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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 21 Dec 2012 17:13:27 -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/21/t1356128046jx0xl41qakt6sku.htm/, Retrieved Fri, 26 Apr 2024 05:16:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=204347, Retrieved Fri, 26 Apr 2024 05:16:08 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact76

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [multiple regressi...] [2012-12-21 21:57:42] [9e2edbc475ba2b100cc6940e0b163d75]
- R  D    [Multiple Regression] [multiple regressi...] [2012-12-21 22:13:27] [f49ded22e47bf433de387b207efbd53c] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1	0	0	0	0	1
1	1	1	0	0	1
0	0	0	0	0	0
0	0	0	0	0	1
0	0	0	0	1	0
1	1	0	0	0	0
1	0	0	0	1	0
0	0	0	0	0	0
0	1	0	0	0	0
0	0	0	0	0	1
1	1	0	0	0	0
0	0	0	0	0	0
1	0	0	0	0	0
0	0	0	0	0	1
1	0	0	0	0	1
0	0	0	0	0	0
0	0	0	0	0	0
0	0	0	0	0	0
0	1	1	0	0	0
0	0	0	0	0	0
0	0	0	0	0	0
1	1	1	0	0	0
0	0	0	0	0	0
1	0	0	0	0	0
1	1	1	0	1	0
0	1	0	0	0	0
0	0	1	0	0	0
1	1	1	0	0	0
1	0	0	0	0	0
0	0	0	0	0	0
1	0	0	0	0	1
1	0	0	0	0	0
0	0	0	0	0	0
0	0	0	0	0	1
1	0	0	0	0	0
0	0	0	0	0	0
1	1	1	0	0	0
0	0	1	0	1	1
0	0	0	0	0	1
0	1	0	0	0	0
0	0	0	0	1	0
0	0	0	0	0	1
0	0	0	0	0	0
0	0	0	0	0	1
1	0	0	0	0	0
1	0	0	0	0	1
1	0	1	0	0	0
0	0	0	0	0	0
0	0	0	0	0	0
0	0	0	0	0	0
1	0	1	0	1	1
1	1	1	0	1	1
0	1	0	0	0	0
0	0	0	0	0	0
0	0	1	1	0	1
0	1	1	0	0	1
1	0	0	0	0	0
0	0	0	0	1	1
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0	1	0	0	0	1
0	1	1	0	0	0
0	1	0	0	0	0
1	0	0	0	0	0
0	0	0	0	1	1
0	0	0	0	0	1
1	0	1	1	0	0
1	0	1	1	1	0
1	0	1	0	0	0

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 'George Udny Yule' @ yule.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204347&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204347&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204347&T=0

As an alternative you can also use a QR Code:  
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'George Udny Yule' @ yule.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
UseLimit[t] = + 0.32455537644354 -0.0269945297428715T20[t] + 0.365952273988531Used[t] + 0.00372611717449325CorrectAnalysis[t] + 0.0106498242646865Useful[t] -0.0933511270843794`Outcome\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
UseLimit[t] =  +  0.32455537644354 -0.0269945297428715T20[t] +  0.365952273988531Used[t] +  0.00372611717449325CorrectAnalysis[t] +  0.0106498242646865Useful[t] -0.0933511270843794`Outcome\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204347&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]UseLimit[t] =  +  0.32455537644354 -0.0269945297428715T20[t] +  0.365952273988531Used[t] +  0.00372611717449325CorrectAnalysis[t] +  0.0106498242646865Useful[t] -0.0933511270843794`Outcome\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204347&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204347&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
UseLimit[t] = + 0.32455537644354 -0.0269945297428715T20[t] + 0.365952273988531Used[t] + 0.00372611717449325CorrectAnalysis[t] + 0.0106498242646865Useful[t] -0.0933511270843794`Outcome\r`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.324555376443540.0843493.84780.0002840.000142
T20-0.02699452974287150.155419-0.17370.8626760.431338
Used0.3659522739885310.1682462.17510.0334450.016722
CorrectAnalysis0.003726117174493250.3217850.01160.9907980.495399
Useful0.01064982426468650.165170.06450.9487970.474399
`Outcome\r`-0.09335112708437940.128349-0.72730.4697660.234883

\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) & 0.32455537644354 & 0.084349 & 3.8478 & 0.000284 & 0.000142 \tabularnewline
T20 & -0.0269945297428715 & 0.155419 & -0.1737 & 0.862676 & 0.431338 \tabularnewline
Used & 0.365952273988531 & 0.168246 & 2.1751 & 0.033445 & 0.016722 \tabularnewline
CorrectAnalysis & 0.00372611717449325 & 0.321785 & 0.0116 & 0.990798 & 0.495399 \tabularnewline
Useful & 0.0106498242646865 & 0.16517 & 0.0645 & 0.948797 & 0.474399 \tabularnewline
`Outcome\r` & -0.0933511270843794 & 0.128349 & -0.7273 & 0.469766 & 0.234883 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204347&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]0.32455537644354[/C][C]0.084349[/C][C]3.8478[/C][C]0.000284[/C][C]0.000142[/C][/ROW]
[ROW][C]T20[/C][C]-0.0269945297428715[/C][C]0.155419[/C][C]-0.1737[/C][C]0.862676[/C][C]0.431338[/C][/ROW]
[ROW][C]Used[/C][C]0.365952273988531[/C][C]0.168246[/C][C]2.1751[/C][C]0.033445[/C][C]0.016722[/C][/ROW]
[ROW][C]CorrectAnalysis[/C][C]0.00372611717449325[/C][C]0.321785[/C][C]0.0116[/C][C]0.990798[/C][C]0.495399[/C][/ROW]
[ROW][C]Useful[/C][C]0.0106498242646865[/C][C]0.16517[/C][C]0.0645[/C][C]0.948797[/C][C]0.474399[/C][/ROW]
[ROW][C]`Outcome\r`[/C][C]-0.0933511270843794[/C][C]0.128349[/C][C]-0.7273[/C][C]0.469766[/C][C]0.234883[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204347&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204347&T=2

As an alternative you can also use a QR Code:  
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)0.324555376443540.0843493.84780.0002840.000142
T20-0.02699452974287150.155419-0.17370.8626760.431338
Used0.3659522739885310.1682462.17510.0334450.016722
CorrectAnalysis0.003726117174493250.3217850.01160.9907980.495399
Useful0.01064982426468650.165170.06450.9487970.474399
`Outcome\r`-0.09335112708437940.128349-0.72730.4697660.234883






Multiple Linear Regression - Regression Statistics
Multiple R0.326701993131307
R-squared0.106734192315969
Adjusted R-squared0.0346966271801598
F-TEST (value)1.48164630654503
F-TEST (DF numerator)5
F-TEST (DF denominator)62
p-value0.208714562809699
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.481006974170742
Sum Squared Residuals14.3447979704553

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.326701993131307 \tabularnewline
R-squared & 0.106734192315969 \tabularnewline
Adjusted R-squared & 0.0346966271801598 \tabularnewline
F-TEST (value) & 1.48164630654503 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 62 \tabularnewline
p-value & 0.208714562809699 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.481006974170742 \tabularnewline
Sum Squared Residuals & 14.3447979704553 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204347&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.326701993131307[/C][/ROW]
[ROW][C]R-squared[/C][C]0.106734192315969[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0346966271801598[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.48164630654503[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]62[/C][/ROW]
[ROW][C]p-value[/C][C]0.208714562809699[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.481006974170742[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]14.3447979704553[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204347&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204347&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.326701993131307
R-squared0.106734192315969
Adjusted R-squared0.0346966271801598
F-TEST (value)1.48164630654503
F-TEST (DF numerator)5
F-TEST (DF denominator)62
p-value0.208714562809699
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.481006974170742
Sum Squared Residuals14.3447979704553






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.2312042493591610.768795750640839
210.570161993604820.42983800639518
300.32455537644354-0.32455537644354
400.231204249359161-0.231204249359161
500.335205200708227-0.335205200708227
610.2975608467006690.702439153299331
710.3352052007082270.664794799291773
800.32455537644354-0.32455537644354
900.297560846700669-0.297560846700669
1000.231204249359161-0.231204249359161
1110.2975608467006690.702439153299331
1200.32455537644354-0.32455537644354
1310.324555376443540.67544462355646
1400.231204249359161-0.231204249359161
1510.2312042493591610.768795750640839
1600.32455537644354-0.32455537644354
1700.32455537644354-0.32455537644354
1800.32455537644354-0.32455537644354
1900.663513120689199-0.663513120689199
2000.32455537644354-0.32455537644354
2100.32455537644354-0.32455537644354
2210.66351312068920.3364868793108
2300.32455537644354-0.32455537644354
2410.324555376443540.67544462355646
2510.6741629449538860.325837055046114
2600.297560846700669-0.297560846700669
2700.690507650432071-0.690507650432071
2810.66351312068920.3364868793108
2910.324555376443540.67544462355646
3000.32455537644354-0.32455537644354
3110.2312042493591610.768795750640839
3210.324555376443540.67544462355646
3300.32455537644354-0.32455537644354
3400.231204249359161-0.231204249359161
3510.324555376443540.67544462355646
3600.32455537644354-0.32455537644354
3710.66351312068920.3364868793108
3800.607806347612378-0.607806347612378
3900.231204249359161-0.231204249359161
4000.297560846700669-0.297560846700669
4100.335205200708227-0.335205200708227
4200.231204249359161-0.231204249359161
4300.32455537644354-0.32455537644354
4400.231204249359161-0.231204249359161
4510.324555376443540.67544462355646
4610.2312042493591610.768795750640839
4710.6905076504320710.309492349567929
4800.32455537644354-0.32455537644354
4900.32455537644354-0.32455537644354
5000.32455537644354-0.32455537644354
5110.6078063476123780.392193652387622
5210.5808118178695070.419188182130493
5300.297560846700669-0.297560846700669
5400.32455537644354-0.32455537644354
5500.600882640522185-0.600882640522185
5600.57016199360482-0.57016199360482
5710.324555376443540.67544462355646
5800.241854073623847-0.241854073623847
5900.335205200708227-0.335205200708227
6000.204209719616289-0.204209719616289
6100.663513120689199-0.663513120689199
6200.297560846700669-0.297560846700669
6310.324555376443540.67544462355646
6400.241854073623847-0.241854073623847
6500.231204249359161-0.231204249359161
6610.6942337676065640.305766232393436
6710.7048835918712510.295116408128749
6810.6905076504320710.309492349567929

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 0.231204249359161 & 0.768795750640839 \tabularnewline
2 & 1 & 0.57016199360482 & 0.42983800639518 \tabularnewline
3 & 0 & 0.32455537644354 & -0.32455537644354 \tabularnewline
4 & 0 & 0.231204249359161 & -0.231204249359161 \tabularnewline
5 & 0 & 0.335205200708227 & -0.335205200708227 \tabularnewline
6 & 1 & 0.297560846700669 & 0.702439153299331 \tabularnewline
7 & 1 & 0.335205200708227 & 0.664794799291773 \tabularnewline
8 & 0 & 0.32455537644354 & -0.32455537644354 \tabularnewline
9 & 0 & 0.297560846700669 & -0.297560846700669 \tabularnewline
10 & 0 & 0.231204249359161 & -0.231204249359161 \tabularnewline
11 & 1 & 0.297560846700669 & 0.702439153299331 \tabularnewline
12 & 0 & 0.32455537644354 & -0.32455537644354 \tabularnewline
13 & 1 & 0.32455537644354 & 0.67544462355646 \tabularnewline
14 & 0 & 0.231204249359161 & -0.231204249359161 \tabularnewline
15 & 1 & 0.231204249359161 & 0.768795750640839 \tabularnewline
16 & 0 & 0.32455537644354 & -0.32455537644354 \tabularnewline
17 & 0 & 0.32455537644354 & -0.32455537644354 \tabularnewline
18 & 0 & 0.32455537644354 & -0.32455537644354 \tabularnewline
19 & 0 & 0.663513120689199 & -0.663513120689199 \tabularnewline
20 & 0 & 0.32455537644354 & -0.32455537644354 \tabularnewline
21 & 0 & 0.32455537644354 & -0.32455537644354 \tabularnewline
22 & 1 & 0.6635131206892 & 0.3364868793108 \tabularnewline
23 & 0 & 0.32455537644354 & -0.32455537644354 \tabularnewline
24 & 1 & 0.32455537644354 & 0.67544462355646 \tabularnewline
25 & 1 & 0.674162944953886 & 0.325837055046114 \tabularnewline
26 & 0 & 0.297560846700669 & -0.297560846700669 \tabularnewline
27 & 0 & 0.690507650432071 & -0.690507650432071 \tabularnewline
28 & 1 & 0.6635131206892 & 0.3364868793108 \tabularnewline
29 & 1 & 0.32455537644354 & 0.67544462355646 \tabularnewline
30 & 0 & 0.32455537644354 & -0.32455537644354 \tabularnewline
31 & 1 & 0.231204249359161 & 0.768795750640839 \tabularnewline
32 & 1 & 0.32455537644354 & 0.67544462355646 \tabularnewline
33 & 0 & 0.32455537644354 & -0.32455537644354 \tabularnewline
34 & 0 & 0.231204249359161 & -0.231204249359161 \tabularnewline
35 & 1 & 0.32455537644354 & 0.67544462355646 \tabularnewline
36 & 0 & 0.32455537644354 & -0.32455537644354 \tabularnewline
37 & 1 & 0.6635131206892 & 0.3364868793108 \tabularnewline
38 & 0 & 0.607806347612378 & -0.607806347612378 \tabularnewline
39 & 0 & 0.231204249359161 & -0.231204249359161 \tabularnewline
40 & 0 & 0.297560846700669 & -0.297560846700669 \tabularnewline
41 & 0 & 0.335205200708227 & -0.335205200708227 \tabularnewline
42 & 0 & 0.231204249359161 & -0.231204249359161 \tabularnewline
43 & 0 & 0.32455537644354 & -0.32455537644354 \tabularnewline
44 & 0 & 0.231204249359161 & -0.231204249359161 \tabularnewline
45 & 1 & 0.32455537644354 & 0.67544462355646 \tabularnewline
46 & 1 & 0.231204249359161 & 0.768795750640839 \tabularnewline
47 & 1 & 0.690507650432071 & 0.309492349567929 \tabularnewline
48 & 0 & 0.32455537644354 & -0.32455537644354 \tabularnewline
49 & 0 & 0.32455537644354 & -0.32455537644354 \tabularnewline
50 & 0 & 0.32455537644354 & -0.32455537644354 \tabularnewline
51 & 1 & 0.607806347612378 & 0.392193652387622 \tabularnewline
52 & 1 & 0.580811817869507 & 0.419188182130493 \tabularnewline
53 & 0 & 0.297560846700669 & -0.297560846700669 \tabularnewline
54 & 0 & 0.32455537644354 & -0.32455537644354 \tabularnewline
55 & 0 & 0.600882640522185 & -0.600882640522185 \tabularnewline
56 & 0 & 0.57016199360482 & -0.57016199360482 \tabularnewline
57 & 1 & 0.32455537644354 & 0.67544462355646 \tabularnewline
58 & 0 & 0.241854073623847 & -0.241854073623847 \tabularnewline
59 & 0 & 0.335205200708227 & -0.335205200708227 \tabularnewline
60 & 0 & 0.204209719616289 & -0.204209719616289 \tabularnewline
61 & 0 & 0.663513120689199 & -0.663513120689199 \tabularnewline
62 & 0 & 0.297560846700669 & -0.297560846700669 \tabularnewline
63 & 1 & 0.32455537644354 & 0.67544462355646 \tabularnewline
64 & 0 & 0.241854073623847 & -0.241854073623847 \tabularnewline
65 & 0 & 0.231204249359161 & -0.231204249359161 \tabularnewline
66 & 1 & 0.694233767606564 & 0.305766232393436 \tabularnewline
67 & 1 & 0.704883591871251 & 0.295116408128749 \tabularnewline
68 & 1 & 0.690507650432071 & 0.309492349567929 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204347&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]0.231204249359161[/C][C]0.768795750640839[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]0.57016199360482[/C][C]0.42983800639518[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.32455537644354[/C][C]-0.32455537644354[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.231204249359161[/C][C]-0.231204249359161[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.335205200708227[/C][C]-0.335205200708227[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]0.297560846700669[/C][C]0.702439153299331[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]0.335205200708227[/C][C]0.664794799291773[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.32455537644354[/C][C]-0.32455537644354[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0.297560846700669[/C][C]-0.297560846700669[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.231204249359161[/C][C]-0.231204249359161[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]0.297560846700669[/C][C]0.702439153299331[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.32455537644354[/C][C]-0.32455537644354[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]0.32455537644354[/C][C]0.67544462355646[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.231204249359161[/C][C]-0.231204249359161[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]0.231204249359161[/C][C]0.768795750640839[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.32455537644354[/C][C]-0.32455537644354[/C][/ROW]
[ROW][C]17[/C][C]0[/C][C]0.32455537644354[/C][C]-0.32455537644354[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.32455537644354[/C][C]-0.32455537644354[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0.663513120689199[/C][C]-0.663513120689199[/C][/ROW]
[ROW][C]20[/C][C]0[/C][C]0.32455537644354[/C][C]-0.32455537644354[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.32455537644354[/C][C]-0.32455537644354[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]0.6635131206892[/C][C]0.3364868793108[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.32455537644354[/C][C]-0.32455537644354[/C][/ROW]
[ROW][C]24[/C][C]1[/C][C]0.32455537644354[/C][C]0.67544462355646[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]0.674162944953886[/C][C]0.325837055046114[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.297560846700669[/C][C]-0.297560846700669[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]0.690507650432071[/C][C]-0.690507650432071[/C][/ROW]
[ROW][C]28[/C][C]1[/C][C]0.6635131206892[/C][C]0.3364868793108[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]0.32455537644354[/C][C]0.67544462355646[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.32455537644354[/C][C]-0.32455537644354[/C][/ROW]
[ROW][C]31[/C][C]1[/C][C]0.231204249359161[/C][C]0.768795750640839[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]0.32455537644354[/C][C]0.67544462355646[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.32455537644354[/C][C]-0.32455537644354[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.231204249359161[/C][C]-0.231204249359161[/C][/ROW]
[ROW][C]35[/C][C]1[/C][C]0.32455537644354[/C][C]0.67544462355646[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.32455537644354[/C][C]-0.32455537644354[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]0.6635131206892[/C][C]0.3364868793108[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.607806347612378[/C][C]-0.607806347612378[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.231204249359161[/C][C]-0.231204249359161[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.297560846700669[/C][C]-0.297560846700669[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0.335205200708227[/C][C]-0.335205200708227[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.231204249359161[/C][C]-0.231204249359161[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0.32455537644354[/C][C]-0.32455537644354[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.231204249359161[/C][C]-0.231204249359161[/C][/ROW]
[ROW][C]45[/C][C]1[/C][C]0.32455537644354[/C][C]0.67544462355646[/C][/ROW]
[ROW][C]46[/C][C]1[/C][C]0.231204249359161[/C][C]0.768795750640839[/C][/ROW]
[ROW][C]47[/C][C]1[/C][C]0.690507650432071[/C][C]0.309492349567929[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0.32455537644354[/C][C]-0.32455537644354[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.32455537644354[/C][C]-0.32455537644354[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.32455537644354[/C][C]-0.32455537644354[/C][/ROW]
[ROW][C]51[/C][C]1[/C][C]0.607806347612378[/C][C]0.392193652387622[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.580811817869507[/C][C]0.419188182130493[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.297560846700669[/C][C]-0.297560846700669[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.32455537644354[/C][C]-0.32455537644354[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.600882640522185[/C][C]-0.600882640522185[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.57016199360482[/C][C]-0.57016199360482[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]0.32455537644354[/C][C]0.67544462355646[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0.241854073623847[/C][C]-0.241854073623847[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0.335205200708227[/C][C]-0.335205200708227[/C][/ROW]
[ROW][C]60[/C][C]0[/C][C]0.204209719616289[/C][C]-0.204209719616289[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.663513120689199[/C][C]-0.663513120689199[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.297560846700669[/C][C]-0.297560846700669[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]0.32455537644354[/C][C]0.67544462355646[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.241854073623847[/C][C]-0.241854073623847[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.231204249359161[/C][C]-0.231204249359161[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]0.694233767606564[/C][C]0.305766232393436[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.704883591871251[/C][C]0.295116408128749[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]0.690507650432071[/C][C]0.309492349567929[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204347&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204347&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.2312042493591610.768795750640839
210.570161993604820.42983800639518
300.32455537644354-0.32455537644354
400.231204249359161-0.231204249359161
500.335205200708227-0.335205200708227
610.2975608467006690.702439153299331
710.3352052007082270.664794799291773
800.32455537644354-0.32455537644354
900.297560846700669-0.297560846700669
1000.231204249359161-0.231204249359161
1110.2975608467006690.702439153299331
1200.32455537644354-0.32455537644354
1310.324555376443540.67544462355646
1400.231204249359161-0.231204249359161
1510.2312042493591610.768795750640839
1600.32455537644354-0.32455537644354
1700.32455537644354-0.32455537644354
1800.32455537644354-0.32455537644354
1900.663513120689199-0.663513120689199
2000.32455537644354-0.32455537644354
2100.32455537644354-0.32455537644354
2210.66351312068920.3364868793108
2300.32455537644354-0.32455537644354
2410.324555376443540.67544462355646
2510.6741629449538860.325837055046114
2600.297560846700669-0.297560846700669
2700.690507650432071-0.690507650432071
2810.66351312068920.3364868793108
2910.324555376443540.67544462355646
3000.32455537644354-0.32455537644354
3110.2312042493591610.768795750640839
3210.324555376443540.67544462355646
3300.32455537644354-0.32455537644354
3400.231204249359161-0.231204249359161
3510.324555376443540.67544462355646
3600.32455537644354-0.32455537644354
3710.66351312068920.3364868793108
3800.607806347612378-0.607806347612378
3900.231204249359161-0.231204249359161
4000.297560846700669-0.297560846700669
4100.335205200708227-0.335205200708227
4200.231204249359161-0.231204249359161
4300.32455537644354-0.32455537644354
4400.231204249359161-0.231204249359161
4510.324555376443540.67544462355646
4610.2312042493591610.768795750640839
4710.6905076504320710.309492349567929
4800.32455537644354-0.32455537644354
4900.32455537644354-0.32455537644354
5000.32455537644354-0.32455537644354
5110.6078063476123780.392193652387622
5210.5808118178695070.419188182130493
5300.297560846700669-0.297560846700669
5400.32455537644354-0.32455537644354
5500.600882640522185-0.600882640522185
5600.57016199360482-0.57016199360482
5710.324555376443540.67544462355646
5800.241854073623847-0.241854073623847
5900.335205200708227-0.335205200708227
6000.204209719616289-0.204209719616289
6100.663513120689199-0.663513120689199
6200.297560846700669-0.297560846700669
6310.324555376443540.67544462355646
6400.241854073623847-0.241854073623847
6500.231204249359161-0.231204249359161
6610.6942337676065640.305766232393436
6710.7048835918712510.295116408128749
6810.6905076504320710.309492349567929






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.900552278717780.198895442564440.0994477212822201
100.8571339270485270.2857321459029450.142866072951473
110.8343261744442080.3313476511115850.165673825555792
120.7490071498225530.5019857003548940.250992850177447
130.8616826454732720.2766347090534560.138317354526728
140.8121084329233240.3757831341533520.187891567076676
150.8599929227398310.2800141545203380.140007077260169
160.812580170012380.3748396599752390.187419829987619
170.7557834259976620.4884331480046770.244216574002338
180.6916411643781280.6167176712437440.308358835621872
190.7003619716717470.5992760566565060.299638028328253
200.6328834748134450.7342330503731110.367116525186555
210.5636329987471110.8727340025057780.436367001252889
220.5699230206478960.8601539587042080.430076979352104
230.5045641996915910.9908716006168180.495435800308409
240.6397170210741250.720565957851750.360282978925875
250.5840931344019440.8318137311961120.415906865598056
260.5778019316491150.8443961367017710.422198068350885
270.6055669802745490.7888660394509010.394433019725451
280.5805467329427840.8389065341144320.419453267057216
290.678347064037060.6433058719258790.32165293596294
300.6342982049796190.7314035900407620.365701795020381
310.7126117212525020.5747765574949970.287388278747498
320.7876794578145280.4246410843709440.212320542185472
330.7534772182468340.4930455635063310.246522781753166
340.7216587174710410.5566825650579180.278341282528959
350.7905636050420280.4188727899159450.209436394957972
360.7563062443598530.4873875112802950.243693755640147
370.7363239957587870.5273520084824250.263676004241212
380.7947658976691760.4104682046616480.205234102330824
390.7528849120454390.4942301759091220.247115087954561
400.727399260795970.5452014784080590.27260073920403
410.6995634751787270.6008730496425460.300436524821273
420.6439749415997730.7120501168004540.356025058400227
430.6047736485045150.7904527029909710.395226351495485
440.5450009762133380.9099980475733240.454999023786662
450.6174837660923960.7650324678152070.382516233907604
460.7841482219079280.4317035561841450.215851778092072
470.7381201069673770.5237597860652460.261879893032623
480.6891231255771550.621753748845690.310876874422845
490.6417612511406740.7164774977186520.358238748859326
500.6024295239420690.7951409521158620.397570476057931
510.5464608746248350.907078250750330.453539125375165
520.7820439872797420.4359120254405160.217956012720258
530.710260512540250.5794789749195010.28973948745975
540.8151681117656290.3696637764687410.184831888234371
550.8948191697896130.2103616604207750.105180830210387
560.8482365693294130.3035268613411740.151763430670587
570.8095212257778820.3809575484442360.190478774222118
580.6874700924980.6250598150040.312529907502
590.6691418790720060.6617162418559880.330858120927994

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.90055227871778 & 0.19889544256444 & 0.0994477212822201 \tabularnewline
10 & 0.857133927048527 & 0.285732145902945 & 0.142866072951473 \tabularnewline
11 & 0.834326174444208 & 0.331347651111585 & 0.165673825555792 \tabularnewline
12 & 0.749007149822553 & 0.501985700354894 & 0.250992850177447 \tabularnewline
13 & 0.861682645473272 & 0.276634709053456 & 0.138317354526728 \tabularnewline
14 & 0.812108432923324 & 0.375783134153352 & 0.187891567076676 \tabularnewline
15 & 0.859992922739831 & 0.280014154520338 & 0.140007077260169 \tabularnewline
16 & 0.81258017001238 & 0.374839659975239 & 0.187419829987619 \tabularnewline
17 & 0.755783425997662 & 0.488433148004677 & 0.244216574002338 \tabularnewline
18 & 0.691641164378128 & 0.616717671243744 & 0.308358835621872 \tabularnewline
19 & 0.700361971671747 & 0.599276056656506 & 0.299638028328253 \tabularnewline
20 & 0.632883474813445 & 0.734233050373111 & 0.367116525186555 \tabularnewline
21 & 0.563632998747111 & 0.872734002505778 & 0.436367001252889 \tabularnewline
22 & 0.569923020647896 & 0.860153958704208 & 0.430076979352104 \tabularnewline
23 & 0.504564199691591 & 0.990871600616818 & 0.495435800308409 \tabularnewline
24 & 0.639717021074125 & 0.72056595785175 & 0.360282978925875 \tabularnewline
25 & 0.584093134401944 & 0.831813731196112 & 0.415906865598056 \tabularnewline
26 & 0.577801931649115 & 0.844396136701771 & 0.422198068350885 \tabularnewline
27 & 0.605566980274549 & 0.788866039450901 & 0.394433019725451 \tabularnewline
28 & 0.580546732942784 & 0.838906534114432 & 0.419453267057216 \tabularnewline
29 & 0.67834706403706 & 0.643305871925879 & 0.32165293596294 \tabularnewline
30 & 0.634298204979619 & 0.731403590040762 & 0.365701795020381 \tabularnewline
31 & 0.712611721252502 & 0.574776557494997 & 0.287388278747498 \tabularnewline
32 & 0.787679457814528 & 0.424641084370944 & 0.212320542185472 \tabularnewline
33 & 0.753477218246834 & 0.493045563506331 & 0.246522781753166 \tabularnewline
34 & 0.721658717471041 & 0.556682565057918 & 0.278341282528959 \tabularnewline
35 & 0.790563605042028 & 0.418872789915945 & 0.209436394957972 \tabularnewline
36 & 0.756306244359853 & 0.487387511280295 & 0.243693755640147 \tabularnewline
37 & 0.736323995758787 & 0.527352008482425 & 0.263676004241212 \tabularnewline
38 & 0.794765897669176 & 0.410468204661648 & 0.205234102330824 \tabularnewline
39 & 0.752884912045439 & 0.494230175909122 & 0.247115087954561 \tabularnewline
40 & 0.72739926079597 & 0.545201478408059 & 0.27260073920403 \tabularnewline
41 & 0.699563475178727 & 0.600873049642546 & 0.300436524821273 \tabularnewline
42 & 0.643974941599773 & 0.712050116800454 & 0.356025058400227 \tabularnewline
43 & 0.604773648504515 & 0.790452702990971 & 0.395226351495485 \tabularnewline
44 & 0.545000976213338 & 0.909998047573324 & 0.454999023786662 \tabularnewline
45 & 0.617483766092396 & 0.765032467815207 & 0.382516233907604 \tabularnewline
46 & 0.784148221907928 & 0.431703556184145 & 0.215851778092072 \tabularnewline
47 & 0.738120106967377 & 0.523759786065246 & 0.261879893032623 \tabularnewline
48 & 0.689123125577155 & 0.62175374884569 & 0.310876874422845 \tabularnewline
49 & 0.641761251140674 & 0.716477497718652 & 0.358238748859326 \tabularnewline
50 & 0.602429523942069 & 0.795140952115862 & 0.397570476057931 \tabularnewline
51 & 0.546460874624835 & 0.90707825075033 & 0.453539125375165 \tabularnewline
52 & 0.782043987279742 & 0.435912025440516 & 0.217956012720258 \tabularnewline
53 & 0.71026051254025 & 0.579478974919501 & 0.28973948745975 \tabularnewline
54 & 0.815168111765629 & 0.369663776468741 & 0.184831888234371 \tabularnewline
55 & 0.894819169789613 & 0.210361660420775 & 0.105180830210387 \tabularnewline
56 & 0.848236569329413 & 0.303526861341174 & 0.151763430670587 \tabularnewline
57 & 0.809521225777882 & 0.380957548444236 & 0.190478774222118 \tabularnewline
58 & 0.687470092498 & 0.625059815004 & 0.312529907502 \tabularnewline
59 & 0.669141879072006 & 0.661716241855988 & 0.330858120927994 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204347&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]0.90055227871778[/C][C]0.19889544256444[/C][C]0.0994477212822201[/C][/ROW]
[ROW][C]10[/C][C]0.857133927048527[/C][C]0.285732145902945[/C][C]0.142866072951473[/C][/ROW]
[ROW][C]11[/C][C]0.834326174444208[/C][C]0.331347651111585[/C][C]0.165673825555792[/C][/ROW]
[ROW][C]12[/C][C]0.749007149822553[/C][C]0.501985700354894[/C][C]0.250992850177447[/C][/ROW]
[ROW][C]13[/C][C]0.861682645473272[/C][C]0.276634709053456[/C][C]0.138317354526728[/C][/ROW]
[ROW][C]14[/C][C]0.812108432923324[/C][C]0.375783134153352[/C][C]0.187891567076676[/C][/ROW]
[ROW][C]15[/C][C]0.859992922739831[/C][C]0.280014154520338[/C][C]0.140007077260169[/C][/ROW]
[ROW][C]16[/C][C]0.81258017001238[/C][C]0.374839659975239[/C][C]0.187419829987619[/C][/ROW]
[ROW][C]17[/C][C]0.755783425997662[/C][C]0.488433148004677[/C][C]0.244216574002338[/C][/ROW]
[ROW][C]18[/C][C]0.691641164378128[/C][C]0.616717671243744[/C][C]0.308358835621872[/C][/ROW]
[ROW][C]19[/C][C]0.700361971671747[/C][C]0.599276056656506[/C][C]0.299638028328253[/C][/ROW]
[ROW][C]20[/C][C]0.632883474813445[/C][C]0.734233050373111[/C][C]0.367116525186555[/C][/ROW]
[ROW][C]21[/C][C]0.563632998747111[/C][C]0.872734002505778[/C][C]0.436367001252889[/C][/ROW]
[ROW][C]22[/C][C]0.569923020647896[/C][C]0.860153958704208[/C][C]0.430076979352104[/C][/ROW]
[ROW][C]23[/C][C]0.504564199691591[/C][C]0.990871600616818[/C][C]0.495435800308409[/C][/ROW]
[ROW][C]24[/C][C]0.639717021074125[/C][C]0.72056595785175[/C][C]0.360282978925875[/C][/ROW]
[ROW][C]25[/C][C]0.584093134401944[/C][C]0.831813731196112[/C][C]0.415906865598056[/C][/ROW]
[ROW][C]26[/C][C]0.577801931649115[/C][C]0.844396136701771[/C][C]0.422198068350885[/C][/ROW]
[ROW][C]27[/C][C]0.605566980274549[/C][C]0.788866039450901[/C][C]0.394433019725451[/C][/ROW]
[ROW][C]28[/C][C]0.580546732942784[/C][C]0.838906534114432[/C][C]0.419453267057216[/C][/ROW]
[ROW][C]29[/C][C]0.67834706403706[/C][C]0.643305871925879[/C][C]0.32165293596294[/C][/ROW]
[ROW][C]30[/C][C]0.634298204979619[/C][C]0.731403590040762[/C][C]0.365701795020381[/C][/ROW]
[ROW][C]31[/C][C]0.712611721252502[/C][C]0.574776557494997[/C][C]0.287388278747498[/C][/ROW]
[ROW][C]32[/C][C]0.787679457814528[/C][C]0.424641084370944[/C][C]0.212320542185472[/C][/ROW]
[ROW][C]33[/C][C]0.753477218246834[/C][C]0.493045563506331[/C][C]0.246522781753166[/C][/ROW]
[ROW][C]34[/C][C]0.721658717471041[/C][C]0.556682565057918[/C][C]0.278341282528959[/C][/ROW]
[ROW][C]35[/C][C]0.790563605042028[/C][C]0.418872789915945[/C][C]0.209436394957972[/C][/ROW]
[ROW][C]36[/C][C]0.756306244359853[/C][C]0.487387511280295[/C][C]0.243693755640147[/C][/ROW]
[ROW][C]37[/C][C]0.736323995758787[/C][C]0.527352008482425[/C][C]0.263676004241212[/C][/ROW]
[ROW][C]38[/C][C]0.794765897669176[/C][C]0.410468204661648[/C][C]0.205234102330824[/C][/ROW]
[ROW][C]39[/C][C]0.752884912045439[/C][C]0.494230175909122[/C][C]0.247115087954561[/C][/ROW]
[ROW][C]40[/C][C]0.72739926079597[/C][C]0.545201478408059[/C][C]0.27260073920403[/C][/ROW]
[ROW][C]41[/C][C]0.699563475178727[/C][C]0.600873049642546[/C][C]0.300436524821273[/C][/ROW]
[ROW][C]42[/C][C]0.643974941599773[/C][C]0.712050116800454[/C][C]0.356025058400227[/C][/ROW]
[ROW][C]43[/C][C]0.604773648504515[/C][C]0.790452702990971[/C][C]0.395226351495485[/C][/ROW]
[ROW][C]44[/C][C]0.545000976213338[/C][C]0.909998047573324[/C][C]0.454999023786662[/C][/ROW]
[ROW][C]45[/C][C]0.617483766092396[/C][C]0.765032467815207[/C][C]0.382516233907604[/C][/ROW]
[ROW][C]46[/C][C]0.784148221907928[/C][C]0.431703556184145[/C][C]0.215851778092072[/C][/ROW]
[ROW][C]47[/C][C]0.738120106967377[/C][C]0.523759786065246[/C][C]0.261879893032623[/C][/ROW]
[ROW][C]48[/C][C]0.689123125577155[/C][C]0.62175374884569[/C][C]0.310876874422845[/C][/ROW]
[ROW][C]49[/C][C]0.641761251140674[/C][C]0.716477497718652[/C][C]0.358238748859326[/C][/ROW]
[ROW][C]50[/C][C]0.602429523942069[/C][C]0.795140952115862[/C][C]0.397570476057931[/C][/ROW]
[ROW][C]51[/C][C]0.546460874624835[/C][C]0.90707825075033[/C][C]0.453539125375165[/C][/ROW]
[ROW][C]52[/C][C]0.782043987279742[/C][C]0.435912025440516[/C][C]0.217956012720258[/C][/ROW]
[ROW][C]53[/C][C]0.71026051254025[/C][C]0.579478974919501[/C][C]0.28973948745975[/C][/ROW]
[ROW][C]54[/C][C]0.815168111765629[/C][C]0.369663776468741[/C][C]0.184831888234371[/C][/ROW]
[ROW][C]55[/C][C]0.894819169789613[/C][C]0.210361660420775[/C][C]0.105180830210387[/C][/ROW]
[ROW][C]56[/C][C]0.848236569329413[/C][C]0.303526861341174[/C][C]0.151763430670587[/C][/ROW]
[ROW][C]57[/C][C]0.809521225777882[/C][C]0.380957548444236[/C][C]0.190478774222118[/C][/ROW]
[ROW][C]58[/C][C]0.687470092498[/C][C]0.625059815004[/C][C]0.312529907502[/C][/ROW]
[ROW][C]59[/C][C]0.669141879072006[/C][C]0.661716241855988[/C][C]0.330858120927994[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204347&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204347&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.900552278717780.198895442564440.0994477212822201
100.8571339270485270.2857321459029450.142866072951473
110.8343261744442080.3313476511115850.165673825555792
120.7490071498225530.5019857003548940.250992850177447
130.8616826454732720.2766347090534560.138317354526728
140.8121084329233240.3757831341533520.187891567076676
150.8599929227398310.2800141545203380.140007077260169
160.812580170012380.3748396599752390.187419829987619
170.7557834259976620.4884331480046770.244216574002338
180.6916411643781280.6167176712437440.308358835621872
190.7003619716717470.5992760566565060.299638028328253
200.6328834748134450.7342330503731110.367116525186555
210.5636329987471110.8727340025057780.436367001252889
220.5699230206478960.8601539587042080.430076979352104
230.5045641996915910.9908716006168180.495435800308409
240.6397170210741250.720565957851750.360282978925875
250.5840931344019440.8318137311961120.415906865598056
260.5778019316491150.8443961367017710.422198068350885
270.6055669802745490.7888660394509010.394433019725451
280.5805467329427840.8389065341144320.419453267057216
290.678347064037060.6433058719258790.32165293596294
300.6342982049796190.7314035900407620.365701795020381
310.7126117212525020.5747765574949970.287388278747498
320.7876794578145280.4246410843709440.212320542185472
330.7534772182468340.4930455635063310.246522781753166
340.7216587174710410.5566825650579180.278341282528959
350.7905636050420280.4188727899159450.209436394957972
360.7563062443598530.4873875112802950.243693755640147
370.7363239957587870.5273520084824250.263676004241212
380.7947658976691760.4104682046616480.205234102330824
390.7528849120454390.4942301759091220.247115087954561
400.727399260795970.5452014784080590.27260073920403
410.6995634751787270.6008730496425460.300436524821273
420.6439749415997730.7120501168004540.356025058400227
430.6047736485045150.7904527029909710.395226351495485
440.5450009762133380.9099980475733240.454999023786662
450.6174837660923960.7650324678152070.382516233907604
460.7841482219079280.4317035561841450.215851778092072
470.7381201069673770.5237597860652460.261879893032623
480.6891231255771550.621753748845690.310876874422845
490.6417612511406740.7164774977186520.358238748859326
500.6024295239420690.7951409521158620.397570476057931
510.5464608746248350.907078250750330.453539125375165
520.7820439872797420.4359120254405160.217956012720258
530.710260512540250.5794789749195010.28973948745975
540.8151681117656290.3696637764687410.184831888234371
550.8948191697896130.2103616604207750.105180830210387
560.8482365693294130.3035268613411740.151763430670587
570.8095212257778820.3809575484442360.190478774222118
580.6874700924980.6250598150040.312529907502
590.6691418790720060.6617162418559880.330858120927994






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=204347&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=204347&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204347&T=6

As an alternative you can also use a QR Code:  
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


Figures (Output of Computation)

Figure 1
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Figure 6
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Figure 7
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
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')
}