0.334232724842343 & 0.668465449684686 & 0.665767275157657 \tabularnewline
65 & 0.299464280429805 & 0.598928560859609 & 0.700535719570195 \tabularnewline
66 & 0.272873397668117 & 0.545746795336234 & 0.727126602331883 \tabularnewline
67 & 0.262704964830312 & 0.525409929660625 & 0.737295035169688 \tabularnewline
68 & 0.269838460737581 & 0.539676921475162 & 0.730161539262419 \tabularnewline
69 & 0.240374532029224 & 0.480749064058448 & 0.759625467970776 \tabularnewline
70 & 0.301095823438051 & 0.602191646876103 & 0.698904176561949 \tabularnewline
71 & 0.373682368549636 & 0.747364737099273 & 0.626317631450364 \tabularnewline
72 & 0.300769273597577 & 0.601538547195153 & 0.699230726402423 \tabularnewline
73 & 0.22583634814313 & 0.451672696286261 & 0.77416365185687 \tabularnewline
74 & 0.51674042677949 & 0.966519146441021 & 0.48325957322051 \tabularnewline
75 & 0.407914845943595 & 0.81582969188719 & 0.592085154056405 \tabularnewline
76 & 0.319213880037223 & 0.638427760074446 & 0.680786119962777 \tabularnewline
77 & 0.335211018964773 & 0.670422037929545 & 0.664788981035227 \tabularnewline
78 & 0.210806989037898 & 0.421613978075796 & 0.789193010962102 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203004&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]8[/C][C]0.795311618070878[/C][C]0.409376763858245[/C][C]0.204688381929122[/C][/ROW]
[ROW][C]9[/C][C]0.864987523540378[/C][C]0.270024952919244[/C][C]0.135012476459622[/C][/ROW]
[ROW][C]10[/C][C]0.804819936802676[/C][C]0.390360126394648[/C][C]0.195180063197324[/C][/ROW]
[ROW][C]11[/C][C]0.747552394708006[/C][C]0.504895210583987[/C][C]0.252447605291994[/C][/ROW]
[ROW][C]12[/C][C]0.65574714484144[/C][C]0.68850571031712[/C][C]0.34425285515856[/C][/ROW]
[ROW][C]13[/C][C]0.562311038295627[/C][C]0.875377923408747[/C][C]0.437688961704373[/C][/ROW]
[ROW][C]14[/C][C]0.471568679625984[/C][C]0.943137359251967[/C][C]0.528431320374016[/C][/ROW]
[ROW][C]15[/C][C]0.559620973740033[/C][C]0.880758052519935[/C][C]0.440379026259967[/C][/ROW]
[ROW][C]16[/C][C]0.507094624220993[/C][C]0.985810751558013[/C][C]0.492905375779007[/C][/ROW]
[ROW][C]17[/C][C]0.425778243940028[/C][C]0.851556487880056[/C][C]0.574221756059972[/C][/ROW]
[ROW][C]18[/C][C]0.355280512297432[/C][C]0.710561024594865[/C][C]0.644719487702568[/C][/ROW]
[ROW][C]19[/C][C]0.493509336675097[/C][C]0.987018673350193[/C][C]0.506490663324903[/C][/ROW]
[ROW][C]20[/C][C]0.543027210225069[/C][C]0.913945579549862[/C][C]0.456972789774931[/C][/ROW]
[ROW][C]21[/C][C]0.499382783590131[/C][C]0.998765567180262[/C][C]0.500617216409869[/C][/ROW]
[ROW][C]22[/C][C]0.450238586309951[/C][C]0.900477172619903[/C][C]0.549761413690049[/C][/ROW]
[ROW][C]23[/C][C]0.468452978560407[/C][C]0.936905957120815[/C][C]0.531547021439593[/C][/ROW]
[ROW][C]24[/C][C]0.451900996130708[/C][C]0.903801992261415[/C][C]0.548099003869292[/C][/ROW]
[ROW][C]25[/C][C]0.396646213497252[/C][C]0.793292426994505[/C][C]0.603353786502748[/C][/ROW]
[ROW][C]26[/C][C]0.496343089023883[/C][C]0.992686178047766[/C][C]0.503656910976117[/C][/ROW]
[ROW][C]27[/C][C]0.474864625198905[/C][C]0.949729250397811[/C][C]0.525135374801095[/C][/ROW]
[ROW][C]28[/C][C]0.529537828360298[/C][C]0.940924343279404[/C][C]0.470462171639702[/C][/ROW]
[ROW][C]29[/C][C]0.506762211356221[/C][C]0.986475577287557[/C][C]0.493237788643779[/C][/ROW]
[ROW][C]30[/C][C]0.526405383479076[/C][C]0.947189233041847[/C][C]0.473594616520924[/C][/ROW]
[ROW][C]31[/C][C]0.523634744687479[/C][C]0.952730510625041[/C][C]0.476365255312521[/C][/ROW]
[ROW][C]32[/C][C]0.508997212408444[/C][C]0.982005575183113[/C][C]0.491002787591556[/C][/ROW]
[ROW][C]33[/C][C]0.488622046865272[/C][C]0.977244093730544[/C][C]0.511377953134728[/C][/ROW]
[ROW][C]34[/C][C]0.482572201131172[/C][C]0.965144402262344[/C][C]0.517427798868828[/C][/ROW]
[ROW][C]35[/C][C]0.465870589777279[/C][C]0.931741179554557[/C][C]0.534129410222721[/C][/ROW]
[ROW][C]36[/C][C]0.449010768273556[/C][C]0.898021536547113[/C][C]0.550989231726444[/C][/ROW]
[ROW][C]37[/C][C]0.480193910985695[/C][C]0.960387821971391[/C][C]0.519806089014305[/C][/ROW]
[ROW][C]38[/C][C]0.456411587017935[/C][C]0.912823174035871[/C][C]0.543588412982065[/C][/ROW]
[ROW][C]39[/C][C]0.4608896416202[/C][C]0.9217792832404[/C][C]0.5391103583798[/C][/ROW]
[ROW][C]40[/C][C]0.455885373026211[/C][C]0.911770746052422[/C][C]0.544114626973789[/C][/ROW]
[ROW][C]41[/C][C]0.448985608987439[/C][C]0.897971217974877[/C][C]0.551014391012561[/C][/ROW]
[ROW][C]42[/C][C]0.421754059263016[/C][C]0.843508118526031[/C][C]0.578245940736984[/C][/ROW]
[ROW][C]43[/C][C]0.424171878124322[/C][C]0.848343756248645[/C][C]0.575828121875678[/C][/ROW]
[ROW][C]44[/C][C]0.430403881557765[/C][C]0.86080776311553[/C][C]0.569596118442235[/C][/ROW]
[ROW][C]45[/C][C]0.422233210098905[/C][C]0.844466420197811[/C][C]0.577766789901095[/C][/ROW]
[ROW][C]46[/C][C]0.422241750322576[/C][C]0.844483500645152[/C][C]0.577758249677424[/C][/ROW]
[ROW][C]47[/C][C]0.415497020147629[/C][C]0.830994040295258[/C][C]0.584502979852371[/C][/ROW]
[ROW][C]48[/C][C]0.414006452566488[/C][C]0.828012905132976[/C][C]0.585993547433512[/C][/ROW]
[ROW][C]49[/C][C]0.41822146799656[/C][C]0.83644293599312[/C][C]0.58177853200344[/C][/ROW]
[ROW][C]50[/C][C]0.407121276434157[/C][C]0.814242552868313[/C][C]0.592878723565843[/C][/ROW]
[ROW][C]51[/C][C]0.477022788424977[/C][C]0.954045576849955[/C][C]0.522977211575023[/C][/ROW]
[ROW][C]52[/C][C]0.490019179251085[/C][C]0.980038358502169[/C][C]0.509980820748915[/C][/ROW]
[ROW][C]53[/C][C]0.491732749081608[/C][C]0.983465498163217[/C][C]0.508267250918392[/C][/ROW]
[ROW][C]54[/C][C]0.465251876703269[/C][C]0.930503753406539[/C][C]0.534748123296731[/C][/ROW]
[ROW][C]55[/C][C]0.459023011079652[/C][C]0.918046022159305[/C][C]0.540976988920348[/C][/ROW]
[ROW][C]56[/C][C]0.409607125992546[/C][C]0.819214251985092[/C][C]0.590392874007454[/C][/ROW]
[ROW][C]57[/C][C]0.375174835287424[/C][C]0.750349670574847[/C][C]0.624825164712576[/C][/ROW]
[ROW][C]58[/C][C]0.378170805144373[/C][C]0.756341610288747[/C][C]0.621829194855627[/C][/ROW]
[ROW][C]59[/C][C]0.404866344884496[/C][C]0.809732689768992[/C][C]0.595133655115504[/C][/ROW]
[ROW][C]60[/C][C]0.418630051558478[/C][C]0.837260103116955[/C][C]0.581369948441522[/C][/ROW]
[ROW][C]61[/C][C]0.393793862118263[/C][C]0.787587724236526[/C][C]0.606206137881737[/C][/ROW]
[ROW][C]62[/C][C]0.391752804879297[/C][C]0.783505609758595[/C][C]0.608247195120703[/C][/ROW]
[ROW][C]63[/C][C]0.356122895259713[/C][C]0.712245790519425[/C][C]0.643877104740287[/C][/ROW]
[ROW][C]64[/C][C]0.334232724842343[/C][C]0.668465449684686[/C][C]0.665767275157657[/C][/ROW]
[ROW][C]65[/C][C]0.299464280429805[/C][C]0.598928560859609[/C][C]0.700535719570195[/C][/ROW]
[ROW][C]66[/C][C]0.272873397668117[/C][C]0.545746795336234[/C][C]0.727126602331883[/C][/ROW]
[ROW][C]67[/C][C]0.262704964830312[/C][C]0.525409929660625[/C][C]0.737295035169688[/C][/ROW]
[ROW][C]68[/C][C]0.269838460737581[/C][C]0.539676921475162[/C][C]0.730161539262419[/C][/ROW]
[ROW][C]69[/C][C]0.240374532029224[/C][C]0.480749064058448[/C][C]0.759625467970776[/C][/ROW]
[ROW][C]70[/C][C]0.301095823438051[/C][C]0.602191646876103[/C][C]0.698904176561949[/C][/ROW]
[ROW][C]71[/C][C]0.373682368549636[/C][C]0.747364737099273[/C][C]0.626317631450364[/C][/ROW]
[ROW][C]72[/C][C]0.300769273597577[/C][C]0.601538547195153[/C][C]0.699230726402423[/C][/ROW]
[ROW][C]73[/C][C]0.22583634814313[/C][C]0.451672696286261[/C][C]0.77416365185687[/C][/ROW]
[ROW][C]74[/C][C]0.51674042677949[/C][C]0.966519146441021[/C][C]0.48325957322051[/C][/ROW]
[ROW][C]75[/C][C]0.407914845943595[/C][C]0.81582969188719[/C][C]0.592085154056405[/C][/ROW]
[ROW][C]76[/C][C]0.319213880037223[/C][C]0.638427760074446[/C][C]0.680786119962777[/C][/ROW]
[ROW][C]77[/C][C]0.335211018964773[/C][C]0.670422037929545[/C][C]0.664788981035227[/C][/ROW]
[ROW][C]78[/C][C]0.210806989037898[/C][C]0.421613978075796[/C][C]0.789193010962102[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203004&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203004&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
80.7953116180708780.4093767638582450.204688381929122
90.8649875235403780.2700249529192440.135012476459622
100.8048199368026760.3903601263946480.195180063197324
110.7475523947080060.5048952105839870.252447605291994
120.655747144841440.688505710317120.34425285515856
130.5623110382956270.8753779234087470.437688961704373
140.4715686796259840.9431373592519670.528431320374016
150.5596209737400330.8807580525199350.440379026259967
160.5070946242209930.9858107515580130.492905375779007
170.4257782439400280.8515564878800560.574221756059972
180.3552805122974320.7105610245948650.644719487702568
190.4935093366750970.9870186733501930.506490663324903
200.5430272102250690.9139455795498620.456972789774931
210.4993827835901310.9987655671802620.500617216409869
220.4502385863099510.9004771726199030.549761413690049
230.4684529785604070.9369059571208150.531547021439593
240.4519009961307080.9038019922614150.548099003869292
250.3966462134972520.7932924269945050.603353786502748
260.4963430890238830.9926861780477660.503656910976117
270.4748646251989050.9497292503978110.525135374801095
280.5295378283602980.9409243432794040.470462171639702
290.5067622113562210.9864755772875570.493237788643779
300.5264053834790760.9471892330418470.473594616520924
310.5236347446874790.9527305106250410.476365255312521
320.5089972124084440.9820055751831130.491002787591556
330.4886220468652720.9772440937305440.511377953134728
340.4825722011311720.9651444022623440.517427798868828
350.4658705897772790.9317411795545570.534129410222721
360.4490107682735560.8980215365471130.550989231726444
370.4801939109856950.9603878219713910.519806089014305
380.4564115870179350.9128231740358710.543588412982065
390.46088964162020.92177928324040.5391103583798
400.4558853730262110.9117707460524220.544114626973789
410.4489856089874390.8979712179748770.551014391012561
420.4217540592630160.8435081185260310.578245940736984
430.4241718781243220.8483437562486450.575828121875678
440.4304038815577650.860807763115530.569596118442235
450.4222332100989050.8444664201978110.577766789901095
460.4222417503225760.8444835006451520.577758249677424
470.4154970201476290.8309940402952580.584502979852371
480.4140064525664880.8280129051329760.585993547433512
490.418221467996560.836442935993120.58177853200344
500.4071212764341570.8142425528683130.592878723565843
510.4770227884249770.9540455768499550.522977211575023
520.4900191792510850.9800383585021690.509980820748915
530.4917327490816080.9834654981632170.508267250918392
540.4652518767032690.9305037534065390.534748123296731
550.4590230110796520.9180460221593050.540976988920348
560.4096071259925460.8192142519850920.590392874007454
570.3751748352874240.7503496705748470.624825164712576
580.3781708051443730.7563416102887470.621829194855627
590.4048663448844960.8097326897689920.595133655115504
600.4186300515584780.8372601031169550.581369948441522
610.3937938621182630.7875877242365260.606206137881737
620.3917528048792970.7835056097585950.608247195120703
630.3561228952597130.7122457905194250.643877104740287
640.3342327248423430.6684654496846860.665767275157657
650.2994642804298050.5989285608596090.700535719570195
660.2728733976681170.5457467953362340.727126602331883
670.2627049648303120.5254099296606250.737295035169688
680.2698384607375810.5396769214751620.730161539262419
690.2403745320292240.4807490640584480.759625467970776
700.3010958234380510.6021916468761030.698904176561949
710.3736823685496360.7473647370992730.626317631450364
720.3007692735975770.6015385471951530.699230726402423
730.225836348143130.4516726962862610.77416365185687
740.516740426779490.9665191464410210.48325957322051
750.4079148459435950.815829691887190.592085154056405
760.3192138800372230.6384277600744460.680786119962777
770.3352110189647730.6704220379295450.664788981035227
780.2108069890378980.4216139780757960.789193010962102







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203004&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203004&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203004&T=6

As an alternative you can also use a QR Code:  

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

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



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