FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationMon, 15 Dec 2014 16:40:42 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/15/t1418661659t4awt3y3hi9tfly.htm/, Retrieved Fri, 01 Nov 2024 00:19:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268738, Retrieved Fri, 01 Nov 2024 00:19:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact114

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] [] [2014-12-15 16:39:07] [eee95947b6243a1febfcd5f41483d733]
-    D      [Multiple Regression] [] [2014-12-15 16:40:42] [ef562ec391a3ad5a7cbe41e167f467b9] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
11	8	7	18	12	20	12,9
16	12	9	22	14	18	12,8
24	24	19	22	25	24	7,4
15	16	12	19	15	20	6,7
17	19	16	25	20	20	12,6
19	16	17	28	21	24	14,8
19	15	9	16	15	21	13,3
28	28	28	28	28	28	11,1
26	21	20	21	11	10	8,2
15	18	16	22	22	22	11,4
26	22	22	24	22	19	6,4
24	22	12	26	24	23	12,0
25	25	18	28	23	24	6,3
15	16	12	20	21	25	11,9
21	19	16	26	20	24	9,3
27	26	21	28	25	28	10,0
26	20	17	23	24	22	13,8
22	19	17	24	21	26	10,8
22	23	18	22	25	21	11,7
20	18	15	21	23	26	10,9
22	21	21	21	22	24	9,9
21	20	12	26	25	25	11,5
8	15	6	23	23	24	8,3
22	19	13	21	19	20	11,7
20	19	19	27	21	24	9,0
17	20	14	23	25	23	10,8
23	19	12	23	24	23	10,4
19	18	10	24	28	24	11,8
22	17	11	27	18	23	13,0
17	8	10	25	26	25	10,8
24	22	22	23	22	22	11,3
18	14	11	24	12	24	10,9
24	21	17	20	20	23	13,3
23	20	14	19	24	27	10,1
20	18	16	21	22	23	14,3
22	24	15	18	23	23	9,3
22	19	15	27	19	24	12,5
15	16	10	25	24	26	7,6
19	16	18	21	16	23	9,2
21	15	10	27	19	20	14,5
20	15	16	23	18	18	12,3
18	14	5	27	25	26	12,6
16	16	10	25	17	25	13,0
24	26	16	24	24	18	13,2
19	18	16	23	22	26	7,7
20	17	15	22	14	15	4,35
6	6	4	24	5	27	12,7
15	22	9	19	25	23	18,1
18	20	18	25	21	23	17,85
21	17	12	24	9	22	17,1
23	20	16	28	15	20	19,1
20	23	17	23	23	21	16,1
20	18	14	19	21	25	13,35
18	13	13	19	9	19	18,4
25	22	20	27	24	25	14,7
16	20	16	24	16	24	10,6
20	20	15	26	20	22	12,6
14	13	10	21	15	28	16,2
22	16	16	25	18	22	13,6
20	16	15	19	21	23	14,1
17	15	16	20	21	19	14,5
22	19	19	26	21	21	16,15
22	19	9	27	20	25	14,75
20	24	19	23	24	23	14,8
17	9	7	18	15	28	12,45
22	22	23	23	24	14	12,65
17	15	14	21	18	23	17,35
22	22	10	23	24	24	8,6
21	22	16	22	24	25	18,4
25	24	12	21	15	15	16,1
19	21	7	24	20	26	17,75
24	25	20	26	26	21	15,25
17	26	9	24	26	26	17,65
22	21	12	22	23	23	16,35
17	14	10	20	13	15	17,65
26	28	19	20	16	16	13,6
20	21	11	18	22	20	14,35
19	16	15	18	21	20	14,75
21	16	14	25	11	21	18,25
24	25	11	28	23	28	9,9
21	21	14	23	18	19	16
19	22	15	20	19	21	18,25
13	9	7	22	15	22	16,85
27	24	22	23	21	17	18,95
22	22	11	20	25	26	15,6
21	10	12	24	12	22	17,1
22	22	17	18	24	17	16,1
22	21	13	23	19	16	15,4
21	20	15	21	21	18	15,4
19	17	11	19	19	17	13,35
11	7	7	19	18	25	19,1
19	14	13	25	23	21	7,6
21	23	7	18	23	27	19,1
19	18	11	22	27	23	14,75
8	17	22	5	6	8	19,25
23	20	15	24	22	22	13,6
17	19	15	28	23	28	12,75
25	19	11	27	20	24	9,85
24	23	10	23	23	25	15,25
22	20	18	24	27	23	11,9
23	19	14	25	24	26	16,35
17	16	16	19	12	22	12,4
22	21	16	24	24	22	18,15
21	20	17	28	24	26	17,75
19	20	14	19	19	21	12,35
19	19	10	23	28	21	15,6
16	19	16	23	23	24	19,3
23	20	16	26	19	18	17,1
23	22	17	25	23	26	18,4
20	19	12	24	20	23	19,05
24	23	17	23	18	25	18,55
25	16	11	22	20	20	19,1
20	18	12	26	21	26	12,85
23	23	8	23	25	19	9,5
21	20	17	22	18	21	4,5
23	23	17	22	28	24	13,6
11	13	7	17	9	6	11,7
27	26	18	22	26	21	13,35
16	13	14	26	12	19	17,6
18	10	13	24	12	24	14,05
23	21	19	27	20	21	16,1
24	24	15	22	25	21	13,35
20	21	15	23	24	26	11,85
20	23	8	22	23	24	11,95
14	16	11	20	22	23	13,2
23	26	17	27	28	26	7,7
16	16	12	20	15	20	14,6

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

\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
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268738&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]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268738&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268738&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






Multiple Linear Regression - Estimated Regression Equation
TOTAAL[t] = + 17.5587 + 0.0166578I1[t] + 0.0762983I2[t] -0.094351I3[t] -0.0955159E1[t] -0.121134E2[t] + 0.00417222E3[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOTAAL[t] =  +  17.5587 +  0.0166578I1[t] +  0.0762983I2[t] -0.094351I3[t] -0.0955159E1[t] -0.121134E2[t] +  0.00417222E3[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268738&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOTAAL[t] =  +  17.5587 +  0.0166578I1[t] +  0.0762983I2[t] -0.094351I3[t] -0.0955159E1[t] -0.121134E2[t] +  0.00417222E3[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268738&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268738&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
TOTAAL[t] = + 17.5587 + 0.0166578I1[t] + 0.0762983I2[t] -0.094351I3[t] -0.0955159E1[t] -0.121134E2[t] + 0.00417222E3[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)17.55872.695756.5131.79907e-098.99534e-10
I10.01665780.121680.13690.8913410.445671
I20.07629830.1198410.63670.5255560.262778
I3-0.0943510.0921532-1.0240.3079660.153983
E1-0.09551590.115491-0.8270.4098540.204927
E2-0.1211340.0914608-1.3240.1878740.0939368
E30.004172220.1035020.040310.9679130.483956

\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) & 17.5587 & 2.69575 & 6.513 & 1.79907e-09 & 8.99534e-10 \tabularnewline
I1 & 0.0166578 & 0.12168 & 0.1369 & 0.891341 & 0.445671 \tabularnewline
I2 & 0.0762983 & 0.119841 & 0.6367 & 0.525556 & 0.262778 \tabularnewline
I3 & -0.094351 & 0.0921532 & -1.024 & 0.307966 & 0.153983 \tabularnewline
E1 & -0.0955159 & 0.115491 & -0.827 & 0.409854 & 0.204927 \tabularnewline
E2 & -0.121134 & 0.0914608 & -1.324 & 0.187874 & 0.0939368 \tabularnewline
E3 & 0.00417222 & 0.103502 & 0.04031 & 0.967913 & 0.483956 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268738&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]17.5587[/C][C]2.69575[/C][C]6.513[/C][C]1.79907e-09[/C][C]8.99534e-10[/C][/ROW]
[ROW][C]I1[/C][C]0.0166578[/C][C]0.12168[/C][C]0.1369[/C][C]0.891341[/C][C]0.445671[/C][/ROW]
[ROW][C]I2[/C][C]0.0762983[/C][C]0.119841[/C][C]0.6367[/C][C]0.525556[/C][C]0.262778[/C][/ROW]
[ROW][C]I3[/C][C]-0.094351[/C][C]0.0921532[/C][C]-1.024[/C][C]0.307966[/C][C]0.153983[/C][/ROW]
[ROW][C]E1[/C][C]-0.0955159[/C][C]0.115491[/C][C]-0.827[/C][C]0.409854[/C][C]0.204927[/C][/ROW]
[ROW][C]E2[/C][C]-0.121134[/C][C]0.0914608[/C][C]-1.324[/C][C]0.187874[/C][C]0.0939368[/C][/ROW]
[ROW][C]E3[/C][C]0.00417222[/C][C]0.103502[/C][C]0.04031[/C][C]0.967913[/C][C]0.483956[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268738&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268738&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)17.55872.695756.5131.79907e-098.99534e-10
I10.01665780.121680.13690.8913410.445671
I20.07629830.1198410.63670.5255560.262778
I3-0.0943510.0921532-1.0240.3079660.153983
E1-0.09551590.115491-0.8270.4098540.204927
E2-0.1211340.0914608-1.3240.1878740.0939368
E30.004172220.1035020.040310.9679130.483956






Multiple Linear Regression - Regression Statistics
Multiple R0.203241
R-squared0.0413071
Adjusted R-squared-0.00662758
F-TEST (value)0.861737
F-TEST (DF numerator)6
F-TEST (DF denominator)120
p-value0.525261
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.50716
Sum Squared Residuals1476.02

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.203241 \tabularnewline
R-squared & 0.0413071 \tabularnewline
Adjusted R-squared & -0.00662758 \tabularnewline
F-TEST (value) & 0.861737 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 120 \tabularnewline
p-value & 0.525261 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.50716 \tabularnewline
Sum Squared Residuals & 1476.02 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268738&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.203241[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0413071[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.00662758[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.861737[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]120[/C][/ROW]
[ROW][C]p-value[/C][C]0.525261[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.50716[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1476.02[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268738&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268738&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 R0.203241
R-squared0.0413071
Adjusted R-squared-0.00662758
F-TEST (value)0.861737
F-TEST (DF numerator)6
F-TEST (DF denominator)120
p-value0.525261
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.50716
Sum Squared Residuals1476.02






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.914.6024-1.7024
212.814.1695-1.3695
37.412.9674-5.56739
46.714.3487-7.64874
512.613.0548-0.454781
614.812.37392.42614
713.314.9128-1.61285
811.111.5702-0.470244
98.214.4104-6.21044
1011.412.9978-1.59779
116.412.7166-6.31656
121213.2101-1.21015
136.312.8239-6.52387
1411.913.5473-1.64728
159.313.0426-3.74258
161012.4248-2.42485
1713.812.90150.898513
1810.813.0431-2.24313
1911.712.9396-1.23961
2010.913.1665-2.2665
219.912.9754-3.0754
2211.512.8948-1.39479
238.313.3875-5.0875
2411.713.9243-2.22432
25912.5262-3.52622
2610.812.9177-2.11766
2710.413.2511-2.85114
2811.812.721-0.921034
291313.521-0.520981
3010.812.0757-1.27566
3111.312.7913-1.49128
3210.914.243-3.34298
3313.313.7197-0.419727
3410.113.5375-3.43749
3514.313.18081.11923
369.313.9316-4.63164
3712.513.1792-0.679212
387.612.8992-5.29917
399.213.5496-4.34962
4014.513.31241.18757
4112.313.2245-0.924517
4212.612.9561-0.356137
431313.7596-0.759598
4413.213.3081-0.108107
457.712.9856-5.28559
464.3514.039-9.689
4712.714.9536-2.25362
4818.113.89084.20924
4917.8512.85044.99958
5017.114.78262.31744
5119.113.55015.54985
5216.113.14742.9526
5313.3513.69-0.33998
5418.414.79813.6019
5514.712.38482.31517
5610.613.7112-3.11116
5712.613.1882-0.588232
5816.214.13422.06576
5913.613.15980.440212
6014.113.43470.665312
6114.513.10191.39814
6216.1512.64253.50746
6314.7513.62841.12164
6414.812.92221.8778
6512.4514.4486-1.99862
6612.6512.3880.262031
6717.3513.57513.77486
688.613.6563-5.05625
6918.413.17325.22682
7016.114.91381.18619
7117.7514.21043.5396
7215.2512.43362.81638
7317.6513.64314.00693
7416.3513.60372.74627
7517.6514.54413.10595
7613.614.5538-0.95376
7714.3514.15540.194552
7814.7513.5011.24897
7918.2514.17564.0744
809.913.4844-3.58436
811613.89182.10816
8218.2514.01424.23577
8316.8513.97492.87511
8418.9513.09415.85588
8515.613.73571.86434
8617.113.88513.21493
8716.113.44422.65583
8815.413.86921.5308
8915.413.54461.85535
9013.3514.089-0.738968
9119.113.72465.37536
927.612.6304-5.03043
9319.114.61024.48982
9414.7512.93471.81532
9519.2515.74233.50771
9613.613.1870.413032
9712.7512.53260.217442
989.8513.4855-3.63545
9915.2513.89121.35883
10011.912.2858-0.385758
10116.3512.88393.46607
10212.414.3764-1.9764
10318.1512.915.24001
10417.7512.35735.39269
10512.3514.0515-1.7015
10615.612.88032.71967
10719.312.88246.41756
10817.113.24833.8517
10918.412.95095.4491
11019.0513.59025.45981
11118.5513.83644.71361
11219.113.71755.38255
11312.8513.2142-0.364243
1149.513.7959-4.29592
1154.513.6363-9.13635
11613.612.69970.900269
11711.715.3844-3.6844
11813.3513.13070.219342
11917.613.63843.96158
12014.0513.74910.300913
12116.112.83743.26259
12213.3513.33230.0177245
12311.8513.0832-1.23323
12411.9514.1046-2.15459
12513.213.4955-0.295495
1267.712.4594-4.75939
12714.614.26990.330117

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 14.6024 & -1.7024 \tabularnewline
2 & 12.8 & 14.1695 & -1.3695 \tabularnewline
3 & 7.4 & 12.9674 & -5.56739 \tabularnewline
4 & 6.7 & 14.3487 & -7.64874 \tabularnewline
5 & 12.6 & 13.0548 & -0.454781 \tabularnewline
6 & 14.8 & 12.3739 & 2.42614 \tabularnewline
7 & 13.3 & 14.9128 & -1.61285 \tabularnewline
8 & 11.1 & 11.5702 & -0.470244 \tabularnewline
9 & 8.2 & 14.4104 & -6.21044 \tabularnewline
10 & 11.4 & 12.9978 & -1.59779 \tabularnewline
11 & 6.4 & 12.7166 & -6.31656 \tabularnewline
12 & 12 & 13.2101 & -1.21015 \tabularnewline
13 & 6.3 & 12.8239 & -6.52387 \tabularnewline
14 & 11.9 & 13.5473 & -1.64728 \tabularnewline
15 & 9.3 & 13.0426 & -3.74258 \tabularnewline
16 & 10 & 12.4248 & -2.42485 \tabularnewline
17 & 13.8 & 12.9015 & 0.898513 \tabularnewline
18 & 10.8 & 13.0431 & -2.24313 \tabularnewline
19 & 11.7 & 12.9396 & -1.23961 \tabularnewline
20 & 10.9 & 13.1665 & -2.2665 \tabularnewline
21 & 9.9 & 12.9754 & -3.0754 \tabularnewline
22 & 11.5 & 12.8948 & -1.39479 \tabularnewline
23 & 8.3 & 13.3875 & -5.0875 \tabularnewline
24 & 11.7 & 13.9243 & -2.22432 \tabularnewline
25 & 9 & 12.5262 & -3.52622 \tabularnewline
26 & 10.8 & 12.9177 & -2.11766 \tabularnewline
27 & 10.4 & 13.2511 & -2.85114 \tabularnewline
28 & 11.8 & 12.721 & -0.921034 \tabularnewline
29 & 13 & 13.521 & -0.520981 \tabularnewline
30 & 10.8 & 12.0757 & -1.27566 \tabularnewline
31 & 11.3 & 12.7913 & -1.49128 \tabularnewline
32 & 10.9 & 14.243 & -3.34298 \tabularnewline
33 & 13.3 & 13.7197 & -0.419727 \tabularnewline
34 & 10.1 & 13.5375 & -3.43749 \tabularnewline
35 & 14.3 & 13.1808 & 1.11923 \tabularnewline
36 & 9.3 & 13.9316 & -4.63164 \tabularnewline
37 & 12.5 & 13.1792 & -0.679212 \tabularnewline
38 & 7.6 & 12.8992 & -5.29917 \tabularnewline
39 & 9.2 & 13.5496 & -4.34962 \tabularnewline
40 & 14.5 & 13.3124 & 1.18757 \tabularnewline
41 & 12.3 & 13.2245 & -0.924517 \tabularnewline
42 & 12.6 & 12.9561 & -0.356137 \tabularnewline
43 & 13 & 13.7596 & -0.759598 \tabularnewline
44 & 13.2 & 13.3081 & -0.108107 \tabularnewline
45 & 7.7 & 12.9856 & -5.28559 \tabularnewline
46 & 4.35 & 14.039 & -9.689 \tabularnewline
47 & 12.7 & 14.9536 & -2.25362 \tabularnewline
48 & 18.1 & 13.8908 & 4.20924 \tabularnewline
49 & 17.85 & 12.8504 & 4.99958 \tabularnewline
50 & 17.1 & 14.7826 & 2.31744 \tabularnewline
51 & 19.1 & 13.5501 & 5.54985 \tabularnewline
52 & 16.1 & 13.1474 & 2.9526 \tabularnewline
53 & 13.35 & 13.69 & -0.33998 \tabularnewline
54 & 18.4 & 14.7981 & 3.6019 \tabularnewline
55 & 14.7 & 12.3848 & 2.31517 \tabularnewline
56 & 10.6 & 13.7112 & -3.11116 \tabularnewline
57 & 12.6 & 13.1882 & -0.588232 \tabularnewline
58 & 16.2 & 14.1342 & 2.06576 \tabularnewline
59 & 13.6 & 13.1598 & 0.440212 \tabularnewline
60 & 14.1 & 13.4347 & 0.665312 \tabularnewline
61 & 14.5 & 13.1019 & 1.39814 \tabularnewline
62 & 16.15 & 12.6425 & 3.50746 \tabularnewline
63 & 14.75 & 13.6284 & 1.12164 \tabularnewline
64 & 14.8 & 12.9222 & 1.8778 \tabularnewline
65 & 12.45 & 14.4486 & -1.99862 \tabularnewline
66 & 12.65 & 12.388 & 0.262031 \tabularnewline
67 & 17.35 & 13.5751 & 3.77486 \tabularnewline
68 & 8.6 & 13.6563 & -5.05625 \tabularnewline
69 & 18.4 & 13.1732 & 5.22682 \tabularnewline
70 & 16.1 & 14.9138 & 1.18619 \tabularnewline
71 & 17.75 & 14.2104 & 3.5396 \tabularnewline
72 & 15.25 & 12.4336 & 2.81638 \tabularnewline
73 & 17.65 & 13.6431 & 4.00693 \tabularnewline
74 & 16.35 & 13.6037 & 2.74627 \tabularnewline
75 & 17.65 & 14.5441 & 3.10595 \tabularnewline
76 & 13.6 & 14.5538 & -0.95376 \tabularnewline
77 & 14.35 & 14.1554 & 0.194552 \tabularnewline
78 & 14.75 & 13.501 & 1.24897 \tabularnewline
79 & 18.25 & 14.1756 & 4.0744 \tabularnewline
80 & 9.9 & 13.4844 & -3.58436 \tabularnewline
81 & 16 & 13.8918 & 2.10816 \tabularnewline
82 & 18.25 & 14.0142 & 4.23577 \tabularnewline
83 & 16.85 & 13.9749 & 2.87511 \tabularnewline
84 & 18.95 & 13.0941 & 5.85588 \tabularnewline
85 & 15.6 & 13.7357 & 1.86434 \tabularnewline
86 & 17.1 & 13.8851 & 3.21493 \tabularnewline
87 & 16.1 & 13.4442 & 2.65583 \tabularnewline
88 & 15.4 & 13.8692 & 1.5308 \tabularnewline
89 & 15.4 & 13.5446 & 1.85535 \tabularnewline
90 & 13.35 & 14.089 & -0.738968 \tabularnewline
91 & 19.1 & 13.7246 & 5.37536 \tabularnewline
92 & 7.6 & 12.6304 & -5.03043 \tabularnewline
93 & 19.1 & 14.6102 & 4.48982 \tabularnewline
94 & 14.75 & 12.9347 & 1.81532 \tabularnewline
95 & 19.25 & 15.7423 & 3.50771 \tabularnewline
96 & 13.6 & 13.187 & 0.413032 \tabularnewline
97 & 12.75 & 12.5326 & 0.217442 \tabularnewline
98 & 9.85 & 13.4855 & -3.63545 \tabularnewline
99 & 15.25 & 13.8912 & 1.35883 \tabularnewline
100 & 11.9 & 12.2858 & -0.385758 \tabularnewline
101 & 16.35 & 12.8839 & 3.46607 \tabularnewline
102 & 12.4 & 14.3764 & -1.9764 \tabularnewline
103 & 18.15 & 12.91 & 5.24001 \tabularnewline
104 & 17.75 & 12.3573 & 5.39269 \tabularnewline
105 & 12.35 & 14.0515 & -1.7015 \tabularnewline
106 & 15.6 & 12.8803 & 2.71967 \tabularnewline
107 & 19.3 & 12.8824 & 6.41756 \tabularnewline
108 & 17.1 & 13.2483 & 3.8517 \tabularnewline
109 & 18.4 & 12.9509 & 5.4491 \tabularnewline
110 & 19.05 & 13.5902 & 5.45981 \tabularnewline
111 & 18.55 & 13.8364 & 4.71361 \tabularnewline
112 & 19.1 & 13.7175 & 5.38255 \tabularnewline
113 & 12.85 & 13.2142 & -0.364243 \tabularnewline
114 & 9.5 & 13.7959 & -4.29592 \tabularnewline
115 & 4.5 & 13.6363 & -9.13635 \tabularnewline
116 & 13.6 & 12.6997 & 0.900269 \tabularnewline
117 & 11.7 & 15.3844 & -3.6844 \tabularnewline
118 & 13.35 & 13.1307 & 0.219342 \tabularnewline
119 & 17.6 & 13.6384 & 3.96158 \tabularnewline
120 & 14.05 & 13.7491 & 0.300913 \tabularnewline
121 & 16.1 & 12.8374 & 3.26259 \tabularnewline
122 & 13.35 & 13.3323 & 0.0177245 \tabularnewline
123 & 11.85 & 13.0832 & -1.23323 \tabularnewline
124 & 11.95 & 14.1046 & -2.15459 \tabularnewline
125 & 13.2 & 13.4955 & -0.295495 \tabularnewline
126 & 7.7 & 12.4594 & -4.75939 \tabularnewline
127 & 14.6 & 14.2699 & 0.330117 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268738&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]14.6024[/C][C]-1.7024[/C][/ROW]
[ROW][C]2[/C][C]12.8[/C][C]14.1695[/C][C]-1.3695[/C][/ROW]
[ROW][C]3[/C][C]7.4[/C][C]12.9674[/C][C]-5.56739[/C][/ROW]
[ROW][C]4[/C][C]6.7[/C][C]14.3487[/C][C]-7.64874[/C][/ROW]
[ROW][C]5[/C][C]12.6[/C][C]13.0548[/C][C]-0.454781[/C][/ROW]
[ROW][C]6[/C][C]14.8[/C][C]12.3739[/C][C]2.42614[/C][/ROW]
[ROW][C]7[/C][C]13.3[/C][C]14.9128[/C][C]-1.61285[/C][/ROW]
[ROW][C]8[/C][C]11.1[/C][C]11.5702[/C][C]-0.470244[/C][/ROW]
[ROW][C]9[/C][C]8.2[/C][C]14.4104[/C][C]-6.21044[/C][/ROW]
[ROW][C]10[/C][C]11.4[/C][C]12.9978[/C][C]-1.59779[/C][/ROW]
[ROW][C]11[/C][C]6.4[/C][C]12.7166[/C][C]-6.31656[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]13.2101[/C][C]-1.21015[/C][/ROW]
[ROW][C]13[/C][C]6.3[/C][C]12.8239[/C][C]-6.52387[/C][/ROW]
[ROW][C]14[/C][C]11.9[/C][C]13.5473[/C][C]-1.64728[/C][/ROW]
[ROW][C]15[/C][C]9.3[/C][C]13.0426[/C][C]-3.74258[/C][/ROW]
[ROW][C]16[/C][C]10[/C][C]12.4248[/C][C]-2.42485[/C][/ROW]
[ROW][C]17[/C][C]13.8[/C][C]12.9015[/C][C]0.898513[/C][/ROW]
[ROW][C]18[/C][C]10.8[/C][C]13.0431[/C][C]-2.24313[/C][/ROW]
[ROW][C]19[/C][C]11.7[/C][C]12.9396[/C][C]-1.23961[/C][/ROW]
[ROW][C]20[/C][C]10.9[/C][C]13.1665[/C][C]-2.2665[/C][/ROW]
[ROW][C]21[/C][C]9.9[/C][C]12.9754[/C][C]-3.0754[/C][/ROW]
[ROW][C]22[/C][C]11.5[/C][C]12.8948[/C][C]-1.39479[/C][/ROW]
[ROW][C]23[/C][C]8.3[/C][C]13.3875[/C][C]-5.0875[/C][/ROW]
[ROW][C]24[/C][C]11.7[/C][C]13.9243[/C][C]-2.22432[/C][/ROW]
[ROW][C]25[/C][C]9[/C][C]12.5262[/C][C]-3.52622[/C][/ROW]
[ROW][C]26[/C][C]10.8[/C][C]12.9177[/C][C]-2.11766[/C][/ROW]
[ROW][C]27[/C][C]10.4[/C][C]13.2511[/C][C]-2.85114[/C][/ROW]
[ROW][C]28[/C][C]11.8[/C][C]12.721[/C][C]-0.921034[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]13.521[/C][C]-0.520981[/C][/ROW]
[ROW][C]30[/C][C]10.8[/C][C]12.0757[/C][C]-1.27566[/C][/ROW]
[ROW][C]31[/C][C]11.3[/C][C]12.7913[/C][C]-1.49128[/C][/ROW]
[ROW][C]32[/C][C]10.9[/C][C]14.243[/C][C]-3.34298[/C][/ROW]
[ROW][C]33[/C][C]13.3[/C][C]13.7197[/C][C]-0.419727[/C][/ROW]
[ROW][C]34[/C][C]10.1[/C][C]13.5375[/C][C]-3.43749[/C][/ROW]
[ROW][C]35[/C][C]14.3[/C][C]13.1808[/C][C]1.11923[/C][/ROW]
[ROW][C]36[/C][C]9.3[/C][C]13.9316[/C][C]-4.63164[/C][/ROW]
[ROW][C]37[/C][C]12.5[/C][C]13.1792[/C][C]-0.679212[/C][/ROW]
[ROW][C]38[/C][C]7.6[/C][C]12.8992[/C][C]-5.29917[/C][/ROW]
[ROW][C]39[/C][C]9.2[/C][C]13.5496[/C][C]-4.34962[/C][/ROW]
[ROW][C]40[/C][C]14.5[/C][C]13.3124[/C][C]1.18757[/C][/ROW]
[ROW][C]41[/C][C]12.3[/C][C]13.2245[/C][C]-0.924517[/C][/ROW]
[ROW][C]42[/C][C]12.6[/C][C]12.9561[/C][C]-0.356137[/C][/ROW]
[ROW][C]43[/C][C]13[/C][C]13.7596[/C][C]-0.759598[/C][/ROW]
[ROW][C]44[/C][C]13.2[/C][C]13.3081[/C][C]-0.108107[/C][/ROW]
[ROW][C]45[/C][C]7.7[/C][C]12.9856[/C][C]-5.28559[/C][/ROW]
[ROW][C]46[/C][C]4.35[/C][C]14.039[/C][C]-9.689[/C][/ROW]
[ROW][C]47[/C][C]12.7[/C][C]14.9536[/C][C]-2.25362[/C][/ROW]
[ROW][C]48[/C][C]18.1[/C][C]13.8908[/C][C]4.20924[/C][/ROW]
[ROW][C]49[/C][C]17.85[/C][C]12.8504[/C][C]4.99958[/C][/ROW]
[ROW][C]50[/C][C]17.1[/C][C]14.7826[/C][C]2.31744[/C][/ROW]
[ROW][C]51[/C][C]19.1[/C][C]13.5501[/C][C]5.54985[/C][/ROW]
[ROW][C]52[/C][C]16.1[/C][C]13.1474[/C][C]2.9526[/C][/ROW]
[ROW][C]53[/C][C]13.35[/C][C]13.69[/C][C]-0.33998[/C][/ROW]
[ROW][C]54[/C][C]18.4[/C][C]14.7981[/C][C]3.6019[/C][/ROW]
[ROW][C]55[/C][C]14.7[/C][C]12.3848[/C][C]2.31517[/C][/ROW]
[ROW][C]56[/C][C]10.6[/C][C]13.7112[/C][C]-3.11116[/C][/ROW]
[ROW][C]57[/C][C]12.6[/C][C]13.1882[/C][C]-0.588232[/C][/ROW]
[ROW][C]58[/C][C]16.2[/C][C]14.1342[/C][C]2.06576[/C][/ROW]
[ROW][C]59[/C][C]13.6[/C][C]13.1598[/C][C]0.440212[/C][/ROW]
[ROW][C]60[/C][C]14.1[/C][C]13.4347[/C][C]0.665312[/C][/ROW]
[ROW][C]61[/C][C]14.5[/C][C]13.1019[/C][C]1.39814[/C][/ROW]
[ROW][C]62[/C][C]16.15[/C][C]12.6425[/C][C]3.50746[/C][/ROW]
[ROW][C]63[/C][C]14.75[/C][C]13.6284[/C][C]1.12164[/C][/ROW]
[ROW][C]64[/C][C]14.8[/C][C]12.9222[/C][C]1.8778[/C][/ROW]
[ROW][C]65[/C][C]12.45[/C][C]14.4486[/C][C]-1.99862[/C][/ROW]
[ROW][C]66[/C][C]12.65[/C][C]12.388[/C][C]0.262031[/C][/ROW]
[ROW][C]67[/C][C]17.35[/C][C]13.5751[/C][C]3.77486[/C][/ROW]
[ROW][C]68[/C][C]8.6[/C][C]13.6563[/C][C]-5.05625[/C][/ROW]
[ROW][C]69[/C][C]18.4[/C][C]13.1732[/C][C]5.22682[/C][/ROW]
[ROW][C]70[/C][C]16.1[/C][C]14.9138[/C][C]1.18619[/C][/ROW]
[ROW][C]71[/C][C]17.75[/C][C]14.2104[/C][C]3.5396[/C][/ROW]
[ROW][C]72[/C][C]15.25[/C][C]12.4336[/C][C]2.81638[/C][/ROW]
[ROW][C]73[/C][C]17.65[/C][C]13.6431[/C][C]4.00693[/C][/ROW]
[ROW][C]74[/C][C]16.35[/C][C]13.6037[/C][C]2.74627[/C][/ROW]
[ROW][C]75[/C][C]17.65[/C][C]14.5441[/C][C]3.10595[/C][/ROW]
[ROW][C]76[/C][C]13.6[/C][C]14.5538[/C][C]-0.95376[/C][/ROW]
[ROW][C]77[/C][C]14.35[/C][C]14.1554[/C][C]0.194552[/C][/ROW]
[ROW][C]78[/C][C]14.75[/C][C]13.501[/C][C]1.24897[/C][/ROW]
[ROW][C]79[/C][C]18.25[/C][C]14.1756[/C][C]4.0744[/C][/ROW]
[ROW][C]80[/C][C]9.9[/C][C]13.4844[/C][C]-3.58436[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]13.8918[/C][C]2.10816[/C][/ROW]
[ROW][C]82[/C][C]18.25[/C][C]14.0142[/C][C]4.23577[/C][/ROW]
[ROW][C]83[/C][C]16.85[/C][C]13.9749[/C][C]2.87511[/C][/ROW]
[ROW][C]84[/C][C]18.95[/C][C]13.0941[/C][C]5.85588[/C][/ROW]
[ROW][C]85[/C][C]15.6[/C][C]13.7357[/C][C]1.86434[/C][/ROW]
[ROW][C]86[/C][C]17.1[/C][C]13.8851[/C][C]3.21493[/C][/ROW]
[ROW][C]87[/C][C]16.1[/C][C]13.4442[/C][C]2.65583[/C][/ROW]
[ROW][C]88[/C][C]15.4[/C][C]13.8692[/C][C]1.5308[/C][/ROW]
[ROW][C]89[/C][C]15.4[/C][C]13.5446[/C][C]1.85535[/C][/ROW]
[ROW][C]90[/C][C]13.35[/C][C]14.089[/C][C]-0.738968[/C][/ROW]
[ROW][C]91[/C][C]19.1[/C][C]13.7246[/C][C]5.37536[/C][/ROW]
[ROW][C]92[/C][C]7.6[/C][C]12.6304[/C][C]-5.03043[/C][/ROW]
[ROW][C]93[/C][C]19.1[/C][C]14.6102[/C][C]4.48982[/C][/ROW]
[ROW][C]94[/C][C]14.75[/C][C]12.9347[/C][C]1.81532[/C][/ROW]
[ROW][C]95[/C][C]19.25[/C][C]15.7423[/C][C]3.50771[/C][/ROW]
[ROW][C]96[/C][C]13.6[/C][C]13.187[/C][C]0.413032[/C][/ROW]
[ROW][C]97[/C][C]12.75[/C][C]12.5326[/C][C]0.217442[/C][/ROW]
[ROW][C]98[/C][C]9.85[/C][C]13.4855[/C][C]-3.63545[/C][/ROW]
[ROW][C]99[/C][C]15.25[/C][C]13.8912[/C][C]1.35883[/C][/ROW]
[ROW][C]100[/C][C]11.9[/C][C]12.2858[/C][C]-0.385758[/C][/ROW]
[ROW][C]101[/C][C]16.35[/C][C]12.8839[/C][C]3.46607[/C][/ROW]
[ROW][C]102[/C][C]12.4[/C][C]14.3764[/C][C]-1.9764[/C][/ROW]
[ROW][C]103[/C][C]18.15[/C][C]12.91[/C][C]5.24001[/C][/ROW]
[ROW][C]104[/C][C]17.75[/C][C]12.3573[/C][C]5.39269[/C][/ROW]
[ROW][C]105[/C][C]12.35[/C][C]14.0515[/C][C]-1.7015[/C][/ROW]
[ROW][C]106[/C][C]15.6[/C][C]12.8803[/C][C]2.71967[/C][/ROW]
[ROW][C]107[/C][C]19.3[/C][C]12.8824[/C][C]6.41756[/C][/ROW]
[ROW][C]108[/C][C]17.1[/C][C]13.2483[/C][C]3.8517[/C][/ROW]
[ROW][C]109[/C][C]18.4[/C][C]12.9509[/C][C]5.4491[/C][/ROW]
[ROW][C]110[/C][C]19.05[/C][C]13.5902[/C][C]5.45981[/C][/ROW]
[ROW][C]111[/C][C]18.55[/C][C]13.8364[/C][C]4.71361[/C][/ROW]
[ROW][C]112[/C][C]19.1[/C][C]13.7175[/C][C]5.38255[/C][/ROW]
[ROW][C]113[/C][C]12.85[/C][C]13.2142[/C][C]-0.364243[/C][/ROW]
[ROW][C]114[/C][C]9.5[/C][C]13.7959[/C][C]-4.29592[/C][/ROW]
[ROW][C]115[/C][C]4.5[/C][C]13.6363[/C][C]-9.13635[/C][/ROW]
[ROW][C]116[/C][C]13.6[/C][C]12.6997[/C][C]0.900269[/C][/ROW]
[ROW][C]117[/C][C]11.7[/C][C]15.3844[/C][C]-3.6844[/C][/ROW]
[ROW][C]118[/C][C]13.35[/C][C]13.1307[/C][C]0.219342[/C][/ROW]
[ROW][C]119[/C][C]17.6[/C][C]13.6384[/C][C]3.96158[/C][/ROW]
[ROW][C]120[/C][C]14.05[/C][C]13.7491[/C][C]0.300913[/C][/ROW]
[ROW][C]121[/C][C]16.1[/C][C]12.8374[/C][C]3.26259[/C][/ROW]
[ROW][C]122[/C][C]13.35[/C][C]13.3323[/C][C]0.0177245[/C][/ROW]
[ROW][C]123[/C][C]11.85[/C][C]13.0832[/C][C]-1.23323[/C][/ROW]
[ROW][C]124[/C][C]11.95[/C][C]14.1046[/C][C]-2.15459[/C][/ROW]
[ROW][C]125[/C][C]13.2[/C][C]13.4955[/C][C]-0.295495[/C][/ROW]
[ROW][C]126[/C][C]7.7[/C][C]12.4594[/C][C]-4.75939[/C][/ROW]
[ROW][C]127[/C][C]14.6[/C][C]14.2699[/C][C]0.330117[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268738&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268738&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
112.914.6024-1.7024
212.814.1695-1.3695
37.412.9674-5.56739
46.714.3487-7.64874
512.613.0548-0.454781
614.812.37392.42614
713.314.9128-1.61285
811.111.5702-0.470244
98.214.4104-6.21044
1011.412.9978-1.59779
116.412.7166-6.31656
121213.2101-1.21015
136.312.8239-6.52387
1411.913.5473-1.64728
159.313.0426-3.74258
161012.4248-2.42485
1713.812.90150.898513
1810.813.0431-2.24313
1911.712.9396-1.23961
2010.913.1665-2.2665
219.912.9754-3.0754
2211.512.8948-1.39479
238.313.3875-5.0875
2411.713.9243-2.22432
25912.5262-3.52622
2610.812.9177-2.11766
2710.413.2511-2.85114
2811.812.721-0.921034
291313.521-0.520981
3010.812.0757-1.27566
3111.312.7913-1.49128
3210.914.243-3.34298
3313.313.7197-0.419727
3410.113.5375-3.43749
3514.313.18081.11923
369.313.9316-4.63164
3712.513.1792-0.679212
387.612.8992-5.29917
399.213.5496-4.34962
4014.513.31241.18757
4112.313.2245-0.924517
4212.612.9561-0.356137
431313.7596-0.759598
4413.213.3081-0.108107
457.712.9856-5.28559
464.3514.039-9.689
4712.714.9536-2.25362
4818.113.89084.20924
4917.8512.85044.99958
5017.114.78262.31744
5119.113.55015.54985
5216.113.14742.9526
5313.3513.69-0.33998
5418.414.79813.6019
5514.712.38482.31517
5610.613.7112-3.11116
5712.613.1882-0.588232
5816.214.13422.06576
5913.613.15980.440212
6014.113.43470.665312
6114.513.10191.39814
6216.1512.64253.50746
6314.7513.62841.12164
6414.812.92221.8778
6512.4514.4486-1.99862
6612.6512.3880.262031
6717.3513.57513.77486
688.613.6563-5.05625
6918.413.17325.22682
7016.114.91381.18619
7117.7514.21043.5396
7215.2512.43362.81638
7317.6513.64314.00693
7416.3513.60372.74627
7517.6514.54413.10595
7613.614.5538-0.95376
7714.3514.15540.194552
7814.7513.5011.24897
7918.2514.17564.0744
809.913.4844-3.58436
811613.89182.10816
8218.2514.01424.23577
8316.8513.97492.87511
8418.9513.09415.85588
8515.613.73571.86434
8617.113.88513.21493
8716.113.44422.65583
8815.413.86921.5308
8915.413.54461.85535
9013.3514.089-0.738968
9119.113.72465.37536
927.612.6304-5.03043
9319.114.61024.48982
9414.7512.93471.81532
9519.2515.74233.50771
9613.613.1870.413032
9712.7512.53260.217442
989.8513.4855-3.63545
9915.2513.89121.35883
10011.912.2858-0.385758
10116.3512.88393.46607
10212.414.3764-1.9764
10318.1512.915.24001
10417.7512.35735.39269
10512.3514.0515-1.7015
10615.612.88032.71967
10719.312.88246.41756
10817.113.24833.8517
10918.412.95095.4491
11019.0513.59025.45981
11118.5513.83644.71361
11219.113.71755.38255
11312.8513.2142-0.364243
1149.513.7959-4.29592
1154.513.6363-9.13635
11613.612.69970.900269
11711.715.3844-3.6844
11813.3513.13070.219342
11917.613.63843.96158
12014.0513.74910.300913
12116.112.83743.26259
12213.3513.33230.0177245
12311.8513.0832-1.23323
12411.9514.1046-2.15459
12513.213.4955-0.295495
1267.712.4594-4.75939
12714.614.26990.330117






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.4257010.8514010.574299
110.4451050.8902090.554895
120.3100820.6201630.689918
130.418130.8362590.58187
140.3036210.6072420.696379
150.2561810.5123620.743819
160.1839470.3678930.816053
170.1316980.2633960.868302
180.08956690.1791340.910433
190.06806930.1361390.931931
200.04778040.09556090.95222
210.03079450.06158890.969206
220.01928710.03857430.980713
230.02101380.04202760.978986
240.01408640.02817280.985914
250.01177540.02355090.988225
260.007238680.01447740.992761
270.006674090.01334820.993326
280.004295950.008591910.995704
290.00267650.005353010.997323
300.008742920.01748580.991257
310.006305070.01261010.993695
320.004396460.008792910.995604
330.003913980.007827960.996086
340.003196050.00639210.996804
350.003631810.007263630.996368
360.002976630.005953260.997023
370.002095490.004190980.997905
380.003611120.007222240.996389
390.004001270.008002540.995999
400.003449190.006898370.996551
410.002285960.004571920.997714
420.001390.002780.99861
430.00108940.002178810.998911
440.001303210.002606430.998697
450.00233130.004662610.997669
460.03118850.0623770.968812
470.02694640.05389290.973054
480.0960110.1920220.903989
490.2278920.4557840.772108
500.2782730.5565460.721727
510.4315890.8631780.568411
520.4676250.935250.532375
530.442920.8858410.55708
540.5243050.951390.475695
550.5088460.9823070.491154
560.5161820.9676350.483818
570.4729920.9459850.527008
580.4544120.9088250.545588
590.4153250.830650.584675
600.3903260.7806530.609674
610.3781860.7563720.621814
620.3890090.7780180.610991
630.3452080.6904150.654792
640.329480.658960.67052
650.3290360.6580720.670964
660.2932840.5865680.706716
670.3096570.6193140.690343
680.3627810.7255620.637219
690.438130.8762610.56187
700.4123520.8247040.587648
710.4249570.8499140.575043
720.4056520.8113040.594348
730.4542590.9085170.545741
740.4341370.8682730.565863
750.434550.86910.56545
760.3872730.7745460.612727
770.3381740.6763470.661826
780.309840.619680.69016
790.3166430.6332860.683357
800.3151190.6302370.684881
810.2842960.5685920.715704
820.3002080.6004170.699792
830.2781610.5563210.721839
840.3342030.6684070.665797
850.2931650.5863290.706835
860.2662310.5324630.733769
870.2371840.4743690.762816
880.2087830.4175660.791217
890.1769680.3539360.823032
900.1433430.2866860.856657
910.1533450.306690.846655
920.2285580.4571170.771442
930.2959590.5919190.704041
940.248820.4976390.75118
950.2640440.5280870.735956
960.2186740.4373480.781326
970.1868960.3737920.813104
980.259150.51830.74085
990.2168720.4337440.783128
1000.2162940.4325890.783706
1010.182140.3642810.81786
1020.1449420.2898830.855058
1030.1487040.2974080.851296
1040.1364530.2729060.863547
1050.1015550.2031090.898445
1060.07873070.1574610.921269
1070.1610270.3220530.838973
1080.1351420.2702830.864858
1090.1621680.3243360.837832
1100.2314060.4628110.768594
1110.3968450.793690.603155
1120.3456580.6913150.654342
1130.2541660.5083310.745834
1140.3211110.6422220.678889
1150.7304430.5391140.269557
1160.6093690.7812620.390631
1170.859650.2806990.14035

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.425701 & 0.851401 & 0.574299 \tabularnewline
11 & 0.445105 & 0.890209 & 0.554895 \tabularnewline
12 & 0.310082 & 0.620163 & 0.689918 \tabularnewline
13 & 0.41813 & 0.836259 & 0.58187 \tabularnewline
14 & 0.303621 & 0.607242 & 0.696379 \tabularnewline
15 & 0.256181 & 0.512362 & 0.743819 \tabularnewline
16 & 0.183947 & 0.367893 & 0.816053 \tabularnewline
17 & 0.131698 & 0.263396 & 0.868302 \tabularnewline
18 & 0.0895669 & 0.179134 & 0.910433 \tabularnewline
19 & 0.0680693 & 0.136139 & 0.931931 \tabularnewline
20 & 0.0477804 & 0.0955609 & 0.95222 \tabularnewline
21 & 0.0307945 & 0.0615889 & 0.969206 \tabularnewline
22 & 0.0192871 & 0.0385743 & 0.980713 \tabularnewline
23 & 0.0210138 & 0.0420276 & 0.978986 \tabularnewline
24 & 0.0140864 & 0.0281728 & 0.985914 \tabularnewline
25 & 0.0117754 & 0.0235509 & 0.988225 \tabularnewline
26 & 0.00723868 & 0.0144774 & 0.992761 \tabularnewline
27 & 0.00667409 & 0.0133482 & 0.993326 \tabularnewline
28 & 0.00429595 & 0.00859191 & 0.995704 \tabularnewline
29 & 0.0026765 & 0.00535301 & 0.997323 \tabularnewline
30 & 0.00874292 & 0.0174858 & 0.991257 \tabularnewline
31 & 0.00630507 & 0.0126101 & 0.993695 \tabularnewline
32 & 0.00439646 & 0.00879291 & 0.995604 \tabularnewline
33 & 0.00391398 & 0.00782796 & 0.996086 \tabularnewline
34 & 0.00319605 & 0.0063921 & 0.996804 \tabularnewline
35 & 0.00363181 & 0.00726363 & 0.996368 \tabularnewline
36 & 0.00297663 & 0.00595326 & 0.997023 \tabularnewline
37 & 0.00209549 & 0.00419098 & 0.997905 \tabularnewline
38 & 0.00361112 & 0.00722224 & 0.996389 \tabularnewline
39 & 0.00400127 & 0.00800254 & 0.995999 \tabularnewline
40 & 0.00344919 & 0.00689837 & 0.996551 \tabularnewline
41 & 0.00228596 & 0.00457192 & 0.997714 \tabularnewline
42 & 0.00139 & 0.00278 & 0.99861 \tabularnewline
43 & 0.0010894 & 0.00217881 & 0.998911 \tabularnewline
44 & 0.00130321 & 0.00260643 & 0.998697 \tabularnewline
45 & 0.0023313 & 0.00466261 & 0.997669 \tabularnewline
46 & 0.0311885 & 0.062377 & 0.968812 \tabularnewline
47 & 0.0269464 & 0.0538929 & 0.973054 \tabularnewline
48 & 0.096011 & 0.192022 & 0.903989 \tabularnewline
49 & 0.227892 & 0.455784 & 0.772108 \tabularnewline
50 & 0.278273 & 0.556546 & 0.721727 \tabularnewline
51 & 0.431589 & 0.863178 & 0.568411 \tabularnewline
52 & 0.467625 & 0.93525 & 0.532375 \tabularnewline
53 & 0.44292 & 0.885841 & 0.55708 \tabularnewline
54 & 0.524305 & 0.95139 & 0.475695 \tabularnewline
55 & 0.508846 & 0.982307 & 0.491154 \tabularnewline
56 & 0.516182 & 0.967635 & 0.483818 \tabularnewline
57 & 0.472992 & 0.945985 & 0.527008 \tabularnewline
58 & 0.454412 & 0.908825 & 0.545588 \tabularnewline
59 & 0.415325 & 0.83065 & 0.584675 \tabularnewline
60 & 0.390326 & 0.780653 & 0.609674 \tabularnewline
61 & 0.378186 & 0.756372 & 0.621814 \tabularnewline
62 & 0.389009 & 0.778018 & 0.610991 \tabularnewline
63 & 0.345208 & 0.690415 & 0.654792 \tabularnewline
64 & 0.32948 & 0.65896 & 0.67052 \tabularnewline
65 & 0.329036 & 0.658072 & 0.670964 \tabularnewline
66 & 0.293284 & 0.586568 & 0.706716 \tabularnewline
67 & 0.309657 & 0.619314 & 0.690343 \tabularnewline
68 & 0.362781 & 0.725562 & 0.637219 \tabularnewline
69 & 0.43813 & 0.876261 & 0.56187 \tabularnewline
70 & 0.412352 & 0.824704 & 0.587648 \tabularnewline
71 & 0.424957 & 0.849914 & 0.575043 \tabularnewline
72 & 0.405652 & 0.811304 & 0.594348 \tabularnewline
73 & 0.454259 & 0.908517 & 0.545741 \tabularnewline
74 & 0.434137 & 0.868273 & 0.565863 \tabularnewline
75 & 0.43455 & 0.8691 & 0.56545 \tabularnewline
76 & 0.387273 & 0.774546 & 0.612727 \tabularnewline
77 & 0.338174 & 0.676347 & 0.661826 \tabularnewline
78 & 0.30984 & 0.61968 & 0.69016 \tabularnewline
79 & 0.316643 & 0.633286 & 0.683357 \tabularnewline
80 & 0.315119 & 0.630237 & 0.684881 \tabularnewline
81 & 0.284296 & 0.568592 & 0.715704 \tabularnewline
82 & 0.300208 & 0.600417 & 0.699792 \tabularnewline
83 & 0.278161 & 0.556321 & 0.721839 \tabularnewline
84 & 0.334203 & 0.668407 & 0.665797 \tabularnewline
85 & 0.293165 & 0.586329 & 0.706835 \tabularnewline
86 & 0.266231 & 0.532463 & 0.733769 \tabularnewline
87 & 0.237184 & 0.474369 & 0.762816 \tabularnewline
88 & 0.208783 & 0.417566 & 0.791217 \tabularnewline
89 & 0.176968 & 0.353936 & 0.823032 \tabularnewline
90 & 0.143343 & 0.286686 & 0.856657 \tabularnewline
91 & 0.153345 & 0.30669 & 0.846655 \tabularnewline
92 & 0.228558 & 0.457117 & 0.771442 \tabularnewline
93 & 0.295959 & 0.591919 & 0.704041 \tabularnewline
94 & 0.24882 & 0.497639 & 0.75118 \tabularnewline
95 & 0.264044 & 0.528087 & 0.735956 \tabularnewline
96 & 0.218674 & 0.437348 & 0.781326 \tabularnewline
97 & 0.186896 & 0.373792 & 0.813104 \tabularnewline
98 & 0.25915 & 0.5183 & 0.74085 \tabularnewline
99 & 0.216872 & 0.433744 & 0.783128 \tabularnewline
100 & 0.216294 & 0.432589 & 0.783706 \tabularnewline
101 & 0.18214 & 0.364281 & 0.81786 \tabularnewline
102 & 0.144942 & 0.289883 & 0.855058 \tabularnewline
103 & 0.148704 & 0.297408 & 0.851296 \tabularnewline
104 & 0.136453 & 0.272906 & 0.863547 \tabularnewline
105 & 0.101555 & 0.203109 & 0.898445 \tabularnewline
106 & 0.0787307 & 0.157461 & 0.921269 \tabularnewline
107 & 0.161027 & 0.322053 & 0.838973 \tabularnewline
108 & 0.135142 & 0.270283 & 0.864858 \tabularnewline
109 & 0.162168 & 0.324336 & 0.837832 \tabularnewline
110 & 0.231406 & 0.462811 & 0.768594 \tabularnewline
111 & 0.396845 & 0.79369 & 0.603155 \tabularnewline
112 & 0.345658 & 0.691315 & 0.654342 \tabularnewline
113 & 0.254166 & 0.508331 & 0.745834 \tabularnewline
114 & 0.321111 & 0.642222 & 0.678889 \tabularnewline
115 & 0.730443 & 0.539114 & 0.269557 \tabularnewline
116 & 0.609369 & 0.781262 & 0.390631 \tabularnewline
117 & 0.85965 & 0.280699 & 0.14035 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268738&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]0.425701[/C][C]0.851401[/C][C]0.574299[/C][/ROW]
[ROW][C]11[/C][C]0.445105[/C][C]0.890209[/C][C]0.554895[/C][/ROW]
[ROW][C]12[/C][C]0.310082[/C][C]0.620163[/C][C]0.689918[/C][/ROW]
[ROW][C]13[/C][C]0.41813[/C][C]0.836259[/C][C]0.58187[/C][/ROW]
[ROW][C]14[/C][C]0.303621[/C][C]0.607242[/C][C]0.696379[/C][/ROW]
[ROW][C]15[/C][C]0.256181[/C][C]0.512362[/C][C]0.743819[/C][/ROW]
[ROW][C]16[/C][C]0.183947[/C][C]0.367893[/C][C]0.816053[/C][/ROW]
[ROW][C]17[/C][C]0.131698[/C][C]0.263396[/C][C]0.868302[/C][/ROW]
[ROW][C]18[/C][C]0.0895669[/C][C]0.179134[/C][C]0.910433[/C][/ROW]
[ROW][C]19[/C][C]0.0680693[/C][C]0.136139[/C][C]0.931931[/C][/ROW]
[ROW][C]20[/C][C]0.0477804[/C][C]0.0955609[/C][C]0.95222[/C][/ROW]
[ROW][C]21[/C][C]0.0307945[/C][C]0.0615889[/C][C]0.969206[/C][/ROW]
[ROW][C]22[/C][C]0.0192871[/C][C]0.0385743[/C][C]0.980713[/C][/ROW]
[ROW][C]23[/C][C]0.0210138[/C][C]0.0420276[/C][C]0.978986[/C][/ROW]
[ROW][C]24[/C][C]0.0140864[/C][C]0.0281728[/C][C]0.985914[/C][/ROW]
[ROW][C]25[/C][C]0.0117754[/C][C]0.0235509[/C][C]0.988225[/C][/ROW]
[ROW][C]26[/C][C]0.00723868[/C][C]0.0144774[/C][C]0.992761[/C][/ROW]
[ROW][C]27[/C][C]0.00667409[/C][C]0.0133482[/C][C]0.993326[/C][/ROW]
[ROW][C]28[/C][C]0.00429595[/C][C]0.00859191[/C][C]0.995704[/C][/ROW]
[ROW][C]29[/C][C]0.0026765[/C][C]0.00535301[/C][C]0.997323[/C][/ROW]
[ROW][C]30[/C][C]0.00874292[/C][C]0.0174858[/C][C]0.991257[/C][/ROW]
[ROW][C]31[/C][C]0.00630507[/C][C]0.0126101[/C][C]0.993695[/C][/ROW]
[ROW][C]32[/C][C]0.00439646[/C][C]0.00879291[/C][C]0.995604[/C][/ROW]
[ROW][C]33[/C][C]0.00391398[/C][C]0.00782796[/C][C]0.996086[/C][/ROW]
[ROW][C]34[/C][C]0.00319605[/C][C]0.0063921[/C][C]0.996804[/C][/ROW]
[ROW][C]35[/C][C]0.00363181[/C][C]0.00726363[/C][C]0.996368[/C][/ROW]
[ROW][C]36[/C][C]0.00297663[/C][C]0.00595326[/C][C]0.997023[/C][/ROW]
[ROW][C]37[/C][C]0.00209549[/C][C]0.00419098[/C][C]0.997905[/C][/ROW]
[ROW][C]38[/C][C]0.00361112[/C][C]0.00722224[/C][C]0.996389[/C][/ROW]
[ROW][C]39[/C][C]0.00400127[/C][C]0.00800254[/C][C]0.995999[/C][/ROW]
[ROW][C]40[/C][C]0.00344919[/C][C]0.00689837[/C][C]0.996551[/C][/ROW]
[ROW][C]41[/C][C]0.00228596[/C][C]0.00457192[/C][C]0.997714[/C][/ROW]
[ROW][C]42[/C][C]0.00139[/C][C]0.00278[/C][C]0.99861[/C][/ROW]
[ROW][C]43[/C][C]0.0010894[/C][C]0.00217881[/C][C]0.998911[/C][/ROW]
[ROW][C]44[/C][C]0.00130321[/C][C]0.00260643[/C][C]0.998697[/C][/ROW]
[ROW][C]45[/C][C]0.0023313[/C][C]0.00466261[/C][C]0.997669[/C][/ROW]
[ROW][C]46[/C][C]0.0311885[/C][C]0.062377[/C][C]0.968812[/C][/ROW]
[ROW][C]47[/C][C]0.0269464[/C][C]0.0538929[/C][C]0.973054[/C][/ROW]
[ROW][C]48[/C][C]0.096011[/C][C]0.192022[/C][C]0.903989[/C][/ROW]
[ROW][C]49[/C][C]0.227892[/C][C]0.455784[/C][C]0.772108[/C][/ROW]
[ROW][C]50[/C][C]0.278273[/C][C]0.556546[/C][C]0.721727[/C][/ROW]
[ROW][C]51[/C][C]0.431589[/C][C]0.863178[/C][C]0.568411[/C][/ROW]
[ROW][C]52[/C][C]0.467625[/C][C]0.93525[/C][C]0.532375[/C][/ROW]
[ROW][C]53[/C][C]0.44292[/C][C]0.885841[/C][C]0.55708[/C][/ROW]
[ROW][C]54[/C][C]0.524305[/C][C]0.95139[/C][C]0.475695[/C][/ROW]
[ROW][C]55[/C][C]0.508846[/C][C]0.982307[/C][C]0.491154[/C][/ROW]
[ROW][C]56[/C][C]0.516182[/C][C]0.967635[/C][C]0.483818[/C][/ROW]
[ROW][C]57[/C][C]0.472992[/C][C]0.945985[/C][C]0.527008[/C][/ROW]
[ROW][C]58[/C][C]0.454412[/C][C]0.908825[/C][C]0.545588[/C][/ROW]
[ROW][C]59[/C][C]0.415325[/C][C]0.83065[/C][C]0.584675[/C][/ROW]
[ROW][C]60[/C][C]0.390326[/C][C]0.780653[/C][C]0.609674[/C][/ROW]
[ROW][C]61[/C][C]0.378186[/C][C]0.756372[/C][C]0.621814[/C][/ROW]
[ROW][C]62[/C][C]0.389009[/C][C]0.778018[/C][C]0.610991[/C][/ROW]
[ROW][C]63[/C][C]0.345208[/C][C]0.690415[/C][C]0.654792[/C][/ROW]
[ROW][C]64[/C][C]0.32948[/C][C]0.65896[/C][C]0.67052[/C][/ROW]
[ROW][C]65[/C][C]0.329036[/C][C]0.658072[/C][C]0.670964[/C][/ROW]
[ROW][C]66[/C][C]0.293284[/C][C]0.586568[/C][C]0.706716[/C][/ROW]
[ROW][C]67[/C][C]0.309657[/C][C]0.619314[/C][C]0.690343[/C][/ROW]
[ROW][C]68[/C][C]0.362781[/C][C]0.725562[/C][C]0.637219[/C][/ROW]
[ROW][C]69[/C][C]0.43813[/C][C]0.876261[/C][C]0.56187[/C][/ROW]
[ROW][C]70[/C][C]0.412352[/C][C]0.824704[/C][C]0.587648[/C][/ROW]
[ROW][C]71[/C][C]0.424957[/C][C]0.849914[/C][C]0.575043[/C][/ROW]
[ROW][C]72[/C][C]0.405652[/C][C]0.811304[/C][C]0.594348[/C][/ROW]
[ROW][C]73[/C][C]0.454259[/C][C]0.908517[/C][C]0.545741[/C][/ROW]
[ROW][C]74[/C][C]0.434137[/C][C]0.868273[/C][C]0.565863[/C][/ROW]
[ROW][C]75[/C][C]0.43455[/C][C]0.8691[/C][C]0.56545[/C][/ROW]
[ROW][C]76[/C][C]0.387273[/C][C]0.774546[/C][C]0.612727[/C][/ROW]
[ROW][C]77[/C][C]0.338174[/C][C]0.676347[/C][C]0.661826[/C][/ROW]
[ROW][C]78[/C][C]0.30984[/C][C]0.61968[/C][C]0.69016[/C][/ROW]
[ROW][C]79[/C][C]0.316643[/C][C]0.633286[/C][C]0.683357[/C][/ROW]
[ROW][C]80[/C][C]0.315119[/C][C]0.630237[/C][C]0.684881[/C][/ROW]
[ROW][C]81[/C][C]0.284296[/C][C]0.568592[/C][C]0.715704[/C][/ROW]
[ROW][C]82[/C][C]0.300208[/C][C]0.600417[/C][C]0.699792[/C][/ROW]
[ROW][C]83[/C][C]0.278161[/C][C]0.556321[/C][C]0.721839[/C][/ROW]
[ROW][C]84[/C][C]0.334203[/C][C]0.668407[/C][C]0.665797[/C][/ROW]
[ROW][C]85[/C][C]0.293165[/C][C]0.586329[/C][C]0.706835[/C][/ROW]
[ROW][C]86[/C][C]0.266231[/C][C]0.532463[/C][C]0.733769[/C][/ROW]
[ROW][C]87[/C][C]0.237184[/C][C]0.474369[/C][C]0.762816[/C][/ROW]
[ROW][C]88[/C][C]0.208783[/C][C]0.417566[/C][C]0.791217[/C][/ROW]
[ROW][C]89[/C][C]0.176968[/C][C]0.353936[/C][C]0.823032[/C][/ROW]
[ROW][C]90[/C][C]0.143343[/C][C]0.286686[/C][C]0.856657[/C][/ROW]
[ROW][C]91[/C][C]0.153345[/C][C]0.30669[/C][C]0.846655[/C][/ROW]
[ROW][C]92[/C][C]0.228558[/C][C]0.457117[/C][C]0.771442[/C][/ROW]
[ROW][C]93[/C][C]0.295959[/C][C]0.591919[/C][C]0.704041[/C][/ROW]
[ROW][C]94[/C][C]0.24882[/C][C]0.497639[/C][C]0.75118[/C][/ROW]
[ROW][C]95[/C][C]0.264044[/C][C]0.528087[/C][C]0.735956[/C][/ROW]
[ROW][C]96[/C][C]0.218674[/C][C]0.437348[/C][C]0.781326[/C][/ROW]
[ROW][C]97[/C][C]0.186896[/C][C]0.373792[/C][C]0.813104[/C][/ROW]
[ROW][C]98[/C][C]0.25915[/C][C]0.5183[/C][C]0.74085[/C][/ROW]
[ROW][C]99[/C][C]0.216872[/C][C]0.433744[/C][C]0.783128[/C][/ROW]
[ROW][C]100[/C][C]0.216294[/C][C]0.432589[/C][C]0.783706[/C][/ROW]
[ROW][C]101[/C][C]0.18214[/C][C]0.364281[/C][C]0.81786[/C][/ROW]
[ROW][C]102[/C][C]0.144942[/C][C]0.289883[/C][C]0.855058[/C][/ROW]
[ROW][C]103[/C][C]0.148704[/C][C]0.297408[/C][C]0.851296[/C][/ROW]
[ROW][C]104[/C][C]0.136453[/C][C]0.272906[/C][C]0.863547[/C][/ROW]
[ROW][C]105[/C][C]0.101555[/C][C]0.203109[/C][C]0.898445[/C][/ROW]
[ROW][C]106[/C][C]0.0787307[/C][C]0.157461[/C][C]0.921269[/C][/ROW]
[ROW][C]107[/C][C]0.161027[/C][C]0.322053[/C][C]0.838973[/C][/ROW]
[ROW][C]108[/C][C]0.135142[/C][C]0.270283[/C][C]0.864858[/C][/ROW]
[ROW][C]109[/C][C]0.162168[/C][C]0.324336[/C][C]0.837832[/C][/ROW]
[ROW][C]110[/C][C]0.231406[/C][C]0.462811[/C][C]0.768594[/C][/ROW]
[ROW][C]111[/C][C]0.396845[/C][C]0.79369[/C][C]0.603155[/C][/ROW]
[ROW][C]112[/C][C]0.345658[/C][C]0.691315[/C][C]0.654342[/C][/ROW]
[ROW][C]113[/C][C]0.254166[/C][C]0.508331[/C][C]0.745834[/C][/ROW]
[ROW][C]114[/C][C]0.321111[/C][C]0.642222[/C][C]0.678889[/C][/ROW]
[ROW][C]115[/C][C]0.730443[/C][C]0.539114[/C][C]0.269557[/C][/ROW]
[ROW][C]116[/C][C]0.609369[/C][C]0.781262[/C][C]0.390631[/C][/ROW]
[ROW][C]117[/C][C]0.85965[/C][C]0.280699[/C][C]0.14035[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268738&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268738&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
100.4257010.8514010.574299
110.4451050.8902090.554895
120.3100820.6201630.689918
130.418130.8362590.58187
140.3036210.6072420.696379
150.2561810.5123620.743819
160.1839470.3678930.816053
170.1316980.2633960.868302
180.08956690.1791340.910433
190.06806930.1361390.931931
200.04778040.09556090.95222
210.03079450.06158890.969206
220.01928710.03857430.980713
230.02101380.04202760.978986
240.01408640.02817280.985914
250.01177540.02355090.988225
260.007238680.01447740.992761
270.006674090.01334820.993326
280.004295950.008591910.995704
290.00267650.005353010.997323
300.008742920.01748580.991257
310.006305070.01261010.993695
320.004396460.008792910.995604
330.003913980.007827960.996086
340.003196050.00639210.996804
350.003631810.007263630.996368
360.002976630.005953260.997023
370.002095490.004190980.997905
380.003611120.007222240.996389
390.004001270.008002540.995999
400.003449190.006898370.996551
410.002285960.004571920.997714
420.001390.002780.99861
430.00108940.002178810.998911
440.001303210.002606430.998697
450.00233130.004662610.997669
460.03118850.0623770.968812
470.02694640.05389290.973054
480.0960110.1920220.903989
490.2278920.4557840.772108
500.2782730.5565460.721727
510.4315890.8631780.568411
520.4676250.935250.532375
530.442920.8858410.55708
540.5243050.951390.475695
550.5088460.9823070.491154
560.5161820.9676350.483818
570.4729920.9459850.527008
580.4544120.9088250.545588
590.4153250.830650.584675
600.3903260.7806530.609674
610.3781860.7563720.621814
620.3890090.7780180.610991
630.3452080.6904150.654792
640.329480.658960.67052
650.3290360.6580720.670964
660.2932840.5865680.706716
670.3096570.6193140.690343
680.3627810.7255620.637219
690.438130.8762610.56187
700.4123520.8247040.587648
710.4249570.8499140.575043
720.4056520.8113040.594348
730.4542590.9085170.545741
740.4341370.8682730.565863
750.434550.86910.56545
760.3872730.7745460.612727
770.3381740.6763470.661826
780.309840.619680.69016
790.3166430.6332860.683357
800.3151190.6302370.684881
810.2842960.5685920.715704
820.3002080.6004170.699792
830.2781610.5563210.721839
840.3342030.6684070.665797
850.2931650.5863290.706835
860.2662310.5324630.733769
870.2371840.4743690.762816
880.2087830.4175660.791217
890.1769680.3539360.823032
900.1433430.2866860.856657
910.1533450.306690.846655
920.2285580.4571170.771442
930.2959590.5919190.704041
940.248820.4976390.75118
950.2640440.5280870.735956
960.2186740.4373480.781326
970.1868960.3737920.813104
980.259150.51830.74085
990.2168720.4337440.783128
1000.2162940.4325890.783706
1010.182140.3642810.81786
1020.1449420.2898830.855058
1030.1487040.2974080.851296
1040.1364530.2729060.863547
1050.1015550.2031090.898445
1060.07873070.1574610.921269
1070.1610270.3220530.838973
1080.1351420.2702830.864858
1090.1621680.3243360.837832
1100.2314060.4628110.768594
1110.3968450.793690.603155
1120.3456580.6913150.654342
1130.2541660.5083310.745834
1140.3211110.6422220.678889
1150.7304430.5391140.269557
1160.6093690.7812620.390631
1170.859650.2806990.14035






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level160.148148NOK
5% type I error level240.222222NOK
10% type I error level280.259259NOK

\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 & 16 & 0.148148 & NOK \tabularnewline
5% type I error level & 24 & 0.222222 & NOK \tabularnewline
10% type I error level & 28 & 0.259259 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268738&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]16[/C][C]0.148148[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]24[/C][C]0.222222[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]28[/C][C]0.259259[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268738&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268738&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 level160.148148NOK
5% type I error level240.222222NOK
10% type I error level280.259259NOK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


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')
}