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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 computationSun, 23 Aug 2015 03:36:22 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Aug/23/t1440297575cbhhyf708ryj6xf.htm/, Retrieved Sun, 10 Nov 2024 17:59:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=280317, Retrieved Sun, 10 Nov 2024 17:59:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact245

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2013-11-04 07:18:26] [0307e7a6407eb638caabc417e3a6b260]
- RMPD    [Multiple Regression] [] [2015-08-23 02:36:22] [3e99441ea7f7f69c8fa4628f6be951c3] [Current]
-    D      [Multiple Regression] [] [2015-08-27 20:23:40] [82473208b72870f966ef7d4d2162cc96]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
11	8	7	18	12	20	12,9
15	18	18	23	20	25	7,4
16	12	9	22	14	18	12,8
19	16	17	28	21	24	14,8
24	22	12	26	24	23	12
25	25	18	28	23	24	6,3
22	20	20	24	24	24	11,3
21	19	16	26	20	24	9,3
27	26	21	28	25	28	10
22	19	17	24	21	26	10,8
20	18	13	20	21	20	13,4
21	20	12	26	25	25	11,5
8	15	6	23	23	24	8,3
22	19	13	21	19	20	11,7
23	19	12	23	24	23	10,4
19	18	10	24	28	24	11,8
24	22	22	23	22	22	11,3
25	22	20	25	26	25	12,7
13	20	8	24	24	27	5,7
20	14	11	24	21	18	8
22	19	15	27	19	24	12,5
15	16	10	25	24	26	7,6
19	16	18	21	16	23	9,2
22	22	22	27	23	23	11,1
16	14	7	24	18	22	12,2
20	15	16	23	18	18	12,3
21	14	16	24	21	19	11,4
20	20	16	21	20	19	8,8
18	14	5	27	25	26	12,6
16	16	10	25	17	25	13
24	26	16	24	24	18	13,2
13	13	8	25	21	22	9,9
20	15	14	23	18	25	10,5
22	18	15	25	22	26	13,4
19	21	9	26	20	26	10,9
15	18	7	16	21	22	10,3
22	25	16	25	25	28	11,4
20	20	16	23	23	22	8,6
21	19	14	26	21	26	13,2
11	16	5	20	22	23	8,8
22	23	22	24	24	23	9
22	23	21	27	23	26	10,3
19	19	13	22	18	23	8,5
25	15	15	22	14	23	13,5
21	18	11	25	20	28	4,9
22	18	20	23	19	24	6,4
21	22	13	24	18	20	9,6
22	23	18	24	22	23	11,6
24	16	12	26	11	20	16,6
23	20	16	28	15	20	19,1
20	18	14	19	21	25	13,35
18	13	13	19	9	19	18,4
22	19	19	26	21	21	16,15
21	22	16	22	24	25	18,4
22	19	14	26	18	15	15,6
22	21	12	22	23	23	16,35
17	14	10	20	13	15	17,65
19	16	16	20	19	20	11,7
20	21	11	18	22	20	14,35
19	16	15	18	21	20	14,75
24	25	11	28	23	28	9,9
13	9	7	22	15	22	16,85
22	22	11	20	25	26	15,6
23	22	19	22	14	21	14,85
19	12	9	21	21	24	11,75
18	17	11	24	18	21	18,45
21	10	12	24	12	22	17,1
17	24	10	17	17	14	19,9
21	18	13	21	24	28	18,45
26	21	19	24	15	22	15
19	21	21	22	22	24	11,35
21	17	13	24	17	21	18,1
11	7	7	19	18	25	19,1
19	14	13	25	23	21	7,6
20	18	12	24	13	21	13,4
17	14	8	21	18	19	13,9
21	20	17	23	21	28	15,25
23	17	18	23	16	23	16,1
22	21	17	23	17	25	17,35
22	23	17	23	20	26	13,15
21	24	18	23	18	25	12,15
22	21	21	21	23	22	18,2
20	8	10	25	9	21	13,6
19	18	11	22	27	23	14,75
16	17	15	23	22	17	14,1
19	16	12	27	12	25	14,9
23	22	21	27	18	19	16,25
23	20	15	24	22	22	13,6
15	20	9	23	22	25	15,65
21	8	14	25	19	25	14,6
20	13	12	25	24	24	19,2
22	20	18	24	27	23	11,9
22	22	11	27	25	26	13,2
23	19	14	25	24	26	16,35
24	11	8	14	16	20	14,35
18	21	12	21	24	20	15,65
21	20	17	28	24	26	17,75
20	21	23	26	26	26	7,65
16	19	16	23	23	24	19,3
18	18	11	21	21	21	15,2
23	20	16	26	19	18	17,1
20	19	12	24	20	23	19,05
24	23	17	23	18	25	18,55
25	16	11	22	20	20	19,1
23	20	13	26	24	23	11,85
27	26	18	22	26	21	13,35
19	18	13	28	28	28	11,4
16	18	13	21	23	14	19,9
16	13	14	26	12	19	17,6
23	21	19	27	20	21	16,1
20	23	8	22	23	24	11,95
4	11	7	15	20	25	15,15
22	20	17	22	20	24	16,85
23	26	17	27	28	26	7,7
20	21	12	24	25	23	12,6
20	18	16	26	24	24	12,35
21	16	16	24	18	23	16,65
19	7	10	22	8	21	13,95
27	21	28	28	27	25	15,7
20	20	19	28	20	26	15,35
21	17	17	24	23	23	15,1
23	19	16	24	24	21	17,75
16	16	12	20	15	20	14,6
20	20	17	26	22	23	16,65

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.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=280317&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.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=280317&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280317&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 time6 seconds
R Server'Gwilym Jenkins' @ jenkins.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
TOT [t] = + 21.8648 + 0.171434I1[t] -0.0845436I2[t] + 0.00789335I3[t] -0.126468E1[t] -0.117634E2[t] -0.222105E3[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT
[t] =  +  21.8648 +  0.171434I1[t] -0.0845436I2[t] +  0.00789335I3[t] -0.126468E1[t] -0.117634E2[t] -0.222105E3[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280317&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT
[t] =  +  21.8648 +  0.171434I1[t] -0.0845436I2[t] +  0.00789335I3[t] -0.126468E1[t] -0.117634E2[t] -0.222105E3[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280317&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280317&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
TOT [t] = + 21.8648 + 0.171434I1[t] -0.0845436I2[t] + 0.00789335I3[t] -0.126468E1[t] -0.117634E2[t] -0.222105E3[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)21.86483.063017.1388.56828e-114.28414e-11
I10.1714340.11361.5090.133970.0669849
I2-0.08454360.105561-0.80090.4248130.212406
I30.007893350.0937220.084220.9330250.466512
E1-0.1264680.124505-1.0160.3118360.155918
E2-0.1176340.0917314-1.2820.2022480.101124
E3-0.2221050.114419-1.9410.05464480.0273224

\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) & 21.8648 & 3.06301 & 7.138 & 8.56828e-11 & 4.28414e-11 \tabularnewline
I1 & 0.171434 & 0.1136 & 1.509 & 0.13397 & 0.0669849 \tabularnewline
I2 & -0.0845436 & 0.105561 & -0.8009 & 0.424813 & 0.212406 \tabularnewline
I3 & 0.00789335 & 0.093722 & 0.08422 & 0.933025 & 0.466512 \tabularnewline
E1 & -0.126468 & 0.124505 & -1.016 & 0.311836 & 0.155918 \tabularnewline
E2 & -0.117634 & 0.0917314 & -1.282 & 0.202248 & 0.101124 \tabularnewline
E3 & -0.222105 & 0.114419 & -1.941 & 0.0546448 & 0.0273224 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280317&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]21.8648[/C][C]3.06301[/C][C]7.138[/C][C]8.56828e-11[/C][C]4.28414e-11[/C][/ROW]
[ROW][C]I1[/C][C]0.171434[/C][C]0.1136[/C][C]1.509[/C][C]0.13397[/C][C]0.0669849[/C][/ROW]
[ROW][C]I2[/C][C]-0.0845436[/C][C]0.105561[/C][C]-0.8009[/C][C]0.424813[/C][C]0.212406[/C][/ROW]
[ROW][C]I3[/C][C]0.00789335[/C][C]0.093722[/C][C]0.08422[/C][C]0.933025[/C][C]0.466512[/C][/ROW]
[ROW][C]E1[/C][C]-0.126468[/C][C]0.124505[/C][C]-1.016[/C][C]0.311836[/C][C]0.155918[/C][/ROW]
[ROW][C]E2[/C][C]-0.117634[/C][C]0.0917314[/C][C]-1.282[/C][C]0.202248[/C][C]0.101124[/C][/ROW]
[ROW][C]E3[/C][C]-0.222105[/C][C]0.114419[/C][C]-1.941[/C][C]0.0546448[/C][C]0.0273224[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280317&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280317&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)21.86483.063017.1388.56828e-114.28414e-11
I10.1714340.11361.5090.133970.0669849
I2-0.08454360.105561-0.80090.4248130.212406
I30.007893350.0937220.084220.9330250.466512
E1-0.1264680.124505-1.0160.3118360.155918
E2-0.1176340.0917314-1.2820.2022480.101124
E3-0.2221050.114419-1.9410.05464480.0273224






Multiple Linear Regression - Regression Statistics
Multiple R0.367144
R-squared0.134794
Adjusted R-squared0.0904249
F-TEST (value)3.03799
F-TEST (DF numerator)6
F-TEST (DF denominator)117
p-value0.00849208
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.32832
Sum Squared Residuals1296.09

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.367144 \tabularnewline
R-squared & 0.134794 \tabularnewline
Adjusted R-squared & 0.0904249 \tabularnewline
F-TEST (value) & 3.03799 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 117 \tabularnewline
p-value & 0.00849208 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.32832 \tabularnewline
Sum Squared Residuals & 1296.09 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280317&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.367144[/C][/ROW]
[ROW][C]R-squared[/C][C]0.134794[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0904249[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.03799[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]117[/C][/ROW]
[ROW][C]p-value[/C][C]0.00849208[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.32832[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1296.09[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280317&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280317&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 R0.367144
R-squared0.134794
Adjusted R-squared0.0904249
F-TEST (value)3.03799
F-TEST (DF numerator)6
F-TEST (DF denominator)117
p-value0.00849208
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.32832
Sum Squared Residuals1296.09






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.914.9993-2.09932
27.412.2425-4.84251
312.815.2372-2.43717
414.812.56162.23842
51212.9942-0.994156
66.312.6019-6.30191
711.312.9144-1.61435
89.313.0135-3.71349
91011.7602-1.76023
1010.812.8839-2.08391
1113.414.4325-1.03251
1211.512.0871-0.587098
138.311.0706-2.77059
1411.714.7996-3.09963
1510.413.4558-3.05576
1611.812.0197-0.21967
1711.313.9099-2.60986
1812.712.67570.0242731
195.710.6104-4.91041
20814.6932-6.69324
2112.513.1682-0.668194
227.611.4029-3.80288
239.214.265-5.06502
2411.112.7214-1.62139
2512.213.4404-1.24041
2612.315.1275-2.82753
2711.414.682-3.28203
288.814.5004-5.70037
2912.611.67620.923769
301312.61990.380148
3113.214.051-0.851016
329.912.5392-2.63918
3310.513.557-3.05701
3413.412.70860.691438
3510.912.0021-1.10207
3610.313.5896-3.28964
3711.411.32750.0724608
388.613.2282-4.62822
3913.212.43590.764143
408.812.2116-3.4116
41912.8986-3.89861
4210.311.9626-1.66264
438.513.6102-5.11018
4413.515.4633-1.96328
454.912.2966-7.39661
466.413.7981-7.39808
479.614.1128-4.5128
4811.613.1023-1.50231
4916.615.6970.90303
5019.114.49554.60453
5113.3513.4563-0.106345
5218.416.27252.12746
5316.1513.75732.39272
5418.412.57315.82691
5515.615.40330.196652
5616.3513.35932.99066
5717.6516.28431.3657
5811.714.6891-2.98911
5914.3514.29840.0516059
6014.7514.69890.0511147
619.911.4868-1.5868
6216.8513.95472.89534
6315.612.61832.98175
6414.8515.0044-0.154392
6511.7513.7219-1.97188
6618.4513.78334.66668
6717.115.3811.71898
6819.915.56984.33017
6918.4512.34776.10227
701515.0106-0.0105635
7111.3512.8116-1.4616
7218.114.4313.66895
7319.113.14115.95893
747.613.5095-5.90954
7513.414.6377-1.23771
7613.914.6655-0.765454
7715.2512.31022.93981
7816.114.61331.48673
7917.3513.53393.81607
8013.1512.78980.360167
8112.1512.9991-0.849121
8218.213.77894.42105
8313.615.8114-2.21143
8414.7512.62022.12976
8514.114.01640.0836164
8614.913.48521.41483
8716.2514.36151.88848
8813.613.7258-0.125797
8915.6511.76713.88288
9014.613.94970.650323
9119.212.97376.22632
9211.912.7678-0.86777
9313.211.7331.46702
9416.3512.55233.79771
9514.3517.0176-2.66756
9615.6513.34872.30125
9717.7511.76925.98084
987.6511.5782-3.92821
9919.312.18287.11718
10015.213.72531.47472
10117.114.72212.37792
10219.0513.28555.76448
10318.5513.59014.95993
10419.115.30773.79232
10511.8512.9997-1.1497
10613.3513.9325-0.582457
10711.410.64910.750941
10819.914.71775.18233
10917.614.69942.90061
11016.113.75082.3492
11111.9512.5937-0.643702
11215.1511.87353.27654
11316.8513.61413.23586
1147.711.2607-3.5607
11512.612.52830.0717344
11612.3512.4561-0.106063
11716.6513.97742.67257
11813.9516.2216-2.27157
11915.712.66923.03075
12015.3512.0843.26596
12115.113.31261.78739
12217.7513.80513.94493
12314.614.6138-0.0137698
12416.6512.75223.89776

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 14.9993 & -2.09932 \tabularnewline
2 & 7.4 & 12.2425 & -4.84251 \tabularnewline
3 & 12.8 & 15.2372 & -2.43717 \tabularnewline
4 & 14.8 & 12.5616 & 2.23842 \tabularnewline
5 & 12 & 12.9942 & -0.994156 \tabularnewline
6 & 6.3 & 12.6019 & -6.30191 \tabularnewline
7 & 11.3 & 12.9144 & -1.61435 \tabularnewline
8 & 9.3 & 13.0135 & -3.71349 \tabularnewline
9 & 10 & 11.7602 & -1.76023 \tabularnewline
10 & 10.8 & 12.8839 & -2.08391 \tabularnewline
11 & 13.4 & 14.4325 & -1.03251 \tabularnewline
12 & 11.5 & 12.0871 & -0.587098 \tabularnewline
13 & 8.3 & 11.0706 & -2.77059 \tabularnewline
14 & 11.7 & 14.7996 & -3.09963 \tabularnewline
15 & 10.4 & 13.4558 & -3.05576 \tabularnewline
16 & 11.8 & 12.0197 & -0.21967 \tabularnewline
17 & 11.3 & 13.9099 & -2.60986 \tabularnewline
18 & 12.7 & 12.6757 & 0.0242731 \tabularnewline
19 & 5.7 & 10.6104 & -4.91041 \tabularnewline
20 & 8 & 14.6932 & -6.69324 \tabularnewline
21 & 12.5 & 13.1682 & -0.668194 \tabularnewline
22 & 7.6 & 11.4029 & -3.80288 \tabularnewline
23 & 9.2 & 14.265 & -5.06502 \tabularnewline
24 & 11.1 & 12.7214 & -1.62139 \tabularnewline
25 & 12.2 & 13.4404 & -1.24041 \tabularnewline
26 & 12.3 & 15.1275 & -2.82753 \tabularnewline
27 & 11.4 & 14.682 & -3.28203 \tabularnewline
28 & 8.8 & 14.5004 & -5.70037 \tabularnewline
29 & 12.6 & 11.6762 & 0.923769 \tabularnewline
30 & 13 & 12.6199 & 0.380148 \tabularnewline
31 & 13.2 & 14.051 & -0.851016 \tabularnewline
32 & 9.9 & 12.5392 & -2.63918 \tabularnewline
33 & 10.5 & 13.557 & -3.05701 \tabularnewline
34 & 13.4 & 12.7086 & 0.691438 \tabularnewline
35 & 10.9 & 12.0021 & -1.10207 \tabularnewline
36 & 10.3 & 13.5896 & -3.28964 \tabularnewline
37 & 11.4 & 11.3275 & 0.0724608 \tabularnewline
38 & 8.6 & 13.2282 & -4.62822 \tabularnewline
39 & 13.2 & 12.4359 & 0.764143 \tabularnewline
40 & 8.8 & 12.2116 & -3.4116 \tabularnewline
41 & 9 & 12.8986 & -3.89861 \tabularnewline
42 & 10.3 & 11.9626 & -1.66264 \tabularnewline
43 & 8.5 & 13.6102 & -5.11018 \tabularnewline
44 & 13.5 & 15.4633 & -1.96328 \tabularnewline
45 & 4.9 & 12.2966 & -7.39661 \tabularnewline
46 & 6.4 & 13.7981 & -7.39808 \tabularnewline
47 & 9.6 & 14.1128 & -4.5128 \tabularnewline
48 & 11.6 & 13.1023 & -1.50231 \tabularnewline
49 & 16.6 & 15.697 & 0.90303 \tabularnewline
50 & 19.1 & 14.4955 & 4.60453 \tabularnewline
51 & 13.35 & 13.4563 & -0.106345 \tabularnewline
52 & 18.4 & 16.2725 & 2.12746 \tabularnewline
53 & 16.15 & 13.7573 & 2.39272 \tabularnewline
54 & 18.4 & 12.5731 & 5.82691 \tabularnewline
55 & 15.6 & 15.4033 & 0.196652 \tabularnewline
56 & 16.35 & 13.3593 & 2.99066 \tabularnewline
57 & 17.65 & 16.2843 & 1.3657 \tabularnewline
58 & 11.7 & 14.6891 & -2.98911 \tabularnewline
59 & 14.35 & 14.2984 & 0.0516059 \tabularnewline
60 & 14.75 & 14.6989 & 0.0511147 \tabularnewline
61 & 9.9 & 11.4868 & -1.5868 \tabularnewline
62 & 16.85 & 13.9547 & 2.89534 \tabularnewline
63 & 15.6 & 12.6183 & 2.98175 \tabularnewline
64 & 14.85 & 15.0044 & -0.154392 \tabularnewline
65 & 11.75 & 13.7219 & -1.97188 \tabularnewline
66 & 18.45 & 13.7833 & 4.66668 \tabularnewline
67 & 17.1 & 15.381 & 1.71898 \tabularnewline
68 & 19.9 & 15.5698 & 4.33017 \tabularnewline
69 & 18.45 & 12.3477 & 6.10227 \tabularnewline
70 & 15 & 15.0106 & -0.0105635 \tabularnewline
71 & 11.35 & 12.8116 & -1.4616 \tabularnewline
72 & 18.1 & 14.431 & 3.66895 \tabularnewline
73 & 19.1 & 13.1411 & 5.95893 \tabularnewline
74 & 7.6 & 13.5095 & -5.90954 \tabularnewline
75 & 13.4 & 14.6377 & -1.23771 \tabularnewline
76 & 13.9 & 14.6655 & -0.765454 \tabularnewline
77 & 15.25 & 12.3102 & 2.93981 \tabularnewline
78 & 16.1 & 14.6133 & 1.48673 \tabularnewline
79 & 17.35 & 13.5339 & 3.81607 \tabularnewline
80 & 13.15 & 12.7898 & 0.360167 \tabularnewline
81 & 12.15 & 12.9991 & -0.849121 \tabularnewline
82 & 18.2 & 13.7789 & 4.42105 \tabularnewline
83 & 13.6 & 15.8114 & -2.21143 \tabularnewline
84 & 14.75 & 12.6202 & 2.12976 \tabularnewline
85 & 14.1 & 14.0164 & 0.0836164 \tabularnewline
86 & 14.9 & 13.4852 & 1.41483 \tabularnewline
87 & 16.25 & 14.3615 & 1.88848 \tabularnewline
88 & 13.6 & 13.7258 & -0.125797 \tabularnewline
89 & 15.65 & 11.7671 & 3.88288 \tabularnewline
90 & 14.6 & 13.9497 & 0.650323 \tabularnewline
91 & 19.2 & 12.9737 & 6.22632 \tabularnewline
92 & 11.9 & 12.7678 & -0.86777 \tabularnewline
93 & 13.2 & 11.733 & 1.46702 \tabularnewline
94 & 16.35 & 12.5523 & 3.79771 \tabularnewline
95 & 14.35 & 17.0176 & -2.66756 \tabularnewline
96 & 15.65 & 13.3487 & 2.30125 \tabularnewline
97 & 17.75 & 11.7692 & 5.98084 \tabularnewline
98 & 7.65 & 11.5782 & -3.92821 \tabularnewline
99 & 19.3 & 12.1828 & 7.11718 \tabularnewline
100 & 15.2 & 13.7253 & 1.47472 \tabularnewline
101 & 17.1 & 14.7221 & 2.37792 \tabularnewline
102 & 19.05 & 13.2855 & 5.76448 \tabularnewline
103 & 18.55 & 13.5901 & 4.95993 \tabularnewline
104 & 19.1 & 15.3077 & 3.79232 \tabularnewline
105 & 11.85 & 12.9997 & -1.1497 \tabularnewline
106 & 13.35 & 13.9325 & -0.582457 \tabularnewline
107 & 11.4 & 10.6491 & 0.750941 \tabularnewline
108 & 19.9 & 14.7177 & 5.18233 \tabularnewline
109 & 17.6 & 14.6994 & 2.90061 \tabularnewline
110 & 16.1 & 13.7508 & 2.3492 \tabularnewline
111 & 11.95 & 12.5937 & -0.643702 \tabularnewline
112 & 15.15 & 11.8735 & 3.27654 \tabularnewline
113 & 16.85 & 13.6141 & 3.23586 \tabularnewline
114 & 7.7 & 11.2607 & -3.5607 \tabularnewline
115 & 12.6 & 12.5283 & 0.0717344 \tabularnewline
116 & 12.35 & 12.4561 & -0.106063 \tabularnewline
117 & 16.65 & 13.9774 & 2.67257 \tabularnewline
118 & 13.95 & 16.2216 & -2.27157 \tabularnewline
119 & 15.7 & 12.6692 & 3.03075 \tabularnewline
120 & 15.35 & 12.084 & 3.26596 \tabularnewline
121 & 15.1 & 13.3126 & 1.78739 \tabularnewline
122 & 17.75 & 13.8051 & 3.94493 \tabularnewline
123 & 14.6 & 14.6138 & -0.0137698 \tabularnewline
124 & 16.65 & 12.7522 & 3.89776 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280317&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]12.9[/C][C]14.9993[/C][C]-2.09932[/C][/ROW]
[ROW][C]2[/C][C]7.4[/C][C]12.2425[/C][C]-4.84251[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]15.2372[/C][C]-2.43717[/C][/ROW]
[ROW][C]4[/C][C]14.8[/C][C]12.5616[/C][C]2.23842[/C][/ROW]
[ROW][C]5[/C][C]12[/C][C]12.9942[/C][C]-0.994156[/C][/ROW]
[ROW][C]6[/C][C]6.3[/C][C]12.6019[/C][C]-6.30191[/C][/ROW]
[ROW][C]7[/C][C]11.3[/C][C]12.9144[/C][C]-1.61435[/C][/ROW]
[ROW][C]8[/C][C]9.3[/C][C]13.0135[/C][C]-3.71349[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]11.7602[/C][C]-1.76023[/C][/ROW]
[ROW][C]10[/C][C]10.8[/C][C]12.8839[/C][C]-2.08391[/C][/ROW]
[ROW][C]11[/C][C]13.4[/C][C]14.4325[/C][C]-1.03251[/C][/ROW]
[ROW][C]12[/C][C]11.5[/C][C]12.0871[/C][C]-0.587098[/C][/ROW]
[ROW][C]13[/C][C]8.3[/C][C]11.0706[/C][C]-2.77059[/C][/ROW]
[ROW][C]14[/C][C]11.7[/C][C]14.7996[/C][C]-3.09963[/C][/ROW]
[ROW][C]15[/C][C]10.4[/C][C]13.4558[/C][C]-3.05576[/C][/ROW]
[ROW][C]16[/C][C]11.8[/C][C]12.0197[/C][C]-0.21967[/C][/ROW]
[ROW][C]17[/C][C]11.3[/C][C]13.9099[/C][C]-2.60986[/C][/ROW]
[ROW][C]18[/C][C]12.7[/C][C]12.6757[/C][C]0.0242731[/C][/ROW]
[ROW][C]19[/C][C]5.7[/C][C]10.6104[/C][C]-4.91041[/C][/ROW]
[ROW][C]20[/C][C]8[/C][C]14.6932[/C][C]-6.69324[/C][/ROW]
[ROW][C]21[/C][C]12.5[/C][C]13.1682[/C][C]-0.668194[/C][/ROW]
[ROW][C]22[/C][C]7.6[/C][C]11.4029[/C][C]-3.80288[/C][/ROW]
[ROW][C]23[/C][C]9.2[/C][C]14.265[/C][C]-5.06502[/C][/ROW]
[ROW][C]24[/C][C]11.1[/C][C]12.7214[/C][C]-1.62139[/C][/ROW]
[ROW][C]25[/C][C]12.2[/C][C]13.4404[/C][C]-1.24041[/C][/ROW]
[ROW][C]26[/C][C]12.3[/C][C]15.1275[/C][C]-2.82753[/C][/ROW]
[ROW][C]27[/C][C]11.4[/C][C]14.682[/C][C]-3.28203[/C][/ROW]
[ROW][C]28[/C][C]8.8[/C][C]14.5004[/C][C]-5.70037[/C][/ROW]
[ROW][C]29[/C][C]12.6[/C][C]11.6762[/C][C]0.923769[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]12.6199[/C][C]0.380148[/C][/ROW]
[ROW][C]31[/C][C]13.2[/C][C]14.051[/C][C]-0.851016[/C][/ROW]
[ROW][C]32[/C][C]9.9[/C][C]12.5392[/C][C]-2.63918[/C][/ROW]
[ROW][C]33[/C][C]10.5[/C][C]13.557[/C][C]-3.05701[/C][/ROW]
[ROW][C]34[/C][C]13.4[/C][C]12.7086[/C][C]0.691438[/C][/ROW]
[ROW][C]35[/C][C]10.9[/C][C]12.0021[/C][C]-1.10207[/C][/ROW]
[ROW][C]36[/C][C]10.3[/C][C]13.5896[/C][C]-3.28964[/C][/ROW]
[ROW][C]37[/C][C]11.4[/C][C]11.3275[/C][C]0.0724608[/C][/ROW]
[ROW][C]38[/C][C]8.6[/C][C]13.2282[/C][C]-4.62822[/C][/ROW]
[ROW][C]39[/C][C]13.2[/C][C]12.4359[/C][C]0.764143[/C][/ROW]
[ROW][C]40[/C][C]8.8[/C][C]12.2116[/C][C]-3.4116[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]12.8986[/C][C]-3.89861[/C][/ROW]
[ROW][C]42[/C][C]10.3[/C][C]11.9626[/C][C]-1.66264[/C][/ROW]
[ROW][C]43[/C][C]8.5[/C][C]13.6102[/C][C]-5.11018[/C][/ROW]
[ROW][C]44[/C][C]13.5[/C][C]15.4633[/C][C]-1.96328[/C][/ROW]
[ROW][C]45[/C][C]4.9[/C][C]12.2966[/C][C]-7.39661[/C][/ROW]
[ROW][C]46[/C][C]6.4[/C][C]13.7981[/C][C]-7.39808[/C][/ROW]
[ROW][C]47[/C][C]9.6[/C][C]14.1128[/C][C]-4.5128[/C][/ROW]
[ROW][C]48[/C][C]11.6[/C][C]13.1023[/C][C]-1.50231[/C][/ROW]
[ROW][C]49[/C][C]16.6[/C][C]15.697[/C][C]0.90303[/C][/ROW]
[ROW][C]50[/C][C]19.1[/C][C]14.4955[/C][C]4.60453[/C][/ROW]
[ROW][C]51[/C][C]13.35[/C][C]13.4563[/C][C]-0.106345[/C][/ROW]
[ROW][C]52[/C][C]18.4[/C][C]16.2725[/C][C]2.12746[/C][/ROW]
[ROW][C]53[/C][C]16.15[/C][C]13.7573[/C][C]2.39272[/C][/ROW]
[ROW][C]54[/C][C]18.4[/C][C]12.5731[/C][C]5.82691[/C][/ROW]
[ROW][C]55[/C][C]15.6[/C][C]15.4033[/C][C]0.196652[/C][/ROW]
[ROW][C]56[/C][C]16.35[/C][C]13.3593[/C][C]2.99066[/C][/ROW]
[ROW][C]57[/C][C]17.65[/C][C]16.2843[/C][C]1.3657[/C][/ROW]
[ROW][C]58[/C][C]11.7[/C][C]14.6891[/C][C]-2.98911[/C][/ROW]
[ROW][C]59[/C][C]14.35[/C][C]14.2984[/C][C]0.0516059[/C][/ROW]
[ROW][C]60[/C][C]14.75[/C][C]14.6989[/C][C]0.0511147[/C][/ROW]
[ROW][C]61[/C][C]9.9[/C][C]11.4868[/C][C]-1.5868[/C][/ROW]
[ROW][C]62[/C][C]16.85[/C][C]13.9547[/C][C]2.89534[/C][/ROW]
[ROW][C]63[/C][C]15.6[/C][C]12.6183[/C][C]2.98175[/C][/ROW]
[ROW][C]64[/C][C]14.85[/C][C]15.0044[/C][C]-0.154392[/C][/ROW]
[ROW][C]65[/C][C]11.75[/C][C]13.7219[/C][C]-1.97188[/C][/ROW]
[ROW][C]66[/C][C]18.45[/C][C]13.7833[/C][C]4.66668[/C][/ROW]
[ROW][C]67[/C][C]17.1[/C][C]15.381[/C][C]1.71898[/C][/ROW]
[ROW][C]68[/C][C]19.9[/C][C]15.5698[/C][C]4.33017[/C][/ROW]
[ROW][C]69[/C][C]18.45[/C][C]12.3477[/C][C]6.10227[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]15.0106[/C][C]-0.0105635[/C][/ROW]
[ROW][C]71[/C][C]11.35[/C][C]12.8116[/C][C]-1.4616[/C][/ROW]
[ROW][C]72[/C][C]18.1[/C][C]14.431[/C][C]3.66895[/C][/ROW]
[ROW][C]73[/C][C]19.1[/C][C]13.1411[/C][C]5.95893[/C][/ROW]
[ROW][C]74[/C][C]7.6[/C][C]13.5095[/C][C]-5.90954[/C][/ROW]
[ROW][C]75[/C][C]13.4[/C][C]14.6377[/C][C]-1.23771[/C][/ROW]
[ROW][C]76[/C][C]13.9[/C][C]14.6655[/C][C]-0.765454[/C][/ROW]
[ROW][C]77[/C][C]15.25[/C][C]12.3102[/C][C]2.93981[/C][/ROW]
[ROW][C]78[/C][C]16.1[/C][C]14.6133[/C][C]1.48673[/C][/ROW]
[ROW][C]79[/C][C]17.35[/C][C]13.5339[/C][C]3.81607[/C][/ROW]
[ROW][C]80[/C][C]13.15[/C][C]12.7898[/C][C]0.360167[/C][/ROW]
[ROW][C]81[/C][C]12.15[/C][C]12.9991[/C][C]-0.849121[/C][/ROW]
[ROW][C]82[/C][C]18.2[/C][C]13.7789[/C][C]4.42105[/C][/ROW]
[ROW][C]83[/C][C]13.6[/C][C]15.8114[/C][C]-2.21143[/C][/ROW]
[ROW][C]84[/C][C]14.75[/C][C]12.6202[/C][C]2.12976[/C][/ROW]
[ROW][C]85[/C][C]14.1[/C][C]14.0164[/C][C]0.0836164[/C][/ROW]
[ROW][C]86[/C][C]14.9[/C][C]13.4852[/C][C]1.41483[/C][/ROW]
[ROW][C]87[/C][C]16.25[/C][C]14.3615[/C][C]1.88848[/C][/ROW]
[ROW][C]88[/C][C]13.6[/C][C]13.7258[/C][C]-0.125797[/C][/ROW]
[ROW][C]89[/C][C]15.65[/C][C]11.7671[/C][C]3.88288[/C][/ROW]
[ROW][C]90[/C][C]14.6[/C][C]13.9497[/C][C]0.650323[/C][/ROW]
[ROW][C]91[/C][C]19.2[/C][C]12.9737[/C][C]6.22632[/C][/ROW]
[ROW][C]92[/C][C]11.9[/C][C]12.7678[/C][C]-0.86777[/C][/ROW]
[ROW][C]93[/C][C]13.2[/C][C]11.733[/C][C]1.46702[/C][/ROW]
[ROW][C]94[/C][C]16.35[/C][C]12.5523[/C][C]3.79771[/C][/ROW]
[ROW][C]95[/C][C]14.35[/C][C]17.0176[/C][C]-2.66756[/C][/ROW]
[ROW][C]96[/C][C]15.65[/C][C]13.3487[/C][C]2.30125[/C][/ROW]
[ROW][C]97[/C][C]17.75[/C][C]11.7692[/C][C]5.98084[/C][/ROW]
[ROW][C]98[/C][C]7.65[/C][C]11.5782[/C][C]-3.92821[/C][/ROW]
[ROW][C]99[/C][C]19.3[/C][C]12.1828[/C][C]7.11718[/C][/ROW]
[ROW][C]100[/C][C]15.2[/C][C]13.7253[/C][C]1.47472[/C][/ROW]
[ROW][C]101[/C][C]17.1[/C][C]14.7221[/C][C]2.37792[/C][/ROW]
[ROW][C]102[/C][C]19.05[/C][C]13.2855[/C][C]5.76448[/C][/ROW]
[ROW][C]103[/C][C]18.55[/C][C]13.5901[/C][C]4.95993[/C][/ROW]
[ROW][C]104[/C][C]19.1[/C][C]15.3077[/C][C]3.79232[/C][/ROW]
[ROW][C]105[/C][C]11.85[/C][C]12.9997[/C][C]-1.1497[/C][/ROW]
[ROW][C]106[/C][C]13.35[/C][C]13.9325[/C][C]-0.582457[/C][/ROW]
[ROW][C]107[/C][C]11.4[/C][C]10.6491[/C][C]0.750941[/C][/ROW]
[ROW][C]108[/C][C]19.9[/C][C]14.7177[/C][C]5.18233[/C][/ROW]
[ROW][C]109[/C][C]17.6[/C][C]14.6994[/C][C]2.90061[/C][/ROW]
[ROW][C]110[/C][C]16.1[/C][C]13.7508[/C][C]2.3492[/C][/ROW]
[ROW][C]111[/C][C]11.95[/C][C]12.5937[/C][C]-0.643702[/C][/ROW]
[ROW][C]112[/C][C]15.15[/C][C]11.8735[/C][C]3.27654[/C][/ROW]
[ROW][C]113[/C][C]16.85[/C][C]13.6141[/C][C]3.23586[/C][/ROW]
[ROW][C]114[/C][C]7.7[/C][C]11.2607[/C][C]-3.5607[/C][/ROW]
[ROW][C]115[/C][C]12.6[/C][C]12.5283[/C][C]0.0717344[/C][/ROW]
[ROW][C]116[/C][C]12.35[/C][C]12.4561[/C][C]-0.106063[/C][/ROW]
[ROW][C]117[/C][C]16.65[/C][C]13.9774[/C][C]2.67257[/C][/ROW]
[ROW][C]118[/C][C]13.95[/C][C]16.2216[/C][C]-2.27157[/C][/ROW]
[ROW][C]119[/C][C]15.7[/C][C]12.6692[/C][C]3.03075[/C][/ROW]
[ROW][C]120[/C][C]15.35[/C][C]12.084[/C][C]3.26596[/C][/ROW]
[ROW][C]121[/C][C]15.1[/C][C]13.3126[/C][C]1.78739[/C][/ROW]
[ROW][C]122[/C][C]17.75[/C][C]13.8051[/C][C]3.94493[/C][/ROW]
[ROW][C]123[/C][C]14.6[/C][C]14.6138[/C][C]-0.0137698[/C][/ROW]
[ROW][C]124[/C][C]16.65[/C][C]12.7522[/C][C]3.89776[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280317&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280317&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
112.914.9993-2.09932
27.412.2425-4.84251
312.815.2372-2.43717
414.812.56162.23842
51212.9942-0.994156
66.312.6019-6.30191
711.312.9144-1.61435
89.313.0135-3.71349
91011.7602-1.76023
1010.812.8839-2.08391
1113.414.4325-1.03251
1211.512.0871-0.587098
138.311.0706-2.77059
1411.714.7996-3.09963
1510.413.4558-3.05576
1611.812.0197-0.21967
1711.313.9099-2.60986
1812.712.67570.0242731
195.710.6104-4.91041
20814.6932-6.69324
2112.513.1682-0.668194
227.611.4029-3.80288
239.214.265-5.06502
2411.112.7214-1.62139
2512.213.4404-1.24041
2612.315.1275-2.82753
2711.414.682-3.28203
288.814.5004-5.70037
2912.611.67620.923769
301312.61990.380148
3113.214.051-0.851016
329.912.5392-2.63918
3310.513.557-3.05701
3413.412.70860.691438
3510.912.0021-1.10207
3610.313.5896-3.28964
3711.411.32750.0724608
388.613.2282-4.62822
3913.212.43590.764143
408.812.2116-3.4116
41912.8986-3.89861
4210.311.9626-1.66264
438.513.6102-5.11018
4413.515.4633-1.96328
454.912.2966-7.39661
466.413.7981-7.39808
479.614.1128-4.5128
4811.613.1023-1.50231
4916.615.6970.90303
5019.114.49554.60453
5113.3513.4563-0.106345
5218.416.27252.12746
5316.1513.75732.39272
5418.412.57315.82691
5515.615.40330.196652
5616.3513.35932.99066
5717.6516.28431.3657
5811.714.6891-2.98911
5914.3514.29840.0516059
6014.7514.69890.0511147
619.911.4868-1.5868
6216.8513.95472.89534
6315.612.61832.98175
6414.8515.0044-0.154392
6511.7513.7219-1.97188
6618.4513.78334.66668
6717.115.3811.71898
6819.915.56984.33017
6918.4512.34776.10227
701515.0106-0.0105635
7111.3512.8116-1.4616
7218.114.4313.66895
7319.113.14115.95893
747.613.5095-5.90954
7513.414.6377-1.23771
7613.914.6655-0.765454
7715.2512.31022.93981
7816.114.61331.48673
7917.3513.53393.81607
8013.1512.78980.360167
8112.1512.9991-0.849121
8218.213.77894.42105
8313.615.8114-2.21143
8414.7512.62022.12976
8514.114.01640.0836164
8614.913.48521.41483
8716.2514.36151.88848
8813.613.7258-0.125797
8915.6511.76713.88288
9014.613.94970.650323
9119.212.97376.22632
9211.912.7678-0.86777
9313.211.7331.46702
9416.3512.55233.79771
9514.3517.0176-2.66756
9615.6513.34872.30125
9717.7511.76925.98084
987.6511.5782-3.92821
9919.312.18287.11718
10015.213.72531.47472
10117.114.72212.37792
10219.0513.28555.76448
10318.5513.59014.95993
10419.115.30773.79232
10511.8512.9997-1.1497
10613.3513.9325-0.582457
10711.410.64910.750941
10819.914.71775.18233
10917.614.69942.90061
11016.113.75082.3492
11111.9512.5937-0.643702
11215.1511.87353.27654
11316.8513.61413.23586
1147.711.2607-3.5607
11512.612.52830.0717344
11612.3512.4561-0.106063
11716.6513.97742.67257
11813.9516.2216-2.27157
11915.712.66923.03075
12015.3512.0843.26596
12115.113.31261.78739
12217.7513.80513.94493
12314.614.6138-0.0137698
12416.6512.75223.89776






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.08475110.1695020.915249
110.06236440.1247290.937636
120.03630710.07261430.963693
130.01405810.02811620.985942
140.005511650.01102330.994488
150.02195710.04391410.978043
160.01238410.02476820.987616
170.005865940.01173190.994134
180.002687860.005375720.997312
190.00130410.002608190.998696
200.05030790.1006160.949692
210.03697350.0739470.963026
220.03921920.07843850.960781
230.04272480.08544960.957275
240.03095550.0619110.969044
250.021890.043780.97811
260.01424590.02849190.985754
270.0122830.0245660.987717
280.01025610.02051210.989744
290.006106420.01221280.993894
300.005691470.01138290.994309
310.01063450.02126890.989366
320.007746750.01549350.992253
330.005829760.01165950.99417
340.004623510.009247020.995376
350.002961570.005923140.997038
360.002157230.004314460.997843
370.001657670.003315340.998342
380.00169510.00339020.998305
390.001294850.00258970.998705
400.001133950.002267890.998866
410.0009214310.001842860.999079
420.0006141060.001228210.999386
430.0009307610.001861520.999069
440.0005815150.001163030.999418
450.01309910.02619830.986901
460.0508620.1017240.949138
470.06159980.12320.9384
480.05837480.116750.941625
490.05745230.1149050.942548
500.1233260.2466520.876674
510.1499910.2999820.850009
520.2006490.4012970.799351
530.2287930.4575850.771207
540.5292240.9415520.470776
550.4822260.9644530.517774
560.5249340.9501330.475066
570.5078430.9843130.492157
580.5178870.9642250.482113
590.4877390.9754780.512261
600.4710910.9421820.528909
610.4486420.8972840.551358
620.4706920.9413850.529308
630.4844110.9688220.515589
640.445630.8912590.55437
650.4322570.8645140.567743
660.5121180.9757630.487882
670.471990.9439790.52801
680.5059590.9880820.494041
690.6637670.6724670.336233
700.6144350.771130.385565
710.6257420.7485170.374258
720.6390810.7218380.360919
730.749180.501640.25082
740.8857230.2285540.114277
750.8716580.2566830.128342
760.8534660.2930690.146534
770.8434290.3131430.156571
780.8121930.3756140.187807
790.8165620.3668760.183438
800.7784170.4431660.221583
810.7589620.4820750.241038
820.7892480.4215050.210752
830.7993040.4013910.200696
840.7707240.4585530.229276
850.769640.460720.23036
860.7298830.5402330.270117
870.6991230.6017530.300877
880.657030.685940.34297
890.640960.7180810.35904
900.5909360.8181270.409064
910.7143810.5712380.285619
920.6842890.6314210.315711
930.6284390.7431230.371561
940.6419090.7161820.358091
950.624710.750580.37529
960.5696250.8607510.430375
970.6874760.6250470.312524
980.8712630.2574740.128737
990.9252210.1495570.0747787
1000.8947730.2104540.105227
1010.8578180.2843650.142182
1020.9300380.1399240.0699622
1030.9670540.0658930.0329465
1040.9848450.03030940.0151547
1050.9734880.05302490.0265125
1060.9604690.07906170.0395309
1070.9439720.1120560.056028
1080.9139850.1720310.0860154
1090.8637220.2725550.136278
1100.7888730.4222530.211127
1110.715640.5687190.28436
1120.6134160.7731690.386584
1130.7359880.5280240.264012
1140.8141840.3716320.185816

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.0847511 & 0.169502 & 0.915249 \tabularnewline
11 & 0.0623644 & 0.124729 & 0.937636 \tabularnewline
12 & 0.0363071 & 0.0726143 & 0.963693 \tabularnewline
13 & 0.0140581 & 0.0281162 & 0.985942 \tabularnewline
14 & 0.00551165 & 0.0110233 & 0.994488 \tabularnewline
15 & 0.0219571 & 0.0439141 & 0.978043 \tabularnewline
16 & 0.0123841 & 0.0247682 & 0.987616 \tabularnewline
17 & 0.00586594 & 0.0117319 & 0.994134 \tabularnewline
18 & 0.00268786 & 0.00537572 & 0.997312 \tabularnewline
19 & 0.0013041 & 0.00260819 & 0.998696 \tabularnewline
20 & 0.0503079 & 0.100616 & 0.949692 \tabularnewline
21 & 0.0369735 & 0.073947 & 0.963026 \tabularnewline
22 & 0.0392192 & 0.0784385 & 0.960781 \tabularnewline
23 & 0.0427248 & 0.0854496 & 0.957275 \tabularnewline
24 & 0.0309555 & 0.061911 & 0.969044 \tabularnewline
25 & 0.02189 & 0.04378 & 0.97811 \tabularnewline
26 & 0.0142459 & 0.0284919 & 0.985754 \tabularnewline
27 & 0.012283 & 0.024566 & 0.987717 \tabularnewline
28 & 0.0102561 & 0.0205121 & 0.989744 \tabularnewline
29 & 0.00610642 & 0.0122128 & 0.993894 \tabularnewline
30 & 0.00569147 & 0.0113829 & 0.994309 \tabularnewline
31 & 0.0106345 & 0.0212689 & 0.989366 \tabularnewline
32 & 0.00774675 & 0.0154935 & 0.992253 \tabularnewline
33 & 0.00582976 & 0.0116595 & 0.99417 \tabularnewline
34 & 0.00462351 & 0.00924702 & 0.995376 \tabularnewline
35 & 0.00296157 & 0.00592314 & 0.997038 \tabularnewline
36 & 0.00215723 & 0.00431446 & 0.997843 \tabularnewline
37 & 0.00165767 & 0.00331534 & 0.998342 \tabularnewline
38 & 0.0016951 & 0.0033902 & 0.998305 \tabularnewline
39 & 0.00129485 & 0.0025897 & 0.998705 \tabularnewline
40 & 0.00113395 & 0.00226789 & 0.998866 \tabularnewline
41 & 0.000921431 & 0.00184286 & 0.999079 \tabularnewline
42 & 0.000614106 & 0.00122821 & 0.999386 \tabularnewline
43 & 0.000930761 & 0.00186152 & 0.999069 \tabularnewline
44 & 0.000581515 & 0.00116303 & 0.999418 \tabularnewline
45 & 0.0130991 & 0.0261983 & 0.986901 \tabularnewline
46 & 0.050862 & 0.101724 & 0.949138 \tabularnewline
47 & 0.0615998 & 0.1232 & 0.9384 \tabularnewline
48 & 0.0583748 & 0.11675 & 0.941625 \tabularnewline
49 & 0.0574523 & 0.114905 & 0.942548 \tabularnewline
50 & 0.123326 & 0.246652 & 0.876674 \tabularnewline
51 & 0.149991 & 0.299982 & 0.850009 \tabularnewline
52 & 0.200649 & 0.401297 & 0.799351 \tabularnewline
53 & 0.228793 & 0.457585 & 0.771207 \tabularnewline
54 & 0.529224 & 0.941552 & 0.470776 \tabularnewline
55 & 0.482226 & 0.964453 & 0.517774 \tabularnewline
56 & 0.524934 & 0.950133 & 0.475066 \tabularnewline
57 & 0.507843 & 0.984313 & 0.492157 \tabularnewline
58 & 0.517887 & 0.964225 & 0.482113 \tabularnewline
59 & 0.487739 & 0.975478 & 0.512261 \tabularnewline
60 & 0.471091 & 0.942182 & 0.528909 \tabularnewline
61 & 0.448642 & 0.897284 & 0.551358 \tabularnewline
62 & 0.470692 & 0.941385 & 0.529308 \tabularnewline
63 & 0.484411 & 0.968822 & 0.515589 \tabularnewline
64 & 0.44563 & 0.891259 & 0.55437 \tabularnewline
65 & 0.432257 & 0.864514 & 0.567743 \tabularnewline
66 & 0.512118 & 0.975763 & 0.487882 \tabularnewline
67 & 0.47199 & 0.943979 & 0.52801 \tabularnewline
68 & 0.505959 & 0.988082 & 0.494041 \tabularnewline
69 & 0.663767 & 0.672467 & 0.336233 \tabularnewline
70 & 0.614435 & 0.77113 & 0.385565 \tabularnewline
71 & 0.625742 & 0.748517 & 0.374258 \tabularnewline
72 & 0.639081 & 0.721838 & 0.360919 \tabularnewline
73 & 0.74918 & 0.50164 & 0.25082 \tabularnewline
74 & 0.885723 & 0.228554 & 0.114277 \tabularnewline
75 & 0.871658 & 0.256683 & 0.128342 \tabularnewline
76 & 0.853466 & 0.293069 & 0.146534 \tabularnewline
77 & 0.843429 & 0.313143 & 0.156571 \tabularnewline
78 & 0.812193 & 0.375614 & 0.187807 \tabularnewline
79 & 0.816562 & 0.366876 & 0.183438 \tabularnewline
80 & 0.778417 & 0.443166 & 0.221583 \tabularnewline
81 & 0.758962 & 0.482075 & 0.241038 \tabularnewline
82 & 0.789248 & 0.421505 & 0.210752 \tabularnewline
83 & 0.799304 & 0.401391 & 0.200696 \tabularnewline
84 & 0.770724 & 0.458553 & 0.229276 \tabularnewline
85 & 0.76964 & 0.46072 & 0.23036 \tabularnewline
86 & 0.729883 & 0.540233 & 0.270117 \tabularnewline
87 & 0.699123 & 0.601753 & 0.300877 \tabularnewline
88 & 0.65703 & 0.68594 & 0.34297 \tabularnewline
89 & 0.64096 & 0.718081 & 0.35904 \tabularnewline
90 & 0.590936 & 0.818127 & 0.409064 \tabularnewline
91 & 0.714381 & 0.571238 & 0.285619 \tabularnewline
92 & 0.684289 & 0.631421 & 0.315711 \tabularnewline
93 & 0.628439 & 0.743123 & 0.371561 \tabularnewline
94 & 0.641909 & 0.716182 & 0.358091 \tabularnewline
95 & 0.62471 & 0.75058 & 0.37529 \tabularnewline
96 & 0.569625 & 0.860751 & 0.430375 \tabularnewline
97 & 0.687476 & 0.625047 & 0.312524 \tabularnewline
98 & 0.871263 & 0.257474 & 0.128737 \tabularnewline
99 & 0.925221 & 0.149557 & 0.0747787 \tabularnewline
100 & 0.894773 & 0.210454 & 0.105227 \tabularnewline
101 & 0.857818 & 0.284365 & 0.142182 \tabularnewline
102 & 0.930038 & 0.139924 & 0.0699622 \tabularnewline
103 & 0.967054 & 0.065893 & 0.0329465 \tabularnewline
104 & 0.984845 & 0.0303094 & 0.0151547 \tabularnewline
105 & 0.973488 & 0.0530249 & 0.0265125 \tabularnewline
106 & 0.960469 & 0.0790617 & 0.0395309 \tabularnewline
107 & 0.943972 & 0.112056 & 0.056028 \tabularnewline
108 & 0.913985 & 0.172031 & 0.0860154 \tabularnewline
109 & 0.863722 & 0.272555 & 0.136278 \tabularnewline
110 & 0.788873 & 0.422253 & 0.211127 \tabularnewline
111 & 0.71564 & 0.568719 & 0.28436 \tabularnewline
112 & 0.613416 & 0.773169 & 0.386584 \tabularnewline
113 & 0.735988 & 0.528024 & 0.264012 \tabularnewline
114 & 0.814184 & 0.371632 & 0.185816 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280317&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]10[/C][C]0.0847511[/C][C]0.169502[/C][C]0.915249[/C][/ROW]
[ROW][C]11[/C][C]0.0623644[/C][C]0.124729[/C][C]0.937636[/C][/ROW]
[ROW][C]12[/C][C]0.0363071[/C][C]0.0726143[/C][C]0.963693[/C][/ROW]
[ROW][C]13[/C][C]0.0140581[/C][C]0.0281162[/C][C]0.985942[/C][/ROW]
[ROW][C]14[/C][C]0.00551165[/C][C]0.0110233[/C][C]0.994488[/C][/ROW]
[ROW][C]15[/C][C]0.0219571[/C][C]0.0439141[/C][C]0.978043[/C][/ROW]
[ROW][C]16[/C][C]0.0123841[/C][C]0.0247682[/C][C]0.987616[/C][/ROW]
[ROW][C]17[/C][C]0.00586594[/C][C]0.0117319[/C][C]0.994134[/C][/ROW]
[ROW][C]18[/C][C]0.00268786[/C][C]0.00537572[/C][C]0.997312[/C][/ROW]
[ROW][C]19[/C][C]0.0013041[/C][C]0.00260819[/C][C]0.998696[/C][/ROW]
[ROW][C]20[/C][C]0.0503079[/C][C]0.100616[/C][C]0.949692[/C][/ROW]
[ROW][C]21[/C][C]0.0369735[/C][C]0.073947[/C][C]0.963026[/C][/ROW]
[ROW][C]22[/C][C]0.0392192[/C][C]0.0784385[/C][C]0.960781[/C][/ROW]
[ROW][C]23[/C][C]0.0427248[/C][C]0.0854496[/C][C]0.957275[/C][/ROW]
[ROW][C]24[/C][C]0.0309555[/C][C]0.061911[/C][C]0.969044[/C][/ROW]
[ROW][C]25[/C][C]0.02189[/C][C]0.04378[/C][C]0.97811[/C][/ROW]
[ROW][C]26[/C][C]0.0142459[/C][C]0.0284919[/C][C]0.985754[/C][/ROW]
[ROW][C]27[/C][C]0.012283[/C][C]0.024566[/C][C]0.987717[/C][/ROW]
[ROW][C]28[/C][C]0.0102561[/C][C]0.0205121[/C][C]0.989744[/C][/ROW]
[ROW][C]29[/C][C]0.00610642[/C][C]0.0122128[/C][C]0.993894[/C][/ROW]
[ROW][C]30[/C][C]0.00569147[/C][C]0.0113829[/C][C]0.994309[/C][/ROW]
[ROW][C]31[/C][C]0.0106345[/C][C]0.0212689[/C][C]0.989366[/C][/ROW]
[ROW][C]32[/C][C]0.00774675[/C][C]0.0154935[/C][C]0.992253[/C][/ROW]
[ROW][C]33[/C][C]0.00582976[/C][C]0.0116595[/C][C]0.99417[/C][/ROW]
[ROW][C]34[/C][C]0.00462351[/C][C]0.00924702[/C][C]0.995376[/C][/ROW]
[ROW][C]35[/C][C]0.00296157[/C][C]0.00592314[/C][C]0.997038[/C][/ROW]
[ROW][C]36[/C][C]0.00215723[/C][C]0.00431446[/C][C]0.997843[/C][/ROW]
[ROW][C]37[/C][C]0.00165767[/C][C]0.00331534[/C][C]0.998342[/C][/ROW]
[ROW][C]38[/C][C]0.0016951[/C][C]0.0033902[/C][C]0.998305[/C][/ROW]
[ROW][C]39[/C][C]0.00129485[/C][C]0.0025897[/C][C]0.998705[/C][/ROW]
[ROW][C]40[/C][C]0.00113395[/C][C]0.00226789[/C][C]0.998866[/C][/ROW]
[ROW][C]41[/C][C]0.000921431[/C][C]0.00184286[/C][C]0.999079[/C][/ROW]
[ROW][C]42[/C][C]0.000614106[/C][C]0.00122821[/C][C]0.999386[/C][/ROW]
[ROW][C]43[/C][C]0.000930761[/C][C]0.00186152[/C][C]0.999069[/C][/ROW]
[ROW][C]44[/C][C]0.000581515[/C][C]0.00116303[/C][C]0.999418[/C][/ROW]
[ROW][C]45[/C][C]0.0130991[/C][C]0.0261983[/C][C]0.986901[/C][/ROW]
[ROW][C]46[/C][C]0.050862[/C][C]0.101724[/C][C]0.949138[/C][/ROW]
[ROW][C]47[/C][C]0.0615998[/C][C]0.1232[/C][C]0.9384[/C][/ROW]
[ROW][C]48[/C][C]0.0583748[/C][C]0.11675[/C][C]0.941625[/C][/ROW]
[ROW][C]49[/C][C]0.0574523[/C][C]0.114905[/C][C]0.942548[/C][/ROW]
[ROW][C]50[/C][C]0.123326[/C][C]0.246652[/C][C]0.876674[/C][/ROW]
[ROW][C]51[/C][C]0.149991[/C][C]0.299982[/C][C]0.850009[/C][/ROW]
[ROW][C]52[/C][C]0.200649[/C][C]0.401297[/C][C]0.799351[/C][/ROW]
[ROW][C]53[/C][C]0.228793[/C][C]0.457585[/C][C]0.771207[/C][/ROW]
[ROW][C]54[/C][C]0.529224[/C][C]0.941552[/C][C]0.470776[/C][/ROW]
[ROW][C]55[/C][C]0.482226[/C][C]0.964453[/C][C]0.517774[/C][/ROW]
[ROW][C]56[/C][C]0.524934[/C][C]0.950133[/C][C]0.475066[/C][/ROW]
[ROW][C]57[/C][C]0.507843[/C][C]0.984313[/C][C]0.492157[/C][/ROW]
[ROW][C]58[/C][C]0.517887[/C][C]0.964225[/C][C]0.482113[/C][/ROW]
[ROW][C]59[/C][C]0.487739[/C][C]0.975478[/C][C]0.512261[/C][/ROW]
[ROW][C]60[/C][C]0.471091[/C][C]0.942182[/C][C]0.528909[/C][/ROW]
[ROW][C]61[/C][C]0.448642[/C][C]0.897284[/C][C]0.551358[/C][/ROW]
[ROW][C]62[/C][C]0.470692[/C][C]0.941385[/C][C]0.529308[/C][/ROW]
[ROW][C]63[/C][C]0.484411[/C][C]0.968822[/C][C]0.515589[/C][/ROW]
[ROW][C]64[/C][C]0.44563[/C][C]0.891259[/C][C]0.55437[/C][/ROW]
[ROW][C]65[/C][C]0.432257[/C][C]0.864514[/C][C]0.567743[/C][/ROW]
[ROW][C]66[/C][C]0.512118[/C][C]0.975763[/C][C]0.487882[/C][/ROW]
[ROW][C]67[/C][C]0.47199[/C][C]0.943979[/C][C]0.52801[/C][/ROW]
[ROW][C]68[/C][C]0.505959[/C][C]0.988082[/C][C]0.494041[/C][/ROW]
[ROW][C]69[/C][C]0.663767[/C][C]0.672467[/C][C]0.336233[/C][/ROW]
[ROW][C]70[/C][C]0.614435[/C][C]0.77113[/C][C]0.385565[/C][/ROW]
[ROW][C]71[/C][C]0.625742[/C][C]0.748517[/C][C]0.374258[/C][/ROW]
[ROW][C]72[/C][C]0.639081[/C][C]0.721838[/C][C]0.360919[/C][/ROW]
[ROW][C]73[/C][C]0.74918[/C][C]0.50164[/C][C]0.25082[/C][/ROW]
[ROW][C]74[/C][C]0.885723[/C][C]0.228554[/C][C]0.114277[/C][/ROW]
[ROW][C]75[/C][C]0.871658[/C][C]0.256683[/C][C]0.128342[/C][/ROW]
[ROW][C]76[/C][C]0.853466[/C][C]0.293069[/C][C]0.146534[/C][/ROW]
[ROW][C]77[/C][C]0.843429[/C][C]0.313143[/C][C]0.156571[/C][/ROW]
[ROW][C]78[/C][C]0.812193[/C][C]0.375614[/C][C]0.187807[/C][/ROW]
[ROW][C]79[/C][C]0.816562[/C][C]0.366876[/C][C]0.183438[/C][/ROW]
[ROW][C]80[/C][C]0.778417[/C][C]0.443166[/C][C]0.221583[/C][/ROW]
[ROW][C]81[/C][C]0.758962[/C][C]0.482075[/C][C]0.241038[/C][/ROW]
[ROW][C]82[/C][C]0.789248[/C][C]0.421505[/C][C]0.210752[/C][/ROW]
[ROW][C]83[/C][C]0.799304[/C][C]0.401391[/C][C]0.200696[/C][/ROW]
[ROW][C]84[/C][C]0.770724[/C][C]0.458553[/C][C]0.229276[/C][/ROW]
[ROW][C]85[/C][C]0.76964[/C][C]0.46072[/C][C]0.23036[/C][/ROW]
[ROW][C]86[/C][C]0.729883[/C][C]0.540233[/C][C]0.270117[/C][/ROW]
[ROW][C]87[/C][C]0.699123[/C][C]0.601753[/C][C]0.300877[/C][/ROW]
[ROW][C]88[/C][C]0.65703[/C][C]0.68594[/C][C]0.34297[/C][/ROW]
[ROW][C]89[/C][C]0.64096[/C][C]0.718081[/C][C]0.35904[/C][/ROW]
[ROW][C]90[/C][C]0.590936[/C][C]0.818127[/C][C]0.409064[/C][/ROW]
[ROW][C]91[/C][C]0.714381[/C][C]0.571238[/C][C]0.285619[/C][/ROW]
[ROW][C]92[/C][C]0.684289[/C][C]0.631421[/C][C]0.315711[/C][/ROW]
[ROW][C]93[/C][C]0.628439[/C][C]0.743123[/C][C]0.371561[/C][/ROW]
[ROW][C]94[/C][C]0.641909[/C][C]0.716182[/C][C]0.358091[/C][/ROW]
[ROW][C]95[/C][C]0.62471[/C][C]0.75058[/C][C]0.37529[/C][/ROW]
[ROW][C]96[/C][C]0.569625[/C][C]0.860751[/C][C]0.430375[/C][/ROW]
[ROW][C]97[/C][C]0.687476[/C][C]0.625047[/C][C]0.312524[/C][/ROW]
[ROW][C]98[/C][C]0.871263[/C][C]0.257474[/C][C]0.128737[/C][/ROW]
[ROW][C]99[/C][C]0.925221[/C][C]0.149557[/C][C]0.0747787[/C][/ROW]
[ROW][C]100[/C][C]0.894773[/C][C]0.210454[/C][C]0.105227[/C][/ROW]
[ROW][C]101[/C][C]0.857818[/C][C]0.284365[/C][C]0.142182[/C][/ROW]
[ROW][C]102[/C][C]0.930038[/C][C]0.139924[/C][C]0.0699622[/C][/ROW]
[ROW][C]103[/C][C]0.967054[/C][C]0.065893[/C][C]0.0329465[/C][/ROW]
[ROW][C]104[/C][C]0.984845[/C][C]0.0303094[/C][C]0.0151547[/C][/ROW]
[ROW][C]105[/C][C]0.973488[/C][C]0.0530249[/C][C]0.0265125[/C][/ROW]
[ROW][C]106[/C][C]0.960469[/C][C]0.0790617[/C][C]0.0395309[/C][/ROW]
[ROW][C]107[/C][C]0.943972[/C][C]0.112056[/C][C]0.056028[/C][/ROW]
[ROW][C]108[/C][C]0.913985[/C][C]0.172031[/C][C]0.0860154[/C][/ROW]
[ROW][C]109[/C][C]0.863722[/C][C]0.272555[/C][C]0.136278[/C][/ROW]
[ROW][C]110[/C][C]0.788873[/C][C]0.422253[/C][C]0.211127[/C][/ROW]
[ROW][C]111[/C][C]0.71564[/C][C]0.568719[/C][C]0.28436[/C][/ROW]
[ROW][C]112[/C][C]0.613416[/C][C]0.773169[/C][C]0.386584[/C][/ROW]
[ROW][C]113[/C][C]0.735988[/C][C]0.528024[/C][C]0.264012[/C][/ROW]
[ROW][C]114[/C][C]0.814184[/C][C]0.371632[/C][C]0.185816[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280317&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280317&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
100.08475110.1695020.915249
110.06236440.1247290.937636
120.03630710.07261430.963693
130.01405810.02811620.985942
140.005511650.01102330.994488
150.02195710.04391410.978043
160.01238410.02476820.987616
170.005865940.01173190.994134
180.002687860.005375720.997312
190.00130410.002608190.998696
200.05030790.1006160.949692
210.03697350.0739470.963026
220.03921920.07843850.960781
230.04272480.08544960.957275
240.03095550.0619110.969044
250.021890.043780.97811
260.01424590.02849190.985754
270.0122830.0245660.987717
280.01025610.02051210.989744
290.006106420.01221280.993894
300.005691470.01138290.994309
310.01063450.02126890.989366
320.007746750.01549350.992253
330.005829760.01165950.99417
340.004623510.009247020.995376
350.002961570.005923140.997038
360.002157230.004314460.997843
370.001657670.003315340.998342
380.00169510.00339020.998305
390.001294850.00258970.998705
400.001133950.002267890.998866
410.0009214310.001842860.999079
420.0006141060.001228210.999386
430.0009307610.001861520.999069
440.0005815150.001163030.999418
450.01309910.02619830.986901
460.0508620.1017240.949138
470.06159980.12320.9384
480.05837480.116750.941625
490.05745230.1149050.942548
500.1233260.2466520.876674
510.1499910.2999820.850009
520.2006490.4012970.799351
530.2287930.4575850.771207
540.5292240.9415520.470776
550.4822260.9644530.517774
560.5249340.9501330.475066
570.5078430.9843130.492157
580.5178870.9642250.482113
590.4877390.9754780.512261
600.4710910.9421820.528909
610.4486420.8972840.551358
620.4706920.9413850.529308
630.4844110.9688220.515589
640.445630.8912590.55437
650.4322570.8645140.567743
660.5121180.9757630.487882
670.471990.9439790.52801
680.5059590.9880820.494041
690.6637670.6724670.336233
700.6144350.771130.385565
710.6257420.7485170.374258
720.6390810.7218380.360919
730.749180.501640.25082
740.8857230.2285540.114277
750.8716580.2566830.128342
760.8534660.2930690.146534
770.8434290.3131430.156571
780.8121930.3756140.187807
790.8165620.3668760.183438
800.7784170.4431660.221583
810.7589620.4820750.241038
820.7892480.4215050.210752
830.7993040.4013910.200696
840.7707240.4585530.229276
850.769640.460720.23036
860.7298830.5402330.270117
870.6991230.6017530.300877
880.657030.685940.34297
890.640960.7180810.35904
900.5909360.8181270.409064
910.7143810.5712380.285619
920.6842890.6314210.315711
930.6284390.7431230.371561
940.6419090.7161820.358091
950.624710.750580.37529
960.5696250.8607510.430375
970.6874760.6250470.312524
980.8712630.2574740.128737
990.9252210.1495570.0747787
1000.8947730.2104540.105227
1010.8578180.2843650.142182
1020.9300380.1399240.0699622
1030.9670540.0658930.0329465
1040.9848450.03030940.0151547
1050.9734880.05302490.0265125
1060.9604690.07906170.0395309
1070.9439720.1120560.056028
1080.9139850.1720310.0860154
1090.8637220.2725550.136278
1100.7888730.4222530.211127
1110.715640.5687190.28436
1120.6134160.7731690.386584
1130.7359880.5280240.264012
1140.8141840.3716320.185816






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level130.12381NOK
5% type I error level290.27619NOK
10% type I error level370.352381NOK

\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 & 13 & 0.12381 & NOK \tabularnewline
5% type I error level & 29 & 0.27619 & NOK \tabularnewline
10% type I error level & 37 & 0.352381 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280317&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]13[/C][C]0.12381[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]29[/C][C]0.27619[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]37[/C][C]0.352381[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280317&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280317&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 level130.12381NOK
5% type I error level290.27619NOK
10% type I error level370.352381NOK


Figures (Output of Computation)

Figure 1
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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):
par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 7 ; 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), 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,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}