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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 03:54:07 -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/t1258714510oqkn6sqjvkro6eq.htm/, Retrieved Thu, 28 Mar 2024 17:02:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58024, Retrieved Thu, 28 Mar 2024 17:02:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact175
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] [SHW WS7] [2009-11-20 10:54:07] [b7e46d23597387652ca7420fdeb9acca] [Current]
-    D        [Multiple Regression] [WS 7.1] [2009-11-20 20:55:39] [d31db4f83c6a129f6d3e47077769e868]
-   P           [Multiple Regression] [WS 7.2] [2009-11-20 21:37:01] [d31db4f83c6a129f6d3e47077769e868]
-   P             [Multiple Regression] [WS 7.3] [2009-11-20 22:04:53] [d31db4f83c6a129f6d3e47077769e868]
-    D              [Multiple Regression] [verbetering] [2009-11-27 10:19:49] [f5d341d4bbba73282fc6e80153a6d315]
-   PD              [Multiple Regression] [Paper Multiple Re...] [2009-12-12 17:59:55] [d31db4f83c6a129f6d3e47077769e868]
-                     [Multiple Regression] [Paper. Multi Regr...] [2009-12-12 18:05:52] [d31db4f83c6a129f6d3e47077769e868]
-                       [Multiple Regression] [Paper. Multi Regr...] [2009-12-12 18:09:49] [d31db4f83c6a129f6d3e47077769e868]
-                         [Multiple Regression] [Paper. Multi Regr...] [2009-12-12 18:12:04] [d31db4f83c6a129f6d3e47077769e868]
-                           [Multiple Regression] [Paper. Multi Regr...] [2009-12-12 18:14:53] [d31db4f83c6a129f6d3e47077769e868]
-                           [Multiple Regression] [Paper. Multi Regr...] [2009-12-12 18:19:05] [d31db4f83c6a129f6d3e47077769e868]
- R  D        [Multiple Regression] [WorkShop7 (SHW)] [2009-11-27 13:36:52] [37daf76adc256428993ec4063536c760]
-    D        [Multiple Regression] [SHW Paper] [2009-12-04 14:16:18] [253127ae8da904b75450fbd69fe4eb21]
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Dataseries X:
8.6	0
8.5	0
8.3	0
7.8	0
7.8	0
8	0
8.6	0
8.9	0
8.9	0
8.6	0
8.3	0
8.3	0
8.3	0
8.4	0
8.5	0
8.4	0
8.6	0
8.5	0
8.5	0
8.5	0
8.5	0
8.5	0
8.5	0
8.5	0
8.5	0
8.5	0
8.5	0
8.5	0
8.6	0
8.4	0
8.1	0
8	0
8	0
8	0
8	0
7.9	0
7.8	0
7.8	0
7.9	0
8.1	0
8	0
7.6	0
7.3	0
7	0
6.8	0
7	0
7.1	0
7.2	0
7.1	1
6.9	1
6.7	1
6.7	1
6.6	1
6.9	1
7.3	1
7.5	1
7.3	1
7.1	1
6.9	1
7.1	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=58024&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=58024&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58024&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] = + 8.14375 -1.13541666666667X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  8.14375 -1.13541666666667X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58024&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  8.14375 -1.13541666666667X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58024&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58024&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] = + 8.14375 -1.13541666666667X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.143750.068563118.777500
X-1.135416666666670.153312-7.405900

\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) & 8.14375 & 0.068563 & 118.7775 & 0 & 0 \tabularnewline
X & -1.13541666666667 & 0.153312 & -7.4059 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58024&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]8.14375[/C][C]0.068563[/C][C]118.7775[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-1.13541666666667[/C][C]0.153312[/C][C]-7.4059[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58024&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58024&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)8.143750.068563118.777500
X-1.135416666666670.153312-7.405900







Multiple Linear Regression - Regression Statistics
Multiple R0.697161272237485
R-squared0.486033839507789
Adjusted R-squared0.477172353982061
F-TEST (value)54.8478963370955
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value6.0921212519105e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.475018904644978
Sum Squared Residuals13.0872916666667

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.697161272237485 \tabularnewline
R-squared & 0.486033839507789 \tabularnewline
Adjusted R-squared & 0.477172353982061 \tabularnewline
F-TEST (value) & 54.8478963370955 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 6.0921212519105e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.475018904644978 \tabularnewline
Sum Squared Residuals & 13.0872916666667 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58024&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.697161272237485[/C][/ROW]
[ROW][C]R-squared[/C][C]0.486033839507789[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.477172353982061[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]54.8478963370955[/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]6.0921212519105e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.475018904644978[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]13.0872916666667[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58024&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58024&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 R0.697161272237485
R-squared0.486033839507789
Adjusted R-squared0.477172353982061
F-TEST (value)54.8478963370955
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value6.0921212519105e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.475018904644978
Sum Squared Residuals13.0872916666667







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
18.68.143750000000020.456249999999984
28.58.143750.356250000000003
38.38.143750.156250000000001
47.88.14375-0.34375
57.88.14375-0.34375
688.14375-0.143750000000000
78.68.143750.45625
88.98.143750.75625
98.98.143750.75625
108.68.143750.45625
118.38.143750.156250000000001
128.38.143750.156250000000001
138.38.143750.156250000000001
148.48.143750.256250000000001
158.58.143750.35625
168.48.143750.256250000000001
178.68.143750.45625
188.58.143750.35625
198.58.143750.35625
208.58.143750.35625
218.58.143750.35625
228.58.143750.35625
238.58.143750.35625
248.58.143750.35625
258.58.143750.35625
268.58.143750.35625
278.58.143750.35625
288.58.143750.35625
298.68.143750.45625
308.48.143750.256250000000001
318.18.14375-0.0437500000000001
3288.14375-0.143750000000000
3388.14375-0.143750000000000
3488.14375-0.143750000000000
3588.14375-0.143750000000000
367.98.14375-0.243749999999999
377.88.14375-0.34375
387.88.14375-0.34375
397.98.14375-0.243749999999999
408.18.14375-0.0437500000000001
4188.14375-0.143750000000000
427.68.14375-0.54375
437.38.14375-0.84375
4478.14375-1.14375
456.88.14375-1.34375
4678.14375-1.14375
477.18.14375-1.04375
487.28.14375-0.94375
497.17.008333333333330.0916666666666664
506.97.00833333333333-0.108333333333333
516.77.00833333333333-0.308333333333333
526.77.00833333333333-0.308333333333333
536.67.00833333333333-0.408333333333334
546.97.00833333333333-0.108333333333333
557.37.008333333333330.291666666666667
567.57.008333333333330.491666666666667
577.37.008333333333330.291666666666667
587.17.008333333333330.0916666666666664
596.97.00833333333333-0.108333333333333
607.17.008333333333330.0916666666666664

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 8.6 & 8.14375000000002 & 0.456249999999984 \tabularnewline
2 & 8.5 & 8.14375 & 0.356250000000003 \tabularnewline
3 & 8.3 & 8.14375 & 0.156250000000001 \tabularnewline
4 & 7.8 & 8.14375 & -0.34375 \tabularnewline
5 & 7.8 & 8.14375 & -0.34375 \tabularnewline
6 & 8 & 8.14375 & -0.143750000000000 \tabularnewline
7 & 8.6 & 8.14375 & 0.45625 \tabularnewline
8 & 8.9 & 8.14375 & 0.75625 \tabularnewline
9 & 8.9 & 8.14375 & 0.75625 \tabularnewline
10 & 8.6 & 8.14375 & 0.45625 \tabularnewline
11 & 8.3 & 8.14375 & 0.156250000000001 \tabularnewline
12 & 8.3 & 8.14375 & 0.156250000000001 \tabularnewline
13 & 8.3 & 8.14375 & 0.156250000000001 \tabularnewline
14 & 8.4 & 8.14375 & 0.256250000000001 \tabularnewline
15 & 8.5 & 8.14375 & 0.35625 \tabularnewline
16 & 8.4 & 8.14375 & 0.256250000000001 \tabularnewline
17 & 8.6 & 8.14375 & 0.45625 \tabularnewline
18 & 8.5 & 8.14375 & 0.35625 \tabularnewline
19 & 8.5 & 8.14375 & 0.35625 \tabularnewline
20 & 8.5 & 8.14375 & 0.35625 \tabularnewline
21 & 8.5 & 8.14375 & 0.35625 \tabularnewline
22 & 8.5 & 8.14375 & 0.35625 \tabularnewline
23 & 8.5 & 8.14375 & 0.35625 \tabularnewline
24 & 8.5 & 8.14375 & 0.35625 \tabularnewline
25 & 8.5 & 8.14375 & 0.35625 \tabularnewline
26 & 8.5 & 8.14375 & 0.35625 \tabularnewline
27 & 8.5 & 8.14375 & 0.35625 \tabularnewline
28 & 8.5 & 8.14375 & 0.35625 \tabularnewline
29 & 8.6 & 8.14375 & 0.45625 \tabularnewline
30 & 8.4 & 8.14375 & 0.256250000000001 \tabularnewline
31 & 8.1 & 8.14375 & -0.0437500000000001 \tabularnewline
32 & 8 & 8.14375 & -0.143750000000000 \tabularnewline
33 & 8 & 8.14375 & -0.143750000000000 \tabularnewline
34 & 8 & 8.14375 & -0.143750000000000 \tabularnewline
35 & 8 & 8.14375 & -0.143750000000000 \tabularnewline
36 & 7.9 & 8.14375 & -0.243749999999999 \tabularnewline
37 & 7.8 & 8.14375 & -0.34375 \tabularnewline
38 & 7.8 & 8.14375 & -0.34375 \tabularnewline
39 & 7.9 & 8.14375 & -0.243749999999999 \tabularnewline
40 & 8.1 & 8.14375 & -0.0437500000000001 \tabularnewline
41 & 8 & 8.14375 & -0.143750000000000 \tabularnewline
42 & 7.6 & 8.14375 & -0.54375 \tabularnewline
43 & 7.3 & 8.14375 & -0.84375 \tabularnewline
44 & 7 & 8.14375 & -1.14375 \tabularnewline
45 & 6.8 & 8.14375 & -1.34375 \tabularnewline
46 & 7 & 8.14375 & -1.14375 \tabularnewline
47 & 7.1 & 8.14375 & -1.04375 \tabularnewline
48 & 7.2 & 8.14375 & -0.94375 \tabularnewline
49 & 7.1 & 7.00833333333333 & 0.0916666666666664 \tabularnewline
50 & 6.9 & 7.00833333333333 & -0.108333333333333 \tabularnewline
51 & 6.7 & 7.00833333333333 & -0.308333333333333 \tabularnewline
52 & 6.7 & 7.00833333333333 & -0.308333333333333 \tabularnewline
53 & 6.6 & 7.00833333333333 & -0.408333333333334 \tabularnewline
54 & 6.9 & 7.00833333333333 & -0.108333333333333 \tabularnewline
55 & 7.3 & 7.00833333333333 & 0.291666666666667 \tabularnewline
56 & 7.5 & 7.00833333333333 & 0.491666666666667 \tabularnewline
57 & 7.3 & 7.00833333333333 & 0.291666666666667 \tabularnewline
58 & 7.1 & 7.00833333333333 & 0.0916666666666664 \tabularnewline
59 & 6.9 & 7.00833333333333 & -0.108333333333333 \tabularnewline
60 & 7.1 & 7.00833333333333 & 0.0916666666666664 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58024&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]8.6[/C][C]8.14375000000002[/C][C]0.456249999999984[/C][/ROW]
[ROW][C]2[/C][C]8.5[/C][C]8.14375[/C][C]0.356250000000003[/C][/ROW]
[ROW][C]3[/C][C]8.3[/C][C]8.14375[/C][C]0.156250000000001[/C][/ROW]
[ROW][C]4[/C][C]7.8[/C][C]8.14375[/C][C]-0.34375[/C][/ROW]
[ROW][C]5[/C][C]7.8[/C][C]8.14375[/C][C]-0.34375[/C][/ROW]
[ROW][C]6[/C][C]8[/C][C]8.14375[/C][C]-0.143750000000000[/C][/ROW]
[ROW][C]7[/C][C]8.6[/C][C]8.14375[/C][C]0.45625[/C][/ROW]
[ROW][C]8[/C][C]8.9[/C][C]8.14375[/C][C]0.75625[/C][/ROW]
[ROW][C]9[/C][C]8.9[/C][C]8.14375[/C][C]0.75625[/C][/ROW]
[ROW][C]10[/C][C]8.6[/C][C]8.14375[/C][C]0.45625[/C][/ROW]
[ROW][C]11[/C][C]8.3[/C][C]8.14375[/C][C]0.156250000000001[/C][/ROW]
[ROW][C]12[/C][C]8.3[/C][C]8.14375[/C][C]0.156250000000001[/C][/ROW]
[ROW][C]13[/C][C]8.3[/C][C]8.14375[/C][C]0.156250000000001[/C][/ROW]
[ROW][C]14[/C][C]8.4[/C][C]8.14375[/C][C]0.256250000000001[/C][/ROW]
[ROW][C]15[/C][C]8.5[/C][C]8.14375[/C][C]0.35625[/C][/ROW]
[ROW][C]16[/C][C]8.4[/C][C]8.14375[/C][C]0.256250000000001[/C][/ROW]
[ROW][C]17[/C][C]8.6[/C][C]8.14375[/C][C]0.45625[/C][/ROW]
[ROW][C]18[/C][C]8.5[/C][C]8.14375[/C][C]0.35625[/C][/ROW]
[ROW][C]19[/C][C]8.5[/C][C]8.14375[/C][C]0.35625[/C][/ROW]
[ROW][C]20[/C][C]8.5[/C][C]8.14375[/C][C]0.35625[/C][/ROW]
[ROW][C]21[/C][C]8.5[/C][C]8.14375[/C][C]0.35625[/C][/ROW]
[ROW][C]22[/C][C]8.5[/C][C]8.14375[/C][C]0.35625[/C][/ROW]
[ROW][C]23[/C][C]8.5[/C][C]8.14375[/C][C]0.35625[/C][/ROW]
[ROW][C]24[/C][C]8.5[/C][C]8.14375[/C][C]0.35625[/C][/ROW]
[ROW][C]25[/C][C]8.5[/C][C]8.14375[/C][C]0.35625[/C][/ROW]
[ROW][C]26[/C][C]8.5[/C][C]8.14375[/C][C]0.35625[/C][/ROW]
[ROW][C]27[/C][C]8.5[/C][C]8.14375[/C][C]0.35625[/C][/ROW]
[ROW][C]28[/C][C]8.5[/C][C]8.14375[/C][C]0.35625[/C][/ROW]
[ROW][C]29[/C][C]8.6[/C][C]8.14375[/C][C]0.45625[/C][/ROW]
[ROW][C]30[/C][C]8.4[/C][C]8.14375[/C][C]0.256250000000001[/C][/ROW]
[ROW][C]31[/C][C]8.1[/C][C]8.14375[/C][C]-0.0437500000000001[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]8.14375[/C][C]-0.143750000000000[/C][/ROW]
[ROW][C]33[/C][C]8[/C][C]8.14375[/C][C]-0.143750000000000[/C][/ROW]
[ROW][C]34[/C][C]8[/C][C]8.14375[/C][C]-0.143750000000000[/C][/ROW]
[ROW][C]35[/C][C]8[/C][C]8.14375[/C][C]-0.143750000000000[/C][/ROW]
[ROW][C]36[/C][C]7.9[/C][C]8.14375[/C][C]-0.243749999999999[/C][/ROW]
[ROW][C]37[/C][C]7.8[/C][C]8.14375[/C][C]-0.34375[/C][/ROW]
[ROW][C]38[/C][C]7.8[/C][C]8.14375[/C][C]-0.34375[/C][/ROW]
[ROW][C]39[/C][C]7.9[/C][C]8.14375[/C][C]-0.243749999999999[/C][/ROW]
[ROW][C]40[/C][C]8.1[/C][C]8.14375[/C][C]-0.0437500000000001[/C][/ROW]
[ROW][C]41[/C][C]8[/C][C]8.14375[/C][C]-0.143750000000000[/C][/ROW]
[ROW][C]42[/C][C]7.6[/C][C]8.14375[/C][C]-0.54375[/C][/ROW]
[ROW][C]43[/C][C]7.3[/C][C]8.14375[/C][C]-0.84375[/C][/ROW]
[ROW][C]44[/C][C]7[/C][C]8.14375[/C][C]-1.14375[/C][/ROW]
[ROW][C]45[/C][C]6.8[/C][C]8.14375[/C][C]-1.34375[/C][/ROW]
[ROW][C]46[/C][C]7[/C][C]8.14375[/C][C]-1.14375[/C][/ROW]
[ROW][C]47[/C][C]7.1[/C][C]8.14375[/C][C]-1.04375[/C][/ROW]
[ROW][C]48[/C][C]7.2[/C][C]8.14375[/C][C]-0.94375[/C][/ROW]
[ROW][C]49[/C][C]7.1[/C][C]7.00833333333333[/C][C]0.0916666666666664[/C][/ROW]
[ROW][C]50[/C][C]6.9[/C][C]7.00833333333333[/C][C]-0.108333333333333[/C][/ROW]
[ROW][C]51[/C][C]6.7[/C][C]7.00833333333333[/C][C]-0.308333333333333[/C][/ROW]
[ROW][C]52[/C][C]6.7[/C][C]7.00833333333333[/C][C]-0.308333333333333[/C][/ROW]
[ROW][C]53[/C][C]6.6[/C][C]7.00833333333333[/C][C]-0.408333333333334[/C][/ROW]
[ROW][C]54[/C][C]6.9[/C][C]7.00833333333333[/C][C]-0.108333333333333[/C][/ROW]
[ROW][C]55[/C][C]7.3[/C][C]7.00833333333333[/C][C]0.291666666666667[/C][/ROW]
[ROW][C]56[/C][C]7.5[/C][C]7.00833333333333[/C][C]0.491666666666667[/C][/ROW]
[ROW][C]57[/C][C]7.3[/C][C]7.00833333333333[/C][C]0.291666666666667[/C][/ROW]
[ROW][C]58[/C][C]7.1[/C][C]7.00833333333333[/C][C]0.0916666666666664[/C][/ROW]
[ROW][C]59[/C][C]6.9[/C][C]7.00833333333333[/C][C]-0.108333333333333[/C][/ROW]
[ROW][C]60[/C][C]7.1[/C][C]7.00833333333333[/C][C]0.0916666666666664[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58024&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58024&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
18.68.143750000000020.456249999999984
28.58.143750.356250000000003
38.38.143750.156250000000001
47.88.14375-0.34375
57.88.14375-0.34375
688.14375-0.143750000000000
78.68.143750.45625
88.98.143750.75625
98.98.143750.75625
108.68.143750.45625
118.38.143750.156250000000001
128.38.143750.156250000000001
138.38.143750.156250000000001
148.48.143750.256250000000001
158.58.143750.35625
168.48.143750.256250000000001
178.68.143750.45625
188.58.143750.35625
198.58.143750.35625
208.58.143750.35625
218.58.143750.35625
228.58.143750.35625
238.58.143750.35625
248.58.143750.35625
258.58.143750.35625
268.58.143750.35625
278.58.143750.35625
288.58.143750.35625
298.68.143750.45625
308.48.143750.256250000000001
318.18.14375-0.0437500000000001
3288.14375-0.143750000000000
3388.14375-0.143750000000000
3488.14375-0.143750000000000
3588.14375-0.143750000000000
367.98.14375-0.243749999999999
377.88.14375-0.34375
387.88.14375-0.34375
397.98.14375-0.243749999999999
408.18.14375-0.0437500000000001
4188.14375-0.143750000000000
427.68.14375-0.54375
437.38.14375-0.84375
4478.14375-1.14375
456.88.14375-1.34375
4678.14375-1.14375
477.18.14375-1.04375
487.28.14375-0.94375
497.17.008333333333330.0916666666666664
506.97.00833333333333-0.108333333333333
516.77.00833333333333-0.308333333333333
526.77.00833333333333-0.308333333333333
536.67.00833333333333-0.408333333333334
546.97.00833333333333-0.108333333333333
557.37.008333333333330.291666666666667
567.57.008333333333330.491666666666667
577.37.008333333333330.291666666666667
587.17.008333333333330.0916666666666664
596.97.00833333333333-0.108333333333333
607.17.008333333333330.0916666666666664







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.511600001785270.976799996429460.48839999821473
60.3632242699992520.7264485399985050.636775730000748
70.3331517105573480.6663034211146960.666848289442652
80.4522477131569410.9044954263138830.547752286843059
90.514153078727140.971693842545720.48584692127286
100.4351934159592570.8703868319185140.564806584040743
110.3358061222384970.6716122444769930.664193877761504
120.2495912682212660.4991825364425320.750408731778734
130.1788588837456720.3577177674913450.821141116254328
140.124698360673830.249396721347660.87530163932617
150.08956427602260750.1791285520452150.910435723977392
160.05955703660106370.1191140732021270.940442963398936
170.04722577542463190.09445155084926380.952774224575368
180.03304158091800440.06608316183600870.966958419081996
190.02308632985921110.04617265971842220.976913670140789
200.01620148278607690.03240296557215380.983798517213923
210.01149709950691900.02299419901383790.988502900493081
220.008316546527516080.01663309305503220.991683453472484
230.006192075670120260.01238415134024050.99380792432988
240.004802295049668940.009604590099337880.995197704950331
250.003937779379977680.007875558759955350.996062220620022
260.003479098255664180.006958196511328360.996520901744336
270.003394183223737520.006788366447475050.996605816776262
280.003776435313366360.007552870626732730.996223564686634
290.007004529043337590.01400905808667520.992995470956662
300.009366649529613590.01873329905922720.990633350470386
310.01233290384413410.02466580768826810.987667096155866
320.01805261267254430.03610522534508860.981947387327456
330.02522105803641630.05044211607283260.974778941963584
340.03465953767459590.06931907534919180.965340462325404
350.04811976546466860.09623953092933730.951880234535331
360.06906205833925730.1381241166785150.930937941660743
370.09980313178277690.1996062635655540.900196868217223
380.1354385172925460.2708770345850930.864561482707454
390.1874418367802360.3748836735604730.812558163219764
400.3786283534832530.7572567069665060.621371646516747
410.7132475909187210.5735048181625580.286752409081279
420.8711223672055160.2577552655889690.128877632794485
430.932387303149950.1352253937001000.0676126968500502
440.9634388132484280.07312237350314310.0365611867515716
450.9850241265333750.02995174693324980.0149758734666249
460.9861103270464950.02777934590700890.0138896729535044
470.9827069479251110.03458610414977850.0172930520748892
480.9744543197889820.05109136042203680.0255456802110184
490.9528727037495080.09425459250098340.0471272962504917
500.917693034371520.1646139312569590.0823069656284795
510.8986911620198630.2026176759602730.101308837980137
520.8863105090191470.2273789819617060.113689490980853
530.9408869755979410.1182260488041180.0591130244020589
540.9202296296237330.1595407407525340.079770370376267
550.8316846074950080.3366307850099830.168315392504992

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.51160000178527 & 0.97679999642946 & 0.48839999821473 \tabularnewline
6 & 0.363224269999252 & 0.726448539998505 & 0.636775730000748 \tabularnewline
7 & 0.333151710557348 & 0.666303421114696 & 0.666848289442652 \tabularnewline
8 & 0.452247713156941 & 0.904495426313883 & 0.547752286843059 \tabularnewline
9 & 0.51415307872714 & 0.97169384254572 & 0.48584692127286 \tabularnewline
10 & 0.435193415959257 & 0.870386831918514 & 0.564806584040743 \tabularnewline
11 & 0.335806122238497 & 0.671612244476993 & 0.664193877761504 \tabularnewline
12 & 0.249591268221266 & 0.499182536442532 & 0.750408731778734 \tabularnewline
13 & 0.178858883745672 & 0.357717767491345 & 0.821141116254328 \tabularnewline
14 & 0.12469836067383 & 0.24939672134766 & 0.87530163932617 \tabularnewline
15 & 0.0895642760226075 & 0.179128552045215 & 0.910435723977392 \tabularnewline
16 & 0.0595570366010637 & 0.119114073202127 & 0.940442963398936 \tabularnewline
17 & 0.0472257754246319 & 0.0944515508492638 & 0.952774224575368 \tabularnewline
18 & 0.0330415809180044 & 0.0660831618360087 & 0.966958419081996 \tabularnewline
19 & 0.0230863298592111 & 0.0461726597184222 & 0.976913670140789 \tabularnewline
20 & 0.0162014827860769 & 0.0324029655721538 & 0.983798517213923 \tabularnewline
21 & 0.0114970995069190 & 0.0229941990138379 & 0.988502900493081 \tabularnewline
22 & 0.00831654652751608 & 0.0166330930550322 & 0.991683453472484 \tabularnewline
23 & 0.00619207567012026 & 0.0123841513402405 & 0.99380792432988 \tabularnewline
24 & 0.00480229504966894 & 0.00960459009933788 & 0.995197704950331 \tabularnewline
25 & 0.00393777937997768 & 0.00787555875995535 & 0.996062220620022 \tabularnewline
26 & 0.00347909825566418 & 0.00695819651132836 & 0.996520901744336 \tabularnewline
27 & 0.00339418322373752 & 0.00678836644747505 & 0.996605816776262 \tabularnewline
28 & 0.00377643531336636 & 0.00755287062673273 & 0.996223564686634 \tabularnewline
29 & 0.00700452904333759 & 0.0140090580866752 & 0.992995470956662 \tabularnewline
30 & 0.00936664952961359 & 0.0187332990592272 & 0.990633350470386 \tabularnewline
31 & 0.0123329038441341 & 0.0246658076882681 & 0.987667096155866 \tabularnewline
32 & 0.0180526126725443 & 0.0361052253450886 & 0.981947387327456 \tabularnewline
33 & 0.0252210580364163 & 0.0504421160728326 & 0.974778941963584 \tabularnewline
34 & 0.0346595376745959 & 0.0693190753491918 & 0.965340462325404 \tabularnewline
35 & 0.0481197654646686 & 0.0962395309293373 & 0.951880234535331 \tabularnewline
36 & 0.0690620583392573 & 0.138124116678515 & 0.930937941660743 \tabularnewline
37 & 0.0998031317827769 & 0.199606263565554 & 0.900196868217223 \tabularnewline
38 & 0.135438517292546 & 0.270877034585093 & 0.864561482707454 \tabularnewline
39 & 0.187441836780236 & 0.374883673560473 & 0.812558163219764 \tabularnewline
40 & 0.378628353483253 & 0.757256706966506 & 0.621371646516747 \tabularnewline
41 & 0.713247590918721 & 0.573504818162558 & 0.286752409081279 \tabularnewline
42 & 0.871122367205516 & 0.257755265588969 & 0.128877632794485 \tabularnewline
43 & 0.93238730314995 & 0.135225393700100 & 0.0676126968500502 \tabularnewline
44 & 0.963438813248428 & 0.0731223735031431 & 0.0365611867515716 \tabularnewline
45 & 0.985024126533375 & 0.0299517469332498 & 0.0149758734666249 \tabularnewline
46 & 0.986110327046495 & 0.0277793459070089 & 0.0138896729535044 \tabularnewline
47 & 0.982706947925111 & 0.0345861041497785 & 0.0172930520748892 \tabularnewline
48 & 0.974454319788982 & 0.0510913604220368 & 0.0255456802110184 \tabularnewline
49 & 0.952872703749508 & 0.0942545925009834 & 0.0471272962504917 \tabularnewline
50 & 0.91769303437152 & 0.164613931256959 & 0.0823069656284795 \tabularnewline
51 & 0.898691162019863 & 0.202617675960273 & 0.101308837980137 \tabularnewline
52 & 0.886310509019147 & 0.227378981961706 & 0.113689490980853 \tabularnewline
53 & 0.940886975597941 & 0.118226048804118 & 0.0591130244020589 \tabularnewline
54 & 0.920229629623733 & 0.159540740752534 & 0.079770370376267 \tabularnewline
55 & 0.831684607495008 & 0.336630785009983 & 0.168315392504992 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58024&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]0.51160000178527[/C][C]0.97679999642946[/C][C]0.48839999821473[/C][/ROW]
[ROW][C]6[/C][C]0.363224269999252[/C][C]0.726448539998505[/C][C]0.636775730000748[/C][/ROW]
[ROW][C]7[/C][C]0.333151710557348[/C][C]0.666303421114696[/C][C]0.666848289442652[/C][/ROW]
[ROW][C]8[/C][C]0.452247713156941[/C][C]0.904495426313883[/C][C]0.547752286843059[/C][/ROW]
[ROW][C]9[/C][C]0.51415307872714[/C][C]0.97169384254572[/C][C]0.48584692127286[/C][/ROW]
[ROW][C]10[/C][C]0.435193415959257[/C][C]0.870386831918514[/C][C]0.564806584040743[/C][/ROW]
[ROW][C]11[/C][C]0.335806122238497[/C][C]0.671612244476993[/C][C]0.664193877761504[/C][/ROW]
[ROW][C]12[/C][C]0.249591268221266[/C][C]0.499182536442532[/C][C]0.750408731778734[/C][/ROW]
[ROW][C]13[/C][C]0.178858883745672[/C][C]0.357717767491345[/C][C]0.821141116254328[/C][/ROW]
[ROW][C]14[/C][C]0.12469836067383[/C][C]0.24939672134766[/C][C]0.87530163932617[/C][/ROW]
[ROW][C]15[/C][C]0.0895642760226075[/C][C]0.179128552045215[/C][C]0.910435723977392[/C][/ROW]
[ROW][C]16[/C][C]0.0595570366010637[/C][C]0.119114073202127[/C][C]0.940442963398936[/C][/ROW]
[ROW][C]17[/C][C]0.0472257754246319[/C][C]0.0944515508492638[/C][C]0.952774224575368[/C][/ROW]
[ROW][C]18[/C][C]0.0330415809180044[/C][C]0.0660831618360087[/C][C]0.966958419081996[/C][/ROW]
[ROW][C]19[/C][C]0.0230863298592111[/C][C]0.0461726597184222[/C][C]0.976913670140789[/C][/ROW]
[ROW][C]20[/C][C]0.0162014827860769[/C][C]0.0324029655721538[/C][C]0.983798517213923[/C][/ROW]
[ROW][C]21[/C][C]0.0114970995069190[/C][C]0.0229941990138379[/C][C]0.988502900493081[/C][/ROW]
[ROW][C]22[/C][C]0.00831654652751608[/C][C]0.0166330930550322[/C][C]0.991683453472484[/C][/ROW]
[ROW][C]23[/C][C]0.00619207567012026[/C][C]0.0123841513402405[/C][C]0.99380792432988[/C][/ROW]
[ROW][C]24[/C][C]0.00480229504966894[/C][C]0.00960459009933788[/C][C]0.995197704950331[/C][/ROW]
[ROW][C]25[/C][C]0.00393777937997768[/C][C]0.00787555875995535[/C][C]0.996062220620022[/C][/ROW]
[ROW][C]26[/C][C]0.00347909825566418[/C][C]0.00695819651132836[/C][C]0.996520901744336[/C][/ROW]
[ROW][C]27[/C][C]0.00339418322373752[/C][C]0.00678836644747505[/C][C]0.996605816776262[/C][/ROW]
[ROW][C]28[/C][C]0.00377643531336636[/C][C]0.00755287062673273[/C][C]0.996223564686634[/C][/ROW]
[ROW][C]29[/C][C]0.00700452904333759[/C][C]0.0140090580866752[/C][C]0.992995470956662[/C][/ROW]
[ROW][C]30[/C][C]0.00936664952961359[/C][C]0.0187332990592272[/C][C]0.990633350470386[/C][/ROW]
[ROW][C]31[/C][C]0.0123329038441341[/C][C]0.0246658076882681[/C][C]0.987667096155866[/C][/ROW]
[ROW][C]32[/C][C]0.0180526126725443[/C][C]0.0361052253450886[/C][C]0.981947387327456[/C][/ROW]
[ROW][C]33[/C][C]0.0252210580364163[/C][C]0.0504421160728326[/C][C]0.974778941963584[/C][/ROW]
[ROW][C]34[/C][C]0.0346595376745959[/C][C]0.0693190753491918[/C][C]0.965340462325404[/C][/ROW]
[ROW][C]35[/C][C]0.0481197654646686[/C][C]0.0962395309293373[/C][C]0.951880234535331[/C][/ROW]
[ROW][C]36[/C][C]0.0690620583392573[/C][C]0.138124116678515[/C][C]0.930937941660743[/C][/ROW]
[ROW][C]37[/C][C]0.0998031317827769[/C][C]0.199606263565554[/C][C]0.900196868217223[/C][/ROW]
[ROW][C]38[/C][C]0.135438517292546[/C][C]0.270877034585093[/C][C]0.864561482707454[/C][/ROW]
[ROW][C]39[/C][C]0.187441836780236[/C][C]0.374883673560473[/C][C]0.812558163219764[/C][/ROW]
[ROW][C]40[/C][C]0.378628353483253[/C][C]0.757256706966506[/C][C]0.621371646516747[/C][/ROW]
[ROW][C]41[/C][C]0.713247590918721[/C][C]0.573504818162558[/C][C]0.286752409081279[/C][/ROW]
[ROW][C]42[/C][C]0.871122367205516[/C][C]0.257755265588969[/C][C]0.128877632794485[/C][/ROW]
[ROW][C]43[/C][C]0.93238730314995[/C][C]0.135225393700100[/C][C]0.0676126968500502[/C][/ROW]
[ROW][C]44[/C][C]0.963438813248428[/C][C]0.0731223735031431[/C][C]0.0365611867515716[/C][/ROW]
[ROW][C]45[/C][C]0.985024126533375[/C][C]0.0299517469332498[/C][C]0.0149758734666249[/C][/ROW]
[ROW][C]46[/C][C]0.986110327046495[/C][C]0.0277793459070089[/C][C]0.0138896729535044[/C][/ROW]
[ROW][C]47[/C][C]0.982706947925111[/C][C]0.0345861041497785[/C][C]0.0172930520748892[/C][/ROW]
[ROW][C]48[/C][C]0.974454319788982[/C][C]0.0510913604220368[/C][C]0.0255456802110184[/C][/ROW]
[ROW][C]49[/C][C]0.952872703749508[/C][C]0.0942545925009834[/C][C]0.0471272962504917[/C][/ROW]
[ROW][C]50[/C][C]0.91769303437152[/C][C]0.164613931256959[/C][C]0.0823069656284795[/C][/ROW]
[ROW][C]51[/C][C]0.898691162019863[/C][C]0.202617675960273[/C][C]0.101308837980137[/C][/ROW]
[ROW][C]52[/C][C]0.886310509019147[/C][C]0.227378981961706[/C][C]0.113689490980853[/C][/ROW]
[ROW][C]53[/C][C]0.940886975597941[/C][C]0.118226048804118[/C][C]0.0591130244020589[/C][/ROW]
[ROW][C]54[/C][C]0.920229629623733[/C][C]0.159540740752534[/C][C]0.079770370376267[/C][/ROW]
[ROW][C]55[/C][C]0.831684607495008[/C][C]0.336630785009983[/C][C]0.168315392504992[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58024&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58024&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
50.511600001785270.976799996429460.48839999821473
60.3632242699992520.7264485399985050.636775730000748
70.3331517105573480.6663034211146960.666848289442652
80.4522477131569410.9044954263138830.547752286843059
90.514153078727140.971693842545720.48584692127286
100.4351934159592570.8703868319185140.564806584040743
110.3358061222384970.6716122444769930.664193877761504
120.2495912682212660.4991825364425320.750408731778734
130.1788588837456720.3577177674913450.821141116254328
140.124698360673830.249396721347660.87530163932617
150.08956427602260750.1791285520452150.910435723977392
160.05955703660106370.1191140732021270.940442963398936
170.04722577542463190.09445155084926380.952774224575368
180.03304158091800440.06608316183600870.966958419081996
190.02308632985921110.04617265971842220.976913670140789
200.01620148278607690.03240296557215380.983798517213923
210.01149709950691900.02299419901383790.988502900493081
220.008316546527516080.01663309305503220.991683453472484
230.006192075670120260.01238415134024050.99380792432988
240.004802295049668940.009604590099337880.995197704950331
250.003937779379977680.007875558759955350.996062220620022
260.003479098255664180.006958196511328360.996520901744336
270.003394183223737520.006788366447475050.996605816776262
280.003776435313366360.007552870626732730.996223564686634
290.007004529043337590.01400905808667520.992995470956662
300.009366649529613590.01873329905922720.990633350470386
310.01233290384413410.02466580768826810.987667096155866
320.01805261267254430.03610522534508860.981947387327456
330.02522105803641630.05044211607283260.974778941963584
340.03465953767459590.06931907534919180.965340462325404
350.04811976546466860.09623953092933730.951880234535331
360.06906205833925730.1381241166785150.930937941660743
370.09980313178277690.1996062635655540.900196868217223
380.1354385172925460.2708770345850930.864561482707454
390.1874418367802360.3748836735604730.812558163219764
400.3786283534832530.7572567069665060.621371646516747
410.7132475909187210.5735048181625580.286752409081279
420.8711223672055160.2577552655889690.128877632794485
430.932387303149950.1352253937001000.0676126968500502
440.9634388132484280.07312237350314310.0365611867515716
450.9850241265333750.02995174693324980.0149758734666249
460.9861103270464950.02777934590700890.0138896729535044
470.9827069479251110.03458610414977850.0172930520748892
480.9744543197889820.05109136042203680.0255456802110184
490.9528727037495080.09425459250098340.0471272962504917
500.917693034371520.1646139312569590.0823069656284795
510.8986911620198630.2026176759602730.101308837980137
520.8863105090191470.2273789819617060.113689490980853
530.9408869755979410.1182260488041180.0591130244020589
540.9202296296237330.1595407407525340.079770370376267
550.8316846074950080.3366307850099830.168315392504992







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.0980392156862745NOK
5% type I error level170.333333333333333NOK
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 & 5 & 0.0980392156862745 & NOK \tabularnewline
5% type I error level & 17 & 0.333333333333333 & NOK \tabularnewline
10% type I error level & 25 & 0.490196078431373 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58024&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]5[/C][C]0.0980392156862745[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]17[/C][C]0.333333333333333[/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=58024&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58024&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 level50.0980392156862745NOK
5% type I error level170.333333333333333NOK
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')
}