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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 06:48:55 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258724979wft5nws4pqo9opm.htm/, Retrieved Fri, 29 Mar 2024 13:21:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58155, Retrieved Fri, 29 Mar 2024 13:21:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact172
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
- R  D      [Multiple Regression] [] [2009-11-20 13:48:55] [4057bfb3a128b4e91b455d276991f7f0] [Current]
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Dataseries X:
22	0
22	0
20	0
21	0
20	0
21	0
21	0
21	0
19	0
21	0
21	0
22	0
19	0
24	0
22	0
22	0
22	0
24	0
22	0
23	0
24	0
21	0
20	0
22	0
23	0
23	0
22	0
20	0
21	1
21	1
20	1
20	1
17	1
18	1
19	1
19	1
20	1
21	1
20	1
21	1
19	1
22	1
20	1
18	1
16	1
17	1
18	1
19	1
18	1
20	1
21	1
18	1
19	1
19	1
19	1
21	1
19	1
19	1
17	1
16	1
16	1
17	1
16	1
15	1
16	1
16	1
16	1
18	1
19	1
16	1
16	1
16	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58155&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58155&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58155&T=0

As an alternative you can also use a 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'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 21.5714285714286 -3.18506493506494X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  21.5714285714286 -3.18506493506494X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58155&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  21.5714285714286 -3.18506493506494X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58155&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58155&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 21.5714285714286 -3.18506493506494X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)21.57142857142860.32046667.312700
X-3.185064935064940.409941-7.769600

\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) & 21.5714285714286 & 0.320466 & 67.3127 & 0 & 0 \tabularnewline
X & -3.18506493506494 & 0.409941 & -7.7696 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58155&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]21.5714285714286[/C][C]0.320466[/C][C]67.3127[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-3.18506493506494[/C][C]0.409941[/C][C]-7.7696[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58155&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58155&T=2

As an alternative you can also use a 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)21.57142857142860.32046667.312700
X-3.185064935064940.409941-7.769600







Multiple Linear Regression - Regression Statistics
Multiple R0.680478106956758
R-squared0.463050454047453
Adjusted R-squared0.455379746248131
F-TEST (value)60.3660660999726
F-TEST (DF numerator)1
F-TEST (DF denominator)70
p-value4.85240736480819e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.69574661617556
Sum Squared Residuals201.288961038961

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.680478106956758 \tabularnewline
R-squared & 0.463050454047453 \tabularnewline
Adjusted R-squared & 0.455379746248131 \tabularnewline
F-TEST (value) & 60.3660660999726 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 70 \tabularnewline
p-value & 4.85240736480819e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.69574661617556 \tabularnewline
Sum Squared Residuals & 201.288961038961 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58155&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.680478106956758[/C][/ROW]
[ROW][C]R-squared[/C][C]0.463050454047453[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.455379746248131[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]60.3660660999726[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]70[/C][/ROW]
[ROW][C]p-value[/C][C]4.85240736480819e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.69574661617556[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]201.288961038961[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58155&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58155&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.680478106956758
R-squared0.463050454047453
Adjusted R-squared0.455379746248131
F-TEST (value)60.3660660999726
F-TEST (DF numerator)1
F-TEST (DF denominator)70
p-value4.85240736480819e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.69574661617556
Sum Squared Residuals201.288961038961







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12221.57142857142850.428571428571511
22221.57142857142860.428571428571439
32021.5714285714286-1.57142857142857
42121.5714285714286-0.571428571428575
52021.5714285714286-1.57142857142857
62121.5714285714286-0.571428571428575
72121.5714285714286-0.571428571428575
82121.5714285714286-0.571428571428575
91921.5714285714286-2.57142857142858
102121.5714285714286-0.571428571428575
112121.5714285714286-0.571428571428575
122221.57142857142860.428571428571425
131921.5714285714286-2.57142857142858
142421.57142857142862.42857142857142
152221.57142857142860.428571428571425
162221.57142857142860.428571428571425
172221.57142857142860.428571428571425
182421.57142857142862.42857142857142
192221.57142857142860.428571428571425
202321.57142857142861.42857142857143
212421.57142857142862.42857142857142
222121.5714285714286-0.571428571428575
232021.5714285714286-1.57142857142857
242221.57142857142860.428571428571425
252321.57142857142861.42857142857143
262321.57142857142861.42857142857143
272221.57142857142860.428571428571425
282021.5714285714286-1.57142857142857
292118.38636363636362.61363636363636
302118.38636363636362.61363636363636
312018.38636363636361.61363636363636
322018.38636363636361.61363636363636
331718.3863636363636-1.38636363636364
341818.3863636363636-0.386363636363637
351918.38636363636360.613636363636363
361918.38636363636360.613636363636363
372018.38636363636361.61363636363636
382118.38636363636362.61363636363636
392018.38636363636361.61363636363636
402118.38636363636362.61363636363636
411918.38636363636360.613636363636363
422218.38636363636363.61363636363636
432018.38636363636361.61363636363636
441818.3863636363636-0.386363636363637
451618.3863636363636-2.38636363636364
461718.3863636363636-1.38636363636364
471818.3863636363636-0.386363636363637
481918.38636363636360.613636363636363
491818.3863636363636-0.386363636363637
502018.38636363636361.61363636363636
512118.38636363636362.61363636363636
521818.3863636363636-0.386363636363637
531918.38636363636360.613636363636363
541918.38636363636360.613636363636363
551918.38636363636360.613636363636363
562118.38636363636362.61363636363636
571918.38636363636360.613636363636363
581918.38636363636360.613636363636363
591718.3863636363636-1.38636363636364
601618.3863636363636-2.38636363636364
611618.3863636363636-2.38636363636364
621718.3863636363636-1.38636363636364
631618.3863636363636-2.38636363636364
641518.3863636363636-3.38636363636364
651618.3863636363636-2.38636363636364
661618.3863636363636-2.38636363636364
671618.3863636363636-2.38636363636364
681818.3863636363636-0.386363636363637
691918.38636363636360.613636363636363
701618.3863636363636-2.38636363636364
711618.3863636363636-2.38636363636364
721618.3863636363636-2.38636363636364

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 22 & 21.5714285714285 & 0.428571428571511 \tabularnewline
2 & 22 & 21.5714285714286 & 0.428571428571439 \tabularnewline
3 & 20 & 21.5714285714286 & -1.57142857142857 \tabularnewline
4 & 21 & 21.5714285714286 & -0.571428571428575 \tabularnewline
5 & 20 & 21.5714285714286 & -1.57142857142857 \tabularnewline
6 & 21 & 21.5714285714286 & -0.571428571428575 \tabularnewline
7 & 21 & 21.5714285714286 & -0.571428571428575 \tabularnewline
8 & 21 & 21.5714285714286 & -0.571428571428575 \tabularnewline
9 & 19 & 21.5714285714286 & -2.57142857142858 \tabularnewline
10 & 21 & 21.5714285714286 & -0.571428571428575 \tabularnewline
11 & 21 & 21.5714285714286 & -0.571428571428575 \tabularnewline
12 & 22 & 21.5714285714286 & 0.428571428571425 \tabularnewline
13 & 19 & 21.5714285714286 & -2.57142857142858 \tabularnewline
14 & 24 & 21.5714285714286 & 2.42857142857142 \tabularnewline
15 & 22 & 21.5714285714286 & 0.428571428571425 \tabularnewline
16 & 22 & 21.5714285714286 & 0.428571428571425 \tabularnewline
17 & 22 & 21.5714285714286 & 0.428571428571425 \tabularnewline
18 & 24 & 21.5714285714286 & 2.42857142857142 \tabularnewline
19 & 22 & 21.5714285714286 & 0.428571428571425 \tabularnewline
20 & 23 & 21.5714285714286 & 1.42857142857143 \tabularnewline
21 & 24 & 21.5714285714286 & 2.42857142857142 \tabularnewline
22 & 21 & 21.5714285714286 & -0.571428571428575 \tabularnewline
23 & 20 & 21.5714285714286 & -1.57142857142857 \tabularnewline
24 & 22 & 21.5714285714286 & 0.428571428571425 \tabularnewline
25 & 23 & 21.5714285714286 & 1.42857142857143 \tabularnewline
26 & 23 & 21.5714285714286 & 1.42857142857143 \tabularnewline
27 & 22 & 21.5714285714286 & 0.428571428571425 \tabularnewline
28 & 20 & 21.5714285714286 & -1.57142857142857 \tabularnewline
29 & 21 & 18.3863636363636 & 2.61363636363636 \tabularnewline
30 & 21 & 18.3863636363636 & 2.61363636363636 \tabularnewline
31 & 20 & 18.3863636363636 & 1.61363636363636 \tabularnewline
32 & 20 & 18.3863636363636 & 1.61363636363636 \tabularnewline
33 & 17 & 18.3863636363636 & -1.38636363636364 \tabularnewline
34 & 18 & 18.3863636363636 & -0.386363636363637 \tabularnewline
35 & 19 & 18.3863636363636 & 0.613636363636363 \tabularnewline
36 & 19 & 18.3863636363636 & 0.613636363636363 \tabularnewline
37 & 20 & 18.3863636363636 & 1.61363636363636 \tabularnewline
38 & 21 & 18.3863636363636 & 2.61363636363636 \tabularnewline
39 & 20 & 18.3863636363636 & 1.61363636363636 \tabularnewline
40 & 21 & 18.3863636363636 & 2.61363636363636 \tabularnewline
41 & 19 & 18.3863636363636 & 0.613636363636363 \tabularnewline
42 & 22 & 18.3863636363636 & 3.61363636363636 \tabularnewline
43 & 20 & 18.3863636363636 & 1.61363636363636 \tabularnewline
44 & 18 & 18.3863636363636 & -0.386363636363637 \tabularnewline
45 & 16 & 18.3863636363636 & -2.38636363636364 \tabularnewline
46 & 17 & 18.3863636363636 & -1.38636363636364 \tabularnewline
47 & 18 & 18.3863636363636 & -0.386363636363637 \tabularnewline
48 & 19 & 18.3863636363636 & 0.613636363636363 \tabularnewline
49 & 18 & 18.3863636363636 & -0.386363636363637 \tabularnewline
50 & 20 & 18.3863636363636 & 1.61363636363636 \tabularnewline
51 & 21 & 18.3863636363636 & 2.61363636363636 \tabularnewline
52 & 18 & 18.3863636363636 & -0.386363636363637 \tabularnewline
53 & 19 & 18.3863636363636 & 0.613636363636363 \tabularnewline
54 & 19 & 18.3863636363636 & 0.613636363636363 \tabularnewline
55 & 19 & 18.3863636363636 & 0.613636363636363 \tabularnewline
56 & 21 & 18.3863636363636 & 2.61363636363636 \tabularnewline
57 & 19 & 18.3863636363636 & 0.613636363636363 \tabularnewline
58 & 19 & 18.3863636363636 & 0.613636363636363 \tabularnewline
59 & 17 & 18.3863636363636 & -1.38636363636364 \tabularnewline
60 & 16 & 18.3863636363636 & -2.38636363636364 \tabularnewline
61 & 16 & 18.3863636363636 & -2.38636363636364 \tabularnewline
62 & 17 & 18.3863636363636 & -1.38636363636364 \tabularnewline
63 & 16 & 18.3863636363636 & -2.38636363636364 \tabularnewline
64 & 15 & 18.3863636363636 & -3.38636363636364 \tabularnewline
65 & 16 & 18.3863636363636 & -2.38636363636364 \tabularnewline
66 & 16 & 18.3863636363636 & -2.38636363636364 \tabularnewline
67 & 16 & 18.3863636363636 & -2.38636363636364 \tabularnewline
68 & 18 & 18.3863636363636 & -0.386363636363637 \tabularnewline
69 & 19 & 18.3863636363636 & 0.613636363636363 \tabularnewline
70 & 16 & 18.3863636363636 & -2.38636363636364 \tabularnewline
71 & 16 & 18.3863636363636 & -2.38636363636364 \tabularnewline
72 & 16 & 18.3863636363636 & -2.38636363636364 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58155&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]22[/C][C]21.5714285714285[/C][C]0.428571428571511[/C][/ROW]
[ROW][C]2[/C][C]22[/C][C]21.5714285714286[/C][C]0.428571428571439[/C][/ROW]
[ROW][C]3[/C][C]20[/C][C]21.5714285714286[/C][C]-1.57142857142857[/C][/ROW]
[ROW][C]4[/C][C]21[/C][C]21.5714285714286[/C][C]-0.571428571428575[/C][/ROW]
[ROW][C]5[/C][C]20[/C][C]21.5714285714286[/C][C]-1.57142857142857[/C][/ROW]
[ROW][C]6[/C][C]21[/C][C]21.5714285714286[/C][C]-0.571428571428575[/C][/ROW]
[ROW][C]7[/C][C]21[/C][C]21.5714285714286[/C][C]-0.571428571428575[/C][/ROW]
[ROW][C]8[/C][C]21[/C][C]21.5714285714286[/C][C]-0.571428571428575[/C][/ROW]
[ROW][C]9[/C][C]19[/C][C]21.5714285714286[/C][C]-2.57142857142858[/C][/ROW]
[ROW][C]10[/C][C]21[/C][C]21.5714285714286[/C][C]-0.571428571428575[/C][/ROW]
[ROW][C]11[/C][C]21[/C][C]21.5714285714286[/C][C]-0.571428571428575[/C][/ROW]
[ROW][C]12[/C][C]22[/C][C]21.5714285714286[/C][C]0.428571428571425[/C][/ROW]
[ROW][C]13[/C][C]19[/C][C]21.5714285714286[/C][C]-2.57142857142858[/C][/ROW]
[ROW][C]14[/C][C]24[/C][C]21.5714285714286[/C][C]2.42857142857142[/C][/ROW]
[ROW][C]15[/C][C]22[/C][C]21.5714285714286[/C][C]0.428571428571425[/C][/ROW]
[ROW][C]16[/C][C]22[/C][C]21.5714285714286[/C][C]0.428571428571425[/C][/ROW]
[ROW][C]17[/C][C]22[/C][C]21.5714285714286[/C][C]0.428571428571425[/C][/ROW]
[ROW][C]18[/C][C]24[/C][C]21.5714285714286[/C][C]2.42857142857142[/C][/ROW]
[ROW][C]19[/C][C]22[/C][C]21.5714285714286[/C][C]0.428571428571425[/C][/ROW]
[ROW][C]20[/C][C]23[/C][C]21.5714285714286[/C][C]1.42857142857143[/C][/ROW]
[ROW][C]21[/C][C]24[/C][C]21.5714285714286[/C][C]2.42857142857142[/C][/ROW]
[ROW][C]22[/C][C]21[/C][C]21.5714285714286[/C][C]-0.571428571428575[/C][/ROW]
[ROW][C]23[/C][C]20[/C][C]21.5714285714286[/C][C]-1.57142857142857[/C][/ROW]
[ROW][C]24[/C][C]22[/C][C]21.5714285714286[/C][C]0.428571428571425[/C][/ROW]
[ROW][C]25[/C][C]23[/C][C]21.5714285714286[/C][C]1.42857142857143[/C][/ROW]
[ROW][C]26[/C][C]23[/C][C]21.5714285714286[/C][C]1.42857142857143[/C][/ROW]
[ROW][C]27[/C][C]22[/C][C]21.5714285714286[/C][C]0.428571428571425[/C][/ROW]
[ROW][C]28[/C][C]20[/C][C]21.5714285714286[/C][C]-1.57142857142857[/C][/ROW]
[ROW][C]29[/C][C]21[/C][C]18.3863636363636[/C][C]2.61363636363636[/C][/ROW]
[ROW][C]30[/C][C]21[/C][C]18.3863636363636[/C][C]2.61363636363636[/C][/ROW]
[ROW][C]31[/C][C]20[/C][C]18.3863636363636[/C][C]1.61363636363636[/C][/ROW]
[ROW][C]32[/C][C]20[/C][C]18.3863636363636[/C][C]1.61363636363636[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]18.3863636363636[/C][C]-1.38636363636364[/C][/ROW]
[ROW][C]34[/C][C]18[/C][C]18.3863636363636[/C][C]-0.386363636363637[/C][/ROW]
[ROW][C]35[/C][C]19[/C][C]18.3863636363636[/C][C]0.613636363636363[/C][/ROW]
[ROW][C]36[/C][C]19[/C][C]18.3863636363636[/C][C]0.613636363636363[/C][/ROW]
[ROW][C]37[/C][C]20[/C][C]18.3863636363636[/C][C]1.61363636363636[/C][/ROW]
[ROW][C]38[/C][C]21[/C][C]18.3863636363636[/C][C]2.61363636363636[/C][/ROW]
[ROW][C]39[/C][C]20[/C][C]18.3863636363636[/C][C]1.61363636363636[/C][/ROW]
[ROW][C]40[/C][C]21[/C][C]18.3863636363636[/C][C]2.61363636363636[/C][/ROW]
[ROW][C]41[/C][C]19[/C][C]18.3863636363636[/C][C]0.613636363636363[/C][/ROW]
[ROW][C]42[/C][C]22[/C][C]18.3863636363636[/C][C]3.61363636363636[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]18.3863636363636[/C][C]1.61363636363636[/C][/ROW]
[ROW][C]44[/C][C]18[/C][C]18.3863636363636[/C][C]-0.386363636363637[/C][/ROW]
[ROW][C]45[/C][C]16[/C][C]18.3863636363636[/C][C]-2.38636363636364[/C][/ROW]
[ROW][C]46[/C][C]17[/C][C]18.3863636363636[/C][C]-1.38636363636364[/C][/ROW]
[ROW][C]47[/C][C]18[/C][C]18.3863636363636[/C][C]-0.386363636363637[/C][/ROW]
[ROW][C]48[/C][C]19[/C][C]18.3863636363636[/C][C]0.613636363636363[/C][/ROW]
[ROW][C]49[/C][C]18[/C][C]18.3863636363636[/C][C]-0.386363636363637[/C][/ROW]
[ROW][C]50[/C][C]20[/C][C]18.3863636363636[/C][C]1.61363636363636[/C][/ROW]
[ROW][C]51[/C][C]21[/C][C]18.3863636363636[/C][C]2.61363636363636[/C][/ROW]
[ROW][C]52[/C][C]18[/C][C]18.3863636363636[/C][C]-0.386363636363637[/C][/ROW]
[ROW][C]53[/C][C]19[/C][C]18.3863636363636[/C][C]0.613636363636363[/C][/ROW]
[ROW][C]54[/C][C]19[/C][C]18.3863636363636[/C][C]0.613636363636363[/C][/ROW]
[ROW][C]55[/C][C]19[/C][C]18.3863636363636[/C][C]0.613636363636363[/C][/ROW]
[ROW][C]56[/C][C]21[/C][C]18.3863636363636[/C][C]2.61363636363636[/C][/ROW]
[ROW][C]57[/C][C]19[/C][C]18.3863636363636[/C][C]0.613636363636363[/C][/ROW]
[ROW][C]58[/C][C]19[/C][C]18.3863636363636[/C][C]0.613636363636363[/C][/ROW]
[ROW][C]59[/C][C]17[/C][C]18.3863636363636[/C][C]-1.38636363636364[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]18.3863636363636[/C][C]-2.38636363636364[/C][/ROW]
[ROW][C]61[/C][C]16[/C][C]18.3863636363636[/C][C]-2.38636363636364[/C][/ROW]
[ROW][C]62[/C][C]17[/C][C]18.3863636363636[/C][C]-1.38636363636364[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]18.3863636363636[/C][C]-2.38636363636364[/C][/ROW]
[ROW][C]64[/C][C]15[/C][C]18.3863636363636[/C][C]-3.38636363636364[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]18.3863636363636[/C][C]-2.38636363636364[/C][/ROW]
[ROW][C]66[/C][C]16[/C][C]18.3863636363636[/C][C]-2.38636363636364[/C][/ROW]
[ROW][C]67[/C][C]16[/C][C]18.3863636363636[/C][C]-2.38636363636364[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]18.3863636363636[/C][C]-0.386363636363637[/C][/ROW]
[ROW][C]69[/C][C]19[/C][C]18.3863636363636[/C][C]0.613636363636363[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]18.3863636363636[/C][C]-2.38636363636364[/C][/ROW]
[ROW][C]71[/C][C]16[/C][C]18.3863636363636[/C][C]-2.38636363636364[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]18.3863636363636[/C][C]-2.38636363636364[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58155&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58155&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12221.57142857142850.428571428571511
22221.57142857142860.428571428571439
32021.5714285714286-1.57142857142857
42121.5714285714286-0.571428571428575
52021.5714285714286-1.57142857142857
62121.5714285714286-0.571428571428575
72121.5714285714286-0.571428571428575
82121.5714285714286-0.571428571428575
91921.5714285714286-2.57142857142858
102121.5714285714286-0.571428571428575
112121.5714285714286-0.571428571428575
122221.57142857142860.428571428571425
131921.5714285714286-2.57142857142858
142421.57142857142862.42857142857142
152221.57142857142860.428571428571425
162221.57142857142860.428571428571425
172221.57142857142860.428571428571425
182421.57142857142862.42857142857142
192221.57142857142860.428571428571425
202321.57142857142861.42857142857143
212421.57142857142862.42857142857142
222121.5714285714286-0.571428571428575
232021.5714285714286-1.57142857142857
242221.57142857142860.428571428571425
252321.57142857142861.42857142857143
262321.57142857142861.42857142857143
272221.57142857142860.428571428571425
282021.5714285714286-1.57142857142857
292118.38636363636362.61363636363636
302118.38636363636362.61363636363636
312018.38636363636361.61363636363636
322018.38636363636361.61363636363636
331718.3863636363636-1.38636363636364
341818.3863636363636-0.386363636363637
351918.38636363636360.613636363636363
361918.38636363636360.613636363636363
372018.38636363636361.61363636363636
382118.38636363636362.61363636363636
392018.38636363636361.61363636363636
402118.38636363636362.61363636363636
411918.38636363636360.613636363636363
422218.38636363636363.61363636363636
432018.38636363636361.61363636363636
441818.3863636363636-0.386363636363637
451618.3863636363636-2.38636363636364
461718.3863636363636-1.38636363636364
471818.3863636363636-0.386363636363637
481918.38636363636360.613636363636363
491818.3863636363636-0.386363636363637
502018.38636363636361.61363636363636
512118.38636363636362.61363636363636
521818.3863636363636-0.386363636363637
531918.38636363636360.613636363636363
541918.38636363636360.613636363636363
551918.38636363636360.613636363636363
562118.38636363636362.61363636363636
571918.38636363636360.613636363636363
581918.38636363636360.613636363636363
591718.3863636363636-1.38636363636364
601618.3863636363636-2.38636363636364
611618.3863636363636-2.38636363636364
621718.3863636363636-1.38636363636364
631618.3863636363636-2.38636363636364
641518.3863636363636-3.38636363636364
651618.3863636363636-2.38636363636364
661618.3863636363636-2.38636363636364
671618.3863636363636-2.38636363636364
681818.3863636363636-0.386363636363637
691918.38636363636360.613636363636363
701618.3863636363636-2.38636363636364
711618.3863636363636-2.38636363636364
721618.3863636363636-2.38636363636364







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.2774767929832130.5549535859664260.722523207016787
60.1422013633552410.2844027267104830.857798636644759
70.0665595880358580.1331191760717160.933440411964142
80.02879250795346310.05758501590692610.971207492046537
90.07557868379608710.1511573675921740.924421316203913
100.04025668343391670.08051336686783330.959743316566083
110.02041927471508910.04083854943017820.97958072528491
120.01719981600012880.03439963200025760.982800183999871
130.03666149301508810.07332298603017620.963338506984912
140.1698902351343150.3397804702686300.830109764865685
150.1331015889910730.2662031779821460.866898411008927
160.1013070157863410.2026140315726820.898692984213659
170.07494251165384950.1498850233076990.92505748834615
180.1509567923889040.3019135847778080.849043207611096
190.1119651682807260.2239303365614520.888034831719274
200.1080092880374060.2160185760748120.891990711962594
210.1640567660569220.3281135321138440.835943233943078
220.1244395575406130.2488791150812270.875560442459387
230.1220464956284970.2440929912569940.877953504371503
240.08952889707149470.1790577941429890.910471102928505
250.0819304306074180.1638608612148360.918069569392582
260.07662179390261660.1532435878052330.923378206097383
270.05783259236856810.1156651847371360.942167407631432
280.05227084137555970.1045416827511190.94772915862444
290.04640426441941760.09280852883883520.953595735580582
300.04248779210661120.08497558421322230.95751220789339
310.03547373449102460.07094746898204910.964526265508975
320.02868857089942120.05737714179884230.971311429100579
330.05810757130010360.1162151426002070.941892428699896
340.05190852289687020.1038170457937400.94809147710313
350.0376129527329190.0752259054658380.962387047267081
360.0266447154490610.0532894308981220.97335528455094
370.02197259988532130.04394519977064260.978027400114679
380.02927661593538040.05855323187076080.97072338406462
390.02508518950595910.05017037901191830.974914810494041
400.03551947381987490.07103894763974980.964480526180125
410.02738951840993970.05477903681987940.97261048159006
420.09186169689174390.1837233937834880.908138303108256
430.09591812355567070.1918362471113410.90408187644433
440.08819714226004260.1763942845200850.911802857739957
450.1643551554849490.3287103109698990.83564484451505
460.1698564889177960.3397129778355920.830143511082204
470.1404906651504580.2809813303009170.859509334849542
480.1177850237712290.2355700475424580.882214976228771
490.09376885376823730.1875377075364750.906231146231763
500.1067687745941080.2135375491882160.893231225405892
510.2235209570775800.4470419141551590.77647904292242
520.1883671454792480.3767342909584960.811632854520752
530.1794601021603190.3589202043206370.820539897839681
540.1767837703279250.353567540655850.823216229672075
550.1821058943124420.3642117886248830.817894105687558
560.5786785458557520.8426429082884960.421321454144248
570.696593108192820.6068137836143610.303406891807181
580.8535819634381070.2928360731237850.146418036561893
590.8266608660263350.3466782679473290.173339133973665
600.8053225480377410.3893549039245180.194677451962259
610.7723479445550130.4553041108899750.227652055444987
620.7116618838890780.5766762322218430.288338116110922
630.6492370788515460.7015258422969070.350762921148454
640.7028921192395150.5942157615209710.297107880760485
650.6220375422800220.7559249154399570.377962457719978
660.5268999333878570.9462001332242860.473100066612143
670.4217464629751930.8434929259503860.578253537024807

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.277476792983213 & 0.554953585966426 & 0.722523207016787 \tabularnewline
6 & 0.142201363355241 & 0.284402726710483 & 0.857798636644759 \tabularnewline
7 & 0.066559588035858 & 0.133119176071716 & 0.933440411964142 \tabularnewline
8 & 0.0287925079534631 & 0.0575850159069261 & 0.971207492046537 \tabularnewline
9 & 0.0755786837960871 & 0.151157367592174 & 0.924421316203913 \tabularnewline
10 & 0.0402566834339167 & 0.0805133668678333 & 0.959743316566083 \tabularnewline
11 & 0.0204192747150891 & 0.0408385494301782 & 0.97958072528491 \tabularnewline
12 & 0.0171998160001288 & 0.0343996320002576 & 0.982800183999871 \tabularnewline
13 & 0.0366614930150881 & 0.0733229860301762 & 0.963338506984912 \tabularnewline
14 & 0.169890235134315 & 0.339780470268630 & 0.830109764865685 \tabularnewline
15 & 0.133101588991073 & 0.266203177982146 & 0.866898411008927 \tabularnewline
16 & 0.101307015786341 & 0.202614031572682 & 0.898692984213659 \tabularnewline
17 & 0.0749425116538495 & 0.149885023307699 & 0.92505748834615 \tabularnewline
18 & 0.150956792388904 & 0.301913584777808 & 0.849043207611096 \tabularnewline
19 & 0.111965168280726 & 0.223930336561452 & 0.888034831719274 \tabularnewline
20 & 0.108009288037406 & 0.216018576074812 & 0.891990711962594 \tabularnewline
21 & 0.164056766056922 & 0.328113532113844 & 0.835943233943078 \tabularnewline
22 & 0.124439557540613 & 0.248879115081227 & 0.875560442459387 \tabularnewline
23 & 0.122046495628497 & 0.244092991256994 & 0.877953504371503 \tabularnewline
24 & 0.0895288970714947 & 0.179057794142989 & 0.910471102928505 \tabularnewline
25 & 0.081930430607418 & 0.163860861214836 & 0.918069569392582 \tabularnewline
26 & 0.0766217939026166 & 0.153243587805233 & 0.923378206097383 \tabularnewline
27 & 0.0578325923685681 & 0.115665184737136 & 0.942167407631432 \tabularnewline
28 & 0.0522708413755597 & 0.104541682751119 & 0.94772915862444 \tabularnewline
29 & 0.0464042644194176 & 0.0928085288388352 & 0.953595735580582 \tabularnewline
30 & 0.0424877921066112 & 0.0849755842132223 & 0.95751220789339 \tabularnewline
31 & 0.0354737344910246 & 0.0709474689820491 & 0.964526265508975 \tabularnewline
32 & 0.0286885708994212 & 0.0573771417988423 & 0.971311429100579 \tabularnewline
33 & 0.0581075713001036 & 0.116215142600207 & 0.941892428699896 \tabularnewline
34 & 0.0519085228968702 & 0.103817045793740 & 0.94809147710313 \tabularnewline
35 & 0.037612952732919 & 0.075225905465838 & 0.962387047267081 \tabularnewline
36 & 0.026644715449061 & 0.053289430898122 & 0.97335528455094 \tabularnewline
37 & 0.0219725998853213 & 0.0439451997706426 & 0.978027400114679 \tabularnewline
38 & 0.0292766159353804 & 0.0585532318707608 & 0.97072338406462 \tabularnewline
39 & 0.0250851895059591 & 0.0501703790119183 & 0.974914810494041 \tabularnewline
40 & 0.0355194738198749 & 0.0710389476397498 & 0.964480526180125 \tabularnewline
41 & 0.0273895184099397 & 0.0547790368198794 & 0.97261048159006 \tabularnewline
42 & 0.0918616968917439 & 0.183723393783488 & 0.908138303108256 \tabularnewline
43 & 0.0959181235556707 & 0.191836247111341 & 0.90408187644433 \tabularnewline
44 & 0.0881971422600426 & 0.176394284520085 & 0.911802857739957 \tabularnewline
45 & 0.164355155484949 & 0.328710310969899 & 0.83564484451505 \tabularnewline
46 & 0.169856488917796 & 0.339712977835592 & 0.830143511082204 \tabularnewline
47 & 0.140490665150458 & 0.280981330300917 & 0.859509334849542 \tabularnewline
48 & 0.117785023771229 & 0.235570047542458 & 0.882214976228771 \tabularnewline
49 & 0.0937688537682373 & 0.187537707536475 & 0.906231146231763 \tabularnewline
50 & 0.106768774594108 & 0.213537549188216 & 0.893231225405892 \tabularnewline
51 & 0.223520957077580 & 0.447041914155159 & 0.77647904292242 \tabularnewline
52 & 0.188367145479248 & 0.376734290958496 & 0.811632854520752 \tabularnewline
53 & 0.179460102160319 & 0.358920204320637 & 0.820539897839681 \tabularnewline
54 & 0.176783770327925 & 0.35356754065585 & 0.823216229672075 \tabularnewline
55 & 0.182105894312442 & 0.364211788624883 & 0.817894105687558 \tabularnewline
56 & 0.578678545855752 & 0.842642908288496 & 0.421321454144248 \tabularnewline
57 & 0.69659310819282 & 0.606813783614361 & 0.303406891807181 \tabularnewline
58 & 0.853581963438107 & 0.292836073123785 & 0.146418036561893 \tabularnewline
59 & 0.826660866026335 & 0.346678267947329 & 0.173339133973665 \tabularnewline
60 & 0.805322548037741 & 0.389354903924518 & 0.194677451962259 \tabularnewline
61 & 0.772347944555013 & 0.455304110889975 & 0.227652055444987 \tabularnewline
62 & 0.711661883889078 & 0.576676232221843 & 0.288338116110922 \tabularnewline
63 & 0.649237078851546 & 0.701525842296907 & 0.350762921148454 \tabularnewline
64 & 0.702892119239515 & 0.594215761520971 & 0.297107880760485 \tabularnewline
65 & 0.622037542280022 & 0.755924915439957 & 0.377962457719978 \tabularnewline
66 & 0.526899933387857 & 0.946200133224286 & 0.473100066612143 \tabularnewline
67 & 0.421746462975193 & 0.843492925950386 & 0.578253537024807 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58155&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]5[/C][C]0.277476792983213[/C][C]0.554953585966426[/C][C]0.722523207016787[/C][/ROW]
[ROW][C]6[/C][C]0.142201363355241[/C][C]0.284402726710483[/C][C]0.857798636644759[/C][/ROW]
[ROW][C]7[/C][C]0.066559588035858[/C][C]0.133119176071716[/C][C]0.933440411964142[/C][/ROW]
[ROW][C]8[/C][C]0.0287925079534631[/C][C]0.0575850159069261[/C][C]0.971207492046537[/C][/ROW]
[ROW][C]9[/C][C]0.0755786837960871[/C][C]0.151157367592174[/C][C]0.924421316203913[/C][/ROW]
[ROW][C]10[/C][C]0.0402566834339167[/C][C]0.0805133668678333[/C][C]0.959743316566083[/C][/ROW]
[ROW][C]11[/C][C]0.0204192747150891[/C][C]0.0408385494301782[/C][C]0.97958072528491[/C][/ROW]
[ROW][C]12[/C][C]0.0171998160001288[/C][C]0.0343996320002576[/C][C]0.982800183999871[/C][/ROW]
[ROW][C]13[/C][C]0.0366614930150881[/C][C]0.0733229860301762[/C][C]0.963338506984912[/C][/ROW]
[ROW][C]14[/C][C]0.169890235134315[/C][C]0.339780470268630[/C][C]0.830109764865685[/C][/ROW]
[ROW][C]15[/C][C]0.133101588991073[/C][C]0.266203177982146[/C][C]0.866898411008927[/C][/ROW]
[ROW][C]16[/C][C]0.101307015786341[/C][C]0.202614031572682[/C][C]0.898692984213659[/C][/ROW]
[ROW][C]17[/C][C]0.0749425116538495[/C][C]0.149885023307699[/C][C]0.92505748834615[/C][/ROW]
[ROW][C]18[/C][C]0.150956792388904[/C][C]0.301913584777808[/C][C]0.849043207611096[/C][/ROW]
[ROW][C]19[/C][C]0.111965168280726[/C][C]0.223930336561452[/C][C]0.888034831719274[/C][/ROW]
[ROW][C]20[/C][C]0.108009288037406[/C][C]0.216018576074812[/C][C]0.891990711962594[/C][/ROW]
[ROW][C]21[/C][C]0.164056766056922[/C][C]0.328113532113844[/C][C]0.835943233943078[/C][/ROW]
[ROW][C]22[/C][C]0.124439557540613[/C][C]0.248879115081227[/C][C]0.875560442459387[/C][/ROW]
[ROW][C]23[/C][C]0.122046495628497[/C][C]0.244092991256994[/C][C]0.877953504371503[/C][/ROW]
[ROW][C]24[/C][C]0.0895288970714947[/C][C]0.179057794142989[/C][C]0.910471102928505[/C][/ROW]
[ROW][C]25[/C][C]0.081930430607418[/C][C]0.163860861214836[/C][C]0.918069569392582[/C][/ROW]
[ROW][C]26[/C][C]0.0766217939026166[/C][C]0.153243587805233[/C][C]0.923378206097383[/C][/ROW]
[ROW][C]27[/C][C]0.0578325923685681[/C][C]0.115665184737136[/C][C]0.942167407631432[/C][/ROW]
[ROW][C]28[/C][C]0.0522708413755597[/C][C]0.104541682751119[/C][C]0.94772915862444[/C][/ROW]
[ROW][C]29[/C][C]0.0464042644194176[/C][C]0.0928085288388352[/C][C]0.953595735580582[/C][/ROW]
[ROW][C]30[/C][C]0.0424877921066112[/C][C]0.0849755842132223[/C][C]0.95751220789339[/C][/ROW]
[ROW][C]31[/C][C]0.0354737344910246[/C][C]0.0709474689820491[/C][C]0.964526265508975[/C][/ROW]
[ROW][C]32[/C][C]0.0286885708994212[/C][C]0.0573771417988423[/C][C]0.971311429100579[/C][/ROW]
[ROW][C]33[/C][C]0.0581075713001036[/C][C]0.116215142600207[/C][C]0.941892428699896[/C][/ROW]
[ROW][C]34[/C][C]0.0519085228968702[/C][C]0.103817045793740[/C][C]0.94809147710313[/C][/ROW]
[ROW][C]35[/C][C]0.037612952732919[/C][C]0.075225905465838[/C][C]0.962387047267081[/C][/ROW]
[ROW][C]36[/C][C]0.026644715449061[/C][C]0.053289430898122[/C][C]0.97335528455094[/C][/ROW]
[ROW][C]37[/C][C]0.0219725998853213[/C][C]0.0439451997706426[/C][C]0.978027400114679[/C][/ROW]
[ROW][C]38[/C][C]0.0292766159353804[/C][C]0.0585532318707608[/C][C]0.97072338406462[/C][/ROW]
[ROW][C]39[/C][C]0.0250851895059591[/C][C]0.0501703790119183[/C][C]0.974914810494041[/C][/ROW]
[ROW][C]40[/C][C]0.0355194738198749[/C][C]0.0710389476397498[/C][C]0.964480526180125[/C][/ROW]
[ROW][C]41[/C][C]0.0273895184099397[/C][C]0.0547790368198794[/C][C]0.97261048159006[/C][/ROW]
[ROW][C]42[/C][C]0.0918616968917439[/C][C]0.183723393783488[/C][C]0.908138303108256[/C][/ROW]
[ROW][C]43[/C][C]0.0959181235556707[/C][C]0.191836247111341[/C][C]0.90408187644433[/C][/ROW]
[ROW][C]44[/C][C]0.0881971422600426[/C][C]0.176394284520085[/C][C]0.911802857739957[/C][/ROW]
[ROW][C]45[/C][C]0.164355155484949[/C][C]0.328710310969899[/C][C]0.83564484451505[/C][/ROW]
[ROW][C]46[/C][C]0.169856488917796[/C][C]0.339712977835592[/C][C]0.830143511082204[/C][/ROW]
[ROW][C]47[/C][C]0.140490665150458[/C][C]0.280981330300917[/C][C]0.859509334849542[/C][/ROW]
[ROW][C]48[/C][C]0.117785023771229[/C][C]0.235570047542458[/C][C]0.882214976228771[/C][/ROW]
[ROW][C]49[/C][C]0.0937688537682373[/C][C]0.187537707536475[/C][C]0.906231146231763[/C][/ROW]
[ROW][C]50[/C][C]0.106768774594108[/C][C]0.213537549188216[/C][C]0.893231225405892[/C][/ROW]
[ROW][C]51[/C][C]0.223520957077580[/C][C]0.447041914155159[/C][C]0.77647904292242[/C][/ROW]
[ROW][C]52[/C][C]0.188367145479248[/C][C]0.376734290958496[/C][C]0.811632854520752[/C][/ROW]
[ROW][C]53[/C][C]0.179460102160319[/C][C]0.358920204320637[/C][C]0.820539897839681[/C][/ROW]
[ROW][C]54[/C][C]0.176783770327925[/C][C]0.35356754065585[/C][C]0.823216229672075[/C][/ROW]
[ROW][C]55[/C][C]0.182105894312442[/C][C]0.364211788624883[/C][C]0.817894105687558[/C][/ROW]
[ROW][C]56[/C][C]0.578678545855752[/C][C]0.842642908288496[/C][C]0.421321454144248[/C][/ROW]
[ROW][C]57[/C][C]0.69659310819282[/C][C]0.606813783614361[/C][C]0.303406891807181[/C][/ROW]
[ROW][C]58[/C][C]0.853581963438107[/C][C]0.292836073123785[/C][C]0.146418036561893[/C][/ROW]
[ROW][C]59[/C][C]0.826660866026335[/C][C]0.346678267947329[/C][C]0.173339133973665[/C][/ROW]
[ROW][C]60[/C][C]0.805322548037741[/C][C]0.389354903924518[/C][C]0.194677451962259[/C][/ROW]
[ROW][C]61[/C][C]0.772347944555013[/C][C]0.455304110889975[/C][C]0.227652055444987[/C][/ROW]
[ROW][C]62[/C][C]0.711661883889078[/C][C]0.576676232221843[/C][C]0.288338116110922[/C][/ROW]
[ROW][C]63[/C][C]0.649237078851546[/C][C]0.701525842296907[/C][C]0.350762921148454[/C][/ROW]
[ROW][C]64[/C][C]0.702892119239515[/C][C]0.594215761520971[/C][C]0.297107880760485[/C][/ROW]
[ROW][C]65[/C][C]0.622037542280022[/C][C]0.755924915439957[/C][C]0.377962457719978[/C][/ROW]
[ROW][C]66[/C][C]0.526899933387857[/C][C]0.946200133224286[/C][C]0.473100066612143[/C][/ROW]
[ROW][C]67[/C][C]0.421746462975193[/C][C]0.843492925950386[/C][C]0.578253537024807[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58155&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58155&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.2774767929832130.5549535859664260.722523207016787
60.1422013633552410.2844027267104830.857798636644759
70.0665595880358580.1331191760717160.933440411964142
80.02879250795346310.05758501590692610.971207492046537
90.07557868379608710.1511573675921740.924421316203913
100.04025668343391670.08051336686783330.959743316566083
110.02041927471508910.04083854943017820.97958072528491
120.01719981600012880.03439963200025760.982800183999871
130.03666149301508810.07332298603017620.963338506984912
140.1698902351343150.3397804702686300.830109764865685
150.1331015889910730.2662031779821460.866898411008927
160.1013070157863410.2026140315726820.898692984213659
170.07494251165384950.1498850233076990.92505748834615
180.1509567923889040.3019135847778080.849043207611096
190.1119651682807260.2239303365614520.888034831719274
200.1080092880374060.2160185760748120.891990711962594
210.1640567660569220.3281135321138440.835943233943078
220.1244395575406130.2488791150812270.875560442459387
230.1220464956284970.2440929912569940.877953504371503
240.08952889707149470.1790577941429890.910471102928505
250.0819304306074180.1638608612148360.918069569392582
260.07662179390261660.1532435878052330.923378206097383
270.05783259236856810.1156651847371360.942167407631432
280.05227084137555970.1045416827511190.94772915862444
290.04640426441941760.09280852883883520.953595735580582
300.04248779210661120.08497558421322230.95751220789339
310.03547373449102460.07094746898204910.964526265508975
320.02868857089942120.05737714179884230.971311429100579
330.05810757130010360.1162151426002070.941892428699896
340.05190852289687020.1038170457937400.94809147710313
350.0376129527329190.0752259054658380.962387047267081
360.0266447154490610.0532894308981220.97335528455094
370.02197259988532130.04394519977064260.978027400114679
380.02927661593538040.05855323187076080.97072338406462
390.02508518950595910.05017037901191830.974914810494041
400.03551947381987490.07103894763974980.964480526180125
410.02738951840993970.05477903681987940.97261048159006
420.09186169689174390.1837233937834880.908138303108256
430.09591812355567070.1918362471113410.90408187644433
440.08819714226004260.1763942845200850.911802857739957
450.1643551554849490.3287103109698990.83564484451505
460.1698564889177960.3397129778355920.830143511082204
470.1404906651504580.2809813303009170.859509334849542
480.1177850237712290.2355700475424580.882214976228771
490.09376885376823730.1875377075364750.906231146231763
500.1067687745941080.2135375491882160.893231225405892
510.2235209570775800.4470419141551590.77647904292242
520.1883671454792480.3767342909584960.811632854520752
530.1794601021603190.3589202043206370.820539897839681
540.1767837703279250.353567540655850.823216229672075
550.1821058943124420.3642117886248830.817894105687558
560.5786785458557520.8426429082884960.421321454144248
570.696593108192820.6068137836143610.303406891807181
580.8535819634381070.2928360731237850.146418036561893
590.8266608660263350.3466782679473290.173339133973665
600.8053225480377410.3893549039245180.194677451962259
610.7723479445550130.4553041108899750.227652055444987
620.7116618838890780.5766762322218430.288338116110922
630.6492370788515460.7015258422969070.350762921148454
640.7028921192395150.5942157615209710.297107880760485
650.6220375422800220.7559249154399570.377962457719978
660.5268999333878570.9462001332242860.473100066612143
670.4217464629751930.8434929259503860.578253537024807







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0476190476190476OK
10% type I error level160.253968253968254NOK

\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 & 3 & 0.0476190476190476 & OK \tabularnewline
10% type I error level & 16 & 0.253968253968254 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58155&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]3[/C][C]0.0476190476190476[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.253968253968254[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58155&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58155&T=6

As an alternative you can also use a 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 level30.0476190476190476OK
10% type I error level160.253968253968254NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], 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,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}