FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 10 Nov 2014 14:38:44 +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/Nov/10/t14156303424ny9gczjy36imb2.htm/, Retrieved Sun, 19 May 2024 15:50:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=253353, Retrieved Sun, 19 May 2024 15:50:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact135

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- RM D    [Multiple Regression] [Ws 7] [2014-11-10 14:38:44] [0015a2406d94cac8c1a56a29b9122359] [Current]
- RMPD      [Central Tendency] [ws 7] [2014-11-13 18:37:13] [55a850ac261e4a7d4f206113c00d6f60]
- RM          [Skewness and Kurtosis Test] [ws 7] [2014-11-13 18:59:25] [55a850ac261e4a7d4f206113c00d6f60]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
41 38 13 14 12
39 32 16 18 11
30 35 19 11 14
31 33 15 12 12
34 37 14 16 21
35 29 13 18 12
39 31 19 14 22
34 36 15 14 11
36 35 14 15 10
37 38 15 15 13
38 31 16 17 10
36 34 16 19 8
38 35 16 10 15
39 38 16 16 14
33 37 17 18 10
32 33 15 14 14
36 32 15 14 14
38 38 20 17 11
39 38 18 14 10
32 32 16 16 13
32 33 16 18 9.5
31 31 16 11 14
39 38 19 14 12
37 39 16 12 14
39 32 17 17 11
41 32 17 9 9
36 35 16 16 11
33 37 15 14 15
33 33 16 15 14
34 33 14 11 13
31 31 15 16 9
27 32 12 13 15
37 31 14 17 10
34 37 16 15 11
34 30 14 14 13
32 33 10 16 8
29 31 10 9 20
36 33 14 15 12
29 31 16 17 10
35 33 16 13 10
37 32 16 15 9
34 33 14 16 14
38 32 20 16 8
35 33 14 12 14
38 28 14 15 11
37 35 11 11 13
38 39 14 15 9
33 34 15 15 11
36 38 16 17 15
38 32 14 13 11
32 38 16 16 10
32 30 14 14 14
32 33 12 11 18
34 38 16 12 14
32 32 9 12 11
37 35 14 15 14.5
39 34 16 16 13
29 34 16 15 9
37 36 15 12 10
35 34 16 12 15
30 28 12 8 20
38 34 16 13 12
34 35 16 11 12
31 35 14 14 14
34 31 16 15 13
35 37 17 10 11
36 35 18 11 17
30 27 18 12 12
39 40 12 15 13
35 37 16 15 14
38 36 10 14 13
31 38 14 16 15
34 39 18 15 13
38 41 18 15 10
34 27 16 13 11
39 30 17 12 19
37 37 16 17 13
34 31 16 13 17
28 31 13 15 13
37 27 16 13 9
33 36 16 15 11
35 37 16 15 9
37 33 15 16 12
32 34 15 15 12
33 31 16 14 13
38 39 14 15 13
33 34 16 14 12
29 32 16 13 15
33 33 15 7 22
31 36 12 17 13
36 32 17 13 15
35 41 16 15 13
32 28 15 14 15
29 30 13 13 12.5
39 36 16 16 11
37 35 16 12 16
35 31 16 14 11
37 34 16 17 11
32 36 14 15 10
38 36 16 17 10
37 35 16 12 16
36 37 20 16 12
32 28 15 11 11
33 39 16 15 16
40 32 13 9 19
38 35 17 16 11
41 39 16 15 16
36 35 16 10 15
43 42 12 10 24
30 34 16 15 14
31 33 16 11 15
32 41 17 13 11
32 33 13 14 15
37 34 12 18 12
37 32 18 16 10
33 40 14 14 14
34 40 14 14 13
33 35 13 14 9
38 36 16 14 15
33 37 13 12 15
31 27 16 14 14
38 39 13 15 11
37 38 16 15 8
36 31 15 15 11
31 33 16 13 11
39 32 15 17 8
44 39 17 17 10
33 36 15 19 11
35 33 12 15 13
32 33 16 13 11
28 32 10 9 20
40 37 16 15 10
27 30 12 15 15
37 38 14 15 12
32 29 15 16 14
28 22 13 11 23
34 35 15 14 14
30 35 11 11 16
35 34 12 15 11
31 35 11 13 12
32 34 16 15 10
30 37 15 16 14
30 35 17 14 12
31 23 16 15 12
40 31 10 16 11
32 27 18 16 12
36 36 13 11 13
32 31 16 12 11
35 32 13 9 19
38 39 10 16 12
42 37 15 13 17
34 38 16 16 9
35 39 16 12 12
38 34 14 9 19
33 31 10 13 18
36 32 17 13 15
32 37 13 14 14
33 36 15 19 11
34 32 16 13 9
32 38 12 12 18
34 36 13 13 16
27 26 13 10 24
31 26 12 14 14
38 33 17 16 20
34 39 15 10 18
24 30 10 11 23
30 33 14 14 12
26 25 11 12 14
34 38 13 9 16
27 37 16 9 18
37 31 12 11 20
36 37 16 16 12
41 35 12 9 12
29 25 9 13 17
36 28 12 16 13
32 35 15 13 9
37 33 12 9 16
30 30 12 12 18
31 31 14 16 10
38 37 12 11 14
36 36 16 14 11
35 30 11 13 9
31 36 19 15 11
38 32 15 14 10
22 28 8 16 11
32 36 16 13 19
36 34 17 14 14
39 31 12 15 12
28 28 11 13 14
32 36 11 11 21
32 36 14 11 13
38 40 16 14 10
32 33 12 15 15
35 37 16 11 16
32 32 13 15 14
37 38 15 12 12
34 31 16 14 19
33 37 16 14 15
33 33 14 8 19
26 32 16 13 13
30 30 16 9 17
24 30 14 15 12
34 31 11 17 11
34 32 12 13 14
33 34 15 15 11
34 36 15 15 13
35 37 16 14 12
35 36 16 16 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'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253353&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]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253353&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253353&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'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
L[t] = + 9.08849 + 0.102088C[t] + 0.0787035S[t] + 0.0667282H[t] -0.111463D[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
L[t] =  +  9.08849 +  0.102088C[t] +  0.0787035S[t] +  0.0667282H[t] -0.111463D[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253353&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]L[t] =  +  9.08849 +  0.102088C[t] +  0.0787035S[t] +  0.0667282H[t] -0.111463D[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253353&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253353&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
L[t] = + 9.08849 + 0.102088C[t] + 0.0787035S[t] + 0.0667282H[t] -0.111463D[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.088492.279483.9879.32966e-054.66483e-05
C0.1020880.0439252.3240.02110740.0105537
S0.07870350.04414671.7830.07611760.0380588
H0.06672820.07392580.90260.3677880.183894
D-0.1114630.0533229-2.090.03783260.0189163

\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) & 9.08849 & 2.27948 & 3.987 & 9.32966e-05 & 4.66483e-05 \tabularnewline
C & 0.102088 & 0.043925 & 2.324 & 0.0211074 & 0.0105537 \tabularnewline
S & 0.0787035 & 0.0441467 & 1.783 & 0.0761176 & 0.0380588 \tabularnewline
H & 0.0667282 & 0.0739258 & 0.9026 & 0.367788 & 0.183894 \tabularnewline
D & -0.111463 & 0.0533229 & -2.09 & 0.0378326 & 0.0189163 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253353&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]9.08849[/C][C]2.27948[/C][C]3.987[/C][C]9.32966e-05[/C][C]4.66483e-05[/C][/ROW]
[ROW][C]C[/C][C]0.102088[/C][C]0.043925[/C][C]2.324[/C][C]0.0211074[/C][C]0.0105537[/C][/ROW]
[ROW][C]S[/C][C]0.0787035[/C][C]0.0441467[/C][C]1.783[/C][C]0.0761176[/C][C]0.0380588[/C][/ROW]
[ROW][C]H[/C][C]0.0667282[/C][C]0.0739258[/C][C]0.9026[/C][C]0.367788[/C][C]0.183894[/C][/ROW]
[ROW][C]D[/C][C]-0.111463[/C][C]0.0533229[/C][C]-2.09[/C][C]0.0378326[/C][C]0.0189163[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253353&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253353&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)9.088492.279483.9879.32966e-054.66483e-05
C0.1020880.0439252.3240.02110740.0105537
S0.07870350.04414671.7830.07611760.0380588
H0.06672820.07392580.90260.3677880.183894
D-0.1114630.0533229-2.090.03783260.0189163






Multiple Linear Regression - Regression Statistics
Multiple R0.357423
R-squared0.127751
Adjusted R-squared0.110564
F-TEST (value)7.43294
F-TEST (DF numerator)4
F-TEST (DF denominator)203
p-value1.31855e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.10889
Sum Squared Residuals902.828

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.357423 \tabularnewline
R-squared & 0.127751 \tabularnewline
Adjusted R-squared & 0.110564 \tabularnewline
F-TEST (value) & 7.43294 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 203 \tabularnewline
p-value & 1.31855e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.10889 \tabularnewline
Sum Squared Residuals & 902.828 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253353&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.357423[/C][/ROW]
[ROW][C]R-squared[/C][C]0.127751[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.110564[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.43294[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]203[/C][/ROW]
[ROW][C]p-value[/C][C]1.31855e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.10889[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]902.828[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253353&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253353&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 R0.357423
R-squared0.127751
Adjusted R-squared0.110564
F-TEST (value)7.43294
F-TEST (DF numerator)4
F-TEST (DF denominator)203
p-value1.31855e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.10889
Sum Squared Residuals902.828






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11315.8615-2.86147
21615.56340.436555
31914.07934.92072
41514.31360.686386
51414.1984-0.19844
61314.8075-1.80752
71913.99175.00826
81515.1009-0.100907
91415.4046-1.40457
101515.4084-0.40838
111615.42740.572612
121615.81570.184295
131614.71781.28221
141615.56780.432179
151715.45591.5441
161514.32620.673767
171514.65590.34412
182015.86684.13315
191815.88022.11978
201614.49241.50755
211615.09470.905271
221613.86662.13345
231915.65733.34271
241615.17540.824564
251715.49671.50328
261715.391.61001
271615.35980.640164
281514.63170.368328
291614.4951.50495
301414.4417-0.441687
311514.75750.242491
321213.5589-1.5589
331415.3253-1.3253
341615.24630.753661
351414.4058-0.405761
361015.1285-5.12847
371012.8601-2.86015
381415.0242-1.02424
391614.50861.4914
401615.01160.988381
411615.3820.61799
421414.6639-0.663865
432015.66234.33771
441414.499-0.49904
451414.9464-0.946358
461114.9054-3.90536
471416.035-2.03502
481514.90810.0918592
491615.21680.783177
501415.1277-1.12772
511615.29910.700942
521414.0901-0.0901226
531213.6802-1.6802
541614.79051.20953
55914.4485-5.44846
561415.0051-1.00508
571615.36450.63553
581614.72271.27728
591515.3852-0.385177
601614.46631.53372
611212.6594-0.659394
621615.17370.82634
631614.71061.28944
641414.3816-0.381552
651614.55121.44881
661715.01481.98521
671814.35743.64258
681813.73934.26069
691215.77-3.76996
701615.0140.985962
711015.2863-5.28633
721414.6397-0.639657
731815.18082.81918
741816.0811.91903
751614.32581.67415
761714.1142.88603
771615.46310.536867
781613.97192.02812
791313.9387-0.938666
801614.8551.14496
811615.06550.934452
821615.57140.428648
831515.1931-0.193054
841514.69460.30541
851614.38241.61762
861415.5892-1.58917
871614.72991.27005
881613.76312.23692
891513.06951.93048
901214.7719-2.7719
911714.47772.52231
921615.44030.559685
931513.82131.17875
941313.8843-0.884325
951615.74480.255198
961614.63771.3623
971614.80951.19052
981615.44990.550052
991415.0749-1.07492
1001615.82090.179094
1011614.63771.3623
1022015.40584.59422
1031514.06690.933081
1041614.74431.25566
1051314.1733-1.17328
1061715.5641.43599
1071615.5610.438954
1081614.51361.48638
1091214.776-2.77599
1101614.26751.73251
1111613.91252.0875
1121715.22351.77648
1131314.2148-1.21477
1141215.4052-3.40521
1151815.33732.66272
1161414.9792-0.979245
1171415.1928-1.1928
1181315.143-2.14304
1191615.06340.936593
1201314.4982-1.49822
1211613.75192.24808
1221315.8121-2.8121
1231615.96570.0343061
1241514.97830.0217066
1251614.49181.50819
1261515.8311-0.831105
1271716.66950.330458
1281515.3325-0.332461
1291214.8107-2.81069
1301614.59391.40611
1311012.8368-2.83676
1321615.97030.029672
1331213.5349-1.53495
1341415.5198-1.51984
1351514.14490.855124
1361311.84881.15121
1371514.68780.312185
1381113.8564-2.85635
1391215.1123-3.11232
1401114.5377-3.53775
1411614.91751.08248
1421514.57030.429672
1431714.50242.49761
1441613.72682.27324
1451015.4534-5.45337
1461814.21043.78961
1471314.882-1.88197
1481614.36981.63024
1491313.6628-0.662838
1501015.7674-5.76736
1511515.2608-0.260808
1521615.61470.385304
1531615.19420.805814
1541414.1265-0.126508
1551013.7583-3.75833
1561714.47772.52231
1571314.641-1.64105
1581515.3325-0.332461
1591614.94231.05771
1601214.1404-2.14044
1611314.4769-1.47687
1621311.88331.11667
1631213.6732-1.67322
1641714.40342.59656
1651514.28990.710135
1661012.0701-2.07007
1671414.345-0.344983
1681112.9506-1.95062
1691314.3674-1.36736
1701613.35112.64888
1711213.8103-1.8103
1721615.40580.59422
1731215.2917-3.29171
174912.9892-3.98923
1751214.586-2.58599
1761514.97420.0257743
1771214.2801-2.2801
1781213.3066-1.30664
1791414.646-0.646046
1801215.0534-3.05339
1811615.30510.694917
1821114.887-3.88697
1831914.86144.13863
1841515.3059-0.305907
185813.3797-5.37968
1861613.93832.0617
1871714.81332.18671
1881215.1731-3.17309
1891113.4576-2.45764
1901113.5819-2.58192
1911414.4736-0.473622
1921615.93550.0644652
1931214.2815-2.2815
1941614.52421.4758
1951314.3143-1.31426
1961515.3197-0.319658
1971613.81572.18431
1981614.63171.36833
1991413.47060.529363
2001613.67972.32026
2011613.21792.78208
2021413.56310.436925
2031114.9076-3.90757
2041214.385-2.38498
2051514.90810.0918592
2061514.94470.05529
2071615.17020.829764
2081614.89061.1094

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 15.8615 & -2.86147 \tabularnewline
2 & 16 & 15.5634 & 0.436555 \tabularnewline
3 & 19 & 14.0793 & 4.92072 \tabularnewline
4 & 15 & 14.3136 & 0.686386 \tabularnewline
5 & 14 & 14.1984 & -0.19844 \tabularnewline
6 & 13 & 14.8075 & -1.80752 \tabularnewline
7 & 19 & 13.9917 & 5.00826 \tabularnewline
8 & 15 & 15.1009 & -0.100907 \tabularnewline
9 & 14 & 15.4046 & -1.40457 \tabularnewline
10 & 15 & 15.4084 & -0.40838 \tabularnewline
11 & 16 & 15.4274 & 0.572612 \tabularnewline
12 & 16 & 15.8157 & 0.184295 \tabularnewline
13 & 16 & 14.7178 & 1.28221 \tabularnewline
14 & 16 & 15.5678 & 0.432179 \tabularnewline
15 & 17 & 15.4559 & 1.5441 \tabularnewline
16 & 15 & 14.3262 & 0.673767 \tabularnewline
17 & 15 & 14.6559 & 0.34412 \tabularnewline
18 & 20 & 15.8668 & 4.13315 \tabularnewline
19 & 18 & 15.8802 & 2.11978 \tabularnewline
20 & 16 & 14.4924 & 1.50755 \tabularnewline
21 & 16 & 15.0947 & 0.905271 \tabularnewline
22 & 16 & 13.8666 & 2.13345 \tabularnewline
23 & 19 & 15.6573 & 3.34271 \tabularnewline
24 & 16 & 15.1754 & 0.824564 \tabularnewline
25 & 17 & 15.4967 & 1.50328 \tabularnewline
26 & 17 & 15.39 & 1.61001 \tabularnewline
27 & 16 & 15.3598 & 0.640164 \tabularnewline
28 & 15 & 14.6317 & 0.368328 \tabularnewline
29 & 16 & 14.495 & 1.50495 \tabularnewline
30 & 14 & 14.4417 & -0.441687 \tabularnewline
31 & 15 & 14.7575 & 0.242491 \tabularnewline
32 & 12 & 13.5589 & -1.5589 \tabularnewline
33 & 14 & 15.3253 & -1.3253 \tabularnewline
34 & 16 & 15.2463 & 0.753661 \tabularnewline
35 & 14 & 14.4058 & -0.405761 \tabularnewline
36 & 10 & 15.1285 & -5.12847 \tabularnewline
37 & 10 & 12.8601 & -2.86015 \tabularnewline
38 & 14 & 15.0242 & -1.02424 \tabularnewline
39 & 16 & 14.5086 & 1.4914 \tabularnewline
40 & 16 & 15.0116 & 0.988381 \tabularnewline
41 & 16 & 15.382 & 0.61799 \tabularnewline
42 & 14 & 14.6639 & -0.663865 \tabularnewline
43 & 20 & 15.6623 & 4.33771 \tabularnewline
44 & 14 & 14.499 & -0.49904 \tabularnewline
45 & 14 & 14.9464 & -0.946358 \tabularnewline
46 & 11 & 14.9054 & -3.90536 \tabularnewline
47 & 14 & 16.035 & -2.03502 \tabularnewline
48 & 15 & 14.9081 & 0.0918592 \tabularnewline
49 & 16 & 15.2168 & 0.783177 \tabularnewline
50 & 14 & 15.1277 & -1.12772 \tabularnewline
51 & 16 & 15.2991 & 0.700942 \tabularnewline
52 & 14 & 14.0901 & -0.0901226 \tabularnewline
53 & 12 & 13.6802 & -1.6802 \tabularnewline
54 & 16 & 14.7905 & 1.20953 \tabularnewline
55 & 9 & 14.4485 & -5.44846 \tabularnewline
56 & 14 & 15.0051 & -1.00508 \tabularnewline
57 & 16 & 15.3645 & 0.63553 \tabularnewline
58 & 16 & 14.7227 & 1.27728 \tabularnewline
59 & 15 & 15.3852 & -0.385177 \tabularnewline
60 & 16 & 14.4663 & 1.53372 \tabularnewline
61 & 12 & 12.6594 & -0.659394 \tabularnewline
62 & 16 & 15.1737 & 0.82634 \tabularnewline
63 & 16 & 14.7106 & 1.28944 \tabularnewline
64 & 14 & 14.3816 & -0.381552 \tabularnewline
65 & 16 & 14.5512 & 1.44881 \tabularnewline
66 & 17 & 15.0148 & 1.98521 \tabularnewline
67 & 18 & 14.3574 & 3.64258 \tabularnewline
68 & 18 & 13.7393 & 4.26069 \tabularnewline
69 & 12 & 15.77 & -3.76996 \tabularnewline
70 & 16 & 15.014 & 0.985962 \tabularnewline
71 & 10 & 15.2863 & -5.28633 \tabularnewline
72 & 14 & 14.6397 & -0.639657 \tabularnewline
73 & 18 & 15.1808 & 2.81918 \tabularnewline
74 & 18 & 16.081 & 1.91903 \tabularnewline
75 & 16 & 14.3258 & 1.67415 \tabularnewline
76 & 17 & 14.114 & 2.88603 \tabularnewline
77 & 16 & 15.4631 & 0.536867 \tabularnewline
78 & 16 & 13.9719 & 2.02812 \tabularnewline
79 & 13 & 13.9387 & -0.938666 \tabularnewline
80 & 16 & 14.855 & 1.14496 \tabularnewline
81 & 16 & 15.0655 & 0.934452 \tabularnewline
82 & 16 & 15.5714 & 0.428648 \tabularnewline
83 & 15 & 15.1931 & -0.193054 \tabularnewline
84 & 15 & 14.6946 & 0.30541 \tabularnewline
85 & 16 & 14.3824 & 1.61762 \tabularnewline
86 & 14 & 15.5892 & -1.58917 \tabularnewline
87 & 16 & 14.7299 & 1.27005 \tabularnewline
88 & 16 & 13.7631 & 2.23692 \tabularnewline
89 & 15 & 13.0695 & 1.93048 \tabularnewline
90 & 12 & 14.7719 & -2.7719 \tabularnewline
91 & 17 & 14.4777 & 2.52231 \tabularnewline
92 & 16 & 15.4403 & 0.559685 \tabularnewline
93 & 15 & 13.8213 & 1.17875 \tabularnewline
94 & 13 & 13.8843 & -0.884325 \tabularnewline
95 & 16 & 15.7448 & 0.255198 \tabularnewline
96 & 16 & 14.6377 & 1.3623 \tabularnewline
97 & 16 & 14.8095 & 1.19052 \tabularnewline
98 & 16 & 15.4499 & 0.550052 \tabularnewline
99 & 14 & 15.0749 & -1.07492 \tabularnewline
100 & 16 & 15.8209 & 0.179094 \tabularnewline
101 & 16 & 14.6377 & 1.3623 \tabularnewline
102 & 20 & 15.4058 & 4.59422 \tabularnewline
103 & 15 & 14.0669 & 0.933081 \tabularnewline
104 & 16 & 14.7443 & 1.25566 \tabularnewline
105 & 13 & 14.1733 & -1.17328 \tabularnewline
106 & 17 & 15.564 & 1.43599 \tabularnewline
107 & 16 & 15.561 & 0.438954 \tabularnewline
108 & 16 & 14.5136 & 1.48638 \tabularnewline
109 & 12 & 14.776 & -2.77599 \tabularnewline
110 & 16 & 14.2675 & 1.73251 \tabularnewline
111 & 16 & 13.9125 & 2.0875 \tabularnewline
112 & 17 & 15.2235 & 1.77648 \tabularnewline
113 & 13 & 14.2148 & -1.21477 \tabularnewline
114 & 12 & 15.4052 & -3.40521 \tabularnewline
115 & 18 & 15.3373 & 2.66272 \tabularnewline
116 & 14 & 14.9792 & -0.979245 \tabularnewline
117 & 14 & 15.1928 & -1.1928 \tabularnewline
118 & 13 & 15.143 & -2.14304 \tabularnewline
119 & 16 & 15.0634 & 0.936593 \tabularnewline
120 & 13 & 14.4982 & -1.49822 \tabularnewline
121 & 16 & 13.7519 & 2.24808 \tabularnewline
122 & 13 & 15.8121 & -2.8121 \tabularnewline
123 & 16 & 15.9657 & 0.0343061 \tabularnewline
124 & 15 & 14.9783 & 0.0217066 \tabularnewline
125 & 16 & 14.4918 & 1.50819 \tabularnewline
126 & 15 & 15.8311 & -0.831105 \tabularnewline
127 & 17 & 16.6695 & 0.330458 \tabularnewline
128 & 15 & 15.3325 & -0.332461 \tabularnewline
129 & 12 & 14.8107 & -2.81069 \tabularnewline
130 & 16 & 14.5939 & 1.40611 \tabularnewline
131 & 10 & 12.8368 & -2.83676 \tabularnewline
132 & 16 & 15.9703 & 0.029672 \tabularnewline
133 & 12 & 13.5349 & -1.53495 \tabularnewline
134 & 14 & 15.5198 & -1.51984 \tabularnewline
135 & 15 & 14.1449 & 0.855124 \tabularnewline
136 & 13 & 11.8488 & 1.15121 \tabularnewline
137 & 15 & 14.6878 & 0.312185 \tabularnewline
138 & 11 & 13.8564 & -2.85635 \tabularnewline
139 & 12 & 15.1123 & -3.11232 \tabularnewline
140 & 11 & 14.5377 & -3.53775 \tabularnewline
141 & 16 & 14.9175 & 1.08248 \tabularnewline
142 & 15 & 14.5703 & 0.429672 \tabularnewline
143 & 17 & 14.5024 & 2.49761 \tabularnewline
144 & 16 & 13.7268 & 2.27324 \tabularnewline
145 & 10 & 15.4534 & -5.45337 \tabularnewline
146 & 18 & 14.2104 & 3.78961 \tabularnewline
147 & 13 & 14.882 & -1.88197 \tabularnewline
148 & 16 & 14.3698 & 1.63024 \tabularnewline
149 & 13 & 13.6628 & -0.662838 \tabularnewline
150 & 10 & 15.7674 & -5.76736 \tabularnewline
151 & 15 & 15.2608 & -0.260808 \tabularnewline
152 & 16 & 15.6147 & 0.385304 \tabularnewline
153 & 16 & 15.1942 & 0.805814 \tabularnewline
154 & 14 & 14.1265 & -0.126508 \tabularnewline
155 & 10 & 13.7583 & -3.75833 \tabularnewline
156 & 17 & 14.4777 & 2.52231 \tabularnewline
157 & 13 & 14.641 & -1.64105 \tabularnewline
158 & 15 & 15.3325 & -0.332461 \tabularnewline
159 & 16 & 14.9423 & 1.05771 \tabularnewline
160 & 12 & 14.1404 & -2.14044 \tabularnewline
161 & 13 & 14.4769 & -1.47687 \tabularnewline
162 & 13 & 11.8833 & 1.11667 \tabularnewline
163 & 12 & 13.6732 & -1.67322 \tabularnewline
164 & 17 & 14.4034 & 2.59656 \tabularnewline
165 & 15 & 14.2899 & 0.710135 \tabularnewline
166 & 10 & 12.0701 & -2.07007 \tabularnewline
167 & 14 & 14.345 & -0.344983 \tabularnewline
168 & 11 & 12.9506 & -1.95062 \tabularnewline
169 & 13 & 14.3674 & -1.36736 \tabularnewline
170 & 16 & 13.3511 & 2.64888 \tabularnewline
171 & 12 & 13.8103 & -1.8103 \tabularnewline
172 & 16 & 15.4058 & 0.59422 \tabularnewline
173 & 12 & 15.2917 & -3.29171 \tabularnewline
174 & 9 & 12.9892 & -3.98923 \tabularnewline
175 & 12 & 14.586 & -2.58599 \tabularnewline
176 & 15 & 14.9742 & 0.0257743 \tabularnewline
177 & 12 & 14.2801 & -2.2801 \tabularnewline
178 & 12 & 13.3066 & -1.30664 \tabularnewline
179 & 14 & 14.646 & -0.646046 \tabularnewline
180 & 12 & 15.0534 & -3.05339 \tabularnewline
181 & 16 & 15.3051 & 0.694917 \tabularnewline
182 & 11 & 14.887 & -3.88697 \tabularnewline
183 & 19 & 14.8614 & 4.13863 \tabularnewline
184 & 15 & 15.3059 & -0.305907 \tabularnewline
185 & 8 & 13.3797 & -5.37968 \tabularnewline
186 & 16 & 13.9383 & 2.0617 \tabularnewline
187 & 17 & 14.8133 & 2.18671 \tabularnewline
188 & 12 & 15.1731 & -3.17309 \tabularnewline
189 & 11 & 13.4576 & -2.45764 \tabularnewline
190 & 11 & 13.5819 & -2.58192 \tabularnewline
191 & 14 & 14.4736 & -0.473622 \tabularnewline
192 & 16 & 15.9355 & 0.0644652 \tabularnewline
193 & 12 & 14.2815 & -2.2815 \tabularnewline
194 & 16 & 14.5242 & 1.4758 \tabularnewline
195 & 13 & 14.3143 & -1.31426 \tabularnewline
196 & 15 & 15.3197 & -0.319658 \tabularnewline
197 & 16 & 13.8157 & 2.18431 \tabularnewline
198 & 16 & 14.6317 & 1.36833 \tabularnewline
199 & 14 & 13.4706 & 0.529363 \tabularnewline
200 & 16 & 13.6797 & 2.32026 \tabularnewline
201 & 16 & 13.2179 & 2.78208 \tabularnewline
202 & 14 & 13.5631 & 0.436925 \tabularnewline
203 & 11 & 14.9076 & -3.90757 \tabularnewline
204 & 12 & 14.385 & -2.38498 \tabularnewline
205 & 15 & 14.9081 & 0.0918592 \tabularnewline
206 & 15 & 14.9447 & 0.05529 \tabularnewline
207 & 16 & 15.1702 & 0.829764 \tabularnewline
208 & 16 & 14.8906 & 1.1094 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253353&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]13[/C][C]15.8615[/C][C]-2.86147[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.5634[/C][C]0.436555[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]14.0793[/C][C]4.92072[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]14.3136[/C][C]0.686386[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]14.1984[/C][C]-0.19844[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.8075[/C][C]-1.80752[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]13.9917[/C][C]5.00826[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]15.1009[/C][C]-0.100907[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.4046[/C][C]-1.40457[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]15.4084[/C][C]-0.40838[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.4274[/C][C]0.572612[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]15.8157[/C][C]0.184295[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]14.7178[/C][C]1.28221[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.5678[/C][C]0.432179[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]15.4559[/C][C]1.5441[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]14.3262[/C][C]0.673767[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.6559[/C][C]0.34412[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]15.8668[/C][C]4.13315[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.8802[/C][C]2.11978[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]14.4924[/C][C]1.50755[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.0947[/C][C]0.905271[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]13.8666[/C][C]2.13345[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]15.6573[/C][C]3.34271[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]15.1754[/C][C]0.824564[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.4967[/C][C]1.50328[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]15.39[/C][C]1.61001[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.3598[/C][C]0.640164[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]14.6317[/C][C]0.368328[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]14.495[/C][C]1.50495[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.4417[/C][C]-0.441687[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]14.7575[/C][C]0.242491[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.5589[/C][C]-1.5589[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.3253[/C][C]-1.3253[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.2463[/C][C]0.753661[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]14.4058[/C][C]-0.405761[/C][/ROW]
[ROW][C]36[/C][C]10[/C][C]15.1285[/C][C]-5.12847[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]12.8601[/C][C]-2.86015[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.0242[/C][C]-1.02424[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.5086[/C][C]1.4914[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.0116[/C][C]0.988381[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.382[/C][C]0.61799[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]14.6639[/C][C]-0.663865[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]15.6623[/C][C]4.33771[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.499[/C][C]-0.49904[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.9464[/C][C]-0.946358[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]14.9054[/C][C]-3.90536[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.035[/C][C]-2.03502[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.9081[/C][C]0.0918592[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.2168[/C][C]0.783177[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.1277[/C][C]-1.12772[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]15.2991[/C][C]0.700942[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.0901[/C][C]-0.0901226[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]13.6802[/C][C]-1.6802[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]14.7905[/C][C]1.20953[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]14.4485[/C][C]-5.44846[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]15.0051[/C][C]-1.00508[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.3645[/C][C]0.63553[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.7227[/C][C]1.27728[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.3852[/C][C]-0.385177[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.4663[/C][C]1.53372[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]12.6594[/C][C]-0.659394[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.1737[/C][C]0.82634[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]14.7106[/C][C]1.28944[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.3816[/C][C]-0.381552[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]14.5512[/C][C]1.44881[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.0148[/C][C]1.98521[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]14.3574[/C][C]3.64258[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]13.7393[/C][C]4.26069[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.77[/C][C]-3.76996[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.014[/C][C]0.985962[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]15.2863[/C][C]-5.28633[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.6397[/C][C]-0.639657[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]15.1808[/C][C]2.81918[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]16.081[/C][C]1.91903[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]14.3258[/C][C]1.67415[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]14.114[/C][C]2.88603[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]15.4631[/C][C]0.536867[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]13.9719[/C][C]2.02812[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]13.9387[/C][C]-0.938666[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]14.855[/C][C]1.14496[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.0655[/C][C]0.934452[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]15.5714[/C][C]0.428648[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.1931[/C][C]-0.193054[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]14.6946[/C][C]0.30541[/C][/ROW]
[ROW][C]85[/C][C]16[/C][C]14.3824[/C][C]1.61762[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]15.5892[/C][C]-1.58917[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]14.7299[/C][C]1.27005[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]13.7631[/C][C]2.23692[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]13.0695[/C][C]1.93048[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]14.7719[/C][C]-2.7719[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]14.4777[/C][C]2.52231[/C][/ROW]
[ROW][C]92[/C][C]16[/C][C]15.4403[/C][C]0.559685[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]13.8213[/C][C]1.17875[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]13.8843[/C][C]-0.884325[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]15.7448[/C][C]0.255198[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]14.6377[/C][C]1.3623[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]14.8095[/C][C]1.19052[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]15.4499[/C][C]0.550052[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]15.0749[/C][C]-1.07492[/C][/ROW]
[ROW][C]100[/C][C]16[/C][C]15.8209[/C][C]0.179094[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]14.6377[/C][C]1.3623[/C][/ROW]
[ROW][C]102[/C][C]20[/C][C]15.4058[/C][C]4.59422[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]14.0669[/C][C]0.933081[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]14.7443[/C][C]1.25566[/C][/ROW]
[ROW][C]105[/C][C]13[/C][C]14.1733[/C][C]-1.17328[/C][/ROW]
[ROW][C]106[/C][C]17[/C][C]15.564[/C][C]1.43599[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.561[/C][C]0.438954[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]14.5136[/C][C]1.48638[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]14.776[/C][C]-2.77599[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]14.2675[/C][C]1.73251[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]13.9125[/C][C]2.0875[/C][/ROW]
[ROW][C]112[/C][C]17[/C][C]15.2235[/C][C]1.77648[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]14.2148[/C][C]-1.21477[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]15.4052[/C][C]-3.40521[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]15.3373[/C][C]2.66272[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]14.9792[/C][C]-0.979245[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.1928[/C][C]-1.1928[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]15.143[/C][C]-2.14304[/C][/ROW]
[ROW][C]119[/C][C]16[/C][C]15.0634[/C][C]0.936593[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]14.4982[/C][C]-1.49822[/C][/ROW]
[ROW][C]121[/C][C]16[/C][C]13.7519[/C][C]2.24808[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]15.8121[/C][C]-2.8121[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]15.9657[/C][C]0.0343061[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]14.9783[/C][C]0.0217066[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]14.4918[/C][C]1.50819[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]15.8311[/C][C]-0.831105[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]16.6695[/C][C]0.330458[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]15.3325[/C][C]-0.332461[/C][/ROW]
[ROW][C]129[/C][C]12[/C][C]14.8107[/C][C]-2.81069[/C][/ROW]
[ROW][C]130[/C][C]16[/C][C]14.5939[/C][C]1.40611[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]12.8368[/C][C]-2.83676[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]15.9703[/C][C]0.029672[/C][/ROW]
[ROW][C]133[/C][C]12[/C][C]13.5349[/C][C]-1.53495[/C][/ROW]
[ROW][C]134[/C][C]14[/C][C]15.5198[/C][C]-1.51984[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]14.1449[/C][C]0.855124[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]11.8488[/C][C]1.15121[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]14.6878[/C][C]0.312185[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]13.8564[/C][C]-2.85635[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]15.1123[/C][C]-3.11232[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]14.5377[/C][C]-3.53775[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]14.9175[/C][C]1.08248[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]14.5703[/C][C]0.429672[/C][/ROW]
[ROW][C]143[/C][C]17[/C][C]14.5024[/C][C]2.49761[/C][/ROW]
[ROW][C]144[/C][C]16[/C][C]13.7268[/C][C]2.27324[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]15.4534[/C][C]-5.45337[/C][/ROW]
[ROW][C]146[/C][C]18[/C][C]14.2104[/C][C]3.78961[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]14.882[/C][C]-1.88197[/C][/ROW]
[ROW][C]148[/C][C]16[/C][C]14.3698[/C][C]1.63024[/C][/ROW]
[ROW][C]149[/C][C]13[/C][C]13.6628[/C][C]-0.662838[/C][/ROW]
[ROW][C]150[/C][C]10[/C][C]15.7674[/C][C]-5.76736[/C][/ROW]
[ROW][C]151[/C][C]15[/C][C]15.2608[/C][C]-0.260808[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]15.6147[/C][C]0.385304[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]15.1942[/C][C]0.805814[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]14.1265[/C][C]-0.126508[/C][/ROW]
[ROW][C]155[/C][C]10[/C][C]13.7583[/C][C]-3.75833[/C][/ROW]
[ROW][C]156[/C][C]17[/C][C]14.4777[/C][C]2.52231[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]14.641[/C][C]-1.64105[/C][/ROW]
[ROW][C]158[/C][C]15[/C][C]15.3325[/C][C]-0.332461[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]14.9423[/C][C]1.05771[/C][/ROW]
[ROW][C]160[/C][C]12[/C][C]14.1404[/C][C]-2.14044[/C][/ROW]
[ROW][C]161[/C][C]13[/C][C]14.4769[/C][C]-1.47687[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]11.8833[/C][C]1.11667[/C][/ROW]
[ROW][C]163[/C][C]12[/C][C]13.6732[/C][C]-1.67322[/C][/ROW]
[ROW][C]164[/C][C]17[/C][C]14.4034[/C][C]2.59656[/C][/ROW]
[ROW][C]165[/C][C]15[/C][C]14.2899[/C][C]0.710135[/C][/ROW]
[ROW][C]166[/C][C]10[/C][C]12.0701[/C][C]-2.07007[/C][/ROW]
[ROW][C]167[/C][C]14[/C][C]14.345[/C][C]-0.344983[/C][/ROW]
[ROW][C]168[/C][C]11[/C][C]12.9506[/C][C]-1.95062[/C][/ROW]
[ROW][C]169[/C][C]13[/C][C]14.3674[/C][C]-1.36736[/C][/ROW]
[ROW][C]170[/C][C]16[/C][C]13.3511[/C][C]2.64888[/C][/ROW]
[ROW][C]171[/C][C]12[/C][C]13.8103[/C][C]-1.8103[/C][/ROW]
[ROW][C]172[/C][C]16[/C][C]15.4058[/C][C]0.59422[/C][/ROW]
[ROW][C]173[/C][C]12[/C][C]15.2917[/C][C]-3.29171[/C][/ROW]
[ROW][C]174[/C][C]9[/C][C]12.9892[/C][C]-3.98923[/C][/ROW]
[ROW][C]175[/C][C]12[/C][C]14.586[/C][C]-2.58599[/C][/ROW]
[ROW][C]176[/C][C]15[/C][C]14.9742[/C][C]0.0257743[/C][/ROW]
[ROW][C]177[/C][C]12[/C][C]14.2801[/C][C]-2.2801[/C][/ROW]
[ROW][C]178[/C][C]12[/C][C]13.3066[/C][C]-1.30664[/C][/ROW]
[ROW][C]179[/C][C]14[/C][C]14.646[/C][C]-0.646046[/C][/ROW]
[ROW][C]180[/C][C]12[/C][C]15.0534[/C][C]-3.05339[/C][/ROW]
[ROW][C]181[/C][C]16[/C][C]15.3051[/C][C]0.694917[/C][/ROW]
[ROW][C]182[/C][C]11[/C][C]14.887[/C][C]-3.88697[/C][/ROW]
[ROW][C]183[/C][C]19[/C][C]14.8614[/C][C]4.13863[/C][/ROW]
[ROW][C]184[/C][C]15[/C][C]15.3059[/C][C]-0.305907[/C][/ROW]
[ROW][C]185[/C][C]8[/C][C]13.3797[/C][C]-5.37968[/C][/ROW]
[ROW][C]186[/C][C]16[/C][C]13.9383[/C][C]2.0617[/C][/ROW]
[ROW][C]187[/C][C]17[/C][C]14.8133[/C][C]2.18671[/C][/ROW]
[ROW][C]188[/C][C]12[/C][C]15.1731[/C][C]-3.17309[/C][/ROW]
[ROW][C]189[/C][C]11[/C][C]13.4576[/C][C]-2.45764[/C][/ROW]
[ROW][C]190[/C][C]11[/C][C]13.5819[/C][C]-2.58192[/C][/ROW]
[ROW][C]191[/C][C]14[/C][C]14.4736[/C][C]-0.473622[/C][/ROW]
[ROW][C]192[/C][C]16[/C][C]15.9355[/C][C]0.0644652[/C][/ROW]
[ROW][C]193[/C][C]12[/C][C]14.2815[/C][C]-2.2815[/C][/ROW]
[ROW][C]194[/C][C]16[/C][C]14.5242[/C][C]1.4758[/C][/ROW]
[ROW][C]195[/C][C]13[/C][C]14.3143[/C][C]-1.31426[/C][/ROW]
[ROW][C]196[/C][C]15[/C][C]15.3197[/C][C]-0.319658[/C][/ROW]
[ROW][C]197[/C][C]16[/C][C]13.8157[/C][C]2.18431[/C][/ROW]
[ROW][C]198[/C][C]16[/C][C]14.6317[/C][C]1.36833[/C][/ROW]
[ROW][C]199[/C][C]14[/C][C]13.4706[/C][C]0.529363[/C][/ROW]
[ROW][C]200[/C][C]16[/C][C]13.6797[/C][C]2.32026[/C][/ROW]
[ROW][C]201[/C][C]16[/C][C]13.2179[/C][C]2.78208[/C][/ROW]
[ROW][C]202[/C][C]14[/C][C]13.5631[/C][C]0.436925[/C][/ROW]
[ROW][C]203[/C][C]11[/C][C]14.9076[/C][C]-3.90757[/C][/ROW]
[ROW][C]204[/C][C]12[/C][C]14.385[/C][C]-2.38498[/C][/ROW]
[ROW][C]205[/C][C]15[/C][C]14.9081[/C][C]0.0918592[/C][/ROW]
[ROW][C]206[/C][C]15[/C][C]14.9447[/C][C]0.05529[/C][/ROW]
[ROW][C]207[/C][C]16[/C][C]15.1702[/C][C]0.829764[/C][/ROW]
[ROW][C]208[/C][C]16[/C][C]14.8906[/C][C]1.1094[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253353&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253353&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
11315.8615-2.86147
21615.56340.436555
31914.07934.92072
41514.31360.686386
51414.1984-0.19844
61314.8075-1.80752
71913.99175.00826
81515.1009-0.100907
91415.4046-1.40457
101515.4084-0.40838
111615.42740.572612
121615.81570.184295
131614.71781.28221
141615.56780.432179
151715.45591.5441
161514.32620.673767
171514.65590.34412
182015.86684.13315
191815.88022.11978
201614.49241.50755
211615.09470.905271
221613.86662.13345
231915.65733.34271
241615.17540.824564
251715.49671.50328
261715.391.61001
271615.35980.640164
281514.63170.368328
291614.4951.50495
301414.4417-0.441687
311514.75750.242491
321213.5589-1.5589
331415.3253-1.3253
341615.24630.753661
351414.4058-0.405761
361015.1285-5.12847
371012.8601-2.86015
381415.0242-1.02424
391614.50861.4914
401615.01160.988381
411615.3820.61799
421414.6639-0.663865
432015.66234.33771
441414.499-0.49904
451414.9464-0.946358
461114.9054-3.90536
471416.035-2.03502
481514.90810.0918592
491615.21680.783177
501415.1277-1.12772
511615.29910.700942
521414.0901-0.0901226
531213.6802-1.6802
541614.79051.20953
55914.4485-5.44846
561415.0051-1.00508
571615.36450.63553
581614.72271.27728
591515.3852-0.385177
601614.46631.53372
611212.6594-0.659394
621615.17370.82634
631614.71061.28944
641414.3816-0.381552
651614.55121.44881
661715.01481.98521
671814.35743.64258
681813.73934.26069
691215.77-3.76996
701615.0140.985962
711015.2863-5.28633
721414.6397-0.639657
731815.18082.81918
741816.0811.91903
751614.32581.67415
761714.1142.88603
771615.46310.536867
781613.97192.02812
791313.9387-0.938666
801614.8551.14496
811615.06550.934452
821615.57140.428648
831515.1931-0.193054
841514.69460.30541
851614.38241.61762
861415.5892-1.58917
871614.72991.27005
881613.76312.23692
891513.06951.93048
901214.7719-2.7719
911714.47772.52231
921615.44030.559685
931513.82131.17875
941313.8843-0.884325
951615.74480.255198
961614.63771.3623
971614.80951.19052
981615.44990.550052
991415.0749-1.07492
1001615.82090.179094
1011614.63771.3623
1022015.40584.59422
1031514.06690.933081
1041614.74431.25566
1051314.1733-1.17328
1061715.5641.43599
1071615.5610.438954
1081614.51361.48638
1091214.776-2.77599
1101614.26751.73251
1111613.91252.0875
1121715.22351.77648
1131314.2148-1.21477
1141215.4052-3.40521
1151815.33732.66272
1161414.9792-0.979245
1171415.1928-1.1928
1181315.143-2.14304
1191615.06340.936593
1201314.4982-1.49822
1211613.75192.24808
1221315.8121-2.8121
1231615.96570.0343061
1241514.97830.0217066
1251614.49181.50819
1261515.8311-0.831105
1271716.66950.330458
1281515.3325-0.332461
1291214.8107-2.81069
1301614.59391.40611
1311012.8368-2.83676
1321615.97030.029672
1331213.5349-1.53495
1341415.5198-1.51984
1351514.14490.855124
1361311.84881.15121
1371514.68780.312185
1381113.8564-2.85635
1391215.1123-3.11232
1401114.5377-3.53775
1411614.91751.08248
1421514.57030.429672
1431714.50242.49761
1441613.72682.27324
1451015.4534-5.45337
1461814.21043.78961
1471314.882-1.88197
1481614.36981.63024
1491313.6628-0.662838
1501015.7674-5.76736
1511515.2608-0.260808
1521615.61470.385304
1531615.19420.805814
1541414.1265-0.126508
1551013.7583-3.75833
1561714.47772.52231
1571314.641-1.64105
1581515.3325-0.332461
1591614.94231.05771
1601214.1404-2.14044
1611314.4769-1.47687
1621311.88331.11667
1631213.6732-1.67322
1641714.40342.59656
1651514.28990.710135
1661012.0701-2.07007
1671414.345-0.344983
1681112.9506-1.95062
1691314.3674-1.36736
1701613.35112.64888
1711213.8103-1.8103
1721615.40580.59422
1731215.2917-3.29171
174912.9892-3.98923
1751214.586-2.58599
1761514.97420.0257743
1771214.2801-2.2801
1781213.3066-1.30664
1791414.646-0.646046
1801215.0534-3.05339
1811615.30510.694917
1821114.887-3.88697
1831914.86144.13863
1841515.3059-0.305907
185813.3797-5.37968
1861613.93832.0617
1871714.81332.18671
1881215.1731-3.17309
1891113.4576-2.45764
1901113.5819-2.58192
1911414.4736-0.473622
1921615.93550.0644652
1931214.2815-2.2815
1941614.52421.4758
1951314.3143-1.31426
1961515.3197-0.319658
1971613.81572.18431
1981614.63171.36833
1991413.47060.529363
2001613.67972.32026
2011613.21792.78208
2021413.56310.436925
2031114.9076-3.90757
2041214.385-2.38498
2051514.90810.0918592
2061514.94470.05529
2071615.17020.829764
2081614.89061.1094






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.7566240.4867530.243376
90.6170560.7658870.382944
100.5193670.9612660.480633
110.4434340.8868680.556566
120.5609510.8780970.439049
130.4680660.9361320.531934
140.4198110.8396220.580189
150.4510890.9021780.548911
160.3902560.7805120.609744
170.3211340.6422680.678866
180.6429030.7141930.357097
190.6335880.7328250.366412
200.5644990.8710030.435501
210.4934340.9868680.506566
220.4263620.8527230.573638
230.4710010.9420010.528999
240.4120940.8241890.587906
250.3703160.7406310.629684
260.3145470.6290950.685453
270.2582420.5164840.741758
280.2211180.4422350.778882
290.1791860.3583710.820814
300.1760510.3521020.823949
310.1388590.2777190.861141
320.1676820.3353640.832318
330.1564510.3129020.843549
340.1238190.2476370.876181
350.1034420.2068830.896558
360.2899030.5798050.710097
370.4228510.8457020.577149
380.3930960.7861920.606904
390.3933620.7867240.606638
400.3500540.7001090.649946
410.3042090.6084170.695791
420.2706190.5412390.729381
430.4046790.8093580.595321
440.3688370.7376740.631163
450.3390450.6780890.660955
460.5138330.9723350.486167
470.5348660.9302670.465134
480.485810.9716210.51419
490.4400340.8800680.559966
500.4120580.8241160.587942
510.3691180.7382370.630882
520.324950.6499010.67505
530.3164870.6329740.683513
540.2836690.5673380.716331
550.5064720.9870560.493528
560.4848290.9696580.515171
570.4417640.8835270.558236
580.4224330.8448660.577567
590.3800260.7600520.619974
600.3570550.7141090.642945
610.3176910.6353820.682309
620.2826320.5652640.717368
630.2607740.5215490.739226
640.2275750.4551490.772425
650.2101610.4203220.789839
660.2054740.4109470.794526
670.2549420.5098850.745058
680.4030920.8061850.596908
690.5246010.9507970.475399
700.4881950.976390.511805
710.7131830.5736330.286817
720.6805660.6388670.319434
730.7042380.5915230.295762
740.694630.610740.30537
750.6785180.6429640.321482
760.697120.6057610.30288
770.6621010.6757980.337899
780.6517750.696450.348225
790.6216820.7566360.378318
800.5931450.8137110.406855
810.5601460.8797080.439854
820.5210340.9579330.478966
830.4826280.9652560.517372
840.4425290.8850590.557471
850.4235540.8471090.576446
860.4101320.8202640.589868
870.3839370.7678740.616063
880.383060.766120.61694
890.3671490.7342980.632851
900.3992140.7984270.600786
910.4112710.8225410.588729
920.3747710.7495410.625229
930.3479540.6959080.652046
940.3195980.6391950.680402
950.2859080.5718150.714092
960.2654680.5309360.734532
970.2448510.4897020.755149
980.2174240.4348470.782576
990.1952760.3905510.804724
1000.1695670.3391340.830433
1010.1553190.3106370.844681
1020.2706190.5412390.729381
1030.2468930.4937860.753107
1040.2255520.4511050.774448
1050.2176710.4353420.782329
1060.2066860.4133730.793314
1070.1843830.3687660.815617
1080.1735330.3470670.826467
1090.1997070.3994140.800293
1100.1899910.3799830.810009
1110.1898230.3796470.810177
1120.1834360.3668720.816564
1130.1682860.3365720.831714
1140.2099080.4198150.790092
1150.2401140.4802280.759886
1160.2166040.4332080.783396
1170.1967040.3934080.803296
1180.1958070.3916140.804193
1190.1777210.3554420.822279
1200.165160.3303190.83484
1210.1746590.3493180.825341
1220.1898290.3796570.810171
1230.1643320.3286630.835668
1240.1438880.2877770.856112
1250.136270.2725390.86373
1260.1186230.2372450.881377
1270.1033830.2067670.896617
1280.08633790.1726760.913662
1290.09658780.1931760.903412
1300.09070560.1814110.909294
1310.1109650.221930.889035
1320.09516440.1903290.904836
1330.08820380.1764080.911796
1340.07811270.1562250.921887
1350.0686140.1372280.931386
1360.06226980.124540.93773
1370.05123520.102470.948765
1380.06214820.1242960.937852
1390.07264760.1452950.927352
1400.1011330.2022670.898867
1410.08979430.1795890.910206
1420.0741390.1482780.925861
1430.08039250.1607850.919608
1440.1114760.2229530.888524
1450.2106150.4212310.789385
1460.3730480.7460960.626952
1470.3578340.7156690.642166
1480.3758670.7517350.624133
1490.3372810.6745620.662719
1500.6534390.6931230.346561
1510.6104520.7790950.389548
1520.5663890.8672210.433611
1530.524540.9509210.47546
1540.480340.9606810.51966
1550.5575780.8848430.442422
1560.631490.737020.36851
1570.6324590.7350820.367541
1580.5920770.8158460.407923
1590.6161640.7676730.383836
1600.6907510.6184980.309249
1610.687610.6247790.31239
1620.6956780.6086440.304322
1630.6761440.6477120.323856
1640.7211180.5577640.278882
1650.6825040.6349920.317496
1660.7022670.5954650.297733
1670.6553460.6893080.344654
1680.6265140.7469720.373486
1690.6409840.7180330.359016
1700.608620.782760.39138
1710.5646840.8706320.435316
1720.5105070.9789860.489493
1730.512590.9748210.48741
1740.5125380.9749230.487462
1750.472950.94590.52705
1760.4163660.8327330.583634
1770.3914150.782830.608585
1780.3429260.6858520.657074
1790.2993680.5987360.700632
1800.4044930.8089860.595507
1810.3514750.7029490.648525
1820.3570250.714050.642975
1830.5457670.9084650.454233
1840.4997440.9994890.500256
1850.7031640.5936720.296836
1860.6573970.6852060.342603
1870.737820.5243610.26218
1880.6860280.6279440.313972
1890.6923430.6153150.307657
1900.9557680.08846430.0442321
1910.9538150.09236990.0461849
1920.9296350.140730.0703649
1930.9700820.05983680.0299184
1940.947130.1057410.0528705
1950.925240.1495210.0747603
1960.8724060.2551880.127594
1970.9002930.1994140.0997071
1980.8373920.3252150.162608
1990.906960.1860810.0930403
2000.7977650.404470.202235

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.756624 & 0.486753 & 0.243376 \tabularnewline
9 & 0.617056 & 0.765887 & 0.382944 \tabularnewline
10 & 0.519367 & 0.961266 & 0.480633 \tabularnewline
11 & 0.443434 & 0.886868 & 0.556566 \tabularnewline
12 & 0.560951 & 0.878097 & 0.439049 \tabularnewline
13 & 0.468066 & 0.936132 & 0.531934 \tabularnewline
14 & 0.419811 & 0.839622 & 0.580189 \tabularnewline
15 & 0.451089 & 0.902178 & 0.548911 \tabularnewline
16 & 0.390256 & 0.780512 & 0.609744 \tabularnewline
17 & 0.321134 & 0.642268 & 0.678866 \tabularnewline
18 & 0.642903 & 0.714193 & 0.357097 \tabularnewline
19 & 0.633588 & 0.732825 & 0.366412 \tabularnewline
20 & 0.564499 & 0.871003 & 0.435501 \tabularnewline
21 & 0.493434 & 0.986868 & 0.506566 \tabularnewline
22 & 0.426362 & 0.852723 & 0.573638 \tabularnewline
23 & 0.471001 & 0.942001 & 0.528999 \tabularnewline
24 & 0.412094 & 0.824189 & 0.587906 \tabularnewline
25 & 0.370316 & 0.740631 & 0.629684 \tabularnewline
26 & 0.314547 & 0.629095 & 0.685453 \tabularnewline
27 & 0.258242 & 0.516484 & 0.741758 \tabularnewline
28 & 0.221118 & 0.442235 & 0.778882 \tabularnewline
29 & 0.179186 & 0.358371 & 0.820814 \tabularnewline
30 & 0.176051 & 0.352102 & 0.823949 \tabularnewline
31 & 0.138859 & 0.277719 & 0.861141 \tabularnewline
32 & 0.167682 & 0.335364 & 0.832318 \tabularnewline
33 & 0.156451 & 0.312902 & 0.843549 \tabularnewline
34 & 0.123819 & 0.247637 & 0.876181 \tabularnewline
35 & 0.103442 & 0.206883 & 0.896558 \tabularnewline
36 & 0.289903 & 0.579805 & 0.710097 \tabularnewline
37 & 0.422851 & 0.845702 & 0.577149 \tabularnewline
38 & 0.393096 & 0.786192 & 0.606904 \tabularnewline
39 & 0.393362 & 0.786724 & 0.606638 \tabularnewline
40 & 0.350054 & 0.700109 & 0.649946 \tabularnewline
41 & 0.304209 & 0.608417 & 0.695791 \tabularnewline
42 & 0.270619 & 0.541239 & 0.729381 \tabularnewline
43 & 0.404679 & 0.809358 & 0.595321 \tabularnewline
44 & 0.368837 & 0.737674 & 0.631163 \tabularnewline
45 & 0.339045 & 0.678089 & 0.660955 \tabularnewline
46 & 0.513833 & 0.972335 & 0.486167 \tabularnewline
47 & 0.534866 & 0.930267 & 0.465134 \tabularnewline
48 & 0.48581 & 0.971621 & 0.51419 \tabularnewline
49 & 0.440034 & 0.880068 & 0.559966 \tabularnewline
50 & 0.412058 & 0.824116 & 0.587942 \tabularnewline
51 & 0.369118 & 0.738237 & 0.630882 \tabularnewline
52 & 0.32495 & 0.649901 & 0.67505 \tabularnewline
53 & 0.316487 & 0.632974 & 0.683513 \tabularnewline
54 & 0.283669 & 0.567338 & 0.716331 \tabularnewline
55 & 0.506472 & 0.987056 & 0.493528 \tabularnewline
56 & 0.484829 & 0.969658 & 0.515171 \tabularnewline
57 & 0.441764 & 0.883527 & 0.558236 \tabularnewline
58 & 0.422433 & 0.844866 & 0.577567 \tabularnewline
59 & 0.380026 & 0.760052 & 0.619974 \tabularnewline
60 & 0.357055 & 0.714109 & 0.642945 \tabularnewline
61 & 0.317691 & 0.635382 & 0.682309 \tabularnewline
62 & 0.282632 & 0.565264 & 0.717368 \tabularnewline
63 & 0.260774 & 0.521549 & 0.739226 \tabularnewline
64 & 0.227575 & 0.455149 & 0.772425 \tabularnewline
65 & 0.210161 & 0.420322 & 0.789839 \tabularnewline
66 & 0.205474 & 0.410947 & 0.794526 \tabularnewline
67 & 0.254942 & 0.509885 & 0.745058 \tabularnewline
68 & 0.403092 & 0.806185 & 0.596908 \tabularnewline
69 & 0.524601 & 0.950797 & 0.475399 \tabularnewline
70 & 0.488195 & 0.97639 & 0.511805 \tabularnewline
71 & 0.713183 & 0.573633 & 0.286817 \tabularnewline
72 & 0.680566 & 0.638867 & 0.319434 \tabularnewline
73 & 0.704238 & 0.591523 & 0.295762 \tabularnewline
74 & 0.69463 & 0.61074 & 0.30537 \tabularnewline
75 & 0.678518 & 0.642964 & 0.321482 \tabularnewline
76 & 0.69712 & 0.605761 & 0.30288 \tabularnewline
77 & 0.662101 & 0.675798 & 0.337899 \tabularnewline
78 & 0.651775 & 0.69645 & 0.348225 \tabularnewline
79 & 0.621682 & 0.756636 & 0.378318 \tabularnewline
80 & 0.593145 & 0.813711 & 0.406855 \tabularnewline
81 & 0.560146 & 0.879708 & 0.439854 \tabularnewline
82 & 0.521034 & 0.957933 & 0.478966 \tabularnewline
83 & 0.482628 & 0.965256 & 0.517372 \tabularnewline
84 & 0.442529 & 0.885059 & 0.557471 \tabularnewline
85 & 0.423554 & 0.847109 & 0.576446 \tabularnewline
86 & 0.410132 & 0.820264 & 0.589868 \tabularnewline
87 & 0.383937 & 0.767874 & 0.616063 \tabularnewline
88 & 0.38306 & 0.76612 & 0.61694 \tabularnewline
89 & 0.367149 & 0.734298 & 0.632851 \tabularnewline
90 & 0.399214 & 0.798427 & 0.600786 \tabularnewline
91 & 0.411271 & 0.822541 & 0.588729 \tabularnewline
92 & 0.374771 & 0.749541 & 0.625229 \tabularnewline
93 & 0.347954 & 0.695908 & 0.652046 \tabularnewline
94 & 0.319598 & 0.639195 & 0.680402 \tabularnewline
95 & 0.285908 & 0.571815 & 0.714092 \tabularnewline
96 & 0.265468 & 0.530936 & 0.734532 \tabularnewline
97 & 0.244851 & 0.489702 & 0.755149 \tabularnewline
98 & 0.217424 & 0.434847 & 0.782576 \tabularnewline
99 & 0.195276 & 0.390551 & 0.804724 \tabularnewline
100 & 0.169567 & 0.339134 & 0.830433 \tabularnewline
101 & 0.155319 & 0.310637 & 0.844681 \tabularnewline
102 & 0.270619 & 0.541239 & 0.729381 \tabularnewline
103 & 0.246893 & 0.493786 & 0.753107 \tabularnewline
104 & 0.225552 & 0.451105 & 0.774448 \tabularnewline
105 & 0.217671 & 0.435342 & 0.782329 \tabularnewline
106 & 0.206686 & 0.413373 & 0.793314 \tabularnewline
107 & 0.184383 & 0.368766 & 0.815617 \tabularnewline
108 & 0.173533 & 0.347067 & 0.826467 \tabularnewline
109 & 0.199707 & 0.399414 & 0.800293 \tabularnewline
110 & 0.189991 & 0.379983 & 0.810009 \tabularnewline
111 & 0.189823 & 0.379647 & 0.810177 \tabularnewline
112 & 0.183436 & 0.366872 & 0.816564 \tabularnewline
113 & 0.168286 & 0.336572 & 0.831714 \tabularnewline
114 & 0.209908 & 0.419815 & 0.790092 \tabularnewline
115 & 0.240114 & 0.480228 & 0.759886 \tabularnewline
116 & 0.216604 & 0.433208 & 0.783396 \tabularnewline
117 & 0.196704 & 0.393408 & 0.803296 \tabularnewline
118 & 0.195807 & 0.391614 & 0.804193 \tabularnewline
119 & 0.177721 & 0.355442 & 0.822279 \tabularnewline
120 & 0.16516 & 0.330319 & 0.83484 \tabularnewline
121 & 0.174659 & 0.349318 & 0.825341 \tabularnewline
122 & 0.189829 & 0.379657 & 0.810171 \tabularnewline
123 & 0.164332 & 0.328663 & 0.835668 \tabularnewline
124 & 0.143888 & 0.287777 & 0.856112 \tabularnewline
125 & 0.13627 & 0.272539 & 0.86373 \tabularnewline
126 & 0.118623 & 0.237245 & 0.881377 \tabularnewline
127 & 0.103383 & 0.206767 & 0.896617 \tabularnewline
128 & 0.0863379 & 0.172676 & 0.913662 \tabularnewline
129 & 0.0965878 & 0.193176 & 0.903412 \tabularnewline
130 & 0.0907056 & 0.181411 & 0.909294 \tabularnewline
131 & 0.110965 & 0.22193 & 0.889035 \tabularnewline
132 & 0.0951644 & 0.190329 & 0.904836 \tabularnewline
133 & 0.0882038 & 0.176408 & 0.911796 \tabularnewline
134 & 0.0781127 & 0.156225 & 0.921887 \tabularnewline
135 & 0.068614 & 0.137228 & 0.931386 \tabularnewline
136 & 0.0622698 & 0.12454 & 0.93773 \tabularnewline
137 & 0.0512352 & 0.10247 & 0.948765 \tabularnewline
138 & 0.0621482 & 0.124296 & 0.937852 \tabularnewline
139 & 0.0726476 & 0.145295 & 0.927352 \tabularnewline
140 & 0.101133 & 0.202267 & 0.898867 \tabularnewline
141 & 0.0897943 & 0.179589 & 0.910206 \tabularnewline
142 & 0.074139 & 0.148278 & 0.925861 \tabularnewline
143 & 0.0803925 & 0.160785 & 0.919608 \tabularnewline
144 & 0.111476 & 0.222953 & 0.888524 \tabularnewline
145 & 0.210615 & 0.421231 & 0.789385 \tabularnewline
146 & 0.373048 & 0.746096 & 0.626952 \tabularnewline
147 & 0.357834 & 0.715669 & 0.642166 \tabularnewline
148 & 0.375867 & 0.751735 & 0.624133 \tabularnewline
149 & 0.337281 & 0.674562 & 0.662719 \tabularnewline
150 & 0.653439 & 0.693123 & 0.346561 \tabularnewline
151 & 0.610452 & 0.779095 & 0.389548 \tabularnewline
152 & 0.566389 & 0.867221 & 0.433611 \tabularnewline
153 & 0.52454 & 0.950921 & 0.47546 \tabularnewline
154 & 0.48034 & 0.960681 & 0.51966 \tabularnewline
155 & 0.557578 & 0.884843 & 0.442422 \tabularnewline
156 & 0.63149 & 0.73702 & 0.36851 \tabularnewline
157 & 0.632459 & 0.735082 & 0.367541 \tabularnewline
158 & 0.592077 & 0.815846 & 0.407923 \tabularnewline
159 & 0.616164 & 0.767673 & 0.383836 \tabularnewline
160 & 0.690751 & 0.618498 & 0.309249 \tabularnewline
161 & 0.68761 & 0.624779 & 0.31239 \tabularnewline
162 & 0.695678 & 0.608644 & 0.304322 \tabularnewline
163 & 0.676144 & 0.647712 & 0.323856 \tabularnewline
164 & 0.721118 & 0.557764 & 0.278882 \tabularnewline
165 & 0.682504 & 0.634992 & 0.317496 \tabularnewline
166 & 0.702267 & 0.595465 & 0.297733 \tabularnewline
167 & 0.655346 & 0.689308 & 0.344654 \tabularnewline
168 & 0.626514 & 0.746972 & 0.373486 \tabularnewline
169 & 0.640984 & 0.718033 & 0.359016 \tabularnewline
170 & 0.60862 & 0.78276 & 0.39138 \tabularnewline
171 & 0.564684 & 0.870632 & 0.435316 \tabularnewline
172 & 0.510507 & 0.978986 & 0.489493 \tabularnewline
173 & 0.51259 & 0.974821 & 0.48741 \tabularnewline
174 & 0.512538 & 0.974923 & 0.487462 \tabularnewline
175 & 0.47295 & 0.9459 & 0.52705 \tabularnewline
176 & 0.416366 & 0.832733 & 0.583634 \tabularnewline
177 & 0.391415 & 0.78283 & 0.608585 \tabularnewline
178 & 0.342926 & 0.685852 & 0.657074 \tabularnewline
179 & 0.299368 & 0.598736 & 0.700632 \tabularnewline
180 & 0.404493 & 0.808986 & 0.595507 \tabularnewline
181 & 0.351475 & 0.702949 & 0.648525 \tabularnewline
182 & 0.357025 & 0.71405 & 0.642975 \tabularnewline
183 & 0.545767 & 0.908465 & 0.454233 \tabularnewline
184 & 0.499744 & 0.999489 & 0.500256 \tabularnewline
185 & 0.703164 & 0.593672 & 0.296836 \tabularnewline
186 & 0.657397 & 0.685206 & 0.342603 \tabularnewline
187 & 0.73782 & 0.524361 & 0.26218 \tabularnewline
188 & 0.686028 & 0.627944 & 0.313972 \tabularnewline
189 & 0.692343 & 0.615315 & 0.307657 \tabularnewline
190 & 0.955768 & 0.0884643 & 0.0442321 \tabularnewline
191 & 0.953815 & 0.0923699 & 0.0461849 \tabularnewline
192 & 0.929635 & 0.14073 & 0.0703649 \tabularnewline
193 & 0.970082 & 0.0598368 & 0.0299184 \tabularnewline
194 & 0.94713 & 0.105741 & 0.0528705 \tabularnewline
195 & 0.92524 & 0.149521 & 0.0747603 \tabularnewline
196 & 0.872406 & 0.255188 & 0.127594 \tabularnewline
197 & 0.900293 & 0.199414 & 0.0997071 \tabularnewline
198 & 0.837392 & 0.325215 & 0.162608 \tabularnewline
199 & 0.90696 & 0.186081 & 0.0930403 \tabularnewline
200 & 0.797765 & 0.40447 & 0.202235 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253353&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]8[/C][C]0.756624[/C][C]0.486753[/C][C]0.243376[/C][/ROW]
[ROW][C]9[/C][C]0.617056[/C][C]0.765887[/C][C]0.382944[/C][/ROW]
[ROW][C]10[/C][C]0.519367[/C][C]0.961266[/C][C]0.480633[/C][/ROW]
[ROW][C]11[/C][C]0.443434[/C][C]0.886868[/C][C]0.556566[/C][/ROW]
[ROW][C]12[/C][C]0.560951[/C][C]0.878097[/C][C]0.439049[/C][/ROW]
[ROW][C]13[/C][C]0.468066[/C][C]0.936132[/C][C]0.531934[/C][/ROW]
[ROW][C]14[/C][C]0.419811[/C][C]0.839622[/C][C]0.580189[/C][/ROW]
[ROW][C]15[/C][C]0.451089[/C][C]0.902178[/C][C]0.548911[/C][/ROW]
[ROW][C]16[/C][C]0.390256[/C][C]0.780512[/C][C]0.609744[/C][/ROW]
[ROW][C]17[/C][C]0.321134[/C][C]0.642268[/C][C]0.678866[/C][/ROW]
[ROW][C]18[/C][C]0.642903[/C][C]0.714193[/C][C]0.357097[/C][/ROW]
[ROW][C]19[/C][C]0.633588[/C][C]0.732825[/C][C]0.366412[/C][/ROW]
[ROW][C]20[/C][C]0.564499[/C][C]0.871003[/C][C]0.435501[/C][/ROW]
[ROW][C]21[/C][C]0.493434[/C][C]0.986868[/C][C]0.506566[/C][/ROW]
[ROW][C]22[/C][C]0.426362[/C][C]0.852723[/C][C]0.573638[/C][/ROW]
[ROW][C]23[/C][C]0.471001[/C][C]0.942001[/C][C]0.528999[/C][/ROW]
[ROW][C]24[/C][C]0.412094[/C][C]0.824189[/C][C]0.587906[/C][/ROW]
[ROW][C]25[/C][C]0.370316[/C][C]0.740631[/C][C]0.629684[/C][/ROW]
[ROW][C]26[/C][C]0.314547[/C][C]0.629095[/C][C]0.685453[/C][/ROW]
[ROW][C]27[/C][C]0.258242[/C][C]0.516484[/C][C]0.741758[/C][/ROW]
[ROW][C]28[/C][C]0.221118[/C][C]0.442235[/C][C]0.778882[/C][/ROW]
[ROW][C]29[/C][C]0.179186[/C][C]0.358371[/C][C]0.820814[/C][/ROW]
[ROW][C]30[/C][C]0.176051[/C][C]0.352102[/C][C]0.823949[/C][/ROW]
[ROW][C]31[/C][C]0.138859[/C][C]0.277719[/C][C]0.861141[/C][/ROW]
[ROW][C]32[/C][C]0.167682[/C][C]0.335364[/C][C]0.832318[/C][/ROW]
[ROW][C]33[/C][C]0.156451[/C][C]0.312902[/C][C]0.843549[/C][/ROW]
[ROW][C]34[/C][C]0.123819[/C][C]0.247637[/C][C]0.876181[/C][/ROW]
[ROW][C]35[/C][C]0.103442[/C][C]0.206883[/C][C]0.896558[/C][/ROW]
[ROW][C]36[/C][C]0.289903[/C][C]0.579805[/C][C]0.710097[/C][/ROW]
[ROW][C]37[/C][C]0.422851[/C][C]0.845702[/C][C]0.577149[/C][/ROW]
[ROW][C]38[/C][C]0.393096[/C][C]0.786192[/C][C]0.606904[/C][/ROW]
[ROW][C]39[/C][C]0.393362[/C][C]0.786724[/C][C]0.606638[/C][/ROW]
[ROW][C]40[/C][C]0.350054[/C][C]0.700109[/C][C]0.649946[/C][/ROW]
[ROW][C]41[/C][C]0.304209[/C][C]0.608417[/C][C]0.695791[/C][/ROW]
[ROW][C]42[/C][C]0.270619[/C][C]0.541239[/C][C]0.729381[/C][/ROW]
[ROW][C]43[/C][C]0.404679[/C][C]0.809358[/C][C]0.595321[/C][/ROW]
[ROW][C]44[/C][C]0.368837[/C][C]0.737674[/C][C]0.631163[/C][/ROW]
[ROW][C]45[/C][C]0.339045[/C][C]0.678089[/C][C]0.660955[/C][/ROW]
[ROW][C]46[/C][C]0.513833[/C][C]0.972335[/C][C]0.486167[/C][/ROW]
[ROW][C]47[/C][C]0.534866[/C][C]0.930267[/C][C]0.465134[/C][/ROW]
[ROW][C]48[/C][C]0.48581[/C][C]0.971621[/C][C]0.51419[/C][/ROW]
[ROW][C]49[/C][C]0.440034[/C][C]0.880068[/C][C]0.559966[/C][/ROW]
[ROW][C]50[/C][C]0.412058[/C][C]0.824116[/C][C]0.587942[/C][/ROW]
[ROW][C]51[/C][C]0.369118[/C][C]0.738237[/C][C]0.630882[/C][/ROW]
[ROW][C]52[/C][C]0.32495[/C][C]0.649901[/C][C]0.67505[/C][/ROW]
[ROW][C]53[/C][C]0.316487[/C][C]0.632974[/C][C]0.683513[/C][/ROW]
[ROW][C]54[/C][C]0.283669[/C][C]0.567338[/C][C]0.716331[/C][/ROW]
[ROW][C]55[/C][C]0.506472[/C][C]0.987056[/C][C]0.493528[/C][/ROW]
[ROW][C]56[/C][C]0.484829[/C][C]0.969658[/C][C]0.515171[/C][/ROW]
[ROW][C]57[/C][C]0.441764[/C][C]0.883527[/C][C]0.558236[/C][/ROW]
[ROW][C]58[/C][C]0.422433[/C][C]0.844866[/C][C]0.577567[/C][/ROW]
[ROW][C]59[/C][C]0.380026[/C][C]0.760052[/C][C]0.619974[/C][/ROW]
[ROW][C]60[/C][C]0.357055[/C][C]0.714109[/C][C]0.642945[/C][/ROW]
[ROW][C]61[/C][C]0.317691[/C][C]0.635382[/C][C]0.682309[/C][/ROW]
[ROW][C]62[/C][C]0.282632[/C][C]0.565264[/C][C]0.717368[/C][/ROW]
[ROW][C]63[/C][C]0.260774[/C][C]0.521549[/C][C]0.739226[/C][/ROW]
[ROW][C]64[/C][C]0.227575[/C][C]0.455149[/C][C]0.772425[/C][/ROW]
[ROW][C]65[/C][C]0.210161[/C][C]0.420322[/C][C]0.789839[/C][/ROW]
[ROW][C]66[/C][C]0.205474[/C][C]0.410947[/C][C]0.794526[/C][/ROW]
[ROW][C]67[/C][C]0.254942[/C][C]0.509885[/C][C]0.745058[/C][/ROW]
[ROW][C]68[/C][C]0.403092[/C][C]0.806185[/C][C]0.596908[/C][/ROW]
[ROW][C]69[/C][C]0.524601[/C][C]0.950797[/C][C]0.475399[/C][/ROW]
[ROW][C]70[/C][C]0.488195[/C][C]0.97639[/C][C]0.511805[/C][/ROW]
[ROW][C]71[/C][C]0.713183[/C][C]0.573633[/C][C]0.286817[/C][/ROW]
[ROW][C]72[/C][C]0.680566[/C][C]0.638867[/C][C]0.319434[/C][/ROW]
[ROW][C]73[/C][C]0.704238[/C][C]0.591523[/C][C]0.295762[/C][/ROW]
[ROW][C]74[/C][C]0.69463[/C][C]0.61074[/C][C]0.30537[/C][/ROW]
[ROW][C]75[/C][C]0.678518[/C][C]0.642964[/C][C]0.321482[/C][/ROW]
[ROW][C]76[/C][C]0.69712[/C][C]0.605761[/C][C]0.30288[/C][/ROW]
[ROW][C]77[/C][C]0.662101[/C][C]0.675798[/C][C]0.337899[/C][/ROW]
[ROW][C]78[/C][C]0.651775[/C][C]0.69645[/C][C]0.348225[/C][/ROW]
[ROW][C]79[/C][C]0.621682[/C][C]0.756636[/C][C]0.378318[/C][/ROW]
[ROW][C]80[/C][C]0.593145[/C][C]0.813711[/C][C]0.406855[/C][/ROW]
[ROW][C]81[/C][C]0.560146[/C][C]0.879708[/C][C]0.439854[/C][/ROW]
[ROW][C]82[/C][C]0.521034[/C][C]0.957933[/C][C]0.478966[/C][/ROW]
[ROW][C]83[/C][C]0.482628[/C][C]0.965256[/C][C]0.517372[/C][/ROW]
[ROW][C]84[/C][C]0.442529[/C][C]0.885059[/C][C]0.557471[/C][/ROW]
[ROW][C]85[/C][C]0.423554[/C][C]0.847109[/C][C]0.576446[/C][/ROW]
[ROW][C]86[/C][C]0.410132[/C][C]0.820264[/C][C]0.589868[/C][/ROW]
[ROW][C]87[/C][C]0.383937[/C][C]0.767874[/C][C]0.616063[/C][/ROW]
[ROW][C]88[/C][C]0.38306[/C][C]0.76612[/C][C]0.61694[/C][/ROW]
[ROW][C]89[/C][C]0.367149[/C][C]0.734298[/C][C]0.632851[/C][/ROW]
[ROW][C]90[/C][C]0.399214[/C][C]0.798427[/C][C]0.600786[/C][/ROW]
[ROW][C]91[/C][C]0.411271[/C][C]0.822541[/C][C]0.588729[/C][/ROW]
[ROW][C]92[/C][C]0.374771[/C][C]0.749541[/C][C]0.625229[/C][/ROW]
[ROW][C]93[/C][C]0.347954[/C][C]0.695908[/C][C]0.652046[/C][/ROW]
[ROW][C]94[/C][C]0.319598[/C][C]0.639195[/C][C]0.680402[/C][/ROW]
[ROW][C]95[/C][C]0.285908[/C][C]0.571815[/C][C]0.714092[/C][/ROW]
[ROW][C]96[/C][C]0.265468[/C][C]0.530936[/C][C]0.734532[/C][/ROW]
[ROW][C]97[/C][C]0.244851[/C][C]0.489702[/C][C]0.755149[/C][/ROW]
[ROW][C]98[/C][C]0.217424[/C][C]0.434847[/C][C]0.782576[/C][/ROW]
[ROW][C]99[/C][C]0.195276[/C][C]0.390551[/C][C]0.804724[/C][/ROW]
[ROW][C]100[/C][C]0.169567[/C][C]0.339134[/C][C]0.830433[/C][/ROW]
[ROW][C]101[/C][C]0.155319[/C][C]0.310637[/C][C]0.844681[/C][/ROW]
[ROW][C]102[/C][C]0.270619[/C][C]0.541239[/C][C]0.729381[/C][/ROW]
[ROW][C]103[/C][C]0.246893[/C][C]0.493786[/C][C]0.753107[/C][/ROW]
[ROW][C]104[/C][C]0.225552[/C][C]0.451105[/C][C]0.774448[/C][/ROW]
[ROW][C]105[/C][C]0.217671[/C][C]0.435342[/C][C]0.782329[/C][/ROW]
[ROW][C]106[/C][C]0.206686[/C][C]0.413373[/C][C]0.793314[/C][/ROW]
[ROW][C]107[/C][C]0.184383[/C][C]0.368766[/C][C]0.815617[/C][/ROW]
[ROW][C]108[/C][C]0.173533[/C][C]0.347067[/C][C]0.826467[/C][/ROW]
[ROW][C]109[/C][C]0.199707[/C][C]0.399414[/C][C]0.800293[/C][/ROW]
[ROW][C]110[/C][C]0.189991[/C][C]0.379983[/C][C]0.810009[/C][/ROW]
[ROW][C]111[/C][C]0.189823[/C][C]0.379647[/C][C]0.810177[/C][/ROW]
[ROW][C]112[/C][C]0.183436[/C][C]0.366872[/C][C]0.816564[/C][/ROW]
[ROW][C]113[/C][C]0.168286[/C][C]0.336572[/C][C]0.831714[/C][/ROW]
[ROW][C]114[/C][C]0.209908[/C][C]0.419815[/C][C]0.790092[/C][/ROW]
[ROW][C]115[/C][C]0.240114[/C][C]0.480228[/C][C]0.759886[/C][/ROW]
[ROW][C]116[/C][C]0.216604[/C][C]0.433208[/C][C]0.783396[/C][/ROW]
[ROW][C]117[/C][C]0.196704[/C][C]0.393408[/C][C]0.803296[/C][/ROW]
[ROW][C]118[/C][C]0.195807[/C][C]0.391614[/C][C]0.804193[/C][/ROW]
[ROW][C]119[/C][C]0.177721[/C][C]0.355442[/C][C]0.822279[/C][/ROW]
[ROW][C]120[/C][C]0.16516[/C][C]0.330319[/C][C]0.83484[/C][/ROW]
[ROW][C]121[/C][C]0.174659[/C][C]0.349318[/C][C]0.825341[/C][/ROW]
[ROW][C]122[/C][C]0.189829[/C][C]0.379657[/C][C]0.810171[/C][/ROW]
[ROW][C]123[/C][C]0.164332[/C][C]0.328663[/C][C]0.835668[/C][/ROW]
[ROW][C]124[/C][C]0.143888[/C][C]0.287777[/C][C]0.856112[/C][/ROW]
[ROW][C]125[/C][C]0.13627[/C][C]0.272539[/C][C]0.86373[/C][/ROW]
[ROW][C]126[/C][C]0.118623[/C][C]0.237245[/C][C]0.881377[/C][/ROW]
[ROW][C]127[/C][C]0.103383[/C][C]0.206767[/C][C]0.896617[/C][/ROW]
[ROW][C]128[/C][C]0.0863379[/C][C]0.172676[/C][C]0.913662[/C][/ROW]
[ROW][C]129[/C][C]0.0965878[/C][C]0.193176[/C][C]0.903412[/C][/ROW]
[ROW][C]130[/C][C]0.0907056[/C][C]0.181411[/C][C]0.909294[/C][/ROW]
[ROW][C]131[/C][C]0.110965[/C][C]0.22193[/C][C]0.889035[/C][/ROW]
[ROW][C]132[/C][C]0.0951644[/C][C]0.190329[/C][C]0.904836[/C][/ROW]
[ROW][C]133[/C][C]0.0882038[/C][C]0.176408[/C][C]0.911796[/C][/ROW]
[ROW][C]134[/C][C]0.0781127[/C][C]0.156225[/C][C]0.921887[/C][/ROW]
[ROW][C]135[/C][C]0.068614[/C][C]0.137228[/C][C]0.931386[/C][/ROW]
[ROW][C]136[/C][C]0.0622698[/C][C]0.12454[/C][C]0.93773[/C][/ROW]
[ROW][C]137[/C][C]0.0512352[/C][C]0.10247[/C][C]0.948765[/C][/ROW]
[ROW][C]138[/C][C]0.0621482[/C][C]0.124296[/C][C]0.937852[/C][/ROW]
[ROW][C]139[/C][C]0.0726476[/C][C]0.145295[/C][C]0.927352[/C][/ROW]
[ROW][C]140[/C][C]0.101133[/C][C]0.202267[/C][C]0.898867[/C][/ROW]
[ROW][C]141[/C][C]0.0897943[/C][C]0.179589[/C][C]0.910206[/C][/ROW]
[ROW][C]142[/C][C]0.074139[/C][C]0.148278[/C][C]0.925861[/C][/ROW]
[ROW][C]143[/C][C]0.0803925[/C][C]0.160785[/C][C]0.919608[/C][/ROW]
[ROW][C]144[/C][C]0.111476[/C][C]0.222953[/C][C]0.888524[/C][/ROW]
[ROW][C]145[/C][C]0.210615[/C][C]0.421231[/C][C]0.789385[/C][/ROW]
[ROW][C]146[/C][C]0.373048[/C][C]0.746096[/C][C]0.626952[/C][/ROW]
[ROW][C]147[/C][C]0.357834[/C][C]0.715669[/C][C]0.642166[/C][/ROW]
[ROW][C]148[/C][C]0.375867[/C][C]0.751735[/C][C]0.624133[/C][/ROW]
[ROW][C]149[/C][C]0.337281[/C][C]0.674562[/C][C]0.662719[/C][/ROW]
[ROW][C]150[/C][C]0.653439[/C][C]0.693123[/C][C]0.346561[/C][/ROW]
[ROW][C]151[/C][C]0.610452[/C][C]0.779095[/C][C]0.389548[/C][/ROW]
[ROW][C]152[/C][C]0.566389[/C][C]0.867221[/C][C]0.433611[/C][/ROW]
[ROW][C]153[/C][C]0.52454[/C][C]0.950921[/C][C]0.47546[/C][/ROW]
[ROW][C]154[/C][C]0.48034[/C][C]0.960681[/C][C]0.51966[/C][/ROW]
[ROW][C]155[/C][C]0.557578[/C][C]0.884843[/C][C]0.442422[/C][/ROW]
[ROW][C]156[/C][C]0.63149[/C][C]0.73702[/C][C]0.36851[/C][/ROW]
[ROW][C]157[/C][C]0.632459[/C][C]0.735082[/C][C]0.367541[/C][/ROW]
[ROW][C]158[/C][C]0.592077[/C][C]0.815846[/C][C]0.407923[/C][/ROW]
[ROW][C]159[/C][C]0.616164[/C][C]0.767673[/C][C]0.383836[/C][/ROW]
[ROW][C]160[/C][C]0.690751[/C][C]0.618498[/C][C]0.309249[/C][/ROW]
[ROW][C]161[/C][C]0.68761[/C][C]0.624779[/C][C]0.31239[/C][/ROW]
[ROW][C]162[/C][C]0.695678[/C][C]0.608644[/C][C]0.304322[/C][/ROW]
[ROW][C]163[/C][C]0.676144[/C][C]0.647712[/C][C]0.323856[/C][/ROW]
[ROW][C]164[/C][C]0.721118[/C][C]0.557764[/C][C]0.278882[/C][/ROW]
[ROW][C]165[/C][C]0.682504[/C][C]0.634992[/C][C]0.317496[/C][/ROW]
[ROW][C]166[/C][C]0.702267[/C][C]0.595465[/C][C]0.297733[/C][/ROW]
[ROW][C]167[/C][C]0.655346[/C][C]0.689308[/C][C]0.344654[/C][/ROW]
[ROW][C]168[/C][C]0.626514[/C][C]0.746972[/C][C]0.373486[/C][/ROW]
[ROW][C]169[/C][C]0.640984[/C][C]0.718033[/C][C]0.359016[/C][/ROW]
[ROW][C]170[/C][C]0.60862[/C][C]0.78276[/C][C]0.39138[/C][/ROW]
[ROW][C]171[/C][C]0.564684[/C][C]0.870632[/C][C]0.435316[/C][/ROW]
[ROW][C]172[/C][C]0.510507[/C][C]0.978986[/C][C]0.489493[/C][/ROW]
[ROW][C]173[/C][C]0.51259[/C][C]0.974821[/C][C]0.48741[/C][/ROW]
[ROW][C]174[/C][C]0.512538[/C][C]0.974923[/C][C]0.487462[/C][/ROW]
[ROW][C]175[/C][C]0.47295[/C][C]0.9459[/C][C]0.52705[/C][/ROW]
[ROW][C]176[/C][C]0.416366[/C][C]0.832733[/C][C]0.583634[/C][/ROW]
[ROW][C]177[/C][C]0.391415[/C][C]0.78283[/C][C]0.608585[/C][/ROW]
[ROW][C]178[/C][C]0.342926[/C][C]0.685852[/C][C]0.657074[/C][/ROW]
[ROW][C]179[/C][C]0.299368[/C][C]0.598736[/C][C]0.700632[/C][/ROW]
[ROW][C]180[/C][C]0.404493[/C][C]0.808986[/C][C]0.595507[/C][/ROW]
[ROW][C]181[/C][C]0.351475[/C][C]0.702949[/C][C]0.648525[/C][/ROW]
[ROW][C]182[/C][C]0.357025[/C][C]0.71405[/C][C]0.642975[/C][/ROW]
[ROW][C]183[/C][C]0.545767[/C][C]0.908465[/C][C]0.454233[/C][/ROW]
[ROW][C]184[/C][C]0.499744[/C][C]0.999489[/C][C]0.500256[/C][/ROW]
[ROW][C]185[/C][C]0.703164[/C][C]0.593672[/C][C]0.296836[/C][/ROW]
[ROW][C]186[/C][C]0.657397[/C][C]0.685206[/C][C]0.342603[/C][/ROW]
[ROW][C]187[/C][C]0.73782[/C][C]0.524361[/C][C]0.26218[/C][/ROW]
[ROW][C]188[/C][C]0.686028[/C][C]0.627944[/C][C]0.313972[/C][/ROW]
[ROW][C]189[/C][C]0.692343[/C][C]0.615315[/C][C]0.307657[/C][/ROW]
[ROW][C]190[/C][C]0.955768[/C][C]0.0884643[/C][C]0.0442321[/C][/ROW]
[ROW][C]191[/C][C]0.953815[/C][C]0.0923699[/C][C]0.0461849[/C][/ROW]
[ROW][C]192[/C][C]0.929635[/C][C]0.14073[/C][C]0.0703649[/C][/ROW]
[ROW][C]193[/C][C]0.970082[/C][C]0.0598368[/C][C]0.0299184[/C][/ROW]
[ROW][C]194[/C][C]0.94713[/C][C]0.105741[/C][C]0.0528705[/C][/ROW]
[ROW][C]195[/C][C]0.92524[/C][C]0.149521[/C][C]0.0747603[/C][/ROW]
[ROW][C]196[/C][C]0.872406[/C][C]0.255188[/C][C]0.127594[/C][/ROW]
[ROW][C]197[/C][C]0.900293[/C][C]0.199414[/C][C]0.0997071[/C][/ROW]
[ROW][C]198[/C][C]0.837392[/C][C]0.325215[/C][C]0.162608[/C][/ROW]
[ROW][C]199[/C][C]0.90696[/C][C]0.186081[/C][C]0.0930403[/C][/ROW]
[ROW][C]200[/C][C]0.797765[/C][C]0.40447[/C][C]0.202235[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253353&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253353&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
80.7566240.4867530.243376
90.6170560.7658870.382944
100.5193670.9612660.480633
110.4434340.8868680.556566
120.5609510.8780970.439049
130.4680660.9361320.531934
140.4198110.8396220.580189
150.4510890.9021780.548911
160.3902560.7805120.609744
170.3211340.6422680.678866
180.6429030.7141930.357097
190.6335880.7328250.366412
200.5644990.8710030.435501
210.4934340.9868680.506566
220.4263620.8527230.573638
230.4710010.9420010.528999
240.4120940.8241890.587906
250.3703160.7406310.629684
260.3145470.6290950.685453
270.2582420.5164840.741758
280.2211180.4422350.778882
290.1791860.3583710.820814
300.1760510.3521020.823949
310.1388590.2777190.861141
320.1676820.3353640.832318
330.1564510.3129020.843549
340.1238190.2476370.876181
350.1034420.2068830.896558
360.2899030.5798050.710097
370.4228510.8457020.577149
380.3930960.7861920.606904
390.3933620.7867240.606638
400.3500540.7001090.649946
410.3042090.6084170.695791
420.2706190.5412390.729381
430.4046790.8093580.595321
440.3688370.7376740.631163
450.3390450.6780890.660955
460.5138330.9723350.486167
470.5348660.9302670.465134
480.485810.9716210.51419
490.4400340.8800680.559966
500.4120580.8241160.587942
510.3691180.7382370.630882
520.324950.6499010.67505
530.3164870.6329740.683513
540.2836690.5673380.716331
550.5064720.9870560.493528
560.4848290.9696580.515171
570.4417640.8835270.558236
580.4224330.8448660.577567
590.3800260.7600520.619974
600.3570550.7141090.642945
610.3176910.6353820.682309
620.2826320.5652640.717368
630.2607740.5215490.739226
640.2275750.4551490.772425
650.2101610.4203220.789839
660.2054740.4109470.794526
670.2549420.5098850.745058
680.4030920.8061850.596908
690.5246010.9507970.475399
700.4881950.976390.511805
710.7131830.5736330.286817
720.6805660.6388670.319434
730.7042380.5915230.295762
740.694630.610740.30537
750.6785180.6429640.321482
760.697120.6057610.30288
770.6621010.6757980.337899
780.6517750.696450.348225
790.6216820.7566360.378318
800.5931450.8137110.406855
810.5601460.8797080.439854
820.5210340.9579330.478966
830.4826280.9652560.517372
840.4425290.8850590.557471
850.4235540.8471090.576446
860.4101320.8202640.589868
870.3839370.7678740.616063
880.383060.766120.61694
890.3671490.7342980.632851
900.3992140.7984270.600786
910.4112710.8225410.588729
920.3747710.7495410.625229
930.3479540.6959080.652046
940.3195980.6391950.680402
950.2859080.5718150.714092
960.2654680.5309360.734532
970.2448510.4897020.755149
980.2174240.4348470.782576
990.1952760.3905510.804724
1000.1695670.3391340.830433
1010.1553190.3106370.844681
1020.2706190.5412390.729381
1030.2468930.4937860.753107
1040.2255520.4511050.774448
1050.2176710.4353420.782329
1060.2066860.4133730.793314
1070.1843830.3687660.815617
1080.1735330.3470670.826467
1090.1997070.3994140.800293
1100.1899910.3799830.810009
1110.1898230.3796470.810177
1120.1834360.3668720.816564
1130.1682860.3365720.831714
1140.2099080.4198150.790092
1150.2401140.4802280.759886
1160.2166040.4332080.783396
1170.1967040.3934080.803296
1180.1958070.3916140.804193
1190.1777210.3554420.822279
1200.165160.3303190.83484
1210.1746590.3493180.825341
1220.1898290.3796570.810171
1230.1643320.3286630.835668
1240.1438880.2877770.856112
1250.136270.2725390.86373
1260.1186230.2372450.881377
1270.1033830.2067670.896617
1280.08633790.1726760.913662
1290.09658780.1931760.903412
1300.09070560.1814110.909294
1310.1109650.221930.889035
1320.09516440.1903290.904836
1330.08820380.1764080.911796
1340.07811270.1562250.921887
1350.0686140.1372280.931386
1360.06226980.124540.93773
1370.05123520.102470.948765
1380.06214820.1242960.937852
1390.07264760.1452950.927352
1400.1011330.2022670.898867
1410.08979430.1795890.910206
1420.0741390.1482780.925861
1430.08039250.1607850.919608
1440.1114760.2229530.888524
1450.2106150.4212310.789385
1460.3730480.7460960.626952
1470.3578340.7156690.642166
1480.3758670.7517350.624133
1490.3372810.6745620.662719
1500.6534390.6931230.346561
1510.6104520.7790950.389548
1520.5663890.8672210.433611
1530.524540.9509210.47546
1540.480340.9606810.51966
1550.5575780.8848430.442422
1560.631490.737020.36851
1570.6324590.7350820.367541
1580.5920770.8158460.407923
1590.6161640.7676730.383836
1600.6907510.6184980.309249
1610.687610.6247790.31239
1620.6956780.6086440.304322
1630.6761440.6477120.323856
1640.7211180.5577640.278882
1650.6825040.6349920.317496
1660.7022670.5954650.297733
1670.6553460.6893080.344654
1680.6265140.7469720.373486
1690.6409840.7180330.359016
1700.608620.782760.39138
1710.5646840.8706320.435316
1720.5105070.9789860.489493
1730.512590.9748210.48741
1740.5125380.9749230.487462
1750.472950.94590.52705
1760.4163660.8327330.583634
1770.3914150.782830.608585
1780.3429260.6858520.657074
1790.2993680.5987360.700632
1800.4044930.8089860.595507
1810.3514750.7029490.648525
1820.3570250.714050.642975
1830.5457670.9084650.454233
1840.4997440.9994890.500256
1850.7031640.5936720.296836
1860.6573970.6852060.342603
1870.737820.5243610.26218
1880.6860280.6279440.313972
1890.6923430.6153150.307657
1900.9557680.08846430.0442321
1910.9538150.09236990.0461849
1920.9296350.140730.0703649
1930.9700820.05983680.0299184
1940.947130.1057410.0528705
1950.925240.1495210.0747603
1960.8724060.2551880.127594
1970.9002930.1994140.0997071
1980.8373920.3252150.162608
1990.906960.1860810.0930403
2000.7977650.404470.202235






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level30.015544OK

\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 & 0 & 0 & OK \tabularnewline
10% type I error level & 3 & 0.015544 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253353&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.015544[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253353&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253353&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 level00OK
10% type I error level30.015544OK


Figures (Output of Computation)

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


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


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


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


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


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


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


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


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


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


Input Parameters & R Code

Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '3'
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')
}