Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 16 Dec 2012 12:05:45 -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/16/t1355677600zkz45t3zlissqsu.htm/, Retrieved Fri, 29 Mar 2024 07:04:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=200481, Retrieved Fri, 29 Mar 2024 07:04:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact80
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2012-12-16 17:05:45] [c8e18a68d7e55abb9d5b5bbd2b98426e] [Current]
Feedback Forum

Post a new message
Dataseries X:
50	50	100	50	100	100
50	50	50	50	50	50
50	50	50	50	50	50
50	50	50	50	50	50
50	50	50	50	50	50
50	50	100	100	50	100
50	50	50	50	50	50
50	50	50	50	100	50
50	50	50	50	50	100
50	50	100	50	50	50
50	50	100	50	100	50
50	50	50	50	50	50
100	50	50	100	50	50
50	50	100	50	100	50
100	50	50	100	50	100
100	50	50	100	100	100
100	100	100	100	100	50
50	50	100	50	100	50
50	50	50	50	50	100
100	100	50	100	100	100
50	50	100	100	50	50
100	50	100	100	50	100
50	50	50	100	50	100
50	50	100	100	50	100
100	50	50	50	100	100
100	50	50	100	50	50
50	50	100	50	50	100
100	50	50	50	50	50
50	50	50	50	50	100
50	50	50	100	50	50
50	50	50	50	50	50
50	50	100	50	50	50
50	50	100	100	50	50
50	50	50	50	100	100
50	50	50	50	50	50
50	50	50	50	50	50
100	50	100	100	100	50
100	50	50	50	50	100
50	50	50	100	50	100
50	50	50	100	100	50
100	100	50	100	50	100
100	50	50	50	50	100
50	50	100	100	50	100
50	50	100	50	100	50
50	50	50	100	50	50
50	50	50	100	50	100
50	50	50	50	50	50
50	50	50	50	50	100
50	50	50	100	50	100
50	50	50	50	50	50
100	50	50	50	100	50
100	100	100	100	100	50
50	50	50	50	50	100
100	100	50	50	50	50
50	50	50	50	50	50
100	50	50	50	100	100
100	50	50	100	50	100
50	50	50	50	50	100
50	50	50	50	50	100
100	100	100	100	100	100
50	50	100	50	100	100
100	50	50	100	50	50
50	50	50	50	50	50
50	50	100	50	100	100
50	50	50	50	50	50
50	50	50	50	50	50
100	100	50	100	100	50
50	50	100	50	50	50
50	50	50	50	50	100
100	50	50	50	50	50
50	50	50	50	50	50
50	50	50	50	50	100
100	50	50	50	50	100
100	50	100	50	50	50
50	50	50	50	50	100
50	50	50	100	100	100
50	50	50	50	50	100
100	50	50	100	50	100
100	100	50	50	100	100
50	50	50	100	100	50
50	50	50	50	50	50
100	50	100	50	50	100
50	50	50	50	50	50
100	100	50	50	50	50
50	50	50	100	50	100
50	50	100	50	50	50




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 9 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200481&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200481&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200481&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 time9 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Used[t] = + 14.8998521934881 + 0.67241862676928Correctanal[t] -0.0951388356000594uselimit[t] + 0.159323996262626useful[t] + 0.0709581898357269T40[t] + 0.0693788122346552outcome[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Used[t] =  +  14.8998521934881 +  0.67241862676928Correctanal[t] -0.0951388356000594uselimit[t] +  0.159323996262626useful[t] +  0.0709581898357269T40[t] +  0.0693788122346552outcome[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200481&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Used[t] =  +  14.8998521934881 +  0.67241862676928Correctanal[t] -0.0951388356000594uselimit[t] +  0.159323996262626useful[t] +  0.0709581898357269T40[t] +  0.0693788122346552outcome[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200481&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200481&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
Used[t] = + 14.8998521934881 + 0.67241862676928Correctanal[t] -0.0951388356000594uselimit[t] + 0.159323996262626useful[t] + 0.0709581898357269T40[t] + 0.0693788122346552outcome[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)14.899852193488112.9479761.15070.2532640.126632
Correctanal0.672418626769280.1560014.31034.6e-052.3e-05
uselimit-0.09513883560005940.10325-0.92140.3595920.179796
useful0.1593239962626260.0970851.64110.1047090.052354
T400.07095818983572690.1093410.6490.5182230.259111
outcome0.06937881223465520.0902460.76880.4442910.222146

\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) & 14.8998521934881 & 12.947976 & 1.1507 & 0.253264 & 0.126632 \tabularnewline
Correctanal & 0.67241862676928 & 0.156001 & 4.3103 & 4.6e-05 & 2.3e-05 \tabularnewline
uselimit & -0.0951388356000594 & 0.10325 & -0.9214 & 0.359592 & 0.179796 \tabularnewline
useful & 0.159323996262626 & 0.097085 & 1.6411 & 0.104709 & 0.052354 \tabularnewline
T40 & 0.0709581898357269 & 0.109341 & 0.649 & 0.518223 & 0.259111 \tabularnewline
outcome & 0.0693788122346552 & 0.090246 & 0.7688 & 0.444291 & 0.222146 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200481&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]14.8998521934881[/C][C]12.947976[/C][C]1.1507[/C][C]0.253264[/C][C]0.126632[/C][/ROW]
[ROW][C]Correctanal[/C][C]0.67241862676928[/C][C]0.156001[/C][C]4.3103[/C][C]4.6e-05[/C][C]2.3e-05[/C][/ROW]
[ROW][C]uselimit[/C][C]-0.0951388356000594[/C][C]0.10325[/C][C]-0.9214[/C][C]0.359592[/C][C]0.179796[/C][/ROW]
[ROW][C]useful[/C][C]0.159323996262626[/C][C]0.097085[/C][C]1.6411[/C][C]0.104709[/C][C]0.052354[/C][/ROW]
[ROW][C]T40[/C][C]0.0709581898357269[/C][C]0.109341[/C][C]0.649[/C][C]0.518223[/C][C]0.259111[/C][/ROW]
[ROW][C]outcome[/C][C]0.0693788122346552[/C][C]0.090246[/C][C]0.7688[/C][C]0.444291[/C][C]0.222146[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200481&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200481&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)14.899852193488112.9479761.15070.2532640.126632
Correctanal0.672418626769280.1560014.31034.6e-052.3e-05
uselimit-0.09513883560005940.10325-0.92140.3595920.179796
useful0.1593239962626260.0970851.64110.1047090.052354
T400.07095818983572690.1093410.6490.5182230.259111
outcome0.06937881223465520.0902460.76880.4442910.222146







Multiple Linear Regression - Regression Statistics
Multiple R0.533704990484674
R-squared0.284841016868246
Adjusted R-squared0.240143580422511
F-TEST (value)6.3726477292285
F-TEST (DF numerator)5
F-TEST (DF denominator)80
p-value4.9740959433775e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation20.5432947228294
Sum Squared Residuals33762.1566455224

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.533704990484674 \tabularnewline
R-squared & 0.284841016868246 \tabularnewline
Adjusted R-squared & 0.240143580422511 \tabularnewline
F-TEST (value) & 6.3726477292285 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 80 \tabularnewline
p-value & 4.9740959433775e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 20.5432947228294 \tabularnewline
Sum Squared Residuals & 33762.1566455224 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200481&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.533704990484674[/C][/ROW]
[ROW][C]R-squared[/C][C]0.284841016868246[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.240143580422511[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.3726477292285[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]80[/C][/ROW]
[ROW][C]p-value[/C][C]4.9740959433775e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]20.5432947228294[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]33762.1566455224[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200481&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200481&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 R0.533704990484674
R-squared0.284841016868246
Adjusted R-squared0.240143580422511
F-TEST (value)6.3726477292285
F-TEST (DF numerator)5
F-TEST (DF denominator)80
p-value4.9740959433775e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation20.5432947228294
Sum Squared Residuals33762.1566455224







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
15061.0067999921157-11.0067999921157
25058.7468916685995-8.74689166859952
35058.7468916685995-8.74689166859952
45058.7468916685995-8.74689166859954
55058.7468916685995-8.74689166859953
65065.4250903134606-15.4250903134606
75058.7468916685995-8.74689166859953
85062.2948011603859-12.2948011603859
95062.2158322803323-12.2158322803323
105053.9899498885966-3.98994988859656
115057.5378593803829-7.5378593803829
125058.7468916685995-8.74689166859953
1310066.713091481730833.2869085182692
145057.5378593803829-7.5378593803829
1510070.182032093463629.8179679065364
1610073.729941585249926.2700584147501
1710099.12499053197820.875009468021765
185057.5378593803829-7.5378593803829
195062.2158322803323-12.2158322803323
20100107.350872923714-7.35087292371396
215061.9561497017279-11.9561497017279
2210065.425090313460634.5749096865394
235070.1820320934636-20.1820320934636
245065.4250903134606-15.4250903134606
2510065.763741772118634.2362582278814
2610066.713091481730833.2869085182692
275057.4588905003293-7.45889050032932
2810058.746891668599541.2531083314005
295062.2158322803323-12.2158322803323
305066.7130914817308-16.7130914817308
315058.7468916685995-8.74689166859953
325053.9899498885966-3.98994988859656
335061.9561497017279-11.9561497017279
345065.7637417721186-15.7637417721186
355058.7468916685995-8.74689166859953
365058.7468916685995-8.74689166859953
3710065.504059193514234.4959408064858
3810062.215832280332337.7841677196677
395070.1820320934636-20.1820320934636
405070.2610009735172-20.2610009735172
41100103.802963431928-3.80296343192762
4210062.215832280332337.7841677196677
435065.4250903134606-15.4250903134606
445057.5378593803829-7.5378593803829
455066.7130914817308-16.7130914817308
465070.1820320934636-20.1820320934636
475058.7468916685995-8.74689166859953
485062.2158322803323-12.2158322803323
495070.1820320934636-20.1820320934636
505058.7468916685995-8.74689166859953
5110062.294801160385937.7051988396141
5210099.12499053197820.875009468021765
535062.2158322803323-12.2158322803323
5410092.36782300706357.63217699293645
555058.7468916685995-8.74689166859953
5610065.763741772118634.2362582278814
5710070.182032093463629.8179679065364
585062.2158322803323-12.2158322803323
595062.2158322803323-12.2158322803323
60100102.593931143711-2.59393114371099
615061.0067999921157-11.0067999921157
6210066.713091481730833.2869085182692
635058.7468916685995-8.74689166859953
645061.0067999921157-11.0067999921157
655058.7468916685995-8.74689166859953
665058.7468916685995-8.74689166859953
67100103.881932311981-3.8819323119812
685053.9899498885966-3.98994988859656
695062.2158322803323-12.2158322803323
7010058.746891668599541.2531083314005
715058.7468916685995-8.74689166859953
725062.2158322803323-12.2158322803323
7310062.215832280332337.7841677196677
7410053.989949888596646.0100501114034
755062.2158322803323-12.2158322803323
765073.7299415852499-23.72994158525
775062.2158322803323-12.2158322803323
7810070.182032093463629.8179679065364
7910099.38467311058270.615326889417349
805070.2610009735172-20.2610009735172
815058.7468916685995-8.74689166859953
8210057.458890500329342.5411094996707
835058.7468916685995-8.74689166859953
8410092.36782300706357.63217699293645
855070.1820320934636-20.1820320934636
865053.9899498885966-3.98994988859656

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 50 & 61.0067999921157 & -11.0067999921157 \tabularnewline
2 & 50 & 58.7468916685995 & -8.74689166859952 \tabularnewline
3 & 50 & 58.7468916685995 & -8.74689166859952 \tabularnewline
4 & 50 & 58.7468916685995 & -8.74689166859954 \tabularnewline
5 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
6 & 50 & 65.4250903134606 & -15.4250903134606 \tabularnewline
7 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
8 & 50 & 62.2948011603859 & -12.2948011603859 \tabularnewline
9 & 50 & 62.2158322803323 & -12.2158322803323 \tabularnewline
10 & 50 & 53.9899498885966 & -3.98994988859656 \tabularnewline
11 & 50 & 57.5378593803829 & -7.5378593803829 \tabularnewline
12 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
13 & 100 & 66.7130914817308 & 33.2869085182692 \tabularnewline
14 & 50 & 57.5378593803829 & -7.5378593803829 \tabularnewline
15 & 100 & 70.1820320934636 & 29.8179679065364 \tabularnewline
16 & 100 & 73.7299415852499 & 26.2700584147501 \tabularnewline
17 & 100 & 99.1249905319782 & 0.875009468021765 \tabularnewline
18 & 50 & 57.5378593803829 & -7.5378593803829 \tabularnewline
19 & 50 & 62.2158322803323 & -12.2158322803323 \tabularnewline
20 & 100 & 107.350872923714 & -7.35087292371396 \tabularnewline
21 & 50 & 61.9561497017279 & -11.9561497017279 \tabularnewline
22 & 100 & 65.4250903134606 & 34.5749096865394 \tabularnewline
23 & 50 & 70.1820320934636 & -20.1820320934636 \tabularnewline
24 & 50 & 65.4250903134606 & -15.4250903134606 \tabularnewline
25 & 100 & 65.7637417721186 & 34.2362582278814 \tabularnewline
26 & 100 & 66.7130914817308 & 33.2869085182692 \tabularnewline
27 & 50 & 57.4588905003293 & -7.45889050032932 \tabularnewline
28 & 100 & 58.7468916685995 & 41.2531083314005 \tabularnewline
29 & 50 & 62.2158322803323 & -12.2158322803323 \tabularnewline
30 & 50 & 66.7130914817308 & -16.7130914817308 \tabularnewline
31 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
32 & 50 & 53.9899498885966 & -3.98994988859656 \tabularnewline
33 & 50 & 61.9561497017279 & -11.9561497017279 \tabularnewline
34 & 50 & 65.7637417721186 & -15.7637417721186 \tabularnewline
35 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
36 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
37 & 100 & 65.5040591935142 & 34.4959408064858 \tabularnewline
38 & 100 & 62.2158322803323 & 37.7841677196677 \tabularnewline
39 & 50 & 70.1820320934636 & -20.1820320934636 \tabularnewline
40 & 50 & 70.2610009735172 & -20.2610009735172 \tabularnewline
41 & 100 & 103.802963431928 & -3.80296343192762 \tabularnewline
42 & 100 & 62.2158322803323 & 37.7841677196677 \tabularnewline
43 & 50 & 65.4250903134606 & -15.4250903134606 \tabularnewline
44 & 50 & 57.5378593803829 & -7.5378593803829 \tabularnewline
45 & 50 & 66.7130914817308 & -16.7130914817308 \tabularnewline
46 & 50 & 70.1820320934636 & -20.1820320934636 \tabularnewline
47 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
48 & 50 & 62.2158322803323 & -12.2158322803323 \tabularnewline
49 & 50 & 70.1820320934636 & -20.1820320934636 \tabularnewline
50 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
51 & 100 & 62.2948011603859 & 37.7051988396141 \tabularnewline
52 & 100 & 99.1249905319782 & 0.875009468021765 \tabularnewline
53 & 50 & 62.2158322803323 & -12.2158322803323 \tabularnewline
54 & 100 & 92.3678230070635 & 7.63217699293645 \tabularnewline
55 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
56 & 100 & 65.7637417721186 & 34.2362582278814 \tabularnewline
57 & 100 & 70.1820320934636 & 29.8179679065364 \tabularnewline
58 & 50 & 62.2158322803323 & -12.2158322803323 \tabularnewline
59 & 50 & 62.2158322803323 & -12.2158322803323 \tabularnewline
60 & 100 & 102.593931143711 & -2.59393114371099 \tabularnewline
61 & 50 & 61.0067999921157 & -11.0067999921157 \tabularnewline
62 & 100 & 66.7130914817308 & 33.2869085182692 \tabularnewline
63 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
64 & 50 & 61.0067999921157 & -11.0067999921157 \tabularnewline
65 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
66 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
67 & 100 & 103.881932311981 & -3.8819323119812 \tabularnewline
68 & 50 & 53.9899498885966 & -3.98994988859656 \tabularnewline
69 & 50 & 62.2158322803323 & -12.2158322803323 \tabularnewline
70 & 100 & 58.7468916685995 & 41.2531083314005 \tabularnewline
71 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
72 & 50 & 62.2158322803323 & -12.2158322803323 \tabularnewline
73 & 100 & 62.2158322803323 & 37.7841677196677 \tabularnewline
74 & 100 & 53.9899498885966 & 46.0100501114034 \tabularnewline
75 & 50 & 62.2158322803323 & -12.2158322803323 \tabularnewline
76 & 50 & 73.7299415852499 & -23.72994158525 \tabularnewline
77 & 50 & 62.2158322803323 & -12.2158322803323 \tabularnewline
78 & 100 & 70.1820320934636 & 29.8179679065364 \tabularnewline
79 & 100 & 99.3846731105827 & 0.615326889417349 \tabularnewline
80 & 50 & 70.2610009735172 & -20.2610009735172 \tabularnewline
81 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
82 & 100 & 57.4588905003293 & 42.5411094996707 \tabularnewline
83 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
84 & 100 & 92.3678230070635 & 7.63217699293645 \tabularnewline
85 & 50 & 70.1820320934636 & -20.1820320934636 \tabularnewline
86 & 50 & 53.9899498885966 & -3.98994988859656 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200481&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]50[/C][C]61.0067999921157[/C][C]-11.0067999921157[/C][/ROW]
[ROW][C]2[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859952[/C][/ROW]
[ROW][C]3[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859952[/C][/ROW]
[ROW][C]4[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859954[/C][/ROW]
[ROW][C]5[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]6[/C][C]50[/C][C]65.4250903134606[/C][C]-15.4250903134606[/C][/ROW]
[ROW][C]7[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]8[/C][C]50[/C][C]62.2948011603859[/C][C]-12.2948011603859[/C][/ROW]
[ROW][C]9[/C][C]50[/C][C]62.2158322803323[/C][C]-12.2158322803323[/C][/ROW]
[ROW][C]10[/C][C]50[/C][C]53.9899498885966[/C][C]-3.98994988859656[/C][/ROW]
[ROW][C]11[/C][C]50[/C][C]57.5378593803829[/C][C]-7.5378593803829[/C][/ROW]
[ROW][C]12[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]13[/C][C]100[/C][C]66.7130914817308[/C][C]33.2869085182692[/C][/ROW]
[ROW][C]14[/C][C]50[/C][C]57.5378593803829[/C][C]-7.5378593803829[/C][/ROW]
[ROW][C]15[/C][C]100[/C][C]70.1820320934636[/C][C]29.8179679065364[/C][/ROW]
[ROW][C]16[/C][C]100[/C][C]73.7299415852499[/C][C]26.2700584147501[/C][/ROW]
[ROW][C]17[/C][C]100[/C][C]99.1249905319782[/C][C]0.875009468021765[/C][/ROW]
[ROW][C]18[/C][C]50[/C][C]57.5378593803829[/C][C]-7.5378593803829[/C][/ROW]
[ROW][C]19[/C][C]50[/C][C]62.2158322803323[/C][C]-12.2158322803323[/C][/ROW]
[ROW][C]20[/C][C]100[/C][C]107.350872923714[/C][C]-7.35087292371396[/C][/ROW]
[ROW][C]21[/C][C]50[/C][C]61.9561497017279[/C][C]-11.9561497017279[/C][/ROW]
[ROW][C]22[/C][C]100[/C][C]65.4250903134606[/C][C]34.5749096865394[/C][/ROW]
[ROW][C]23[/C][C]50[/C][C]70.1820320934636[/C][C]-20.1820320934636[/C][/ROW]
[ROW][C]24[/C][C]50[/C][C]65.4250903134606[/C][C]-15.4250903134606[/C][/ROW]
[ROW][C]25[/C][C]100[/C][C]65.7637417721186[/C][C]34.2362582278814[/C][/ROW]
[ROW][C]26[/C][C]100[/C][C]66.7130914817308[/C][C]33.2869085182692[/C][/ROW]
[ROW][C]27[/C][C]50[/C][C]57.4588905003293[/C][C]-7.45889050032932[/C][/ROW]
[ROW][C]28[/C][C]100[/C][C]58.7468916685995[/C][C]41.2531083314005[/C][/ROW]
[ROW][C]29[/C][C]50[/C][C]62.2158322803323[/C][C]-12.2158322803323[/C][/ROW]
[ROW][C]30[/C][C]50[/C][C]66.7130914817308[/C][C]-16.7130914817308[/C][/ROW]
[ROW][C]31[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]32[/C][C]50[/C][C]53.9899498885966[/C][C]-3.98994988859656[/C][/ROW]
[ROW][C]33[/C][C]50[/C][C]61.9561497017279[/C][C]-11.9561497017279[/C][/ROW]
[ROW][C]34[/C][C]50[/C][C]65.7637417721186[/C][C]-15.7637417721186[/C][/ROW]
[ROW][C]35[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]36[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]37[/C][C]100[/C][C]65.5040591935142[/C][C]34.4959408064858[/C][/ROW]
[ROW][C]38[/C][C]100[/C][C]62.2158322803323[/C][C]37.7841677196677[/C][/ROW]
[ROW][C]39[/C][C]50[/C][C]70.1820320934636[/C][C]-20.1820320934636[/C][/ROW]
[ROW][C]40[/C][C]50[/C][C]70.2610009735172[/C][C]-20.2610009735172[/C][/ROW]
[ROW][C]41[/C][C]100[/C][C]103.802963431928[/C][C]-3.80296343192762[/C][/ROW]
[ROW][C]42[/C][C]100[/C][C]62.2158322803323[/C][C]37.7841677196677[/C][/ROW]
[ROW][C]43[/C][C]50[/C][C]65.4250903134606[/C][C]-15.4250903134606[/C][/ROW]
[ROW][C]44[/C][C]50[/C][C]57.5378593803829[/C][C]-7.5378593803829[/C][/ROW]
[ROW][C]45[/C][C]50[/C][C]66.7130914817308[/C][C]-16.7130914817308[/C][/ROW]
[ROW][C]46[/C][C]50[/C][C]70.1820320934636[/C][C]-20.1820320934636[/C][/ROW]
[ROW][C]47[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]48[/C][C]50[/C][C]62.2158322803323[/C][C]-12.2158322803323[/C][/ROW]
[ROW][C]49[/C][C]50[/C][C]70.1820320934636[/C][C]-20.1820320934636[/C][/ROW]
[ROW][C]50[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]51[/C][C]100[/C][C]62.2948011603859[/C][C]37.7051988396141[/C][/ROW]
[ROW][C]52[/C][C]100[/C][C]99.1249905319782[/C][C]0.875009468021765[/C][/ROW]
[ROW][C]53[/C][C]50[/C][C]62.2158322803323[/C][C]-12.2158322803323[/C][/ROW]
[ROW][C]54[/C][C]100[/C][C]92.3678230070635[/C][C]7.63217699293645[/C][/ROW]
[ROW][C]55[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]56[/C][C]100[/C][C]65.7637417721186[/C][C]34.2362582278814[/C][/ROW]
[ROW][C]57[/C][C]100[/C][C]70.1820320934636[/C][C]29.8179679065364[/C][/ROW]
[ROW][C]58[/C][C]50[/C][C]62.2158322803323[/C][C]-12.2158322803323[/C][/ROW]
[ROW][C]59[/C][C]50[/C][C]62.2158322803323[/C][C]-12.2158322803323[/C][/ROW]
[ROW][C]60[/C][C]100[/C][C]102.593931143711[/C][C]-2.59393114371099[/C][/ROW]
[ROW][C]61[/C][C]50[/C][C]61.0067999921157[/C][C]-11.0067999921157[/C][/ROW]
[ROW][C]62[/C][C]100[/C][C]66.7130914817308[/C][C]33.2869085182692[/C][/ROW]
[ROW][C]63[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]64[/C][C]50[/C][C]61.0067999921157[/C][C]-11.0067999921157[/C][/ROW]
[ROW][C]65[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]66[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]67[/C][C]100[/C][C]103.881932311981[/C][C]-3.8819323119812[/C][/ROW]
[ROW][C]68[/C][C]50[/C][C]53.9899498885966[/C][C]-3.98994988859656[/C][/ROW]
[ROW][C]69[/C][C]50[/C][C]62.2158322803323[/C][C]-12.2158322803323[/C][/ROW]
[ROW][C]70[/C][C]100[/C][C]58.7468916685995[/C][C]41.2531083314005[/C][/ROW]
[ROW][C]71[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]72[/C][C]50[/C][C]62.2158322803323[/C][C]-12.2158322803323[/C][/ROW]
[ROW][C]73[/C][C]100[/C][C]62.2158322803323[/C][C]37.7841677196677[/C][/ROW]
[ROW][C]74[/C][C]100[/C][C]53.9899498885966[/C][C]46.0100501114034[/C][/ROW]
[ROW][C]75[/C][C]50[/C][C]62.2158322803323[/C][C]-12.2158322803323[/C][/ROW]
[ROW][C]76[/C][C]50[/C][C]73.7299415852499[/C][C]-23.72994158525[/C][/ROW]
[ROW][C]77[/C][C]50[/C][C]62.2158322803323[/C][C]-12.2158322803323[/C][/ROW]
[ROW][C]78[/C][C]100[/C][C]70.1820320934636[/C][C]29.8179679065364[/C][/ROW]
[ROW][C]79[/C][C]100[/C][C]99.3846731105827[/C][C]0.615326889417349[/C][/ROW]
[ROW][C]80[/C][C]50[/C][C]70.2610009735172[/C][C]-20.2610009735172[/C][/ROW]
[ROW][C]81[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]82[/C][C]100[/C][C]57.4588905003293[/C][C]42.5411094996707[/C][/ROW]
[ROW][C]83[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]84[/C][C]100[/C][C]92.3678230070635[/C][C]7.63217699293645[/C][/ROW]
[ROW][C]85[/C][C]50[/C][C]70.1820320934636[/C][C]-20.1820320934636[/C][/ROW]
[ROW][C]86[/C][C]50[/C][C]53.9899498885966[/C][C]-3.98994988859656[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200481&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200481&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
15061.0067999921157-11.0067999921157
25058.7468916685995-8.74689166859952
35058.7468916685995-8.74689166859952
45058.7468916685995-8.74689166859954
55058.7468916685995-8.74689166859953
65065.4250903134606-15.4250903134606
75058.7468916685995-8.74689166859953
85062.2948011603859-12.2948011603859
95062.2158322803323-12.2158322803323
105053.9899498885966-3.98994988859656
115057.5378593803829-7.5378593803829
125058.7468916685995-8.74689166859953
1310066.713091481730833.2869085182692
145057.5378593803829-7.5378593803829
1510070.182032093463629.8179679065364
1610073.729941585249926.2700584147501
1710099.12499053197820.875009468021765
185057.5378593803829-7.5378593803829
195062.2158322803323-12.2158322803323
20100107.350872923714-7.35087292371396
215061.9561497017279-11.9561497017279
2210065.425090313460634.5749096865394
235070.1820320934636-20.1820320934636
245065.4250903134606-15.4250903134606
2510065.763741772118634.2362582278814
2610066.713091481730833.2869085182692
275057.4588905003293-7.45889050032932
2810058.746891668599541.2531083314005
295062.2158322803323-12.2158322803323
305066.7130914817308-16.7130914817308
315058.7468916685995-8.74689166859953
325053.9899498885966-3.98994988859656
335061.9561497017279-11.9561497017279
345065.7637417721186-15.7637417721186
355058.7468916685995-8.74689166859953
365058.7468916685995-8.74689166859953
3710065.504059193514234.4959408064858
3810062.215832280332337.7841677196677
395070.1820320934636-20.1820320934636
405070.2610009735172-20.2610009735172
41100103.802963431928-3.80296343192762
4210062.215832280332337.7841677196677
435065.4250903134606-15.4250903134606
445057.5378593803829-7.5378593803829
455066.7130914817308-16.7130914817308
465070.1820320934636-20.1820320934636
475058.7468916685995-8.74689166859953
485062.2158322803323-12.2158322803323
495070.1820320934636-20.1820320934636
505058.7468916685995-8.74689166859953
5110062.294801160385937.7051988396141
5210099.12499053197820.875009468021765
535062.2158322803323-12.2158322803323
5410092.36782300706357.63217699293645
555058.7468916685995-8.74689166859953
5610065.763741772118634.2362582278814
5710070.182032093463629.8179679065364
585062.2158322803323-12.2158322803323
595062.2158322803323-12.2158322803323
60100102.593931143711-2.59393114371099
615061.0067999921157-11.0067999921157
6210066.713091481730833.2869085182692
635058.7468916685995-8.74689166859953
645061.0067999921157-11.0067999921157
655058.7468916685995-8.74689166859953
665058.7468916685995-8.74689166859953
67100103.881932311981-3.8819323119812
685053.9899498885966-3.98994988859656
695062.2158322803323-12.2158322803323
7010058.746891668599541.2531083314005
715058.7468916685995-8.74689166859953
725062.2158322803323-12.2158322803323
7310062.215832280332337.7841677196677
7410053.989949888596646.0100501114034
755062.2158322803323-12.2158322803323
765073.7299415852499-23.72994158525
775062.2158322803323-12.2158322803323
7810070.182032093463629.8179679065364
7910099.38467311058270.615326889417349
805070.2610009735172-20.2610009735172
815058.7468916685995-8.74689166859953
8210057.458890500329342.5411094996707
835058.7468916685995-8.74689166859953
8410092.36782300706357.63217699293645
855070.1820320934636-20.1820320934636
865053.9899498885966-3.98994988859656







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
96.77054496660541e-951.35410899332108e-941
103.08728550853195e-646.17457101706389e-641
118.18422347986106e-791.63684469597221e-781
128.50530669564521e-951.70106133912904e-941
130.02759896341053730.05519792682107470.972401036589463
140.01213132415944210.02426264831888420.987868675840558
150.02033087031491650.0406617406298330.979669129685083
160.0109672263305130.0219344526610260.989032773669487
170.00496182679983060.00992365359966120.995038173200169
180.002245372053917150.004490744107834290.997754627946083
190.0009785574012493920.001957114802498780.999021442598751
200.0004839835349864080.0009679670699728160.999516016465014
210.001729865501827640.003459731003655270.998270134498172
220.009432916448797580.01886583289759520.990567083551202
230.04057157743726670.08114315487453330.959428422562733
240.04102776969471920.08205553938943850.958972230305281
250.1040790834794890.2081581669589790.895920916520511
260.1212570283959370.2425140567918750.878742971604063
270.1024183222698460.2048366445396910.897581677730154
280.3083598108905390.6167196217810770.691640189109461
290.2578526332466820.5157052664933640.742147366753318
300.3107186005772650.621437201154530.689281399422735
310.2567426732998580.5134853465997160.743257326700142
320.2151698585923150.430339717184630.784830141407685
330.1880246144194720.3760492288389450.811975385580528
340.1786220120633180.3572440241266360.821377987936682
350.1418869766485760.2837739532971520.858113023351424
360.1109560518080520.2219121036161030.889043948191948
370.144786911059920.2895738221198390.85521308894008
380.2976963366403150.595392673280630.702303663359685
390.3230770482904450.646154096580890.676922951709555
400.3629097090993410.7258194181986830.637090290900659
410.3025598368988280.6051196737976560.697440163101172
420.4455996960620060.8911993921240120.554400303937994
430.4198912153495810.8397824306991620.580108784650419
440.3704910695383510.7409821390767020.629508930461649
450.3524158651350560.7048317302701110.647584134864944
460.3529136749755620.7058273499511230.647086325024438
470.3037122421844240.6074244843688470.696287757815576
480.2640014144206160.5280028288412330.735998585579384
490.26541380552250.5308276110450.7345861944775
500.2234485059754970.4468970119509940.776551494024503
510.3886530062565110.7773060125130210.611346993743489
520.3306034108954810.6612068217909620.669396589104519
530.2912762448184740.5825524896369480.708723755181526
540.2444188740948330.4888377481896670.755581125905167
550.2008338029705330.4016676059410670.799166197029467
560.4670962392955260.9341924785910510.532903760704474
570.4878003105229470.9756006210458950.512199689477053
580.4370038476667270.8740076953334540.562996152333273
590.3891702090323750.778340418064750.610829790967625
600.3591428555878060.7182857111756120.640857144412194
610.2979626835031880.5959253670063750.702037316496812
620.3614473827466630.7228947654933270.638552617253337
630.2967330938919170.5934661877838340.703266906108083
640.2462723065708710.4925446131417410.753727693429129
650.1917343244135340.3834686488270690.808265675586466
660.1452030242547760.2904060485095530.854796975745224
670.1057269401267440.2114538802534890.894273059873256
680.1134291250595680.2268582501191350.886570874940432
690.09021946201394760.1804389240278950.909780537986052
700.2993644495142980.5987288990285950.700635550485702
710.2218989973197690.4437979946395380.778101002680231
720.1805618043904210.3611236087808420.819438195609579
730.4047873390207330.8095746780414660.595212660979267
740.5204307904123910.9591384191752180.479569209587609
750.4110507162849320.8221014325698630.588949283715068
760.3221590667910250.644318133582050.677840933208975
770.2531740431858190.5063480863716370.746825956814181

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 6.77054496660541e-95 & 1.35410899332108e-94 & 1 \tabularnewline
10 & 3.08728550853195e-64 & 6.17457101706389e-64 & 1 \tabularnewline
11 & 8.18422347986106e-79 & 1.63684469597221e-78 & 1 \tabularnewline
12 & 8.50530669564521e-95 & 1.70106133912904e-94 & 1 \tabularnewline
13 & 0.0275989634105373 & 0.0551979268210747 & 0.972401036589463 \tabularnewline
14 & 0.0121313241594421 & 0.0242626483188842 & 0.987868675840558 \tabularnewline
15 & 0.0203308703149165 & 0.040661740629833 & 0.979669129685083 \tabularnewline
16 & 0.010967226330513 & 0.021934452661026 & 0.989032773669487 \tabularnewline
17 & 0.0049618267998306 & 0.0099236535996612 & 0.995038173200169 \tabularnewline
18 & 0.00224537205391715 & 0.00449074410783429 & 0.997754627946083 \tabularnewline
19 & 0.000978557401249392 & 0.00195711480249878 & 0.999021442598751 \tabularnewline
20 & 0.000483983534986408 & 0.000967967069972816 & 0.999516016465014 \tabularnewline
21 & 0.00172986550182764 & 0.00345973100365527 & 0.998270134498172 \tabularnewline
22 & 0.00943291644879758 & 0.0188658328975952 & 0.990567083551202 \tabularnewline
23 & 0.0405715774372667 & 0.0811431548745333 & 0.959428422562733 \tabularnewline
24 & 0.0410277696947192 & 0.0820555393894385 & 0.958972230305281 \tabularnewline
25 & 0.104079083479489 & 0.208158166958979 & 0.895920916520511 \tabularnewline
26 & 0.121257028395937 & 0.242514056791875 & 0.878742971604063 \tabularnewline
27 & 0.102418322269846 & 0.204836644539691 & 0.897581677730154 \tabularnewline
28 & 0.308359810890539 & 0.616719621781077 & 0.691640189109461 \tabularnewline
29 & 0.257852633246682 & 0.515705266493364 & 0.742147366753318 \tabularnewline
30 & 0.310718600577265 & 0.62143720115453 & 0.689281399422735 \tabularnewline
31 & 0.256742673299858 & 0.513485346599716 & 0.743257326700142 \tabularnewline
32 & 0.215169858592315 & 0.43033971718463 & 0.784830141407685 \tabularnewline
33 & 0.188024614419472 & 0.376049228838945 & 0.811975385580528 \tabularnewline
34 & 0.178622012063318 & 0.357244024126636 & 0.821377987936682 \tabularnewline
35 & 0.141886976648576 & 0.283773953297152 & 0.858113023351424 \tabularnewline
36 & 0.110956051808052 & 0.221912103616103 & 0.889043948191948 \tabularnewline
37 & 0.14478691105992 & 0.289573822119839 & 0.85521308894008 \tabularnewline
38 & 0.297696336640315 & 0.59539267328063 & 0.702303663359685 \tabularnewline
39 & 0.323077048290445 & 0.64615409658089 & 0.676922951709555 \tabularnewline
40 & 0.362909709099341 & 0.725819418198683 & 0.637090290900659 \tabularnewline
41 & 0.302559836898828 & 0.605119673797656 & 0.697440163101172 \tabularnewline
42 & 0.445599696062006 & 0.891199392124012 & 0.554400303937994 \tabularnewline
43 & 0.419891215349581 & 0.839782430699162 & 0.580108784650419 \tabularnewline
44 & 0.370491069538351 & 0.740982139076702 & 0.629508930461649 \tabularnewline
45 & 0.352415865135056 & 0.704831730270111 & 0.647584134864944 \tabularnewline
46 & 0.352913674975562 & 0.705827349951123 & 0.647086325024438 \tabularnewline
47 & 0.303712242184424 & 0.607424484368847 & 0.696287757815576 \tabularnewline
48 & 0.264001414420616 & 0.528002828841233 & 0.735998585579384 \tabularnewline
49 & 0.2654138055225 & 0.530827611045 & 0.7345861944775 \tabularnewline
50 & 0.223448505975497 & 0.446897011950994 & 0.776551494024503 \tabularnewline
51 & 0.388653006256511 & 0.777306012513021 & 0.611346993743489 \tabularnewline
52 & 0.330603410895481 & 0.661206821790962 & 0.669396589104519 \tabularnewline
53 & 0.291276244818474 & 0.582552489636948 & 0.708723755181526 \tabularnewline
54 & 0.244418874094833 & 0.488837748189667 & 0.755581125905167 \tabularnewline
55 & 0.200833802970533 & 0.401667605941067 & 0.799166197029467 \tabularnewline
56 & 0.467096239295526 & 0.934192478591051 & 0.532903760704474 \tabularnewline
57 & 0.487800310522947 & 0.975600621045895 & 0.512199689477053 \tabularnewline
58 & 0.437003847666727 & 0.874007695333454 & 0.562996152333273 \tabularnewline
59 & 0.389170209032375 & 0.77834041806475 & 0.610829790967625 \tabularnewline
60 & 0.359142855587806 & 0.718285711175612 & 0.640857144412194 \tabularnewline
61 & 0.297962683503188 & 0.595925367006375 & 0.702037316496812 \tabularnewline
62 & 0.361447382746663 & 0.722894765493327 & 0.638552617253337 \tabularnewline
63 & 0.296733093891917 & 0.593466187783834 & 0.703266906108083 \tabularnewline
64 & 0.246272306570871 & 0.492544613141741 & 0.753727693429129 \tabularnewline
65 & 0.191734324413534 & 0.383468648827069 & 0.808265675586466 \tabularnewline
66 & 0.145203024254776 & 0.290406048509553 & 0.854796975745224 \tabularnewline
67 & 0.105726940126744 & 0.211453880253489 & 0.894273059873256 \tabularnewline
68 & 0.113429125059568 & 0.226858250119135 & 0.886570874940432 \tabularnewline
69 & 0.0902194620139476 & 0.180438924027895 & 0.909780537986052 \tabularnewline
70 & 0.299364449514298 & 0.598728899028595 & 0.700635550485702 \tabularnewline
71 & 0.221898997319769 & 0.443797994639538 & 0.778101002680231 \tabularnewline
72 & 0.180561804390421 & 0.361123608780842 & 0.819438195609579 \tabularnewline
73 & 0.404787339020733 & 0.809574678041466 & 0.595212660979267 \tabularnewline
74 & 0.520430790412391 & 0.959138419175218 & 0.479569209587609 \tabularnewline
75 & 0.411050716284932 & 0.822101432569863 & 0.588949283715068 \tabularnewline
76 & 0.322159066791025 & 0.64431813358205 & 0.677840933208975 \tabularnewline
77 & 0.253174043185819 & 0.506348086371637 & 0.746825956814181 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200481&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]9[/C][C]6.77054496660541e-95[/C][C]1.35410899332108e-94[/C][C]1[/C][/ROW]
[ROW][C]10[/C][C]3.08728550853195e-64[/C][C]6.17457101706389e-64[/C][C]1[/C][/ROW]
[ROW][C]11[/C][C]8.18422347986106e-79[/C][C]1.63684469597221e-78[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]8.50530669564521e-95[/C][C]1.70106133912904e-94[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]0.0275989634105373[/C][C]0.0551979268210747[/C][C]0.972401036589463[/C][/ROW]
[ROW][C]14[/C][C]0.0121313241594421[/C][C]0.0242626483188842[/C][C]0.987868675840558[/C][/ROW]
[ROW][C]15[/C][C]0.0203308703149165[/C][C]0.040661740629833[/C][C]0.979669129685083[/C][/ROW]
[ROW][C]16[/C][C]0.010967226330513[/C][C]0.021934452661026[/C][C]0.989032773669487[/C][/ROW]
[ROW][C]17[/C][C]0.0049618267998306[/C][C]0.0099236535996612[/C][C]0.995038173200169[/C][/ROW]
[ROW][C]18[/C][C]0.00224537205391715[/C][C]0.00449074410783429[/C][C]0.997754627946083[/C][/ROW]
[ROW][C]19[/C][C]0.000978557401249392[/C][C]0.00195711480249878[/C][C]0.999021442598751[/C][/ROW]
[ROW][C]20[/C][C]0.000483983534986408[/C][C]0.000967967069972816[/C][C]0.999516016465014[/C][/ROW]
[ROW][C]21[/C][C]0.00172986550182764[/C][C]0.00345973100365527[/C][C]0.998270134498172[/C][/ROW]
[ROW][C]22[/C][C]0.00943291644879758[/C][C]0.0188658328975952[/C][C]0.990567083551202[/C][/ROW]
[ROW][C]23[/C][C]0.0405715774372667[/C][C]0.0811431548745333[/C][C]0.959428422562733[/C][/ROW]
[ROW][C]24[/C][C]0.0410277696947192[/C][C]0.0820555393894385[/C][C]0.958972230305281[/C][/ROW]
[ROW][C]25[/C][C]0.104079083479489[/C][C]0.208158166958979[/C][C]0.895920916520511[/C][/ROW]
[ROW][C]26[/C][C]0.121257028395937[/C][C]0.242514056791875[/C][C]0.878742971604063[/C][/ROW]
[ROW][C]27[/C][C]0.102418322269846[/C][C]0.204836644539691[/C][C]0.897581677730154[/C][/ROW]
[ROW][C]28[/C][C]0.308359810890539[/C][C]0.616719621781077[/C][C]0.691640189109461[/C][/ROW]
[ROW][C]29[/C][C]0.257852633246682[/C][C]0.515705266493364[/C][C]0.742147366753318[/C][/ROW]
[ROW][C]30[/C][C]0.310718600577265[/C][C]0.62143720115453[/C][C]0.689281399422735[/C][/ROW]
[ROW][C]31[/C][C]0.256742673299858[/C][C]0.513485346599716[/C][C]0.743257326700142[/C][/ROW]
[ROW][C]32[/C][C]0.215169858592315[/C][C]0.43033971718463[/C][C]0.784830141407685[/C][/ROW]
[ROW][C]33[/C][C]0.188024614419472[/C][C]0.376049228838945[/C][C]0.811975385580528[/C][/ROW]
[ROW][C]34[/C][C]0.178622012063318[/C][C]0.357244024126636[/C][C]0.821377987936682[/C][/ROW]
[ROW][C]35[/C][C]0.141886976648576[/C][C]0.283773953297152[/C][C]0.858113023351424[/C][/ROW]
[ROW][C]36[/C][C]0.110956051808052[/C][C]0.221912103616103[/C][C]0.889043948191948[/C][/ROW]
[ROW][C]37[/C][C]0.14478691105992[/C][C]0.289573822119839[/C][C]0.85521308894008[/C][/ROW]
[ROW][C]38[/C][C]0.297696336640315[/C][C]0.59539267328063[/C][C]0.702303663359685[/C][/ROW]
[ROW][C]39[/C][C]0.323077048290445[/C][C]0.64615409658089[/C][C]0.676922951709555[/C][/ROW]
[ROW][C]40[/C][C]0.362909709099341[/C][C]0.725819418198683[/C][C]0.637090290900659[/C][/ROW]
[ROW][C]41[/C][C]0.302559836898828[/C][C]0.605119673797656[/C][C]0.697440163101172[/C][/ROW]
[ROW][C]42[/C][C]0.445599696062006[/C][C]0.891199392124012[/C][C]0.554400303937994[/C][/ROW]
[ROW][C]43[/C][C]0.419891215349581[/C][C]0.839782430699162[/C][C]0.580108784650419[/C][/ROW]
[ROW][C]44[/C][C]0.370491069538351[/C][C]0.740982139076702[/C][C]0.629508930461649[/C][/ROW]
[ROW][C]45[/C][C]0.352415865135056[/C][C]0.704831730270111[/C][C]0.647584134864944[/C][/ROW]
[ROW][C]46[/C][C]0.352913674975562[/C][C]0.705827349951123[/C][C]0.647086325024438[/C][/ROW]
[ROW][C]47[/C][C]0.303712242184424[/C][C]0.607424484368847[/C][C]0.696287757815576[/C][/ROW]
[ROW][C]48[/C][C]0.264001414420616[/C][C]0.528002828841233[/C][C]0.735998585579384[/C][/ROW]
[ROW][C]49[/C][C]0.2654138055225[/C][C]0.530827611045[/C][C]0.7345861944775[/C][/ROW]
[ROW][C]50[/C][C]0.223448505975497[/C][C]0.446897011950994[/C][C]0.776551494024503[/C][/ROW]
[ROW][C]51[/C][C]0.388653006256511[/C][C]0.777306012513021[/C][C]0.611346993743489[/C][/ROW]
[ROW][C]52[/C][C]0.330603410895481[/C][C]0.661206821790962[/C][C]0.669396589104519[/C][/ROW]
[ROW][C]53[/C][C]0.291276244818474[/C][C]0.582552489636948[/C][C]0.708723755181526[/C][/ROW]
[ROW][C]54[/C][C]0.244418874094833[/C][C]0.488837748189667[/C][C]0.755581125905167[/C][/ROW]
[ROW][C]55[/C][C]0.200833802970533[/C][C]0.401667605941067[/C][C]0.799166197029467[/C][/ROW]
[ROW][C]56[/C][C]0.467096239295526[/C][C]0.934192478591051[/C][C]0.532903760704474[/C][/ROW]
[ROW][C]57[/C][C]0.487800310522947[/C][C]0.975600621045895[/C][C]0.512199689477053[/C][/ROW]
[ROW][C]58[/C][C]0.437003847666727[/C][C]0.874007695333454[/C][C]0.562996152333273[/C][/ROW]
[ROW][C]59[/C][C]0.389170209032375[/C][C]0.77834041806475[/C][C]0.610829790967625[/C][/ROW]
[ROW][C]60[/C][C]0.359142855587806[/C][C]0.718285711175612[/C][C]0.640857144412194[/C][/ROW]
[ROW][C]61[/C][C]0.297962683503188[/C][C]0.595925367006375[/C][C]0.702037316496812[/C][/ROW]
[ROW][C]62[/C][C]0.361447382746663[/C][C]0.722894765493327[/C][C]0.638552617253337[/C][/ROW]
[ROW][C]63[/C][C]0.296733093891917[/C][C]0.593466187783834[/C][C]0.703266906108083[/C][/ROW]
[ROW][C]64[/C][C]0.246272306570871[/C][C]0.492544613141741[/C][C]0.753727693429129[/C][/ROW]
[ROW][C]65[/C][C]0.191734324413534[/C][C]0.383468648827069[/C][C]0.808265675586466[/C][/ROW]
[ROW][C]66[/C][C]0.145203024254776[/C][C]0.290406048509553[/C][C]0.854796975745224[/C][/ROW]
[ROW][C]67[/C][C]0.105726940126744[/C][C]0.211453880253489[/C][C]0.894273059873256[/C][/ROW]
[ROW][C]68[/C][C]0.113429125059568[/C][C]0.226858250119135[/C][C]0.886570874940432[/C][/ROW]
[ROW][C]69[/C][C]0.0902194620139476[/C][C]0.180438924027895[/C][C]0.909780537986052[/C][/ROW]
[ROW][C]70[/C][C]0.299364449514298[/C][C]0.598728899028595[/C][C]0.700635550485702[/C][/ROW]
[ROW][C]71[/C][C]0.221898997319769[/C][C]0.443797994639538[/C][C]0.778101002680231[/C][/ROW]
[ROW][C]72[/C][C]0.180561804390421[/C][C]0.361123608780842[/C][C]0.819438195609579[/C][/ROW]
[ROW][C]73[/C][C]0.404787339020733[/C][C]0.809574678041466[/C][C]0.595212660979267[/C][/ROW]
[ROW][C]74[/C][C]0.520430790412391[/C][C]0.959138419175218[/C][C]0.479569209587609[/C][/ROW]
[ROW][C]75[/C][C]0.411050716284932[/C][C]0.822101432569863[/C][C]0.588949283715068[/C][/ROW]
[ROW][C]76[/C][C]0.322159066791025[/C][C]0.64431813358205[/C][C]0.677840933208975[/C][/ROW]
[ROW][C]77[/C][C]0.253174043185819[/C][C]0.506348086371637[/C][C]0.746825956814181[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200481&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200481&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
96.77054496660541e-951.35410899332108e-941
103.08728550853195e-646.17457101706389e-641
118.18422347986106e-791.63684469597221e-781
128.50530669564521e-951.70106133912904e-941
130.02759896341053730.05519792682107470.972401036589463
140.01213132415944210.02426264831888420.987868675840558
150.02033087031491650.0406617406298330.979669129685083
160.0109672263305130.0219344526610260.989032773669487
170.00496182679983060.00992365359966120.995038173200169
180.002245372053917150.004490744107834290.997754627946083
190.0009785574012493920.001957114802498780.999021442598751
200.0004839835349864080.0009679670699728160.999516016465014
210.001729865501827640.003459731003655270.998270134498172
220.009432916448797580.01886583289759520.990567083551202
230.04057157743726670.08114315487453330.959428422562733
240.04102776969471920.08205553938943850.958972230305281
250.1040790834794890.2081581669589790.895920916520511
260.1212570283959370.2425140567918750.878742971604063
270.1024183222698460.2048366445396910.897581677730154
280.3083598108905390.6167196217810770.691640189109461
290.2578526332466820.5157052664933640.742147366753318
300.3107186005772650.621437201154530.689281399422735
310.2567426732998580.5134853465997160.743257326700142
320.2151698585923150.430339717184630.784830141407685
330.1880246144194720.3760492288389450.811975385580528
340.1786220120633180.3572440241266360.821377987936682
350.1418869766485760.2837739532971520.858113023351424
360.1109560518080520.2219121036161030.889043948191948
370.144786911059920.2895738221198390.85521308894008
380.2976963366403150.595392673280630.702303663359685
390.3230770482904450.646154096580890.676922951709555
400.3629097090993410.7258194181986830.637090290900659
410.3025598368988280.6051196737976560.697440163101172
420.4455996960620060.8911993921240120.554400303937994
430.4198912153495810.8397824306991620.580108784650419
440.3704910695383510.7409821390767020.629508930461649
450.3524158651350560.7048317302701110.647584134864944
460.3529136749755620.7058273499511230.647086325024438
470.3037122421844240.6074244843688470.696287757815576
480.2640014144206160.5280028288412330.735998585579384
490.26541380552250.5308276110450.7345861944775
500.2234485059754970.4468970119509940.776551494024503
510.3886530062565110.7773060125130210.611346993743489
520.3306034108954810.6612068217909620.669396589104519
530.2912762448184740.5825524896369480.708723755181526
540.2444188740948330.4888377481896670.755581125905167
550.2008338029705330.4016676059410670.799166197029467
560.4670962392955260.9341924785910510.532903760704474
570.4878003105229470.9756006210458950.512199689477053
580.4370038476667270.8740076953334540.562996152333273
590.3891702090323750.778340418064750.610829790967625
600.3591428555878060.7182857111756120.640857144412194
610.2979626835031880.5959253670063750.702037316496812
620.3614473827466630.7228947654933270.638552617253337
630.2967330938919170.5934661877838340.703266906108083
640.2462723065708710.4925446131417410.753727693429129
650.1917343244135340.3834686488270690.808265675586466
660.1452030242547760.2904060485095530.854796975745224
670.1057269401267440.2114538802534890.894273059873256
680.1134291250595680.2268582501191350.886570874940432
690.09021946201394760.1804389240278950.909780537986052
700.2993644495142980.5987288990285950.700635550485702
710.2218989973197690.4437979946395380.778101002680231
720.1805618043904210.3611236087808420.819438195609579
730.4047873390207330.8095746780414660.595212660979267
740.5204307904123910.9591384191752180.479569209587609
750.4110507162849320.8221014325698630.588949283715068
760.3221590667910250.644318133582050.677840933208975
770.2531740431858190.5063480863716370.746825956814181







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level90.130434782608696NOK
5% type I error level130.188405797101449NOK
10% type I error level160.231884057971014NOK

\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 & 9 & 0.130434782608696 & NOK \tabularnewline
5% type I error level & 13 & 0.188405797101449 & NOK \tabularnewline
10% type I error level & 16 & 0.231884057971014 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200481&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]9[/C][C]0.130434782608696[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]13[/C][C]0.188405797101449[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.231884057971014[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200481&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200481&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 level90.130434782608696NOK
5% type I error level130.188405797101449NOK
10% type I error level160.231884057971014NOK



Parameters (Session):
par1 = 2 ; 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 ;
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')
}