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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 computationSat, 17 Dec 2011 19:47:01 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/17/t1324169557lfq09rnoxompgfx.htm/, Retrieved Thu, 18 Apr 2024 22:29:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=156595, Retrieved Thu, 18 Apr 2024 22:29:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact97

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [] [2011-12-12 12:00:59] [77e355412ccdb651b3c7eae41c3da865]
-   PD      [Multiple Regression] [] [2011-12-18 00:47:01] [2be7aedefc35278abdba659ba29c8de8] [Current]
- RMP         [Kendall tau Correlation Matrix] [] [2011-12-20 18:45:28] [77e355412ccdb651b3c7eae41c3da865]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
11	8	7	18	12	20	4	2
16	12	9	22	14	18	5	0
24	24	19	22	25	24	4	0
15	16	12	19	15	20	4	4
17	19	16	25	20	20	9	0
19	16	17	28	21	24	8	-1
19	15	9	16	15	21	11	0
28	28	28	28	28	28	4	1
26	21	20	21	11	10	4	0
15	18	16	22	22	22	6	3
26	22	22	24	22	19	4	-1
24	22	12	26	24	23	4	4
25	25	18	28	23	24	4	1
22	20	20	24	24	24	11	0
15	16	12	20	21	25	4	-2
21	19	16	26	20	24	4	-4
27	26	21	28	25	28	6	2
26	20	17	23	24	22	8	2
22	19	17	24	21	26	5	-4
22	23	18	22	25	21	9	2
20	18	15	21	23	26	4	2
21	16	20	25	20	23	7	0
22	21	21	21	22	24	4	-3
21	20	12	26	25	25	4	2
8	15	6	23	23	24	7	0
22	19	13	21	19	20	12	4
20	19	19	27	21	24	7	2
17	20	14	23	25	23	8	2
23	19	12	23	24	23	4	-4
20	19	17	19	24	21	9	3
20	19	9	23	21	21	4	3
19	18	10	24	28	24	4	2
22	17	11	27	18	23	4	-1
18	22	16	25	26	24	4	-3
18	14	11	24	12	24	4	3
23	24	20	28	20	25	4	0
24	21	17	20	20	23	4	0
23	20	14	19	24	27	4	0
20	18	16	21	22	23	12	3
22	24	15	18	23	23	4	0
22	19	15	27	19	24	5	2
15	16	10	25	24	26	15	-1
19	16	18	21	16	23	10	3
21	15	10	27	19	20	5	2
20	15	16	23	18	18	9	2
18	14	5	27	25	26	4	-2
16	16	10	25	17	25	7	0
17	13	8	19	17	23	5	-2
24	26	16	24	24	18	4	0
19	18	16	23	22	26	4	6
24	21	24	24	20	23	8	-3
19	19	18	22	19	20	5	3
20	15	14	23	18	25	4	0
19	21	9	26	20	26	4	-2
21	17	21	26	21	24	6	1
15	18	7	16	21	22	10	0
22	25	16	25	25	28	4	2
14	12	8	20	21	24	11	2
11	16	5	20	22	23	14	-3
16	11	10	19	12	17	11	-2
22	23	22	24	24	23	4	1
25	19	17	27	18	27	4	-4
22	18	20	23	19	24	5	1
22	23	18	24	22	23	4	0

Tables (Output of Computation)





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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156595&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156595&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156595&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 time4 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
test[t] = + 3.0081632803644 -0.0458583460107966I1[t] -0.0056787017022851I2[t] + 0.0487015324041718I3[t] -0.0421523402417902E1[t] + 0.0633028504811332E2[t] -0.114410684681444E3[t] + 0.00882487607121334A[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
test[t] =  +  3.0081632803644 -0.0458583460107966I1[t] -0.0056787017022851I2[t] +  0.0487015324041718I3[t] -0.0421523402417902E1[t] +  0.0633028504811332E2[t] -0.114410684681444E3[t] +  0.00882487607121334A[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156595&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]test[t] =  +  3.0081632803644 -0.0458583460107966I1[t] -0.0056787017022851I2[t] +  0.0487015324041718I3[t] -0.0421523402417902E1[t] +  0.0633028504811332E2[t] -0.114410684681444E3[t] +  0.00882487607121334A[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156595&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156595&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
test[t] = + 3.0081632803644 -0.0458583460107966I1[t] -0.0056787017022851I2[t] + 0.0487015324041718I3[t] -0.0421523402417902E1[t] + 0.0633028504811332E2[t] -0.114410684681444E3[t] + 0.00882487607121334A[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.00816328036443.3931370.88650.3791160.189558
I1-0.04585834601079660.130539-0.35130.7266810.36334
I2-0.00567870170228510.150675-0.03770.970070.485035
I30.04870153240417180.0913810.53290.5961770.298089
E1-0.04215234024179020.115508-0.36490.7165380.358269
E20.06330285048113320.1220550.51860.6060530.303027
E3-0.1144106846814440.125135-0.91430.3644790.18224
A0.008824876071213340.1143580.07720.9387640.469382

\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) & 3.0081632803644 & 3.393137 & 0.8865 & 0.379116 & 0.189558 \tabularnewline
I1 & -0.0458583460107966 & 0.130539 & -0.3513 & 0.726681 & 0.36334 \tabularnewline
I2 & -0.0056787017022851 & 0.150675 & -0.0377 & 0.97007 & 0.485035 \tabularnewline
I3 & 0.0487015324041718 & 0.091381 & 0.5329 & 0.596177 & 0.298089 \tabularnewline
E1 & -0.0421523402417902 & 0.115508 & -0.3649 & 0.716538 & 0.358269 \tabularnewline
E2 & 0.0633028504811332 & 0.122055 & 0.5186 & 0.606053 & 0.303027 \tabularnewline
E3 & -0.114410684681444 & 0.125135 & -0.9143 & 0.364479 & 0.18224 \tabularnewline
A & 0.00882487607121334 & 0.114358 & 0.0772 & 0.938764 & 0.469382 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156595&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]3.0081632803644[/C][C]3.393137[/C][C]0.8865[/C][C]0.379116[/C][C]0.189558[/C][/ROW]
[ROW][C]I1[/C][C]-0.0458583460107966[/C][C]0.130539[/C][C]-0.3513[/C][C]0.726681[/C][C]0.36334[/C][/ROW]
[ROW][C]I2[/C][C]-0.0056787017022851[/C][C]0.150675[/C][C]-0.0377[/C][C]0.97007[/C][C]0.485035[/C][/ROW]
[ROW][C]I3[/C][C]0.0487015324041718[/C][C]0.091381[/C][C]0.5329[/C][C]0.596177[/C][C]0.298089[/C][/ROW]
[ROW][C]E1[/C][C]-0.0421523402417902[/C][C]0.115508[/C][C]-0.3649[/C][C]0.716538[/C][C]0.358269[/C][/ROW]
[ROW][C]E2[/C][C]0.0633028504811332[/C][C]0.122055[/C][C]0.5186[/C][C]0.606053[/C][C]0.303027[/C][/ROW]
[ROW][C]E3[/C][C]-0.114410684681444[/C][C]0.125135[/C][C]-0.9143[/C][C]0.364479[/C][C]0.18224[/C][/ROW]
[ROW][C]A[/C][C]0.00882487607121334[/C][C]0.114358[/C][C]0.0772[/C][C]0.938764[/C][C]0.469382[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156595&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156595&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)3.00816328036443.3931370.88650.3791160.189558
I1-0.04585834601079660.130539-0.35130.7266810.36334
I2-0.00567870170228510.150675-0.03770.970070.485035
I30.04870153240417180.0913810.53290.5961770.298089
E1-0.04215234024179020.115508-0.36490.7165380.358269
E20.06330285048113320.1220550.51860.6060530.303027
E3-0.1144106846814440.125135-0.91430.3644790.18224
A0.008824876071213340.1143580.07720.9387640.469382






Multiple Linear Regression - Regression Statistics
Multiple R0.173164937220105
R-squared0.0299860954824428
Adjusted R-squared-0.0912656425822518
F-TEST (value)0.247304458979743
F-TEST (DF numerator)7
F-TEST (DF denominator)56
p-value0.971014839632296
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.34674468971515
Sum Squared Residuals308.403795767551

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.173164937220105 \tabularnewline
R-squared & 0.0299860954824428 \tabularnewline
Adjusted R-squared & -0.0912656425822518 \tabularnewline
F-TEST (value) & 0.247304458979743 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 56 \tabularnewline
p-value & 0.971014839632296 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.34674468971515 \tabularnewline
Sum Squared Residuals & 308.403795767551 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156595&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.173164937220105[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0299860954824428[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0912656425822518[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.247304458979743[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]56[/C][/ROW]
[ROW][C]p-value[/C][C]0.971014839632296[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.34674468971515[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]308.403795767551[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156595&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156595&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 R0.173164937220105
R-squared0.0299860954824428
Adjusted R-squared-0.0912656425822518
F-TEST (value)0.247304458979743
F-TEST (DF numerator)7
F-TEST (DF denominator)56
p-value0.971014839632296
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.34674468971515
Sum Squared Residuals308.403795767551






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
120.5471804795339031.4528195204661
200.58821959290833-0.58821959290833
300.64126609956884-0.64126609956884
440.7095813550949043.2904186449051
500.903359278894133-0.903359278894133
6-10.347758439342339-1.34775843934234
700.459542544083906-0.459542544083906
810.352783471620940.64721652837906
901.37294906410442-1.37294906410442
1030.998521396729122.00147860327088
11-11.00485159964457-2.00485159964457
1240.1942112493773583.80578875062264
1310.1615077770385780.838492222961422
1400.718565732337525-0.718565732337525
15-20.475192694332691-2.47519269433269
16-40.176006435527312-4.17600643552731
172-0.1031703050938692.10317030509387
1820.6335268324728621.36647316752714
19-40.106460659593727-4.10646065959373
2021.077316395288950.922683604711054
2120.3506908341256851.64930916587432
2200.57088632338773-0.57088632338773
23-30.699665750304022-3.69966575030402
2420.1776251719325621.82237482806744
2500.650704616817502-0.650704616817502
2640.6597440903273033.3402559096727
2720.4155945172036081.58440548279639
2820.8490395151572051.15096048484279
29-40.383562721220381-4.38356272122038
3031.206200531959741.79379946804026
3130.4139459799597432.58605402004026
3220.5719201191588061.42807988084119
33-1-0.156349525622386-0.843650474377614
34-30.718514811581437-3.71851481158144
353-0.3236508033152163.32365080331522
3600.0519869994459563-0.0519869994459563
3700.44310025262671-0.44310025262671
3800.186253706567822-0.186253706567822
3930.7499205786627632.25007942133724
4000.694591006660085-0.694591006660085
412-0.01518375753936582.01518375753937
42-10.339599431860698-1.3395994318607
4330.5070725378572472.49292746214275
4420.2675244719854891.73247552801451
4520.9750193965550871.02498060344491
46-2-0.148201331573775-1.85179866842622
470-0.1055671914062930.105567191406293
48-20.232293161552624-2.23229316155262
4901.02266067607571-1.02266067607571
5060.2976431815759395.70235681842406
51-30.650701122773605-3.6507011227736
5230.9369003176402672.06309968235973
5300.032617158620566-0.032617158620566
54-2-0.313366372047756-1.68663362795224
5510.5118241035763020.488175896423698
5600.785118300346036-0.785118300346036
572-0.002900266775515362.00290026677552
5820.5251445347158781.47485546528412
59-30.69808833210283-3.69808833210283
60-20.809811087886615-2.80981108788662
6110.8515692442219640.148430755778036
62-4-0.470715511360091-3.52928448863991
6310.4026119671509390.597388032849061
6400.53015741364301-0.53015741364301

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 0.547180479533903 & 1.4528195204661 \tabularnewline
2 & 0 & 0.58821959290833 & -0.58821959290833 \tabularnewline
3 & 0 & 0.64126609956884 & -0.64126609956884 \tabularnewline
4 & 4 & 0.709581355094904 & 3.2904186449051 \tabularnewline
5 & 0 & 0.903359278894133 & -0.903359278894133 \tabularnewline
6 & -1 & 0.347758439342339 & -1.34775843934234 \tabularnewline
7 & 0 & 0.459542544083906 & -0.459542544083906 \tabularnewline
8 & 1 & 0.35278347162094 & 0.64721652837906 \tabularnewline
9 & 0 & 1.37294906410442 & -1.37294906410442 \tabularnewline
10 & 3 & 0.99852139672912 & 2.00147860327088 \tabularnewline
11 & -1 & 1.00485159964457 & -2.00485159964457 \tabularnewline
12 & 4 & 0.194211249377358 & 3.80578875062264 \tabularnewline
13 & 1 & 0.161507777038578 & 0.838492222961422 \tabularnewline
14 & 0 & 0.718565732337525 & -0.718565732337525 \tabularnewline
15 & -2 & 0.475192694332691 & -2.47519269433269 \tabularnewline
16 & -4 & 0.176006435527312 & -4.17600643552731 \tabularnewline
17 & 2 & -0.103170305093869 & 2.10317030509387 \tabularnewline
18 & 2 & 0.633526832472862 & 1.36647316752714 \tabularnewline
19 & -4 & 0.106460659593727 & -4.10646065959373 \tabularnewline
20 & 2 & 1.07731639528895 & 0.922683604711054 \tabularnewline
21 & 2 & 0.350690834125685 & 1.64930916587432 \tabularnewline
22 & 0 & 0.57088632338773 & -0.57088632338773 \tabularnewline
23 & -3 & 0.699665750304022 & -3.69966575030402 \tabularnewline
24 & 2 & 0.177625171932562 & 1.82237482806744 \tabularnewline
25 & 0 & 0.650704616817502 & -0.650704616817502 \tabularnewline
26 & 4 & 0.659744090327303 & 3.3402559096727 \tabularnewline
27 & 2 & 0.415594517203608 & 1.58440548279639 \tabularnewline
28 & 2 & 0.849039515157205 & 1.15096048484279 \tabularnewline
29 & -4 & 0.383562721220381 & -4.38356272122038 \tabularnewline
30 & 3 & 1.20620053195974 & 1.79379946804026 \tabularnewline
31 & 3 & 0.413945979959743 & 2.58605402004026 \tabularnewline
32 & 2 & 0.571920119158806 & 1.42807988084119 \tabularnewline
33 & -1 & -0.156349525622386 & -0.843650474377614 \tabularnewline
34 & -3 & 0.718514811581437 & -3.71851481158144 \tabularnewline
35 & 3 & -0.323650803315216 & 3.32365080331522 \tabularnewline
36 & 0 & 0.0519869994459563 & -0.0519869994459563 \tabularnewline
37 & 0 & 0.44310025262671 & -0.44310025262671 \tabularnewline
38 & 0 & 0.186253706567822 & -0.186253706567822 \tabularnewline
39 & 3 & 0.749920578662763 & 2.25007942133724 \tabularnewline
40 & 0 & 0.694591006660085 & -0.694591006660085 \tabularnewline
41 & 2 & -0.0151837575393658 & 2.01518375753937 \tabularnewline
42 & -1 & 0.339599431860698 & -1.3395994318607 \tabularnewline
43 & 3 & 0.507072537857247 & 2.49292746214275 \tabularnewline
44 & 2 & 0.267524471985489 & 1.73247552801451 \tabularnewline
45 & 2 & 0.975019396555087 & 1.02498060344491 \tabularnewline
46 & -2 & -0.148201331573775 & -1.85179866842622 \tabularnewline
47 & 0 & -0.105567191406293 & 0.105567191406293 \tabularnewline
48 & -2 & 0.232293161552624 & -2.23229316155262 \tabularnewline
49 & 0 & 1.02266067607571 & -1.02266067607571 \tabularnewline
50 & 6 & 0.297643181575939 & 5.70235681842406 \tabularnewline
51 & -3 & 0.650701122773605 & -3.6507011227736 \tabularnewline
52 & 3 & 0.936900317640267 & 2.06309968235973 \tabularnewline
53 & 0 & 0.032617158620566 & -0.032617158620566 \tabularnewline
54 & -2 & -0.313366372047756 & -1.68663362795224 \tabularnewline
55 & 1 & 0.511824103576302 & 0.488175896423698 \tabularnewline
56 & 0 & 0.785118300346036 & -0.785118300346036 \tabularnewline
57 & 2 & -0.00290026677551536 & 2.00290026677552 \tabularnewline
58 & 2 & 0.525144534715878 & 1.47485546528412 \tabularnewline
59 & -3 & 0.69808833210283 & -3.69808833210283 \tabularnewline
60 & -2 & 0.809811087886615 & -2.80981108788662 \tabularnewline
61 & 1 & 0.851569244221964 & 0.148430755778036 \tabularnewline
62 & -4 & -0.470715511360091 & -3.52928448863991 \tabularnewline
63 & 1 & 0.402611967150939 & 0.597388032849061 \tabularnewline
64 & 0 & 0.53015741364301 & -0.53015741364301 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156595&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]2[/C][C]0.547180479533903[/C][C]1.4528195204661[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.58821959290833[/C][C]-0.58821959290833[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.64126609956884[/C][C]-0.64126609956884[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]0.709581355094904[/C][C]3.2904186449051[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.903359278894133[/C][C]-0.903359278894133[/C][/ROW]
[ROW][C]6[/C][C]-1[/C][C]0.347758439342339[/C][C]-1.34775843934234[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.459542544083906[/C][C]-0.459542544083906[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]0.35278347162094[/C][C]0.64721652837906[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]1.37294906410442[/C][C]-1.37294906410442[/C][/ROW]
[ROW][C]10[/C][C]3[/C][C]0.99852139672912[/C][C]2.00147860327088[/C][/ROW]
[ROW][C]11[/C][C]-1[/C][C]1.00485159964457[/C][C]-2.00485159964457[/C][/ROW]
[ROW][C]12[/C][C]4[/C][C]0.194211249377358[/C][C]3.80578875062264[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]0.161507777038578[/C][C]0.838492222961422[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.718565732337525[/C][C]-0.718565732337525[/C][/ROW]
[ROW][C]15[/C][C]-2[/C][C]0.475192694332691[/C][C]-2.47519269433269[/C][/ROW]
[ROW][C]16[/C][C]-4[/C][C]0.176006435527312[/C][C]-4.17600643552731[/C][/ROW]
[ROW][C]17[/C][C]2[/C][C]-0.103170305093869[/C][C]2.10317030509387[/C][/ROW]
[ROW][C]18[/C][C]2[/C][C]0.633526832472862[/C][C]1.36647316752714[/C][/ROW]
[ROW][C]19[/C][C]-4[/C][C]0.106460659593727[/C][C]-4.10646065959373[/C][/ROW]
[ROW][C]20[/C][C]2[/C][C]1.07731639528895[/C][C]0.922683604711054[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]0.350690834125685[/C][C]1.64930916587432[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.57088632338773[/C][C]-0.57088632338773[/C][/ROW]
[ROW][C]23[/C][C]-3[/C][C]0.699665750304022[/C][C]-3.69966575030402[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]0.177625171932562[/C][C]1.82237482806744[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.650704616817502[/C][C]-0.650704616817502[/C][/ROW]
[ROW][C]26[/C][C]4[/C][C]0.659744090327303[/C][C]3.3402559096727[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]0.415594517203608[/C][C]1.58440548279639[/C][/ROW]
[ROW][C]28[/C][C]2[/C][C]0.849039515157205[/C][C]1.15096048484279[/C][/ROW]
[ROW][C]29[/C][C]-4[/C][C]0.383562721220381[/C][C]-4.38356272122038[/C][/ROW]
[ROW][C]30[/C][C]3[/C][C]1.20620053195974[/C][C]1.79379946804026[/C][/ROW]
[ROW][C]31[/C][C]3[/C][C]0.413945979959743[/C][C]2.58605402004026[/C][/ROW]
[ROW][C]32[/C][C]2[/C][C]0.571920119158806[/C][C]1.42807988084119[/C][/ROW]
[ROW][C]33[/C][C]-1[/C][C]-0.156349525622386[/C][C]-0.843650474377614[/C][/ROW]
[ROW][C]34[/C][C]-3[/C][C]0.718514811581437[/C][C]-3.71851481158144[/C][/ROW]
[ROW][C]35[/C][C]3[/C][C]-0.323650803315216[/C][C]3.32365080331522[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.0519869994459563[/C][C]-0.0519869994459563[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.44310025262671[/C][C]-0.44310025262671[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.186253706567822[/C][C]-0.186253706567822[/C][/ROW]
[ROW][C]39[/C][C]3[/C][C]0.749920578662763[/C][C]2.25007942133724[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.694591006660085[/C][C]-0.694591006660085[/C][/ROW]
[ROW][C]41[/C][C]2[/C][C]-0.0151837575393658[/C][C]2.01518375753937[/C][/ROW]
[ROW][C]42[/C][C]-1[/C][C]0.339599431860698[/C][C]-1.3395994318607[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]0.507072537857247[/C][C]2.49292746214275[/C][/ROW]
[ROW][C]44[/C][C]2[/C][C]0.267524471985489[/C][C]1.73247552801451[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]0.975019396555087[/C][C]1.02498060344491[/C][/ROW]
[ROW][C]46[/C][C]-2[/C][C]-0.148201331573775[/C][C]-1.85179866842622[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]-0.105567191406293[/C][C]0.105567191406293[/C][/ROW]
[ROW][C]48[/C][C]-2[/C][C]0.232293161552624[/C][C]-2.23229316155262[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]1.02266067607571[/C][C]-1.02266067607571[/C][/ROW]
[ROW][C]50[/C][C]6[/C][C]0.297643181575939[/C][C]5.70235681842406[/C][/ROW]
[ROW][C]51[/C][C]-3[/C][C]0.650701122773605[/C][C]-3.6507011227736[/C][/ROW]
[ROW][C]52[/C][C]3[/C][C]0.936900317640267[/C][C]2.06309968235973[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.032617158620566[/C][C]-0.032617158620566[/C][/ROW]
[ROW][C]54[/C][C]-2[/C][C]-0.313366372047756[/C][C]-1.68663362795224[/C][/ROW]
[ROW][C]55[/C][C]1[/C][C]0.511824103576302[/C][C]0.488175896423698[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.785118300346036[/C][C]-0.785118300346036[/C][/ROW]
[ROW][C]57[/C][C]2[/C][C]-0.00290026677551536[/C][C]2.00290026677552[/C][/ROW]
[ROW][C]58[/C][C]2[/C][C]0.525144534715878[/C][C]1.47485546528412[/C][/ROW]
[ROW][C]59[/C][C]-3[/C][C]0.69808833210283[/C][C]-3.69808833210283[/C][/ROW]
[ROW][C]60[/C][C]-2[/C][C]0.809811087886615[/C][C]-2.80981108788662[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]0.851569244221964[/C][C]0.148430755778036[/C][/ROW]
[ROW][C]62[/C][C]-4[/C][C]-0.470715511360091[/C][C]-3.52928448863991[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]0.402611967150939[/C][C]0.597388032849061[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.53015741364301[/C][C]-0.53015741364301[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156595&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156595&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
120.5471804795339031.4528195204661
200.58821959290833-0.58821959290833
300.64126609956884-0.64126609956884
440.7095813550949043.2904186449051
500.903359278894133-0.903359278894133
6-10.347758439342339-1.34775843934234
700.459542544083906-0.459542544083906
810.352783471620940.64721652837906
901.37294906410442-1.37294906410442
1030.998521396729122.00147860327088
11-11.00485159964457-2.00485159964457
1240.1942112493773583.80578875062264
1310.1615077770385780.838492222961422
1400.718565732337525-0.718565732337525
15-20.475192694332691-2.47519269433269
16-40.176006435527312-4.17600643552731
172-0.1031703050938692.10317030509387
1820.6335268324728621.36647316752714
19-40.106460659593727-4.10646065959373
2021.077316395288950.922683604711054
2120.3506908341256851.64930916587432
2200.57088632338773-0.57088632338773
23-30.699665750304022-3.69966575030402
2420.1776251719325621.82237482806744
2500.650704616817502-0.650704616817502
2640.6597440903273033.3402559096727
2720.4155945172036081.58440548279639
2820.8490395151572051.15096048484279
29-40.383562721220381-4.38356272122038
3031.206200531959741.79379946804026
3130.4139459799597432.58605402004026
3220.5719201191588061.42807988084119
33-1-0.156349525622386-0.843650474377614
34-30.718514811581437-3.71851481158144
353-0.3236508033152163.32365080331522
3600.0519869994459563-0.0519869994459563
3700.44310025262671-0.44310025262671
3800.186253706567822-0.186253706567822
3930.7499205786627632.25007942133724
4000.694591006660085-0.694591006660085
412-0.01518375753936582.01518375753937
42-10.339599431860698-1.3395994318607
4330.5070725378572472.49292746214275
4420.2675244719854891.73247552801451
4520.9750193965550871.02498060344491
46-2-0.148201331573775-1.85179866842622
470-0.1055671914062930.105567191406293
48-20.232293161552624-2.23229316155262
4901.02266067607571-1.02266067607571
5060.2976431815759395.70235681842406
51-30.650701122773605-3.6507011227736
5230.9369003176402672.06309968235973
5300.032617158620566-0.032617158620566
54-2-0.313366372047756-1.68663362795224
5510.5118241035763020.488175896423698
5600.785118300346036-0.785118300346036
572-0.002900266775515362.00290026677552
5820.5251445347158781.47485546528412
59-30.69808833210283-3.69808833210283
60-20.809811087886615-2.80981108788662
6110.8515692442219640.148430755778036
62-4-0.470715511360091-3.52928448863991
6310.4026119671509390.597388032849061
6400.53015741364301-0.53015741364301






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.03817600237860670.07635200475721340.961823997621393
120.1653939028605950.3307878057211910.834606097139405
130.1434618602197630.2869237204395270.856538139780237
140.1141906231497380.2283812462994770.885809376850262
150.3470297133127850.6940594266255710.652970286687215
160.5752778983815220.8494442032369550.424722101618478
170.5384968798348090.9230062403303830.461503120165191
180.4976201067210060.9952402134420120.502379893278994
190.5823437135057480.8353125729885040.417656286494252
200.4951272082785430.9902544165570870.504872791721457
210.44693799421250.8938759884250.5530620057875
220.401113416228790.8022268324575790.59888658377121
230.4629605654270920.9259211308541840.537039434572908
240.3968257219588380.7936514439176770.603174278041162
250.394209024678720.788418049357440.60579097532128
260.4649516325947470.9299032651894940.535048367405253
270.4446545389777880.8893090779555770.555345461022212
280.368759066894190.737518133788380.63124093310581
290.5694148243571140.8611703512857730.430585175642886
300.5307115057997510.9385769884004980.469288494200249
310.5375604056347810.9248791887304380.462439594365219
320.4811841400352790.9623682800705570.518815859964721
330.4041649312852280.8083298625704550.595835068714772
340.6026617116061060.7946765767877880.397338288393894
350.6778966742014170.6442066515971660.322103325798583
360.5997429266561770.8005141466876470.400257073343823
370.5171440682496010.9657118635007990.482855931750399
380.4325140635740280.8650281271480570.567485936425972
390.4818787145926430.9637574291852860.518121285407357
400.4044815676885540.8089631353771090.595518432311446
410.4186055005770670.8372110011541330.581394499422934
420.4012009303584940.8024018607169880.598799069641506
430.5064834235540710.9870331528918580.493516576445929
440.590178717579980.8196425648400410.40982128242002
450.7011622011454540.5976755977090920.298837798854546
460.6768343842483850.6463312315032310.323165615751615
470.6055242849886190.7889514300227620.394475715011381
480.8380162557433320.3239674885133360.161983744256668
490.8007090012867580.3985819974264850.199290998713242
500.8587002538338410.2825994923323180.141299746166159
510.7867173927506130.4265652144987750.213282607249387
520.7250411994369930.5499176011260150.274958800563007
530.6656448334693760.6687103330612470.334355166530624

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.0381760023786067 & 0.0763520047572134 & 0.961823997621393 \tabularnewline
12 & 0.165393902860595 & 0.330787805721191 & 0.834606097139405 \tabularnewline
13 & 0.143461860219763 & 0.286923720439527 & 0.856538139780237 \tabularnewline
14 & 0.114190623149738 & 0.228381246299477 & 0.885809376850262 \tabularnewline
15 & 0.347029713312785 & 0.694059426625571 & 0.652970286687215 \tabularnewline
16 & 0.575277898381522 & 0.849444203236955 & 0.424722101618478 \tabularnewline
17 & 0.538496879834809 & 0.923006240330383 & 0.461503120165191 \tabularnewline
18 & 0.497620106721006 & 0.995240213442012 & 0.502379893278994 \tabularnewline
19 & 0.582343713505748 & 0.835312572988504 & 0.417656286494252 \tabularnewline
20 & 0.495127208278543 & 0.990254416557087 & 0.504872791721457 \tabularnewline
21 & 0.4469379942125 & 0.893875988425 & 0.5530620057875 \tabularnewline
22 & 0.40111341622879 & 0.802226832457579 & 0.59888658377121 \tabularnewline
23 & 0.462960565427092 & 0.925921130854184 & 0.537039434572908 \tabularnewline
24 & 0.396825721958838 & 0.793651443917677 & 0.603174278041162 \tabularnewline
25 & 0.39420902467872 & 0.78841804935744 & 0.60579097532128 \tabularnewline
26 & 0.464951632594747 & 0.929903265189494 & 0.535048367405253 \tabularnewline
27 & 0.444654538977788 & 0.889309077955577 & 0.555345461022212 \tabularnewline
28 & 0.36875906689419 & 0.73751813378838 & 0.63124093310581 \tabularnewline
29 & 0.569414824357114 & 0.861170351285773 & 0.430585175642886 \tabularnewline
30 & 0.530711505799751 & 0.938576988400498 & 0.469288494200249 \tabularnewline
31 & 0.537560405634781 & 0.924879188730438 & 0.462439594365219 \tabularnewline
32 & 0.481184140035279 & 0.962368280070557 & 0.518815859964721 \tabularnewline
33 & 0.404164931285228 & 0.808329862570455 & 0.595835068714772 \tabularnewline
34 & 0.602661711606106 & 0.794676576787788 & 0.397338288393894 \tabularnewline
35 & 0.677896674201417 & 0.644206651597166 & 0.322103325798583 \tabularnewline
36 & 0.599742926656177 & 0.800514146687647 & 0.400257073343823 \tabularnewline
37 & 0.517144068249601 & 0.965711863500799 & 0.482855931750399 \tabularnewline
38 & 0.432514063574028 & 0.865028127148057 & 0.567485936425972 \tabularnewline
39 & 0.481878714592643 & 0.963757429185286 & 0.518121285407357 \tabularnewline
40 & 0.404481567688554 & 0.808963135377109 & 0.595518432311446 \tabularnewline
41 & 0.418605500577067 & 0.837211001154133 & 0.581394499422934 \tabularnewline
42 & 0.401200930358494 & 0.802401860716988 & 0.598799069641506 \tabularnewline
43 & 0.506483423554071 & 0.987033152891858 & 0.493516576445929 \tabularnewline
44 & 0.59017871757998 & 0.819642564840041 & 0.40982128242002 \tabularnewline
45 & 0.701162201145454 & 0.597675597709092 & 0.298837798854546 \tabularnewline
46 & 0.676834384248385 & 0.646331231503231 & 0.323165615751615 \tabularnewline
47 & 0.605524284988619 & 0.788951430022762 & 0.394475715011381 \tabularnewline
48 & 0.838016255743332 & 0.323967488513336 & 0.161983744256668 \tabularnewline
49 & 0.800709001286758 & 0.398581997426485 & 0.199290998713242 \tabularnewline
50 & 0.858700253833841 & 0.282599492332318 & 0.141299746166159 \tabularnewline
51 & 0.786717392750613 & 0.426565214498775 & 0.213282607249387 \tabularnewline
52 & 0.725041199436993 & 0.549917601126015 & 0.274958800563007 \tabularnewline
53 & 0.665644833469376 & 0.668710333061247 & 0.334355166530624 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156595&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]11[/C][C]0.0381760023786067[/C][C]0.0763520047572134[/C][C]0.961823997621393[/C][/ROW]
[ROW][C]12[/C][C]0.165393902860595[/C][C]0.330787805721191[/C][C]0.834606097139405[/C][/ROW]
[ROW][C]13[/C][C]0.143461860219763[/C][C]0.286923720439527[/C][C]0.856538139780237[/C][/ROW]
[ROW][C]14[/C][C]0.114190623149738[/C][C]0.228381246299477[/C][C]0.885809376850262[/C][/ROW]
[ROW][C]15[/C][C]0.347029713312785[/C][C]0.694059426625571[/C][C]0.652970286687215[/C][/ROW]
[ROW][C]16[/C][C]0.575277898381522[/C][C]0.849444203236955[/C][C]0.424722101618478[/C][/ROW]
[ROW][C]17[/C][C]0.538496879834809[/C][C]0.923006240330383[/C][C]0.461503120165191[/C][/ROW]
[ROW][C]18[/C][C]0.497620106721006[/C][C]0.995240213442012[/C][C]0.502379893278994[/C][/ROW]
[ROW][C]19[/C][C]0.582343713505748[/C][C]0.835312572988504[/C][C]0.417656286494252[/C][/ROW]
[ROW][C]20[/C][C]0.495127208278543[/C][C]0.990254416557087[/C][C]0.504872791721457[/C][/ROW]
[ROW][C]21[/C][C]0.4469379942125[/C][C]0.893875988425[/C][C]0.5530620057875[/C][/ROW]
[ROW][C]22[/C][C]0.40111341622879[/C][C]0.802226832457579[/C][C]0.59888658377121[/C][/ROW]
[ROW][C]23[/C][C]0.462960565427092[/C][C]0.925921130854184[/C][C]0.537039434572908[/C][/ROW]
[ROW][C]24[/C][C]0.396825721958838[/C][C]0.793651443917677[/C][C]0.603174278041162[/C][/ROW]
[ROW][C]25[/C][C]0.39420902467872[/C][C]0.78841804935744[/C][C]0.60579097532128[/C][/ROW]
[ROW][C]26[/C][C]0.464951632594747[/C][C]0.929903265189494[/C][C]0.535048367405253[/C][/ROW]
[ROW][C]27[/C][C]0.444654538977788[/C][C]0.889309077955577[/C][C]0.555345461022212[/C][/ROW]
[ROW][C]28[/C][C]0.36875906689419[/C][C]0.73751813378838[/C][C]0.63124093310581[/C][/ROW]
[ROW][C]29[/C][C]0.569414824357114[/C][C]0.861170351285773[/C][C]0.430585175642886[/C][/ROW]
[ROW][C]30[/C][C]0.530711505799751[/C][C]0.938576988400498[/C][C]0.469288494200249[/C][/ROW]
[ROW][C]31[/C][C]0.537560405634781[/C][C]0.924879188730438[/C][C]0.462439594365219[/C][/ROW]
[ROW][C]32[/C][C]0.481184140035279[/C][C]0.962368280070557[/C][C]0.518815859964721[/C][/ROW]
[ROW][C]33[/C][C]0.404164931285228[/C][C]0.808329862570455[/C][C]0.595835068714772[/C][/ROW]
[ROW][C]34[/C][C]0.602661711606106[/C][C]0.794676576787788[/C][C]0.397338288393894[/C][/ROW]
[ROW][C]35[/C][C]0.677896674201417[/C][C]0.644206651597166[/C][C]0.322103325798583[/C][/ROW]
[ROW][C]36[/C][C]0.599742926656177[/C][C]0.800514146687647[/C][C]0.400257073343823[/C][/ROW]
[ROW][C]37[/C][C]0.517144068249601[/C][C]0.965711863500799[/C][C]0.482855931750399[/C][/ROW]
[ROW][C]38[/C][C]0.432514063574028[/C][C]0.865028127148057[/C][C]0.567485936425972[/C][/ROW]
[ROW][C]39[/C][C]0.481878714592643[/C][C]0.963757429185286[/C][C]0.518121285407357[/C][/ROW]
[ROW][C]40[/C][C]0.404481567688554[/C][C]0.808963135377109[/C][C]0.595518432311446[/C][/ROW]
[ROW][C]41[/C][C]0.418605500577067[/C][C]0.837211001154133[/C][C]0.581394499422934[/C][/ROW]
[ROW][C]42[/C][C]0.401200930358494[/C][C]0.802401860716988[/C][C]0.598799069641506[/C][/ROW]
[ROW][C]43[/C][C]0.506483423554071[/C][C]0.987033152891858[/C][C]0.493516576445929[/C][/ROW]
[ROW][C]44[/C][C]0.59017871757998[/C][C]0.819642564840041[/C][C]0.40982128242002[/C][/ROW]
[ROW][C]45[/C][C]0.701162201145454[/C][C]0.597675597709092[/C][C]0.298837798854546[/C][/ROW]
[ROW][C]46[/C][C]0.676834384248385[/C][C]0.646331231503231[/C][C]0.323165615751615[/C][/ROW]
[ROW][C]47[/C][C]0.605524284988619[/C][C]0.788951430022762[/C][C]0.394475715011381[/C][/ROW]
[ROW][C]48[/C][C]0.838016255743332[/C][C]0.323967488513336[/C][C]0.161983744256668[/C][/ROW]
[ROW][C]49[/C][C]0.800709001286758[/C][C]0.398581997426485[/C][C]0.199290998713242[/C][/ROW]
[ROW][C]50[/C][C]0.858700253833841[/C][C]0.282599492332318[/C][C]0.141299746166159[/C][/ROW]
[ROW][C]51[/C][C]0.786717392750613[/C][C]0.426565214498775[/C][C]0.213282607249387[/C][/ROW]
[ROW][C]52[/C][C]0.725041199436993[/C][C]0.549917601126015[/C][C]0.274958800563007[/C][/ROW]
[ROW][C]53[/C][C]0.665644833469376[/C][C]0.668710333061247[/C][C]0.334355166530624[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156595&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156595&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
110.03817600237860670.07635200475721340.961823997621393
120.1653939028605950.3307878057211910.834606097139405
130.1434618602197630.2869237204395270.856538139780237
140.1141906231497380.2283812462994770.885809376850262
150.3470297133127850.6940594266255710.652970286687215
160.5752778983815220.8494442032369550.424722101618478
170.5384968798348090.9230062403303830.461503120165191
180.4976201067210060.9952402134420120.502379893278994
190.5823437135057480.8353125729885040.417656286494252
200.4951272082785430.9902544165570870.504872791721457
210.44693799421250.8938759884250.5530620057875
220.401113416228790.8022268324575790.59888658377121
230.4629605654270920.9259211308541840.537039434572908
240.3968257219588380.7936514439176770.603174278041162
250.394209024678720.788418049357440.60579097532128
260.4649516325947470.9299032651894940.535048367405253
270.4446545389777880.8893090779555770.555345461022212
280.368759066894190.737518133788380.63124093310581
290.5694148243571140.8611703512857730.430585175642886
300.5307115057997510.9385769884004980.469288494200249
310.5375604056347810.9248791887304380.462439594365219
320.4811841400352790.9623682800705570.518815859964721
330.4041649312852280.8083298625704550.595835068714772
340.6026617116061060.7946765767877880.397338288393894
350.6778966742014170.6442066515971660.322103325798583
360.5997429266561770.8005141466876470.400257073343823
370.5171440682496010.9657118635007990.482855931750399
380.4325140635740280.8650281271480570.567485936425972
390.4818787145926430.9637574291852860.518121285407357
400.4044815676885540.8089631353771090.595518432311446
410.4186055005770670.8372110011541330.581394499422934
420.4012009303584940.8024018607169880.598799069641506
430.5064834235540710.9870331528918580.493516576445929
440.590178717579980.8196425648400410.40982128242002
450.7011622011454540.5976755977090920.298837798854546
460.6768343842483850.6463312315032310.323165615751615
470.6055242849886190.7889514300227620.394475715011381
480.8380162557433320.3239674885133360.161983744256668
490.8007090012867580.3985819974264850.199290998713242
500.8587002538338410.2825994923323180.141299746166159
510.7867173927506130.4265652144987750.213282607249387
520.7250411994369930.5499176011260150.274958800563007
530.6656448334693760.6687103330612470.334355166530624






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.0232558139534884OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.0232558139534884 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156595&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.0232558139534884[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156595&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156595&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 level10.0232558139534884OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
Parameters (R input):
par1 = 8 ; 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')
}