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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 computationWed, 17 Dec 2014 14:47:20 +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/17/t1418828728rih0cl30jvsirsa.htm/, Retrieved Thu, 16 May 2024 18:15:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270378, Retrieved Thu, 16 May 2024 18:15:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact74

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]
-  MPD    [Multiple Regression] [] [2014-12-17 14:47:20] [d043def4c969c6fe6dac6c6c71a7875a] [Current]
-   PD      [Multiple Regression] [] [2014-12-17 15:56:54] [23dea497e8c7f4d7527c256c4e83e065]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
8 17 22 5 6 8 28 19.25
11 12 10 14 19 23 23 11.6
4 11 7 15 20 25 19 15.15
15 18 17 23 20 23 18 10.95
18 18 11 21 21 21 17 15.2
22 16 17 18 20 18 17 12.6
14 16 11 20 22 23 16 13.2
15 14 15 20 13 20 16 9.95
16 18 13 21 23 14 16 19.9
16 16 13 16 25 24 15 8.1
12 14 9 20 23 19 15 12.9
17 17 16 17 13 17 15 14.85
18 10 13 24 12 24 14 14.05
19 15 18 17 16 16 14 10.95
20 21 23 26 26 26 12 7.65
18 11 9 16 17 15 12 12.65
19 21 21 22 22 24 11 11.35
17 15 16 20 21 19 11 14.5
23 23 17 22 28 24 10 13.6
24 18 20 22 19 23 10 14.9
23 17 18 23 16 23 10 16.1
17 16 16 19 12 22 10 12.4
21 17 13 24 17 21 10 18.1
21 16 14 25 11 21 10 18.25
21 24 18 23 18 25 9 12.15
22 21 17 23 17 25 9 17.35
20 21 12 24 25 23 9 12.6
19 14 13 25 23 21 9 7.6
20 18 12 24 13 21 9 13.4
16 17 15 23 22 17 9 14.1
17 24 10 17 17 14 9 19.9
15 22 9 19 25 23 8 18.1
23 20 13 26 24 23 8 11.85
20 20 17 26 22 23 8 16.65
19 19 10 23 28 21 8 15.6
24 25 20 26 26 21 8 15.25
20 23 17 23 23 21 8 16.1
21 20 15 21 21 18 8 15.4
19 17 11 19 19 17 8 13.35
22 21 13 23 19 16 8 15.4
25 24 12 21 15 15 8 16.1
14 13 10 21 15 28 7 16.2
23 26 17 27 28 26 7 7.7
19 14 13 20 23 26 7 11.15
22 23 17 23 20 26 7 13.15
18 18 14 23 18 26 7 14.75
15 11 11 19 16 25 7 15.85
21 24 22 25 26 24 7 15.4
20 16 15 19 21 23 7 14.1
22 21 21 21 23 22 7 18.2
22 19 19 26 21 21 7 16.15
21 18 8 24 15 20 7 11.2
18 13 13 19 9 19 7 18.4
17 14 10 20 13 15 7 17.65
21 18 13 21 24 28 6 18.45
24 25 11 28 23 28 6 9.9
21 18 11 26 20 28 6 16.6
20 18 15 24 23 27 6 17.6
17 26 9 24 26 26 6 17.65
21 22 16 22 24 25 6 18.4
20 21 12 27 20 25 6 12.6
16 19 16 23 23 24 6 19.3
21 19 14 24 18 24 6 11.2
19 21 16 24 23 22 6 14.6
18 17 11 24 18 21 6 18.45
21 20 17 22 18 21 6 4.5
25 16 11 22 20 20 6 19.1
19 19 12 23 16 18 6 13.4
20 17 15 22 14 15 6 4.35
17 19 15 28 23 28 5 12.75
22 22 11 20 25 26 5 15.6
20 21 15 23 24 26 5 11.85
23 21 18 24 20 26 5 10.95
24 23 10 23 23 25 5 15.25
23 15 18 23 18 25 5 11.9
24 23 17 23 18 25 5 18.55
20 23 8 22 23 24 5 11.95
20 23 11 21 22 24 5 15.1
22 21 19 25 23 23 5 15.6
21 17 17 24 23 23 5 15.1
18 20 18 25 21 23 5 17.85
20 19 12 24 20 23 5 19.05
21 16 16 24 18 23 5 16.65
21 21 23 25 22 22 5 12.4
20 20 15 26 20 22 5 12.6
24 24 15 22 25 21 5 13.35
23 21 19 27 20 21 5 16.1
19 22 15 20 19 21 5 18.25
19 20 14 19 19 21 5 12.35
23 22 19 22 14 21 5 14.85
28 19 19 24 15 20 5 13.85
16 16 12 20 15 20 5 14.6
18 12 12 21 14 20 5 7.85
21 21 14 23 18 19 5 16
17 14 8 21 18 19 5 13.9
27 24 22 23 21 17 5 18.95
19 18 13 28 28 28 4 11.4
22 19 16 26 24 28 4 14.6
21 20 17 23 21 28 4 15.25
17 9 7 18 15 28 4 12.45
21 23 7 18 23 27 4 19.1
24 20 22 27 8 27 4 14.6
6 6 4 24 5 27 4 12.7
22 22 11 27 25 26 4 13.2
21 20 17 28 24 26 4 17.75
23 19 14 25 24 26 4 16.35
23 22 17 25 23 26 4 18.4
20 18 12 26 21 26 4 12.85
20 20 19 28 20 26 4 15.35
19 21 7 24 20 26 4 17.75
25 23 19 27 28 25 4 13.1
27 21 28 28 27 25 4 15.7
28 28 24 24 26 25 4 15.95
25 22 20 27 24 25 4 14.7
15 20 9 23 22 25 4 15.65
20 18 14 19 21 25 4 13.35
22 19 9 27 20 25 4 14.75
21 8 14 25 19 25 4 14.6
23 18 17 26 18 25 4 15.9
11 7 7 19 18 25 4 19.1
19 16 12 27 12 25 4 14.9
17 22 8 25 26 24 4 12.2
17 20 19 25 25 24 4 7.85
20 18 16 26 24 24 4 12.35
20 13 12 25 24 24 4 19.2
22 22 10 23 24 24 4 8.6
19 12 9 21 21 24 4 11.75
25 19 11 27 20 24 4 9.85
22 20 17 22 20 24 4 16.85
18 14 14 19 19 24 4 10.35
21 19 17 22 18 24 4 14.9
16 20 16 24 16 24 4 10.6
17 17 13 23 16 24 4 15.35
24 16 19 25 9 24 4 9.6
22 20 18 24 27 23 4 11.9
19 18 11 22 27 23 4 14.75
20 24 19 23 24 23 4 14.8
22 21 12 22 23 23 4 16.35
23 24 19 21 23 23 4 16.85
22 20 18 21 21 23 4 15.2
17 15 14 21 18 23 4 17.35
22 21 16 24 24 22 4 18.15
23 20 15 24 22 22 4 13.6
22 16 16 25 18 22 4 13.6
26 21 19 24 15 22 4 15
13 9 7 22 15 22 4 16.85
21 10 12 24 12 22 4 17.1
21 17 12 24 9 22 4 17.1
27 26 18 22 26 21 4 13.35
23 19 16 24 24 21 4 17.75
26 25 21 28 22 21 4 18.9
20 8 10 25 9 21 4 13.6
19 7 10 22 8 21 4 13.95
18 21 12 21 24 20 4 15.65
20 21 11 18 22 20 4 14.35
19 16 15 18 21 20 4 14.75
19 16 16 20 19 20 4 11.7
24 11 8 14 16 20 4 14.35
23 20 16 28 15 20 4 19.1
24 16 12 26 11 20 4 16.6
23 23 8 23 25 19 4 9.5
23 22 21 27 18 19 4 16.25
16 13 14 26 12 19 4 17.6
23 20 16 26 19 18 4 17.1
22 22 17 18 24 17 4 16.1
22 19 13 21 22 17 4 17.75
26 28 19 20 16 16 4 13.6
22 19 14 26 18 15 4 15.6
22 22 23 23 24 14 4 12.65
22 21 20 19 17 14 4 13.6
11 13 7 17 9 6 4 11.7

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 8 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270378&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270378&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270378&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 time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 17.1767 -0.0207027AMS.I1[t] + 0.103957AMS.I2[t] -0.0151581AMS.I3[t] -0.0622512AMS.E1[t] -0.0635471AMS.E2[t] -0.0304285AMS.E3[t] -0.0955906AMS.A[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  17.1767 -0.0207027AMS.I1[t] +  0.103957AMS.I2[t] -0.0151581AMS.I3[t] -0.0622512AMS.E1[t] -0.0635471AMS.E2[t] -0.0304285AMS.E3[t] -0.0955906AMS.A[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270378&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  17.1767 -0.0207027AMS.I1[t] +  0.103957AMS.I2[t] -0.0151581AMS.I3[t] -0.0622512AMS.E1[t] -0.0635471AMS.E2[t] -0.0304285AMS.E3[t] -0.0955906AMS.A[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270378&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270378&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] = + 17.1767 -0.0207027AMS.I1[t] + 0.103957AMS.I2[t] -0.0151581AMS.I3[t] -0.0622512AMS.E1[t] -0.0635471AMS.E2[t] -0.0304285AMS.E3[t] -0.0955906AMS.A[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)17.17672.59446.6214.93127e-102.46564e-10
AMS.I1-0.02070270.0936525-0.22110.8253230.412662
AMS.I20.1039570.0805861.290.1988750.0994373
AMS.I3-0.01515810.0696405-0.21770.8279650.413982
AMS.E1-0.06225120.0944084-0.65940.5105810.255291
AMS.E2-0.06354710.0621791-1.0220.3082950.154148
AMS.E3-0.03042850.0752055-0.40460.6862980.343149
AMS.A-0.09559060.0765368-1.2490.2134740.106737

\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) & 17.1767 & 2.5944 & 6.621 & 4.93127e-10 & 2.46564e-10 \tabularnewline
AMS.I1 & -0.0207027 & 0.0936525 & -0.2211 & 0.825323 & 0.412662 \tabularnewline
AMS.I2 & 0.103957 & 0.080586 & 1.29 & 0.198875 & 0.0994373 \tabularnewline
AMS.I3 & -0.0151581 & 0.0696405 & -0.2177 & 0.827965 & 0.413982 \tabularnewline
AMS.E1 & -0.0622512 & 0.0944084 & -0.6594 & 0.510581 & 0.255291 \tabularnewline
AMS.E2 & -0.0635471 & 0.0621791 & -1.022 & 0.308295 & 0.154148 \tabularnewline
AMS.E3 & -0.0304285 & 0.0752055 & -0.4046 & 0.686298 & 0.343149 \tabularnewline
AMS.A & -0.0955906 & 0.0765368 & -1.249 & 0.213474 & 0.106737 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270378&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]17.1767[/C][C]2.5944[/C][C]6.621[/C][C]4.93127e-10[/C][C]2.46564e-10[/C][/ROW]
[ROW][C]AMS.I1[/C][C]-0.0207027[/C][C]0.0936525[/C][C]-0.2211[/C][C]0.825323[/C][C]0.412662[/C][/ROW]
[ROW][C]AMS.I2[/C][C]0.103957[/C][C]0.080586[/C][C]1.29[/C][C]0.198875[/C][C]0.0994373[/C][/ROW]
[ROW][C]AMS.I3[/C][C]-0.0151581[/C][C]0.0696405[/C][C]-0.2177[/C][C]0.827965[/C][C]0.413982[/C][/ROW]
[ROW][C]AMS.E1[/C][C]-0.0622512[/C][C]0.0944084[/C][C]-0.6594[/C][C]0.510581[/C][C]0.255291[/C][/ROW]
[ROW][C]AMS.E2[/C][C]-0.0635471[/C][C]0.0621791[/C][C]-1.022[/C][C]0.308295[/C][C]0.154148[/C][/ROW]
[ROW][C]AMS.E3[/C][C]-0.0304285[/C][C]0.0752055[/C][C]-0.4046[/C][C]0.686298[/C][C]0.343149[/C][/ROW]
[ROW][C]AMS.A[/C][C]-0.0955906[/C][C]0.0765368[/C][C]-1.249[/C][C]0.213474[/C][C]0.106737[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270378&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270378&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)17.17672.59446.6214.93127e-102.46564e-10
AMS.I1-0.02070270.0936525-0.22110.8253230.412662
AMS.I20.1039570.0805861.290.1988750.0994373
AMS.I3-0.01515810.0696405-0.21770.8279650.413982
AMS.E1-0.06225120.0944084-0.65940.5105810.255291
AMS.E2-0.06354710.0621791-1.0220.3082950.154148
AMS.E3-0.03042850.0752055-0.40460.6862980.343149
AMS.A-0.09559060.0765368-1.2490.2134740.106737






Multiple Linear Regression - Regression Statistics
Multiple R0.165949
R-squared0.0275389
Adjusted R-squared-0.0142232
F-TEST (value)0.659423
F-TEST (DF numerator)7
F-TEST (DF denominator)163
p-value0.706084
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.05005
Sum Squared Residuals1516.35

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.165949 \tabularnewline
R-squared & 0.0275389 \tabularnewline
Adjusted R-squared & -0.0142232 \tabularnewline
F-TEST (value) & 0.659423 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 163 \tabularnewline
p-value & 0.706084 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.05005 \tabularnewline
Sum Squared Residuals & 1516.35 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270378&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.165949[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0275389[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0142232[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.659423[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]163[/C][/ROW]
[ROW][C]p-value[/C][C]0.706084[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.05005[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1516.35[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270378&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270378&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 R0.165949
R-squared0.0275389
Adjusted R-squared-0.0142232
F-TEST (value)0.659423
F-TEST (DF numerator)7
F-TEST (DF denominator)163
p-value0.706084
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.05005
Sum Squared Residuals1516.35






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
119.2514.83244.41762
211.613.0675-1.46754
315.1513.34971.80031
410.9513.3565-2.40651
515.213.60281.59725
612.613.5627-0.962666
713.213.5111-0.311088
89.9513.885-3.93505
919.913.79536.10466
108.113.5629-5.46289
1112.913.5287-0.628654
1214.8514.5140.336015
1314.0513.32140.728561
1410.9514.1697-3.21973
157.6513.3881-5.73814
1612.6514.1313-1.48134
1711.3514.0988-2.7488
1814.513.93240.567553
1913.613.9988-0.398842
2014.914.01520.884766
2116.114.09072.00931
2212.414.6749-2.27488
2318.114.14293.95706
2418.2514.34293.90714
2512.1514.7674-2.61743
2617.3514.51362.83644
2712.614.121-1.52099
287.613.5245-5.92453
2913.414.6325-1.23254
3014.114.178-0.0779588
3119.915.74334.15673
3218.114.78083.31922
3311.8513.9744-2.1244
3416.6514.1032.54703
3515.613.99211.60785
3615.2514.30110.948865
3716.114.59891.5011
3815.414.63950.760473
3913.3514.7117-1.36172
4015.414.81650.583454
4116.115.49060.609415
4216.214.30511.89487
437.714.2254-6.52537
4411.1513.8748-2.72483
4513.1514.6916-1.54159
4614.7514.42720.322819
4715.8514.21361.63641
4815.414.29551.10447
4914.114.3124-0.212353
5018.214.47863.72139
5116.1514.14732.00272
5211.214.767-3.56698
5318.414.95653.44352
5417.6514.93192.71811
5518.4514.15824.29182
569.914.4819-4.58188
5716.614.13142.46857
5817.614.05583.54421
5917.6514.88032.76971
6018.414.55763.84243
6112.614.4779-1.87788
6219.314.38094.91906
6311.214.5632-3.36323
6414.614.52540.0746497
6518.4514.55423.89582
664.514.8375-10.3375
6719.114.33314.76686
6813.415.0069-1.60686
694.3515.0134-10.6634
7012.7514.038-1.28802
7115.614.73880.861224
7211.8514.4924-2.64239
7310.9514.5767-3.62674
7415.2514.78730.462745
7511.914.1728-2.27277
7618.5514.99893.55112
7711.9514.9931-3.04306
7815.115.07340.0266143
7915.614.42071.17932
8015.114.11810.981878
8117.8514.54183.30821
8219.0514.61324.43683
8316.6514.34712.30294
8412.414.4747-2.07472
8512.614.5776-1.97758
8613.3514.8723-1.52229
8716.114.5271.57303
8818.2515.27372.97632
8912.3515.1432-2.79317
9014.8515.3235-0.473467
9113.8514.7505-0.900462
9214.615.0421-0.442137
937.8514.5862-6.7362
941615.08110.918876
9513.914.6517-0.751689
9618.9515.01773.93227
9711.413.7008-2.30082
9814.614.07590.524111
9915.2514.56280.687214
10012.4514.3462-1.89619
10119.115.24083.85917
10214.615.0324-0.432423
10312.714.5999-1.89992
10413.214.3986-1.19861
10517.7514.12173.62825
10616.3514.20862.14139
10718.414.53863.86145
10812.8514.3255-1.47547
10915.3514.36630.98368
11017.7514.92192.82812
11113.114.159-1.05898
11215.713.77451.92547
11315.9514.85471.09529
11414.714.29410.405947
11515.6514.8360.813995
11613.3514.7613-1.41134
11714.7514.46520.284781
11814.613.45471.14534
11915.914.40861.49136
12019.114.10094.99911
12114.914.67840.221641
12212.214.6694-2.46941
1237.8514.3583-6.5083
12412.3514.1351-1.78505
12519.213.73825.46185
1268.614.7872-6.18718
12711.7514.14-2.39002
1289.8514.4032-4.55322
12916.8514.78962.0604
13010.3514.5444-4.19444
13114.914.83340.0665647
13210.615.0587-4.45866
13315.3514.83380.516192
1349.614.8143-5.21431
13511.914.2355-2.33553
13614.7514.32030.429663
13714.814.9305-0.1305
13816.3514.80911.54087
13916.8515.05641.79356
14015.214.80360.39643
14117.3514.63862.71143
14218.1514.59093.55912
14313.614.6085-1.00847
14413.614.3901-0.790124
1451515.0345-0.0345147
14616.8514.36262.48743
14717.114.29132.80875
14817.115.20961.89041
14913.3515.0047-1.65467
15017.7514.39273.35731
15118.914.75664.14338
15213.614.2932-0.693177
15313.9514.4602-0.510224
15415.6514.98190.66807
15514.3515.2695-0.919531
15614.7514.7734-0.0233645
15711.714.7608-3.0608
15814.3514.8229-0.472914
15919.114.854.25001
16016.614.85281.74721
1619.514.9893-5.48934
16216.2514.88421.36585
16317.614.64312.9569
16417.114.78122.31884
16516.115.20530.894676
16617.7514.89442.85557
16713.616.1302-2.53024
16815.614.88310.716941
16912.6514.8944-2.24441
17013.615.5298-1.92976
17111.715.9992-4.2992

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 19.25 & 14.8324 & 4.41762 \tabularnewline
2 & 11.6 & 13.0675 & -1.46754 \tabularnewline
3 & 15.15 & 13.3497 & 1.80031 \tabularnewline
4 & 10.95 & 13.3565 & -2.40651 \tabularnewline
5 & 15.2 & 13.6028 & 1.59725 \tabularnewline
6 & 12.6 & 13.5627 & -0.962666 \tabularnewline
7 & 13.2 & 13.5111 & -0.311088 \tabularnewline
8 & 9.95 & 13.885 & -3.93505 \tabularnewline
9 & 19.9 & 13.7953 & 6.10466 \tabularnewline
10 & 8.1 & 13.5629 & -5.46289 \tabularnewline
11 & 12.9 & 13.5287 & -0.628654 \tabularnewline
12 & 14.85 & 14.514 & 0.336015 \tabularnewline
13 & 14.05 & 13.3214 & 0.728561 \tabularnewline
14 & 10.95 & 14.1697 & -3.21973 \tabularnewline
15 & 7.65 & 13.3881 & -5.73814 \tabularnewline
16 & 12.65 & 14.1313 & -1.48134 \tabularnewline
17 & 11.35 & 14.0988 & -2.7488 \tabularnewline
18 & 14.5 & 13.9324 & 0.567553 \tabularnewline
19 & 13.6 & 13.9988 & -0.398842 \tabularnewline
20 & 14.9 & 14.0152 & 0.884766 \tabularnewline
21 & 16.1 & 14.0907 & 2.00931 \tabularnewline
22 & 12.4 & 14.6749 & -2.27488 \tabularnewline
23 & 18.1 & 14.1429 & 3.95706 \tabularnewline
24 & 18.25 & 14.3429 & 3.90714 \tabularnewline
25 & 12.15 & 14.7674 & -2.61743 \tabularnewline
26 & 17.35 & 14.5136 & 2.83644 \tabularnewline
27 & 12.6 & 14.121 & -1.52099 \tabularnewline
28 & 7.6 & 13.5245 & -5.92453 \tabularnewline
29 & 13.4 & 14.6325 & -1.23254 \tabularnewline
30 & 14.1 & 14.178 & -0.0779588 \tabularnewline
31 & 19.9 & 15.7433 & 4.15673 \tabularnewline
32 & 18.1 & 14.7808 & 3.31922 \tabularnewline
33 & 11.85 & 13.9744 & -2.1244 \tabularnewline
34 & 16.65 & 14.103 & 2.54703 \tabularnewline
35 & 15.6 & 13.9921 & 1.60785 \tabularnewline
36 & 15.25 & 14.3011 & 0.948865 \tabularnewline
37 & 16.1 & 14.5989 & 1.5011 \tabularnewline
38 & 15.4 & 14.6395 & 0.760473 \tabularnewline
39 & 13.35 & 14.7117 & -1.36172 \tabularnewline
40 & 15.4 & 14.8165 & 0.583454 \tabularnewline
41 & 16.1 & 15.4906 & 0.609415 \tabularnewline
42 & 16.2 & 14.3051 & 1.89487 \tabularnewline
43 & 7.7 & 14.2254 & -6.52537 \tabularnewline
44 & 11.15 & 13.8748 & -2.72483 \tabularnewline
45 & 13.15 & 14.6916 & -1.54159 \tabularnewline
46 & 14.75 & 14.4272 & 0.322819 \tabularnewline
47 & 15.85 & 14.2136 & 1.63641 \tabularnewline
48 & 15.4 & 14.2955 & 1.10447 \tabularnewline
49 & 14.1 & 14.3124 & -0.212353 \tabularnewline
50 & 18.2 & 14.4786 & 3.72139 \tabularnewline
51 & 16.15 & 14.1473 & 2.00272 \tabularnewline
52 & 11.2 & 14.767 & -3.56698 \tabularnewline
53 & 18.4 & 14.9565 & 3.44352 \tabularnewline
54 & 17.65 & 14.9319 & 2.71811 \tabularnewline
55 & 18.45 & 14.1582 & 4.29182 \tabularnewline
56 & 9.9 & 14.4819 & -4.58188 \tabularnewline
57 & 16.6 & 14.1314 & 2.46857 \tabularnewline
58 & 17.6 & 14.0558 & 3.54421 \tabularnewline
59 & 17.65 & 14.8803 & 2.76971 \tabularnewline
60 & 18.4 & 14.5576 & 3.84243 \tabularnewline
61 & 12.6 & 14.4779 & -1.87788 \tabularnewline
62 & 19.3 & 14.3809 & 4.91906 \tabularnewline
63 & 11.2 & 14.5632 & -3.36323 \tabularnewline
64 & 14.6 & 14.5254 & 0.0746497 \tabularnewline
65 & 18.45 & 14.5542 & 3.89582 \tabularnewline
66 & 4.5 & 14.8375 & -10.3375 \tabularnewline
67 & 19.1 & 14.3331 & 4.76686 \tabularnewline
68 & 13.4 & 15.0069 & -1.60686 \tabularnewline
69 & 4.35 & 15.0134 & -10.6634 \tabularnewline
70 & 12.75 & 14.038 & -1.28802 \tabularnewline
71 & 15.6 & 14.7388 & 0.861224 \tabularnewline
72 & 11.85 & 14.4924 & -2.64239 \tabularnewline
73 & 10.95 & 14.5767 & -3.62674 \tabularnewline
74 & 15.25 & 14.7873 & 0.462745 \tabularnewline
75 & 11.9 & 14.1728 & -2.27277 \tabularnewline
76 & 18.55 & 14.9989 & 3.55112 \tabularnewline
77 & 11.95 & 14.9931 & -3.04306 \tabularnewline
78 & 15.1 & 15.0734 & 0.0266143 \tabularnewline
79 & 15.6 & 14.4207 & 1.17932 \tabularnewline
80 & 15.1 & 14.1181 & 0.981878 \tabularnewline
81 & 17.85 & 14.5418 & 3.30821 \tabularnewline
82 & 19.05 & 14.6132 & 4.43683 \tabularnewline
83 & 16.65 & 14.3471 & 2.30294 \tabularnewline
84 & 12.4 & 14.4747 & -2.07472 \tabularnewline
85 & 12.6 & 14.5776 & -1.97758 \tabularnewline
86 & 13.35 & 14.8723 & -1.52229 \tabularnewline
87 & 16.1 & 14.527 & 1.57303 \tabularnewline
88 & 18.25 & 15.2737 & 2.97632 \tabularnewline
89 & 12.35 & 15.1432 & -2.79317 \tabularnewline
90 & 14.85 & 15.3235 & -0.473467 \tabularnewline
91 & 13.85 & 14.7505 & -0.900462 \tabularnewline
92 & 14.6 & 15.0421 & -0.442137 \tabularnewline
93 & 7.85 & 14.5862 & -6.7362 \tabularnewline
94 & 16 & 15.0811 & 0.918876 \tabularnewline
95 & 13.9 & 14.6517 & -0.751689 \tabularnewline
96 & 18.95 & 15.0177 & 3.93227 \tabularnewline
97 & 11.4 & 13.7008 & -2.30082 \tabularnewline
98 & 14.6 & 14.0759 & 0.524111 \tabularnewline
99 & 15.25 & 14.5628 & 0.687214 \tabularnewline
100 & 12.45 & 14.3462 & -1.89619 \tabularnewline
101 & 19.1 & 15.2408 & 3.85917 \tabularnewline
102 & 14.6 & 15.0324 & -0.432423 \tabularnewline
103 & 12.7 & 14.5999 & -1.89992 \tabularnewline
104 & 13.2 & 14.3986 & -1.19861 \tabularnewline
105 & 17.75 & 14.1217 & 3.62825 \tabularnewline
106 & 16.35 & 14.2086 & 2.14139 \tabularnewline
107 & 18.4 & 14.5386 & 3.86145 \tabularnewline
108 & 12.85 & 14.3255 & -1.47547 \tabularnewline
109 & 15.35 & 14.3663 & 0.98368 \tabularnewline
110 & 17.75 & 14.9219 & 2.82812 \tabularnewline
111 & 13.1 & 14.159 & -1.05898 \tabularnewline
112 & 15.7 & 13.7745 & 1.92547 \tabularnewline
113 & 15.95 & 14.8547 & 1.09529 \tabularnewline
114 & 14.7 & 14.2941 & 0.405947 \tabularnewline
115 & 15.65 & 14.836 & 0.813995 \tabularnewline
116 & 13.35 & 14.7613 & -1.41134 \tabularnewline
117 & 14.75 & 14.4652 & 0.284781 \tabularnewline
118 & 14.6 & 13.4547 & 1.14534 \tabularnewline
119 & 15.9 & 14.4086 & 1.49136 \tabularnewline
120 & 19.1 & 14.1009 & 4.99911 \tabularnewline
121 & 14.9 & 14.6784 & 0.221641 \tabularnewline
122 & 12.2 & 14.6694 & -2.46941 \tabularnewline
123 & 7.85 & 14.3583 & -6.5083 \tabularnewline
124 & 12.35 & 14.1351 & -1.78505 \tabularnewline
125 & 19.2 & 13.7382 & 5.46185 \tabularnewline
126 & 8.6 & 14.7872 & -6.18718 \tabularnewline
127 & 11.75 & 14.14 & -2.39002 \tabularnewline
128 & 9.85 & 14.4032 & -4.55322 \tabularnewline
129 & 16.85 & 14.7896 & 2.0604 \tabularnewline
130 & 10.35 & 14.5444 & -4.19444 \tabularnewline
131 & 14.9 & 14.8334 & 0.0665647 \tabularnewline
132 & 10.6 & 15.0587 & -4.45866 \tabularnewline
133 & 15.35 & 14.8338 & 0.516192 \tabularnewline
134 & 9.6 & 14.8143 & -5.21431 \tabularnewline
135 & 11.9 & 14.2355 & -2.33553 \tabularnewline
136 & 14.75 & 14.3203 & 0.429663 \tabularnewline
137 & 14.8 & 14.9305 & -0.1305 \tabularnewline
138 & 16.35 & 14.8091 & 1.54087 \tabularnewline
139 & 16.85 & 15.0564 & 1.79356 \tabularnewline
140 & 15.2 & 14.8036 & 0.39643 \tabularnewline
141 & 17.35 & 14.6386 & 2.71143 \tabularnewline
142 & 18.15 & 14.5909 & 3.55912 \tabularnewline
143 & 13.6 & 14.6085 & -1.00847 \tabularnewline
144 & 13.6 & 14.3901 & -0.790124 \tabularnewline
145 & 15 & 15.0345 & -0.0345147 \tabularnewline
146 & 16.85 & 14.3626 & 2.48743 \tabularnewline
147 & 17.1 & 14.2913 & 2.80875 \tabularnewline
148 & 17.1 & 15.2096 & 1.89041 \tabularnewline
149 & 13.35 & 15.0047 & -1.65467 \tabularnewline
150 & 17.75 & 14.3927 & 3.35731 \tabularnewline
151 & 18.9 & 14.7566 & 4.14338 \tabularnewline
152 & 13.6 & 14.2932 & -0.693177 \tabularnewline
153 & 13.95 & 14.4602 & -0.510224 \tabularnewline
154 & 15.65 & 14.9819 & 0.66807 \tabularnewline
155 & 14.35 & 15.2695 & -0.919531 \tabularnewline
156 & 14.75 & 14.7734 & -0.0233645 \tabularnewline
157 & 11.7 & 14.7608 & -3.0608 \tabularnewline
158 & 14.35 & 14.8229 & -0.472914 \tabularnewline
159 & 19.1 & 14.85 & 4.25001 \tabularnewline
160 & 16.6 & 14.8528 & 1.74721 \tabularnewline
161 & 9.5 & 14.9893 & -5.48934 \tabularnewline
162 & 16.25 & 14.8842 & 1.36585 \tabularnewline
163 & 17.6 & 14.6431 & 2.9569 \tabularnewline
164 & 17.1 & 14.7812 & 2.31884 \tabularnewline
165 & 16.1 & 15.2053 & 0.894676 \tabularnewline
166 & 17.75 & 14.8944 & 2.85557 \tabularnewline
167 & 13.6 & 16.1302 & -2.53024 \tabularnewline
168 & 15.6 & 14.8831 & 0.716941 \tabularnewline
169 & 12.65 & 14.8944 & -2.24441 \tabularnewline
170 & 13.6 & 15.5298 & -1.92976 \tabularnewline
171 & 11.7 & 15.9992 & -4.2992 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270378&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]19.25[/C][C]14.8324[/C][C]4.41762[/C][/ROW]
[ROW][C]2[/C][C]11.6[/C][C]13.0675[/C][C]-1.46754[/C][/ROW]
[ROW][C]3[/C][C]15.15[/C][C]13.3497[/C][C]1.80031[/C][/ROW]
[ROW][C]4[/C][C]10.95[/C][C]13.3565[/C][C]-2.40651[/C][/ROW]
[ROW][C]5[/C][C]15.2[/C][C]13.6028[/C][C]1.59725[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]13.5627[/C][C]-0.962666[/C][/ROW]
[ROW][C]7[/C][C]13.2[/C][C]13.5111[/C][C]-0.311088[/C][/ROW]
[ROW][C]8[/C][C]9.95[/C][C]13.885[/C][C]-3.93505[/C][/ROW]
[ROW][C]9[/C][C]19.9[/C][C]13.7953[/C][C]6.10466[/C][/ROW]
[ROW][C]10[/C][C]8.1[/C][C]13.5629[/C][C]-5.46289[/C][/ROW]
[ROW][C]11[/C][C]12.9[/C][C]13.5287[/C][C]-0.628654[/C][/ROW]
[ROW][C]12[/C][C]14.85[/C][C]14.514[/C][C]0.336015[/C][/ROW]
[ROW][C]13[/C][C]14.05[/C][C]13.3214[/C][C]0.728561[/C][/ROW]
[ROW][C]14[/C][C]10.95[/C][C]14.1697[/C][C]-3.21973[/C][/ROW]
[ROW][C]15[/C][C]7.65[/C][C]13.3881[/C][C]-5.73814[/C][/ROW]
[ROW][C]16[/C][C]12.65[/C][C]14.1313[/C][C]-1.48134[/C][/ROW]
[ROW][C]17[/C][C]11.35[/C][C]14.0988[/C][C]-2.7488[/C][/ROW]
[ROW][C]18[/C][C]14.5[/C][C]13.9324[/C][C]0.567553[/C][/ROW]
[ROW][C]19[/C][C]13.6[/C][C]13.9988[/C][C]-0.398842[/C][/ROW]
[ROW][C]20[/C][C]14.9[/C][C]14.0152[/C][C]0.884766[/C][/ROW]
[ROW][C]21[/C][C]16.1[/C][C]14.0907[/C][C]2.00931[/C][/ROW]
[ROW][C]22[/C][C]12.4[/C][C]14.6749[/C][C]-2.27488[/C][/ROW]
[ROW][C]23[/C][C]18.1[/C][C]14.1429[/C][C]3.95706[/C][/ROW]
[ROW][C]24[/C][C]18.25[/C][C]14.3429[/C][C]3.90714[/C][/ROW]
[ROW][C]25[/C][C]12.15[/C][C]14.7674[/C][C]-2.61743[/C][/ROW]
[ROW][C]26[/C][C]17.35[/C][C]14.5136[/C][C]2.83644[/C][/ROW]
[ROW][C]27[/C][C]12.6[/C][C]14.121[/C][C]-1.52099[/C][/ROW]
[ROW][C]28[/C][C]7.6[/C][C]13.5245[/C][C]-5.92453[/C][/ROW]
[ROW][C]29[/C][C]13.4[/C][C]14.6325[/C][C]-1.23254[/C][/ROW]
[ROW][C]30[/C][C]14.1[/C][C]14.178[/C][C]-0.0779588[/C][/ROW]
[ROW][C]31[/C][C]19.9[/C][C]15.7433[/C][C]4.15673[/C][/ROW]
[ROW][C]32[/C][C]18.1[/C][C]14.7808[/C][C]3.31922[/C][/ROW]
[ROW][C]33[/C][C]11.85[/C][C]13.9744[/C][C]-2.1244[/C][/ROW]
[ROW][C]34[/C][C]16.65[/C][C]14.103[/C][C]2.54703[/C][/ROW]
[ROW][C]35[/C][C]15.6[/C][C]13.9921[/C][C]1.60785[/C][/ROW]
[ROW][C]36[/C][C]15.25[/C][C]14.3011[/C][C]0.948865[/C][/ROW]
[ROW][C]37[/C][C]16.1[/C][C]14.5989[/C][C]1.5011[/C][/ROW]
[ROW][C]38[/C][C]15.4[/C][C]14.6395[/C][C]0.760473[/C][/ROW]
[ROW][C]39[/C][C]13.35[/C][C]14.7117[/C][C]-1.36172[/C][/ROW]
[ROW][C]40[/C][C]15.4[/C][C]14.8165[/C][C]0.583454[/C][/ROW]
[ROW][C]41[/C][C]16.1[/C][C]15.4906[/C][C]0.609415[/C][/ROW]
[ROW][C]42[/C][C]16.2[/C][C]14.3051[/C][C]1.89487[/C][/ROW]
[ROW][C]43[/C][C]7.7[/C][C]14.2254[/C][C]-6.52537[/C][/ROW]
[ROW][C]44[/C][C]11.15[/C][C]13.8748[/C][C]-2.72483[/C][/ROW]
[ROW][C]45[/C][C]13.15[/C][C]14.6916[/C][C]-1.54159[/C][/ROW]
[ROW][C]46[/C][C]14.75[/C][C]14.4272[/C][C]0.322819[/C][/ROW]
[ROW][C]47[/C][C]15.85[/C][C]14.2136[/C][C]1.63641[/C][/ROW]
[ROW][C]48[/C][C]15.4[/C][C]14.2955[/C][C]1.10447[/C][/ROW]
[ROW][C]49[/C][C]14.1[/C][C]14.3124[/C][C]-0.212353[/C][/ROW]
[ROW][C]50[/C][C]18.2[/C][C]14.4786[/C][C]3.72139[/C][/ROW]
[ROW][C]51[/C][C]16.15[/C][C]14.1473[/C][C]2.00272[/C][/ROW]
[ROW][C]52[/C][C]11.2[/C][C]14.767[/C][C]-3.56698[/C][/ROW]
[ROW][C]53[/C][C]18.4[/C][C]14.9565[/C][C]3.44352[/C][/ROW]
[ROW][C]54[/C][C]17.65[/C][C]14.9319[/C][C]2.71811[/C][/ROW]
[ROW][C]55[/C][C]18.45[/C][C]14.1582[/C][C]4.29182[/C][/ROW]
[ROW][C]56[/C][C]9.9[/C][C]14.4819[/C][C]-4.58188[/C][/ROW]
[ROW][C]57[/C][C]16.6[/C][C]14.1314[/C][C]2.46857[/C][/ROW]
[ROW][C]58[/C][C]17.6[/C][C]14.0558[/C][C]3.54421[/C][/ROW]
[ROW][C]59[/C][C]17.65[/C][C]14.8803[/C][C]2.76971[/C][/ROW]
[ROW][C]60[/C][C]18.4[/C][C]14.5576[/C][C]3.84243[/C][/ROW]
[ROW][C]61[/C][C]12.6[/C][C]14.4779[/C][C]-1.87788[/C][/ROW]
[ROW][C]62[/C][C]19.3[/C][C]14.3809[/C][C]4.91906[/C][/ROW]
[ROW][C]63[/C][C]11.2[/C][C]14.5632[/C][C]-3.36323[/C][/ROW]
[ROW][C]64[/C][C]14.6[/C][C]14.5254[/C][C]0.0746497[/C][/ROW]
[ROW][C]65[/C][C]18.45[/C][C]14.5542[/C][C]3.89582[/C][/ROW]
[ROW][C]66[/C][C]4.5[/C][C]14.8375[/C][C]-10.3375[/C][/ROW]
[ROW][C]67[/C][C]19.1[/C][C]14.3331[/C][C]4.76686[/C][/ROW]
[ROW][C]68[/C][C]13.4[/C][C]15.0069[/C][C]-1.60686[/C][/ROW]
[ROW][C]69[/C][C]4.35[/C][C]15.0134[/C][C]-10.6634[/C][/ROW]
[ROW][C]70[/C][C]12.75[/C][C]14.038[/C][C]-1.28802[/C][/ROW]
[ROW][C]71[/C][C]15.6[/C][C]14.7388[/C][C]0.861224[/C][/ROW]
[ROW][C]72[/C][C]11.85[/C][C]14.4924[/C][C]-2.64239[/C][/ROW]
[ROW][C]73[/C][C]10.95[/C][C]14.5767[/C][C]-3.62674[/C][/ROW]
[ROW][C]74[/C][C]15.25[/C][C]14.7873[/C][C]0.462745[/C][/ROW]
[ROW][C]75[/C][C]11.9[/C][C]14.1728[/C][C]-2.27277[/C][/ROW]
[ROW][C]76[/C][C]18.55[/C][C]14.9989[/C][C]3.55112[/C][/ROW]
[ROW][C]77[/C][C]11.95[/C][C]14.9931[/C][C]-3.04306[/C][/ROW]
[ROW][C]78[/C][C]15.1[/C][C]15.0734[/C][C]0.0266143[/C][/ROW]
[ROW][C]79[/C][C]15.6[/C][C]14.4207[/C][C]1.17932[/C][/ROW]
[ROW][C]80[/C][C]15.1[/C][C]14.1181[/C][C]0.981878[/C][/ROW]
[ROW][C]81[/C][C]17.85[/C][C]14.5418[/C][C]3.30821[/C][/ROW]
[ROW][C]82[/C][C]19.05[/C][C]14.6132[/C][C]4.43683[/C][/ROW]
[ROW][C]83[/C][C]16.65[/C][C]14.3471[/C][C]2.30294[/C][/ROW]
[ROW][C]84[/C][C]12.4[/C][C]14.4747[/C][C]-2.07472[/C][/ROW]
[ROW][C]85[/C][C]12.6[/C][C]14.5776[/C][C]-1.97758[/C][/ROW]
[ROW][C]86[/C][C]13.35[/C][C]14.8723[/C][C]-1.52229[/C][/ROW]
[ROW][C]87[/C][C]16.1[/C][C]14.527[/C][C]1.57303[/C][/ROW]
[ROW][C]88[/C][C]18.25[/C][C]15.2737[/C][C]2.97632[/C][/ROW]
[ROW][C]89[/C][C]12.35[/C][C]15.1432[/C][C]-2.79317[/C][/ROW]
[ROW][C]90[/C][C]14.85[/C][C]15.3235[/C][C]-0.473467[/C][/ROW]
[ROW][C]91[/C][C]13.85[/C][C]14.7505[/C][C]-0.900462[/C][/ROW]
[ROW][C]92[/C][C]14.6[/C][C]15.0421[/C][C]-0.442137[/C][/ROW]
[ROW][C]93[/C][C]7.85[/C][C]14.5862[/C][C]-6.7362[/C][/ROW]
[ROW][C]94[/C][C]16[/C][C]15.0811[/C][C]0.918876[/C][/ROW]
[ROW][C]95[/C][C]13.9[/C][C]14.6517[/C][C]-0.751689[/C][/ROW]
[ROW][C]96[/C][C]18.95[/C][C]15.0177[/C][C]3.93227[/C][/ROW]
[ROW][C]97[/C][C]11.4[/C][C]13.7008[/C][C]-2.30082[/C][/ROW]
[ROW][C]98[/C][C]14.6[/C][C]14.0759[/C][C]0.524111[/C][/ROW]
[ROW][C]99[/C][C]15.25[/C][C]14.5628[/C][C]0.687214[/C][/ROW]
[ROW][C]100[/C][C]12.45[/C][C]14.3462[/C][C]-1.89619[/C][/ROW]
[ROW][C]101[/C][C]19.1[/C][C]15.2408[/C][C]3.85917[/C][/ROW]
[ROW][C]102[/C][C]14.6[/C][C]15.0324[/C][C]-0.432423[/C][/ROW]
[ROW][C]103[/C][C]12.7[/C][C]14.5999[/C][C]-1.89992[/C][/ROW]
[ROW][C]104[/C][C]13.2[/C][C]14.3986[/C][C]-1.19861[/C][/ROW]
[ROW][C]105[/C][C]17.75[/C][C]14.1217[/C][C]3.62825[/C][/ROW]
[ROW][C]106[/C][C]16.35[/C][C]14.2086[/C][C]2.14139[/C][/ROW]
[ROW][C]107[/C][C]18.4[/C][C]14.5386[/C][C]3.86145[/C][/ROW]
[ROW][C]108[/C][C]12.85[/C][C]14.3255[/C][C]-1.47547[/C][/ROW]
[ROW][C]109[/C][C]15.35[/C][C]14.3663[/C][C]0.98368[/C][/ROW]
[ROW][C]110[/C][C]17.75[/C][C]14.9219[/C][C]2.82812[/C][/ROW]
[ROW][C]111[/C][C]13.1[/C][C]14.159[/C][C]-1.05898[/C][/ROW]
[ROW][C]112[/C][C]15.7[/C][C]13.7745[/C][C]1.92547[/C][/ROW]
[ROW][C]113[/C][C]15.95[/C][C]14.8547[/C][C]1.09529[/C][/ROW]
[ROW][C]114[/C][C]14.7[/C][C]14.2941[/C][C]0.405947[/C][/ROW]
[ROW][C]115[/C][C]15.65[/C][C]14.836[/C][C]0.813995[/C][/ROW]
[ROW][C]116[/C][C]13.35[/C][C]14.7613[/C][C]-1.41134[/C][/ROW]
[ROW][C]117[/C][C]14.75[/C][C]14.4652[/C][C]0.284781[/C][/ROW]
[ROW][C]118[/C][C]14.6[/C][C]13.4547[/C][C]1.14534[/C][/ROW]
[ROW][C]119[/C][C]15.9[/C][C]14.4086[/C][C]1.49136[/C][/ROW]
[ROW][C]120[/C][C]19.1[/C][C]14.1009[/C][C]4.99911[/C][/ROW]
[ROW][C]121[/C][C]14.9[/C][C]14.6784[/C][C]0.221641[/C][/ROW]
[ROW][C]122[/C][C]12.2[/C][C]14.6694[/C][C]-2.46941[/C][/ROW]
[ROW][C]123[/C][C]7.85[/C][C]14.3583[/C][C]-6.5083[/C][/ROW]
[ROW][C]124[/C][C]12.35[/C][C]14.1351[/C][C]-1.78505[/C][/ROW]
[ROW][C]125[/C][C]19.2[/C][C]13.7382[/C][C]5.46185[/C][/ROW]
[ROW][C]126[/C][C]8.6[/C][C]14.7872[/C][C]-6.18718[/C][/ROW]
[ROW][C]127[/C][C]11.75[/C][C]14.14[/C][C]-2.39002[/C][/ROW]
[ROW][C]128[/C][C]9.85[/C][C]14.4032[/C][C]-4.55322[/C][/ROW]
[ROW][C]129[/C][C]16.85[/C][C]14.7896[/C][C]2.0604[/C][/ROW]
[ROW][C]130[/C][C]10.35[/C][C]14.5444[/C][C]-4.19444[/C][/ROW]
[ROW][C]131[/C][C]14.9[/C][C]14.8334[/C][C]0.0665647[/C][/ROW]
[ROW][C]132[/C][C]10.6[/C][C]15.0587[/C][C]-4.45866[/C][/ROW]
[ROW][C]133[/C][C]15.35[/C][C]14.8338[/C][C]0.516192[/C][/ROW]
[ROW][C]134[/C][C]9.6[/C][C]14.8143[/C][C]-5.21431[/C][/ROW]
[ROW][C]135[/C][C]11.9[/C][C]14.2355[/C][C]-2.33553[/C][/ROW]
[ROW][C]136[/C][C]14.75[/C][C]14.3203[/C][C]0.429663[/C][/ROW]
[ROW][C]137[/C][C]14.8[/C][C]14.9305[/C][C]-0.1305[/C][/ROW]
[ROW][C]138[/C][C]16.35[/C][C]14.8091[/C][C]1.54087[/C][/ROW]
[ROW][C]139[/C][C]16.85[/C][C]15.0564[/C][C]1.79356[/C][/ROW]
[ROW][C]140[/C][C]15.2[/C][C]14.8036[/C][C]0.39643[/C][/ROW]
[ROW][C]141[/C][C]17.35[/C][C]14.6386[/C][C]2.71143[/C][/ROW]
[ROW][C]142[/C][C]18.15[/C][C]14.5909[/C][C]3.55912[/C][/ROW]
[ROW][C]143[/C][C]13.6[/C][C]14.6085[/C][C]-1.00847[/C][/ROW]
[ROW][C]144[/C][C]13.6[/C][C]14.3901[/C][C]-0.790124[/C][/ROW]
[ROW][C]145[/C][C]15[/C][C]15.0345[/C][C]-0.0345147[/C][/ROW]
[ROW][C]146[/C][C]16.85[/C][C]14.3626[/C][C]2.48743[/C][/ROW]
[ROW][C]147[/C][C]17.1[/C][C]14.2913[/C][C]2.80875[/C][/ROW]
[ROW][C]148[/C][C]17.1[/C][C]15.2096[/C][C]1.89041[/C][/ROW]
[ROW][C]149[/C][C]13.35[/C][C]15.0047[/C][C]-1.65467[/C][/ROW]
[ROW][C]150[/C][C]17.75[/C][C]14.3927[/C][C]3.35731[/C][/ROW]
[ROW][C]151[/C][C]18.9[/C][C]14.7566[/C][C]4.14338[/C][/ROW]
[ROW][C]152[/C][C]13.6[/C][C]14.2932[/C][C]-0.693177[/C][/ROW]
[ROW][C]153[/C][C]13.95[/C][C]14.4602[/C][C]-0.510224[/C][/ROW]
[ROW][C]154[/C][C]15.65[/C][C]14.9819[/C][C]0.66807[/C][/ROW]
[ROW][C]155[/C][C]14.35[/C][C]15.2695[/C][C]-0.919531[/C][/ROW]
[ROW][C]156[/C][C]14.75[/C][C]14.7734[/C][C]-0.0233645[/C][/ROW]
[ROW][C]157[/C][C]11.7[/C][C]14.7608[/C][C]-3.0608[/C][/ROW]
[ROW][C]158[/C][C]14.35[/C][C]14.8229[/C][C]-0.472914[/C][/ROW]
[ROW][C]159[/C][C]19.1[/C][C]14.85[/C][C]4.25001[/C][/ROW]
[ROW][C]160[/C][C]16.6[/C][C]14.8528[/C][C]1.74721[/C][/ROW]
[ROW][C]161[/C][C]9.5[/C][C]14.9893[/C][C]-5.48934[/C][/ROW]
[ROW][C]162[/C][C]16.25[/C][C]14.8842[/C][C]1.36585[/C][/ROW]
[ROW][C]163[/C][C]17.6[/C][C]14.6431[/C][C]2.9569[/C][/ROW]
[ROW][C]164[/C][C]17.1[/C][C]14.7812[/C][C]2.31884[/C][/ROW]
[ROW][C]165[/C][C]16.1[/C][C]15.2053[/C][C]0.894676[/C][/ROW]
[ROW][C]166[/C][C]17.75[/C][C]14.8944[/C][C]2.85557[/C][/ROW]
[ROW][C]167[/C][C]13.6[/C][C]16.1302[/C][C]-2.53024[/C][/ROW]
[ROW][C]168[/C][C]15.6[/C][C]14.8831[/C][C]0.716941[/C][/ROW]
[ROW][C]169[/C][C]12.65[/C][C]14.8944[/C][C]-2.24441[/C][/ROW]
[ROW][C]170[/C][C]13.6[/C][C]15.5298[/C][C]-1.92976[/C][/ROW]
[ROW][C]171[/C][C]11.7[/C][C]15.9992[/C][C]-4.2992[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270378&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270378&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
119.2514.83244.41762
211.613.0675-1.46754
315.1513.34971.80031
410.9513.3565-2.40651
515.213.60281.59725
612.613.5627-0.962666
713.213.5111-0.311088
89.9513.885-3.93505
919.913.79536.10466
108.113.5629-5.46289
1112.913.5287-0.628654
1214.8514.5140.336015
1314.0513.32140.728561
1410.9514.1697-3.21973
157.6513.3881-5.73814
1612.6514.1313-1.48134
1711.3514.0988-2.7488
1814.513.93240.567553
1913.613.9988-0.398842
2014.914.01520.884766
2116.114.09072.00931
2212.414.6749-2.27488
2318.114.14293.95706
2418.2514.34293.90714
2512.1514.7674-2.61743
2617.3514.51362.83644
2712.614.121-1.52099
287.613.5245-5.92453
2913.414.6325-1.23254
3014.114.178-0.0779588
3119.915.74334.15673
3218.114.78083.31922
3311.8513.9744-2.1244
3416.6514.1032.54703
3515.613.99211.60785
3615.2514.30110.948865
3716.114.59891.5011
3815.414.63950.760473
3913.3514.7117-1.36172
4015.414.81650.583454
4116.115.49060.609415
4216.214.30511.89487
437.714.2254-6.52537
4411.1513.8748-2.72483
4513.1514.6916-1.54159
4614.7514.42720.322819
4715.8514.21361.63641
4815.414.29551.10447
4914.114.3124-0.212353
5018.214.47863.72139
5116.1514.14732.00272
5211.214.767-3.56698
5318.414.95653.44352
5417.6514.93192.71811
5518.4514.15824.29182
569.914.4819-4.58188
5716.614.13142.46857
5817.614.05583.54421
5917.6514.88032.76971
6018.414.55763.84243
6112.614.4779-1.87788
6219.314.38094.91906
6311.214.5632-3.36323
6414.614.52540.0746497
6518.4514.55423.89582
664.514.8375-10.3375
6719.114.33314.76686
6813.415.0069-1.60686
694.3515.0134-10.6634
7012.7514.038-1.28802
7115.614.73880.861224
7211.8514.4924-2.64239
7310.9514.5767-3.62674
7415.2514.78730.462745
7511.914.1728-2.27277
7618.5514.99893.55112
7711.9514.9931-3.04306
7815.115.07340.0266143
7915.614.42071.17932
8015.114.11810.981878
8117.8514.54183.30821
8219.0514.61324.43683
8316.6514.34712.30294
8412.414.4747-2.07472
8512.614.5776-1.97758
8613.3514.8723-1.52229
8716.114.5271.57303
8818.2515.27372.97632
8912.3515.1432-2.79317
9014.8515.3235-0.473467
9113.8514.7505-0.900462
9214.615.0421-0.442137
937.8514.5862-6.7362
941615.08110.918876
9513.914.6517-0.751689
9618.9515.01773.93227
9711.413.7008-2.30082
9814.614.07590.524111
9915.2514.56280.687214
10012.4514.3462-1.89619
10119.115.24083.85917
10214.615.0324-0.432423
10312.714.5999-1.89992
10413.214.3986-1.19861
10517.7514.12173.62825
10616.3514.20862.14139
10718.414.53863.86145
10812.8514.3255-1.47547
10915.3514.36630.98368
11017.7514.92192.82812
11113.114.159-1.05898
11215.713.77451.92547
11315.9514.85471.09529
11414.714.29410.405947
11515.6514.8360.813995
11613.3514.7613-1.41134
11714.7514.46520.284781
11814.613.45471.14534
11915.914.40861.49136
12019.114.10094.99911
12114.914.67840.221641
12212.214.6694-2.46941
1237.8514.3583-6.5083
12412.3514.1351-1.78505
12519.213.73825.46185
1268.614.7872-6.18718
12711.7514.14-2.39002
1289.8514.4032-4.55322
12916.8514.78962.0604
13010.3514.5444-4.19444
13114.914.83340.0665647
13210.615.0587-4.45866
13315.3514.83380.516192
1349.614.8143-5.21431
13511.914.2355-2.33553
13614.7514.32030.429663
13714.814.9305-0.1305
13816.3514.80911.54087
13916.8515.05641.79356
14015.214.80360.39643
14117.3514.63862.71143
14218.1514.59093.55912
14313.614.6085-1.00847
14413.614.3901-0.790124
1451515.0345-0.0345147
14616.8514.36262.48743
14717.114.29132.80875
14817.115.20961.89041
14913.3515.0047-1.65467
15017.7514.39273.35731
15118.914.75664.14338
15213.614.2932-0.693177
15313.9514.4602-0.510224
15415.6514.98190.66807
15514.3515.2695-0.919531
15614.7514.7734-0.0233645
15711.714.7608-3.0608
15814.3514.8229-0.472914
15919.114.854.25001
16016.614.85281.74721
1619.514.9893-5.48934
16216.2514.88421.36585
16317.614.64312.9569
16417.114.78122.31884
16516.115.20530.894676
16617.7514.89442.85557
16713.616.1302-2.53024
16815.614.88310.716941
16912.6514.8944-2.24441
17013.615.5298-1.92976
17111.715.9992-4.2992






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.4777620.9555240.522238
120.3522210.7044430.647779
130.478390.956780.52161
140.3676140.7352280.632386
150.3283830.6567660.671617
160.251110.5022210.74889
170.2431320.4862640.756868
180.2500710.5001420.749929
190.2527970.5055940.747203
200.3763280.7526570.623672
210.3944680.7889360.605532
220.3616510.7233020.638349
230.3374870.6749740.662513
240.2828770.5657550.717123
250.3001480.6002960.699852
260.3051120.6102240.694888
270.2822830.5645670.717717
280.3804930.7609860.619507
290.420540.841080.57946
300.3611160.7222310.638884
310.3203810.6407620.679619
320.3272420.6544840.672758
330.2994770.5989530.700523
340.3199320.6398650.680068
350.2851230.5702450.714877
360.2399070.4798150.760093
370.201310.402620.79869
380.1621730.3243460.837827
390.1472240.2944480.852776
400.1304220.2608440.869578
410.1376570.2753150.862343
420.1350070.2700130.864993
430.2545690.5091370.745431
440.2269580.4539170.773042
450.1926880.3853770.807312
460.1596130.3192250.840387
470.1451870.2903730.854813
480.1386440.2772890.861356
490.1158060.2316130.884194
500.1580320.3160630.841968
510.1428440.2856880.857156
520.1874480.3748970.812552
530.1761890.3523780.823811
540.1583150.316630.841685
550.2355840.4711690.764416
560.2651380.5302750.734862
570.2692890.5385780.730711
580.2968510.5937030.703149
590.2820350.564070.717965
600.3100630.6201250.689937
610.2865250.5730510.713475
620.3433620.6867240.656638
630.3591940.7183880.640806
640.3228320.6456640.677168
650.3536860.7073710.646314
660.8262750.3474490.173725
670.8795880.2408240.120412
680.8707860.2584270.129214
690.9909210.01815720.00907861
700.9882960.02340780.0117039
710.9846690.0306620.015331
720.9836130.03277470.0163874
730.9852690.02946280.0147314
740.9804430.03911420.0195571
750.9782860.04342780.0217139
760.9800720.0398560.019928
770.9815160.03696880.0184844
780.9758720.04825550.0241277
790.9700050.05998950.0299947
800.9623830.07523430.0376171
810.9634840.07303250.0365163
820.972480.05503950.0275197
830.9696880.06062430.0303121
840.9645320.07093680.0354684
850.9587770.08244590.0412229
860.9504850.09902950.0495147
870.9416430.1167130.0583567
880.9452690.1094630.0547313
890.9424690.1150620.0575311
900.9283740.1432530.0716265
910.9129920.1740170.0870085
920.8966220.2067550.103378
930.9617190.07656130.0382806
940.9513210.0973590.0486795
950.9476240.1047520.0523762
960.9431680.1136640.0568319
970.9416690.1166610.0583307
980.9279680.1440640.0720319
990.9113750.177250.0886252
1000.9000290.1999420.0999712
1010.9229080.1541850.0770925
1020.9049520.1900950.0950477
1030.8908710.2182570.109129
1040.8724630.2550750.127537
1050.8768790.2462420.123121
1060.8619430.2761140.138057
1070.8777530.2444940.122247
1080.860190.279620.13981
1090.833690.332620.16631
1100.8434190.3131610.156581
1110.8203270.3593470.179673
1120.7942860.4114280.205714
1130.7659580.4680840.234042
1140.7268010.5463980.273199
1150.7002770.5994470.299723
1160.6614790.6770420.338521
1170.6157690.7684620.384231
1180.5849020.8301970.415098
1190.5429360.9141280.457064
1200.6386670.7226670.361333
1210.5912470.8175060.408753
1220.5556130.8887740.444387
1230.7502920.4994160.249708
1240.7503520.4992950.249648
1250.7915350.4169310.208465
1260.8793270.2413460.120673
1270.8735020.2529960.126498
1280.9435350.1129310.0564654
1290.9361930.1276150.0638075
1300.9498420.1003160.0501581
1310.9327370.1345260.0672628
1320.9622250.07555030.0377752
1330.9485990.1028020.0514011
1340.9912690.01746170.00873086
1350.9949560.01008840.00504418
1360.9922730.01545350.00772673
1370.9903590.01928170.00964087
1380.9855970.02880660.0144033
1390.9791910.04161850.0208092
1400.9696710.06065790.0303289
1410.9595490.0809020.040451
1420.9559170.08816510.0440826
1430.9490520.1018960.0509481
1440.9515160.09696850.0484842
1450.9360910.1278180.0639092
1460.9149490.1701020.0850511
1470.8864910.2270170.113509
1480.8492070.3015860.150793
1490.8306920.3386160.169308
1500.799440.401120.20056
1510.7649710.4700580.235029
1520.7474680.5050650.252532
1530.7419990.5160010.258001
1540.691010.617980.30899
1550.6282580.7434840.371742
1560.5284620.9430760.471538
1570.6028470.7943070.397153
1580.5575840.8848310.442416
1590.5334250.933150.466575
1600.5754870.8490270.424513

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.477762 & 0.955524 & 0.522238 \tabularnewline
12 & 0.352221 & 0.704443 & 0.647779 \tabularnewline
13 & 0.47839 & 0.95678 & 0.52161 \tabularnewline
14 & 0.367614 & 0.735228 & 0.632386 \tabularnewline
15 & 0.328383 & 0.656766 & 0.671617 \tabularnewline
16 & 0.25111 & 0.502221 & 0.74889 \tabularnewline
17 & 0.243132 & 0.486264 & 0.756868 \tabularnewline
18 & 0.250071 & 0.500142 & 0.749929 \tabularnewline
19 & 0.252797 & 0.505594 & 0.747203 \tabularnewline
20 & 0.376328 & 0.752657 & 0.623672 \tabularnewline
21 & 0.394468 & 0.788936 & 0.605532 \tabularnewline
22 & 0.361651 & 0.723302 & 0.638349 \tabularnewline
23 & 0.337487 & 0.674974 & 0.662513 \tabularnewline
24 & 0.282877 & 0.565755 & 0.717123 \tabularnewline
25 & 0.300148 & 0.600296 & 0.699852 \tabularnewline
26 & 0.305112 & 0.610224 & 0.694888 \tabularnewline
27 & 0.282283 & 0.564567 & 0.717717 \tabularnewline
28 & 0.380493 & 0.760986 & 0.619507 \tabularnewline
29 & 0.42054 & 0.84108 & 0.57946 \tabularnewline
30 & 0.361116 & 0.722231 & 0.638884 \tabularnewline
31 & 0.320381 & 0.640762 & 0.679619 \tabularnewline
32 & 0.327242 & 0.654484 & 0.672758 \tabularnewline
33 & 0.299477 & 0.598953 & 0.700523 \tabularnewline
34 & 0.319932 & 0.639865 & 0.680068 \tabularnewline
35 & 0.285123 & 0.570245 & 0.714877 \tabularnewline
36 & 0.239907 & 0.479815 & 0.760093 \tabularnewline
37 & 0.20131 & 0.40262 & 0.79869 \tabularnewline
38 & 0.162173 & 0.324346 & 0.837827 \tabularnewline
39 & 0.147224 & 0.294448 & 0.852776 \tabularnewline
40 & 0.130422 & 0.260844 & 0.869578 \tabularnewline
41 & 0.137657 & 0.275315 & 0.862343 \tabularnewline
42 & 0.135007 & 0.270013 & 0.864993 \tabularnewline
43 & 0.254569 & 0.509137 & 0.745431 \tabularnewline
44 & 0.226958 & 0.453917 & 0.773042 \tabularnewline
45 & 0.192688 & 0.385377 & 0.807312 \tabularnewline
46 & 0.159613 & 0.319225 & 0.840387 \tabularnewline
47 & 0.145187 & 0.290373 & 0.854813 \tabularnewline
48 & 0.138644 & 0.277289 & 0.861356 \tabularnewline
49 & 0.115806 & 0.231613 & 0.884194 \tabularnewline
50 & 0.158032 & 0.316063 & 0.841968 \tabularnewline
51 & 0.142844 & 0.285688 & 0.857156 \tabularnewline
52 & 0.187448 & 0.374897 & 0.812552 \tabularnewline
53 & 0.176189 & 0.352378 & 0.823811 \tabularnewline
54 & 0.158315 & 0.31663 & 0.841685 \tabularnewline
55 & 0.235584 & 0.471169 & 0.764416 \tabularnewline
56 & 0.265138 & 0.530275 & 0.734862 \tabularnewline
57 & 0.269289 & 0.538578 & 0.730711 \tabularnewline
58 & 0.296851 & 0.593703 & 0.703149 \tabularnewline
59 & 0.282035 & 0.56407 & 0.717965 \tabularnewline
60 & 0.310063 & 0.620125 & 0.689937 \tabularnewline
61 & 0.286525 & 0.573051 & 0.713475 \tabularnewline
62 & 0.343362 & 0.686724 & 0.656638 \tabularnewline
63 & 0.359194 & 0.718388 & 0.640806 \tabularnewline
64 & 0.322832 & 0.645664 & 0.677168 \tabularnewline
65 & 0.353686 & 0.707371 & 0.646314 \tabularnewline
66 & 0.826275 & 0.347449 & 0.173725 \tabularnewline
67 & 0.879588 & 0.240824 & 0.120412 \tabularnewline
68 & 0.870786 & 0.258427 & 0.129214 \tabularnewline
69 & 0.990921 & 0.0181572 & 0.00907861 \tabularnewline
70 & 0.988296 & 0.0234078 & 0.0117039 \tabularnewline
71 & 0.984669 & 0.030662 & 0.015331 \tabularnewline
72 & 0.983613 & 0.0327747 & 0.0163874 \tabularnewline
73 & 0.985269 & 0.0294628 & 0.0147314 \tabularnewline
74 & 0.980443 & 0.0391142 & 0.0195571 \tabularnewline
75 & 0.978286 & 0.0434278 & 0.0217139 \tabularnewline
76 & 0.980072 & 0.039856 & 0.019928 \tabularnewline
77 & 0.981516 & 0.0369688 & 0.0184844 \tabularnewline
78 & 0.975872 & 0.0482555 & 0.0241277 \tabularnewline
79 & 0.970005 & 0.0599895 & 0.0299947 \tabularnewline
80 & 0.962383 & 0.0752343 & 0.0376171 \tabularnewline
81 & 0.963484 & 0.0730325 & 0.0365163 \tabularnewline
82 & 0.97248 & 0.0550395 & 0.0275197 \tabularnewline
83 & 0.969688 & 0.0606243 & 0.0303121 \tabularnewline
84 & 0.964532 & 0.0709368 & 0.0354684 \tabularnewline
85 & 0.958777 & 0.0824459 & 0.0412229 \tabularnewline
86 & 0.950485 & 0.0990295 & 0.0495147 \tabularnewline
87 & 0.941643 & 0.116713 & 0.0583567 \tabularnewline
88 & 0.945269 & 0.109463 & 0.0547313 \tabularnewline
89 & 0.942469 & 0.115062 & 0.0575311 \tabularnewline
90 & 0.928374 & 0.143253 & 0.0716265 \tabularnewline
91 & 0.912992 & 0.174017 & 0.0870085 \tabularnewline
92 & 0.896622 & 0.206755 & 0.103378 \tabularnewline
93 & 0.961719 & 0.0765613 & 0.0382806 \tabularnewline
94 & 0.951321 & 0.097359 & 0.0486795 \tabularnewline
95 & 0.947624 & 0.104752 & 0.0523762 \tabularnewline
96 & 0.943168 & 0.113664 & 0.0568319 \tabularnewline
97 & 0.941669 & 0.116661 & 0.0583307 \tabularnewline
98 & 0.927968 & 0.144064 & 0.0720319 \tabularnewline
99 & 0.911375 & 0.17725 & 0.0886252 \tabularnewline
100 & 0.900029 & 0.199942 & 0.0999712 \tabularnewline
101 & 0.922908 & 0.154185 & 0.0770925 \tabularnewline
102 & 0.904952 & 0.190095 & 0.0950477 \tabularnewline
103 & 0.890871 & 0.218257 & 0.109129 \tabularnewline
104 & 0.872463 & 0.255075 & 0.127537 \tabularnewline
105 & 0.876879 & 0.246242 & 0.123121 \tabularnewline
106 & 0.861943 & 0.276114 & 0.138057 \tabularnewline
107 & 0.877753 & 0.244494 & 0.122247 \tabularnewline
108 & 0.86019 & 0.27962 & 0.13981 \tabularnewline
109 & 0.83369 & 0.33262 & 0.16631 \tabularnewline
110 & 0.843419 & 0.313161 & 0.156581 \tabularnewline
111 & 0.820327 & 0.359347 & 0.179673 \tabularnewline
112 & 0.794286 & 0.411428 & 0.205714 \tabularnewline
113 & 0.765958 & 0.468084 & 0.234042 \tabularnewline
114 & 0.726801 & 0.546398 & 0.273199 \tabularnewline
115 & 0.700277 & 0.599447 & 0.299723 \tabularnewline
116 & 0.661479 & 0.677042 & 0.338521 \tabularnewline
117 & 0.615769 & 0.768462 & 0.384231 \tabularnewline
118 & 0.584902 & 0.830197 & 0.415098 \tabularnewline
119 & 0.542936 & 0.914128 & 0.457064 \tabularnewline
120 & 0.638667 & 0.722667 & 0.361333 \tabularnewline
121 & 0.591247 & 0.817506 & 0.408753 \tabularnewline
122 & 0.555613 & 0.888774 & 0.444387 \tabularnewline
123 & 0.750292 & 0.499416 & 0.249708 \tabularnewline
124 & 0.750352 & 0.499295 & 0.249648 \tabularnewline
125 & 0.791535 & 0.416931 & 0.208465 \tabularnewline
126 & 0.879327 & 0.241346 & 0.120673 \tabularnewline
127 & 0.873502 & 0.252996 & 0.126498 \tabularnewline
128 & 0.943535 & 0.112931 & 0.0564654 \tabularnewline
129 & 0.936193 & 0.127615 & 0.0638075 \tabularnewline
130 & 0.949842 & 0.100316 & 0.0501581 \tabularnewline
131 & 0.932737 & 0.134526 & 0.0672628 \tabularnewline
132 & 0.962225 & 0.0755503 & 0.0377752 \tabularnewline
133 & 0.948599 & 0.102802 & 0.0514011 \tabularnewline
134 & 0.991269 & 0.0174617 & 0.00873086 \tabularnewline
135 & 0.994956 & 0.0100884 & 0.00504418 \tabularnewline
136 & 0.992273 & 0.0154535 & 0.00772673 \tabularnewline
137 & 0.990359 & 0.0192817 & 0.00964087 \tabularnewline
138 & 0.985597 & 0.0288066 & 0.0144033 \tabularnewline
139 & 0.979191 & 0.0416185 & 0.0208092 \tabularnewline
140 & 0.969671 & 0.0606579 & 0.0303289 \tabularnewline
141 & 0.959549 & 0.080902 & 0.040451 \tabularnewline
142 & 0.955917 & 0.0881651 & 0.0440826 \tabularnewline
143 & 0.949052 & 0.101896 & 0.0509481 \tabularnewline
144 & 0.951516 & 0.0969685 & 0.0484842 \tabularnewline
145 & 0.936091 & 0.127818 & 0.0639092 \tabularnewline
146 & 0.914949 & 0.170102 & 0.0850511 \tabularnewline
147 & 0.886491 & 0.227017 & 0.113509 \tabularnewline
148 & 0.849207 & 0.301586 & 0.150793 \tabularnewline
149 & 0.830692 & 0.338616 & 0.169308 \tabularnewline
150 & 0.79944 & 0.40112 & 0.20056 \tabularnewline
151 & 0.764971 & 0.470058 & 0.235029 \tabularnewline
152 & 0.747468 & 0.505065 & 0.252532 \tabularnewline
153 & 0.741999 & 0.516001 & 0.258001 \tabularnewline
154 & 0.69101 & 0.61798 & 0.30899 \tabularnewline
155 & 0.628258 & 0.743484 & 0.371742 \tabularnewline
156 & 0.528462 & 0.943076 & 0.471538 \tabularnewline
157 & 0.602847 & 0.794307 & 0.397153 \tabularnewline
158 & 0.557584 & 0.884831 & 0.442416 \tabularnewline
159 & 0.533425 & 0.93315 & 0.466575 \tabularnewline
160 & 0.575487 & 0.849027 & 0.424513 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270378&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.477762[/C][C]0.955524[/C][C]0.522238[/C][/ROW]
[ROW][C]12[/C][C]0.352221[/C][C]0.704443[/C][C]0.647779[/C][/ROW]
[ROW][C]13[/C][C]0.47839[/C][C]0.95678[/C][C]0.52161[/C][/ROW]
[ROW][C]14[/C][C]0.367614[/C][C]0.735228[/C][C]0.632386[/C][/ROW]
[ROW][C]15[/C][C]0.328383[/C][C]0.656766[/C][C]0.671617[/C][/ROW]
[ROW][C]16[/C][C]0.25111[/C][C]0.502221[/C][C]0.74889[/C][/ROW]
[ROW][C]17[/C][C]0.243132[/C][C]0.486264[/C][C]0.756868[/C][/ROW]
[ROW][C]18[/C][C]0.250071[/C][C]0.500142[/C][C]0.749929[/C][/ROW]
[ROW][C]19[/C][C]0.252797[/C][C]0.505594[/C][C]0.747203[/C][/ROW]
[ROW][C]20[/C][C]0.376328[/C][C]0.752657[/C][C]0.623672[/C][/ROW]
[ROW][C]21[/C][C]0.394468[/C][C]0.788936[/C][C]0.605532[/C][/ROW]
[ROW][C]22[/C][C]0.361651[/C][C]0.723302[/C][C]0.638349[/C][/ROW]
[ROW][C]23[/C][C]0.337487[/C][C]0.674974[/C][C]0.662513[/C][/ROW]
[ROW][C]24[/C][C]0.282877[/C][C]0.565755[/C][C]0.717123[/C][/ROW]
[ROW][C]25[/C][C]0.300148[/C][C]0.600296[/C][C]0.699852[/C][/ROW]
[ROW][C]26[/C][C]0.305112[/C][C]0.610224[/C][C]0.694888[/C][/ROW]
[ROW][C]27[/C][C]0.282283[/C][C]0.564567[/C][C]0.717717[/C][/ROW]
[ROW][C]28[/C][C]0.380493[/C][C]0.760986[/C][C]0.619507[/C][/ROW]
[ROW][C]29[/C][C]0.42054[/C][C]0.84108[/C][C]0.57946[/C][/ROW]
[ROW][C]30[/C][C]0.361116[/C][C]0.722231[/C][C]0.638884[/C][/ROW]
[ROW][C]31[/C][C]0.320381[/C][C]0.640762[/C][C]0.679619[/C][/ROW]
[ROW][C]32[/C][C]0.327242[/C][C]0.654484[/C][C]0.672758[/C][/ROW]
[ROW][C]33[/C][C]0.299477[/C][C]0.598953[/C][C]0.700523[/C][/ROW]
[ROW][C]34[/C][C]0.319932[/C][C]0.639865[/C][C]0.680068[/C][/ROW]
[ROW][C]35[/C][C]0.285123[/C][C]0.570245[/C][C]0.714877[/C][/ROW]
[ROW][C]36[/C][C]0.239907[/C][C]0.479815[/C][C]0.760093[/C][/ROW]
[ROW][C]37[/C][C]0.20131[/C][C]0.40262[/C][C]0.79869[/C][/ROW]
[ROW][C]38[/C][C]0.162173[/C][C]0.324346[/C][C]0.837827[/C][/ROW]
[ROW][C]39[/C][C]0.147224[/C][C]0.294448[/C][C]0.852776[/C][/ROW]
[ROW][C]40[/C][C]0.130422[/C][C]0.260844[/C][C]0.869578[/C][/ROW]
[ROW][C]41[/C][C]0.137657[/C][C]0.275315[/C][C]0.862343[/C][/ROW]
[ROW][C]42[/C][C]0.135007[/C][C]0.270013[/C][C]0.864993[/C][/ROW]
[ROW][C]43[/C][C]0.254569[/C][C]0.509137[/C][C]0.745431[/C][/ROW]
[ROW][C]44[/C][C]0.226958[/C][C]0.453917[/C][C]0.773042[/C][/ROW]
[ROW][C]45[/C][C]0.192688[/C][C]0.385377[/C][C]0.807312[/C][/ROW]
[ROW][C]46[/C][C]0.159613[/C][C]0.319225[/C][C]0.840387[/C][/ROW]
[ROW][C]47[/C][C]0.145187[/C][C]0.290373[/C][C]0.854813[/C][/ROW]
[ROW][C]48[/C][C]0.138644[/C][C]0.277289[/C][C]0.861356[/C][/ROW]
[ROW][C]49[/C][C]0.115806[/C][C]0.231613[/C][C]0.884194[/C][/ROW]
[ROW][C]50[/C][C]0.158032[/C][C]0.316063[/C][C]0.841968[/C][/ROW]
[ROW][C]51[/C][C]0.142844[/C][C]0.285688[/C][C]0.857156[/C][/ROW]
[ROW][C]52[/C][C]0.187448[/C][C]0.374897[/C][C]0.812552[/C][/ROW]
[ROW][C]53[/C][C]0.176189[/C][C]0.352378[/C][C]0.823811[/C][/ROW]
[ROW][C]54[/C][C]0.158315[/C][C]0.31663[/C][C]0.841685[/C][/ROW]
[ROW][C]55[/C][C]0.235584[/C][C]0.471169[/C][C]0.764416[/C][/ROW]
[ROW][C]56[/C][C]0.265138[/C][C]0.530275[/C][C]0.734862[/C][/ROW]
[ROW][C]57[/C][C]0.269289[/C][C]0.538578[/C][C]0.730711[/C][/ROW]
[ROW][C]58[/C][C]0.296851[/C][C]0.593703[/C][C]0.703149[/C][/ROW]
[ROW][C]59[/C][C]0.282035[/C][C]0.56407[/C][C]0.717965[/C][/ROW]
[ROW][C]60[/C][C]0.310063[/C][C]0.620125[/C][C]0.689937[/C][/ROW]
[ROW][C]61[/C][C]0.286525[/C][C]0.573051[/C][C]0.713475[/C][/ROW]
[ROW][C]62[/C][C]0.343362[/C][C]0.686724[/C][C]0.656638[/C][/ROW]
[ROW][C]63[/C][C]0.359194[/C][C]0.718388[/C][C]0.640806[/C][/ROW]
[ROW][C]64[/C][C]0.322832[/C][C]0.645664[/C][C]0.677168[/C][/ROW]
[ROW][C]65[/C][C]0.353686[/C][C]0.707371[/C][C]0.646314[/C][/ROW]
[ROW][C]66[/C][C]0.826275[/C][C]0.347449[/C][C]0.173725[/C][/ROW]
[ROW][C]67[/C][C]0.879588[/C][C]0.240824[/C][C]0.120412[/C][/ROW]
[ROW][C]68[/C][C]0.870786[/C][C]0.258427[/C][C]0.129214[/C][/ROW]
[ROW][C]69[/C][C]0.990921[/C][C]0.0181572[/C][C]0.00907861[/C][/ROW]
[ROW][C]70[/C][C]0.988296[/C][C]0.0234078[/C][C]0.0117039[/C][/ROW]
[ROW][C]71[/C][C]0.984669[/C][C]0.030662[/C][C]0.015331[/C][/ROW]
[ROW][C]72[/C][C]0.983613[/C][C]0.0327747[/C][C]0.0163874[/C][/ROW]
[ROW][C]73[/C][C]0.985269[/C][C]0.0294628[/C][C]0.0147314[/C][/ROW]
[ROW][C]74[/C][C]0.980443[/C][C]0.0391142[/C][C]0.0195571[/C][/ROW]
[ROW][C]75[/C][C]0.978286[/C][C]0.0434278[/C][C]0.0217139[/C][/ROW]
[ROW][C]76[/C][C]0.980072[/C][C]0.039856[/C][C]0.019928[/C][/ROW]
[ROW][C]77[/C][C]0.981516[/C][C]0.0369688[/C][C]0.0184844[/C][/ROW]
[ROW][C]78[/C][C]0.975872[/C][C]0.0482555[/C][C]0.0241277[/C][/ROW]
[ROW][C]79[/C][C]0.970005[/C][C]0.0599895[/C][C]0.0299947[/C][/ROW]
[ROW][C]80[/C][C]0.962383[/C][C]0.0752343[/C][C]0.0376171[/C][/ROW]
[ROW][C]81[/C][C]0.963484[/C][C]0.0730325[/C][C]0.0365163[/C][/ROW]
[ROW][C]82[/C][C]0.97248[/C][C]0.0550395[/C][C]0.0275197[/C][/ROW]
[ROW][C]83[/C][C]0.969688[/C][C]0.0606243[/C][C]0.0303121[/C][/ROW]
[ROW][C]84[/C][C]0.964532[/C][C]0.0709368[/C][C]0.0354684[/C][/ROW]
[ROW][C]85[/C][C]0.958777[/C][C]0.0824459[/C][C]0.0412229[/C][/ROW]
[ROW][C]86[/C][C]0.950485[/C][C]0.0990295[/C][C]0.0495147[/C][/ROW]
[ROW][C]87[/C][C]0.941643[/C][C]0.116713[/C][C]0.0583567[/C][/ROW]
[ROW][C]88[/C][C]0.945269[/C][C]0.109463[/C][C]0.0547313[/C][/ROW]
[ROW][C]89[/C][C]0.942469[/C][C]0.115062[/C][C]0.0575311[/C][/ROW]
[ROW][C]90[/C][C]0.928374[/C][C]0.143253[/C][C]0.0716265[/C][/ROW]
[ROW][C]91[/C][C]0.912992[/C][C]0.174017[/C][C]0.0870085[/C][/ROW]
[ROW][C]92[/C][C]0.896622[/C][C]0.206755[/C][C]0.103378[/C][/ROW]
[ROW][C]93[/C][C]0.961719[/C][C]0.0765613[/C][C]0.0382806[/C][/ROW]
[ROW][C]94[/C][C]0.951321[/C][C]0.097359[/C][C]0.0486795[/C][/ROW]
[ROW][C]95[/C][C]0.947624[/C][C]0.104752[/C][C]0.0523762[/C][/ROW]
[ROW][C]96[/C][C]0.943168[/C][C]0.113664[/C][C]0.0568319[/C][/ROW]
[ROW][C]97[/C][C]0.941669[/C][C]0.116661[/C][C]0.0583307[/C][/ROW]
[ROW][C]98[/C][C]0.927968[/C][C]0.144064[/C][C]0.0720319[/C][/ROW]
[ROW][C]99[/C][C]0.911375[/C][C]0.17725[/C][C]0.0886252[/C][/ROW]
[ROW][C]100[/C][C]0.900029[/C][C]0.199942[/C][C]0.0999712[/C][/ROW]
[ROW][C]101[/C][C]0.922908[/C][C]0.154185[/C][C]0.0770925[/C][/ROW]
[ROW][C]102[/C][C]0.904952[/C][C]0.190095[/C][C]0.0950477[/C][/ROW]
[ROW][C]103[/C][C]0.890871[/C][C]0.218257[/C][C]0.109129[/C][/ROW]
[ROW][C]104[/C][C]0.872463[/C][C]0.255075[/C][C]0.127537[/C][/ROW]
[ROW][C]105[/C][C]0.876879[/C][C]0.246242[/C][C]0.123121[/C][/ROW]
[ROW][C]106[/C][C]0.861943[/C][C]0.276114[/C][C]0.138057[/C][/ROW]
[ROW][C]107[/C][C]0.877753[/C][C]0.244494[/C][C]0.122247[/C][/ROW]
[ROW][C]108[/C][C]0.86019[/C][C]0.27962[/C][C]0.13981[/C][/ROW]
[ROW][C]109[/C][C]0.83369[/C][C]0.33262[/C][C]0.16631[/C][/ROW]
[ROW][C]110[/C][C]0.843419[/C][C]0.313161[/C][C]0.156581[/C][/ROW]
[ROW][C]111[/C][C]0.820327[/C][C]0.359347[/C][C]0.179673[/C][/ROW]
[ROW][C]112[/C][C]0.794286[/C][C]0.411428[/C][C]0.205714[/C][/ROW]
[ROW][C]113[/C][C]0.765958[/C][C]0.468084[/C][C]0.234042[/C][/ROW]
[ROW][C]114[/C][C]0.726801[/C][C]0.546398[/C][C]0.273199[/C][/ROW]
[ROW][C]115[/C][C]0.700277[/C][C]0.599447[/C][C]0.299723[/C][/ROW]
[ROW][C]116[/C][C]0.661479[/C][C]0.677042[/C][C]0.338521[/C][/ROW]
[ROW][C]117[/C][C]0.615769[/C][C]0.768462[/C][C]0.384231[/C][/ROW]
[ROW][C]118[/C][C]0.584902[/C][C]0.830197[/C][C]0.415098[/C][/ROW]
[ROW][C]119[/C][C]0.542936[/C][C]0.914128[/C][C]0.457064[/C][/ROW]
[ROW][C]120[/C][C]0.638667[/C][C]0.722667[/C][C]0.361333[/C][/ROW]
[ROW][C]121[/C][C]0.591247[/C][C]0.817506[/C][C]0.408753[/C][/ROW]
[ROW][C]122[/C][C]0.555613[/C][C]0.888774[/C][C]0.444387[/C][/ROW]
[ROW][C]123[/C][C]0.750292[/C][C]0.499416[/C][C]0.249708[/C][/ROW]
[ROW][C]124[/C][C]0.750352[/C][C]0.499295[/C][C]0.249648[/C][/ROW]
[ROW][C]125[/C][C]0.791535[/C][C]0.416931[/C][C]0.208465[/C][/ROW]
[ROW][C]126[/C][C]0.879327[/C][C]0.241346[/C][C]0.120673[/C][/ROW]
[ROW][C]127[/C][C]0.873502[/C][C]0.252996[/C][C]0.126498[/C][/ROW]
[ROW][C]128[/C][C]0.943535[/C][C]0.112931[/C][C]0.0564654[/C][/ROW]
[ROW][C]129[/C][C]0.936193[/C][C]0.127615[/C][C]0.0638075[/C][/ROW]
[ROW][C]130[/C][C]0.949842[/C][C]0.100316[/C][C]0.0501581[/C][/ROW]
[ROW][C]131[/C][C]0.932737[/C][C]0.134526[/C][C]0.0672628[/C][/ROW]
[ROW][C]132[/C][C]0.962225[/C][C]0.0755503[/C][C]0.0377752[/C][/ROW]
[ROW][C]133[/C][C]0.948599[/C][C]0.102802[/C][C]0.0514011[/C][/ROW]
[ROW][C]134[/C][C]0.991269[/C][C]0.0174617[/C][C]0.00873086[/C][/ROW]
[ROW][C]135[/C][C]0.994956[/C][C]0.0100884[/C][C]0.00504418[/C][/ROW]
[ROW][C]136[/C][C]0.992273[/C][C]0.0154535[/C][C]0.00772673[/C][/ROW]
[ROW][C]137[/C][C]0.990359[/C][C]0.0192817[/C][C]0.00964087[/C][/ROW]
[ROW][C]138[/C][C]0.985597[/C][C]0.0288066[/C][C]0.0144033[/C][/ROW]
[ROW][C]139[/C][C]0.979191[/C][C]0.0416185[/C][C]0.0208092[/C][/ROW]
[ROW][C]140[/C][C]0.969671[/C][C]0.0606579[/C][C]0.0303289[/C][/ROW]
[ROW][C]141[/C][C]0.959549[/C][C]0.080902[/C][C]0.040451[/C][/ROW]
[ROW][C]142[/C][C]0.955917[/C][C]0.0881651[/C][C]0.0440826[/C][/ROW]
[ROW][C]143[/C][C]0.949052[/C][C]0.101896[/C][C]0.0509481[/C][/ROW]
[ROW][C]144[/C][C]0.951516[/C][C]0.0969685[/C][C]0.0484842[/C][/ROW]
[ROW][C]145[/C][C]0.936091[/C][C]0.127818[/C][C]0.0639092[/C][/ROW]
[ROW][C]146[/C][C]0.914949[/C][C]0.170102[/C][C]0.0850511[/C][/ROW]
[ROW][C]147[/C][C]0.886491[/C][C]0.227017[/C][C]0.113509[/C][/ROW]
[ROW][C]148[/C][C]0.849207[/C][C]0.301586[/C][C]0.150793[/C][/ROW]
[ROW][C]149[/C][C]0.830692[/C][C]0.338616[/C][C]0.169308[/C][/ROW]
[ROW][C]150[/C][C]0.79944[/C][C]0.40112[/C][C]0.20056[/C][/ROW]
[ROW][C]151[/C][C]0.764971[/C][C]0.470058[/C][C]0.235029[/C][/ROW]
[ROW][C]152[/C][C]0.747468[/C][C]0.505065[/C][C]0.252532[/C][/ROW]
[ROW][C]153[/C][C]0.741999[/C][C]0.516001[/C][C]0.258001[/C][/ROW]
[ROW][C]154[/C][C]0.69101[/C][C]0.61798[/C][C]0.30899[/C][/ROW]
[ROW][C]155[/C][C]0.628258[/C][C]0.743484[/C][C]0.371742[/C][/ROW]
[ROW][C]156[/C][C]0.528462[/C][C]0.943076[/C][C]0.471538[/C][/ROW]
[ROW][C]157[/C][C]0.602847[/C][C]0.794307[/C][C]0.397153[/C][/ROW]
[ROW][C]158[/C][C]0.557584[/C][C]0.884831[/C][C]0.442416[/C][/ROW]
[ROW][C]159[/C][C]0.533425[/C][C]0.93315[/C][C]0.466575[/C][/ROW]
[ROW][C]160[/C][C]0.575487[/C][C]0.849027[/C][C]0.424513[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270378&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270378&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.4777620.9555240.522238
120.3522210.7044430.647779
130.478390.956780.52161
140.3676140.7352280.632386
150.3283830.6567660.671617
160.251110.5022210.74889
170.2431320.4862640.756868
180.2500710.5001420.749929
190.2527970.5055940.747203
200.3763280.7526570.623672
210.3944680.7889360.605532
220.3616510.7233020.638349
230.3374870.6749740.662513
240.2828770.5657550.717123
250.3001480.6002960.699852
260.3051120.6102240.694888
270.2822830.5645670.717717
280.3804930.7609860.619507
290.420540.841080.57946
300.3611160.7222310.638884
310.3203810.6407620.679619
320.3272420.6544840.672758
330.2994770.5989530.700523
340.3199320.6398650.680068
350.2851230.5702450.714877
360.2399070.4798150.760093
370.201310.402620.79869
380.1621730.3243460.837827
390.1472240.2944480.852776
400.1304220.2608440.869578
410.1376570.2753150.862343
420.1350070.2700130.864993
430.2545690.5091370.745431
440.2269580.4539170.773042
450.1926880.3853770.807312
460.1596130.3192250.840387
470.1451870.2903730.854813
480.1386440.2772890.861356
490.1158060.2316130.884194
500.1580320.3160630.841968
510.1428440.2856880.857156
520.1874480.3748970.812552
530.1761890.3523780.823811
540.1583150.316630.841685
550.2355840.4711690.764416
560.2651380.5302750.734862
570.2692890.5385780.730711
580.2968510.5937030.703149
590.2820350.564070.717965
600.3100630.6201250.689937
610.2865250.5730510.713475
620.3433620.6867240.656638
630.3591940.7183880.640806
640.3228320.6456640.677168
650.3536860.7073710.646314
660.8262750.3474490.173725
670.8795880.2408240.120412
680.8707860.2584270.129214
690.9909210.01815720.00907861
700.9882960.02340780.0117039
710.9846690.0306620.015331
720.9836130.03277470.0163874
730.9852690.02946280.0147314
740.9804430.03911420.0195571
750.9782860.04342780.0217139
760.9800720.0398560.019928
770.9815160.03696880.0184844
780.9758720.04825550.0241277
790.9700050.05998950.0299947
800.9623830.07523430.0376171
810.9634840.07303250.0365163
820.972480.05503950.0275197
830.9696880.06062430.0303121
840.9645320.07093680.0354684
850.9587770.08244590.0412229
860.9504850.09902950.0495147
870.9416430.1167130.0583567
880.9452690.1094630.0547313
890.9424690.1150620.0575311
900.9283740.1432530.0716265
910.9129920.1740170.0870085
920.8966220.2067550.103378
930.9617190.07656130.0382806
940.9513210.0973590.0486795
950.9476240.1047520.0523762
960.9431680.1136640.0568319
970.9416690.1166610.0583307
980.9279680.1440640.0720319
990.9113750.177250.0886252
1000.9000290.1999420.0999712
1010.9229080.1541850.0770925
1020.9049520.1900950.0950477
1030.8908710.2182570.109129
1040.8724630.2550750.127537
1050.8768790.2462420.123121
1060.8619430.2761140.138057
1070.8777530.2444940.122247
1080.860190.279620.13981
1090.833690.332620.16631
1100.8434190.3131610.156581
1110.8203270.3593470.179673
1120.7942860.4114280.205714
1130.7659580.4680840.234042
1140.7268010.5463980.273199
1150.7002770.5994470.299723
1160.6614790.6770420.338521
1170.6157690.7684620.384231
1180.5849020.8301970.415098
1190.5429360.9141280.457064
1200.6386670.7226670.361333
1210.5912470.8175060.408753
1220.5556130.8887740.444387
1230.7502920.4994160.249708
1240.7503520.4992950.249648
1250.7915350.4169310.208465
1260.8793270.2413460.120673
1270.8735020.2529960.126498
1280.9435350.1129310.0564654
1290.9361930.1276150.0638075
1300.9498420.1003160.0501581
1310.9327370.1345260.0672628
1320.9622250.07555030.0377752
1330.9485990.1028020.0514011
1340.9912690.01746170.00873086
1350.9949560.01008840.00504418
1360.9922730.01545350.00772673
1370.9903590.01928170.00964087
1380.9855970.02880660.0144033
1390.9791910.04161850.0208092
1400.9696710.06065790.0303289
1410.9595490.0809020.040451
1420.9559170.08816510.0440826
1430.9490520.1018960.0509481
1440.9515160.09696850.0484842
1450.9360910.1278180.0639092
1460.9149490.1701020.0850511
1470.8864910.2270170.113509
1480.8492070.3015860.150793
1490.8306920.3386160.169308
1500.799440.401120.20056
1510.7649710.4700580.235029
1520.7474680.5050650.252532
1530.7419990.5160010.258001
1540.691010.617980.30899
1550.6282580.7434840.371742
1560.5284620.9430760.471538
1570.6028470.7943070.397153
1580.5575840.8848310.442416
1590.5334250.933150.466575
1600.5754870.8490270.424513






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level160.106667NOK
10% type I error level310.206667NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 16 & 0.106667 & NOK \tabularnewline
10% type I error level & 31 & 0.206667 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270378&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]16[/C][C]0.106667[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]31[/C][C]0.206667[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270378&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270378&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level160.106667NOK
10% type I error level310.206667NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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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 = 8 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 8 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$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')
}