Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 09:33:52 -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/19/t12586485083emtcip43bfdwns.htm/, Retrieved Thu, 28 Mar 2024 08:33:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57812, Retrieved Thu, 28 Mar 2024 08:33:14 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact192
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]
-    D      [Multiple Regression] [Workshop 7] [2009-11-19 16:33:52] [5cd0e65b1f56b3935a0672588b930e12] [Current]
-   P         [Multiple Regression] [] [2009-11-19 17:52:10] [85be98bd9ebcfd4d73e77f8552419c9a]
-   PD          [Multiple Regression] [2e link] [2009-11-20 15:46:37] [4fe1472705bb0a32f118ba3ca90ffa8e]
-    D        [Multiple Regression] [1e link] [2009-11-20 15:40:59] [4fe1472705bb0a32f118ba3ca90ffa8e]
-   PD          [Multiple Regression] [4e link] [2009-11-28 08:23:38] [4fe1472705bb0a32f118ba3ca90ffa8e]
-    D          [Multiple Regression] [1e link] [2009-11-28 08:28:10] [4fe1472705bb0a32f118ba3ca90ffa8e]
-   PD          [Multiple Regression] [2e link] [2009-11-28 08:31:03] [4fe1472705bb0a32f118ba3ca90ffa8e]
-   PD          [Multiple Regression] [3e link] [2009-11-28 08:33:23] [4fe1472705bb0a32f118ba3ca90ffa8e]
-   P         [Multiple Regression] [WS7 review 1] [2009-11-27 09:57:05] [28b3d6edac7521eb1dd8ebf456c2a09f]
-   P         [Multiple Regression] [] [2009-11-27 09:57:05] [28b3d6edac7521eb1dd8ebf456c2a09f]
Feedback Forum

Post a new message
Dataseries X:
 2.11 	0 
 2.09 	0 
 2.05 	0 
 2.08 	0 
 2.06 	0 
 2.06 	0 
 2.08 	0 
 2.07 	0 
 2.06 	0 
 2.07 	0 
 2.06 	0 
 2.09 	0 
 2.07 	0 
 2.09 	0 
 2.28 	0 
 2.33 	0 
 2.35 	0 
 2.52 	0 
 2.63 	0 
 2.58 	0 
 2.70 	0 
 2.81 	0 
 2.97 	0 
 3.04 	0 
 3.28 	0 
 3.33 	0 
 3.50 	0 
 3.56 	0 
 3.57 	0 
 3.69 	0 
 3.82 	0 
 3.79 	0 
 3.96 	0 
 4.06 	0 
 4.05 	0 
 4.03 	0 
 3.94 	0 
 4.02 	0 
 3.88 	0 
 4.02 	0 
 4.03 	0 
 4.09 	0 
 3.99 	0 
 4.01 	0 
 4.01 	0 
 4.19 	0 
 4.30 	0 
 4.27 	0 
 3.82 	0 
 3.15 	1 
 2.49 	1 
 1.81 	1 
 1.26 	1 
 1.06 	1 
 0.84 	1 
 0.78 	1 
 0.70 	1 
 0.36 	1 
 0.35 	1 
 0.36 	1 




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57812&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]3 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=57812&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57812&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 3.11142857142857 -1.91506493506493X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  3.11142857142857 -1.91506493506493X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57812&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  3.11142857142857 -1.91506493506493X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57812&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57812&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] = + 3.11142857142857 -1.91506493506493X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.111428571428570.12366525.160200
X-1.915064935064930.288818-6.630700

\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) & 3.11142857142857 & 0.123665 & 25.1602 & 0 & 0 \tabularnewline
X & -1.91506493506493 & 0.288818 & -6.6307 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57812&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]3.11142857142857[/C][C]0.123665[/C][C]25.1602[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-1.91506493506493[/C][C]0.288818[/C][C]-6.6307[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57812&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57812&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)3.111428571428570.12366525.160200
X-1.915064935064930.288818-6.630700







Multiple Linear Regression - Regression Statistics
Multiple R0.656645782998115
R-squared0.431183684329208
Adjusted R-squared0.421376506472815
F-TEST (value)43.9661328307749
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value1.21933612096115e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.865651583823886
Sum Squared Residuals43.4624545454545

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.656645782998115 \tabularnewline
R-squared & 0.431183684329208 \tabularnewline
Adjusted R-squared & 0.421376506472815 \tabularnewline
F-TEST (value) & 43.9661328307749 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 1.21933612096115e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.865651583823886 \tabularnewline
Sum Squared Residuals & 43.4624545454545 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57812&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.656645782998115[/C][/ROW]
[ROW][C]R-squared[/C][C]0.431183684329208[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.421376506472815[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]43.9661328307749[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]1.21933612096115e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.865651583823886[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]43.4624545454545[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57812&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57812&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 R0.656645782998115
R-squared0.431183684329208
Adjusted R-squared0.421376506472815
F-TEST (value)43.9661328307749
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value1.21933612096115e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.865651583823886
Sum Squared Residuals43.4624545454545







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12.113.11142857142857-1.00142857142857
22.093.11142857142857-1.02142857142857
32.053.11142857142857-1.06142857142857
42.083.11142857142857-1.03142857142857
52.063.11142857142857-1.05142857142857
62.063.11142857142857-1.05142857142857
72.083.11142857142857-1.03142857142857
82.073.11142857142857-1.04142857142857
92.063.11142857142857-1.05142857142857
102.073.11142857142857-1.04142857142857
112.063.11142857142857-1.05142857142857
122.093.11142857142857-1.02142857142857
132.073.11142857142857-1.04142857142857
142.093.11142857142857-1.02142857142857
152.283.11142857142857-0.831428571428572
162.333.11142857142857-0.781428571428571
172.353.11142857142857-0.761428571428571
182.523.11142857142857-0.591428571428571
192.633.11142857142857-0.481428571428572
202.583.11142857142857-0.531428571428571
212.73.11142857142857-0.411428571428571
222.813.11142857142857-0.301428571428571
232.973.11142857142857-0.141428571428571
243.043.11142857142857-0.0714285714285714
253.283.111428571428570.168571428571428
263.333.111428571428570.218571428571429
273.53.111428571428570.388571428571429
283.563.111428571428570.448571428571429
293.573.111428571428570.458571428571428
303.693.111428571428570.578571428571429
313.823.111428571428570.708571428571428
323.793.111428571428570.678571428571429
333.963.111428571428570.848571428571429
344.063.111428571428570.948571428571428
354.053.111428571428570.938571428571428
364.033.111428571428570.91857142857143
373.943.111428571428570.828571428571429
384.023.111428571428570.908571428571428
393.883.111428571428570.768571428571428
404.023.111428571428570.908571428571428
414.033.111428571428570.91857142857143
424.093.111428571428570.978571428571428
433.993.111428571428570.87857142857143
444.013.111428571428570.898571428571428
454.013.111428571428570.898571428571428
464.193.111428571428571.07857142857143
474.33.111428571428571.18857142857143
484.273.111428571428571.15857142857143
493.823.111428571428570.708571428571428
503.151.196363636363641.95363636363636
512.491.196363636363641.29363636363636
521.811.196363636363640.613636363636364
531.261.196363636363640.0636363636363636
541.061.19636363636364-0.136363636363636
550.841.19636363636364-0.356363636363637
560.781.19636363636364-0.416363636363636
570.71.19636363636364-0.496363636363637
580.361.19636363636364-0.836363636363637
590.351.19636363636364-0.846363636363636
600.361.19636363636364-0.836363636363637

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2.11 & 3.11142857142857 & -1.00142857142857 \tabularnewline
2 & 2.09 & 3.11142857142857 & -1.02142857142857 \tabularnewline
3 & 2.05 & 3.11142857142857 & -1.06142857142857 \tabularnewline
4 & 2.08 & 3.11142857142857 & -1.03142857142857 \tabularnewline
5 & 2.06 & 3.11142857142857 & -1.05142857142857 \tabularnewline
6 & 2.06 & 3.11142857142857 & -1.05142857142857 \tabularnewline
7 & 2.08 & 3.11142857142857 & -1.03142857142857 \tabularnewline
8 & 2.07 & 3.11142857142857 & -1.04142857142857 \tabularnewline
9 & 2.06 & 3.11142857142857 & -1.05142857142857 \tabularnewline
10 & 2.07 & 3.11142857142857 & -1.04142857142857 \tabularnewline
11 & 2.06 & 3.11142857142857 & -1.05142857142857 \tabularnewline
12 & 2.09 & 3.11142857142857 & -1.02142857142857 \tabularnewline
13 & 2.07 & 3.11142857142857 & -1.04142857142857 \tabularnewline
14 & 2.09 & 3.11142857142857 & -1.02142857142857 \tabularnewline
15 & 2.28 & 3.11142857142857 & -0.831428571428572 \tabularnewline
16 & 2.33 & 3.11142857142857 & -0.781428571428571 \tabularnewline
17 & 2.35 & 3.11142857142857 & -0.761428571428571 \tabularnewline
18 & 2.52 & 3.11142857142857 & -0.591428571428571 \tabularnewline
19 & 2.63 & 3.11142857142857 & -0.481428571428572 \tabularnewline
20 & 2.58 & 3.11142857142857 & -0.531428571428571 \tabularnewline
21 & 2.7 & 3.11142857142857 & -0.411428571428571 \tabularnewline
22 & 2.81 & 3.11142857142857 & -0.301428571428571 \tabularnewline
23 & 2.97 & 3.11142857142857 & -0.141428571428571 \tabularnewline
24 & 3.04 & 3.11142857142857 & -0.0714285714285714 \tabularnewline
25 & 3.28 & 3.11142857142857 & 0.168571428571428 \tabularnewline
26 & 3.33 & 3.11142857142857 & 0.218571428571429 \tabularnewline
27 & 3.5 & 3.11142857142857 & 0.388571428571429 \tabularnewline
28 & 3.56 & 3.11142857142857 & 0.448571428571429 \tabularnewline
29 & 3.57 & 3.11142857142857 & 0.458571428571428 \tabularnewline
30 & 3.69 & 3.11142857142857 & 0.578571428571429 \tabularnewline
31 & 3.82 & 3.11142857142857 & 0.708571428571428 \tabularnewline
32 & 3.79 & 3.11142857142857 & 0.678571428571429 \tabularnewline
33 & 3.96 & 3.11142857142857 & 0.848571428571429 \tabularnewline
34 & 4.06 & 3.11142857142857 & 0.948571428571428 \tabularnewline
35 & 4.05 & 3.11142857142857 & 0.938571428571428 \tabularnewline
36 & 4.03 & 3.11142857142857 & 0.91857142857143 \tabularnewline
37 & 3.94 & 3.11142857142857 & 0.828571428571429 \tabularnewline
38 & 4.02 & 3.11142857142857 & 0.908571428571428 \tabularnewline
39 & 3.88 & 3.11142857142857 & 0.768571428571428 \tabularnewline
40 & 4.02 & 3.11142857142857 & 0.908571428571428 \tabularnewline
41 & 4.03 & 3.11142857142857 & 0.91857142857143 \tabularnewline
42 & 4.09 & 3.11142857142857 & 0.978571428571428 \tabularnewline
43 & 3.99 & 3.11142857142857 & 0.87857142857143 \tabularnewline
44 & 4.01 & 3.11142857142857 & 0.898571428571428 \tabularnewline
45 & 4.01 & 3.11142857142857 & 0.898571428571428 \tabularnewline
46 & 4.19 & 3.11142857142857 & 1.07857142857143 \tabularnewline
47 & 4.3 & 3.11142857142857 & 1.18857142857143 \tabularnewline
48 & 4.27 & 3.11142857142857 & 1.15857142857143 \tabularnewline
49 & 3.82 & 3.11142857142857 & 0.708571428571428 \tabularnewline
50 & 3.15 & 1.19636363636364 & 1.95363636363636 \tabularnewline
51 & 2.49 & 1.19636363636364 & 1.29363636363636 \tabularnewline
52 & 1.81 & 1.19636363636364 & 0.613636363636364 \tabularnewline
53 & 1.26 & 1.19636363636364 & 0.0636363636363636 \tabularnewline
54 & 1.06 & 1.19636363636364 & -0.136363636363636 \tabularnewline
55 & 0.84 & 1.19636363636364 & -0.356363636363637 \tabularnewline
56 & 0.78 & 1.19636363636364 & -0.416363636363636 \tabularnewline
57 & 0.7 & 1.19636363636364 & -0.496363636363637 \tabularnewline
58 & 0.36 & 1.19636363636364 & -0.836363636363637 \tabularnewline
59 & 0.35 & 1.19636363636364 & -0.846363636363636 \tabularnewline
60 & 0.36 & 1.19636363636364 & -0.836363636363637 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57812&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]2.11[/C][C]3.11142857142857[/C][C]-1.00142857142857[/C][/ROW]
[ROW][C]2[/C][C]2.09[/C][C]3.11142857142857[/C][C]-1.02142857142857[/C][/ROW]
[ROW][C]3[/C][C]2.05[/C][C]3.11142857142857[/C][C]-1.06142857142857[/C][/ROW]
[ROW][C]4[/C][C]2.08[/C][C]3.11142857142857[/C][C]-1.03142857142857[/C][/ROW]
[ROW][C]5[/C][C]2.06[/C][C]3.11142857142857[/C][C]-1.05142857142857[/C][/ROW]
[ROW][C]6[/C][C]2.06[/C][C]3.11142857142857[/C][C]-1.05142857142857[/C][/ROW]
[ROW][C]7[/C][C]2.08[/C][C]3.11142857142857[/C][C]-1.03142857142857[/C][/ROW]
[ROW][C]8[/C][C]2.07[/C][C]3.11142857142857[/C][C]-1.04142857142857[/C][/ROW]
[ROW][C]9[/C][C]2.06[/C][C]3.11142857142857[/C][C]-1.05142857142857[/C][/ROW]
[ROW][C]10[/C][C]2.07[/C][C]3.11142857142857[/C][C]-1.04142857142857[/C][/ROW]
[ROW][C]11[/C][C]2.06[/C][C]3.11142857142857[/C][C]-1.05142857142857[/C][/ROW]
[ROW][C]12[/C][C]2.09[/C][C]3.11142857142857[/C][C]-1.02142857142857[/C][/ROW]
[ROW][C]13[/C][C]2.07[/C][C]3.11142857142857[/C][C]-1.04142857142857[/C][/ROW]
[ROW][C]14[/C][C]2.09[/C][C]3.11142857142857[/C][C]-1.02142857142857[/C][/ROW]
[ROW][C]15[/C][C]2.28[/C][C]3.11142857142857[/C][C]-0.831428571428572[/C][/ROW]
[ROW][C]16[/C][C]2.33[/C][C]3.11142857142857[/C][C]-0.781428571428571[/C][/ROW]
[ROW][C]17[/C][C]2.35[/C][C]3.11142857142857[/C][C]-0.761428571428571[/C][/ROW]
[ROW][C]18[/C][C]2.52[/C][C]3.11142857142857[/C][C]-0.591428571428571[/C][/ROW]
[ROW][C]19[/C][C]2.63[/C][C]3.11142857142857[/C][C]-0.481428571428572[/C][/ROW]
[ROW][C]20[/C][C]2.58[/C][C]3.11142857142857[/C][C]-0.531428571428571[/C][/ROW]
[ROW][C]21[/C][C]2.7[/C][C]3.11142857142857[/C][C]-0.411428571428571[/C][/ROW]
[ROW][C]22[/C][C]2.81[/C][C]3.11142857142857[/C][C]-0.301428571428571[/C][/ROW]
[ROW][C]23[/C][C]2.97[/C][C]3.11142857142857[/C][C]-0.141428571428571[/C][/ROW]
[ROW][C]24[/C][C]3.04[/C][C]3.11142857142857[/C][C]-0.0714285714285714[/C][/ROW]
[ROW][C]25[/C][C]3.28[/C][C]3.11142857142857[/C][C]0.168571428571428[/C][/ROW]
[ROW][C]26[/C][C]3.33[/C][C]3.11142857142857[/C][C]0.218571428571429[/C][/ROW]
[ROW][C]27[/C][C]3.5[/C][C]3.11142857142857[/C][C]0.388571428571429[/C][/ROW]
[ROW][C]28[/C][C]3.56[/C][C]3.11142857142857[/C][C]0.448571428571429[/C][/ROW]
[ROW][C]29[/C][C]3.57[/C][C]3.11142857142857[/C][C]0.458571428571428[/C][/ROW]
[ROW][C]30[/C][C]3.69[/C][C]3.11142857142857[/C][C]0.578571428571429[/C][/ROW]
[ROW][C]31[/C][C]3.82[/C][C]3.11142857142857[/C][C]0.708571428571428[/C][/ROW]
[ROW][C]32[/C][C]3.79[/C][C]3.11142857142857[/C][C]0.678571428571429[/C][/ROW]
[ROW][C]33[/C][C]3.96[/C][C]3.11142857142857[/C][C]0.848571428571429[/C][/ROW]
[ROW][C]34[/C][C]4.06[/C][C]3.11142857142857[/C][C]0.948571428571428[/C][/ROW]
[ROW][C]35[/C][C]4.05[/C][C]3.11142857142857[/C][C]0.938571428571428[/C][/ROW]
[ROW][C]36[/C][C]4.03[/C][C]3.11142857142857[/C][C]0.91857142857143[/C][/ROW]
[ROW][C]37[/C][C]3.94[/C][C]3.11142857142857[/C][C]0.828571428571429[/C][/ROW]
[ROW][C]38[/C][C]4.02[/C][C]3.11142857142857[/C][C]0.908571428571428[/C][/ROW]
[ROW][C]39[/C][C]3.88[/C][C]3.11142857142857[/C][C]0.768571428571428[/C][/ROW]
[ROW][C]40[/C][C]4.02[/C][C]3.11142857142857[/C][C]0.908571428571428[/C][/ROW]
[ROW][C]41[/C][C]4.03[/C][C]3.11142857142857[/C][C]0.91857142857143[/C][/ROW]
[ROW][C]42[/C][C]4.09[/C][C]3.11142857142857[/C][C]0.978571428571428[/C][/ROW]
[ROW][C]43[/C][C]3.99[/C][C]3.11142857142857[/C][C]0.87857142857143[/C][/ROW]
[ROW][C]44[/C][C]4.01[/C][C]3.11142857142857[/C][C]0.898571428571428[/C][/ROW]
[ROW][C]45[/C][C]4.01[/C][C]3.11142857142857[/C][C]0.898571428571428[/C][/ROW]
[ROW][C]46[/C][C]4.19[/C][C]3.11142857142857[/C][C]1.07857142857143[/C][/ROW]
[ROW][C]47[/C][C]4.3[/C][C]3.11142857142857[/C][C]1.18857142857143[/C][/ROW]
[ROW][C]48[/C][C]4.27[/C][C]3.11142857142857[/C][C]1.15857142857143[/C][/ROW]
[ROW][C]49[/C][C]3.82[/C][C]3.11142857142857[/C][C]0.708571428571428[/C][/ROW]
[ROW][C]50[/C][C]3.15[/C][C]1.19636363636364[/C][C]1.95363636363636[/C][/ROW]
[ROW][C]51[/C][C]2.49[/C][C]1.19636363636364[/C][C]1.29363636363636[/C][/ROW]
[ROW][C]52[/C][C]1.81[/C][C]1.19636363636364[/C][C]0.613636363636364[/C][/ROW]
[ROW][C]53[/C][C]1.26[/C][C]1.19636363636364[/C][C]0.0636363636363636[/C][/ROW]
[ROW][C]54[/C][C]1.06[/C][C]1.19636363636364[/C][C]-0.136363636363636[/C][/ROW]
[ROW][C]55[/C][C]0.84[/C][C]1.19636363636364[/C][C]-0.356363636363637[/C][/ROW]
[ROW][C]56[/C][C]0.78[/C][C]1.19636363636364[/C][C]-0.416363636363636[/C][/ROW]
[ROW][C]57[/C][C]0.7[/C][C]1.19636363636364[/C][C]-0.496363636363637[/C][/ROW]
[ROW][C]58[/C][C]0.36[/C][C]1.19636363636364[/C][C]-0.836363636363637[/C][/ROW]
[ROW][C]59[/C][C]0.35[/C][C]1.19636363636364[/C][C]-0.846363636363636[/C][/ROW]
[ROW][C]60[/C][C]0.36[/C][C]1.19636363636364[/C][C]-0.836363636363637[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57812&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57812&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
12.113.11142857142857-1.00142857142857
22.093.11142857142857-1.02142857142857
32.053.11142857142857-1.06142857142857
42.083.11142857142857-1.03142857142857
52.063.11142857142857-1.05142857142857
62.063.11142857142857-1.05142857142857
72.083.11142857142857-1.03142857142857
82.073.11142857142857-1.04142857142857
92.063.11142857142857-1.05142857142857
102.073.11142857142857-1.04142857142857
112.063.11142857142857-1.05142857142857
122.093.11142857142857-1.02142857142857
132.073.11142857142857-1.04142857142857
142.093.11142857142857-1.02142857142857
152.283.11142857142857-0.831428571428572
162.333.11142857142857-0.781428571428571
172.353.11142857142857-0.761428571428571
182.523.11142857142857-0.591428571428571
192.633.11142857142857-0.481428571428572
202.583.11142857142857-0.531428571428571
212.73.11142857142857-0.411428571428571
222.813.11142857142857-0.301428571428571
232.973.11142857142857-0.141428571428571
243.043.11142857142857-0.0714285714285714
253.283.111428571428570.168571428571428
263.333.111428571428570.218571428571429
273.53.111428571428570.388571428571429
283.563.111428571428570.448571428571429
293.573.111428571428570.458571428571428
303.693.111428571428570.578571428571429
313.823.111428571428570.708571428571428
323.793.111428571428570.678571428571429
333.963.111428571428570.848571428571429
344.063.111428571428570.948571428571428
354.053.111428571428570.938571428571428
364.033.111428571428570.91857142857143
373.943.111428571428570.828571428571429
384.023.111428571428570.908571428571428
393.883.111428571428570.768571428571428
404.023.111428571428570.908571428571428
414.033.111428571428570.91857142857143
424.093.111428571428570.978571428571428
433.993.111428571428570.87857142857143
444.013.111428571428570.898571428571428
454.013.111428571428570.898571428571428
464.193.111428571428571.07857142857143
474.33.111428571428571.18857142857143
484.273.111428571428571.15857142857143
493.823.111428571428570.708571428571428
503.151.196363636363641.95363636363636
512.491.196363636363641.29363636363636
521.811.196363636363640.613636363636364
531.261.196363636363640.0636363636363636
541.061.19636363636364-0.136363636363636
550.841.19636363636364-0.356363636363637
560.781.19636363636364-0.416363636363636
570.71.19636363636364-0.496363636363637
580.361.19636363636364-0.836363636363637
590.351.19636363636364-0.846363636363636
600.361.19636363636364-0.836363636363637







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
54.92583207289033e-059.85166414578066e-050.999950741679271
61.74754472862918e-063.49508945725836e-060.999998252455271
75.0105776046226e-081.00211552092452e-070.999999949894224
81.40386465382395e-092.80772930764790e-090.999999998596135
94.86784975658016e-119.73569951316031e-110.999999999951321
101.36409523544758e-122.72819047089517e-120.999999999998636
114.89555080371057e-149.79110160742114e-140.999999999999951
122.36464312045412e-154.72928624090825e-150.999999999999998
138.04500219998019e-171.60900043999604e-161
144.64620433407642e-189.29240866815284e-181
151.79591377650468e-123.59182755300937e-120.999999999998204
167.02073602644622e-111.40414720528924e-100.999999999929793
174.52415508815239e-109.04831017630479e-100.999999999547585
182.27204815422638e-084.54409630845276e-080.999999977279519
196.26834000981579e-071.25366800196316e-060.999999373166
202.87666122074002e-065.75332244148004e-060.99999712333878
212.06819084352559e-054.13638168705118e-050.999979318091565
220.0001591190398390740.0003182380796781490.99984088096016
230.001334113348090710.002668226696181410.99866588665191
240.006714863980234130.01342972796046830.993285136019766
250.03399402553717650.06798805107435290.966005974462824
260.09341394014887080.1868278802977420.90658605985113
270.2043766448765700.4087532897531390.79562335512343
280.3305126240730920.6610252481461830.669487375926908
290.4385916290830930.8771832581661860.561408370916907
300.541044038723710.917911922552580.45895596127629
310.6322100943558620.7355798112882760.367789905644138
320.6854112894368220.6291774211263560.314588710563178
330.7375710419578030.5248579160843940.262428958042197
340.7786587028980260.4426825942039490.221341297101974
350.7989625311308380.4020749377383230.201037468869161
360.8047129995791050.3905740008417900.195287000420895
370.7952143075414640.4095713849170710.204785692458536
380.7840545621425510.4318908757148970.215945437857449
390.758836882830090.482326234339820.24116311716991
400.7341246650896040.5317506698207930.265875334910396
410.7028582618099660.5942834763800680.297141738190034
420.6681394255609290.6637211488781430.331860574439071
430.6203336009032330.7593327981935330.379666399096767
440.5669042273714270.8661915452571470.433095772628573
450.5078377164437040.9843245671125910.492162283556296
460.4531084469077330.9062168938154670.546891553092266
470.4035274106478770.8070548212957540.596472589352123
480.3523277632643580.7046555265287170.647672236735642
490.2732227510179580.5464455020359170.726777248982042
500.6490566570899550.701886685820090.350943342910045
510.9116437796710420.1767124406579160.088356220328958
520.9779266257214440.04414674855711130.0220733742785557
530.9841416854230910.03171662915381750.0158583145769087
540.984367101861680.03126579627663980.0156328981383199
550.9698400394682920.06031992106341690.0301599605317084

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 4.92583207289033e-05 & 9.85166414578066e-05 & 0.999950741679271 \tabularnewline
6 & 1.74754472862918e-06 & 3.49508945725836e-06 & 0.999998252455271 \tabularnewline
7 & 5.0105776046226e-08 & 1.00211552092452e-07 & 0.999999949894224 \tabularnewline
8 & 1.40386465382395e-09 & 2.80772930764790e-09 & 0.999999998596135 \tabularnewline
9 & 4.86784975658016e-11 & 9.73569951316031e-11 & 0.999999999951321 \tabularnewline
10 & 1.36409523544758e-12 & 2.72819047089517e-12 & 0.999999999998636 \tabularnewline
11 & 4.89555080371057e-14 & 9.79110160742114e-14 & 0.999999999999951 \tabularnewline
12 & 2.36464312045412e-15 & 4.72928624090825e-15 & 0.999999999999998 \tabularnewline
13 & 8.04500219998019e-17 & 1.60900043999604e-16 & 1 \tabularnewline
14 & 4.64620433407642e-18 & 9.29240866815284e-18 & 1 \tabularnewline
15 & 1.79591377650468e-12 & 3.59182755300937e-12 & 0.999999999998204 \tabularnewline
16 & 7.02073602644622e-11 & 1.40414720528924e-10 & 0.999999999929793 \tabularnewline
17 & 4.52415508815239e-10 & 9.04831017630479e-10 & 0.999999999547585 \tabularnewline
18 & 2.27204815422638e-08 & 4.54409630845276e-08 & 0.999999977279519 \tabularnewline
19 & 6.26834000981579e-07 & 1.25366800196316e-06 & 0.999999373166 \tabularnewline
20 & 2.87666122074002e-06 & 5.75332244148004e-06 & 0.99999712333878 \tabularnewline
21 & 2.06819084352559e-05 & 4.13638168705118e-05 & 0.999979318091565 \tabularnewline
22 & 0.000159119039839074 & 0.000318238079678149 & 0.99984088096016 \tabularnewline
23 & 0.00133411334809071 & 0.00266822669618141 & 0.99866588665191 \tabularnewline
24 & 0.00671486398023413 & 0.0134297279604683 & 0.993285136019766 \tabularnewline
25 & 0.0339940255371765 & 0.0679880510743529 & 0.966005974462824 \tabularnewline
26 & 0.0934139401488708 & 0.186827880297742 & 0.90658605985113 \tabularnewline
27 & 0.204376644876570 & 0.408753289753139 & 0.79562335512343 \tabularnewline
28 & 0.330512624073092 & 0.661025248146183 & 0.669487375926908 \tabularnewline
29 & 0.438591629083093 & 0.877183258166186 & 0.561408370916907 \tabularnewline
30 & 0.54104403872371 & 0.91791192255258 & 0.45895596127629 \tabularnewline
31 & 0.632210094355862 & 0.735579811288276 & 0.367789905644138 \tabularnewline
32 & 0.685411289436822 & 0.629177421126356 & 0.314588710563178 \tabularnewline
33 & 0.737571041957803 & 0.524857916084394 & 0.262428958042197 \tabularnewline
34 & 0.778658702898026 & 0.442682594203949 & 0.221341297101974 \tabularnewline
35 & 0.798962531130838 & 0.402074937738323 & 0.201037468869161 \tabularnewline
36 & 0.804712999579105 & 0.390574000841790 & 0.195287000420895 \tabularnewline
37 & 0.795214307541464 & 0.409571384917071 & 0.204785692458536 \tabularnewline
38 & 0.784054562142551 & 0.431890875714897 & 0.215945437857449 \tabularnewline
39 & 0.75883688283009 & 0.48232623433982 & 0.24116311716991 \tabularnewline
40 & 0.734124665089604 & 0.531750669820793 & 0.265875334910396 \tabularnewline
41 & 0.702858261809966 & 0.594283476380068 & 0.297141738190034 \tabularnewline
42 & 0.668139425560929 & 0.663721148878143 & 0.331860574439071 \tabularnewline
43 & 0.620333600903233 & 0.759332798193533 & 0.379666399096767 \tabularnewline
44 & 0.566904227371427 & 0.866191545257147 & 0.433095772628573 \tabularnewline
45 & 0.507837716443704 & 0.984324567112591 & 0.492162283556296 \tabularnewline
46 & 0.453108446907733 & 0.906216893815467 & 0.546891553092266 \tabularnewline
47 & 0.403527410647877 & 0.807054821295754 & 0.596472589352123 \tabularnewline
48 & 0.352327763264358 & 0.704655526528717 & 0.647672236735642 \tabularnewline
49 & 0.273222751017958 & 0.546445502035917 & 0.726777248982042 \tabularnewline
50 & 0.649056657089955 & 0.70188668582009 & 0.350943342910045 \tabularnewline
51 & 0.911643779671042 & 0.176712440657916 & 0.088356220328958 \tabularnewline
52 & 0.977926625721444 & 0.0441467485571113 & 0.0220733742785557 \tabularnewline
53 & 0.984141685423091 & 0.0317166291538175 & 0.0158583145769087 \tabularnewline
54 & 0.98436710186168 & 0.0312657962766398 & 0.0156328981383199 \tabularnewline
55 & 0.969840039468292 & 0.0603199210634169 & 0.0301599605317084 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57812&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]4.92583207289033e-05[/C][C]9.85166414578066e-05[/C][C]0.999950741679271[/C][/ROW]
[ROW][C]6[/C][C]1.74754472862918e-06[/C][C]3.49508945725836e-06[/C][C]0.999998252455271[/C][/ROW]
[ROW][C]7[/C][C]5.0105776046226e-08[/C][C]1.00211552092452e-07[/C][C]0.999999949894224[/C][/ROW]
[ROW][C]8[/C][C]1.40386465382395e-09[/C][C]2.80772930764790e-09[/C][C]0.999999998596135[/C][/ROW]
[ROW][C]9[/C][C]4.86784975658016e-11[/C][C]9.73569951316031e-11[/C][C]0.999999999951321[/C][/ROW]
[ROW][C]10[/C][C]1.36409523544758e-12[/C][C]2.72819047089517e-12[/C][C]0.999999999998636[/C][/ROW]
[ROW][C]11[/C][C]4.89555080371057e-14[/C][C]9.79110160742114e-14[/C][C]0.999999999999951[/C][/ROW]
[ROW][C]12[/C][C]2.36464312045412e-15[/C][C]4.72928624090825e-15[/C][C]0.999999999999998[/C][/ROW]
[ROW][C]13[/C][C]8.04500219998019e-17[/C][C]1.60900043999604e-16[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]4.64620433407642e-18[/C][C]9.29240866815284e-18[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]1.79591377650468e-12[/C][C]3.59182755300937e-12[/C][C]0.999999999998204[/C][/ROW]
[ROW][C]16[/C][C]7.02073602644622e-11[/C][C]1.40414720528924e-10[/C][C]0.999999999929793[/C][/ROW]
[ROW][C]17[/C][C]4.52415508815239e-10[/C][C]9.04831017630479e-10[/C][C]0.999999999547585[/C][/ROW]
[ROW][C]18[/C][C]2.27204815422638e-08[/C][C]4.54409630845276e-08[/C][C]0.999999977279519[/C][/ROW]
[ROW][C]19[/C][C]6.26834000981579e-07[/C][C]1.25366800196316e-06[/C][C]0.999999373166[/C][/ROW]
[ROW][C]20[/C][C]2.87666122074002e-06[/C][C]5.75332244148004e-06[/C][C]0.99999712333878[/C][/ROW]
[ROW][C]21[/C][C]2.06819084352559e-05[/C][C]4.13638168705118e-05[/C][C]0.999979318091565[/C][/ROW]
[ROW][C]22[/C][C]0.000159119039839074[/C][C]0.000318238079678149[/C][C]0.99984088096016[/C][/ROW]
[ROW][C]23[/C][C]0.00133411334809071[/C][C]0.00266822669618141[/C][C]0.99866588665191[/C][/ROW]
[ROW][C]24[/C][C]0.00671486398023413[/C][C]0.0134297279604683[/C][C]0.993285136019766[/C][/ROW]
[ROW][C]25[/C][C]0.0339940255371765[/C][C]0.0679880510743529[/C][C]0.966005974462824[/C][/ROW]
[ROW][C]26[/C][C]0.0934139401488708[/C][C]0.186827880297742[/C][C]0.90658605985113[/C][/ROW]
[ROW][C]27[/C][C]0.204376644876570[/C][C]0.408753289753139[/C][C]0.79562335512343[/C][/ROW]
[ROW][C]28[/C][C]0.330512624073092[/C][C]0.661025248146183[/C][C]0.669487375926908[/C][/ROW]
[ROW][C]29[/C][C]0.438591629083093[/C][C]0.877183258166186[/C][C]0.561408370916907[/C][/ROW]
[ROW][C]30[/C][C]0.54104403872371[/C][C]0.91791192255258[/C][C]0.45895596127629[/C][/ROW]
[ROW][C]31[/C][C]0.632210094355862[/C][C]0.735579811288276[/C][C]0.367789905644138[/C][/ROW]
[ROW][C]32[/C][C]0.685411289436822[/C][C]0.629177421126356[/C][C]0.314588710563178[/C][/ROW]
[ROW][C]33[/C][C]0.737571041957803[/C][C]0.524857916084394[/C][C]0.262428958042197[/C][/ROW]
[ROW][C]34[/C][C]0.778658702898026[/C][C]0.442682594203949[/C][C]0.221341297101974[/C][/ROW]
[ROW][C]35[/C][C]0.798962531130838[/C][C]0.402074937738323[/C][C]0.201037468869161[/C][/ROW]
[ROW][C]36[/C][C]0.804712999579105[/C][C]0.390574000841790[/C][C]0.195287000420895[/C][/ROW]
[ROW][C]37[/C][C]0.795214307541464[/C][C]0.409571384917071[/C][C]0.204785692458536[/C][/ROW]
[ROW][C]38[/C][C]0.784054562142551[/C][C]0.431890875714897[/C][C]0.215945437857449[/C][/ROW]
[ROW][C]39[/C][C]0.75883688283009[/C][C]0.48232623433982[/C][C]0.24116311716991[/C][/ROW]
[ROW][C]40[/C][C]0.734124665089604[/C][C]0.531750669820793[/C][C]0.265875334910396[/C][/ROW]
[ROW][C]41[/C][C]0.702858261809966[/C][C]0.594283476380068[/C][C]0.297141738190034[/C][/ROW]
[ROW][C]42[/C][C]0.668139425560929[/C][C]0.663721148878143[/C][C]0.331860574439071[/C][/ROW]
[ROW][C]43[/C][C]0.620333600903233[/C][C]0.759332798193533[/C][C]0.379666399096767[/C][/ROW]
[ROW][C]44[/C][C]0.566904227371427[/C][C]0.866191545257147[/C][C]0.433095772628573[/C][/ROW]
[ROW][C]45[/C][C]0.507837716443704[/C][C]0.984324567112591[/C][C]0.492162283556296[/C][/ROW]
[ROW][C]46[/C][C]0.453108446907733[/C][C]0.906216893815467[/C][C]0.546891553092266[/C][/ROW]
[ROW][C]47[/C][C]0.403527410647877[/C][C]0.807054821295754[/C][C]0.596472589352123[/C][/ROW]
[ROW][C]48[/C][C]0.352327763264358[/C][C]0.704655526528717[/C][C]0.647672236735642[/C][/ROW]
[ROW][C]49[/C][C]0.273222751017958[/C][C]0.546445502035917[/C][C]0.726777248982042[/C][/ROW]
[ROW][C]50[/C][C]0.649056657089955[/C][C]0.70188668582009[/C][C]0.350943342910045[/C][/ROW]
[ROW][C]51[/C][C]0.911643779671042[/C][C]0.176712440657916[/C][C]0.088356220328958[/C][/ROW]
[ROW][C]52[/C][C]0.977926625721444[/C][C]0.0441467485571113[/C][C]0.0220733742785557[/C][/ROW]
[ROW][C]53[/C][C]0.984141685423091[/C][C]0.0317166291538175[/C][C]0.0158583145769087[/C][/ROW]
[ROW][C]54[/C][C]0.98436710186168[/C][C]0.0312657962766398[/C][C]0.0156328981383199[/C][/ROW]
[ROW][C]55[/C][C]0.969840039468292[/C][C]0.0603199210634169[/C][C]0.0301599605317084[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57812&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57812&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
54.92583207289033e-059.85166414578066e-050.999950741679271
61.74754472862918e-063.49508945725836e-060.999998252455271
75.0105776046226e-081.00211552092452e-070.999999949894224
81.40386465382395e-092.80772930764790e-090.999999998596135
94.86784975658016e-119.73569951316031e-110.999999999951321
101.36409523544758e-122.72819047089517e-120.999999999998636
114.89555080371057e-149.79110160742114e-140.999999999999951
122.36464312045412e-154.72928624090825e-150.999999999999998
138.04500219998019e-171.60900043999604e-161
144.64620433407642e-189.29240866815284e-181
151.79591377650468e-123.59182755300937e-120.999999999998204
167.02073602644622e-111.40414720528924e-100.999999999929793
174.52415508815239e-109.04831017630479e-100.999999999547585
182.27204815422638e-084.54409630845276e-080.999999977279519
196.26834000981579e-071.25366800196316e-060.999999373166
202.87666122074002e-065.75332244148004e-060.99999712333878
212.06819084352559e-054.13638168705118e-050.999979318091565
220.0001591190398390740.0003182380796781490.99984088096016
230.001334113348090710.002668226696181410.99866588665191
240.006714863980234130.01342972796046830.993285136019766
250.03399402553717650.06798805107435290.966005974462824
260.09341394014887080.1868278802977420.90658605985113
270.2043766448765700.4087532897531390.79562335512343
280.3305126240730920.6610252481461830.669487375926908
290.4385916290830930.8771832581661860.561408370916907
300.541044038723710.917911922552580.45895596127629
310.6322100943558620.7355798112882760.367789905644138
320.6854112894368220.6291774211263560.314588710563178
330.7375710419578030.5248579160843940.262428958042197
340.7786587028980260.4426825942039490.221341297101974
350.7989625311308380.4020749377383230.201037468869161
360.8047129995791050.3905740008417900.195287000420895
370.7952143075414640.4095713849170710.204785692458536
380.7840545621425510.4318908757148970.215945437857449
390.758836882830090.482326234339820.24116311716991
400.7341246650896040.5317506698207930.265875334910396
410.7028582618099660.5942834763800680.297141738190034
420.6681394255609290.6637211488781430.331860574439071
430.6203336009032330.7593327981935330.379666399096767
440.5669042273714270.8661915452571470.433095772628573
450.5078377164437040.9843245671125910.492162283556296
460.4531084469077330.9062168938154670.546891553092266
470.4035274106478770.8070548212957540.596472589352123
480.3523277632643580.7046555265287170.647672236735642
490.2732227510179580.5464455020359170.726777248982042
500.6490566570899550.701886685820090.350943342910045
510.9116437796710420.1767124406579160.088356220328958
520.9779266257214440.04414674855711130.0220733742785557
530.9841416854230910.03171662915381750.0158583145769087
540.984367101861680.03126579627663980.0156328981383199
550.9698400394682920.06031992106341690.0301599605317084







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level190.372549019607843NOK
5% type I error level230.450980392156863NOK
10% type I error level250.490196078431373NOK

\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 & 19 & 0.372549019607843 & NOK \tabularnewline
5% type I error level & 23 & 0.450980392156863 & NOK \tabularnewline
10% type I error level & 25 & 0.490196078431373 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57812&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]19[/C][C]0.372549019607843[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]23[/C][C]0.450980392156863[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]25[/C][C]0.490196078431373[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57812&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57812&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 level190.372549019607843NOK
5% type I error level230.450980392156863NOK
10% type I error level250.490196078431373NOK



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')
}