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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 computationTue, 02 Dec 2014 16:27:00 +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/02/t1417537658jfcp7u7cp4e8v55.htm/, Retrieved Thu, 16 May 2024 11:50:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=262784, Retrieved Thu, 16 May 2024 11:50:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact60

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-02 16:27:00] [61a57b1a717662ce9f6e819e563a5fa9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
'12.9' 26 50 4
'12.8' 37 54 5
'7.4' 67 71 4
'6.7' 43 54 4
'12.6' 52 65 9
'14.8' 52 73 8
'13.3' 43 52 11
'11.1' 84 84 4
'8.2' 67 42 4
'11.4' 49 66 6
'6.4' 70 65 4
12 58 73 4
'6.3' 68 75 4
'11.3' 62 72 11
'11.9' 43 66 4
'9.3' 56 70 4
10 74 81 6
'13.8' 63 69 8
'10.8' 58 71 5
'11.7' 63 68 9
'10.9' 53 70 4
'16.1' 57 68 7
'9.9' 64 67 4
'11.5' 53 76 4
'8.3' 29 70 7
'11.7' 54 60 12
9 58 72 7
'10.8' 51 71 8
'10.4' 54 70 4
'12.7' 56 64 9
'11.8' 47 76 4
13 50 68 4
'10.8' 35 76 4
'12.3' 30 65 7
'11.3' 68 67 4
'11.6' 56 75 4
'10.9' 43 60 4
'12.1' 67 73 4
'13.3' 62 63 4
'10.1' 57 70 4
'14.3' 54 66 12
'9.3' 61 64 4
'12.5' 56 70 5
'7.6' 41 75 15
'9.2' 53 60 10
'14.5' 46 66 5
'12.3' 51 59 9
'12.6' 37 78 4
13 42 67 7
'12.6' 38 59 5
'13.2' 66 66 4
'7.7' 53 71 4
'10.5' 49 66 4
'10.9' 49 72 4
'4.3' 59 71 6
'10.3' 40 59 10
'11.4' 63 78 4
'5.6' 34 65 11
'8.8' 32 65 14
9 67 71 4
'9.6' 61 72 4
'6.4' 60 66 5
'11.6' 63 69 4
'4.35' 52 51 6
'12.7' 16 56 4
'18.1' 46 67 8
'17.85' 56 69 5
'16.6' 52 57 4
'12.6' 55 56 17
'17.1' 50 55 4
'19.1' 59 63 4
'16.1' 60 67 8
'13.35' 52 65 4
'18.4' 44 47 7
'14.7' 67 76 4
'10.6' 52 64 4
'12.6' 55 68 5
'16.2' 37 64 7
'13.6' 54 65 4
'18.9' 72 71 4
'14.1' 51 63 7
'14.5' 48 60 11
'16.15' 60 68 7
'14.75' 50 72 4
'14.8' 63 70 4
'12.45' 33 61 4
'12.65' 67 61 4
'17.35' 46 62 4
'8.6' 54 71 4
'18.4' 59 71 6
'16.1' 61 51 8
'11.6' 33 56 23
'17.75' 47 70 4
'15.25' 69 73 8
'17.65' 52 76 6
'16.35' 55 68 4
'17.65' 41 48 7
'13.6' 73 52 4
'14.35' 52 60 4
'14.75' 50 59 4
'18.25' 51 57 10
'9.9' 60 79 6
16 56 60 5
'18.25' 56 60 5
'16.85' 29 59 4
'14.6' 66 62 4
'13.85' 66 59 5
'18.95' 73 61 5
'15.6' 55 71 5
'14.85' 64 57 5
'11.75' 40 66 4
'18.45' 46 63 6
'15.9' 58 69 4
'17.1' 43 58 4
'16.1' 61 59 4
'19.9' 51 48 9
'10.95' 50 66 18
'18.45' 52 73 6
'15.1' 54 67 5
15 66 61 4
'11.35' 61 68 11
'15.95' 80 75 4
'18.1' 51 62 10
'14.6' 56 69 6
'15.4' 56 58 8
'15.4' 56 60 8
'17.6' 53 74 6
'13.35' 47 55 8
'19.1' 25 62 4
'15.35' 47 63 4
'7.6' 46 69 9
'13.4' 50 58 9
'13.9' 39 58 5
'19.1' 51 68 4
'15.25' 58 72 4
'12.9' 35 62 15
'16.1' 58 62 10
'17.35' 60 65 9
'13.15' 62 69 7
'12.15' 63 66 9
'12.6' 53 72 6
'10.35' 46 62 4
'15.4' 67 75 7
'9.6' 59 58 4
'18.2' 64 66 7
'13.6' 38 55 4
'14.85' 50 47 15
'14.75' 48 72 4
'14.1' 48 62 9
'14.9' 47 64 4
'16.25' 66 64 4
'19.25' 47 19 28
'13.6' 63 50 4
'13.6' 58 68 4
'15.65' 44 70 4
'12.75' 51 79 5
'14.6' 43 69 4
'9.85' 55 71 4
'12.65' 38 48 12
'19.2' 45 73 4
'16.6' 50 74 6
'11.2' 54 66 6
'15.25' 57 71 5
'11.9' 60 74 4
'13.2' 55 78 4
'16.35' 56 75 4
'12.4' 49 53 10
'15.85' 37 60 7
'18.15' 59 70 4
'11.15' 46 69 7
'15.65' 51 65 4
'17.75' 58 78 4
'7.65' 64 78 12
'12.35' 53 59 5
'15.6' 48 72 8
'19.3' 51 70 6
'15.2' 47 63 17
'17.1' 59 63 4
'15.6' 62 71 5
'18.4' 62 74 4
'19.05' 51 67 5
'18.55' 64 66 5
'19.1' 52 62 6
'13.1' 67 80 4
'12.85' 50 73 4
'9.5' 54 67 4
'4.5' 58 61 6
'11.85' 56 73 8
'13.6' 63 74 10
'11.7' 31 32 4
'12.4' 65 69 5
'13.35' 71 69 4
'11.4' 50 84 4
'14.9' 57 64 4
'19.9' 47 58 16
'11.2' 47 59 7
'14.6' 57 78 4
'17.6' 43 57 4
'14.05' 41 60 14
'16.1' 63 68 5
'13.35' 63 68 5
'11.85' 56 73 5
'11.95' 51 69 5
'14.75' 50 67 7
'15.15' 22 60 19
'13.2' 41 65 16
'16.85' 59 66 4
'7.85' 56 74 4
'7.7' 66 81 7
'12.6' 53 72 9
'7.85' 42 55 5
'10.95' 52 49 14
'12.35' 54 74 4
'9.95' 44 53 16
'14.9' 62 64 10
'16.65' 53 65 5
'13.4' 50 57 6
'13.95' 36 51 4
'15.7' 76 80 4
'16.85' 66 67 4
'10.95' 62 70 5
'15.35' 59 74 4
'12.2' 47 75 4
'15.1' 55 70 5
'17.75' 58 69 4
'15.2' 60 65 4
'14.6' 44 55 5
'16.65' 57 71 8
'8.1' 45 65 15

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 7 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=262784&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=262784&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=262784&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 time7 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 17.2261 + 0.0131662AMS.I[t] -0.0616066AMS.E[t] -0.0612917AMS.A[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  17.2261 +  0.0131662AMS.I[t] -0.0616066AMS.E[t] -0.0612917AMS.A[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=262784&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  17.2261 +  0.0131662AMS.I[t] -0.0616066AMS.E[t] -0.0612917AMS.A[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=262784&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=262784&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] = + 17.2261 + 0.0131662AMS.I[t] -0.0616066AMS.E[t] -0.0612917AMS.A[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)17.22612.094528.2241.56755e-147.83776e-15
AMS.I0.01316620.02257520.58320.5603330.280167
AMS.E-0.06160660.0285956-2.1540.03227080.0161354
AMS.A-0.06129170.0639787-0.9580.3390910.169545

\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) & 17.2261 & 2.09452 & 8.224 & 1.56755e-14 & 7.83776e-15 \tabularnewline
AMS.I & 0.0131662 & 0.0225752 & 0.5832 & 0.560333 & 0.280167 \tabularnewline
AMS.E & -0.0616066 & 0.0285956 & -2.154 & 0.0322708 & 0.0161354 \tabularnewline
AMS.A & -0.0612917 & 0.0639787 & -0.958 & 0.339091 & 0.169545 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=262784&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]17.2261[/C][C]2.09452[/C][C]8.224[/C][C]1.56755e-14[/C][C]7.83776e-15[/C][/ROW]
[ROW][C]AMS.I[/C][C]0.0131662[/C][C]0.0225752[/C][C]0.5832[/C][C]0.560333[/C][C]0.280167[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.0616066[/C][C]0.0285956[/C][C]-2.154[/C][C]0.0322708[/C][C]0.0161354[/C][/ROW]
[ROW][C]AMS.A[/C][C]-0.0612917[/C][C]0.0639787[/C][C]-0.958[/C][C]0.339091[/C][C]0.169545[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=262784&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=262784&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)17.22612.094528.2241.56755e-147.83776e-15
AMS.I0.01316620.02257520.58320.5603330.280167
AMS.E-0.06160660.0285956-2.1540.03227080.0161354
AMS.A-0.06129170.0639787-0.9580.3390910.169545






Multiple Linear Regression - Regression Statistics
Multiple R0.144225
R-squared0.0208007
Adjusted R-squared0.00774475
F-TEST (value)1.5932
F-TEST (DF numerator)3
F-TEST (DF denominator)225
p-value0.191856
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.31241
Sum Squared Residuals2468.72

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.144225 \tabularnewline
R-squared & 0.0208007 \tabularnewline
Adjusted R-squared & 0.00774475 \tabularnewline
F-TEST (value) & 1.5932 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 225 \tabularnewline
p-value & 0.191856 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.31241 \tabularnewline
Sum Squared Residuals & 2468.72 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=262784&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.144225[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0208007[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.00774475[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.5932[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]225[/C][/ROW]
[ROW][C]p-value[/C][C]0.191856[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.31241[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2468.72[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=262784&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=262784&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 R0.144225
R-squared0.0208007
Adjusted R-squared0.00774475
F-TEST (value)1.5932
F-TEST (DF numerator)3
F-TEST (DF denominator)225
p-value0.191856
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.31241
Sum Squared Residuals2468.72






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.914.2429-1.34291
212.814.08-1.28002
37.413.489-6.08899
46.714.2203-7.52031
512.613.3547-0.754675
614.812.92311.87689
713.313.9145-0.614482
811.112.9119-1.81193
98.215.2756-7.07558
1011.413.4374-2.03744
116.413.8981-7.49812
121213.2473-1.24728
136.313.2557-6.95573
1411.312.9325-1.63251
1511.913.481-1.58103
169.313.4058-4.10576
171012.8425-2.8425
1813.813.31440.485632
1910.813.3092-2.5092
2011.713.3147-1.61468
2110.913.3663-2.46627
2216.113.35832.74173
239.913.6959-3.79591
2411.512.9966-1.49663
258.312.8664-4.5664
2611.713.5052-1.80517
27913.125-4.12501
2810.813.0332-2.23316
2910.413.3794-2.97943
3012.713.4689-0.768946
3111.812.9176-1.11763
321313.45-0.449981
3310.812.7596-1.95964
3412.313.1876-0.887603
3511.313.7486-2.44858
3611.613.0977-1.49773
3710.913.8507-2.95067
3812.113.3658-1.26577
3913.313.916-0.616008
4010.113.4189-3.31893
4114.313.13551.16447
429.313.8412-4.54124
4312.513.3445-0.844473
447.612.226-4.62603
459.213.6146-4.41458
4614.513.45921.04076
4712.313.7111-1.41115
4812.612.6628-0.0627553
491313.2224-0.222384
5012.613.7852-1.18515
5113.213.7839-0.583853
527.713.3047-5.60466
5310.513.56-3.06003
5410.913.1904-2.29039
554.313.2611-8.96107
5610.313.505-3.20503
5711.413.0051-1.60508
585.612.9951-7.3951
598.812.7849-3.98489
60913.489-4.48899
619.613.3484-3.74838
626.413.6436-7.24356
6311.613.5595-1.95953
644.3514.401-10.051
6512.713.7416-1.04161
6618.113.21384.88624
6717.8513.40614.44392
6816.614.1542.44601
6912.613.4583-0.858299
7017.114.25092.84913
7119.113.87655.22349
7216.113.39812.70192
7313.3513.6611-0.311133
7418.414.48083.91915
7514.713.1811.51905
7610.613.7227-3.12274
7712.613.4545-0.85452
7816.213.34142.85863
7913.613.6875-0.0874655
8018.913.55485.34518
8114.113.58730.512695
8214.513.48751.01254
8316.1513.39782.75223
8414.7513.20361.54645
8514.813.49791.30207
8612.4513.6574-1.2074
8712.6514.1051-1.45505
8817.3513.7673.58304
898.613.3178-4.71783
9018.413.26115.13893
9116.114.3971.70305
9211.612.8009-1.20089
9317.7513.28734.46273
9415.2513.14692.10306
9517.6512.86094.78912
9616.3513.51582.83419
9717.6514.37973.27026
9813.614.7385-1.13851
9914.3513.96920.380834
10014.7514.00440.74556
10118.2513.77314.47693
1029.912.7814-2.88139
1031613.96052.03946
10418.2513.96054.28946
10516.8513.7283.12205
10614.614.03030.569721
10713.8514.1538-0.303807
10818.9514.12284.82724
10915.613.26972.3303
11014.8514.25070.599312
11111.7513.4415-1.69153
11218.4513.58284.86723
11315.913.49372.4063
11417.113.97393.12612
11516.114.14931.95073
11619.914.38885.51118
11710.9512.7151-1.76511
11818.4513.04575.4043
11915.113.5031.59704
1201514.09190.908114
12111.3513.1658-1.81577
12215.9513.41372.53628
12318.113.4654.63496
12414.613.34481.25521
12515.413.89991.50012
12615.413.77671.62334
12717.612.99734.60274
12813.3513.9662-0.616202
12919.113.49055.60953
13015.3513.71851.63148
1317.613.0293-5.42925
13213.413.7596-0.359589
13313.913.85990.0400724
13419.113.46315.63685
13515.2513.30891.94112
13612.912.9479-0.0479201
13716.113.55722.5428
13817.3513.463.89
13913.1513.3625-0.212493
14012.1513.4379-1.2879
14112.613.1205-0.52047
14210.3513.767-3.41696
14315.413.05872.34132
1449.614.1845-4.58454
14518.213.57364.62635
14613.614.0929-0.492873
14714.8514.06950.780489
14814.7513.17721.57278
14914.113.48680.61317
15014.913.65691.24309
15116.2513.90712.34293
15219.2514.95824.29179
15313.614.7301-1.13006
15413.613.55530.0446896
15515.6513.24782.40223
15612.7512.72420.0258168
15714.613.29621.30379
1589.8513.331-3.48099
15912.6514.0338-1.38379
16019.213.07616.12388
16116.612.95783.64224
16211.213.5033-2.30328
16315.2513.2961.95397
16411.913.212-1.312
16513.212.89970.300254
16616.3513.09773.25227
16712.413.9932-1.59316
16815.8513.58782.2622
16918.1513.44534.70474
17011.1513.1518-2.00183
17115.6513.6482.00203
17217.7512.93924.81076
1737.6512.5279-4.87791
17412.3513.9826-1.63265
17515.612.93212.66794
17619.313.21746.08265
17715.212.92172.27828
17817.113.87653.22349
17915.613.36192.23814
18018.413.23835.16166
18119.0513.46355.58654
18218.5513.69624.85377
18319.113.72345.37663
18413.112.93450.165473
18512.8513.1419-0.291948
1869.513.5643-4.06425
1874.513.864-9.36397
18811.8512.9758-1.12578
18913.612.88380.716248
19011.715.4177-3.71766
19112.413.5246-1.12458
19213.3513.6649-0.314864
19311.412.4643-1.06428
19414.913.78861.11143
19519.913.2916.60895
19611.213.7811-2.58107
19714.612.92611.67392
19817.614.03553.56451
19914.0513.21140.838578
20016.113.55982.54015
20113.3513.5598-0.20985
20211.8513.1597-1.30965
20311.9513.3402-1.39025
20414.7513.32771.42229
20515.1512.65482.49519
20613.212.78080.419194
20716.8513.69173.15831
2087.8513.1593-5.30934
2097.712.6759-4.97588
21012.612.9366-0.336595
2117.8514.0842-6.23425
21210.9514.0339-3.08392
21312.3513.133-0.783006
2149.9513.5596-3.60958
21514.913.48671.41335
21616.6513.6133.03699
21713.414.0051-0.60507
21813.9514.313-0.362967
21915.713.0532.64698
22016.8513.72223.12775
22110.9513.4235-2.47347
22215.3513.19882.15116
22312.212.9792-0.779237
22415.113.33131.76869
22517.7513.49374.2563
22615.213.76651.43354
22714.614.11060.489422
22816.6513.11223.53784
2298.112.8948-4.79476

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 14.2429 & -1.34291 \tabularnewline
2 & 12.8 & 14.08 & -1.28002 \tabularnewline
3 & 7.4 & 13.489 & -6.08899 \tabularnewline
4 & 6.7 & 14.2203 & -7.52031 \tabularnewline
5 & 12.6 & 13.3547 & -0.754675 \tabularnewline
6 & 14.8 & 12.9231 & 1.87689 \tabularnewline
7 & 13.3 & 13.9145 & -0.614482 \tabularnewline
8 & 11.1 & 12.9119 & -1.81193 \tabularnewline
9 & 8.2 & 15.2756 & -7.07558 \tabularnewline
10 & 11.4 & 13.4374 & -2.03744 \tabularnewline
11 & 6.4 & 13.8981 & -7.49812 \tabularnewline
12 & 12 & 13.2473 & -1.24728 \tabularnewline
13 & 6.3 & 13.2557 & -6.95573 \tabularnewline
14 & 11.3 & 12.9325 & -1.63251 \tabularnewline
15 & 11.9 & 13.481 & -1.58103 \tabularnewline
16 & 9.3 & 13.4058 & -4.10576 \tabularnewline
17 & 10 & 12.8425 & -2.8425 \tabularnewline
18 & 13.8 & 13.3144 & 0.485632 \tabularnewline
19 & 10.8 & 13.3092 & -2.5092 \tabularnewline
20 & 11.7 & 13.3147 & -1.61468 \tabularnewline
21 & 10.9 & 13.3663 & -2.46627 \tabularnewline
22 & 16.1 & 13.3583 & 2.74173 \tabularnewline
23 & 9.9 & 13.6959 & -3.79591 \tabularnewline
24 & 11.5 & 12.9966 & -1.49663 \tabularnewline
25 & 8.3 & 12.8664 & -4.5664 \tabularnewline
26 & 11.7 & 13.5052 & -1.80517 \tabularnewline
27 & 9 & 13.125 & -4.12501 \tabularnewline
28 & 10.8 & 13.0332 & -2.23316 \tabularnewline
29 & 10.4 & 13.3794 & -2.97943 \tabularnewline
30 & 12.7 & 13.4689 & -0.768946 \tabularnewline
31 & 11.8 & 12.9176 & -1.11763 \tabularnewline
32 & 13 & 13.45 & -0.449981 \tabularnewline
33 & 10.8 & 12.7596 & -1.95964 \tabularnewline
34 & 12.3 & 13.1876 & -0.887603 \tabularnewline
35 & 11.3 & 13.7486 & -2.44858 \tabularnewline
36 & 11.6 & 13.0977 & -1.49773 \tabularnewline
37 & 10.9 & 13.8507 & -2.95067 \tabularnewline
38 & 12.1 & 13.3658 & -1.26577 \tabularnewline
39 & 13.3 & 13.916 & -0.616008 \tabularnewline
40 & 10.1 & 13.4189 & -3.31893 \tabularnewline
41 & 14.3 & 13.1355 & 1.16447 \tabularnewline
42 & 9.3 & 13.8412 & -4.54124 \tabularnewline
43 & 12.5 & 13.3445 & -0.844473 \tabularnewline
44 & 7.6 & 12.226 & -4.62603 \tabularnewline
45 & 9.2 & 13.6146 & -4.41458 \tabularnewline
46 & 14.5 & 13.4592 & 1.04076 \tabularnewline
47 & 12.3 & 13.7111 & -1.41115 \tabularnewline
48 & 12.6 & 12.6628 & -0.0627553 \tabularnewline
49 & 13 & 13.2224 & -0.222384 \tabularnewline
50 & 12.6 & 13.7852 & -1.18515 \tabularnewline
51 & 13.2 & 13.7839 & -0.583853 \tabularnewline
52 & 7.7 & 13.3047 & -5.60466 \tabularnewline
53 & 10.5 & 13.56 & -3.06003 \tabularnewline
54 & 10.9 & 13.1904 & -2.29039 \tabularnewline
55 & 4.3 & 13.2611 & -8.96107 \tabularnewline
56 & 10.3 & 13.505 & -3.20503 \tabularnewline
57 & 11.4 & 13.0051 & -1.60508 \tabularnewline
58 & 5.6 & 12.9951 & -7.3951 \tabularnewline
59 & 8.8 & 12.7849 & -3.98489 \tabularnewline
60 & 9 & 13.489 & -4.48899 \tabularnewline
61 & 9.6 & 13.3484 & -3.74838 \tabularnewline
62 & 6.4 & 13.6436 & -7.24356 \tabularnewline
63 & 11.6 & 13.5595 & -1.95953 \tabularnewline
64 & 4.35 & 14.401 & -10.051 \tabularnewline
65 & 12.7 & 13.7416 & -1.04161 \tabularnewline
66 & 18.1 & 13.2138 & 4.88624 \tabularnewline
67 & 17.85 & 13.4061 & 4.44392 \tabularnewline
68 & 16.6 & 14.154 & 2.44601 \tabularnewline
69 & 12.6 & 13.4583 & -0.858299 \tabularnewline
70 & 17.1 & 14.2509 & 2.84913 \tabularnewline
71 & 19.1 & 13.8765 & 5.22349 \tabularnewline
72 & 16.1 & 13.3981 & 2.70192 \tabularnewline
73 & 13.35 & 13.6611 & -0.311133 \tabularnewline
74 & 18.4 & 14.4808 & 3.91915 \tabularnewline
75 & 14.7 & 13.181 & 1.51905 \tabularnewline
76 & 10.6 & 13.7227 & -3.12274 \tabularnewline
77 & 12.6 & 13.4545 & -0.85452 \tabularnewline
78 & 16.2 & 13.3414 & 2.85863 \tabularnewline
79 & 13.6 & 13.6875 & -0.0874655 \tabularnewline
80 & 18.9 & 13.5548 & 5.34518 \tabularnewline
81 & 14.1 & 13.5873 & 0.512695 \tabularnewline
82 & 14.5 & 13.4875 & 1.01254 \tabularnewline
83 & 16.15 & 13.3978 & 2.75223 \tabularnewline
84 & 14.75 & 13.2036 & 1.54645 \tabularnewline
85 & 14.8 & 13.4979 & 1.30207 \tabularnewline
86 & 12.45 & 13.6574 & -1.2074 \tabularnewline
87 & 12.65 & 14.1051 & -1.45505 \tabularnewline
88 & 17.35 & 13.767 & 3.58304 \tabularnewline
89 & 8.6 & 13.3178 & -4.71783 \tabularnewline
90 & 18.4 & 13.2611 & 5.13893 \tabularnewline
91 & 16.1 & 14.397 & 1.70305 \tabularnewline
92 & 11.6 & 12.8009 & -1.20089 \tabularnewline
93 & 17.75 & 13.2873 & 4.46273 \tabularnewline
94 & 15.25 & 13.1469 & 2.10306 \tabularnewline
95 & 17.65 & 12.8609 & 4.78912 \tabularnewline
96 & 16.35 & 13.5158 & 2.83419 \tabularnewline
97 & 17.65 & 14.3797 & 3.27026 \tabularnewline
98 & 13.6 & 14.7385 & -1.13851 \tabularnewline
99 & 14.35 & 13.9692 & 0.380834 \tabularnewline
100 & 14.75 & 14.0044 & 0.74556 \tabularnewline
101 & 18.25 & 13.7731 & 4.47693 \tabularnewline
102 & 9.9 & 12.7814 & -2.88139 \tabularnewline
103 & 16 & 13.9605 & 2.03946 \tabularnewline
104 & 18.25 & 13.9605 & 4.28946 \tabularnewline
105 & 16.85 & 13.728 & 3.12205 \tabularnewline
106 & 14.6 & 14.0303 & 0.569721 \tabularnewline
107 & 13.85 & 14.1538 & -0.303807 \tabularnewline
108 & 18.95 & 14.1228 & 4.82724 \tabularnewline
109 & 15.6 & 13.2697 & 2.3303 \tabularnewline
110 & 14.85 & 14.2507 & 0.599312 \tabularnewline
111 & 11.75 & 13.4415 & -1.69153 \tabularnewline
112 & 18.45 & 13.5828 & 4.86723 \tabularnewline
113 & 15.9 & 13.4937 & 2.4063 \tabularnewline
114 & 17.1 & 13.9739 & 3.12612 \tabularnewline
115 & 16.1 & 14.1493 & 1.95073 \tabularnewline
116 & 19.9 & 14.3888 & 5.51118 \tabularnewline
117 & 10.95 & 12.7151 & -1.76511 \tabularnewline
118 & 18.45 & 13.0457 & 5.4043 \tabularnewline
119 & 15.1 & 13.503 & 1.59704 \tabularnewline
120 & 15 & 14.0919 & 0.908114 \tabularnewline
121 & 11.35 & 13.1658 & -1.81577 \tabularnewline
122 & 15.95 & 13.4137 & 2.53628 \tabularnewline
123 & 18.1 & 13.465 & 4.63496 \tabularnewline
124 & 14.6 & 13.3448 & 1.25521 \tabularnewline
125 & 15.4 & 13.8999 & 1.50012 \tabularnewline
126 & 15.4 & 13.7767 & 1.62334 \tabularnewline
127 & 17.6 & 12.9973 & 4.60274 \tabularnewline
128 & 13.35 & 13.9662 & -0.616202 \tabularnewline
129 & 19.1 & 13.4905 & 5.60953 \tabularnewline
130 & 15.35 & 13.7185 & 1.63148 \tabularnewline
131 & 7.6 & 13.0293 & -5.42925 \tabularnewline
132 & 13.4 & 13.7596 & -0.359589 \tabularnewline
133 & 13.9 & 13.8599 & 0.0400724 \tabularnewline
134 & 19.1 & 13.4631 & 5.63685 \tabularnewline
135 & 15.25 & 13.3089 & 1.94112 \tabularnewline
136 & 12.9 & 12.9479 & -0.0479201 \tabularnewline
137 & 16.1 & 13.5572 & 2.5428 \tabularnewline
138 & 17.35 & 13.46 & 3.89 \tabularnewline
139 & 13.15 & 13.3625 & -0.212493 \tabularnewline
140 & 12.15 & 13.4379 & -1.2879 \tabularnewline
141 & 12.6 & 13.1205 & -0.52047 \tabularnewline
142 & 10.35 & 13.767 & -3.41696 \tabularnewline
143 & 15.4 & 13.0587 & 2.34132 \tabularnewline
144 & 9.6 & 14.1845 & -4.58454 \tabularnewline
145 & 18.2 & 13.5736 & 4.62635 \tabularnewline
146 & 13.6 & 14.0929 & -0.492873 \tabularnewline
147 & 14.85 & 14.0695 & 0.780489 \tabularnewline
148 & 14.75 & 13.1772 & 1.57278 \tabularnewline
149 & 14.1 & 13.4868 & 0.61317 \tabularnewline
150 & 14.9 & 13.6569 & 1.24309 \tabularnewline
151 & 16.25 & 13.9071 & 2.34293 \tabularnewline
152 & 19.25 & 14.9582 & 4.29179 \tabularnewline
153 & 13.6 & 14.7301 & -1.13006 \tabularnewline
154 & 13.6 & 13.5553 & 0.0446896 \tabularnewline
155 & 15.65 & 13.2478 & 2.40223 \tabularnewline
156 & 12.75 & 12.7242 & 0.0258168 \tabularnewline
157 & 14.6 & 13.2962 & 1.30379 \tabularnewline
158 & 9.85 & 13.331 & -3.48099 \tabularnewline
159 & 12.65 & 14.0338 & -1.38379 \tabularnewline
160 & 19.2 & 13.0761 & 6.12388 \tabularnewline
161 & 16.6 & 12.9578 & 3.64224 \tabularnewline
162 & 11.2 & 13.5033 & -2.30328 \tabularnewline
163 & 15.25 & 13.296 & 1.95397 \tabularnewline
164 & 11.9 & 13.212 & -1.312 \tabularnewline
165 & 13.2 & 12.8997 & 0.300254 \tabularnewline
166 & 16.35 & 13.0977 & 3.25227 \tabularnewline
167 & 12.4 & 13.9932 & -1.59316 \tabularnewline
168 & 15.85 & 13.5878 & 2.2622 \tabularnewline
169 & 18.15 & 13.4453 & 4.70474 \tabularnewline
170 & 11.15 & 13.1518 & -2.00183 \tabularnewline
171 & 15.65 & 13.648 & 2.00203 \tabularnewline
172 & 17.75 & 12.9392 & 4.81076 \tabularnewline
173 & 7.65 & 12.5279 & -4.87791 \tabularnewline
174 & 12.35 & 13.9826 & -1.63265 \tabularnewline
175 & 15.6 & 12.9321 & 2.66794 \tabularnewline
176 & 19.3 & 13.2174 & 6.08265 \tabularnewline
177 & 15.2 & 12.9217 & 2.27828 \tabularnewline
178 & 17.1 & 13.8765 & 3.22349 \tabularnewline
179 & 15.6 & 13.3619 & 2.23814 \tabularnewline
180 & 18.4 & 13.2383 & 5.16166 \tabularnewline
181 & 19.05 & 13.4635 & 5.58654 \tabularnewline
182 & 18.55 & 13.6962 & 4.85377 \tabularnewline
183 & 19.1 & 13.7234 & 5.37663 \tabularnewline
184 & 13.1 & 12.9345 & 0.165473 \tabularnewline
185 & 12.85 & 13.1419 & -0.291948 \tabularnewline
186 & 9.5 & 13.5643 & -4.06425 \tabularnewline
187 & 4.5 & 13.864 & -9.36397 \tabularnewline
188 & 11.85 & 12.9758 & -1.12578 \tabularnewline
189 & 13.6 & 12.8838 & 0.716248 \tabularnewline
190 & 11.7 & 15.4177 & -3.71766 \tabularnewline
191 & 12.4 & 13.5246 & -1.12458 \tabularnewline
192 & 13.35 & 13.6649 & -0.314864 \tabularnewline
193 & 11.4 & 12.4643 & -1.06428 \tabularnewline
194 & 14.9 & 13.7886 & 1.11143 \tabularnewline
195 & 19.9 & 13.291 & 6.60895 \tabularnewline
196 & 11.2 & 13.7811 & -2.58107 \tabularnewline
197 & 14.6 & 12.9261 & 1.67392 \tabularnewline
198 & 17.6 & 14.0355 & 3.56451 \tabularnewline
199 & 14.05 & 13.2114 & 0.838578 \tabularnewline
200 & 16.1 & 13.5598 & 2.54015 \tabularnewline
201 & 13.35 & 13.5598 & -0.20985 \tabularnewline
202 & 11.85 & 13.1597 & -1.30965 \tabularnewline
203 & 11.95 & 13.3402 & -1.39025 \tabularnewline
204 & 14.75 & 13.3277 & 1.42229 \tabularnewline
205 & 15.15 & 12.6548 & 2.49519 \tabularnewline
206 & 13.2 & 12.7808 & 0.419194 \tabularnewline
207 & 16.85 & 13.6917 & 3.15831 \tabularnewline
208 & 7.85 & 13.1593 & -5.30934 \tabularnewline
209 & 7.7 & 12.6759 & -4.97588 \tabularnewline
210 & 12.6 & 12.9366 & -0.336595 \tabularnewline
211 & 7.85 & 14.0842 & -6.23425 \tabularnewline
212 & 10.95 & 14.0339 & -3.08392 \tabularnewline
213 & 12.35 & 13.133 & -0.783006 \tabularnewline
214 & 9.95 & 13.5596 & -3.60958 \tabularnewline
215 & 14.9 & 13.4867 & 1.41335 \tabularnewline
216 & 16.65 & 13.613 & 3.03699 \tabularnewline
217 & 13.4 & 14.0051 & -0.60507 \tabularnewline
218 & 13.95 & 14.313 & -0.362967 \tabularnewline
219 & 15.7 & 13.053 & 2.64698 \tabularnewline
220 & 16.85 & 13.7222 & 3.12775 \tabularnewline
221 & 10.95 & 13.4235 & -2.47347 \tabularnewline
222 & 15.35 & 13.1988 & 2.15116 \tabularnewline
223 & 12.2 & 12.9792 & -0.779237 \tabularnewline
224 & 15.1 & 13.3313 & 1.76869 \tabularnewline
225 & 17.75 & 13.4937 & 4.2563 \tabularnewline
226 & 15.2 & 13.7665 & 1.43354 \tabularnewline
227 & 14.6 & 14.1106 & 0.489422 \tabularnewline
228 & 16.65 & 13.1122 & 3.53784 \tabularnewline
229 & 8.1 & 12.8948 & -4.79476 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=262784&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]14.2429[/C][C]-1.34291[/C][/ROW]
[ROW][C]2[/C][C]12.8[/C][C]14.08[/C][C]-1.28002[/C][/ROW]
[ROW][C]3[/C][C]7.4[/C][C]13.489[/C][C]-6.08899[/C][/ROW]
[ROW][C]4[/C][C]6.7[/C][C]14.2203[/C][C]-7.52031[/C][/ROW]
[ROW][C]5[/C][C]12.6[/C][C]13.3547[/C][C]-0.754675[/C][/ROW]
[ROW][C]6[/C][C]14.8[/C][C]12.9231[/C][C]1.87689[/C][/ROW]
[ROW][C]7[/C][C]13.3[/C][C]13.9145[/C][C]-0.614482[/C][/ROW]
[ROW][C]8[/C][C]11.1[/C][C]12.9119[/C][C]-1.81193[/C][/ROW]
[ROW][C]9[/C][C]8.2[/C][C]15.2756[/C][C]-7.07558[/C][/ROW]
[ROW][C]10[/C][C]11.4[/C][C]13.4374[/C][C]-2.03744[/C][/ROW]
[ROW][C]11[/C][C]6.4[/C][C]13.8981[/C][C]-7.49812[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]13.2473[/C][C]-1.24728[/C][/ROW]
[ROW][C]13[/C][C]6.3[/C][C]13.2557[/C][C]-6.95573[/C][/ROW]
[ROW][C]14[/C][C]11.3[/C][C]12.9325[/C][C]-1.63251[/C][/ROW]
[ROW][C]15[/C][C]11.9[/C][C]13.481[/C][C]-1.58103[/C][/ROW]
[ROW][C]16[/C][C]9.3[/C][C]13.4058[/C][C]-4.10576[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]12.8425[/C][C]-2.8425[/C][/ROW]
[ROW][C]18[/C][C]13.8[/C][C]13.3144[/C][C]0.485632[/C][/ROW]
[ROW][C]19[/C][C]10.8[/C][C]13.3092[/C][C]-2.5092[/C][/ROW]
[ROW][C]20[/C][C]11.7[/C][C]13.3147[/C][C]-1.61468[/C][/ROW]
[ROW][C]21[/C][C]10.9[/C][C]13.3663[/C][C]-2.46627[/C][/ROW]
[ROW][C]22[/C][C]16.1[/C][C]13.3583[/C][C]2.74173[/C][/ROW]
[ROW][C]23[/C][C]9.9[/C][C]13.6959[/C][C]-3.79591[/C][/ROW]
[ROW][C]24[/C][C]11.5[/C][C]12.9966[/C][C]-1.49663[/C][/ROW]
[ROW][C]25[/C][C]8.3[/C][C]12.8664[/C][C]-4.5664[/C][/ROW]
[ROW][C]26[/C][C]11.7[/C][C]13.5052[/C][C]-1.80517[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]13.125[/C][C]-4.12501[/C][/ROW]
[ROW][C]28[/C][C]10.8[/C][C]13.0332[/C][C]-2.23316[/C][/ROW]
[ROW][C]29[/C][C]10.4[/C][C]13.3794[/C][C]-2.97943[/C][/ROW]
[ROW][C]30[/C][C]12.7[/C][C]13.4689[/C][C]-0.768946[/C][/ROW]
[ROW][C]31[/C][C]11.8[/C][C]12.9176[/C][C]-1.11763[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]13.45[/C][C]-0.449981[/C][/ROW]
[ROW][C]33[/C][C]10.8[/C][C]12.7596[/C][C]-1.95964[/C][/ROW]
[ROW][C]34[/C][C]12.3[/C][C]13.1876[/C][C]-0.887603[/C][/ROW]
[ROW][C]35[/C][C]11.3[/C][C]13.7486[/C][C]-2.44858[/C][/ROW]
[ROW][C]36[/C][C]11.6[/C][C]13.0977[/C][C]-1.49773[/C][/ROW]
[ROW][C]37[/C][C]10.9[/C][C]13.8507[/C][C]-2.95067[/C][/ROW]
[ROW][C]38[/C][C]12.1[/C][C]13.3658[/C][C]-1.26577[/C][/ROW]
[ROW][C]39[/C][C]13.3[/C][C]13.916[/C][C]-0.616008[/C][/ROW]
[ROW][C]40[/C][C]10.1[/C][C]13.4189[/C][C]-3.31893[/C][/ROW]
[ROW][C]41[/C][C]14.3[/C][C]13.1355[/C][C]1.16447[/C][/ROW]
[ROW][C]42[/C][C]9.3[/C][C]13.8412[/C][C]-4.54124[/C][/ROW]
[ROW][C]43[/C][C]12.5[/C][C]13.3445[/C][C]-0.844473[/C][/ROW]
[ROW][C]44[/C][C]7.6[/C][C]12.226[/C][C]-4.62603[/C][/ROW]
[ROW][C]45[/C][C]9.2[/C][C]13.6146[/C][C]-4.41458[/C][/ROW]
[ROW][C]46[/C][C]14.5[/C][C]13.4592[/C][C]1.04076[/C][/ROW]
[ROW][C]47[/C][C]12.3[/C][C]13.7111[/C][C]-1.41115[/C][/ROW]
[ROW][C]48[/C][C]12.6[/C][C]12.6628[/C][C]-0.0627553[/C][/ROW]
[ROW][C]49[/C][C]13[/C][C]13.2224[/C][C]-0.222384[/C][/ROW]
[ROW][C]50[/C][C]12.6[/C][C]13.7852[/C][C]-1.18515[/C][/ROW]
[ROW][C]51[/C][C]13.2[/C][C]13.7839[/C][C]-0.583853[/C][/ROW]
[ROW][C]52[/C][C]7.7[/C][C]13.3047[/C][C]-5.60466[/C][/ROW]
[ROW][C]53[/C][C]10.5[/C][C]13.56[/C][C]-3.06003[/C][/ROW]
[ROW][C]54[/C][C]10.9[/C][C]13.1904[/C][C]-2.29039[/C][/ROW]
[ROW][C]55[/C][C]4.3[/C][C]13.2611[/C][C]-8.96107[/C][/ROW]
[ROW][C]56[/C][C]10.3[/C][C]13.505[/C][C]-3.20503[/C][/ROW]
[ROW][C]57[/C][C]11.4[/C][C]13.0051[/C][C]-1.60508[/C][/ROW]
[ROW][C]58[/C][C]5.6[/C][C]12.9951[/C][C]-7.3951[/C][/ROW]
[ROW][C]59[/C][C]8.8[/C][C]12.7849[/C][C]-3.98489[/C][/ROW]
[ROW][C]60[/C][C]9[/C][C]13.489[/C][C]-4.48899[/C][/ROW]
[ROW][C]61[/C][C]9.6[/C][C]13.3484[/C][C]-3.74838[/C][/ROW]
[ROW][C]62[/C][C]6.4[/C][C]13.6436[/C][C]-7.24356[/C][/ROW]
[ROW][C]63[/C][C]11.6[/C][C]13.5595[/C][C]-1.95953[/C][/ROW]
[ROW][C]64[/C][C]4.35[/C][C]14.401[/C][C]-10.051[/C][/ROW]
[ROW][C]65[/C][C]12.7[/C][C]13.7416[/C][C]-1.04161[/C][/ROW]
[ROW][C]66[/C][C]18.1[/C][C]13.2138[/C][C]4.88624[/C][/ROW]
[ROW][C]67[/C][C]17.85[/C][C]13.4061[/C][C]4.44392[/C][/ROW]
[ROW][C]68[/C][C]16.6[/C][C]14.154[/C][C]2.44601[/C][/ROW]
[ROW][C]69[/C][C]12.6[/C][C]13.4583[/C][C]-0.858299[/C][/ROW]
[ROW][C]70[/C][C]17.1[/C][C]14.2509[/C][C]2.84913[/C][/ROW]
[ROW][C]71[/C][C]19.1[/C][C]13.8765[/C][C]5.22349[/C][/ROW]
[ROW][C]72[/C][C]16.1[/C][C]13.3981[/C][C]2.70192[/C][/ROW]
[ROW][C]73[/C][C]13.35[/C][C]13.6611[/C][C]-0.311133[/C][/ROW]
[ROW][C]74[/C][C]18.4[/C][C]14.4808[/C][C]3.91915[/C][/ROW]
[ROW][C]75[/C][C]14.7[/C][C]13.181[/C][C]1.51905[/C][/ROW]
[ROW][C]76[/C][C]10.6[/C][C]13.7227[/C][C]-3.12274[/C][/ROW]
[ROW][C]77[/C][C]12.6[/C][C]13.4545[/C][C]-0.85452[/C][/ROW]
[ROW][C]78[/C][C]16.2[/C][C]13.3414[/C][C]2.85863[/C][/ROW]
[ROW][C]79[/C][C]13.6[/C][C]13.6875[/C][C]-0.0874655[/C][/ROW]
[ROW][C]80[/C][C]18.9[/C][C]13.5548[/C][C]5.34518[/C][/ROW]
[ROW][C]81[/C][C]14.1[/C][C]13.5873[/C][C]0.512695[/C][/ROW]
[ROW][C]82[/C][C]14.5[/C][C]13.4875[/C][C]1.01254[/C][/ROW]
[ROW][C]83[/C][C]16.15[/C][C]13.3978[/C][C]2.75223[/C][/ROW]
[ROW][C]84[/C][C]14.75[/C][C]13.2036[/C][C]1.54645[/C][/ROW]
[ROW][C]85[/C][C]14.8[/C][C]13.4979[/C][C]1.30207[/C][/ROW]
[ROW][C]86[/C][C]12.45[/C][C]13.6574[/C][C]-1.2074[/C][/ROW]
[ROW][C]87[/C][C]12.65[/C][C]14.1051[/C][C]-1.45505[/C][/ROW]
[ROW][C]88[/C][C]17.35[/C][C]13.767[/C][C]3.58304[/C][/ROW]
[ROW][C]89[/C][C]8.6[/C][C]13.3178[/C][C]-4.71783[/C][/ROW]
[ROW][C]90[/C][C]18.4[/C][C]13.2611[/C][C]5.13893[/C][/ROW]
[ROW][C]91[/C][C]16.1[/C][C]14.397[/C][C]1.70305[/C][/ROW]
[ROW][C]92[/C][C]11.6[/C][C]12.8009[/C][C]-1.20089[/C][/ROW]
[ROW][C]93[/C][C]17.75[/C][C]13.2873[/C][C]4.46273[/C][/ROW]
[ROW][C]94[/C][C]15.25[/C][C]13.1469[/C][C]2.10306[/C][/ROW]
[ROW][C]95[/C][C]17.65[/C][C]12.8609[/C][C]4.78912[/C][/ROW]
[ROW][C]96[/C][C]16.35[/C][C]13.5158[/C][C]2.83419[/C][/ROW]
[ROW][C]97[/C][C]17.65[/C][C]14.3797[/C][C]3.27026[/C][/ROW]
[ROW][C]98[/C][C]13.6[/C][C]14.7385[/C][C]-1.13851[/C][/ROW]
[ROW][C]99[/C][C]14.35[/C][C]13.9692[/C][C]0.380834[/C][/ROW]
[ROW][C]100[/C][C]14.75[/C][C]14.0044[/C][C]0.74556[/C][/ROW]
[ROW][C]101[/C][C]18.25[/C][C]13.7731[/C][C]4.47693[/C][/ROW]
[ROW][C]102[/C][C]9.9[/C][C]12.7814[/C][C]-2.88139[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]13.9605[/C][C]2.03946[/C][/ROW]
[ROW][C]104[/C][C]18.25[/C][C]13.9605[/C][C]4.28946[/C][/ROW]
[ROW][C]105[/C][C]16.85[/C][C]13.728[/C][C]3.12205[/C][/ROW]
[ROW][C]106[/C][C]14.6[/C][C]14.0303[/C][C]0.569721[/C][/ROW]
[ROW][C]107[/C][C]13.85[/C][C]14.1538[/C][C]-0.303807[/C][/ROW]
[ROW][C]108[/C][C]18.95[/C][C]14.1228[/C][C]4.82724[/C][/ROW]
[ROW][C]109[/C][C]15.6[/C][C]13.2697[/C][C]2.3303[/C][/ROW]
[ROW][C]110[/C][C]14.85[/C][C]14.2507[/C][C]0.599312[/C][/ROW]
[ROW][C]111[/C][C]11.75[/C][C]13.4415[/C][C]-1.69153[/C][/ROW]
[ROW][C]112[/C][C]18.45[/C][C]13.5828[/C][C]4.86723[/C][/ROW]
[ROW][C]113[/C][C]15.9[/C][C]13.4937[/C][C]2.4063[/C][/ROW]
[ROW][C]114[/C][C]17.1[/C][C]13.9739[/C][C]3.12612[/C][/ROW]
[ROW][C]115[/C][C]16.1[/C][C]14.1493[/C][C]1.95073[/C][/ROW]
[ROW][C]116[/C][C]19.9[/C][C]14.3888[/C][C]5.51118[/C][/ROW]
[ROW][C]117[/C][C]10.95[/C][C]12.7151[/C][C]-1.76511[/C][/ROW]
[ROW][C]118[/C][C]18.45[/C][C]13.0457[/C][C]5.4043[/C][/ROW]
[ROW][C]119[/C][C]15.1[/C][C]13.503[/C][C]1.59704[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]14.0919[/C][C]0.908114[/C][/ROW]
[ROW][C]121[/C][C]11.35[/C][C]13.1658[/C][C]-1.81577[/C][/ROW]
[ROW][C]122[/C][C]15.95[/C][C]13.4137[/C][C]2.53628[/C][/ROW]
[ROW][C]123[/C][C]18.1[/C][C]13.465[/C][C]4.63496[/C][/ROW]
[ROW][C]124[/C][C]14.6[/C][C]13.3448[/C][C]1.25521[/C][/ROW]
[ROW][C]125[/C][C]15.4[/C][C]13.8999[/C][C]1.50012[/C][/ROW]
[ROW][C]126[/C][C]15.4[/C][C]13.7767[/C][C]1.62334[/C][/ROW]
[ROW][C]127[/C][C]17.6[/C][C]12.9973[/C][C]4.60274[/C][/ROW]
[ROW][C]128[/C][C]13.35[/C][C]13.9662[/C][C]-0.616202[/C][/ROW]
[ROW][C]129[/C][C]19.1[/C][C]13.4905[/C][C]5.60953[/C][/ROW]
[ROW][C]130[/C][C]15.35[/C][C]13.7185[/C][C]1.63148[/C][/ROW]
[ROW][C]131[/C][C]7.6[/C][C]13.0293[/C][C]-5.42925[/C][/ROW]
[ROW][C]132[/C][C]13.4[/C][C]13.7596[/C][C]-0.359589[/C][/ROW]
[ROW][C]133[/C][C]13.9[/C][C]13.8599[/C][C]0.0400724[/C][/ROW]
[ROW][C]134[/C][C]19.1[/C][C]13.4631[/C][C]5.63685[/C][/ROW]
[ROW][C]135[/C][C]15.25[/C][C]13.3089[/C][C]1.94112[/C][/ROW]
[ROW][C]136[/C][C]12.9[/C][C]12.9479[/C][C]-0.0479201[/C][/ROW]
[ROW][C]137[/C][C]16.1[/C][C]13.5572[/C][C]2.5428[/C][/ROW]
[ROW][C]138[/C][C]17.35[/C][C]13.46[/C][C]3.89[/C][/ROW]
[ROW][C]139[/C][C]13.15[/C][C]13.3625[/C][C]-0.212493[/C][/ROW]
[ROW][C]140[/C][C]12.15[/C][C]13.4379[/C][C]-1.2879[/C][/ROW]
[ROW][C]141[/C][C]12.6[/C][C]13.1205[/C][C]-0.52047[/C][/ROW]
[ROW][C]142[/C][C]10.35[/C][C]13.767[/C][C]-3.41696[/C][/ROW]
[ROW][C]143[/C][C]15.4[/C][C]13.0587[/C][C]2.34132[/C][/ROW]
[ROW][C]144[/C][C]9.6[/C][C]14.1845[/C][C]-4.58454[/C][/ROW]
[ROW][C]145[/C][C]18.2[/C][C]13.5736[/C][C]4.62635[/C][/ROW]
[ROW][C]146[/C][C]13.6[/C][C]14.0929[/C][C]-0.492873[/C][/ROW]
[ROW][C]147[/C][C]14.85[/C][C]14.0695[/C][C]0.780489[/C][/ROW]
[ROW][C]148[/C][C]14.75[/C][C]13.1772[/C][C]1.57278[/C][/ROW]
[ROW][C]149[/C][C]14.1[/C][C]13.4868[/C][C]0.61317[/C][/ROW]
[ROW][C]150[/C][C]14.9[/C][C]13.6569[/C][C]1.24309[/C][/ROW]
[ROW][C]151[/C][C]16.25[/C][C]13.9071[/C][C]2.34293[/C][/ROW]
[ROW][C]152[/C][C]19.25[/C][C]14.9582[/C][C]4.29179[/C][/ROW]
[ROW][C]153[/C][C]13.6[/C][C]14.7301[/C][C]-1.13006[/C][/ROW]
[ROW][C]154[/C][C]13.6[/C][C]13.5553[/C][C]0.0446896[/C][/ROW]
[ROW][C]155[/C][C]15.65[/C][C]13.2478[/C][C]2.40223[/C][/ROW]
[ROW][C]156[/C][C]12.75[/C][C]12.7242[/C][C]0.0258168[/C][/ROW]
[ROW][C]157[/C][C]14.6[/C][C]13.2962[/C][C]1.30379[/C][/ROW]
[ROW][C]158[/C][C]9.85[/C][C]13.331[/C][C]-3.48099[/C][/ROW]
[ROW][C]159[/C][C]12.65[/C][C]14.0338[/C][C]-1.38379[/C][/ROW]
[ROW][C]160[/C][C]19.2[/C][C]13.0761[/C][C]6.12388[/C][/ROW]
[ROW][C]161[/C][C]16.6[/C][C]12.9578[/C][C]3.64224[/C][/ROW]
[ROW][C]162[/C][C]11.2[/C][C]13.5033[/C][C]-2.30328[/C][/ROW]
[ROW][C]163[/C][C]15.25[/C][C]13.296[/C][C]1.95397[/C][/ROW]
[ROW][C]164[/C][C]11.9[/C][C]13.212[/C][C]-1.312[/C][/ROW]
[ROW][C]165[/C][C]13.2[/C][C]12.8997[/C][C]0.300254[/C][/ROW]
[ROW][C]166[/C][C]16.35[/C][C]13.0977[/C][C]3.25227[/C][/ROW]
[ROW][C]167[/C][C]12.4[/C][C]13.9932[/C][C]-1.59316[/C][/ROW]
[ROW][C]168[/C][C]15.85[/C][C]13.5878[/C][C]2.2622[/C][/ROW]
[ROW][C]169[/C][C]18.15[/C][C]13.4453[/C][C]4.70474[/C][/ROW]
[ROW][C]170[/C][C]11.15[/C][C]13.1518[/C][C]-2.00183[/C][/ROW]
[ROW][C]171[/C][C]15.65[/C][C]13.648[/C][C]2.00203[/C][/ROW]
[ROW][C]172[/C][C]17.75[/C][C]12.9392[/C][C]4.81076[/C][/ROW]
[ROW][C]173[/C][C]7.65[/C][C]12.5279[/C][C]-4.87791[/C][/ROW]
[ROW][C]174[/C][C]12.35[/C][C]13.9826[/C][C]-1.63265[/C][/ROW]
[ROW][C]175[/C][C]15.6[/C][C]12.9321[/C][C]2.66794[/C][/ROW]
[ROW][C]176[/C][C]19.3[/C][C]13.2174[/C][C]6.08265[/C][/ROW]
[ROW][C]177[/C][C]15.2[/C][C]12.9217[/C][C]2.27828[/C][/ROW]
[ROW][C]178[/C][C]17.1[/C][C]13.8765[/C][C]3.22349[/C][/ROW]
[ROW][C]179[/C][C]15.6[/C][C]13.3619[/C][C]2.23814[/C][/ROW]
[ROW][C]180[/C][C]18.4[/C][C]13.2383[/C][C]5.16166[/C][/ROW]
[ROW][C]181[/C][C]19.05[/C][C]13.4635[/C][C]5.58654[/C][/ROW]
[ROW][C]182[/C][C]18.55[/C][C]13.6962[/C][C]4.85377[/C][/ROW]
[ROW][C]183[/C][C]19.1[/C][C]13.7234[/C][C]5.37663[/C][/ROW]
[ROW][C]184[/C][C]13.1[/C][C]12.9345[/C][C]0.165473[/C][/ROW]
[ROW][C]185[/C][C]12.85[/C][C]13.1419[/C][C]-0.291948[/C][/ROW]
[ROW][C]186[/C][C]9.5[/C][C]13.5643[/C][C]-4.06425[/C][/ROW]
[ROW][C]187[/C][C]4.5[/C][C]13.864[/C][C]-9.36397[/C][/ROW]
[ROW][C]188[/C][C]11.85[/C][C]12.9758[/C][C]-1.12578[/C][/ROW]
[ROW][C]189[/C][C]13.6[/C][C]12.8838[/C][C]0.716248[/C][/ROW]
[ROW][C]190[/C][C]11.7[/C][C]15.4177[/C][C]-3.71766[/C][/ROW]
[ROW][C]191[/C][C]12.4[/C][C]13.5246[/C][C]-1.12458[/C][/ROW]
[ROW][C]192[/C][C]13.35[/C][C]13.6649[/C][C]-0.314864[/C][/ROW]
[ROW][C]193[/C][C]11.4[/C][C]12.4643[/C][C]-1.06428[/C][/ROW]
[ROW][C]194[/C][C]14.9[/C][C]13.7886[/C][C]1.11143[/C][/ROW]
[ROW][C]195[/C][C]19.9[/C][C]13.291[/C][C]6.60895[/C][/ROW]
[ROW][C]196[/C][C]11.2[/C][C]13.7811[/C][C]-2.58107[/C][/ROW]
[ROW][C]197[/C][C]14.6[/C][C]12.9261[/C][C]1.67392[/C][/ROW]
[ROW][C]198[/C][C]17.6[/C][C]14.0355[/C][C]3.56451[/C][/ROW]
[ROW][C]199[/C][C]14.05[/C][C]13.2114[/C][C]0.838578[/C][/ROW]
[ROW][C]200[/C][C]16.1[/C][C]13.5598[/C][C]2.54015[/C][/ROW]
[ROW][C]201[/C][C]13.35[/C][C]13.5598[/C][C]-0.20985[/C][/ROW]
[ROW][C]202[/C][C]11.85[/C][C]13.1597[/C][C]-1.30965[/C][/ROW]
[ROW][C]203[/C][C]11.95[/C][C]13.3402[/C][C]-1.39025[/C][/ROW]
[ROW][C]204[/C][C]14.75[/C][C]13.3277[/C][C]1.42229[/C][/ROW]
[ROW][C]205[/C][C]15.15[/C][C]12.6548[/C][C]2.49519[/C][/ROW]
[ROW][C]206[/C][C]13.2[/C][C]12.7808[/C][C]0.419194[/C][/ROW]
[ROW][C]207[/C][C]16.85[/C][C]13.6917[/C][C]3.15831[/C][/ROW]
[ROW][C]208[/C][C]7.85[/C][C]13.1593[/C][C]-5.30934[/C][/ROW]
[ROW][C]209[/C][C]7.7[/C][C]12.6759[/C][C]-4.97588[/C][/ROW]
[ROW][C]210[/C][C]12.6[/C][C]12.9366[/C][C]-0.336595[/C][/ROW]
[ROW][C]211[/C][C]7.85[/C][C]14.0842[/C][C]-6.23425[/C][/ROW]
[ROW][C]212[/C][C]10.95[/C][C]14.0339[/C][C]-3.08392[/C][/ROW]
[ROW][C]213[/C][C]12.35[/C][C]13.133[/C][C]-0.783006[/C][/ROW]
[ROW][C]214[/C][C]9.95[/C][C]13.5596[/C][C]-3.60958[/C][/ROW]
[ROW][C]215[/C][C]14.9[/C][C]13.4867[/C][C]1.41335[/C][/ROW]
[ROW][C]216[/C][C]16.65[/C][C]13.613[/C][C]3.03699[/C][/ROW]
[ROW][C]217[/C][C]13.4[/C][C]14.0051[/C][C]-0.60507[/C][/ROW]
[ROW][C]218[/C][C]13.95[/C][C]14.313[/C][C]-0.362967[/C][/ROW]
[ROW][C]219[/C][C]15.7[/C][C]13.053[/C][C]2.64698[/C][/ROW]
[ROW][C]220[/C][C]16.85[/C][C]13.7222[/C][C]3.12775[/C][/ROW]
[ROW][C]221[/C][C]10.95[/C][C]13.4235[/C][C]-2.47347[/C][/ROW]
[ROW][C]222[/C][C]15.35[/C][C]13.1988[/C][C]2.15116[/C][/ROW]
[ROW][C]223[/C][C]12.2[/C][C]12.9792[/C][C]-0.779237[/C][/ROW]
[ROW][C]224[/C][C]15.1[/C][C]13.3313[/C][C]1.76869[/C][/ROW]
[ROW][C]225[/C][C]17.75[/C][C]13.4937[/C][C]4.2563[/C][/ROW]
[ROW][C]226[/C][C]15.2[/C][C]13.7665[/C][C]1.43354[/C][/ROW]
[ROW][C]227[/C][C]14.6[/C][C]14.1106[/C][C]0.489422[/C][/ROW]
[ROW][C]228[/C][C]16.65[/C][C]13.1122[/C][C]3.53784[/C][/ROW]
[ROW][C]229[/C][C]8.1[/C][C]12.8948[/C][C]-4.79476[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=262784&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=262784&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
112.914.2429-1.34291
212.814.08-1.28002
37.413.489-6.08899
46.714.2203-7.52031
512.613.3547-0.754675
614.812.92311.87689
713.313.9145-0.614482
811.112.9119-1.81193
98.215.2756-7.07558
1011.413.4374-2.03744
116.413.8981-7.49812
121213.2473-1.24728
136.313.2557-6.95573
1411.312.9325-1.63251
1511.913.481-1.58103
169.313.4058-4.10576
171012.8425-2.8425
1813.813.31440.485632
1910.813.3092-2.5092
2011.713.3147-1.61468
2110.913.3663-2.46627
2216.113.35832.74173
239.913.6959-3.79591
2411.512.9966-1.49663
258.312.8664-4.5664
2611.713.5052-1.80517
27913.125-4.12501
2810.813.0332-2.23316
2910.413.3794-2.97943
3012.713.4689-0.768946
3111.812.9176-1.11763
321313.45-0.449981
3310.812.7596-1.95964
3412.313.1876-0.887603
3511.313.7486-2.44858
3611.613.0977-1.49773
3710.913.8507-2.95067
3812.113.3658-1.26577
3913.313.916-0.616008
4010.113.4189-3.31893
4114.313.13551.16447
429.313.8412-4.54124
4312.513.3445-0.844473
447.612.226-4.62603
459.213.6146-4.41458
4614.513.45921.04076
4712.313.7111-1.41115
4812.612.6628-0.0627553
491313.2224-0.222384
5012.613.7852-1.18515
5113.213.7839-0.583853
527.713.3047-5.60466
5310.513.56-3.06003
5410.913.1904-2.29039
554.313.2611-8.96107
5610.313.505-3.20503
5711.413.0051-1.60508
585.612.9951-7.3951
598.812.7849-3.98489
60913.489-4.48899
619.613.3484-3.74838
626.413.6436-7.24356
6311.613.5595-1.95953
644.3514.401-10.051
6512.713.7416-1.04161
6618.113.21384.88624
6717.8513.40614.44392
6816.614.1542.44601
6912.613.4583-0.858299
7017.114.25092.84913
7119.113.87655.22349
7216.113.39812.70192
7313.3513.6611-0.311133
7418.414.48083.91915
7514.713.1811.51905
7610.613.7227-3.12274
7712.613.4545-0.85452
7816.213.34142.85863
7913.613.6875-0.0874655
8018.913.55485.34518
8114.113.58730.512695
8214.513.48751.01254
8316.1513.39782.75223
8414.7513.20361.54645
8514.813.49791.30207
8612.4513.6574-1.2074
8712.6514.1051-1.45505
8817.3513.7673.58304
898.613.3178-4.71783
9018.413.26115.13893
9116.114.3971.70305
9211.612.8009-1.20089
9317.7513.28734.46273
9415.2513.14692.10306
9517.6512.86094.78912
9616.3513.51582.83419
9717.6514.37973.27026
9813.614.7385-1.13851
9914.3513.96920.380834
10014.7514.00440.74556
10118.2513.77314.47693
1029.912.7814-2.88139
1031613.96052.03946
10418.2513.96054.28946
10516.8513.7283.12205
10614.614.03030.569721
10713.8514.1538-0.303807
10818.9514.12284.82724
10915.613.26972.3303
11014.8514.25070.599312
11111.7513.4415-1.69153
11218.4513.58284.86723
11315.913.49372.4063
11417.113.97393.12612
11516.114.14931.95073
11619.914.38885.51118
11710.9512.7151-1.76511
11818.4513.04575.4043
11915.113.5031.59704
1201514.09190.908114
12111.3513.1658-1.81577
12215.9513.41372.53628
12318.113.4654.63496
12414.613.34481.25521
12515.413.89991.50012
12615.413.77671.62334
12717.612.99734.60274
12813.3513.9662-0.616202
12919.113.49055.60953
13015.3513.71851.63148
1317.613.0293-5.42925
13213.413.7596-0.359589
13313.913.85990.0400724
13419.113.46315.63685
13515.2513.30891.94112
13612.912.9479-0.0479201
13716.113.55722.5428
13817.3513.463.89
13913.1513.3625-0.212493
14012.1513.4379-1.2879
14112.613.1205-0.52047
14210.3513.767-3.41696
14315.413.05872.34132
1449.614.1845-4.58454
14518.213.57364.62635
14613.614.0929-0.492873
14714.8514.06950.780489
14814.7513.17721.57278
14914.113.48680.61317
15014.913.65691.24309
15116.2513.90712.34293
15219.2514.95824.29179
15313.614.7301-1.13006
15413.613.55530.0446896
15515.6513.24782.40223
15612.7512.72420.0258168
15714.613.29621.30379
1589.8513.331-3.48099
15912.6514.0338-1.38379
16019.213.07616.12388
16116.612.95783.64224
16211.213.5033-2.30328
16315.2513.2961.95397
16411.913.212-1.312
16513.212.89970.300254
16616.3513.09773.25227
16712.413.9932-1.59316
16815.8513.58782.2622
16918.1513.44534.70474
17011.1513.1518-2.00183
17115.6513.6482.00203
17217.7512.93924.81076
1737.6512.5279-4.87791
17412.3513.9826-1.63265
17515.612.93212.66794
17619.313.21746.08265
17715.212.92172.27828
17817.113.87653.22349
17915.613.36192.23814
18018.413.23835.16166
18119.0513.46355.58654
18218.5513.69624.85377
18319.113.72345.37663
18413.112.93450.165473
18512.8513.1419-0.291948
1869.513.5643-4.06425
1874.513.864-9.36397
18811.8512.9758-1.12578
18913.612.88380.716248
19011.715.4177-3.71766
19112.413.5246-1.12458
19213.3513.6649-0.314864
19311.412.4643-1.06428
19414.913.78861.11143
19519.913.2916.60895
19611.213.7811-2.58107
19714.612.92611.67392
19817.614.03553.56451
19914.0513.21140.838578
20016.113.55982.54015
20113.3513.5598-0.20985
20211.8513.1597-1.30965
20311.9513.3402-1.39025
20414.7513.32771.42229
20515.1512.65482.49519
20613.212.78080.419194
20716.8513.69173.15831
2087.8513.1593-5.30934
2097.712.6759-4.97588
21012.612.9366-0.336595
2117.8514.0842-6.23425
21210.9514.0339-3.08392
21312.3513.133-0.783006
2149.9513.5596-3.60958
21514.913.48671.41335
21616.6513.6133.03699
21713.414.0051-0.60507
21813.9514.313-0.362967
21915.713.0532.64698
22016.8513.72223.12775
22110.9513.4235-2.47347
22215.3513.19882.15116
22312.212.9792-0.779237
22415.113.33131.76869
22517.7513.49374.2563
22615.213.76651.43354
22714.614.11060.489422
22816.6513.11223.53784
2298.112.8948-4.79476






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.1130870.2261730.886913
80.1963130.3926260.803687
90.2606220.5212450.739378
100.1627890.3255770.837211
110.1471680.2943370.852832
120.1019570.2039140.898043
130.1353530.2707060.864647
140.1094580.2189160.890542
150.07025340.1405070.929747
160.04682470.09364940.953175
170.02827970.05655940.97172
180.02828450.05656910.971715
190.016980.033960.98302
200.009794120.01958820.990206
210.005613070.01122610.994387
220.01803630.03607270.981964
230.01197940.02395870.988021
240.007208590.01441720.992791
250.03826290.07652570.961737
260.03065210.06130410.969348
270.02916570.05833140.970834
280.02157120.04314240.978429
290.01484770.02969550.985152
300.01009090.02018180.989909
310.006848460.01369690.993152
320.006464230.01292850.993536
330.004417430.008834870.995583
340.002786680.005573360.997213
350.002228420.004456830.997772
360.001507670.003015340.998492
370.0009722220.001944440.999028
380.0008223040.001644610.999178
390.001078420.002156840.998922
400.0007246450.001449290.999275
410.0004838180.0009676360.999516
420.0003596490.0007192970.99964
430.0002636970.0005273950.999736
440.00233010.00466020.99767
450.002369030.004738060.997631
460.00279450.005588990.997206
470.001958180.003916350.998042
480.001356550.00271310.998643
490.0009674350.001934870.999033
500.0006720810.001344160.999328
510.0006533690.001306740.999347
520.00101440.002028790.998986
530.0007437090.001487420.999256
540.0005151890.001030380.999485
550.005391460.01078290.994609
560.004641650.009283290.995358
570.003528960.007057920.996471
580.01605280.03210560.983947
590.01709160.03418320.982908
600.01692020.03384040.98308
610.01539680.03079360.984603
620.03149710.06299430.968503
630.02677150.05354290.973229
640.1098060.2196120.890194
650.09549190.1909840.904508
660.205270.4105410.79473
670.3438510.6877030.656149
680.4262730.8525460.573727
690.3986530.7973060.601347
700.4839240.9678480.516076
710.6671390.6657210.332861
720.7023950.5952110.297605
730.6784850.6430290.321515
740.7503620.4992770.249638
750.7512740.4974510.248726
760.7425280.5149450.257472
770.7171340.5657330.282866
780.7323910.5352180.267609
790.7091030.5817940.290897
800.8170310.3659390.182969
810.7992070.4015870.200793
820.7832580.4334830.216742
830.7950780.4098440.204922
840.7844030.4311950.215597
850.772440.455120.22756
860.74850.5029990.2515
870.7252360.5495270.274764
880.7530410.4939170.246959
890.784850.4303010.21515
900.8461410.3077180.153859
910.8387170.3225670.161283
920.8164730.3670540.183527
930.8506650.298670.149335
940.8453170.3093650.154683
950.87830.2433990.1217
960.8793670.2412660.120633
970.8868310.2263380.113169
980.8719560.2560880.128044
990.8541280.2917440.145872
1000.8356670.3286660.164333
1010.8628850.274230.137115
1020.8599250.2801510.140075
1030.8500780.2998440.149922
1040.8700240.2599520.129976
1050.869370.261260.13063
1060.8510880.2978230.148912
1070.8303250.3393490.169675
1080.8599480.2801040.140052
1090.8518880.2962250.148112
1100.8304350.3391290.169565
1110.8147640.3704720.185236
1120.8456330.3087340.154367
1130.8364290.3271420.163571
1140.8334510.3330970.166549
1150.8172330.3655350.182767
1160.8582090.2835810.141791
1170.844160.3116790.15584
1180.8817980.2364040.118202
1190.8670840.2658310.132916
1200.8471340.3057320.152866
1210.8337520.3324960.166248
1220.8245960.3508080.175404
1230.8472060.3055880.152794
1240.8274830.3450350.172517
1250.8066630.3866740.193337
1260.785350.42930.21465
1270.8102580.3794850.189742
1280.7845730.4308540.215427
1290.8352120.3295760.164788
1300.81610.36780.1839
1310.8624610.2750770.137539
1320.8407510.3184980.159249
1330.8159930.3680130.184007
1340.8603040.2793910.139696
1350.8448050.3103910.155195
1360.8212840.3574320.178716
1370.8086570.3826860.191343
1380.8154940.3690130.184506
1390.7897310.4205370.210269
1400.7686240.4627520.231376
1410.7405060.5189880.259494
1420.7454490.5091020.254551
1430.7265290.5469420.273471
1440.76310.47380.2369
1450.7851320.4297360.214868
1460.7558160.4883680.244184
1470.7242390.5515220.275761
1480.6961160.6077670.303884
1490.6608220.6783560.339178
1500.6271670.7456660.372833
1510.6042670.7914650.395733
1520.6697890.6604210.330211
1530.6347550.730490.365245
1540.5966650.8066690.403335
1550.5703270.8593470.429673
1560.5362420.9275170.463758
1570.4985710.9971430.501429
1580.5202440.9595130.479756
1590.4834740.9669480.516526
1600.5536880.8926250.446312
1610.5500850.899830.449915
1620.5338280.9323430.466172
1630.5013330.9973340.498667
1640.4740610.9481210.525939
1650.4350860.8701730.564914
1660.4202080.8404160.579792
1670.3858050.7716090.614195
1680.3615630.7231260.638437
1690.3878380.7756760.612162
1700.3667810.7335620.633219
1710.3366120.6732240.663388
1720.3606760.7213520.639324
1730.436790.873580.56321
1740.4035570.8071130.596443
1750.3807350.7614690.619265
1760.4718430.9436860.528157
1770.4440570.8881140.555943
1780.4357160.8714320.564284
1790.4041410.8082820.595859
1800.4522410.9044820.547759
1810.5412710.9174580.458729
1820.5923240.8153520.407676
1830.6842620.6314760.315738
1840.6391780.7216450.360822
1850.5915680.8168640.408432
1860.6079520.7840960.392048
1870.8860760.2278480.113924
1880.8644360.2711280.135564
1890.8336280.3327440.166372
1900.8289140.3421720.171086
1910.8017850.396430.198215
1920.7667210.4665580.233279
1930.7258890.5482230.274111
1940.6802360.6395270.319764
1950.8423620.3152750.157638
1960.8294560.3410890.170544
1970.7956820.4086360.204318
1980.8059040.3881920.194096
1990.7750330.4499330.224967
2000.7508260.4983480.249174
2010.6994350.601130.300565
2020.6548730.6902550.345127
2030.6069840.7860310.393016
2040.5561030.8877940.443897
2050.7065880.5868250.293412
2060.7784120.4431760.221588
2070.7456260.5087480.254374
2080.8590040.2819920.140996
2090.9601580.0796850.0398425
2100.93890.1221990.0610997
2110.9887250.0225510.0112755
2120.9829830.0340340.017017
2130.9770170.0459660.022983
2140.9603230.07935430.0396772
2150.9401010.1197980.0598992
2160.9269810.1460380.0730192
2170.8845780.2308430.115422
2180.8169470.3661050.183053
2190.7272910.5454190.272709
2200.6074350.785130.392565
2210.8618580.2762840.138142
2220.7466320.5067370.253368

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.113087 & 0.226173 & 0.886913 \tabularnewline
8 & 0.196313 & 0.392626 & 0.803687 \tabularnewline
9 & 0.260622 & 0.521245 & 0.739378 \tabularnewline
10 & 0.162789 & 0.325577 & 0.837211 \tabularnewline
11 & 0.147168 & 0.294337 & 0.852832 \tabularnewline
12 & 0.101957 & 0.203914 & 0.898043 \tabularnewline
13 & 0.135353 & 0.270706 & 0.864647 \tabularnewline
14 & 0.109458 & 0.218916 & 0.890542 \tabularnewline
15 & 0.0702534 & 0.140507 & 0.929747 \tabularnewline
16 & 0.0468247 & 0.0936494 & 0.953175 \tabularnewline
17 & 0.0282797 & 0.0565594 & 0.97172 \tabularnewline
18 & 0.0282845 & 0.0565691 & 0.971715 \tabularnewline
19 & 0.01698 & 0.03396 & 0.98302 \tabularnewline
20 & 0.00979412 & 0.0195882 & 0.990206 \tabularnewline
21 & 0.00561307 & 0.0112261 & 0.994387 \tabularnewline
22 & 0.0180363 & 0.0360727 & 0.981964 \tabularnewline
23 & 0.0119794 & 0.0239587 & 0.988021 \tabularnewline
24 & 0.00720859 & 0.0144172 & 0.992791 \tabularnewline
25 & 0.0382629 & 0.0765257 & 0.961737 \tabularnewline
26 & 0.0306521 & 0.0613041 & 0.969348 \tabularnewline
27 & 0.0291657 & 0.0583314 & 0.970834 \tabularnewline
28 & 0.0215712 & 0.0431424 & 0.978429 \tabularnewline
29 & 0.0148477 & 0.0296955 & 0.985152 \tabularnewline
30 & 0.0100909 & 0.0201818 & 0.989909 \tabularnewline
31 & 0.00684846 & 0.0136969 & 0.993152 \tabularnewline
32 & 0.00646423 & 0.0129285 & 0.993536 \tabularnewline
33 & 0.00441743 & 0.00883487 & 0.995583 \tabularnewline
34 & 0.00278668 & 0.00557336 & 0.997213 \tabularnewline
35 & 0.00222842 & 0.00445683 & 0.997772 \tabularnewline
36 & 0.00150767 & 0.00301534 & 0.998492 \tabularnewline
37 & 0.000972222 & 0.00194444 & 0.999028 \tabularnewline
38 & 0.000822304 & 0.00164461 & 0.999178 \tabularnewline
39 & 0.00107842 & 0.00215684 & 0.998922 \tabularnewline
40 & 0.000724645 & 0.00144929 & 0.999275 \tabularnewline
41 & 0.000483818 & 0.000967636 & 0.999516 \tabularnewline
42 & 0.000359649 & 0.000719297 & 0.99964 \tabularnewline
43 & 0.000263697 & 0.000527395 & 0.999736 \tabularnewline
44 & 0.0023301 & 0.0046602 & 0.99767 \tabularnewline
45 & 0.00236903 & 0.00473806 & 0.997631 \tabularnewline
46 & 0.0027945 & 0.00558899 & 0.997206 \tabularnewline
47 & 0.00195818 & 0.00391635 & 0.998042 \tabularnewline
48 & 0.00135655 & 0.0027131 & 0.998643 \tabularnewline
49 & 0.000967435 & 0.00193487 & 0.999033 \tabularnewline
50 & 0.000672081 & 0.00134416 & 0.999328 \tabularnewline
51 & 0.000653369 & 0.00130674 & 0.999347 \tabularnewline
52 & 0.0010144 & 0.00202879 & 0.998986 \tabularnewline
53 & 0.000743709 & 0.00148742 & 0.999256 \tabularnewline
54 & 0.000515189 & 0.00103038 & 0.999485 \tabularnewline
55 & 0.00539146 & 0.0107829 & 0.994609 \tabularnewline
56 & 0.00464165 & 0.00928329 & 0.995358 \tabularnewline
57 & 0.00352896 & 0.00705792 & 0.996471 \tabularnewline
58 & 0.0160528 & 0.0321056 & 0.983947 \tabularnewline
59 & 0.0170916 & 0.0341832 & 0.982908 \tabularnewline
60 & 0.0169202 & 0.0338404 & 0.98308 \tabularnewline
61 & 0.0153968 & 0.0307936 & 0.984603 \tabularnewline
62 & 0.0314971 & 0.0629943 & 0.968503 \tabularnewline
63 & 0.0267715 & 0.0535429 & 0.973229 \tabularnewline
64 & 0.109806 & 0.219612 & 0.890194 \tabularnewline
65 & 0.0954919 & 0.190984 & 0.904508 \tabularnewline
66 & 0.20527 & 0.410541 & 0.79473 \tabularnewline
67 & 0.343851 & 0.687703 & 0.656149 \tabularnewline
68 & 0.426273 & 0.852546 & 0.573727 \tabularnewline
69 & 0.398653 & 0.797306 & 0.601347 \tabularnewline
70 & 0.483924 & 0.967848 & 0.516076 \tabularnewline
71 & 0.667139 & 0.665721 & 0.332861 \tabularnewline
72 & 0.702395 & 0.595211 & 0.297605 \tabularnewline
73 & 0.678485 & 0.643029 & 0.321515 \tabularnewline
74 & 0.750362 & 0.499277 & 0.249638 \tabularnewline
75 & 0.751274 & 0.497451 & 0.248726 \tabularnewline
76 & 0.742528 & 0.514945 & 0.257472 \tabularnewline
77 & 0.717134 & 0.565733 & 0.282866 \tabularnewline
78 & 0.732391 & 0.535218 & 0.267609 \tabularnewline
79 & 0.709103 & 0.581794 & 0.290897 \tabularnewline
80 & 0.817031 & 0.365939 & 0.182969 \tabularnewline
81 & 0.799207 & 0.401587 & 0.200793 \tabularnewline
82 & 0.783258 & 0.433483 & 0.216742 \tabularnewline
83 & 0.795078 & 0.409844 & 0.204922 \tabularnewline
84 & 0.784403 & 0.431195 & 0.215597 \tabularnewline
85 & 0.77244 & 0.45512 & 0.22756 \tabularnewline
86 & 0.7485 & 0.502999 & 0.2515 \tabularnewline
87 & 0.725236 & 0.549527 & 0.274764 \tabularnewline
88 & 0.753041 & 0.493917 & 0.246959 \tabularnewline
89 & 0.78485 & 0.430301 & 0.21515 \tabularnewline
90 & 0.846141 & 0.307718 & 0.153859 \tabularnewline
91 & 0.838717 & 0.322567 & 0.161283 \tabularnewline
92 & 0.816473 & 0.367054 & 0.183527 \tabularnewline
93 & 0.850665 & 0.29867 & 0.149335 \tabularnewline
94 & 0.845317 & 0.309365 & 0.154683 \tabularnewline
95 & 0.8783 & 0.243399 & 0.1217 \tabularnewline
96 & 0.879367 & 0.241266 & 0.120633 \tabularnewline
97 & 0.886831 & 0.226338 & 0.113169 \tabularnewline
98 & 0.871956 & 0.256088 & 0.128044 \tabularnewline
99 & 0.854128 & 0.291744 & 0.145872 \tabularnewline
100 & 0.835667 & 0.328666 & 0.164333 \tabularnewline
101 & 0.862885 & 0.27423 & 0.137115 \tabularnewline
102 & 0.859925 & 0.280151 & 0.140075 \tabularnewline
103 & 0.850078 & 0.299844 & 0.149922 \tabularnewline
104 & 0.870024 & 0.259952 & 0.129976 \tabularnewline
105 & 0.86937 & 0.26126 & 0.13063 \tabularnewline
106 & 0.851088 & 0.297823 & 0.148912 \tabularnewline
107 & 0.830325 & 0.339349 & 0.169675 \tabularnewline
108 & 0.859948 & 0.280104 & 0.140052 \tabularnewline
109 & 0.851888 & 0.296225 & 0.148112 \tabularnewline
110 & 0.830435 & 0.339129 & 0.169565 \tabularnewline
111 & 0.814764 & 0.370472 & 0.185236 \tabularnewline
112 & 0.845633 & 0.308734 & 0.154367 \tabularnewline
113 & 0.836429 & 0.327142 & 0.163571 \tabularnewline
114 & 0.833451 & 0.333097 & 0.166549 \tabularnewline
115 & 0.817233 & 0.365535 & 0.182767 \tabularnewline
116 & 0.858209 & 0.283581 & 0.141791 \tabularnewline
117 & 0.84416 & 0.311679 & 0.15584 \tabularnewline
118 & 0.881798 & 0.236404 & 0.118202 \tabularnewline
119 & 0.867084 & 0.265831 & 0.132916 \tabularnewline
120 & 0.847134 & 0.305732 & 0.152866 \tabularnewline
121 & 0.833752 & 0.332496 & 0.166248 \tabularnewline
122 & 0.824596 & 0.350808 & 0.175404 \tabularnewline
123 & 0.847206 & 0.305588 & 0.152794 \tabularnewline
124 & 0.827483 & 0.345035 & 0.172517 \tabularnewline
125 & 0.806663 & 0.386674 & 0.193337 \tabularnewline
126 & 0.78535 & 0.4293 & 0.21465 \tabularnewline
127 & 0.810258 & 0.379485 & 0.189742 \tabularnewline
128 & 0.784573 & 0.430854 & 0.215427 \tabularnewline
129 & 0.835212 & 0.329576 & 0.164788 \tabularnewline
130 & 0.8161 & 0.3678 & 0.1839 \tabularnewline
131 & 0.862461 & 0.275077 & 0.137539 \tabularnewline
132 & 0.840751 & 0.318498 & 0.159249 \tabularnewline
133 & 0.815993 & 0.368013 & 0.184007 \tabularnewline
134 & 0.860304 & 0.279391 & 0.139696 \tabularnewline
135 & 0.844805 & 0.310391 & 0.155195 \tabularnewline
136 & 0.821284 & 0.357432 & 0.178716 \tabularnewline
137 & 0.808657 & 0.382686 & 0.191343 \tabularnewline
138 & 0.815494 & 0.369013 & 0.184506 \tabularnewline
139 & 0.789731 & 0.420537 & 0.210269 \tabularnewline
140 & 0.768624 & 0.462752 & 0.231376 \tabularnewline
141 & 0.740506 & 0.518988 & 0.259494 \tabularnewline
142 & 0.745449 & 0.509102 & 0.254551 \tabularnewline
143 & 0.726529 & 0.546942 & 0.273471 \tabularnewline
144 & 0.7631 & 0.4738 & 0.2369 \tabularnewline
145 & 0.785132 & 0.429736 & 0.214868 \tabularnewline
146 & 0.755816 & 0.488368 & 0.244184 \tabularnewline
147 & 0.724239 & 0.551522 & 0.275761 \tabularnewline
148 & 0.696116 & 0.607767 & 0.303884 \tabularnewline
149 & 0.660822 & 0.678356 & 0.339178 \tabularnewline
150 & 0.627167 & 0.745666 & 0.372833 \tabularnewline
151 & 0.604267 & 0.791465 & 0.395733 \tabularnewline
152 & 0.669789 & 0.660421 & 0.330211 \tabularnewline
153 & 0.634755 & 0.73049 & 0.365245 \tabularnewline
154 & 0.596665 & 0.806669 & 0.403335 \tabularnewline
155 & 0.570327 & 0.859347 & 0.429673 \tabularnewline
156 & 0.536242 & 0.927517 & 0.463758 \tabularnewline
157 & 0.498571 & 0.997143 & 0.501429 \tabularnewline
158 & 0.520244 & 0.959513 & 0.479756 \tabularnewline
159 & 0.483474 & 0.966948 & 0.516526 \tabularnewline
160 & 0.553688 & 0.892625 & 0.446312 \tabularnewline
161 & 0.550085 & 0.89983 & 0.449915 \tabularnewline
162 & 0.533828 & 0.932343 & 0.466172 \tabularnewline
163 & 0.501333 & 0.997334 & 0.498667 \tabularnewline
164 & 0.474061 & 0.948121 & 0.525939 \tabularnewline
165 & 0.435086 & 0.870173 & 0.564914 \tabularnewline
166 & 0.420208 & 0.840416 & 0.579792 \tabularnewline
167 & 0.385805 & 0.771609 & 0.614195 \tabularnewline
168 & 0.361563 & 0.723126 & 0.638437 \tabularnewline
169 & 0.387838 & 0.775676 & 0.612162 \tabularnewline
170 & 0.366781 & 0.733562 & 0.633219 \tabularnewline
171 & 0.336612 & 0.673224 & 0.663388 \tabularnewline
172 & 0.360676 & 0.721352 & 0.639324 \tabularnewline
173 & 0.43679 & 0.87358 & 0.56321 \tabularnewline
174 & 0.403557 & 0.807113 & 0.596443 \tabularnewline
175 & 0.380735 & 0.761469 & 0.619265 \tabularnewline
176 & 0.471843 & 0.943686 & 0.528157 \tabularnewline
177 & 0.444057 & 0.888114 & 0.555943 \tabularnewline
178 & 0.435716 & 0.871432 & 0.564284 \tabularnewline
179 & 0.404141 & 0.808282 & 0.595859 \tabularnewline
180 & 0.452241 & 0.904482 & 0.547759 \tabularnewline
181 & 0.541271 & 0.917458 & 0.458729 \tabularnewline
182 & 0.592324 & 0.815352 & 0.407676 \tabularnewline
183 & 0.684262 & 0.631476 & 0.315738 \tabularnewline
184 & 0.639178 & 0.721645 & 0.360822 \tabularnewline
185 & 0.591568 & 0.816864 & 0.408432 \tabularnewline
186 & 0.607952 & 0.784096 & 0.392048 \tabularnewline
187 & 0.886076 & 0.227848 & 0.113924 \tabularnewline
188 & 0.864436 & 0.271128 & 0.135564 \tabularnewline
189 & 0.833628 & 0.332744 & 0.166372 \tabularnewline
190 & 0.828914 & 0.342172 & 0.171086 \tabularnewline
191 & 0.801785 & 0.39643 & 0.198215 \tabularnewline
192 & 0.766721 & 0.466558 & 0.233279 \tabularnewline
193 & 0.725889 & 0.548223 & 0.274111 \tabularnewline
194 & 0.680236 & 0.639527 & 0.319764 \tabularnewline
195 & 0.842362 & 0.315275 & 0.157638 \tabularnewline
196 & 0.829456 & 0.341089 & 0.170544 \tabularnewline
197 & 0.795682 & 0.408636 & 0.204318 \tabularnewline
198 & 0.805904 & 0.388192 & 0.194096 \tabularnewline
199 & 0.775033 & 0.449933 & 0.224967 \tabularnewline
200 & 0.750826 & 0.498348 & 0.249174 \tabularnewline
201 & 0.699435 & 0.60113 & 0.300565 \tabularnewline
202 & 0.654873 & 0.690255 & 0.345127 \tabularnewline
203 & 0.606984 & 0.786031 & 0.393016 \tabularnewline
204 & 0.556103 & 0.887794 & 0.443897 \tabularnewline
205 & 0.706588 & 0.586825 & 0.293412 \tabularnewline
206 & 0.778412 & 0.443176 & 0.221588 \tabularnewline
207 & 0.745626 & 0.508748 & 0.254374 \tabularnewline
208 & 0.859004 & 0.281992 & 0.140996 \tabularnewline
209 & 0.960158 & 0.079685 & 0.0398425 \tabularnewline
210 & 0.9389 & 0.122199 & 0.0610997 \tabularnewline
211 & 0.988725 & 0.022551 & 0.0112755 \tabularnewline
212 & 0.982983 & 0.034034 & 0.017017 \tabularnewline
213 & 0.977017 & 0.045966 & 0.022983 \tabularnewline
214 & 0.960323 & 0.0793543 & 0.0396772 \tabularnewline
215 & 0.940101 & 0.119798 & 0.0598992 \tabularnewline
216 & 0.926981 & 0.146038 & 0.0730192 \tabularnewline
217 & 0.884578 & 0.230843 & 0.115422 \tabularnewline
218 & 0.816947 & 0.366105 & 0.183053 \tabularnewline
219 & 0.727291 & 0.545419 & 0.272709 \tabularnewline
220 & 0.607435 & 0.78513 & 0.392565 \tabularnewline
221 & 0.861858 & 0.276284 & 0.138142 \tabularnewline
222 & 0.746632 & 0.506737 & 0.253368 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=262784&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]7[/C][C]0.113087[/C][C]0.226173[/C][C]0.886913[/C][/ROW]
[ROW][C]8[/C][C]0.196313[/C][C]0.392626[/C][C]0.803687[/C][/ROW]
[ROW][C]9[/C][C]0.260622[/C][C]0.521245[/C][C]0.739378[/C][/ROW]
[ROW][C]10[/C][C]0.162789[/C][C]0.325577[/C][C]0.837211[/C][/ROW]
[ROW][C]11[/C][C]0.147168[/C][C]0.294337[/C][C]0.852832[/C][/ROW]
[ROW][C]12[/C][C]0.101957[/C][C]0.203914[/C][C]0.898043[/C][/ROW]
[ROW][C]13[/C][C]0.135353[/C][C]0.270706[/C][C]0.864647[/C][/ROW]
[ROW][C]14[/C][C]0.109458[/C][C]0.218916[/C][C]0.890542[/C][/ROW]
[ROW][C]15[/C][C]0.0702534[/C][C]0.140507[/C][C]0.929747[/C][/ROW]
[ROW][C]16[/C][C]0.0468247[/C][C]0.0936494[/C][C]0.953175[/C][/ROW]
[ROW][C]17[/C][C]0.0282797[/C][C]0.0565594[/C][C]0.97172[/C][/ROW]
[ROW][C]18[/C][C]0.0282845[/C][C]0.0565691[/C][C]0.971715[/C][/ROW]
[ROW][C]19[/C][C]0.01698[/C][C]0.03396[/C][C]0.98302[/C][/ROW]
[ROW][C]20[/C][C]0.00979412[/C][C]0.0195882[/C][C]0.990206[/C][/ROW]
[ROW][C]21[/C][C]0.00561307[/C][C]0.0112261[/C][C]0.994387[/C][/ROW]
[ROW][C]22[/C][C]0.0180363[/C][C]0.0360727[/C][C]0.981964[/C][/ROW]
[ROW][C]23[/C][C]0.0119794[/C][C]0.0239587[/C][C]0.988021[/C][/ROW]
[ROW][C]24[/C][C]0.00720859[/C][C]0.0144172[/C][C]0.992791[/C][/ROW]
[ROW][C]25[/C][C]0.0382629[/C][C]0.0765257[/C][C]0.961737[/C][/ROW]
[ROW][C]26[/C][C]0.0306521[/C][C]0.0613041[/C][C]0.969348[/C][/ROW]
[ROW][C]27[/C][C]0.0291657[/C][C]0.0583314[/C][C]0.970834[/C][/ROW]
[ROW][C]28[/C][C]0.0215712[/C][C]0.0431424[/C][C]0.978429[/C][/ROW]
[ROW][C]29[/C][C]0.0148477[/C][C]0.0296955[/C][C]0.985152[/C][/ROW]
[ROW][C]30[/C][C]0.0100909[/C][C]0.0201818[/C][C]0.989909[/C][/ROW]
[ROW][C]31[/C][C]0.00684846[/C][C]0.0136969[/C][C]0.993152[/C][/ROW]
[ROW][C]32[/C][C]0.00646423[/C][C]0.0129285[/C][C]0.993536[/C][/ROW]
[ROW][C]33[/C][C]0.00441743[/C][C]0.00883487[/C][C]0.995583[/C][/ROW]
[ROW][C]34[/C][C]0.00278668[/C][C]0.00557336[/C][C]0.997213[/C][/ROW]
[ROW][C]35[/C][C]0.00222842[/C][C]0.00445683[/C][C]0.997772[/C][/ROW]
[ROW][C]36[/C][C]0.00150767[/C][C]0.00301534[/C][C]0.998492[/C][/ROW]
[ROW][C]37[/C][C]0.000972222[/C][C]0.00194444[/C][C]0.999028[/C][/ROW]
[ROW][C]38[/C][C]0.000822304[/C][C]0.00164461[/C][C]0.999178[/C][/ROW]
[ROW][C]39[/C][C]0.00107842[/C][C]0.00215684[/C][C]0.998922[/C][/ROW]
[ROW][C]40[/C][C]0.000724645[/C][C]0.00144929[/C][C]0.999275[/C][/ROW]
[ROW][C]41[/C][C]0.000483818[/C][C]0.000967636[/C][C]0.999516[/C][/ROW]
[ROW][C]42[/C][C]0.000359649[/C][C]0.000719297[/C][C]0.99964[/C][/ROW]
[ROW][C]43[/C][C]0.000263697[/C][C]0.000527395[/C][C]0.999736[/C][/ROW]
[ROW][C]44[/C][C]0.0023301[/C][C]0.0046602[/C][C]0.99767[/C][/ROW]
[ROW][C]45[/C][C]0.00236903[/C][C]0.00473806[/C][C]0.997631[/C][/ROW]
[ROW][C]46[/C][C]0.0027945[/C][C]0.00558899[/C][C]0.997206[/C][/ROW]
[ROW][C]47[/C][C]0.00195818[/C][C]0.00391635[/C][C]0.998042[/C][/ROW]
[ROW][C]48[/C][C]0.00135655[/C][C]0.0027131[/C][C]0.998643[/C][/ROW]
[ROW][C]49[/C][C]0.000967435[/C][C]0.00193487[/C][C]0.999033[/C][/ROW]
[ROW][C]50[/C][C]0.000672081[/C][C]0.00134416[/C][C]0.999328[/C][/ROW]
[ROW][C]51[/C][C]0.000653369[/C][C]0.00130674[/C][C]0.999347[/C][/ROW]
[ROW][C]52[/C][C]0.0010144[/C][C]0.00202879[/C][C]0.998986[/C][/ROW]
[ROW][C]53[/C][C]0.000743709[/C][C]0.00148742[/C][C]0.999256[/C][/ROW]
[ROW][C]54[/C][C]0.000515189[/C][C]0.00103038[/C][C]0.999485[/C][/ROW]
[ROW][C]55[/C][C]0.00539146[/C][C]0.0107829[/C][C]0.994609[/C][/ROW]
[ROW][C]56[/C][C]0.00464165[/C][C]0.00928329[/C][C]0.995358[/C][/ROW]
[ROW][C]57[/C][C]0.00352896[/C][C]0.00705792[/C][C]0.996471[/C][/ROW]
[ROW][C]58[/C][C]0.0160528[/C][C]0.0321056[/C][C]0.983947[/C][/ROW]
[ROW][C]59[/C][C]0.0170916[/C][C]0.0341832[/C][C]0.982908[/C][/ROW]
[ROW][C]60[/C][C]0.0169202[/C][C]0.0338404[/C][C]0.98308[/C][/ROW]
[ROW][C]61[/C][C]0.0153968[/C][C]0.0307936[/C][C]0.984603[/C][/ROW]
[ROW][C]62[/C][C]0.0314971[/C][C]0.0629943[/C][C]0.968503[/C][/ROW]
[ROW][C]63[/C][C]0.0267715[/C][C]0.0535429[/C][C]0.973229[/C][/ROW]
[ROW][C]64[/C][C]0.109806[/C][C]0.219612[/C][C]0.890194[/C][/ROW]
[ROW][C]65[/C][C]0.0954919[/C][C]0.190984[/C][C]0.904508[/C][/ROW]
[ROW][C]66[/C][C]0.20527[/C][C]0.410541[/C][C]0.79473[/C][/ROW]
[ROW][C]67[/C][C]0.343851[/C][C]0.687703[/C][C]0.656149[/C][/ROW]
[ROW][C]68[/C][C]0.426273[/C][C]0.852546[/C][C]0.573727[/C][/ROW]
[ROW][C]69[/C][C]0.398653[/C][C]0.797306[/C][C]0.601347[/C][/ROW]
[ROW][C]70[/C][C]0.483924[/C][C]0.967848[/C][C]0.516076[/C][/ROW]
[ROW][C]71[/C][C]0.667139[/C][C]0.665721[/C][C]0.332861[/C][/ROW]
[ROW][C]72[/C][C]0.702395[/C][C]0.595211[/C][C]0.297605[/C][/ROW]
[ROW][C]73[/C][C]0.678485[/C][C]0.643029[/C][C]0.321515[/C][/ROW]
[ROW][C]74[/C][C]0.750362[/C][C]0.499277[/C][C]0.249638[/C][/ROW]
[ROW][C]75[/C][C]0.751274[/C][C]0.497451[/C][C]0.248726[/C][/ROW]
[ROW][C]76[/C][C]0.742528[/C][C]0.514945[/C][C]0.257472[/C][/ROW]
[ROW][C]77[/C][C]0.717134[/C][C]0.565733[/C][C]0.282866[/C][/ROW]
[ROW][C]78[/C][C]0.732391[/C][C]0.535218[/C][C]0.267609[/C][/ROW]
[ROW][C]79[/C][C]0.709103[/C][C]0.581794[/C][C]0.290897[/C][/ROW]
[ROW][C]80[/C][C]0.817031[/C][C]0.365939[/C][C]0.182969[/C][/ROW]
[ROW][C]81[/C][C]0.799207[/C][C]0.401587[/C][C]0.200793[/C][/ROW]
[ROW][C]82[/C][C]0.783258[/C][C]0.433483[/C][C]0.216742[/C][/ROW]
[ROW][C]83[/C][C]0.795078[/C][C]0.409844[/C][C]0.204922[/C][/ROW]
[ROW][C]84[/C][C]0.784403[/C][C]0.431195[/C][C]0.215597[/C][/ROW]
[ROW][C]85[/C][C]0.77244[/C][C]0.45512[/C][C]0.22756[/C][/ROW]
[ROW][C]86[/C][C]0.7485[/C][C]0.502999[/C][C]0.2515[/C][/ROW]
[ROW][C]87[/C][C]0.725236[/C][C]0.549527[/C][C]0.274764[/C][/ROW]
[ROW][C]88[/C][C]0.753041[/C][C]0.493917[/C][C]0.246959[/C][/ROW]
[ROW][C]89[/C][C]0.78485[/C][C]0.430301[/C][C]0.21515[/C][/ROW]
[ROW][C]90[/C][C]0.846141[/C][C]0.307718[/C][C]0.153859[/C][/ROW]
[ROW][C]91[/C][C]0.838717[/C][C]0.322567[/C][C]0.161283[/C][/ROW]
[ROW][C]92[/C][C]0.816473[/C][C]0.367054[/C][C]0.183527[/C][/ROW]
[ROW][C]93[/C][C]0.850665[/C][C]0.29867[/C][C]0.149335[/C][/ROW]
[ROW][C]94[/C][C]0.845317[/C][C]0.309365[/C][C]0.154683[/C][/ROW]
[ROW][C]95[/C][C]0.8783[/C][C]0.243399[/C][C]0.1217[/C][/ROW]
[ROW][C]96[/C][C]0.879367[/C][C]0.241266[/C][C]0.120633[/C][/ROW]
[ROW][C]97[/C][C]0.886831[/C][C]0.226338[/C][C]0.113169[/C][/ROW]
[ROW][C]98[/C][C]0.871956[/C][C]0.256088[/C][C]0.128044[/C][/ROW]
[ROW][C]99[/C][C]0.854128[/C][C]0.291744[/C][C]0.145872[/C][/ROW]
[ROW][C]100[/C][C]0.835667[/C][C]0.328666[/C][C]0.164333[/C][/ROW]
[ROW][C]101[/C][C]0.862885[/C][C]0.27423[/C][C]0.137115[/C][/ROW]
[ROW][C]102[/C][C]0.859925[/C][C]0.280151[/C][C]0.140075[/C][/ROW]
[ROW][C]103[/C][C]0.850078[/C][C]0.299844[/C][C]0.149922[/C][/ROW]
[ROW][C]104[/C][C]0.870024[/C][C]0.259952[/C][C]0.129976[/C][/ROW]
[ROW][C]105[/C][C]0.86937[/C][C]0.26126[/C][C]0.13063[/C][/ROW]
[ROW][C]106[/C][C]0.851088[/C][C]0.297823[/C][C]0.148912[/C][/ROW]
[ROW][C]107[/C][C]0.830325[/C][C]0.339349[/C][C]0.169675[/C][/ROW]
[ROW][C]108[/C][C]0.859948[/C][C]0.280104[/C][C]0.140052[/C][/ROW]
[ROW][C]109[/C][C]0.851888[/C][C]0.296225[/C][C]0.148112[/C][/ROW]
[ROW][C]110[/C][C]0.830435[/C][C]0.339129[/C][C]0.169565[/C][/ROW]
[ROW][C]111[/C][C]0.814764[/C][C]0.370472[/C][C]0.185236[/C][/ROW]
[ROW][C]112[/C][C]0.845633[/C][C]0.308734[/C][C]0.154367[/C][/ROW]
[ROW][C]113[/C][C]0.836429[/C][C]0.327142[/C][C]0.163571[/C][/ROW]
[ROW][C]114[/C][C]0.833451[/C][C]0.333097[/C][C]0.166549[/C][/ROW]
[ROW][C]115[/C][C]0.817233[/C][C]0.365535[/C][C]0.182767[/C][/ROW]
[ROW][C]116[/C][C]0.858209[/C][C]0.283581[/C][C]0.141791[/C][/ROW]
[ROW][C]117[/C][C]0.84416[/C][C]0.311679[/C][C]0.15584[/C][/ROW]
[ROW][C]118[/C][C]0.881798[/C][C]0.236404[/C][C]0.118202[/C][/ROW]
[ROW][C]119[/C][C]0.867084[/C][C]0.265831[/C][C]0.132916[/C][/ROW]
[ROW][C]120[/C][C]0.847134[/C][C]0.305732[/C][C]0.152866[/C][/ROW]
[ROW][C]121[/C][C]0.833752[/C][C]0.332496[/C][C]0.166248[/C][/ROW]
[ROW][C]122[/C][C]0.824596[/C][C]0.350808[/C][C]0.175404[/C][/ROW]
[ROW][C]123[/C][C]0.847206[/C][C]0.305588[/C][C]0.152794[/C][/ROW]
[ROW][C]124[/C][C]0.827483[/C][C]0.345035[/C][C]0.172517[/C][/ROW]
[ROW][C]125[/C][C]0.806663[/C][C]0.386674[/C][C]0.193337[/C][/ROW]
[ROW][C]126[/C][C]0.78535[/C][C]0.4293[/C][C]0.21465[/C][/ROW]
[ROW][C]127[/C][C]0.810258[/C][C]0.379485[/C][C]0.189742[/C][/ROW]
[ROW][C]128[/C][C]0.784573[/C][C]0.430854[/C][C]0.215427[/C][/ROW]
[ROW][C]129[/C][C]0.835212[/C][C]0.329576[/C][C]0.164788[/C][/ROW]
[ROW][C]130[/C][C]0.8161[/C][C]0.3678[/C][C]0.1839[/C][/ROW]
[ROW][C]131[/C][C]0.862461[/C][C]0.275077[/C][C]0.137539[/C][/ROW]
[ROW][C]132[/C][C]0.840751[/C][C]0.318498[/C][C]0.159249[/C][/ROW]
[ROW][C]133[/C][C]0.815993[/C][C]0.368013[/C][C]0.184007[/C][/ROW]
[ROW][C]134[/C][C]0.860304[/C][C]0.279391[/C][C]0.139696[/C][/ROW]
[ROW][C]135[/C][C]0.844805[/C][C]0.310391[/C][C]0.155195[/C][/ROW]
[ROW][C]136[/C][C]0.821284[/C][C]0.357432[/C][C]0.178716[/C][/ROW]
[ROW][C]137[/C][C]0.808657[/C][C]0.382686[/C][C]0.191343[/C][/ROW]
[ROW][C]138[/C][C]0.815494[/C][C]0.369013[/C][C]0.184506[/C][/ROW]
[ROW][C]139[/C][C]0.789731[/C][C]0.420537[/C][C]0.210269[/C][/ROW]
[ROW][C]140[/C][C]0.768624[/C][C]0.462752[/C][C]0.231376[/C][/ROW]
[ROW][C]141[/C][C]0.740506[/C][C]0.518988[/C][C]0.259494[/C][/ROW]
[ROW][C]142[/C][C]0.745449[/C][C]0.509102[/C][C]0.254551[/C][/ROW]
[ROW][C]143[/C][C]0.726529[/C][C]0.546942[/C][C]0.273471[/C][/ROW]
[ROW][C]144[/C][C]0.7631[/C][C]0.4738[/C][C]0.2369[/C][/ROW]
[ROW][C]145[/C][C]0.785132[/C][C]0.429736[/C][C]0.214868[/C][/ROW]
[ROW][C]146[/C][C]0.755816[/C][C]0.488368[/C][C]0.244184[/C][/ROW]
[ROW][C]147[/C][C]0.724239[/C][C]0.551522[/C][C]0.275761[/C][/ROW]
[ROW][C]148[/C][C]0.696116[/C][C]0.607767[/C][C]0.303884[/C][/ROW]
[ROW][C]149[/C][C]0.660822[/C][C]0.678356[/C][C]0.339178[/C][/ROW]
[ROW][C]150[/C][C]0.627167[/C][C]0.745666[/C][C]0.372833[/C][/ROW]
[ROW][C]151[/C][C]0.604267[/C][C]0.791465[/C][C]0.395733[/C][/ROW]
[ROW][C]152[/C][C]0.669789[/C][C]0.660421[/C][C]0.330211[/C][/ROW]
[ROW][C]153[/C][C]0.634755[/C][C]0.73049[/C][C]0.365245[/C][/ROW]
[ROW][C]154[/C][C]0.596665[/C][C]0.806669[/C][C]0.403335[/C][/ROW]
[ROW][C]155[/C][C]0.570327[/C][C]0.859347[/C][C]0.429673[/C][/ROW]
[ROW][C]156[/C][C]0.536242[/C][C]0.927517[/C][C]0.463758[/C][/ROW]
[ROW][C]157[/C][C]0.498571[/C][C]0.997143[/C][C]0.501429[/C][/ROW]
[ROW][C]158[/C][C]0.520244[/C][C]0.959513[/C][C]0.479756[/C][/ROW]
[ROW][C]159[/C][C]0.483474[/C][C]0.966948[/C][C]0.516526[/C][/ROW]
[ROW][C]160[/C][C]0.553688[/C][C]0.892625[/C][C]0.446312[/C][/ROW]
[ROW][C]161[/C][C]0.550085[/C][C]0.89983[/C][C]0.449915[/C][/ROW]
[ROW][C]162[/C][C]0.533828[/C][C]0.932343[/C][C]0.466172[/C][/ROW]
[ROW][C]163[/C][C]0.501333[/C][C]0.997334[/C][C]0.498667[/C][/ROW]
[ROW][C]164[/C][C]0.474061[/C][C]0.948121[/C][C]0.525939[/C][/ROW]
[ROW][C]165[/C][C]0.435086[/C][C]0.870173[/C][C]0.564914[/C][/ROW]
[ROW][C]166[/C][C]0.420208[/C][C]0.840416[/C][C]0.579792[/C][/ROW]
[ROW][C]167[/C][C]0.385805[/C][C]0.771609[/C][C]0.614195[/C][/ROW]
[ROW][C]168[/C][C]0.361563[/C][C]0.723126[/C][C]0.638437[/C][/ROW]
[ROW][C]169[/C][C]0.387838[/C][C]0.775676[/C][C]0.612162[/C][/ROW]
[ROW][C]170[/C][C]0.366781[/C][C]0.733562[/C][C]0.633219[/C][/ROW]
[ROW][C]171[/C][C]0.336612[/C][C]0.673224[/C][C]0.663388[/C][/ROW]
[ROW][C]172[/C][C]0.360676[/C][C]0.721352[/C][C]0.639324[/C][/ROW]
[ROW][C]173[/C][C]0.43679[/C][C]0.87358[/C][C]0.56321[/C][/ROW]
[ROW][C]174[/C][C]0.403557[/C][C]0.807113[/C][C]0.596443[/C][/ROW]
[ROW][C]175[/C][C]0.380735[/C][C]0.761469[/C][C]0.619265[/C][/ROW]
[ROW][C]176[/C][C]0.471843[/C][C]0.943686[/C][C]0.528157[/C][/ROW]
[ROW][C]177[/C][C]0.444057[/C][C]0.888114[/C][C]0.555943[/C][/ROW]
[ROW][C]178[/C][C]0.435716[/C][C]0.871432[/C][C]0.564284[/C][/ROW]
[ROW][C]179[/C][C]0.404141[/C][C]0.808282[/C][C]0.595859[/C][/ROW]
[ROW][C]180[/C][C]0.452241[/C][C]0.904482[/C][C]0.547759[/C][/ROW]
[ROW][C]181[/C][C]0.541271[/C][C]0.917458[/C][C]0.458729[/C][/ROW]
[ROW][C]182[/C][C]0.592324[/C][C]0.815352[/C][C]0.407676[/C][/ROW]
[ROW][C]183[/C][C]0.684262[/C][C]0.631476[/C][C]0.315738[/C][/ROW]
[ROW][C]184[/C][C]0.639178[/C][C]0.721645[/C][C]0.360822[/C][/ROW]
[ROW][C]185[/C][C]0.591568[/C][C]0.816864[/C][C]0.408432[/C][/ROW]
[ROW][C]186[/C][C]0.607952[/C][C]0.784096[/C][C]0.392048[/C][/ROW]
[ROW][C]187[/C][C]0.886076[/C][C]0.227848[/C][C]0.113924[/C][/ROW]
[ROW][C]188[/C][C]0.864436[/C][C]0.271128[/C][C]0.135564[/C][/ROW]
[ROW][C]189[/C][C]0.833628[/C][C]0.332744[/C][C]0.166372[/C][/ROW]
[ROW][C]190[/C][C]0.828914[/C][C]0.342172[/C][C]0.171086[/C][/ROW]
[ROW][C]191[/C][C]0.801785[/C][C]0.39643[/C][C]0.198215[/C][/ROW]
[ROW][C]192[/C][C]0.766721[/C][C]0.466558[/C][C]0.233279[/C][/ROW]
[ROW][C]193[/C][C]0.725889[/C][C]0.548223[/C][C]0.274111[/C][/ROW]
[ROW][C]194[/C][C]0.680236[/C][C]0.639527[/C][C]0.319764[/C][/ROW]
[ROW][C]195[/C][C]0.842362[/C][C]0.315275[/C][C]0.157638[/C][/ROW]
[ROW][C]196[/C][C]0.829456[/C][C]0.341089[/C][C]0.170544[/C][/ROW]
[ROW][C]197[/C][C]0.795682[/C][C]0.408636[/C][C]0.204318[/C][/ROW]
[ROW][C]198[/C][C]0.805904[/C][C]0.388192[/C][C]0.194096[/C][/ROW]
[ROW][C]199[/C][C]0.775033[/C][C]0.449933[/C][C]0.224967[/C][/ROW]
[ROW][C]200[/C][C]0.750826[/C][C]0.498348[/C][C]0.249174[/C][/ROW]
[ROW][C]201[/C][C]0.699435[/C][C]0.60113[/C][C]0.300565[/C][/ROW]
[ROW][C]202[/C][C]0.654873[/C][C]0.690255[/C][C]0.345127[/C][/ROW]
[ROW][C]203[/C][C]0.606984[/C][C]0.786031[/C][C]0.393016[/C][/ROW]
[ROW][C]204[/C][C]0.556103[/C][C]0.887794[/C][C]0.443897[/C][/ROW]
[ROW][C]205[/C][C]0.706588[/C][C]0.586825[/C][C]0.293412[/C][/ROW]
[ROW][C]206[/C][C]0.778412[/C][C]0.443176[/C][C]0.221588[/C][/ROW]
[ROW][C]207[/C][C]0.745626[/C][C]0.508748[/C][C]0.254374[/C][/ROW]
[ROW][C]208[/C][C]0.859004[/C][C]0.281992[/C][C]0.140996[/C][/ROW]
[ROW][C]209[/C][C]0.960158[/C][C]0.079685[/C][C]0.0398425[/C][/ROW]
[ROW][C]210[/C][C]0.9389[/C][C]0.122199[/C][C]0.0610997[/C][/ROW]
[ROW][C]211[/C][C]0.988725[/C][C]0.022551[/C][C]0.0112755[/C][/ROW]
[ROW][C]212[/C][C]0.982983[/C][C]0.034034[/C][C]0.017017[/C][/ROW]
[ROW][C]213[/C][C]0.977017[/C][C]0.045966[/C][C]0.022983[/C][/ROW]
[ROW][C]214[/C][C]0.960323[/C][C]0.0793543[/C][C]0.0396772[/C][/ROW]
[ROW][C]215[/C][C]0.940101[/C][C]0.119798[/C][C]0.0598992[/C][/ROW]
[ROW][C]216[/C][C]0.926981[/C][C]0.146038[/C][C]0.0730192[/C][/ROW]
[ROW][C]217[/C][C]0.884578[/C][C]0.230843[/C][C]0.115422[/C][/ROW]
[ROW][C]218[/C][C]0.816947[/C][C]0.366105[/C][C]0.183053[/C][/ROW]
[ROW][C]219[/C][C]0.727291[/C][C]0.545419[/C][C]0.272709[/C][/ROW]
[ROW][C]220[/C][C]0.607435[/C][C]0.78513[/C][C]0.392565[/C][/ROW]
[ROW][C]221[/C][C]0.861858[/C][C]0.276284[/C][C]0.138142[/C][/ROW]
[ROW][C]222[/C][C]0.746632[/C][C]0.506737[/C][C]0.253368[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=262784&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=262784&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
70.1130870.2261730.886913
80.1963130.3926260.803687
90.2606220.5212450.739378
100.1627890.3255770.837211
110.1471680.2943370.852832
120.1019570.2039140.898043
130.1353530.2707060.864647
140.1094580.2189160.890542
150.07025340.1405070.929747
160.04682470.09364940.953175
170.02827970.05655940.97172
180.02828450.05656910.971715
190.016980.033960.98302
200.009794120.01958820.990206
210.005613070.01122610.994387
220.01803630.03607270.981964
230.01197940.02395870.988021
240.007208590.01441720.992791
250.03826290.07652570.961737
260.03065210.06130410.969348
270.02916570.05833140.970834
280.02157120.04314240.978429
290.01484770.02969550.985152
300.01009090.02018180.989909
310.006848460.01369690.993152
320.006464230.01292850.993536
330.004417430.008834870.995583
340.002786680.005573360.997213
350.002228420.004456830.997772
360.001507670.003015340.998492
370.0009722220.001944440.999028
380.0008223040.001644610.999178
390.001078420.002156840.998922
400.0007246450.001449290.999275
410.0004838180.0009676360.999516
420.0003596490.0007192970.99964
430.0002636970.0005273950.999736
440.00233010.00466020.99767
450.002369030.004738060.997631
460.00279450.005588990.997206
470.001958180.003916350.998042
480.001356550.00271310.998643
490.0009674350.001934870.999033
500.0006720810.001344160.999328
510.0006533690.001306740.999347
520.00101440.002028790.998986
530.0007437090.001487420.999256
540.0005151890.001030380.999485
550.005391460.01078290.994609
560.004641650.009283290.995358
570.003528960.007057920.996471
580.01605280.03210560.983947
590.01709160.03418320.982908
600.01692020.03384040.98308
610.01539680.03079360.984603
620.03149710.06299430.968503
630.02677150.05354290.973229
640.1098060.2196120.890194
650.09549190.1909840.904508
660.205270.4105410.79473
670.3438510.6877030.656149
680.4262730.8525460.573727
690.3986530.7973060.601347
700.4839240.9678480.516076
710.6671390.6657210.332861
720.7023950.5952110.297605
730.6784850.6430290.321515
740.7503620.4992770.249638
750.7512740.4974510.248726
760.7425280.5149450.257472
770.7171340.5657330.282866
780.7323910.5352180.267609
790.7091030.5817940.290897
800.8170310.3659390.182969
810.7992070.4015870.200793
820.7832580.4334830.216742
830.7950780.4098440.204922
840.7844030.4311950.215597
850.772440.455120.22756
860.74850.5029990.2515
870.7252360.5495270.274764
880.7530410.4939170.246959
890.784850.4303010.21515
900.8461410.3077180.153859
910.8387170.3225670.161283
920.8164730.3670540.183527
930.8506650.298670.149335
940.8453170.3093650.154683
950.87830.2433990.1217
960.8793670.2412660.120633
970.8868310.2263380.113169
980.8719560.2560880.128044
990.8541280.2917440.145872
1000.8356670.3286660.164333
1010.8628850.274230.137115
1020.8599250.2801510.140075
1030.8500780.2998440.149922
1040.8700240.2599520.129976
1050.869370.261260.13063
1060.8510880.2978230.148912
1070.8303250.3393490.169675
1080.8599480.2801040.140052
1090.8518880.2962250.148112
1100.8304350.3391290.169565
1110.8147640.3704720.185236
1120.8456330.3087340.154367
1130.8364290.3271420.163571
1140.8334510.3330970.166549
1150.8172330.3655350.182767
1160.8582090.2835810.141791
1170.844160.3116790.15584
1180.8817980.2364040.118202
1190.8670840.2658310.132916
1200.8471340.3057320.152866
1210.8337520.3324960.166248
1220.8245960.3508080.175404
1230.8472060.3055880.152794
1240.8274830.3450350.172517
1250.8066630.3866740.193337
1260.785350.42930.21465
1270.8102580.3794850.189742
1280.7845730.4308540.215427
1290.8352120.3295760.164788
1300.81610.36780.1839
1310.8624610.2750770.137539
1320.8407510.3184980.159249
1330.8159930.3680130.184007
1340.8603040.2793910.139696
1350.8448050.3103910.155195
1360.8212840.3574320.178716
1370.8086570.3826860.191343
1380.8154940.3690130.184506
1390.7897310.4205370.210269
1400.7686240.4627520.231376
1410.7405060.5189880.259494
1420.7454490.5091020.254551
1430.7265290.5469420.273471
1440.76310.47380.2369
1450.7851320.4297360.214868
1460.7558160.4883680.244184
1470.7242390.5515220.275761
1480.6961160.6077670.303884
1490.6608220.6783560.339178
1500.6271670.7456660.372833
1510.6042670.7914650.395733
1520.6697890.6604210.330211
1530.6347550.730490.365245
1540.5966650.8066690.403335
1550.5703270.8593470.429673
1560.5362420.9275170.463758
1570.4985710.9971430.501429
1580.5202440.9595130.479756
1590.4834740.9669480.516526
1600.5536880.8926250.446312
1610.5500850.899830.449915
1620.5338280.9323430.466172
1630.5013330.9973340.498667
1640.4740610.9481210.525939
1650.4350860.8701730.564914
1660.4202080.8404160.579792
1670.3858050.7716090.614195
1680.3615630.7231260.638437
1690.3878380.7756760.612162
1700.3667810.7335620.633219
1710.3366120.6732240.663388
1720.3606760.7213520.639324
1730.436790.873580.56321
1740.4035570.8071130.596443
1750.3807350.7614690.619265
1760.4718430.9436860.528157
1770.4440570.8881140.555943
1780.4357160.8714320.564284
1790.4041410.8082820.595859
1800.4522410.9044820.547759
1810.5412710.9174580.458729
1820.5923240.8153520.407676
1830.6842620.6314760.315738
1840.6391780.7216450.360822
1850.5915680.8168640.408432
1860.6079520.7840960.392048
1870.8860760.2278480.113924
1880.8644360.2711280.135564
1890.8336280.3327440.166372
1900.8289140.3421720.171086
1910.8017850.396430.198215
1920.7667210.4665580.233279
1930.7258890.5482230.274111
1940.6802360.6395270.319764
1950.8423620.3152750.157638
1960.8294560.3410890.170544
1970.7956820.4086360.204318
1980.8059040.3881920.194096
1990.7750330.4499330.224967
2000.7508260.4983480.249174
2010.6994350.601130.300565
2020.6548730.6902550.345127
2030.6069840.7860310.393016
2040.5561030.8877940.443897
2050.7065880.5868250.293412
2060.7784120.4431760.221588
2070.7456260.5087480.254374
2080.8590040.2819920.140996
2090.9601580.0796850.0398425
2100.93890.1221990.0610997
2110.9887250.0225510.0112755
2120.9829830.0340340.017017
2130.9770170.0459660.022983
2140.9603230.07935430.0396772
2150.9401010.1197980.0598992
2160.9269810.1460380.0730192
2170.8845780.2308430.115422
2180.8169470.3661050.183053
2190.7272910.5454190.272709
2200.6074350.785130.392565
2210.8618580.2762840.138142
2220.7466320.5067370.253368






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level240.111111NOK
5% type I error level430.199074NOK
10% type I error level530.24537NOK

\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 & 24 & 0.111111 & NOK \tabularnewline
5% type I error level & 43 & 0.199074 & NOK \tabularnewline
10% type I error level & 53 & 0.24537 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=262784&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]24[/C][C]0.111111[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]43[/C][C]0.199074[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]53[/C][C]0.24537[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=262784&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=262784&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 level240.111111NOK
5% type I error level430.199074NOK
10% type I error level530.24537NOK


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

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