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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 computationSun, 14 Dec 2014 16:28:33 +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/14/t1418574608l1nb7vvkxym26ke.htm/, Retrieved Thu, 16 May 2024 22:08:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267737, Retrieved Thu, 16 May 2024 22:08:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact91

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2014-12-11 17:20:38] [2add28fdfb91c9297fa0715b27e01e1f]
- R PD    [Multiple Regression] [] [2014-12-14 16:28:33] [b4b65124834fa3a3e625dd03af063494] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
21 13 13 13 21 12.9
22 8 13 16 21 12.2
21 14 11 11 21 12.8
21 16 14 10 17 7.4
21 14 15 9 22 6.7
21 13 14 8 12 12.6
21 15 11 26 19 14.8
23 13 13 10 21 13.3
22 20 16 10 20 11.1
25 17 14 8 19 8.2
21 15 14 13 22 11.4
23 16 15 11 14 6.4
22 12 15 8 19 10.6
21 17 13 12 18 12
21 11 14 24 18 6.3
25 16 11 21 14 11.3
21 16 12 5 20 11.9
21 15 14 14 20 9.3
20 13 13 11 20 9.6
24 14 12 9 15 10
23 19 15 8 15 6.4
21 16 15 17 22 13.8
24 17 14 18 21 10.8
23 10 14 16 17 13.8
21 15 12 23 24 11.7
22 14 12 9 9 10.9
20 14 12 14 29 16.1
18 16 15 13 19 13.4
21 15 14 10 22 9.9
22 17 16 8 16 11.5
22 14 12 10 15 8.3
21 16 12 19 19 11.7
21 15 14 11 24 9
25 16 16 16 18 9.7
22 16 15 12 22 10.8
22 10 12 11 20 10.3
20 8 14 11 9 10.4
21 17 13 10 13 12.7
21 14 14 13 21 9.3
21 10 16 14 25 11.8
22 14 12 8 22 5.9
21 12 14 11 22 11.4
24 16 15 11 23 13
22 16 13 13 23 10.8
22 16 16 15 18 12.3
21 8 16 15 22 11.3
22 16 12 16 14 11.8
19 15 12 12 19 7.9
22 8 16 12 16 12.7
23 13 12 17 25 12.3
20 14 15 14 22 11.6
20 13 12 15 16 6.7
23 16 13 12 18 10.9
20 19 12 13 20 12.1
23 19 14 7 17 13.3
21 14 14 8 26 10.1
22 15 11 16 20 5.7
21 13 10 20 12 14.3
21 10 12 14 8 8
19 16 11 10 28 13.3
22 15 16 16 27 9.3
21 11 14 11 22 12.5
21 9 14 26 20 7.6
21 16 15 9 18 15.9
21 12 15 15 19 9.2
21 12 14 12 20 9.1
21 14 13 21 17 11.1
22 14 11 20 18 13
22 13 16 20 17 14.5
18 15 12 10 18 12.2
21 17 15 15 24 12.3
23 14 14 10 19 11.4
19 11 15 16 21 8.8
19 9 14 9 23 14.6
21 7 13 17 14 12.6
21 13 6 10 27 NA
21 15 12 19 15 13
21 12 12 13 24 12.6
20 15 14 8 18 13.2
19 14 14 11 12 9.9
21 16 15 9 24 7.7
19 14 11 12 21 10.5
19 13 13 10 13 13.4
19 16 14 9 21 10.9
20 13 16 14 19 4.3
19 16 13 14 10 10.3
19 16 14 10 22 11.8
19 16 16 8 18 11.2
20 10 11 13 21 11.4
19 12 13 9 23 8.6
18 12 13 14 17 13.2
19 12 15 8 21 12.6
21 12 12 16 19 5.6
18 19 13 14 20 9.9
18 14 12 14 21 8.8
19 13 14 8 19 7.7
21 16 14 11 20 9
20 15 16 11 26 7.3
24 12 15 13 21 11.4
22 8 14 12 24 13.6
21 10 13 13 20 7.9
21 16 14 9 21 10.7
19 16 15 10 18 10.3
19 10 14 12 9 8.3
20 18 12 11 15 9.6
18 12 7 13 19 14.2
19 16 12 17 12 8.5
19 10 15 15 8 13.5
20 14 12 15 15 4.9
21 12 13 14 18 6.4
18 11 11 10 16 9.6
19 15 14 15 19 11.6
19 7 13 14 18 11.1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'George Udny Yule' @ yule.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 & 5 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267737&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267737&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267737&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 time5 seconds
R Server'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
NUMERACY[t] = + 10.5471 + 0.0284404AGE[t] + 0.101298CONF[t] + 0.354909STRES[t] -0.0153985DEPR[t] + 0.167581EX[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
NUMERACY[t] =  +  10.5471 +  0.0284404AGE[t] +  0.101298CONF[t] +  0.354909STRES[t] -0.0153985DEPR[t] +  0.167581EX[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267737&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]NUMERACY[t] =  +  10.5471 +  0.0284404AGE[t] +  0.101298CONF[t] +  0.354909STRES[t] -0.0153985DEPR[t] +  0.167581EX[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267737&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267737&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
NUMERACY[t] = + 10.5471 + 0.0284404AGE[t] + 0.101298CONF[t] + 0.354909STRES[t] -0.0153985DEPR[t] + 0.167581EX[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.54716.439431.6380.1044080.0522042
AGE0.02844040.2587690.10990.9126920.456346
CONF0.1012980.1477560.68560.4944770.247239
STRES0.3549090.2584461.3730.1725740.0862872
DEPR-0.01539850.104253-0.14770.8828570.441428
EX0.1675810.1628661.0290.3058430.152921

\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) & 10.5471 & 6.43943 & 1.638 & 0.104408 & 0.0522042 \tabularnewline
AGE & 0.0284404 & 0.258769 & 0.1099 & 0.912692 & 0.456346 \tabularnewline
CONF & 0.101298 & 0.147756 & 0.6856 & 0.494477 & 0.247239 \tabularnewline
STRES & 0.354909 & 0.258446 & 1.373 & 0.172574 & 0.0862872 \tabularnewline
DEPR & -0.0153985 & 0.104253 & -0.1477 & 0.882857 & 0.441428 \tabularnewline
EX & 0.167581 & 0.162866 & 1.029 & 0.305843 & 0.152921 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267737&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]10.5471[/C][C]6.43943[/C][C]1.638[/C][C]0.104408[/C][C]0.0522042[/C][/ROW]
[ROW][C]AGE[/C][C]0.0284404[/C][C]0.258769[/C][C]0.1099[/C][C]0.912692[/C][C]0.456346[/C][/ROW]
[ROW][C]CONF[/C][C]0.101298[/C][C]0.147756[/C][C]0.6856[/C][C]0.494477[/C][C]0.247239[/C][/ROW]
[ROW][C]STRES[/C][C]0.354909[/C][C]0.258446[/C][C]1.373[/C][C]0.172574[/C][C]0.0862872[/C][/ROW]
[ROW][C]DEPR[/C][C]-0.0153985[/C][C]0.104253[/C][C]-0.1477[/C][C]0.882857[/C][C]0.441428[/C][/ROW]
[ROW][C]EX[/C][C]0.167581[/C][C]0.162866[/C][C]1.029[/C][C]0.305843[/C][C]0.152921[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267737&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267737&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)10.54716.439431.6380.1044080.0522042
AGE0.02844040.2587690.10990.9126920.456346
CONF0.1012980.1477560.68560.4944770.247239
STRES0.3549090.2584461.3730.1725740.0862872
DEPR-0.01539850.104253-0.14770.8828570.441428
EX0.1675810.1628661.0290.3058430.152921






Multiple Linear Regression - Regression Statistics
Multiple R0.182001
R-squared0.0331243
Adjusted R-squared-0.012483
F-TEST (value)0.726294
F-TEST (DF numerator)5
F-TEST (DF denominator)106
p-value0.605199
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.22297
Sum Squared Residuals1890.35

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.182001 \tabularnewline
R-squared & 0.0331243 \tabularnewline
Adjusted R-squared & -0.012483 \tabularnewline
F-TEST (value) & 0.726294 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 106 \tabularnewline
p-value & 0.605199 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.22297 \tabularnewline
Sum Squared Residuals & 1890.35 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267737&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.182001[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0331243[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.012483[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.726294[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]106[/C][/ROW]
[ROW][C]p-value[/C][C]0.605199[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]4.22297[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1890.35[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267737&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267737&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 R0.182001
R-squared0.0331243
Adjusted R-squared-0.012483
F-TEST (value)0.726294
F-TEST (DF numerator)5
F-TEST (DF denominator)106
p-value0.605199
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.22297
Sum Squared Residuals1890.35






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12119.03671.96331
22118.39512.60486
32118.44222.55779
41718.82-1.81999
52218.87043.1296
61219.4183-7.41832
71918.64770.352311
82119.20681.7932
92020.5835-0.583492
101919.1999-0.199912
112219.34282.65718
121419.0488-5.0488
131919.3652-0.365205
141819.3065-1.30646
151817.91360.086419
161418.3532-4.35321
172018.94131.05872
182018.97551.0245
192018.4861.51397
201518.444-3.44401
211519.3989-4.39889
222220.13961.86037
232119.45321.5468
241719.2492-2.24921
252418.52935.47071
26918.5379-9.53795
272919.27559.7245
281920.0489-1.04887
292219.13762.86235
301620.3774-4.37743
311518.0868-3.08684
321918.69220.307816
332418.97145.02858
341819.9366-1.93662
352219.74232.25768
362018.00141.99859
37918.4685-9.46851
381319.4546-6.45456
392118.88962.1104
402519.59785.40221
412217.71544.28456
422219.06972.93027
432320.18332.81672
442319.01713.9829
451820.3024-2.30241
462219.2962.704
471418.7836-4.78358
481918.0050.995014
491619.6052-3.60525
502518.57656.42348
512219.58612.41389
521617.5835-1.58354
531819.0777-1.0777
542019.12710.872939
551720.2157-3.21569
562619.10076.89934
572017.30512.69488
581218.0988-6.09878
59817.5413-9.54134
602818.68719.31289
612719.6837.31704
622219.15282.84723
632017.8982.10196
641820.6147-2.61474
651918.99440.00563867
662018.66891.33111
671718.7132-1.71315
681818.3656-0.365576
691720.2902-3.2902
701818.7279-0.727943
712420.02043.97965
721919.3446-0.3446
732118.75382.24625
742319.2763.72399
751418.317-4.31703
762730.8087-3.80874
77159.530215.46979
782425.693-1.69302
791824.9641-6.96407
80127.240574.75943
812420.98453.01551
822127.1098-6.10979
831311.3651.63496
842120.61640.383622
851927.8326-8.83259
86107.500472.49953
872224.1405-2.14053
881814.74323.25684
892116.21954.7805
902324.8849-1.88495
911715.6151.38495
922119.31091.68906
931918.0410.958987
942016.99533.00473
952120.54030.459713
961918.07270.927278
972013.36796.63209
982624.47921.52084
992116.04634.95375
1002421.89492.10511
1012018.38841.61159
1022122.604-1.604
1031827.2753-9.27535
104912.6376-3.63761
1051512.93852.06153
1061925.1298-6.12984
1071223.4555-11.4555
108810.3832-2.38319
1091514.83070.169287
1101819.5321-1.53213
1111616.2887-0.28866
1121919.055-0.0549751
11318NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 21 & 19.0367 & 1.96331 \tabularnewline
2 & 21 & 18.3951 & 2.60486 \tabularnewline
3 & 21 & 18.4422 & 2.55779 \tabularnewline
4 & 17 & 18.82 & -1.81999 \tabularnewline
5 & 22 & 18.8704 & 3.1296 \tabularnewline
6 & 12 & 19.4183 & -7.41832 \tabularnewline
7 & 19 & 18.6477 & 0.352311 \tabularnewline
8 & 21 & 19.2068 & 1.7932 \tabularnewline
9 & 20 & 20.5835 & -0.583492 \tabularnewline
10 & 19 & 19.1999 & -0.199912 \tabularnewline
11 & 22 & 19.3428 & 2.65718 \tabularnewline
12 & 14 & 19.0488 & -5.0488 \tabularnewline
13 & 19 & 19.3652 & -0.365205 \tabularnewline
14 & 18 & 19.3065 & -1.30646 \tabularnewline
15 & 18 & 17.9136 & 0.086419 \tabularnewline
16 & 14 & 18.3532 & -4.35321 \tabularnewline
17 & 20 & 18.9413 & 1.05872 \tabularnewline
18 & 20 & 18.9755 & 1.0245 \tabularnewline
19 & 20 & 18.486 & 1.51397 \tabularnewline
20 & 15 & 18.444 & -3.44401 \tabularnewline
21 & 15 & 19.3989 & -4.39889 \tabularnewline
22 & 22 & 20.1396 & 1.86037 \tabularnewline
23 & 21 & 19.4532 & 1.5468 \tabularnewline
24 & 17 & 19.2492 & -2.24921 \tabularnewline
25 & 24 & 18.5293 & 5.47071 \tabularnewline
26 & 9 & 18.5379 & -9.53795 \tabularnewline
27 & 29 & 19.2755 & 9.7245 \tabularnewline
28 & 19 & 20.0489 & -1.04887 \tabularnewline
29 & 22 & 19.1376 & 2.86235 \tabularnewline
30 & 16 & 20.3774 & -4.37743 \tabularnewline
31 & 15 & 18.0868 & -3.08684 \tabularnewline
32 & 19 & 18.6922 & 0.307816 \tabularnewline
33 & 24 & 18.9714 & 5.02858 \tabularnewline
34 & 18 & 19.9366 & -1.93662 \tabularnewline
35 & 22 & 19.7423 & 2.25768 \tabularnewline
36 & 20 & 18.0014 & 1.99859 \tabularnewline
37 & 9 & 18.4685 & -9.46851 \tabularnewline
38 & 13 & 19.4546 & -6.45456 \tabularnewline
39 & 21 & 18.8896 & 2.1104 \tabularnewline
40 & 25 & 19.5978 & 5.40221 \tabularnewline
41 & 22 & 17.7154 & 4.28456 \tabularnewline
42 & 22 & 19.0697 & 2.93027 \tabularnewline
43 & 23 & 20.1833 & 2.81672 \tabularnewline
44 & 23 & 19.0171 & 3.9829 \tabularnewline
45 & 18 & 20.3024 & -2.30241 \tabularnewline
46 & 22 & 19.296 & 2.704 \tabularnewline
47 & 14 & 18.7836 & -4.78358 \tabularnewline
48 & 19 & 18.005 & 0.995014 \tabularnewline
49 & 16 & 19.6052 & -3.60525 \tabularnewline
50 & 25 & 18.5765 & 6.42348 \tabularnewline
51 & 22 & 19.5861 & 2.41389 \tabularnewline
52 & 16 & 17.5835 & -1.58354 \tabularnewline
53 & 18 & 19.0777 & -1.0777 \tabularnewline
54 & 20 & 19.1271 & 0.872939 \tabularnewline
55 & 17 & 20.2157 & -3.21569 \tabularnewline
56 & 26 & 19.1007 & 6.89934 \tabularnewline
57 & 20 & 17.3051 & 2.69488 \tabularnewline
58 & 12 & 18.0988 & -6.09878 \tabularnewline
59 & 8 & 17.5413 & -9.54134 \tabularnewline
60 & 28 & 18.6871 & 9.31289 \tabularnewline
61 & 27 & 19.683 & 7.31704 \tabularnewline
62 & 22 & 19.1528 & 2.84723 \tabularnewline
63 & 20 & 17.898 & 2.10196 \tabularnewline
64 & 18 & 20.6147 & -2.61474 \tabularnewline
65 & 19 & 18.9944 & 0.00563867 \tabularnewline
66 & 20 & 18.6689 & 1.33111 \tabularnewline
67 & 17 & 18.7132 & -1.71315 \tabularnewline
68 & 18 & 18.3656 & -0.365576 \tabularnewline
69 & 17 & 20.2902 & -3.2902 \tabularnewline
70 & 18 & 18.7279 & -0.727943 \tabularnewline
71 & 24 & 20.0204 & 3.97965 \tabularnewline
72 & 19 & 19.3446 & -0.3446 \tabularnewline
73 & 21 & 18.7538 & 2.24625 \tabularnewline
74 & 23 & 19.276 & 3.72399 \tabularnewline
75 & 14 & 18.317 & -4.31703 \tabularnewline
76 & 27 & 30.8087 & -3.80874 \tabularnewline
77 & 15 & 9.53021 & 5.46979 \tabularnewline
78 & 24 & 25.693 & -1.69302 \tabularnewline
79 & 18 & 24.9641 & -6.96407 \tabularnewline
80 & 12 & 7.24057 & 4.75943 \tabularnewline
81 & 24 & 20.9845 & 3.01551 \tabularnewline
82 & 21 & 27.1098 & -6.10979 \tabularnewline
83 & 13 & 11.365 & 1.63496 \tabularnewline
84 & 21 & 20.6164 & 0.383622 \tabularnewline
85 & 19 & 27.8326 & -8.83259 \tabularnewline
86 & 10 & 7.50047 & 2.49953 \tabularnewline
87 & 22 & 24.1405 & -2.14053 \tabularnewline
88 & 18 & 14.7432 & 3.25684 \tabularnewline
89 & 21 & 16.2195 & 4.7805 \tabularnewline
90 & 23 & 24.8849 & -1.88495 \tabularnewline
91 & 17 & 15.615 & 1.38495 \tabularnewline
92 & 21 & 19.3109 & 1.68906 \tabularnewline
93 & 19 & 18.041 & 0.958987 \tabularnewline
94 & 20 & 16.9953 & 3.00473 \tabularnewline
95 & 21 & 20.5403 & 0.459713 \tabularnewline
96 & 19 & 18.0727 & 0.927278 \tabularnewline
97 & 20 & 13.3679 & 6.63209 \tabularnewline
98 & 26 & 24.4792 & 1.52084 \tabularnewline
99 & 21 & 16.0463 & 4.95375 \tabularnewline
100 & 24 & 21.8949 & 2.10511 \tabularnewline
101 & 20 & 18.3884 & 1.61159 \tabularnewline
102 & 21 & 22.604 & -1.604 \tabularnewline
103 & 18 & 27.2753 & -9.27535 \tabularnewline
104 & 9 & 12.6376 & -3.63761 \tabularnewline
105 & 15 & 12.9385 & 2.06153 \tabularnewline
106 & 19 & 25.1298 & -6.12984 \tabularnewline
107 & 12 & 23.4555 & -11.4555 \tabularnewline
108 & 8 & 10.3832 & -2.38319 \tabularnewline
109 & 15 & 14.8307 & 0.169287 \tabularnewline
110 & 18 & 19.5321 & -1.53213 \tabularnewline
111 & 16 & 16.2887 & -0.28866 \tabularnewline
112 & 19 & 19.055 & -0.0549751 \tabularnewline
113 & 18 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267737&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]21[/C][C]19.0367[/C][C]1.96331[/C][/ROW]
[ROW][C]2[/C][C]21[/C][C]18.3951[/C][C]2.60486[/C][/ROW]
[ROW][C]3[/C][C]21[/C][C]18.4422[/C][C]2.55779[/C][/ROW]
[ROW][C]4[/C][C]17[/C][C]18.82[/C][C]-1.81999[/C][/ROW]
[ROW][C]5[/C][C]22[/C][C]18.8704[/C][C]3.1296[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]19.4183[/C][C]-7.41832[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]18.6477[/C][C]0.352311[/C][/ROW]
[ROW][C]8[/C][C]21[/C][C]19.2068[/C][C]1.7932[/C][/ROW]
[ROW][C]9[/C][C]20[/C][C]20.5835[/C][C]-0.583492[/C][/ROW]
[ROW][C]10[/C][C]19[/C][C]19.1999[/C][C]-0.199912[/C][/ROW]
[ROW][C]11[/C][C]22[/C][C]19.3428[/C][C]2.65718[/C][/ROW]
[ROW][C]12[/C][C]14[/C][C]19.0488[/C][C]-5.0488[/C][/ROW]
[ROW][C]13[/C][C]19[/C][C]19.3652[/C][C]-0.365205[/C][/ROW]
[ROW][C]14[/C][C]18[/C][C]19.3065[/C][C]-1.30646[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]17.9136[/C][C]0.086419[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]18.3532[/C][C]-4.35321[/C][/ROW]
[ROW][C]17[/C][C]20[/C][C]18.9413[/C][C]1.05872[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]18.9755[/C][C]1.0245[/C][/ROW]
[ROW][C]19[/C][C]20[/C][C]18.486[/C][C]1.51397[/C][/ROW]
[ROW][C]20[/C][C]15[/C][C]18.444[/C][C]-3.44401[/C][/ROW]
[ROW][C]21[/C][C]15[/C][C]19.3989[/C][C]-4.39889[/C][/ROW]
[ROW][C]22[/C][C]22[/C][C]20.1396[/C][C]1.86037[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]19.4532[/C][C]1.5468[/C][/ROW]
[ROW][C]24[/C][C]17[/C][C]19.2492[/C][C]-2.24921[/C][/ROW]
[ROW][C]25[/C][C]24[/C][C]18.5293[/C][C]5.47071[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]18.5379[/C][C]-9.53795[/C][/ROW]
[ROW][C]27[/C][C]29[/C][C]19.2755[/C][C]9.7245[/C][/ROW]
[ROW][C]28[/C][C]19[/C][C]20.0489[/C][C]-1.04887[/C][/ROW]
[ROW][C]29[/C][C]22[/C][C]19.1376[/C][C]2.86235[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]20.3774[/C][C]-4.37743[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]18.0868[/C][C]-3.08684[/C][/ROW]
[ROW][C]32[/C][C]19[/C][C]18.6922[/C][C]0.307816[/C][/ROW]
[ROW][C]33[/C][C]24[/C][C]18.9714[/C][C]5.02858[/C][/ROW]
[ROW][C]34[/C][C]18[/C][C]19.9366[/C][C]-1.93662[/C][/ROW]
[ROW][C]35[/C][C]22[/C][C]19.7423[/C][C]2.25768[/C][/ROW]
[ROW][C]36[/C][C]20[/C][C]18.0014[/C][C]1.99859[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]18.4685[/C][C]-9.46851[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]19.4546[/C][C]-6.45456[/C][/ROW]
[ROW][C]39[/C][C]21[/C][C]18.8896[/C][C]2.1104[/C][/ROW]
[ROW][C]40[/C][C]25[/C][C]19.5978[/C][C]5.40221[/C][/ROW]
[ROW][C]41[/C][C]22[/C][C]17.7154[/C][C]4.28456[/C][/ROW]
[ROW][C]42[/C][C]22[/C][C]19.0697[/C][C]2.93027[/C][/ROW]
[ROW][C]43[/C][C]23[/C][C]20.1833[/C][C]2.81672[/C][/ROW]
[ROW][C]44[/C][C]23[/C][C]19.0171[/C][C]3.9829[/C][/ROW]
[ROW][C]45[/C][C]18[/C][C]20.3024[/C][C]-2.30241[/C][/ROW]
[ROW][C]46[/C][C]22[/C][C]19.296[/C][C]2.704[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]18.7836[/C][C]-4.78358[/C][/ROW]
[ROW][C]48[/C][C]19[/C][C]18.005[/C][C]0.995014[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]19.6052[/C][C]-3.60525[/C][/ROW]
[ROW][C]50[/C][C]25[/C][C]18.5765[/C][C]6.42348[/C][/ROW]
[ROW][C]51[/C][C]22[/C][C]19.5861[/C][C]2.41389[/C][/ROW]
[ROW][C]52[/C][C]16[/C][C]17.5835[/C][C]-1.58354[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]19.0777[/C][C]-1.0777[/C][/ROW]
[ROW][C]54[/C][C]20[/C][C]19.1271[/C][C]0.872939[/C][/ROW]
[ROW][C]55[/C][C]17[/C][C]20.2157[/C][C]-3.21569[/C][/ROW]
[ROW][C]56[/C][C]26[/C][C]19.1007[/C][C]6.89934[/C][/ROW]
[ROW][C]57[/C][C]20[/C][C]17.3051[/C][C]2.69488[/C][/ROW]
[ROW][C]58[/C][C]12[/C][C]18.0988[/C][C]-6.09878[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]17.5413[/C][C]-9.54134[/C][/ROW]
[ROW][C]60[/C][C]28[/C][C]18.6871[/C][C]9.31289[/C][/ROW]
[ROW][C]61[/C][C]27[/C][C]19.683[/C][C]7.31704[/C][/ROW]
[ROW][C]62[/C][C]22[/C][C]19.1528[/C][C]2.84723[/C][/ROW]
[ROW][C]63[/C][C]20[/C][C]17.898[/C][C]2.10196[/C][/ROW]
[ROW][C]64[/C][C]18[/C][C]20.6147[/C][C]-2.61474[/C][/ROW]
[ROW][C]65[/C][C]19[/C][C]18.9944[/C][C]0.00563867[/C][/ROW]
[ROW][C]66[/C][C]20[/C][C]18.6689[/C][C]1.33111[/C][/ROW]
[ROW][C]67[/C][C]17[/C][C]18.7132[/C][C]-1.71315[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]18.3656[/C][C]-0.365576[/C][/ROW]
[ROW][C]69[/C][C]17[/C][C]20.2902[/C][C]-3.2902[/C][/ROW]
[ROW][C]70[/C][C]18[/C][C]18.7279[/C][C]-0.727943[/C][/ROW]
[ROW][C]71[/C][C]24[/C][C]20.0204[/C][C]3.97965[/C][/ROW]
[ROW][C]72[/C][C]19[/C][C]19.3446[/C][C]-0.3446[/C][/ROW]
[ROW][C]73[/C][C]21[/C][C]18.7538[/C][C]2.24625[/C][/ROW]
[ROW][C]74[/C][C]23[/C][C]19.276[/C][C]3.72399[/C][/ROW]
[ROW][C]75[/C][C]14[/C][C]18.317[/C][C]-4.31703[/C][/ROW]
[ROW][C]76[/C][C]27[/C][C]30.8087[/C][C]-3.80874[/C][/ROW]
[ROW][C]77[/C][C]15[/C][C]9.53021[/C][C]5.46979[/C][/ROW]
[ROW][C]78[/C][C]24[/C][C]25.693[/C][C]-1.69302[/C][/ROW]
[ROW][C]79[/C][C]18[/C][C]24.9641[/C][C]-6.96407[/C][/ROW]
[ROW][C]80[/C][C]12[/C][C]7.24057[/C][C]4.75943[/C][/ROW]
[ROW][C]81[/C][C]24[/C][C]20.9845[/C][C]3.01551[/C][/ROW]
[ROW][C]82[/C][C]21[/C][C]27.1098[/C][C]-6.10979[/C][/ROW]
[ROW][C]83[/C][C]13[/C][C]11.365[/C][C]1.63496[/C][/ROW]
[ROW][C]84[/C][C]21[/C][C]20.6164[/C][C]0.383622[/C][/ROW]
[ROW][C]85[/C][C]19[/C][C]27.8326[/C][C]-8.83259[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]7.50047[/C][C]2.49953[/C][/ROW]
[ROW][C]87[/C][C]22[/C][C]24.1405[/C][C]-2.14053[/C][/ROW]
[ROW][C]88[/C][C]18[/C][C]14.7432[/C][C]3.25684[/C][/ROW]
[ROW][C]89[/C][C]21[/C][C]16.2195[/C][C]4.7805[/C][/ROW]
[ROW][C]90[/C][C]23[/C][C]24.8849[/C][C]-1.88495[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]15.615[/C][C]1.38495[/C][/ROW]
[ROW][C]92[/C][C]21[/C][C]19.3109[/C][C]1.68906[/C][/ROW]
[ROW][C]93[/C][C]19[/C][C]18.041[/C][C]0.958987[/C][/ROW]
[ROW][C]94[/C][C]20[/C][C]16.9953[/C][C]3.00473[/C][/ROW]
[ROW][C]95[/C][C]21[/C][C]20.5403[/C][C]0.459713[/C][/ROW]
[ROW][C]96[/C][C]19[/C][C]18.0727[/C][C]0.927278[/C][/ROW]
[ROW][C]97[/C][C]20[/C][C]13.3679[/C][C]6.63209[/C][/ROW]
[ROW][C]98[/C][C]26[/C][C]24.4792[/C][C]1.52084[/C][/ROW]
[ROW][C]99[/C][C]21[/C][C]16.0463[/C][C]4.95375[/C][/ROW]
[ROW][C]100[/C][C]24[/C][C]21.8949[/C][C]2.10511[/C][/ROW]
[ROW][C]101[/C][C]20[/C][C]18.3884[/C][C]1.61159[/C][/ROW]
[ROW][C]102[/C][C]21[/C][C]22.604[/C][C]-1.604[/C][/ROW]
[ROW][C]103[/C][C]18[/C][C]27.2753[/C][C]-9.27535[/C][/ROW]
[ROW][C]104[/C][C]9[/C][C]12.6376[/C][C]-3.63761[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]12.9385[/C][C]2.06153[/C][/ROW]
[ROW][C]106[/C][C]19[/C][C]25.1298[/C][C]-6.12984[/C][/ROW]
[ROW][C]107[/C][C]12[/C][C]23.4555[/C][C]-11.4555[/C][/ROW]
[ROW][C]108[/C][C]8[/C][C]10.3832[/C][C]-2.38319[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]14.8307[/C][C]0.169287[/C][/ROW]
[ROW][C]110[/C][C]18[/C][C]19.5321[/C][C]-1.53213[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]16.2887[/C][C]-0.28866[/C][/ROW]
[ROW][C]112[/C][C]19[/C][C]19.055[/C][C]-0.0549751[/C][/ROW]
[ROW][C]113[/C][C]18[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267737&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267737&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
12119.03671.96331
22118.39512.60486
32118.44222.55779
41718.82-1.81999
52218.87043.1296
61219.4183-7.41832
71918.64770.352311
82119.20681.7932
92020.5835-0.583492
101919.1999-0.199912
112219.34282.65718
121419.0488-5.0488
131919.3652-0.365205
141819.3065-1.30646
151817.91360.086419
161418.3532-4.35321
172018.94131.05872
182018.97551.0245
192018.4861.51397
201518.444-3.44401
211519.3989-4.39889
222220.13961.86037
232119.45321.5468
241719.2492-2.24921
252418.52935.47071
26918.5379-9.53795
272919.27559.7245
281920.0489-1.04887
292219.13762.86235
301620.3774-4.37743
311518.0868-3.08684
321918.69220.307816
332418.97145.02858
341819.9366-1.93662
352219.74232.25768
362018.00141.99859
37918.4685-9.46851
381319.4546-6.45456
392118.88962.1104
402519.59785.40221
412217.71544.28456
422219.06972.93027
432320.18332.81672
442319.01713.9829
451820.3024-2.30241
462219.2962.704
471418.7836-4.78358
481918.0050.995014
491619.6052-3.60525
502518.57656.42348
512219.58612.41389
521617.5835-1.58354
531819.0777-1.0777
542019.12710.872939
551720.2157-3.21569
562619.10076.89934
572017.30512.69488
581218.0988-6.09878
59817.5413-9.54134
602818.68719.31289
612719.6837.31704
622219.15282.84723
632017.8982.10196
641820.6147-2.61474
651918.99440.00563867
662018.66891.33111
671718.7132-1.71315
681818.3656-0.365576
691720.2902-3.2902
701818.7279-0.727943
712420.02043.97965
721919.3446-0.3446
732118.75382.24625
742319.2763.72399
751418.317-4.31703
762730.8087-3.80874
77159.530215.46979
782425.693-1.69302
791824.9641-6.96407
80127.240574.75943
812420.98453.01551
822127.1098-6.10979
831311.3651.63496
842120.61640.383622
851927.8326-8.83259
86107.500472.49953
872224.1405-2.14053
881814.74323.25684
892116.21954.7805
902324.8849-1.88495
911715.6151.38495
922119.31091.68906
931918.0410.958987
942016.99533.00473
952120.54030.459713
961918.07270.927278
972013.36796.63209
982624.47921.52084
992116.04634.95375
1002421.89492.10511
1012018.38841.61159
1022122.604-1.604
1031827.2753-9.27535
104912.6376-3.63761
1051512.93852.06153
1061925.1298-6.12984
1071223.4555-11.4555
108810.3832-2.38319
1091514.83070.169287
1101819.5321-1.53213
1111616.2887-0.28866
1121919.055-0.0549751
11318NANA






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.5762790.8474420.423721
100.5468440.9063120.453156
110.4643940.9287880.535606
120.5299440.9401110.470056
130.409010.8180210.59099
140.3075910.6151810.692409
150.2212090.4424190.778791
160.2109130.4218260.789087
170.1467530.2935070.853247
180.101280.2025590.89872
190.06551260.1310250.934487
200.04985980.09971960.95014
210.03700820.07401650.962992
220.02454190.04908370.975458
230.0222970.04459410.977703
240.01778190.03556390.982218
250.02157180.04314360.978428
260.1198670.2397340.880133
270.2547560.5095110.745244
280.273080.5461590.72692
290.2476410.4952830.752359
300.2413560.4827120.758644
310.2148310.4296620.785169
320.1729410.3458820.827059
330.2025490.4050970.797451
340.1688040.3376080.831196
350.1450270.2900530.854973
360.1195190.2390380.880481
370.34660.69320.6534
380.4564120.9128240.543588
390.4125510.8251010.587449
400.4473120.8946240.552688
410.4778030.9556060.522197
420.4418120.8836230.558188
430.4228520.8457040.577148
440.4106020.8212040.589398
450.3737740.7475480.626226
460.3379140.6758280.662086
470.3643220.7286440.635678
480.3118090.6236170.688191
490.3047950.6095910.695205
500.363610.727220.63639
510.327190.6543810.67281
520.2907360.5814720.709264
530.2528750.505750.747125
540.2108710.4217420.789129
550.2193890.4387780.780611
560.2763420.5526840.723658
570.240210.480420.75979
580.3051080.6102170.694892
590.5776950.8446110.422305
600.7570.4860010.243
610.8335650.332870.166435
620.8073920.3852150.192608
630.8079790.3840430.192021
640.7920910.4158190.207909
650.7492240.5015520.250776
660.7019420.5961170.298058
670.6636390.6727210.336361
680.6107640.7784710.389236
690.5746650.850670.425335
700.5241340.9517310.475866
710.5638650.8722690.436135
720.5342150.931570.465785
730.5749850.850030.425015
740.5697960.8604080.430204
750.5411670.9176670.458833
760.5039470.9921050.496053
770.5276640.9446730.472336
780.4930270.9860540.506973
790.6001620.7996760.399838
800.5665250.8669510.433475
810.5259180.9481630.474082
820.60760.78480.3924
830.5449890.9100210.455011
840.4976490.9952980.502351
850.6529490.6941020.347051
860.6078710.7842580.392129
870.5563650.8872710.443635
880.510550.97890.48945
890.4947270.9894540.505273
900.4305480.8610960.569452
910.3650260.7300530.634974
920.3041710.6083420.695829
930.283390.566780.71661
940.3646230.7292470.635377
950.2847930.5695870.715207
960.212940.425880.78706
970.5428630.9142740.457137
980.5103470.9793050.489653
990.4155260.8310520.584474
1000.3277880.6555760.672212
1010.2288970.4577940.771103
1020.2240110.4480220.775989
1030.2317590.4635190.768241
1040.1494560.2989130.850544

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.576279 & 0.847442 & 0.423721 \tabularnewline
10 & 0.546844 & 0.906312 & 0.453156 \tabularnewline
11 & 0.464394 & 0.928788 & 0.535606 \tabularnewline
12 & 0.529944 & 0.940111 & 0.470056 \tabularnewline
13 & 0.40901 & 0.818021 & 0.59099 \tabularnewline
14 & 0.307591 & 0.615181 & 0.692409 \tabularnewline
15 & 0.221209 & 0.442419 & 0.778791 \tabularnewline
16 & 0.210913 & 0.421826 & 0.789087 \tabularnewline
17 & 0.146753 & 0.293507 & 0.853247 \tabularnewline
18 & 0.10128 & 0.202559 & 0.89872 \tabularnewline
19 & 0.0655126 & 0.131025 & 0.934487 \tabularnewline
20 & 0.0498598 & 0.0997196 & 0.95014 \tabularnewline
21 & 0.0370082 & 0.0740165 & 0.962992 \tabularnewline
22 & 0.0245419 & 0.0490837 & 0.975458 \tabularnewline
23 & 0.022297 & 0.0445941 & 0.977703 \tabularnewline
24 & 0.0177819 & 0.0355639 & 0.982218 \tabularnewline
25 & 0.0215718 & 0.0431436 & 0.978428 \tabularnewline
26 & 0.119867 & 0.239734 & 0.880133 \tabularnewline
27 & 0.254756 & 0.509511 & 0.745244 \tabularnewline
28 & 0.27308 & 0.546159 & 0.72692 \tabularnewline
29 & 0.247641 & 0.495283 & 0.752359 \tabularnewline
30 & 0.241356 & 0.482712 & 0.758644 \tabularnewline
31 & 0.214831 & 0.429662 & 0.785169 \tabularnewline
32 & 0.172941 & 0.345882 & 0.827059 \tabularnewline
33 & 0.202549 & 0.405097 & 0.797451 \tabularnewline
34 & 0.168804 & 0.337608 & 0.831196 \tabularnewline
35 & 0.145027 & 0.290053 & 0.854973 \tabularnewline
36 & 0.119519 & 0.239038 & 0.880481 \tabularnewline
37 & 0.3466 & 0.6932 & 0.6534 \tabularnewline
38 & 0.456412 & 0.912824 & 0.543588 \tabularnewline
39 & 0.412551 & 0.825101 & 0.587449 \tabularnewline
40 & 0.447312 & 0.894624 & 0.552688 \tabularnewline
41 & 0.477803 & 0.955606 & 0.522197 \tabularnewline
42 & 0.441812 & 0.883623 & 0.558188 \tabularnewline
43 & 0.422852 & 0.845704 & 0.577148 \tabularnewline
44 & 0.410602 & 0.821204 & 0.589398 \tabularnewline
45 & 0.373774 & 0.747548 & 0.626226 \tabularnewline
46 & 0.337914 & 0.675828 & 0.662086 \tabularnewline
47 & 0.364322 & 0.728644 & 0.635678 \tabularnewline
48 & 0.311809 & 0.623617 & 0.688191 \tabularnewline
49 & 0.304795 & 0.609591 & 0.695205 \tabularnewline
50 & 0.36361 & 0.72722 & 0.63639 \tabularnewline
51 & 0.32719 & 0.654381 & 0.67281 \tabularnewline
52 & 0.290736 & 0.581472 & 0.709264 \tabularnewline
53 & 0.252875 & 0.50575 & 0.747125 \tabularnewline
54 & 0.210871 & 0.421742 & 0.789129 \tabularnewline
55 & 0.219389 & 0.438778 & 0.780611 \tabularnewline
56 & 0.276342 & 0.552684 & 0.723658 \tabularnewline
57 & 0.24021 & 0.48042 & 0.75979 \tabularnewline
58 & 0.305108 & 0.610217 & 0.694892 \tabularnewline
59 & 0.577695 & 0.844611 & 0.422305 \tabularnewline
60 & 0.757 & 0.486001 & 0.243 \tabularnewline
61 & 0.833565 & 0.33287 & 0.166435 \tabularnewline
62 & 0.807392 & 0.385215 & 0.192608 \tabularnewline
63 & 0.807979 & 0.384043 & 0.192021 \tabularnewline
64 & 0.792091 & 0.415819 & 0.207909 \tabularnewline
65 & 0.749224 & 0.501552 & 0.250776 \tabularnewline
66 & 0.701942 & 0.596117 & 0.298058 \tabularnewline
67 & 0.663639 & 0.672721 & 0.336361 \tabularnewline
68 & 0.610764 & 0.778471 & 0.389236 \tabularnewline
69 & 0.574665 & 0.85067 & 0.425335 \tabularnewline
70 & 0.524134 & 0.951731 & 0.475866 \tabularnewline
71 & 0.563865 & 0.872269 & 0.436135 \tabularnewline
72 & 0.534215 & 0.93157 & 0.465785 \tabularnewline
73 & 0.574985 & 0.85003 & 0.425015 \tabularnewline
74 & 0.569796 & 0.860408 & 0.430204 \tabularnewline
75 & 0.541167 & 0.917667 & 0.458833 \tabularnewline
76 & 0.503947 & 0.992105 & 0.496053 \tabularnewline
77 & 0.527664 & 0.944673 & 0.472336 \tabularnewline
78 & 0.493027 & 0.986054 & 0.506973 \tabularnewline
79 & 0.600162 & 0.799676 & 0.399838 \tabularnewline
80 & 0.566525 & 0.866951 & 0.433475 \tabularnewline
81 & 0.525918 & 0.948163 & 0.474082 \tabularnewline
82 & 0.6076 & 0.7848 & 0.3924 \tabularnewline
83 & 0.544989 & 0.910021 & 0.455011 \tabularnewline
84 & 0.497649 & 0.995298 & 0.502351 \tabularnewline
85 & 0.652949 & 0.694102 & 0.347051 \tabularnewline
86 & 0.607871 & 0.784258 & 0.392129 \tabularnewline
87 & 0.556365 & 0.887271 & 0.443635 \tabularnewline
88 & 0.51055 & 0.9789 & 0.48945 \tabularnewline
89 & 0.494727 & 0.989454 & 0.505273 \tabularnewline
90 & 0.430548 & 0.861096 & 0.569452 \tabularnewline
91 & 0.365026 & 0.730053 & 0.634974 \tabularnewline
92 & 0.304171 & 0.608342 & 0.695829 \tabularnewline
93 & 0.28339 & 0.56678 & 0.71661 \tabularnewline
94 & 0.364623 & 0.729247 & 0.635377 \tabularnewline
95 & 0.284793 & 0.569587 & 0.715207 \tabularnewline
96 & 0.21294 & 0.42588 & 0.78706 \tabularnewline
97 & 0.542863 & 0.914274 & 0.457137 \tabularnewline
98 & 0.510347 & 0.979305 & 0.489653 \tabularnewline
99 & 0.415526 & 0.831052 & 0.584474 \tabularnewline
100 & 0.327788 & 0.655576 & 0.672212 \tabularnewline
101 & 0.228897 & 0.457794 & 0.771103 \tabularnewline
102 & 0.224011 & 0.448022 & 0.775989 \tabularnewline
103 & 0.231759 & 0.463519 & 0.768241 \tabularnewline
104 & 0.149456 & 0.298913 & 0.850544 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267737&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]9[/C][C]0.576279[/C][C]0.847442[/C][C]0.423721[/C][/ROW]
[ROW][C]10[/C][C]0.546844[/C][C]0.906312[/C][C]0.453156[/C][/ROW]
[ROW][C]11[/C][C]0.464394[/C][C]0.928788[/C][C]0.535606[/C][/ROW]
[ROW][C]12[/C][C]0.529944[/C][C]0.940111[/C][C]0.470056[/C][/ROW]
[ROW][C]13[/C][C]0.40901[/C][C]0.818021[/C][C]0.59099[/C][/ROW]
[ROW][C]14[/C][C]0.307591[/C][C]0.615181[/C][C]0.692409[/C][/ROW]
[ROW][C]15[/C][C]0.221209[/C][C]0.442419[/C][C]0.778791[/C][/ROW]
[ROW][C]16[/C][C]0.210913[/C][C]0.421826[/C][C]0.789087[/C][/ROW]
[ROW][C]17[/C][C]0.146753[/C][C]0.293507[/C][C]0.853247[/C][/ROW]
[ROW][C]18[/C][C]0.10128[/C][C]0.202559[/C][C]0.89872[/C][/ROW]
[ROW][C]19[/C][C]0.0655126[/C][C]0.131025[/C][C]0.934487[/C][/ROW]
[ROW][C]20[/C][C]0.0498598[/C][C]0.0997196[/C][C]0.95014[/C][/ROW]
[ROW][C]21[/C][C]0.0370082[/C][C]0.0740165[/C][C]0.962992[/C][/ROW]
[ROW][C]22[/C][C]0.0245419[/C][C]0.0490837[/C][C]0.975458[/C][/ROW]
[ROW][C]23[/C][C]0.022297[/C][C]0.0445941[/C][C]0.977703[/C][/ROW]
[ROW][C]24[/C][C]0.0177819[/C][C]0.0355639[/C][C]0.982218[/C][/ROW]
[ROW][C]25[/C][C]0.0215718[/C][C]0.0431436[/C][C]0.978428[/C][/ROW]
[ROW][C]26[/C][C]0.119867[/C][C]0.239734[/C][C]0.880133[/C][/ROW]
[ROW][C]27[/C][C]0.254756[/C][C]0.509511[/C][C]0.745244[/C][/ROW]
[ROW][C]28[/C][C]0.27308[/C][C]0.546159[/C][C]0.72692[/C][/ROW]
[ROW][C]29[/C][C]0.247641[/C][C]0.495283[/C][C]0.752359[/C][/ROW]
[ROW][C]30[/C][C]0.241356[/C][C]0.482712[/C][C]0.758644[/C][/ROW]
[ROW][C]31[/C][C]0.214831[/C][C]0.429662[/C][C]0.785169[/C][/ROW]
[ROW][C]32[/C][C]0.172941[/C][C]0.345882[/C][C]0.827059[/C][/ROW]
[ROW][C]33[/C][C]0.202549[/C][C]0.405097[/C][C]0.797451[/C][/ROW]
[ROW][C]34[/C][C]0.168804[/C][C]0.337608[/C][C]0.831196[/C][/ROW]
[ROW][C]35[/C][C]0.145027[/C][C]0.290053[/C][C]0.854973[/C][/ROW]
[ROW][C]36[/C][C]0.119519[/C][C]0.239038[/C][C]0.880481[/C][/ROW]
[ROW][C]37[/C][C]0.3466[/C][C]0.6932[/C][C]0.6534[/C][/ROW]
[ROW][C]38[/C][C]0.456412[/C][C]0.912824[/C][C]0.543588[/C][/ROW]
[ROW][C]39[/C][C]0.412551[/C][C]0.825101[/C][C]0.587449[/C][/ROW]
[ROW][C]40[/C][C]0.447312[/C][C]0.894624[/C][C]0.552688[/C][/ROW]
[ROW][C]41[/C][C]0.477803[/C][C]0.955606[/C][C]0.522197[/C][/ROW]
[ROW][C]42[/C][C]0.441812[/C][C]0.883623[/C][C]0.558188[/C][/ROW]
[ROW][C]43[/C][C]0.422852[/C][C]0.845704[/C][C]0.577148[/C][/ROW]
[ROW][C]44[/C][C]0.410602[/C][C]0.821204[/C][C]0.589398[/C][/ROW]
[ROW][C]45[/C][C]0.373774[/C][C]0.747548[/C][C]0.626226[/C][/ROW]
[ROW][C]46[/C][C]0.337914[/C][C]0.675828[/C][C]0.662086[/C][/ROW]
[ROW][C]47[/C][C]0.364322[/C][C]0.728644[/C][C]0.635678[/C][/ROW]
[ROW][C]48[/C][C]0.311809[/C][C]0.623617[/C][C]0.688191[/C][/ROW]
[ROW][C]49[/C][C]0.304795[/C][C]0.609591[/C][C]0.695205[/C][/ROW]
[ROW][C]50[/C][C]0.36361[/C][C]0.72722[/C][C]0.63639[/C][/ROW]
[ROW][C]51[/C][C]0.32719[/C][C]0.654381[/C][C]0.67281[/C][/ROW]
[ROW][C]52[/C][C]0.290736[/C][C]0.581472[/C][C]0.709264[/C][/ROW]
[ROW][C]53[/C][C]0.252875[/C][C]0.50575[/C][C]0.747125[/C][/ROW]
[ROW][C]54[/C][C]0.210871[/C][C]0.421742[/C][C]0.789129[/C][/ROW]
[ROW][C]55[/C][C]0.219389[/C][C]0.438778[/C][C]0.780611[/C][/ROW]
[ROW][C]56[/C][C]0.276342[/C][C]0.552684[/C][C]0.723658[/C][/ROW]
[ROW][C]57[/C][C]0.24021[/C][C]0.48042[/C][C]0.75979[/C][/ROW]
[ROW][C]58[/C][C]0.305108[/C][C]0.610217[/C][C]0.694892[/C][/ROW]
[ROW][C]59[/C][C]0.577695[/C][C]0.844611[/C][C]0.422305[/C][/ROW]
[ROW][C]60[/C][C]0.757[/C][C]0.486001[/C][C]0.243[/C][/ROW]
[ROW][C]61[/C][C]0.833565[/C][C]0.33287[/C][C]0.166435[/C][/ROW]
[ROW][C]62[/C][C]0.807392[/C][C]0.385215[/C][C]0.192608[/C][/ROW]
[ROW][C]63[/C][C]0.807979[/C][C]0.384043[/C][C]0.192021[/C][/ROW]
[ROW][C]64[/C][C]0.792091[/C][C]0.415819[/C][C]0.207909[/C][/ROW]
[ROW][C]65[/C][C]0.749224[/C][C]0.501552[/C][C]0.250776[/C][/ROW]
[ROW][C]66[/C][C]0.701942[/C][C]0.596117[/C][C]0.298058[/C][/ROW]
[ROW][C]67[/C][C]0.663639[/C][C]0.672721[/C][C]0.336361[/C][/ROW]
[ROW][C]68[/C][C]0.610764[/C][C]0.778471[/C][C]0.389236[/C][/ROW]
[ROW][C]69[/C][C]0.574665[/C][C]0.85067[/C][C]0.425335[/C][/ROW]
[ROW][C]70[/C][C]0.524134[/C][C]0.951731[/C][C]0.475866[/C][/ROW]
[ROW][C]71[/C][C]0.563865[/C][C]0.872269[/C][C]0.436135[/C][/ROW]
[ROW][C]72[/C][C]0.534215[/C][C]0.93157[/C][C]0.465785[/C][/ROW]
[ROW][C]73[/C][C]0.574985[/C][C]0.85003[/C][C]0.425015[/C][/ROW]
[ROW][C]74[/C][C]0.569796[/C][C]0.860408[/C][C]0.430204[/C][/ROW]
[ROW][C]75[/C][C]0.541167[/C][C]0.917667[/C][C]0.458833[/C][/ROW]
[ROW][C]76[/C][C]0.503947[/C][C]0.992105[/C][C]0.496053[/C][/ROW]
[ROW][C]77[/C][C]0.527664[/C][C]0.944673[/C][C]0.472336[/C][/ROW]
[ROW][C]78[/C][C]0.493027[/C][C]0.986054[/C][C]0.506973[/C][/ROW]
[ROW][C]79[/C][C]0.600162[/C][C]0.799676[/C][C]0.399838[/C][/ROW]
[ROW][C]80[/C][C]0.566525[/C][C]0.866951[/C][C]0.433475[/C][/ROW]
[ROW][C]81[/C][C]0.525918[/C][C]0.948163[/C][C]0.474082[/C][/ROW]
[ROW][C]82[/C][C]0.6076[/C][C]0.7848[/C][C]0.3924[/C][/ROW]
[ROW][C]83[/C][C]0.544989[/C][C]0.910021[/C][C]0.455011[/C][/ROW]
[ROW][C]84[/C][C]0.497649[/C][C]0.995298[/C][C]0.502351[/C][/ROW]
[ROW][C]85[/C][C]0.652949[/C][C]0.694102[/C][C]0.347051[/C][/ROW]
[ROW][C]86[/C][C]0.607871[/C][C]0.784258[/C][C]0.392129[/C][/ROW]
[ROW][C]87[/C][C]0.556365[/C][C]0.887271[/C][C]0.443635[/C][/ROW]
[ROW][C]88[/C][C]0.51055[/C][C]0.9789[/C][C]0.48945[/C][/ROW]
[ROW][C]89[/C][C]0.494727[/C][C]0.989454[/C][C]0.505273[/C][/ROW]
[ROW][C]90[/C][C]0.430548[/C][C]0.861096[/C][C]0.569452[/C][/ROW]
[ROW][C]91[/C][C]0.365026[/C][C]0.730053[/C][C]0.634974[/C][/ROW]
[ROW][C]92[/C][C]0.304171[/C][C]0.608342[/C][C]0.695829[/C][/ROW]
[ROW][C]93[/C][C]0.28339[/C][C]0.56678[/C][C]0.71661[/C][/ROW]
[ROW][C]94[/C][C]0.364623[/C][C]0.729247[/C][C]0.635377[/C][/ROW]
[ROW][C]95[/C][C]0.284793[/C][C]0.569587[/C][C]0.715207[/C][/ROW]
[ROW][C]96[/C][C]0.21294[/C][C]0.42588[/C][C]0.78706[/C][/ROW]
[ROW][C]97[/C][C]0.542863[/C][C]0.914274[/C][C]0.457137[/C][/ROW]
[ROW][C]98[/C][C]0.510347[/C][C]0.979305[/C][C]0.489653[/C][/ROW]
[ROW][C]99[/C][C]0.415526[/C][C]0.831052[/C][C]0.584474[/C][/ROW]
[ROW][C]100[/C][C]0.327788[/C][C]0.655576[/C][C]0.672212[/C][/ROW]
[ROW][C]101[/C][C]0.228897[/C][C]0.457794[/C][C]0.771103[/C][/ROW]
[ROW][C]102[/C][C]0.224011[/C][C]0.448022[/C][C]0.775989[/C][/ROW]
[ROW][C]103[/C][C]0.231759[/C][C]0.463519[/C][C]0.768241[/C][/ROW]
[ROW][C]104[/C][C]0.149456[/C][C]0.298913[/C][C]0.850544[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267737&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267737&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
90.5762790.8474420.423721
100.5468440.9063120.453156
110.4643940.9287880.535606
120.5299440.9401110.470056
130.409010.8180210.59099
140.3075910.6151810.692409
150.2212090.4424190.778791
160.2109130.4218260.789087
170.1467530.2935070.853247
180.101280.2025590.89872
190.06551260.1310250.934487
200.04985980.09971960.95014
210.03700820.07401650.962992
220.02454190.04908370.975458
230.0222970.04459410.977703
240.01778190.03556390.982218
250.02157180.04314360.978428
260.1198670.2397340.880133
270.2547560.5095110.745244
280.273080.5461590.72692
290.2476410.4952830.752359
300.2413560.4827120.758644
310.2148310.4296620.785169
320.1729410.3458820.827059
330.2025490.4050970.797451
340.1688040.3376080.831196
350.1450270.2900530.854973
360.1195190.2390380.880481
370.34660.69320.6534
380.4564120.9128240.543588
390.4125510.8251010.587449
400.4473120.8946240.552688
410.4778030.9556060.522197
420.4418120.8836230.558188
430.4228520.8457040.577148
440.4106020.8212040.589398
450.3737740.7475480.626226
460.3379140.6758280.662086
470.3643220.7286440.635678
480.3118090.6236170.688191
490.3047950.6095910.695205
500.363610.727220.63639
510.327190.6543810.67281
520.2907360.5814720.709264
530.2528750.505750.747125
540.2108710.4217420.789129
550.2193890.4387780.780611
560.2763420.5526840.723658
570.240210.480420.75979
580.3051080.6102170.694892
590.5776950.8446110.422305
600.7570.4860010.243
610.8335650.332870.166435
620.8073920.3852150.192608
630.8079790.3840430.192021
640.7920910.4158190.207909
650.7492240.5015520.250776
660.7019420.5961170.298058
670.6636390.6727210.336361
680.6107640.7784710.389236
690.5746650.850670.425335
700.5241340.9517310.475866
710.5638650.8722690.436135
720.5342150.931570.465785
730.5749850.850030.425015
740.5697960.8604080.430204
750.5411670.9176670.458833
760.5039470.9921050.496053
770.5276640.9446730.472336
780.4930270.9860540.506973
790.6001620.7996760.399838
800.5665250.8669510.433475
810.5259180.9481630.474082
820.60760.78480.3924
830.5449890.9100210.455011
840.4976490.9952980.502351
850.6529490.6941020.347051
860.6078710.7842580.392129
870.5563650.8872710.443635
880.510550.97890.48945
890.4947270.9894540.505273
900.4305480.8610960.569452
910.3650260.7300530.634974
920.3041710.6083420.695829
930.283390.566780.71661
940.3646230.7292470.635377
950.2847930.5695870.715207
960.212940.425880.78706
970.5428630.9142740.457137
980.5103470.9793050.489653
990.4155260.8310520.584474
1000.3277880.6555760.672212
1010.2288970.4577940.771103
1020.2240110.4480220.775989
1030.2317590.4635190.768241
1040.1494560.2989130.850544






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level40.0416667OK
10% type I error level60.0625OK

\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 & 4 & 0.0416667 & OK \tabularnewline
10% type I error level & 6 & 0.0625 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267737&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]4[/C][C]0.0416667[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]6[/C][C]0.0625[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267737&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267737&T=6

As an alternative you can also use a QR Code:  
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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 level40.0416667OK
10% type I error level60.0625OK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 5 ; 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 <- '5'
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')
}