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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, 16 Dec 2014 11:03:20 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/16/t1418727846i89fkyol0k7blcv.htm/, Retrieved Thu, 16 May 2024 20:41:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=269311, Retrieved Thu, 16 May 2024 20:41:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact87

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Paper11] [2014-12-16 11:03:20] [f8a15a4749f25af1f83725a9fa901b6e] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
4.35 1 0 52 51 6 16 23 48
12.7 1 0 16 56 4 16 22 50
18.1 1 0 46 67 8 16 21 150
17.85 1 0 56 69 5 16 25 154
16.6 0 1 52 57 4 12 30 109
12.6 1 1 55 56 17 15 17 68
17.1 1 0 50 55 4 14 27 194
19.1 0 0 59 63 4 15 23 158
16.1 1 0 60 67 8 16 23 159
13.35 0 0 52 65 4 13 18 67
18.4 0 0 44 47 7 10 18 147
14.7 1 0 67 76 4 17 23 39
10.6 1 0 52 64 4 15 19 100
12.6 1 0 55 68 5 18 15 111
16.2 1 0 37 64 7 16 20 138
13.6 1 0 54 65 4 20 16 101
18.9 1 1 72 71 4 16 24 131
14.1 1 0 51 63 7 17 25 101
14.5 1 0 48 60 11 16 25 114
16.15 0 0 60 68 7 15 19 165
14.75 1 0 50 72 4 13 19 114
14.8 1 0 63 70 4 16 16 111
12.45 1 0 33 61 4 16 19 75
12.65 1 0 67 61 4 16 19 82
17.35 1 0 46 62 4 17 23 121
8.6 1 0 54 71 4 20 21 32
18.4 0 0 59 71 6 14 22 150
16.1 1 0 61 51 8 17 19 117
11.6 1 1 33 56 23 6 20 71
17.75 1 0 47 70 4 16 20 165
15.25 1 0 69 73 8 15 3 154
17.65 1 0 52 76 6 16 23 126
16.35 0 0 55 68 4 16 23 149
17.65 0 0 41 48 7 14 20 145
13.6 1 0 73 52 4 16 15 120
14.35 0 0 52 60 4 16 16 109
14.75 0 0 50 59 4 16 7 132
18.25 1 0 51 57 10 14 24 172
9.9 0 0 60 79 6 14 17 169
16 1 0 56 60 5 16 24 114
18.25 1 0 56 60 5 16 24 156
16.85 0 0 29 59 4 15 19 172
14.6 1 1 66 62 4 16 25 68
13.85 1 1 66 59 5 16 20 89
18.95 1 0 73 61 5 18 28 167
15.6 0 0 55 71 5 15 23 113
14.85 0 1 64 57 5 16 27 115
11.75 0 1 40 66 4 16 18 78
18.45 0 1 46 63 6 16 28 118
15.9 1 1 58 69 4 17 21 87
17.1 0 0 43 58 4 14 19 173
16.1 1 0 61 59 4 18 23 2
19.9 0 1 51 48 9 9 27 162
10.95 1 1 50 66 18 15 22 49
18.45 0 1 52 73 6 14 28 122
15.1 1 1 54 67 5 15 25 96
15 0 1 66 61 4 13 21 100
11.35 0 1 61 68 11 16 22 82
15.95 1 1 80 75 4 20 28 100
18.1 0 1 51 62 10 14 20 115
14.6 1 1 56 69 6 12 29 141
15.4 1 0 56 58 8 15 25 165
15.4 1 0 56 60 8 15 25 165
17.6 1 1 53 74 6 15 20 110
13.35 1 0 47 55 8 16 20 118
19.1 0 0 25 62 4 11 16 158
15.35 1 1 47 63 4 16 20 146
7.6 0 0 46 69 9 7 20 49
13.4 0 1 50 58 9 11 23 90
13.9 0 1 39 58 5 9 18 121
19.1 1 0 51 68 4 15 25 155
15.25 0 1 58 72 4 16 18 104
12.9 1 1 35 62 15 14 19 147
16.1 0 1 58 62 10 15 25 110
17.35 0 1 60 65 9 13 25 108
13.15 0 1 62 69 7 13 25 113
12.15 0 1 63 66 9 12 24 115
12.6 1 1 53 72 6 16 19 61
10.35 1 1 46 62 4 14 26 60
15.4 1 1 67 75 7 16 10 109
9.6 1 1 59 58 4 14 17 68
18.2 0 1 64 66 7 15 13 111
13.6 0 1 38 55 4 10 17 77
14.85 1 1 50 47 15 16 30 73
14.75 0 0 48 72 4 14 25 151
14.1 0 1 48 62 9 16 4 89
14.9 0 1 47 64 4 12 16 78
16.25 0 1 66 64 4 16 21 110
19.25 1 0 47 19 28 16 23 220
13.6 1 1 63 50 4 15 22 65
13.6 0 0 58 68 4 14 17 141
15.65 0 1 44 70 4 16 20 117
12.75 1 0 51 79 5 11 20 122
14.6 0 1 43 69 4 15 22 63
9.85 1 0 55 71 4 18 16 44
12.65 1 1 38 48 12 13 23 52
19.2 0 1 45 73 4 7 0 131
16.6 1 1 50 74 6 7 18 101
11.2 1 1 54 66 6 17 25 42
15.25 1 0 57 71 5 18 23 152
11.9 0 0 60 74 4 15 12 107
13.2 0 1 55 78 4 8 18 77
16.35 0 0 56 75 4 13 24 154
12.4 1 0 49 53 10 13 11 103
15.85 1 1 37 60 7 15 18 96
18.15 1 0 59 70 4 18 23 175
11.15 1 1 46 69 7 16 24 57
15.65 0 1 51 65 4 14 29 112
17.75 0 0 58 78 4 15 18 143
7.65 0 1 64 78 12 19 15 49
12.35 1 0 53 59 5 16 29 110
15.6 1 0 48 72 8 12 16 131
19.3 0 0 51 70 6 16 19 167
15.2 0 1 47 63 17 11 22 56
17.1 0 0 59 63 4 16 16 137
15.6 1 1 62 71 5 15 23 86
18.4 1 0 62 74 4 19 23 121
19.05 0 0 51 67 5 15 19 149
18.55 0 0 64 66 5 14 4 168
19.1 0 0 52 62 6 14 20 140
13.1 1 1 67 80 4 17 24 88
12.85 1 0 50 73 4 16 20 168
9.5 1 0 54 67 4 20 4 94
4.5 1 0 58 61 6 16 24 51
11.85 0 1 56 73 8 9 22 48
13.6 1 0 63 74 10 13 16 145
11.7 1 0 31 32 4 15 3 66
12.4 1 1 65 69 5 19 15 85
13.35 0 0 71 69 4 16 24 109
11.4 0 1 50 84 4 17 17 63
14.9 1 1 57 64 4 16 20 102
19.9 0 1 47 58 16 9 27 162
11.2 1 1 47 59 7 11 26 86
14.6 1 1 57 78 4 14 23 114
17.6 0 0 43 57 4 19 17 164
14.05 1 0 41 60 14 13 20 119
16.1 0 0 63 68 5 14 22 126
13.35 1 0 63 68 5 15 19 132
11.85 1 0 56 73 5 15 24 142
11.95 0 0 51 69 5 14 19 83
14.75 1 1 50 67 7 16 23 94
15.15 0 1 22 60 19 17 15 81
13.2 1 0 41 65 16 12 27 166
16.85 0 1 59 66 4 15 26 110
7.85 1 1 56 74 4 17 22 64
7.7 0 0 66 81 7 15 22 93
12.6 0 1 53 72 9 10 18 104
7.85 1 1 42 55 5 16 15 105
10.95 1 1 52 49 14 15 22 49
12.35 0 1 54 74 4 11 27 88
9.95 1 1 44 53 16 16 10 95
14.9 1 1 62 64 10 16 20 102
16.65 0 1 53 65 5 16 17 99
13.4 1 1 50 57 6 14 23 63
13.95 0 1 36 51 4 14 19 76
15.7 0 1 76 80 4 16 13 109
16.85 1 1 66 67 4 16 27 117
10.95 1 1 62 70 5 18 23 57
15.35 0 1 59 74 4 14 16 120
12.2 1 1 47 75 4 20 25 73
15.1 0 1 55 70 5 15 2 91
17.75 0 1 58 69 4 16 26 108
15.2 1 1 60 65 4 16 20 105
14.6 0 0 44 55 5 16 23 117
16.65 0 1 57 71 8 12 22 119
8.1 1 1 45 65 15 8 24 31

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'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269311&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269311&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269311&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'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 9.32888 -0.84663gender[t] + 1.31789Course_id[t] + 0.00267471AMS.I[t] -0.0370435AMS.E[t] -0.0941882AMS.A[t] + 0.0543571CONFSTATTOT[t] + 0.0449417NUMERACYTOT[t] + 0.0562376LFM[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  9.32888 -0.84663gender[t] +  1.31789Course_id[t] +  0.00267471AMS.I[t] -0.0370435AMS.E[t] -0.0941882AMS.A[t] +  0.0543571CONFSTATTOT[t] +  0.0449417NUMERACYTOT[t] +  0.0562376LFM[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269311&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  9.32888 -0.84663gender[t] +  1.31789Course_id[t] +  0.00267471AMS.I[t] -0.0370435AMS.E[t] -0.0941882AMS.A[t] +  0.0543571CONFSTATTOT[t] +  0.0449417NUMERACYTOT[t] +  0.0562376LFM[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269311&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269311&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] = + 9.32888 -0.84663gender[t] + 1.31789Course_id[t] + 0.00267471AMS.I[t] -0.0370435AMS.E[t] -0.0941882AMS.A[t] + 0.0543571CONFSTATTOT[t] + 0.0449417NUMERACYTOT[t] + 0.0562376LFM[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.328882.13624.3672.27736e-051.13868e-05
gender-0.846630.390093-2.170.03148480.0157424
Course_id1.317890.4140163.1830.001756470.000878237
AMS.I0.002674710.01986660.13460.8930740.446537
AMS.E-0.03704350.022686-1.6330.1044980.0522491
AMS.A-0.09418820.0507216-1.8570.06519010.032595
CONFSTATTOT0.05435710.0762760.71260.4771280.238564
NUMERACYTOT0.04494170.03209231.40.1633710.0816853
LFM0.05623760.0052100710.791.14976e-205.74878e-21

\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) & 9.32888 & 2.1362 & 4.367 & 2.27736e-05 & 1.13868e-05 \tabularnewline
gender & -0.84663 & 0.390093 & -2.17 & 0.0314848 & 0.0157424 \tabularnewline
Course_id & 1.31789 & 0.414016 & 3.183 & 0.00175647 & 0.000878237 \tabularnewline
AMS.I & 0.00267471 & 0.0198666 & 0.1346 & 0.893074 & 0.446537 \tabularnewline
AMS.E & -0.0370435 & 0.022686 & -1.633 & 0.104498 & 0.0522491 \tabularnewline
AMS.A & -0.0941882 & 0.0507216 & -1.857 & 0.0651901 & 0.032595 \tabularnewline
CONFSTATTOT & 0.0543571 & 0.076276 & 0.7126 & 0.477128 & 0.238564 \tabularnewline
NUMERACYTOT & 0.0449417 & 0.0320923 & 1.4 & 0.163371 & 0.0816853 \tabularnewline
LFM & 0.0562376 & 0.00521007 & 10.79 & 1.14976e-20 & 5.74878e-21 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269311&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]9.32888[/C][C]2.1362[/C][C]4.367[/C][C]2.27736e-05[/C][C]1.13868e-05[/C][/ROW]
[ROW][C]gender[/C][C]-0.84663[/C][C]0.390093[/C][C]-2.17[/C][C]0.0314848[/C][C]0.0157424[/C][/ROW]
[ROW][C]Course_id[/C][C]1.31789[/C][C]0.414016[/C][C]3.183[/C][C]0.00175647[/C][C]0.000878237[/C][/ROW]
[ROW][C]AMS.I[/C][C]0.00267471[/C][C]0.0198666[/C][C]0.1346[/C][C]0.893074[/C][C]0.446537[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.0370435[/C][C]0.022686[/C][C]-1.633[/C][C]0.104498[/C][C]0.0522491[/C][/ROW]
[ROW][C]AMS.A[/C][C]-0.0941882[/C][C]0.0507216[/C][C]-1.857[/C][C]0.0651901[/C][C]0.032595[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]0.0543571[/C][C]0.076276[/C][C]0.7126[/C][C]0.477128[/C][C]0.238564[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]0.0449417[/C][C]0.0320923[/C][C]1.4[/C][C]0.163371[/C][C]0.0816853[/C][/ROW]
[ROW][C]LFM[/C][C]0.0562376[/C][C]0.00521007[/C][C]10.79[/C][C]1.14976e-20[/C][C]5.74878e-21[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269311&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269311&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)9.328882.13624.3672.27736e-051.13868e-05
gender-0.846630.390093-2.170.03148480.0157424
Course_id1.317890.4140163.1830.001756470.000878237
AMS.I0.002674710.01986660.13460.8930740.446537
AMS.E-0.03704350.022686-1.6330.1044980.0522491
AMS.A-0.09418820.0507216-1.8570.06519010.032595
CONFSTATTOT0.05435710.0762760.71260.4771280.238564
NUMERACYTOT0.04494170.03209231.40.1633710.0816853
LFM0.05623760.0052100710.791.14976e-205.74878e-21






Multiple Linear Regression - Regression Statistics
Multiple R0.700591
R-squared0.490828
Adjusted R-squared0.464882
F-TEST (value)18.9179
F-TEST (DF numerator)8
F-TEST (DF denominator)157
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.22964
Sum Squared Residuals780.493

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.700591 \tabularnewline
R-squared & 0.490828 \tabularnewline
Adjusted R-squared & 0.464882 \tabularnewline
F-TEST (value) & 18.9179 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.22964 \tabularnewline
Sum Squared Residuals & 780.493 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269311&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.700591[/C][/ROW]
[ROW][C]R-squared[/C][C]0.490828[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.464882[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]18.9179[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]157[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.22964[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]780.493[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269311&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269311&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 R0.700591
R-squared0.490828
Adjusted R-squared0.464882
F-TEST (value)18.9179
F-TEST (DF numerator)8
F-TEST (DF denominator)157
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.22964
Sum Squared Residuals780.493






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.3510.7698-6.41976
212.710.74421.95584
318.115.6192.481
417.8516.25891.59106
516.616.42810.171945
612.611.67510.924867
717.119.0864-1.98636
819.117.51081.58925
916.116.2525-0.152465
1013.3511.96691.38311
1118.416.66571.73434
1214.79.620395.07961
1310.613.1668-2.56681
1412.613.5344-0.934386
1516.215.08041.11955
1613.613.32830.27169
1718.916.30132.59868
1814.113.35320.746788
1914.513.75630.743703
2016.1517.2595-1.10954
2114.7513.54371.20628
2214.813.51211.28789
2312.4511.87550.574467
2412.6512.36010.289864
2517.3514.69432.65568
268.69.45036-0.850359
2718.416.47681.92317
2816.114.36041.73955
2911.610.86550.734516
3017.7516.68591.06408
3115.2514.81990.430102
3217.6514.23023.41979
3316.3516.8631-0.513054
3417.6516.81540.834576
3513.614.6668-1.06684
3614.3514.5873-0.23728
3714.7515.508-0.757966
3818.2517.07781.17223
399.917.027-7.12696
401614.29791.70212
4118.2516.65991.59014
4216.8518.1862-1.33625
4314.613.12061.47937
4413.8514.0939-0.243855
4518.9517.57541.37461
4615.614.57881.02118
4714.8516.786-1.93599
4811.7513.9973-2.24733
4918.4516.63511.81495
5015.913.7832.11697
5117.118.2626-1.16262
5216.18.207647.89236
5319.918.97050.92947
5410.9510.35330.596671
5518.4516.39692.0531
5615.114.32940.770575
571515.4611-0.461071
5811.3513.7248-2.37481
5915.9514.82841.12163
6018.115.67182.42824
6114.616.7139-2.11389
6215.416.9481-1.54811
6315.416.874-1.47402
6417.614.53593.06412
6513.3514.2216-0.871647
6619.116.92482.17516
6715.3517.1946-1.8446
687.610.0832-2.48319
6913.414.4773-1.07726
7013.916.2345-2.33453
7119.116.37872.72132
7215.2515.2854-0.0353912
7312.916.0661-3.16605
7416.115.68840.411643
7517.3515.45561.89442
7613.1515.7823-2.63232
7712.1515.7209-3.57092
7812.611.86370.736266
7910.3512.5535-2.20346
8015.413.99081.40921
819.612.7818-3.18183
8218.215.35572.84427
8313.613.9721-0.372136
8414.8513.10331.74669
8514.7516.7898-2.0398
8614.113.68540.41461
8714.913.78281.11717
8816.2516.07540.174612
8919.2519.5425-0.292515
9013.613.19920.400766
9113.616.0428-2.44281
9215.6516.143-0.493005
9312.7513.579-0.829031
9414.613.17611.42393
959.859.794460.0555369
9612.6511.65810.991915
9719.215.43383.76617
9816.613.4973.10303
9911.211.3442-0.144162
10015.2516.0939-0.843882
10111.913.7435-1.84347
10213.213.10180.0981675
10316.3516.7695-0.419483
10412.412.7016-0.301596
10515.8514.04031.80971
10618.1517.52390.626071
10711.1511.8617-0.711711
10815.6516.3615-0.711518
10917.7515.88421.86585
1107.6511.2608-3.61085
11112.3514.3267-1.97666
11215.613.92851.67152
11319.317.42241.8776
11415.211.57353.62651
11517.116.06951.03047
11615.613.55042.04961
11718.414.40133.9987
11819.0516.56112.48891
11918.5516.97291.57707
12019.116.13922.96076
12113.113.5907-0.49069
12212.8516.7515-3.90152
1239.512.3213-2.82126
1244.510.629-6.12903
12511.8511.51620.333796
12613.614.5478-0.947818
12711.711.66490.0351173
12812.413.4342-1.03416
12913.3514.6642-1.31424
13011.412.5231-1.12314
13114.914.70980.190157
13219.917.93011.96992
13311.213.6838-2.48381
13414.614.8922-0.292196
13517.617.9754-0.375422
13614.0513.34840.701582
13716.115.34310.756858
13813.3514.7535-1.40347
13911.8515.3366-3.48661
14011.9512.721-0.770958
14114.7513.98230.767651
14215.1512.84692.30313
14313.215.8782-2.67823
14416.8516.15290.697071
1457.8512.3439-4.49394
1467.712.8797-5.17974
14712.614.4749-1.87493
1487.8514.8529-7.00293
14910.9511.3652-0.415171
15012.3514.4335-2.08349
1519.9513.1092-3.15921
15214.914.15810.741913
15316.6515.1111.539
15413.412.59490.805109
15513.9514.366-0.416035
15615.715.09370.606332
15716.8515.78091.06906
15810.9512.1196-1.16961
15915.3515.9152-0.565184
16012.213.0869-0.886862
16115.113.75281.34725
16217.7515.9811.76899
16315.214.84950.350464
16414.615.4214-0.821405
16516.6515.74890.90109
1668.19.35667-1.25667

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4.35 & 10.7698 & -6.41976 \tabularnewline
2 & 12.7 & 10.7442 & 1.95584 \tabularnewline
3 & 18.1 & 15.619 & 2.481 \tabularnewline
4 & 17.85 & 16.2589 & 1.59106 \tabularnewline
5 & 16.6 & 16.4281 & 0.171945 \tabularnewline
6 & 12.6 & 11.6751 & 0.924867 \tabularnewline
7 & 17.1 & 19.0864 & -1.98636 \tabularnewline
8 & 19.1 & 17.5108 & 1.58925 \tabularnewline
9 & 16.1 & 16.2525 & -0.152465 \tabularnewline
10 & 13.35 & 11.9669 & 1.38311 \tabularnewline
11 & 18.4 & 16.6657 & 1.73434 \tabularnewline
12 & 14.7 & 9.62039 & 5.07961 \tabularnewline
13 & 10.6 & 13.1668 & -2.56681 \tabularnewline
14 & 12.6 & 13.5344 & -0.934386 \tabularnewline
15 & 16.2 & 15.0804 & 1.11955 \tabularnewline
16 & 13.6 & 13.3283 & 0.27169 \tabularnewline
17 & 18.9 & 16.3013 & 2.59868 \tabularnewline
18 & 14.1 & 13.3532 & 0.746788 \tabularnewline
19 & 14.5 & 13.7563 & 0.743703 \tabularnewline
20 & 16.15 & 17.2595 & -1.10954 \tabularnewline
21 & 14.75 & 13.5437 & 1.20628 \tabularnewline
22 & 14.8 & 13.5121 & 1.28789 \tabularnewline
23 & 12.45 & 11.8755 & 0.574467 \tabularnewline
24 & 12.65 & 12.3601 & 0.289864 \tabularnewline
25 & 17.35 & 14.6943 & 2.65568 \tabularnewline
26 & 8.6 & 9.45036 & -0.850359 \tabularnewline
27 & 18.4 & 16.4768 & 1.92317 \tabularnewline
28 & 16.1 & 14.3604 & 1.73955 \tabularnewline
29 & 11.6 & 10.8655 & 0.734516 \tabularnewline
30 & 17.75 & 16.6859 & 1.06408 \tabularnewline
31 & 15.25 & 14.8199 & 0.430102 \tabularnewline
32 & 17.65 & 14.2302 & 3.41979 \tabularnewline
33 & 16.35 & 16.8631 & -0.513054 \tabularnewline
34 & 17.65 & 16.8154 & 0.834576 \tabularnewline
35 & 13.6 & 14.6668 & -1.06684 \tabularnewline
36 & 14.35 & 14.5873 & -0.23728 \tabularnewline
37 & 14.75 & 15.508 & -0.757966 \tabularnewline
38 & 18.25 & 17.0778 & 1.17223 \tabularnewline
39 & 9.9 & 17.027 & -7.12696 \tabularnewline
40 & 16 & 14.2979 & 1.70212 \tabularnewline
41 & 18.25 & 16.6599 & 1.59014 \tabularnewline
42 & 16.85 & 18.1862 & -1.33625 \tabularnewline
43 & 14.6 & 13.1206 & 1.47937 \tabularnewline
44 & 13.85 & 14.0939 & -0.243855 \tabularnewline
45 & 18.95 & 17.5754 & 1.37461 \tabularnewline
46 & 15.6 & 14.5788 & 1.02118 \tabularnewline
47 & 14.85 & 16.786 & -1.93599 \tabularnewline
48 & 11.75 & 13.9973 & -2.24733 \tabularnewline
49 & 18.45 & 16.6351 & 1.81495 \tabularnewline
50 & 15.9 & 13.783 & 2.11697 \tabularnewline
51 & 17.1 & 18.2626 & -1.16262 \tabularnewline
52 & 16.1 & 8.20764 & 7.89236 \tabularnewline
53 & 19.9 & 18.9705 & 0.92947 \tabularnewline
54 & 10.95 & 10.3533 & 0.596671 \tabularnewline
55 & 18.45 & 16.3969 & 2.0531 \tabularnewline
56 & 15.1 & 14.3294 & 0.770575 \tabularnewline
57 & 15 & 15.4611 & -0.461071 \tabularnewline
58 & 11.35 & 13.7248 & -2.37481 \tabularnewline
59 & 15.95 & 14.8284 & 1.12163 \tabularnewline
60 & 18.1 & 15.6718 & 2.42824 \tabularnewline
61 & 14.6 & 16.7139 & -2.11389 \tabularnewline
62 & 15.4 & 16.9481 & -1.54811 \tabularnewline
63 & 15.4 & 16.874 & -1.47402 \tabularnewline
64 & 17.6 & 14.5359 & 3.06412 \tabularnewline
65 & 13.35 & 14.2216 & -0.871647 \tabularnewline
66 & 19.1 & 16.9248 & 2.17516 \tabularnewline
67 & 15.35 & 17.1946 & -1.8446 \tabularnewline
68 & 7.6 & 10.0832 & -2.48319 \tabularnewline
69 & 13.4 & 14.4773 & -1.07726 \tabularnewline
70 & 13.9 & 16.2345 & -2.33453 \tabularnewline
71 & 19.1 & 16.3787 & 2.72132 \tabularnewline
72 & 15.25 & 15.2854 & -0.0353912 \tabularnewline
73 & 12.9 & 16.0661 & -3.16605 \tabularnewline
74 & 16.1 & 15.6884 & 0.411643 \tabularnewline
75 & 17.35 & 15.4556 & 1.89442 \tabularnewline
76 & 13.15 & 15.7823 & -2.63232 \tabularnewline
77 & 12.15 & 15.7209 & -3.57092 \tabularnewline
78 & 12.6 & 11.8637 & 0.736266 \tabularnewline
79 & 10.35 & 12.5535 & -2.20346 \tabularnewline
80 & 15.4 & 13.9908 & 1.40921 \tabularnewline
81 & 9.6 & 12.7818 & -3.18183 \tabularnewline
82 & 18.2 & 15.3557 & 2.84427 \tabularnewline
83 & 13.6 & 13.9721 & -0.372136 \tabularnewline
84 & 14.85 & 13.1033 & 1.74669 \tabularnewline
85 & 14.75 & 16.7898 & -2.0398 \tabularnewline
86 & 14.1 & 13.6854 & 0.41461 \tabularnewline
87 & 14.9 & 13.7828 & 1.11717 \tabularnewline
88 & 16.25 & 16.0754 & 0.174612 \tabularnewline
89 & 19.25 & 19.5425 & -0.292515 \tabularnewline
90 & 13.6 & 13.1992 & 0.400766 \tabularnewline
91 & 13.6 & 16.0428 & -2.44281 \tabularnewline
92 & 15.65 & 16.143 & -0.493005 \tabularnewline
93 & 12.75 & 13.579 & -0.829031 \tabularnewline
94 & 14.6 & 13.1761 & 1.42393 \tabularnewline
95 & 9.85 & 9.79446 & 0.0555369 \tabularnewline
96 & 12.65 & 11.6581 & 0.991915 \tabularnewline
97 & 19.2 & 15.4338 & 3.76617 \tabularnewline
98 & 16.6 & 13.497 & 3.10303 \tabularnewline
99 & 11.2 & 11.3442 & -0.144162 \tabularnewline
100 & 15.25 & 16.0939 & -0.843882 \tabularnewline
101 & 11.9 & 13.7435 & -1.84347 \tabularnewline
102 & 13.2 & 13.1018 & 0.0981675 \tabularnewline
103 & 16.35 & 16.7695 & -0.419483 \tabularnewline
104 & 12.4 & 12.7016 & -0.301596 \tabularnewline
105 & 15.85 & 14.0403 & 1.80971 \tabularnewline
106 & 18.15 & 17.5239 & 0.626071 \tabularnewline
107 & 11.15 & 11.8617 & -0.711711 \tabularnewline
108 & 15.65 & 16.3615 & -0.711518 \tabularnewline
109 & 17.75 & 15.8842 & 1.86585 \tabularnewline
110 & 7.65 & 11.2608 & -3.61085 \tabularnewline
111 & 12.35 & 14.3267 & -1.97666 \tabularnewline
112 & 15.6 & 13.9285 & 1.67152 \tabularnewline
113 & 19.3 & 17.4224 & 1.8776 \tabularnewline
114 & 15.2 & 11.5735 & 3.62651 \tabularnewline
115 & 17.1 & 16.0695 & 1.03047 \tabularnewline
116 & 15.6 & 13.5504 & 2.04961 \tabularnewline
117 & 18.4 & 14.4013 & 3.9987 \tabularnewline
118 & 19.05 & 16.5611 & 2.48891 \tabularnewline
119 & 18.55 & 16.9729 & 1.57707 \tabularnewline
120 & 19.1 & 16.1392 & 2.96076 \tabularnewline
121 & 13.1 & 13.5907 & -0.49069 \tabularnewline
122 & 12.85 & 16.7515 & -3.90152 \tabularnewline
123 & 9.5 & 12.3213 & -2.82126 \tabularnewline
124 & 4.5 & 10.629 & -6.12903 \tabularnewline
125 & 11.85 & 11.5162 & 0.333796 \tabularnewline
126 & 13.6 & 14.5478 & -0.947818 \tabularnewline
127 & 11.7 & 11.6649 & 0.0351173 \tabularnewline
128 & 12.4 & 13.4342 & -1.03416 \tabularnewline
129 & 13.35 & 14.6642 & -1.31424 \tabularnewline
130 & 11.4 & 12.5231 & -1.12314 \tabularnewline
131 & 14.9 & 14.7098 & 0.190157 \tabularnewline
132 & 19.9 & 17.9301 & 1.96992 \tabularnewline
133 & 11.2 & 13.6838 & -2.48381 \tabularnewline
134 & 14.6 & 14.8922 & -0.292196 \tabularnewline
135 & 17.6 & 17.9754 & -0.375422 \tabularnewline
136 & 14.05 & 13.3484 & 0.701582 \tabularnewline
137 & 16.1 & 15.3431 & 0.756858 \tabularnewline
138 & 13.35 & 14.7535 & -1.40347 \tabularnewline
139 & 11.85 & 15.3366 & -3.48661 \tabularnewline
140 & 11.95 & 12.721 & -0.770958 \tabularnewline
141 & 14.75 & 13.9823 & 0.767651 \tabularnewline
142 & 15.15 & 12.8469 & 2.30313 \tabularnewline
143 & 13.2 & 15.8782 & -2.67823 \tabularnewline
144 & 16.85 & 16.1529 & 0.697071 \tabularnewline
145 & 7.85 & 12.3439 & -4.49394 \tabularnewline
146 & 7.7 & 12.8797 & -5.17974 \tabularnewline
147 & 12.6 & 14.4749 & -1.87493 \tabularnewline
148 & 7.85 & 14.8529 & -7.00293 \tabularnewline
149 & 10.95 & 11.3652 & -0.415171 \tabularnewline
150 & 12.35 & 14.4335 & -2.08349 \tabularnewline
151 & 9.95 & 13.1092 & -3.15921 \tabularnewline
152 & 14.9 & 14.1581 & 0.741913 \tabularnewline
153 & 16.65 & 15.111 & 1.539 \tabularnewline
154 & 13.4 & 12.5949 & 0.805109 \tabularnewline
155 & 13.95 & 14.366 & -0.416035 \tabularnewline
156 & 15.7 & 15.0937 & 0.606332 \tabularnewline
157 & 16.85 & 15.7809 & 1.06906 \tabularnewline
158 & 10.95 & 12.1196 & -1.16961 \tabularnewline
159 & 15.35 & 15.9152 & -0.565184 \tabularnewline
160 & 12.2 & 13.0869 & -0.886862 \tabularnewline
161 & 15.1 & 13.7528 & 1.34725 \tabularnewline
162 & 17.75 & 15.981 & 1.76899 \tabularnewline
163 & 15.2 & 14.8495 & 0.350464 \tabularnewline
164 & 14.6 & 15.4214 & -0.821405 \tabularnewline
165 & 16.65 & 15.7489 & 0.90109 \tabularnewline
166 & 8.1 & 9.35667 & -1.25667 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269311&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]4.35[/C][C]10.7698[/C][C]-6.41976[/C][/ROW]
[ROW][C]2[/C][C]12.7[/C][C]10.7442[/C][C]1.95584[/C][/ROW]
[ROW][C]3[/C][C]18.1[/C][C]15.619[/C][C]2.481[/C][/ROW]
[ROW][C]4[/C][C]17.85[/C][C]16.2589[/C][C]1.59106[/C][/ROW]
[ROW][C]5[/C][C]16.6[/C][C]16.4281[/C][C]0.171945[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.6751[/C][C]0.924867[/C][/ROW]
[ROW][C]7[/C][C]17.1[/C][C]19.0864[/C][C]-1.98636[/C][/ROW]
[ROW][C]8[/C][C]19.1[/C][C]17.5108[/C][C]1.58925[/C][/ROW]
[ROW][C]9[/C][C]16.1[/C][C]16.2525[/C][C]-0.152465[/C][/ROW]
[ROW][C]10[/C][C]13.35[/C][C]11.9669[/C][C]1.38311[/C][/ROW]
[ROW][C]11[/C][C]18.4[/C][C]16.6657[/C][C]1.73434[/C][/ROW]
[ROW][C]12[/C][C]14.7[/C][C]9.62039[/C][C]5.07961[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]13.1668[/C][C]-2.56681[/C][/ROW]
[ROW][C]14[/C][C]12.6[/C][C]13.5344[/C][C]-0.934386[/C][/ROW]
[ROW][C]15[/C][C]16.2[/C][C]15.0804[/C][C]1.11955[/C][/ROW]
[ROW][C]16[/C][C]13.6[/C][C]13.3283[/C][C]0.27169[/C][/ROW]
[ROW][C]17[/C][C]18.9[/C][C]16.3013[/C][C]2.59868[/C][/ROW]
[ROW][C]18[/C][C]14.1[/C][C]13.3532[/C][C]0.746788[/C][/ROW]
[ROW][C]19[/C][C]14.5[/C][C]13.7563[/C][C]0.743703[/C][/ROW]
[ROW][C]20[/C][C]16.15[/C][C]17.2595[/C][C]-1.10954[/C][/ROW]
[ROW][C]21[/C][C]14.75[/C][C]13.5437[/C][C]1.20628[/C][/ROW]
[ROW][C]22[/C][C]14.8[/C][C]13.5121[/C][C]1.28789[/C][/ROW]
[ROW][C]23[/C][C]12.45[/C][C]11.8755[/C][C]0.574467[/C][/ROW]
[ROW][C]24[/C][C]12.65[/C][C]12.3601[/C][C]0.289864[/C][/ROW]
[ROW][C]25[/C][C]17.35[/C][C]14.6943[/C][C]2.65568[/C][/ROW]
[ROW][C]26[/C][C]8.6[/C][C]9.45036[/C][C]-0.850359[/C][/ROW]
[ROW][C]27[/C][C]18.4[/C][C]16.4768[/C][C]1.92317[/C][/ROW]
[ROW][C]28[/C][C]16.1[/C][C]14.3604[/C][C]1.73955[/C][/ROW]
[ROW][C]29[/C][C]11.6[/C][C]10.8655[/C][C]0.734516[/C][/ROW]
[ROW][C]30[/C][C]17.75[/C][C]16.6859[/C][C]1.06408[/C][/ROW]
[ROW][C]31[/C][C]15.25[/C][C]14.8199[/C][C]0.430102[/C][/ROW]
[ROW][C]32[/C][C]17.65[/C][C]14.2302[/C][C]3.41979[/C][/ROW]
[ROW][C]33[/C][C]16.35[/C][C]16.8631[/C][C]-0.513054[/C][/ROW]
[ROW][C]34[/C][C]17.65[/C][C]16.8154[/C][C]0.834576[/C][/ROW]
[ROW][C]35[/C][C]13.6[/C][C]14.6668[/C][C]-1.06684[/C][/ROW]
[ROW][C]36[/C][C]14.35[/C][C]14.5873[/C][C]-0.23728[/C][/ROW]
[ROW][C]37[/C][C]14.75[/C][C]15.508[/C][C]-0.757966[/C][/ROW]
[ROW][C]38[/C][C]18.25[/C][C]17.0778[/C][C]1.17223[/C][/ROW]
[ROW][C]39[/C][C]9.9[/C][C]17.027[/C][C]-7.12696[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.2979[/C][C]1.70212[/C][/ROW]
[ROW][C]41[/C][C]18.25[/C][C]16.6599[/C][C]1.59014[/C][/ROW]
[ROW][C]42[/C][C]16.85[/C][C]18.1862[/C][C]-1.33625[/C][/ROW]
[ROW][C]43[/C][C]14.6[/C][C]13.1206[/C][C]1.47937[/C][/ROW]
[ROW][C]44[/C][C]13.85[/C][C]14.0939[/C][C]-0.243855[/C][/ROW]
[ROW][C]45[/C][C]18.95[/C][C]17.5754[/C][C]1.37461[/C][/ROW]
[ROW][C]46[/C][C]15.6[/C][C]14.5788[/C][C]1.02118[/C][/ROW]
[ROW][C]47[/C][C]14.85[/C][C]16.786[/C][C]-1.93599[/C][/ROW]
[ROW][C]48[/C][C]11.75[/C][C]13.9973[/C][C]-2.24733[/C][/ROW]
[ROW][C]49[/C][C]18.45[/C][C]16.6351[/C][C]1.81495[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]13.783[/C][C]2.11697[/C][/ROW]
[ROW][C]51[/C][C]17.1[/C][C]18.2626[/C][C]-1.16262[/C][/ROW]
[ROW][C]52[/C][C]16.1[/C][C]8.20764[/C][C]7.89236[/C][/ROW]
[ROW][C]53[/C][C]19.9[/C][C]18.9705[/C][C]0.92947[/C][/ROW]
[ROW][C]54[/C][C]10.95[/C][C]10.3533[/C][C]0.596671[/C][/ROW]
[ROW][C]55[/C][C]18.45[/C][C]16.3969[/C][C]2.0531[/C][/ROW]
[ROW][C]56[/C][C]15.1[/C][C]14.3294[/C][C]0.770575[/C][/ROW]
[ROW][C]57[/C][C]15[/C][C]15.4611[/C][C]-0.461071[/C][/ROW]
[ROW][C]58[/C][C]11.35[/C][C]13.7248[/C][C]-2.37481[/C][/ROW]
[ROW][C]59[/C][C]15.95[/C][C]14.8284[/C][C]1.12163[/C][/ROW]
[ROW][C]60[/C][C]18.1[/C][C]15.6718[/C][C]2.42824[/C][/ROW]
[ROW][C]61[/C][C]14.6[/C][C]16.7139[/C][C]-2.11389[/C][/ROW]
[ROW][C]62[/C][C]15.4[/C][C]16.9481[/C][C]-1.54811[/C][/ROW]
[ROW][C]63[/C][C]15.4[/C][C]16.874[/C][C]-1.47402[/C][/ROW]
[ROW][C]64[/C][C]17.6[/C][C]14.5359[/C][C]3.06412[/C][/ROW]
[ROW][C]65[/C][C]13.35[/C][C]14.2216[/C][C]-0.871647[/C][/ROW]
[ROW][C]66[/C][C]19.1[/C][C]16.9248[/C][C]2.17516[/C][/ROW]
[ROW][C]67[/C][C]15.35[/C][C]17.1946[/C][C]-1.8446[/C][/ROW]
[ROW][C]68[/C][C]7.6[/C][C]10.0832[/C][C]-2.48319[/C][/ROW]
[ROW][C]69[/C][C]13.4[/C][C]14.4773[/C][C]-1.07726[/C][/ROW]
[ROW][C]70[/C][C]13.9[/C][C]16.2345[/C][C]-2.33453[/C][/ROW]
[ROW][C]71[/C][C]19.1[/C][C]16.3787[/C][C]2.72132[/C][/ROW]
[ROW][C]72[/C][C]15.25[/C][C]15.2854[/C][C]-0.0353912[/C][/ROW]
[ROW][C]73[/C][C]12.9[/C][C]16.0661[/C][C]-3.16605[/C][/ROW]
[ROW][C]74[/C][C]16.1[/C][C]15.6884[/C][C]0.411643[/C][/ROW]
[ROW][C]75[/C][C]17.35[/C][C]15.4556[/C][C]1.89442[/C][/ROW]
[ROW][C]76[/C][C]13.15[/C][C]15.7823[/C][C]-2.63232[/C][/ROW]
[ROW][C]77[/C][C]12.15[/C][C]15.7209[/C][C]-3.57092[/C][/ROW]
[ROW][C]78[/C][C]12.6[/C][C]11.8637[/C][C]0.736266[/C][/ROW]
[ROW][C]79[/C][C]10.35[/C][C]12.5535[/C][C]-2.20346[/C][/ROW]
[ROW][C]80[/C][C]15.4[/C][C]13.9908[/C][C]1.40921[/C][/ROW]
[ROW][C]81[/C][C]9.6[/C][C]12.7818[/C][C]-3.18183[/C][/ROW]
[ROW][C]82[/C][C]18.2[/C][C]15.3557[/C][C]2.84427[/C][/ROW]
[ROW][C]83[/C][C]13.6[/C][C]13.9721[/C][C]-0.372136[/C][/ROW]
[ROW][C]84[/C][C]14.85[/C][C]13.1033[/C][C]1.74669[/C][/ROW]
[ROW][C]85[/C][C]14.75[/C][C]16.7898[/C][C]-2.0398[/C][/ROW]
[ROW][C]86[/C][C]14.1[/C][C]13.6854[/C][C]0.41461[/C][/ROW]
[ROW][C]87[/C][C]14.9[/C][C]13.7828[/C][C]1.11717[/C][/ROW]
[ROW][C]88[/C][C]16.25[/C][C]16.0754[/C][C]0.174612[/C][/ROW]
[ROW][C]89[/C][C]19.25[/C][C]19.5425[/C][C]-0.292515[/C][/ROW]
[ROW][C]90[/C][C]13.6[/C][C]13.1992[/C][C]0.400766[/C][/ROW]
[ROW][C]91[/C][C]13.6[/C][C]16.0428[/C][C]-2.44281[/C][/ROW]
[ROW][C]92[/C][C]15.65[/C][C]16.143[/C][C]-0.493005[/C][/ROW]
[ROW][C]93[/C][C]12.75[/C][C]13.579[/C][C]-0.829031[/C][/ROW]
[ROW][C]94[/C][C]14.6[/C][C]13.1761[/C][C]1.42393[/C][/ROW]
[ROW][C]95[/C][C]9.85[/C][C]9.79446[/C][C]0.0555369[/C][/ROW]
[ROW][C]96[/C][C]12.65[/C][C]11.6581[/C][C]0.991915[/C][/ROW]
[ROW][C]97[/C][C]19.2[/C][C]15.4338[/C][C]3.76617[/C][/ROW]
[ROW][C]98[/C][C]16.6[/C][C]13.497[/C][C]3.10303[/C][/ROW]
[ROW][C]99[/C][C]11.2[/C][C]11.3442[/C][C]-0.144162[/C][/ROW]
[ROW][C]100[/C][C]15.25[/C][C]16.0939[/C][C]-0.843882[/C][/ROW]
[ROW][C]101[/C][C]11.9[/C][C]13.7435[/C][C]-1.84347[/C][/ROW]
[ROW][C]102[/C][C]13.2[/C][C]13.1018[/C][C]0.0981675[/C][/ROW]
[ROW][C]103[/C][C]16.35[/C][C]16.7695[/C][C]-0.419483[/C][/ROW]
[ROW][C]104[/C][C]12.4[/C][C]12.7016[/C][C]-0.301596[/C][/ROW]
[ROW][C]105[/C][C]15.85[/C][C]14.0403[/C][C]1.80971[/C][/ROW]
[ROW][C]106[/C][C]18.15[/C][C]17.5239[/C][C]0.626071[/C][/ROW]
[ROW][C]107[/C][C]11.15[/C][C]11.8617[/C][C]-0.711711[/C][/ROW]
[ROW][C]108[/C][C]15.65[/C][C]16.3615[/C][C]-0.711518[/C][/ROW]
[ROW][C]109[/C][C]17.75[/C][C]15.8842[/C][C]1.86585[/C][/ROW]
[ROW][C]110[/C][C]7.65[/C][C]11.2608[/C][C]-3.61085[/C][/ROW]
[ROW][C]111[/C][C]12.35[/C][C]14.3267[/C][C]-1.97666[/C][/ROW]
[ROW][C]112[/C][C]15.6[/C][C]13.9285[/C][C]1.67152[/C][/ROW]
[ROW][C]113[/C][C]19.3[/C][C]17.4224[/C][C]1.8776[/C][/ROW]
[ROW][C]114[/C][C]15.2[/C][C]11.5735[/C][C]3.62651[/C][/ROW]
[ROW][C]115[/C][C]17.1[/C][C]16.0695[/C][C]1.03047[/C][/ROW]
[ROW][C]116[/C][C]15.6[/C][C]13.5504[/C][C]2.04961[/C][/ROW]
[ROW][C]117[/C][C]18.4[/C][C]14.4013[/C][C]3.9987[/C][/ROW]
[ROW][C]118[/C][C]19.05[/C][C]16.5611[/C][C]2.48891[/C][/ROW]
[ROW][C]119[/C][C]18.55[/C][C]16.9729[/C][C]1.57707[/C][/ROW]
[ROW][C]120[/C][C]19.1[/C][C]16.1392[/C][C]2.96076[/C][/ROW]
[ROW][C]121[/C][C]13.1[/C][C]13.5907[/C][C]-0.49069[/C][/ROW]
[ROW][C]122[/C][C]12.85[/C][C]16.7515[/C][C]-3.90152[/C][/ROW]
[ROW][C]123[/C][C]9.5[/C][C]12.3213[/C][C]-2.82126[/C][/ROW]
[ROW][C]124[/C][C]4.5[/C][C]10.629[/C][C]-6.12903[/C][/ROW]
[ROW][C]125[/C][C]11.85[/C][C]11.5162[/C][C]0.333796[/C][/ROW]
[ROW][C]126[/C][C]13.6[/C][C]14.5478[/C][C]-0.947818[/C][/ROW]
[ROW][C]127[/C][C]11.7[/C][C]11.6649[/C][C]0.0351173[/C][/ROW]
[ROW][C]128[/C][C]12.4[/C][C]13.4342[/C][C]-1.03416[/C][/ROW]
[ROW][C]129[/C][C]13.35[/C][C]14.6642[/C][C]-1.31424[/C][/ROW]
[ROW][C]130[/C][C]11.4[/C][C]12.5231[/C][C]-1.12314[/C][/ROW]
[ROW][C]131[/C][C]14.9[/C][C]14.7098[/C][C]0.190157[/C][/ROW]
[ROW][C]132[/C][C]19.9[/C][C]17.9301[/C][C]1.96992[/C][/ROW]
[ROW][C]133[/C][C]11.2[/C][C]13.6838[/C][C]-2.48381[/C][/ROW]
[ROW][C]134[/C][C]14.6[/C][C]14.8922[/C][C]-0.292196[/C][/ROW]
[ROW][C]135[/C][C]17.6[/C][C]17.9754[/C][C]-0.375422[/C][/ROW]
[ROW][C]136[/C][C]14.05[/C][C]13.3484[/C][C]0.701582[/C][/ROW]
[ROW][C]137[/C][C]16.1[/C][C]15.3431[/C][C]0.756858[/C][/ROW]
[ROW][C]138[/C][C]13.35[/C][C]14.7535[/C][C]-1.40347[/C][/ROW]
[ROW][C]139[/C][C]11.85[/C][C]15.3366[/C][C]-3.48661[/C][/ROW]
[ROW][C]140[/C][C]11.95[/C][C]12.721[/C][C]-0.770958[/C][/ROW]
[ROW][C]141[/C][C]14.75[/C][C]13.9823[/C][C]0.767651[/C][/ROW]
[ROW][C]142[/C][C]15.15[/C][C]12.8469[/C][C]2.30313[/C][/ROW]
[ROW][C]143[/C][C]13.2[/C][C]15.8782[/C][C]-2.67823[/C][/ROW]
[ROW][C]144[/C][C]16.85[/C][C]16.1529[/C][C]0.697071[/C][/ROW]
[ROW][C]145[/C][C]7.85[/C][C]12.3439[/C][C]-4.49394[/C][/ROW]
[ROW][C]146[/C][C]7.7[/C][C]12.8797[/C][C]-5.17974[/C][/ROW]
[ROW][C]147[/C][C]12.6[/C][C]14.4749[/C][C]-1.87493[/C][/ROW]
[ROW][C]148[/C][C]7.85[/C][C]14.8529[/C][C]-7.00293[/C][/ROW]
[ROW][C]149[/C][C]10.95[/C][C]11.3652[/C][C]-0.415171[/C][/ROW]
[ROW][C]150[/C][C]12.35[/C][C]14.4335[/C][C]-2.08349[/C][/ROW]
[ROW][C]151[/C][C]9.95[/C][C]13.1092[/C][C]-3.15921[/C][/ROW]
[ROW][C]152[/C][C]14.9[/C][C]14.1581[/C][C]0.741913[/C][/ROW]
[ROW][C]153[/C][C]16.65[/C][C]15.111[/C][C]1.539[/C][/ROW]
[ROW][C]154[/C][C]13.4[/C][C]12.5949[/C][C]0.805109[/C][/ROW]
[ROW][C]155[/C][C]13.95[/C][C]14.366[/C][C]-0.416035[/C][/ROW]
[ROW][C]156[/C][C]15.7[/C][C]15.0937[/C][C]0.606332[/C][/ROW]
[ROW][C]157[/C][C]16.85[/C][C]15.7809[/C][C]1.06906[/C][/ROW]
[ROW][C]158[/C][C]10.95[/C][C]12.1196[/C][C]-1.16961[/C][/ROW]
[ROW][C]159[/C][C]15.35[/C][C]15.9152[/C][C]-0.565184[/C][/ROW]
[ROW][C]160[/C][C]12.2[/C][C]13.0869[/C][C]-0.886862[/C][/ROW]
[ROW][C]161[/C][C]15.1[/C][C]13.7528[/C][C]1.34725[/C][/ROW]
[ROW][C]162[/C][C]17.75[/C][C]15.981[/C][C]1.76899[/C][/ROW]
[ROW][C]163[/C][C]15.2[/C][C]14.8495[/C][C]0.350464[/C][/ROW]
[ROW][C]164[/C][C]14.6[/C][C]15.4214[/C][C]-0.821405[/C][/ROW]
[ROW][C]165[/C][C]16.65[/C][C]15.7489[/C][C]0.90109[/C][/ROW]
[ROW][C]166[/C][C]8.1[/C][C]9.35667[/C][C]-1.25667[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269311&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269311&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
14.3510.7698-6.41976
212.710.74421.95584
318.115.6192.481
417.8516.25891.59106
516.616.42810.171945
612.611.67510.924867
717.119.0864-1.98636
819.117.51081.58925
916.116.2525-0.152465
1013.3511.96691.38311
1118.416.66571.73434
1214.79.620395.07961
1310.613.1668-2.56681
1412.613.5344-0.934386
1516.215.08041.11955
1613.613.32830.27169
1718.916.30132.59868
1814.113.35320.746788
1914.513.75630.743703
2016.1517.2595-1.10954
2114.7513.54371.20628
2214.813.51211.28789
2312.4511.87550.574467
2412.6512.36010.289864
2517.3514.69432.65568
268.69.45036-0.850359
2718.416.47681.92317
2816.114.36041.73955
2911.610.86550.734516
3017.7516.68591.06408
3115.2514.81990.430102
3217.6514.23023.41979
3316.3516.8631-0.513054
3417.6516.81540.834576
3513.614.6668-1.06684
3614.3514.5873-0.23728
3714.7515.508-0.757966
3818.2517.07781.17223
399.917.027-7.12696
401614.29791.70212
4118.2516.65991.59014
4216.8518.1862-1.33625
4314.613.12061.47937
4413.8514.0939-0.243855
4518.9517.57541.37461
4615.614.57881.02118
4714.8516.786-1.93599
4811.7513.9973-2.24733
4918.4516.63511.81495
5015.913.7832.11697
5117.118.2626-1.16262
5216.18.207647.89236
5319.918.97050.92947
5410.9510.35330.596671
5518.4516.39692.0531
5615.114.32940.770575
571515.4611-0.461071
5811.3513.7248-2.37481
5915.9514.82841.12163
6018.115.67182.42824
6114.616.7139-2.11389
6215.416.9481-1.54811
6315.416.874-1.47402
6417.614.53593.06412
6513.3514.2216-0.871647
6619.116.92482.17516
6715.3517.1946-1.8446
687.610.0832-2.48319
6913.414.4773-1.07726
7013.916.2345-2.33453
7119.116.37872.72132
7215.2515.2854-0.0353912
7312.916.0661-3.16605
7416.115.68840.411643
7517.3515.45561.89442
7613.1515.7823-2.63232
7712.1515.7209-3.57092
7812.611.86370.736266
7910.3512.5535-2.20346
8015.413.99081.40921
819.612.7818-3.18183
8218.215.35572.84427
8313.613.9721-0.372136
8414.8513.10331.74669
8514.7516.7898-2.0398
8614.113.68540.41461
8714.913.78281.11717
8816.2516.07540.174612
8919.2519.5425-0.292515
9013.613.19920.400766
9113.616.0428-2.44281
9215.6516.143-0.493005
9312.7513.579-0.829031
9414.613.17611.42393
959.859.794460.0555369
9612.6511.65810.991915
9719.215.43383.76617
9816.613.4973.10303
9911.211.3442-0.144162
10015.2516.0939-0.843882
10111.913.7435-1.84347
10213.213.10180.0981675
10316.3516.7695-0.419483
10412.412.7016-0.301596
10515.8514.04031.80971
10618.1517.52390.626071
10711.1511.8617-0.711711
10815.6516.3615-0.711518
10917.7515.88421.86585
1107.6511.2608-3.61085
11112.3514.3267-1.97666
11215.613.92851.67152
11319.317.42241.8776
11415.211.57353.62651
11517.116.06951.03047
11615.613.55042.04961
11718.414.40133.9987
11819.0516.56112.48891
11918.5516.97291.57707
12019.116.13922.96076
12113.113.5907-0.49069
12212.8516.7515-3.90152
1239.512.3213-2.82126
1244.510.629-6.12903
12511.8511.51620.333796
12613.614.5478-0.947818
12711.711.66490.0351173
12812.413.4342-1.03416
12913.3514.6642-1.31424
13011.412.5231-1.12314
13114.914.70980.190157
13219.917.93011.96992
13311.213.6838-2.48381
13414.614.8922-0.292196
13517.617.9754-0.375422
13614.0513.34840.701582
13716.115.34310.756858
13813.3514.7535-1.40347
13911.8515.3366-3.48661
14011.9512.721-0.770958
14114.7513.98230.767651
14215.1512.84692.30313
14313.215.8782-2.67823
14416.8516.15290.697071
1457.8512.3439-4.49394
1467.712.8797-5.17974
14712.614.4749-1.87493
1487.8514.8529-7.00293
14910.9511.3652-0.415171
15012.3514.4335-2.08349
1519.9513.1092-3.15921
15214.914.15810.741913
15316.6515.1111.539
15413.412.59490.805109
15513.9514.366-0.416035
15615.715.09370.606332
15716.8515.78091.06906
15810.9512.1196-1.16961
15915.3515.9152-0.565184
16012.213.0869-0.886862
16115.113.75281.34725
16217.7515.9811.76899
16315.214.84950.350464
16414.615.4214-0.821405
16516.6515.74890.90109
1668.19.35667-1.25667






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.7884370.4231260.211563
130.7310810.5378380.268919
140.6703280.6593440.329672
150.5812550.8374890.418745
160.6357110.7285770.364289
170.6236060.7527870.376394
180.5336310.9327380.466369
190.436570.873140.56343
200.589920.8201590.41008
210.5613740.8772530.438626
220.4976440.9952880.502356
230.4161360.8322730.583864
240.410270.820540.58973
250.4493360.8986720.550664
260.4307570.8615130.569243
270.3702310.7404610.629769
280.5155290.9689410.484471
290.4662850.932570.533715
300.4055770.8111540.594423
310.3471250.6942510.652875
320.3296090.6592180.670391
330.3129630.6259270.687037
340.2827830.5655660.717217
350.2363960.4727910.763604
360.1927220.3854440.807278
370.156470.3129390.84353
380.1291790.2583570.870821
390.7529970.4940060.247003
400.723840.552320.27616
410.6900850.619830.309915
420.6532920.6934160.346708
430.6078730.7842540.392127
440.5630090.8739820.436991
450.5205120.9589760.479488
460.4747240.9494470.525276
470.4617270.9234530.538273
480.4456550.8913110.554345
490.427890.8557790.57211
500.4054110.8108210.594589
510.3612120.7224250.638788
520.8314360.3371280.168564
530.8058830.3882350.194117
540.7781130.4437730.221887
550.7589680.4820650.241032
560.7256940.5486120.274306
570.6853890.6292220.314611
580.6920380.6159230.307962
590.6617180.6765640.338282
600.6878970.6242050.312103
610.7228210.5543580.277179
620.709470.581060.29053
630.6908670.6182660.309133
640.7180720.5638560.281928
650.6844160.6311670.315584
660.6866170.6267670.313383
670.67520.64960.3248
680.7118840.5762320.288116
690.6812610.6374790.318739
700.6924570.6150870.307543
710.7164770.5670450.283523
720.675630.648740.32437
730.7275160.5449670.272484
740.6887650.6224690.311235
750.6753480.6493040.324652
760.7044790.5910410.295521
770.7813280.4373440.218672
780.7557550.4884910.244245
790.7662180.4675630.233782
800.7494830.5010340.250517
810.7809360.4381280.219064
820.8085260.3829470.191474
830.7843340.4313320.215666
840.7779560.4440880.222044
850.7772960.4454070.222704
860.745250.50950.25475
870.7169530.5660950.283047
880.6764410.6471190.323559
890.6400960.7198070.359904
900.6002930.7994140.399707
910.6133020.7733950.386698
920.5765540.8468930.423446
930.5335080.9329850.466492
940.5052030.9895940.494797
950.5130010.9739990.486999
960.482870.9657410.51713
970.5548960.8902080.445104
980.6037350.7925290.396265
990.5784160.8431690.421584
1000.5377670.9244650.462233
1010.5139580.9720840.486042
1020.4666360.9332710.533364
1030.4216420.8432850.578358
1040.3786720.7573440.621328
1050.3758480.7516960.624152
1060.3368750.6737510.663125
1070.3103310.6206630.689669
1080.2796130.5592270.720387
1090.2714110.5428210.728589
1100.349040.698080.65096
1110.3248240.6496490.675176
1120.3770620.7541250.622938
1130.3609740.7219490.639026
1140.4234740.8469480.576526
1150.3813360.7626710.618664
1160.4139020.8278040.586098
1170.7006420.5987160.299358
1180.7355380.5289240.264462
1190.7062990.5874010.293701
1200.7775210.4449580.222479
1210.7491490.5017020.250851
1220.7610640.4778730.238936
1230.7396090.5207830.260391
1240.8646590.2706810.135341
1250.8380050.323990.161995
1260.8131740.3736520.186826
1270.8222540.3554920.177746
1280.7836660.4326690.216334
1290.7503540.4992910.249646
1300.7023920.5952150.297608
1310.6630760.6738470.336924
1320.6106490.7787020.389351
1330.5756660.8486670.424334
1340.5477090.9045830.452291
1350.4824620.9649240.517538
1360.555130.889740.44487
1370.5200450.9599110.479955
1380.5161010.9677980.483899
1390.4777010.9554020.522299
1400.5008450.9983090.499155
1410.4897840.9795680.510216
1420.4662180.9324360.533782
1430.4995050.9990110.500495
1440.4393660.8787320.560634
1450.4924240.9848480.507576
1460.6795740.6408520.320426
1470.6134420.7731160.386558
1480.9508240.09835130.0491757
1490.9130640.1738710.0869357
1500.9602460.07950780.0397539
1510.9799720.04005530.0200276
1520.9783320.04333680.0216684
1530.9432160.1135680.0567838
1540.9466860.1066280.0533142

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.788437 & 0.423126 & 0.211563 \tabularnewline
13 & 0.731081 & 0.537838 & 0.268919 \tabularnewline
14 & 0.670328 & 0.659344 & 0.329672 \tabularnewline
15 & 0.581255 & 0.837489 & 0.418745 \tabularnewline
16 & 0.635711 & 0.728577 & 0.364289 \tabularnewline
17 & 0.623606 & 0.752787 & 0.376394 \tabularnewline
18 & 0.533631 & 0.932738 & 0.466369 \tabularnewline
19 & 0.43657 & 0.87314 & 0.56343 \tabularnewline
20 & 0.58992 & 0.820159 & 0.41008 \tabularnewline
21 & 0.561374 & 0.877253 & 0.438626 \tabularnewline
22 & 0.497644 & 0.995288 & 0.502356 \tabularnewline
23 & 0.416136 & 0.832273 & 0.583864 \tabularnewline
24 & 0.41027 & 0.82054 & 0.58973 \tabularnewline
25 & 0.449336 & 0.898672 & 0.550664 \tabularnewline
26 & 0.430757 & 0.861513 & 0.569243 \tabularnewline
27 & 0.370231 & 0.740461 & 0.629769 \tabularnewline
28 & 0.515529 & 0.968941 & 0.484471 \tabularnewline
29 & 0.466285 & 0.93257 & 0.533715 \tabularnewline
30 & 0.405577 & 0.811154 & 0.594423 \tabularnewline
31 & 0.347125 & 0.694251 & 0.652875 \tabularnewline
32 & 0.329609 & 0.659218 & 0.670391 \tabularnewline
33 & 0.312963 & 0.625927 & 0.687037 \tabularnewline
34 & 0.282783 & 0.565566 & 0.717217 \tabularnewline
35 & 0.236396 & 0.472791 & 0.763604 \tabularnewline
36 & 0.192722 & 0.385444 & 0.807278 \tabularnewline
37 & 0.15647 & 0.312939 & 0.84353 \tabularnewline
38 & 0.129179 & 0.258357 & 0.870821 \tabularnewline
39 & 0.752997 & 0.494006 & 0.247003 \tabularnewline
40 & 0.72384 & 0.55232 & 0.27616 \tabularnewline
41 & 0.690085 & 0.61983 & 0.309915 \tabularnewline
42 & 0.653292 & 0.693416 & 0.346708 \tabularnewline
43 & 0.607873 & 0.784254 & 0.392127 \tabularnewline
44 & 0.563009 & 0.873982 & 0.436991 \tabularnewline
45 & 0.520512 & 0.958976 & 0.479488 \tabularnewline
46 & 0.474724 & 0.949447 & 0.525276 \tabularnewline
47 & 0.461727 & 0.923453 & 0.538273 \tabularnewline
48 & 0.445655 & 0.891311 & 0.554345 \tabularnewline
49 & 0.42789 & 0.855779 & 0.57211 \tabularnewline
50 & 0.405411 & 0.810821 & 0.594589 \tabularnewline
51 & 0.361212 & 0.722425 & 0.638788 \tabularnewline
52 & 0.831436 & 0.337128 & 0.168564 \tabularnewline
53 & 0.805883 & 0.388235 & 0.194117 \tabularnewline
54 & 0.778113 & 0.443773 & 0.221887 \tabularnewline
55 & 0.758968 & 0.482065 & 0.241032 \tabularnewline
56 & 0.725694 & 0.548612 & 0.274306 \tabularnewline
57 & 0.685389 & 0.629222 & 0.314611 \tabularnewline
58 & 0.692038 & 0.615923 & 0.307962 \tabularnewline
59 & 0.661718 & 0.676564 & 0.338282 \tabularnewline
60 & 0.687897 & 0.624205 & 0.312103 \tabularnewline
61 & 0.722821 & 0.554358 & 0.277179 \tabularnewline
62 & 0.70947 & 0.58106 & 0.29053 \tabularnewline
63 & 0.690867 & 0.618266 & 0.309133 \tabularnewline
64 & 0.718072 & 0.563856 & 0.281928 \tabularnewline
65 & 0.684416 & 0.631167 & 0.315584 \tabularnewline
66 & 0.686617 & 0.626767 & 0.313383 \tabularnewline
67 & 0.6752 & 0.6496 & 0.3248 \tabularnewline
68 & 0.711884 & 0.576232 & 0.288116 \tabularnewline
69 & 0.681261 & 0.637479 & 0.318739 \tabularnewline
70 & 0.692457 & 0.615087 & 0.307543 \tabularnewline
71 & 0.716477 & 0.567045 & 0.283523 \tabularnewline
72 & 0.67563 & 0.64874 & 0.32437 \tabularnewline
73 & 0.727516 & 0.544967 & 0.272484 \tabularnewline
74 & 0.688765 & 0.622469 & 0.311235 \tabularnewline
75 & 0.675348 & 0.649304 & 0.324652 \tabularnewline
76 & 0.704479 & 0.591041 & 0.295521 \tabularnewline
77 & 0.781328 & 0.437344 & 0.218672 \tabularnewline
78 & 0.755755 & 0.488491 & 0.244245 \tabularnewline
79 & 0.766218 & 0.467563 & 0.233782 \tabularnewline
80 & 0.749483 & 0.501034 & 0.250517 \tabularnewline
81 & 0.780936 & 0.438128 & 0.219064 \tabularnewline
82 & 0.808526 & 0.382947 & 0.191474 \tabularnewline
83 & 0.784334 & 0.431332 & 0.215666 \tabularnewline
84 & 0.777956 & 0.444088 & 0.222044 \tabularnewline
85 & 0.777296 & 0.445407 & 0.222704 \tabularnewline
86 & 0.74525 & 0.5095 & 0.25475 \tabularnewline
87 & 0.716953 & 0.566095 & 0.283047 \tabularnewline
88 & 0.676441 & 0.647119 & 0.323559 \tabularnewline
89 & 0.640096 & 0.719807 & 0.359904 \tabularnewline
90 & 0.600293 & 0.799414 & 0.399707 \tabularnewline
91 & 0.613302 & 0.773395 & 0.386698 \tabularnewline
92 & 0.576554 & 0.846893 & 0.423446 \tabularnewline
93 & 0.533508 & 0.932985 & 0.466492 \tabularnewline
94 & 0.505203 & 0.989594 & 0.494797 \tabularnewline
95 & 0.513001 & 0.973999 & 0.486999 \tabularnewline
96 & 0.48287 & 0.965741 & 0.51713 \tabularnewline
97 & 0.554896 & 0.890208 & 0.445104 \tabularnewline
98 & 0.603735 & 0.792529 & 0.396265 \tabularnewline
99 & 0.578416 & 0.843169 & 0.421584 \tabularnewline
100 & 0.537767 & 0.924465 & 0.462233 \tabularnewline
101 & 0.513958 & 0.972084 & 0.486042 \tabularnewline
102 & 0.466636 & 0.933271 & 0.533364 \tabularnewline
103 & 0.421642 & 0.843285 & 0.578358 \tabularnewline
104 & 0.378672 & 0.757344 & 0.621328 \tabularnewline
105 & 0.375848 & 0.751696 & 0.624152 \tabularnewline
106 & 0.336875 & 0.673751 & 0.663125 \tabularnewline
107 & 0.310331 & 0.620663 & 0.689669 \tabularnewline
108 & 0.279613 & 0.559227 & 0.720387 \tabularnewline
109 & 0.271411 & 0.542821 & 0.728589 \tabularnewline
110 & 0.34904 & 0.69808 & 0.65096 \tabularnewline
111 & 0.324824 & 0.649649 & 0.675176 \tabularnewline
112 & 0.377062 & 0.754125 & 0.622938 \tabularnewline
113 & 0.360974 & 0.721949 & 0.639026 \tabularnewline
114 & 0.423474 & 0.846948 & 0.576526 \tabularnewline
115 & 0.381336 & 0.762671 & 0.618664 \tabularnewline
116 & 0.413902 & 0.827804 & 0.586098 \tabularnewline
117 & 0.700642 & 0.598716 & 0.299358 \tabularnewline
118 & 0.735538 & 0.528924 & 0.264462 \tabularnewline
119 & 0.706299 & 0.587401 & 0.293701 \tabularnewline
120 & 0.777521 & 0.444958 & 0.222479 \tabularnewline
121 & 0.749149 & 0.501702 & 0.250851 \tabularnewline
122 & 0.761064 & 0.477873 & 0.238936 \tabularnewline
123 & 0.739609 & 0.520783 & 0.260391 \tabularnewline
124 & 0.864659 & 0.270681 & 0.135341 \tabularnewline
125 & 0.838005 & 0.32399 & 0.161995 \tabularnewline
126 & 0.813174 & 0.373652 & 0.186826 \tabularnewline
127 & 0.822254 & 0.355492 & 0.177746 \tabularnewline
128 & 0.783666 & 0.432669 & 0.216334 \tabularnewline
129 & 0.750354 & 0.499291 & 0.249646 \tabularnewline
130 & 0.702392 & 0.595215 & 0.297608 \tabularnewline
131 & 0.663076 & 0.673847 & 0.336924 \tabularnewline
132 & 0.610649 & 0.778702 & 0.389351 \tabularnewline
133 & 0.575666 & 0.848667 & 0.424334 \tabularnewline
134 & 0.547709 & 0.904583 & 0.452291 \tabularnewline
135 & 0.482462 & 0.964924 & 0.517538 \tabularnewline
136 & 0.55513 & 0.88974 & 0.44487 \tabularnewline
137 & 0.520045 & 0.959911 & 0.479955 \tabularnewline
138 & 0.516101 & 0.967798 & 0.483899 \tabularnewline
139 & 0.477701 & 0.955402 & 0.522299 \tabularnewline
140 & 0.500845 & 0.998309 & 0.499155 \tabularnewline
141 & 0.489784 & 0.979568 & 0.510216 \tabularnewline
142 & 0.466218 & 0.932436 & 0.533782 \tabularnewline
143 & 0.499505 & 0.999011 & 0.500495 \tabularnewline
144 & 0.439366 & 0.878732 & 0.560634 \tabularnewline
145 & 0.492424 & 0.984848 & 0.507576 \tabularnewline
146 & 0.679574 & 0.640852 & 0.320426 \tabularnewline
147 & 0.613442 & 0.773116 & 0.386558 \tabularnewline
148 & 0.950824 & 0.0983513 & 0.0491757 \tabularnewline
149 & 0.913064 & 0.173871 & 0.0869357 \tabularnewline
150 & 0.960246 & 0.0795078 & 0.0397539 \tabularnewline
151 & 0.979972 & 0.0400553 & 0.0200276 \tabularnewline
152 & 0.978332 & 0.0433368 & 0.0216684 \tabularnewline
153 & 0.943216 & 0.113568 & 0.0567838 \tabularnewline
154 & 0.946686 & 0.106628 & 0.0533142 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269311&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]12[/C][C]0.788437[/C][C]0.423126[/C][C]0.211563[/C][/ROW]
[ROW][C]13[/C][C]0.731081[/C][C]0.537838[/C][C]0.268919[/C][/ROW]
[ROW][C]14[/C][C]0.670328[/C][C]0.659344[/C][C]0.329672[/C][/ROW]
[ROW][C]15[/C][C]0.581255[/C][C]0.837489[/C][C]0.418745[/C][/ROW]
[ROW][C]16[/C][C]0.635711[/C][C]0.728577[/C][C]0.364289[/C][/ROW]
[ROW][C]17[/C][C]0.623606[/C][C]0.752787[/C][C]0.376394[/C][/ROW]
[ROW][C]18[/C][C]0.533631[/C][C]0.932738[/C][C]0.466369[/C][/ROW]
[ROW][C]19[/C][C]0.43657[/C][C]0.87314[/C][C]0.56343[/C][/ROW]
[ROW][C]20[/C][C]0.58992[/C][C]0.820159[/C][C]0.41008[/C][/ROW]
[ROW][C]21[/C][C]0.561374[/C][C]0.877253[/C][C]0.438626[/C][/ROW]
[ROW][C]22[/C][C]0.497644[/C][C]0.995288[/C][C]0.502356[/C][/ROW]
[ROW][C]23[/C][C]0.416136[/C][C]0.832273[/C][C]0.583864[/C][/ROW]
[ROW][C]24[/C][C]0.41027[/C][C]0.82054[/C][C]0.58973[/C][/ROW]
[ROW][C]25[/C][C]0.449336[/C][C]0.898672[/C][C]0.550664[/C][/ROW]
[ROW][C]26[/C][C]0.430757[/C][C]0.861513[/C][C]0.569243[/C][/ROW]
[ROW][C]27[/C][C]0.370231[/C][C]0.740461[/C][C]0.629769[/C][/ROW]
[ROW][C]28[/C][C]0.515529[/C][C]0.968941[/C][C]0.484471[/C][/ROW]
[ROW][C]29[/C][C]0.466285[/C][C]0.93257[/C][C]0.533715[/C][/ROW]
[ROW][C]30[/C][C]0.405577[/C][C]0.811154[/C][C]0.594423[/C][/ROW]
[ROW][C]31[/C][C]0.347125[/C][C]0.694251[/C][C]0.652875[/C][/ROW]
[ROW][C]32[/C][C]0.329609[/C][C]0.659218[/C][C]0.670391[/C][/ROW]
[ROW][C]33[/C][C]0.312963[/C][C]0.625927[/C][C]0.687037[/C][/ROW]
[ROW][C]34[/C][C]0.282783[/C][C]0.565566[/C][C]0.717217[/C][/ROW]
[ROW][C]35[/C][C]0.236396[/C][C]0.472791[/C][C]0.763604[/C][/ROW]
[ROW][C]36[/C][C]0.192722[/C][C]0.385444[/C][C]0.807278[/C][/ROW]
[ROW][C]37[/C][C]0.15647[/C][C]0.312939[/C][C]0.84353[/C][/ROW]
[ROW][C]38[/C][C]0.129179[/C][C]0.258357[/C][C]0.870821[/C][/ROW]
[ROW][C]39[/C][C]0.752997[/C][C]0.494006[/C][C]0.247003[/C][/ROW]
[ROW][C]40[/C][C]0.72384[/C][C]0.55232[/C][C]0.27616[/C][/ROW]
[ROW][C]41[/C][C]0.690085[/C][C]0.61983[/C][C]0.309915[/C][/ROW]
[ROW][C]42[/C][C]0.653292[/C][C]0.693416[/C][C]0.346708[/C][/ROW]
[ROW][C]43[/C][C]0.607873[/C][C]0.784254[/C][C]0.392127[/C][/ROW]
[ROW][C]44[/C][C]0.563009[/C][C]0.873982[/C][C]0.436991[/C][/ROW]
[ROW][C]45[/C][C]0.520512[/C][C]0.958976[/C][C]0.479488[/C][/ROW]
[ROW][C]46[/C][C]0.474724[/C][C]0.949447[/C][C]0.525276[/C][/ROW]
[ROW][C]47[/C][C]0.461727[/C][C]0.923453[/C][C]0.538273[/C][/ROW]
[ROW][C]48[/C][C]0.445655[/C][C]0.891311[/C][C]0.554345[/C][/ROW]
[ROW][C]49[/C][C]0.42789[/C][C]0.855779[/C][C]0.57211[/C][/ROW]
[ROW][C]50[/C][C]0.405411[/C][C]0.810821[/C][C]0.594589[/C][/ROW]
[ROW][C]51[/C][C]0.361212[/C][C]0.722425[/C][C]0.638788[/C][/ROW]
[ROW][C]52[/C][C]0.831436[/C][C]0.337128[/C][C]0.168564[/C][/ROW]
[ROW][C]53[/C][C]0.805883[/C][C]0.388235[/C][C]0.194117[/C][/ROW]
[ROW][C]54[/C][C]0.778113[/C][C]0.443773[/C][C]0.221887[/C][/ROW]
[ROW][C]55[/C][C]0.758968[/C][C]0.482065[/C][C]0.241032[/C][/ROW]
[ROW][C]56[/C][C]0.725694[/C][C]0.548612[/C][C]0.274306[/C][/ROW]
[ROW][C]57[/C][C]0.685389[/C][C]0.629222[/C][C]0.314611[/C][/ROW]
[ROW][C]58[/C][C]0.692038[/C][C]0.615923[/C][C]0.307962[/C][/ROW]
[ROW][C]59[/C][C]0.661718[/C][C]0.676564[/C][C]0.338282[/C][/ROW]
[ROW][C]60[/C][C]0.687897[/C][C]0.624205[/C][C]0.312103[/C][/ROW]
[ROW][C]61[/C][C]0.722821[/C][C]0.554358[/C][C]0.277179[/C][/ROW]
[ROW][C]62[/C][C]0.70947[/C][C]0.58106[/C][C]0.29053[/C][/ROW]
[ROW][C]63[/C][C]0.690867[/C][C]0.618266[/C][C]0.309133[/C][/ROW]
[ROW][C]64[/C][C]0.718072[/C][C]0.563856[/C][C]0.281928[/C][/ROW]
[ROW][C]65[/C][C]0.684416[/C][C]0.631167[/C][C]0.315584[/C][/ROW]
[ROW][C]66[/C][C]0.686617[/C][C]0.626767[/C][C]0.313383[/C][/ROW]
[ROW][C]67[/C][C]0.6752[/C][C]0.6496[/C][C]0.3248[/C][/ROW]
[ROW][C]68[/C][C]0.711884[/C][C]0.576232[/C][C]0.288116[/C][/ROW]
[ROW][C]69[/C][C]0.681261[/C][C]0.637479[/C][C]0.318739[/C][/ROW]
[ROW][C]70[/C][C]0.692457[/C][C]0.615087[/C][C]0.307543[/C][/ROW]
[ROW][C]71[/C][C]0.716477[/C][C]0.567045[/C][C]0.283523[/C][/ROW]
[ROW][C]72[/C][C]0.67563[/C][C]0.64874[/C][C]0.32437[/C][/ROW]
[ROW][C]73[/C][C]0.727516[/C][C]0.544967[/C][C]0.272484[/C][/ROW]
[ROW][C]74[/C][C]0.688765[/C][C]0.622469[/C][C]0.311235[/C][/ROW]
[ROW][C]75[/C][C]0.675348[/C][C]0.649304[/C][C]0.324652[/C][/ROW]
[ROW][C]76[/C][C]0.704479[/C][C]0.591041[/C][C]0.295521[/C][/ROW]
[ROW][C]77[/C][C]0.781328[/C][C]0.437344[/C][C]0.218672[/C][/ROW]
[ROW][C]78[/C][C]0.755755[/C][C]0.488491[/C][C]0.244245[/C][/ROW]
[ROW][C]79[/C][C]0.766218[/C][C]0.467563[/C][C]0.233782[/C][/ROW]
[ROW][C]80[/C][C]0.749483[/C][C]0.501034[/C][C]0.250517[/C][/ROW]
[ROW][C]81[/C][C]0.780936[/C][C]0.438128[/C][C]0.219064[/C][/ROW]
[ROW][C]82[/C][C]0.808526[/C][C]0.382947[/C][C]0.191474[/C][/ROW]
[ROW][C]83[/C][C]0.784334[/C][C]0.431332[/C][C]0.215666[/C][/ROW]
[ROW][C]84[/C][C]0.777956[/C][C]0.444088[/C][C]0.222044[/C][/ROW]
[ROW][C]85[/C][C]0.777296[/C][C]0.445407[/C][C]0.222704[/C][/ROW]
[ROW][C]86[/C][C]0.74525[/C][C]0.5095[/C][C]0.25475[/C][/ROW]
[ROW][C]87[/C][C]0.716953[/C][C]0.566095[/C][C]0.283047[/C][/ROW]
[ROW][C]88[/C][C]0.676441[/C][C]0.647119[/C][C]0.323559[/C][/ROW]
[ROW][C]89[/C][C]0.640096[/C][C]0.719807[/C][C]0.359904[/C][/ROW]
[ROW][C]90[/C][C]0.600293[/C][C]0.799414[/C][C]0.399707[/C][/ROW]
[ROW][C]91[/C][C]0.613302[/C][C]0.773395[/C][C]0.386698[/C][/ROW]
[ROW][C]92[/C][C]0.576554[/C][C]0.846893[/C][C]0.423446[/C][/ROW]
[ROW][C]93[/C][C]0.533508[/C][C]0.932985[/C][C]0.466492[/C][/ROW]
[ROW][C]94[/C][C]0.505203[/C][C]0.989594[/C][C]0.494797[/C][/ROW]
[ROW][C]95[/C][C]0.513001[/C][C]0.973999[/C][C]0.486999[/C][/ROW]
[ROW][C]96[/C][C]0.48287[/C][C]0.965741[/C][C]0.51713[/C][/ROW]
[ROW][C]97[/C][C]0.554896[/C][C]0.890208[/C][C]0.445104[/C][/ROW]
[ROW][C]98[/C][C]0.603735[/C][C]0.792529[/C][C]0.396265[/C][/ROW]
[ROW][C]99[/C][C]0.578416[/C][C]0.843169[/C][C]0.421584[/C][/ROW]
[ROW][C]100[/C][C]0.537767[/C][C]0.924465[/C][C]0.462233[/C][/ROW]
[ROW][C]101[/C][C]0.513958[/C][C]0.972084[/C][C]0.486042[/C][/ROW]
[ROW][C]102[/C][C]0.466636[/C][C]0.933271[/C][C]0.533364[/C][/ROW]
[ROW][C]103[/C][C]0.421642[/C][C]0.843285[/C][C]0.578358[/C][/ROW]
[ROW][C]104[/C][C]0.378672[/C][C]0.757344[/C][C]0.621328[/C][/ROW]
[ROW][C]105[/C][C]0.375848[/C][C]0.751696[/C][C]0.624152[/C][/ROW]
[ROW][C]106[/C][C]0.336875[/C][C]0.673751[/C][C]0.663125[/C][/ROW]
[ROW][C]107[/C][C]0.310331[/C][C]0.620663[/C][C]0.689669[/C][/ROW]
[ROW][C]108[/C][C]0.279613[/C][C]0.559227[/C][C]0.720387[/C][/ROW]
[ROW][C]109[/C][C]0.271411[/C][C]0.542821[/C][C]0.728589[/C][/ROW]
[ROW][C]110[/C][C]0.34904[/C][C]0.69808[/C][C]0.65096[/C][/ROW]
[ROW][C]111[/C][C]0.324824[/C][C]0.649649[/C][C]0.675176[/C][/ROW]
[ROW][C]112[/C][C]0.377062[/C][C]0.754125[/C][C]0.622938[/C][/ROW]
[ROW][C]113[/C][C]0.360974[/C][C]0.721949[/C][C]0.639026[/C][/ROW]
[ROW][C]114[/C][C]0.423474[/C][C]0.846948[/C][C]0.576526[/C][/ROW]
[ROW][C]115[/C][C]0.381336[/C][C]0.762671[/C][C]0.618664[/C][/ROW]
[ROW][C]116[/C][C]0.413902[/C][C]0.827804[/C][C]0.586098[/C][/ROW]
[ROW][C]117[/C][C]0.700642[/C][C]0.598716[/C][C]0.299358[/C][/ROW]
[ROW][C]118[/C][C]0.735538[/C][C]0.528924[/C][C]0.264462[/C][/ROW]
[ROW][C]119[/C][C]0.706299[/C][C]0.587401[/C][C]0.293701[/C][/ROW]
[ROW][C]120[/C][C]0.777521[/C][C]0.444958[/C][C]0.222479[/C][/ROW]
[ROW][C]121[/C][C]0.749149[/C][C]0.501702[/C][C]0.250851[/C][/ROW]
[ROW][C]122[/C][C]0.761064[/C][C]0.477873[/C][C]0.238936[/C][/ROW]
[ROW][C]123[/C][C]0.739609[/C][C]0.520783[/C][C]0.260391[/C][/ROW]
[ROW][C]124[/C][C]0.864659[/C][C]0.270681[/C][C]0.135341[/C][/ROW]
[ROW][C]125[/C][C]0.838005[/C][C]0.32399[/C][C]0.161995[/C][/ROW]
[ROW][C]126[/C][C]0.813174[/C][C]0.373652[/C][C]0.186826[/C][/ROW]
[ROW][C]127[/C][C]0.822254[/C][C]0.355492[/C][C]0.177746[/C][/ROW]
[ROW][C]128[/C][C]0.783666[/C][C]0.432669[/C][C]0.216334[/C][/ROW]
[ROW][C]129[/C][C]0.750354[/C][C]0.499291[/C][C]0.249646[/C][/ROW]
[ROW][C]130[/C][C]0.702392[/C][C]0.595215[/C][C]0.297608[/C][/ROW]
[ROW][C]131[/C][C]0.663076[/C][C]0.673847[/C][C]0.336924[/C][/ROW]
[ROW][C]132[/C][C]0.610649[/C][C]0.778702[/C][C]0.389351[/C][/ROW]
[ROW][C]133[/C][C]0.575666[/C][C]0.848667[/C][C]0.424334[/C][/ROW]
[ROW][C]134[/C][C]0.547709[/C][C]0.904583[/C][C]0.452291[/C][/ROW]
[ROW][C]135[/C][C]0.482462[/C][C]0.964924[/C][C]0.517538[/C][/ROW]
[ROW][C]136[/C][C]0.55513[/C][C]0.88974[/C][C]0.44487[/C][/ROW]
[ROW][C]137[/C][C]0.520045[/C][C]0.959911[/C][C]0.479955[/C][/ROW]
[ROW][C]138[/C][C]0.516101[/C][C]0.967798[/C][C]0.483899[/C][/ROW]
[ROW][C]139[/C][C]0.477701[/C][C]0.955402[/C][C]0.522299[/C][/ROW]
[ROW][C]140[/C][C]0.500845[/C][C]0.998309[/C][C]0.499155[/C][/ROW]
[ROW][C]141[/C][C]0.489784[/C][C]0.979568[/C][C]0.510216[/C][/ROW]
[ROW][C]142[/C][C]0.466218[/C][C]0.932436[/C][C]0.533782[/C][/ROW]
[ROW][C]143[/C][C]0.499505[/C][C]0.999011[/C][C]0.500495[/C][/ROW]
[ROW][C]144[/C][C]0.439366[/C][C]0.878732[/C][C]0.560634[/C][/ROW]
[ROW][C]145[/C][C]0.492424[/C][C]0.984848[/C][C]0.507576[/C][/ROW]
[ROW][C]146[/C][C]0.679574[/C][C]0.640852[/C][C]0.320426[/C][/ROW]
[ROW][C]147[/C][C]0.613442[/C][C]0.773116[/C][C]0.386558[/C][/ROW]
[ROW][C]148[/C][C]0.950824[/C][C]0.0983513[/C][C]0.0491757[/C][/ROW]
[ROW][C]149[/C][C]0.913064[/C][C]0.173871[/C][C]0.0869357[/C][/ROW]
[ROW][C]150[/C][C]0.960246[/C][C]0.0795078[/C][C]0.0397539[/C][/ROW]
[ROW][C]151[/C][C]0.979972[/C][C]0.0400553[/C][C]0.0200276[/C][/ROW]
[ROW][C]152[/C][C]0.978332[/C][C]0.0433368[/C][C]0.0216684[/C][/ROW]
[ROW][C]153[/C][C]0.943216[/C][C]0.113568[/C][C]0.0567838[/C][/ROW]
[ROW][C]154[/C][C]0.946686[/C][C]0.106628[/C][C]0.0533142[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269311&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269311&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
120.7884370.4231260.211563
130.7310810.5378380.268919
140.6703280.6593440.329672
150.5812550.8374890.418745
160.6357110.7285770.364289
170.6236060.7527870.376394
180.5336310.9327380.466369
190.436570.873140.56343
200.589920.8201590.41008
210.5613740.8772530.438626
220.4976440.9952880.502356
230.4161360.8322730.583864
240.410270.820540.58973
250.4493360.8986720.550664
260.4307570.8615130.569243
270.3702310.7404610.629769
280.5155290.9689410.484471
290.4662850.932570.533715
300.4055770.8111540.594423
310.3471250.6942510.652875
320.3296090.6592180.670391
330.3129630.6259270.687037
340.2827830.5655660.717217
350.2363960.4727910.763604
360.1927220.3854440.807278
370.156470.3129390.84353
380.1291790.2583570.870821
390.7529970.4940060.247003
400.723840.552320.27616
410.6900850.619830.309915
420.6532920.6934160.346708
430.6078730.7842540.392127
440.5630090.8739820.436991
450.5205120.9589760.479488
460.4747240.9494470.525276
470.4617270.9234530.538273
480.4456550.8913110.554345
490.427890.8557790.57211
500.4054110.8108210.594589
510.3612120.7224250.638788
520.8314360.3371280.168564
530.8058830.3882350.194117
540.7781130.4437730.221887
550.7589680.4820650.241032
560.7256940.5486120.274306
570.6853890.6292220.314611
580.6920380.6159230.307962
590.6617180.6765640.338282
600.6878970.6242050.312103
610.7228210.5543580.277179
620.709470.581060.29053
630.6908670.6182660.309133
640.7180720.5638560.281928
650.6844160.6311670.315584
660.6866170.6267670.313383
670.67520.64960.3248
680.7118840.5762320.288116
690.6812610.6374790.318739
700.6924570.6150870.307543
710.7164770.5670450.283523
720.675630.648740.32437
730.7275160.5449670.272484
740.6887650.6224690.311235
750.6753480.6493040.324652
760.7044790.5910410.295521
770.7813280.4373440.218672
780.7557550.4884910.244245
790.7662180.4675630.233782
800.7494830.5010340.250517
810.7809360.4381280.219064
820.8085260.3829470.191474
830.7843340.4313320.215666
840.7779560.4440880.222044
850.7772960.4454070.222704
860.745250.50950.25475
870.7169530.5660950.283047
880.6764410.6471190.323559
890.6400960.7198070.359904
900.6002930.7994140.399707
910.6133020.7733950.386698
920.5765540.8468930.423446
930.5335080.9329850.466492
940.5052030.9895940.494797
950.5130010.9739990.486999
960.482870.9657410.51713
970.5548960.8902080.445104
980.6037350.7925290.396265
990.5784160.8431690.421584
1000.5377670.9244650.462233
1010.5139580.9720840.486042
1020.4666360.9332710.533364
1030.4216420.8432850.578358
1040.3786720.7573440.621328
1050.3758480.7516960.624152
1060.3368750.6737510.663125
1070.3103310.6206630.689669
1080.2796130.5592270.720387
1090.2714110.5428210.728589
1100.349040.698080.65096
1110.3248240.6496490.675176
1120.3770620.7541250.622938
1130.3609740.7219490.639026
1140.4234740.8469480.576526
1150.3813360.7626710.618664
1160.4139020.8278040.586098
1170.7006420.5987160.299358
1180.7355380.5289240.264462
1190.7062990.5874010.293701
1200.7775210.4449580.222479
1210.7491490.5017020.250851
1220.7610640.4778730.238936
1230.7396090.5207830.260391
1240.8646590.2706810.135341
1250.8380050.323990.161995
1260.8131740.3736520.186826
1270.8222540.3554920.177746
1280.7836660.4326690.216334
1290.7503540.4992910.249646
1300.7023920.5952150.297608
1310.6630760.6738470.336924
1320.6106490.7787020.389351
1330.5756660.8486670.424334
1340.5477090.9045830.452291
1350.4824620.9649240.517538
1360.555130.889740.44487
1370.5200450.9599110.479955
1380.5161010.9677980.483899
1390.4777010.9554020.522299
1400.5008450.9983090.499155
1410.4897840.9795680.510216
1420.4662180.9324360.533782
1430.4995050.9990110.500495
1440.4393660.8787320.560634
1450.4924240.9848480.507576
1460.6795740.6408520.320426
1470.6134420.7731160.386558
1480.9508240.09835130.0491757
1490.9130640.1738710.0869357
1500.9602460.07950780.0397539
1510.9799720.04005530.0200276
1520.9783320.04333680.0216684
1530.9432160.1135680.0567838
1540.9466860.1066280.0533142






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.013986OK
10% type I error level40.027972OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 2 & 0.013986 & OK \tabularnewline
10% type I error level & 4 & 0.027972 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269311&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]2[/C][C]0.013986[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.027972[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269311&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269311&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.013986OK
10% type I error level40.027972OK


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')
}