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 16:41:15 -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/t12586741395knzm14g5a05125.htm/, Retrieved Fri, 29 Mar 2024 10:06:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57974, Retrieved Fri, 29 Mar 2024 10:06:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsWS 7
Estimated Impact156
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] [WS 7] [2009-11-19 23:41:15] [52b85b290d6f50b0921ad6729b8a5af2] [Current]
-    D        [Multiple Regression] [WS 7 Model 1] [2009-11-20 18:02:51] [9717cb857c153ca3061376906953b329]
-   P           [Multiple Regression] [WS 7 Model 2] [2009-11-20 18:37:24] [9717cb857c153ca3061376906953b329]
-   P             [Multiple Regression] [WS 7 Model 3] [2009-11-20 18:55:39] [9717cb857c153ca3061376906953b329]
-    D              [Multiple Regression] [WS 7 Model 4] [2009-11-22 16:57:43] [9717cb857c153ca3061376906953b329]
-    D                [Multiple Regression] [WS 7 Model 5] [2009-11-22 20:04:45] [9717cb857c153ca3061376906953b329]
-    D                  [Multiple Regression] [WS 7 Model 6] [2009-11-23 16:53:08] [9717cb857c153ca3061376906953b329]
Feedback Forum

Post a new message
Dataseries X:
286602	0
283042	0
276687	0
277915	0
277128	0
277103	0
275037	0
270150	0
267140	0
264993	0
287259	0
291186	0
292300	0
288186	0
281477	0
282656	0
280190	0
280408	0
276836	0
275216	0
274352	0
271311	0
289802	0
290726	0
292300	0
278506	0
269826	0
265861	0
269034	0
264176	0
255198	0
253353	0
246057	0
235372	0
258556	0
260993	0
254663	0
250643	0
243422	0
247105	0
248541	0
245039	0
237080	0
237085	0
225554	0
226839	0
247934	0
248333	1
246969	1
245098	1
246263	1
255765	1
264319	1
268347	1
273046	1
273963	1
267430	1
271993	1
292710	1
295881	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57974&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]4 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=57974&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57974&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
nwwmb[t] = + 266613.595744681 -1219.98036006547dummy_variable[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
nwwmb[t] =  +  266613.595744681 -1219.98036006547dummy_variable[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57974&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]nwwmb[t] =  +  266613.595744681 -1219.98036006547dummy_variable[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57974&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57974&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
nwwmb[t] = + 266613.595744681 -1219.98036006547dummy_variable[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)266613.5957446812643.821107100.84400
dummy_variable-1219.980360065475679.838858-0.21480.8306830.415342

\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) & 266613.595744681 & 2643.821107 & 100.844 & 0 & 0 \tabularnewline
dummy_variable & -1219.98036006547 & 5679.838858 & -0.2148 & 0.830683 & 0.415342 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57974&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]266613.595744681[/C][C]2643.821107[/C][C]100.844[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]dummy_variable[/C][C]-1219.98036006547[/C][C]5679.838858[/C][C]-0.2148[/C][C]0.830683[/C][C]0.415342[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57974&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57974&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)266613.5957446812643.821107100.84400
dummy_variable-1219.980360065475679.838858-0.21480.8306830.415342







Multiple Linear Regression - Regression Statistics
Multiple R0.0281922780235840
R-squared0.00079480454015906
Adjusted R-squared-0.0164328712436312
F-TEST (value)0.0461353319004817
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.830683425738388
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation18125.1243374551
Sum Squared Residuals19054167670.3961

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.0281922780235840 \tabularnewline
R-squared & 0.00079480454015906 \tabularnewline
Adjusted R-squared & -0.0164328712436312 \tabularnewline
F-TEST (value) & 0.0461353319004817 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0.830683425738388 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 18125.1243374551 \tabularnewline
Sum Squared Residuals & 19054167670.3961 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57974&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.0281922780235840[/C][/ROW]
[ROW][C]R-squared[/C][C]0.00079480454015906[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0164328712436312[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.0461353319004817[/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]0.830683425738388[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]18125.1243374551[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]19054167670.3961[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57974&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57974&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 R0.0281922780235840
R-squared0.00079480454015906
Adjusted R-squared-0.0164328712436312
F-TEST (value)0.0461353319004817
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.830683425738388
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation18125.1243374551
Sum Squared Residuals19054167670.3961







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1286602266613.59574468019988.4042553195
2283042266613.59574468116428.4042553191
3276687266613.59574468110073.4042553191
4277915266613.59574468111301.4042553191
5277128266613.59574468110514.4042553191
6277103266613.59574468110489.4042553191
7275037266613.5957446818423.40425531914
8270150266613.5957446813536.40425531914
9267140266613.595744681526.404255319142
10264993266613.595744681-1620.59574468086
11287259266613.59574468120645.4042553191
12291186266613.59574468124572.4042553191
13292300266613.59574468125686.4042553191
14288186266613.59574468121572.4042553191
15281477266613.59574468114863.4042553191
16282656266613.59574468116042.4042553191
17280190266613.59574468113576.4042553191
18280408266613.59574468113794.4042553191
19276836266613.59574468110222.4042553191
20275216266613.5957446818602.40425531914
21274352266613.5957446817738.40425531914
22271311266613.5957446814697.40425531914
23289802266613.59574468123188.4042553191
24290726266613.59574468124112.4042553191
25292300266613.59574468125686.4042553191
26278506266613.59574468111892.4042553191
27269826266613.5957446813212.40425531914
28265861266613.595744681-752.595744680858
29269034266613.5957446812420.40425531914
30264176266613.595744681-2437.59574468086
31255198266613.595744681-11415.5957446809
32253353266613.595744681-13260.5957446809
33246057266613.595744681-20556.5957446809
34235372266613.595744681-31241.5957446809
35258556266613.595744681-8057.59574468086
36260993266613.595744681-5620.59574468086
37254663266613.595744681-11950.5957446809
38250643266613.595744681-15970.5957446809
39243422266613.595744681-23191.5957446809
40247105266613.595744681-19508.5957446809
41248541266613.595744681-18072.5957446809
42245039266613.595744681-21574.5957446809
43237080266613.595744681-29533.5957446809
44237085266613.595744681-29528.5957446809
45225554266613.595744681-41059.5957446809
46226839266613.595744681-39774.5957446809
47247934266613.595744681-18679.5957446809
48248333265393.615384615-17060.6153846154
49246969265393.615384615-18424.6153846154
50245098265393.615384615-20295.6153846154
51246263265393.615384615-19130.6153846154
52255765265393.615384615-9628.61538461538
53264319265393.615384615-1074.61538461538
54268347265393.6153846152953.38461538462
55273046265393.6153846157652.38461538462
56273963265393.6153846158569.38461538462
57267430265393.6153846152036.38461538462
58271993265393.6153846156599.38461538462
59292710265393.61538461527316.3846153846
60295881265393.61538461530487.3846153846

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 286602 & 266613.595744680 & 19988.4042553195 \tabularnewline
2 & 283042 & 266613.595744681 & 16428.4042553191 \tabularnewline
3 & 276687 & 266613.595744681 & 10073.4042553191 \tabularnewline
4 & 277915 & 266613.595744681 & 11301.4042553191 \tabularnewline
5 & 277128 & 266613.595744681 & 10514.4042553191 \tabularnewline
6 & 277103 & 266613.595744681 & 10489.4042553191 \tabularnewline
7 & 275037 & 266613.595744681 & 8423.40425531914 \tabularnewline
8 & 270150 & 266613.595744681 & 3536.40425531914 \tabularnewline
9 & 267140 & 266613.595744681 & 526.404255319142 \tabularnewline
10 & 264993 & 266613.595744681 & -1620.59574468086 \tabularnewline
11 & 287259 & 266613.595744681 & 20645.4042553191 \tabularnewline
12 & 291186 & 266613.595744681 & 24572.4042553191 \tabularnewline
13 & 292300 & 266613.595744681 & 25686.4042553191 \tabularnewline
14 & 288186 & 266613.595744681 & 21572.4042553191 \tabularnewline
15 & 281477 & 266613.595744681 & 14863.4042553191 \tabularnewline
16 & 282656 & 266613.595744681 & 16042.4042553191 \tabularnewline
17 & 280190 & 266613.595744681 & 13576.4042553191 \tabularnewline
18 & 280408 & 266613.595744681 & 13794.4042553191 \tabularnewline
19 & 276836 & 266613.595744681 & 10222.4042553191 \tabularnewline
20 & 275216 & 266613.595744681 & 8602.40425531914 \tabularnewline
21 & 274352 & 266613.595744681 & 7738.40425531914 \tabularnewline
22 & 271311 & 266613.595744681 & 4697.40425531914 \tabularnewline
23 & 289802 & 266613.595744681 & 23188.4042553191 \tabularnewline
24 & 290726 & 266613.595744681 & 24112.4042553191 \tabularnewline
25 & 292300 & 266613.595744681 & 25686.4042553191 \tabularnewline
26 & 278506 & 266613.595744681 & 11892.4042553191 \tabularnewline
27 & 269826 & 266613.595744681 & 3212.40425531914 \tabularnewline
28 & 265861 & 266613.595744681 & -752.595744680858 \tabularnewline
29 & 269034 & 266613.595744681 & 2420.40425531914 \tabularnewline
30 & 264176 & 266613.595744681 & -2437.59574468086 \tabularnewline
31 & 255198 & 266613.595744681 & -11415.5957446809 \tabularnewline
32 & 253353 & 266613.595744681 & -13260.5957446809 \tabularnewline
33 & 246057 & 266613.595744681 & -20556.5957446809 \tabularnewline
34 & 235372 & 266613.595744681 & -31241.5957446809 \tabularnewline
35 & 258556 & 266613.595744681 & -8057.59574468086 \tabularnewline
36 & 260993 & 266613.595744681 & -5620.59574468086 \tabularnewline
37 & 254663 & 266613.595744681 & -11950.5957446809 \tabularnewline
38 & 250643 & 266613.595744681 & -15970.5957446809 \tabularnewline
39 & 243422 & 266613.595744681 & -23191.5957446809 \tabularnewline
40 & 247105 & 266613.595744681 & -19508.5957446809 \tabularnewline
41 & 248541 & 266613.595744681 & -18072.5957446809 \tabularnewline
42 & 245039 & 266613.595744681 & -21574.5957446809 \tabularnewline
43 & 237080 & 266613.595744681 & -29533.5957446809 \tabularnewline
44 & 237085 & 266613.595744681 & -29528.5957446809 \tabularnewline
45 & 225554 & 266613.595744681 & -41059.5957446809 \tabularnewline
46 & 226839 & 266613.595744681 & -39774.5957446809 \tabularnewline
47 & 247934 & 266613.595744681 & -18679.5957446809 \tabularnewline
48 & 248333 & 265393.615384615 & -17060.6153846154 \tabularnewline
49 & 246969 & 265393.615384615 & -18424.6153846154 \tabularnewline
50 & 245098 & 265393.615384615 & -20295.6153846154 \tabularnewline
51 & 246263 & 265393.615384615 & -19130.6153846154 \tabularnewline
52 & 255765 & 265393.615384615 & -9628.61538461538 \tabularnewline
53 & 264319 & 265393.615384615 & -1074.61538461538 \tabularnewline
54 & 268347 & 265393.615384615 & 2953.38461538462 \tabularnewline
55 & 273046 & 265393.615384615 & 7652.38461538462 \tabularnewline
56 & 273963 & 265393.615384615 & 8569.38461538462 \tabularnewline
57 & 267430 & 265393.615384615 & 2036.38461538462 \tabularnewline
58 & 271993 & 265393.615384615 & 6599.38461538462 \tabularnewline
59 & 292710 & 265393.615384615 & 27316.3846153846 \tabularnewline
60 & 295881 & 265393.615384615 & 30487.3846153846 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57974&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]286602[/C][C]266613.595744680[/C][C]19988.4042553195[/C][/ROW]
[ROW][C]2[/C][C]283042[/C][C]266613.595744681[/C][C]16428.4042553191[/C][/ROW]
[ROW][C]3[/C][C]276687[/C][C]266613.595744681[/C][C]10073.4042553191[/C][/ROW]
[ROW][C]4[/C][C]277915[/C][C]266613.595744681[/C][C]11301.4042553191[/C][/ROW]
[ROW][C]5[/C][C]277128[/C][C]266613.595744681[/C][C]10514.4042553191[/C][/ROW]
[ROW][C]6[/C][C]277103[/C][C]266613.595744681[/C][C]10489.4042553191[/C][/ROW]
[ROW][C]7[/C][C]275037[/C][C]266613.595744681[/C][C]8423.40425531914[/C][/ROW]
[ROW][C]8[/C][C]270150[/C][C]266613.595744681[/C][C]3536.40425531914[/C][/ROW]
[ROW][C]9[/C][C]267140[/C][C]266613.595744681[/C][C]526.404255319142[/C][/ROW]
[ROW][C]10[/C][C]264993[/C][C]266613.595744681[/C][C]-1620.59574468086[/C][/ROW]
[ROW][C]11[/C][C]287259[/C][C]266613.595744681[/C][C]20645.4042553191[/C][/ROW]
[ROW][C]12[/C][C]291186[/C][C]266613.595744681[/C][C]24572.4042553191[/C][/ROW]
[ROW][C]13[/C][C]292300[/C][C]266613.595744681[/C][C]25686.4042553191[/C][/ROW]
[ROW][C]14[/C][C]288186[/C][C]266613.595744681[/C][C]21572.4042553191[/C][/ROW]
[ROW][C]15[/C][C]281477[/C][C]266613.595744681[/C][C]14863.4042553191[/C][/ROW]
[ROW][C]16[/C][C]282656[/C][C]266613.595744681[/C][C]16042.4042553191[/C][/ROW]
[ROW][C]17[/C][C]280190[/C][C]266613.595744681[/C][C]13576.4042553191[/C][/ROW]
[ROW][C]18[/C][C]280408[/C][C]266613.595744681[/C][C]13794.4042553191[/C][/ROW]
[ROW][C]19[/C][C]276836[/C][C]266613.595744681[/C][C]10222.4042553191[/C][/ROW]
[ROW][C]20[/C][C]275216[/C][C]266613.595744681[/C][C]8602.40425531914[/C][/ROW]
[ROW][C]21[/C][C]274352[/C][C]266613.595744681[/C][C]7738.40425531914[/C][/ROW]
[ROW][C]22[/C][C]271311[/C][C]266613.595744681[/C][C]4697.40425531914[/C][/ROW]
[ROW][C]23[/C][C]289802[/C][C]266613.595744681[/C][C]23188.4042553191[/C][/ROW]
[ROW][C]24[/C][C]290726[/C][C]266613.595744681[/C][C]24112.4042553191[/C][/ROW]
[ROW][C]25[/C][C]292300[/C][C]266613.595744681[/C][C]25686.4042553191[/C][/ROW]
[ROW][C]26[/C][C]278506[/C][C]266613.595744681[/C][C]11892.4042553191[/C][/ROW]
[ROW][C]27[/C][C]269826[/C][C]266613.595744681[/C][C]3212.40425531914[/C][/ROW]
[ROW][C]28[/C][C]265861[/C][C]266613.595744681[/C][C]-752.595744680858[/C][/ROW]
[ROW][C]29[/C][C]269034[/C][C]266613.595744681[/C][C]2420.40425531914[/C][/ROW]
[ROW][C]30[/C][C]264176[/C][C]266613.595744681[/C][C]-2437.59574468086[/C][/ROW]
[ROW][C]31[/C][C]255198[/C][C]266613.595744681[/C][C]-11415.5957446809[/C][/ROW]
[ROW][C]32[/C][C]253353[/C][C]266613.595744681[/C][C]-13260.5957446809[/C][/ROW]
[ROW][C]33[/C][C]246057[/C][C]266613.595744681[/C][C]-20556.5957446809[/C][/ROW]
[ROW][C]34[/C][C]235372[/C][C]266613.595744681[/C][C]-31241.5957446809[/C][/ROW]
[ROW][C]35[/C][C]258556[/C][C]266613.595744681[/C][C]-8057.59574468086[/C][/ROW]
[ROW][C]36[/C][C]260993[/C][C]266613.595744681[/C][C]-5620.59574468086[/C][/ROW]
[ROW][C]37[/C][C]254663[/C][C]266613.595744681[/C][C]-11950.5957446809[/C][/ROW]
[ROW][C]38[/C][C]250643[/C][C]266613.595744681[/C][C]-15970.5957446809[/C][/ROW]
[ROW][C]39[/C][C]243422[/C][C]266613.595744681[/C][C]-23191.5957446809[/C][/ROW]
[ROW][C]40[/C][C]247105[/C][C]266613.595744681[/C][C]-19508.5957446809[/C][/ROW]
[ROW][C]41[/C][C]248541[/C][C]266613.595744681[/C][C]-18072.5957446809[/C][/ROW]
[ROW][C]42[/C][C]245039[/C][C]266613.595744681[/C][C]-21574.5957446809[/C][/ROW]
[ROW][C]43[/C][C]237080[/C][C]266613.595744681[/C][C]-29533.5957446809[/C][/ROW]
[ROW][C]44[/C][C]237085[/C][C]266613.595744681[/C][C]-29528.5957446809[/C][/ROW]
[ROW][C]45[/C][C]225554[/C][C]266613.595744681[/C][C]-41059.5957446809[/C][/ROW]
[ROW][C]46[/C][C]226839[/C][C]266613.595744681[/C][C]-39774.5957446809[/C][/ROW]
[ROW][C]47[/C][C]247934[/C][C]266613.595744681[/C][C]-18679.5957446809[/C][/ROW]
[ROW][C]48[/C][C]248333[/C][C]265393.615384615[/C][C]-17060.6153846154[/C][/ROW]
[ROW][C]49[/C][C]246969[/C][C]265393.615384615[/C][C]-18424.6153846154[/C][/ROW]
[ROW][C]50[/C][C]245098[/C][C]265393.615384615[/C][C]-20295.6153846154[/C][/ROW]
[ROW][C]51[/C][C]246263[/C][C]265393.615384615[/C][C]-19130.6153846154[/C][/ROW]
[ROW][C]52[/C][C]255765[/C][C]265393.615384615[/C][C]-9628.61538461538[/C][/ROW]
[ROW][C]53[/C][C]264319[/C][C]265393.615384615[/C][C]-1074.61538461538[/C][/ROW]
[ROW][C]54[/C][C]268347[/C][C]265393.615384615[/C][C]2953.38461538462[/C][/ROW]
[ROW][C]55[/C][C]273046[/C][C]265393.615384615[/C][C]7652.38461538462[/C][/ROW]
[ROW][C]56[/C][C]273963[/C][C]265393.615384615[/C][C]8569.38461538462[/C][/ROW]
[ROW][C]57[/C][C]267430[/C][C]265393.615384615[/C][C]2036.38461538462[/C][/ROW]
[ROW][C]58[/C][C]271993[/C][C]265393.615384615[/C][C]6599.38461538462[/C][/ROW]
[ROW][C]59[/C][C]292710[/C][C]265393.615384615[/C][C]27316.3846153846[/C][/ROW]
[ROW][C]60[/C][C]295881[/C][C]265393.615384615[/C][C]30487.3846153846[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57974&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57974&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
1286602266613.59574468019988.4042553195
2283042266613.59574468116428.4042553191
3276687266613.59574468110073.4042553191
4277915266613.59574468111301.4042553191
5277128266613.59574468110514.4042553191
6277103266613.59574468110489.4042553191
7275037266613.5957446818423.40425531914
8270150266613.5957446813536.40425531914
9267140266613.595744681526.404255319142
10264993266613.595744681-1620.59574468086
11287259266613.59574468120645.4042553191
12291186266613.59574468124572.4042553191
13292300266613.59574468125686.4042553191
14288186266613.59574468121572.4042553191
15281477266613.59574468114863.4042553191
16282656266613.59574468116042.4042553191
17280190266613.59574468113576.4042553191
18280408266613.59574468113794.4042553191
19276836266613.59574468110222.4042553191
20275216266613.5957446818602.40425531914
21274352266613.5957446817738.40425531914
22271311266613.5957446814697.40425531914
23289802266613.59574468123188.4042553191
24290726266613.59574468124112.4042553191
25292300266613.59574468125686.4042553191
26278506266613.59574468111892.4042553191
27269826266613.5957446813212.40425531914
28265861266613.595744681-752.595744680858
29269034266613.5957446812420.40425531914
30264176266613.595744681-2437.59574468086
31255198266613.595744681-11415.5957446809
32253353266613.595744681-13260.5957446809
33246057266613.595744681-20556.5957446809
34235372266613.595744681-31241.5957446809
35258556266613.595744681-8057.59574468086
36260993266613.595744681-5620.59574468086
37254663266613.595744681-11950.5957446809
38250643266613.595744681-15970.5957446809
39243422266613.595744681-23191.5957446809
40247105266613.595744681-19508.5957446809
41248541266613.595744681-18072.5957446809
42245039266613.595744681-21574.5957446809
43237080266613.595744681-29533.5957446809
44237085266613.595744681-29528.5957446809
45225554266613.595744681-41059.5957446809
46226839266613.595744681-39774.5957446809
47247934266613.595744681-18679.5957446809
48248333265393.615384615-17060.6153846154
49246969265393.615384615-18424.6153846154
50245098265393.615384615-20295.6153846154
51246263265393.615384615-19130.6153846154
52255765265393.615384615-9628.61538461538
53264319265393.615384615-1074.61538461538
54268347265393.6153846152953.38461538462
55273046265393.6153846157652.38461538462
56273963265393.6153846158569.38461538462
57267430265393.6153846152036.38461538462
58271993265393.6153846156599.38461538462
59292710265393.61538461527316.3846153846
60295881265393.61538461530487.3846153846







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.02676862029067190.05353724058134370.973231379709328
60.007243317027148910.01448663405429780.992756682972851
70.00247571338284310.00495142676568620.997524286617157
80.002254085799664230.004508171599328460.997745914200336
90.002581898083698080.005163796167396160.997418101916302
100.002866716629146240.005733433258292480.997133283370854
110.003067846746281770.006135693492563550.996932153253718
120.005001695679990270.01000339135998050.99499830432001
130.007341919815458780.01468383963091760.992658080184541
140.005954716853638540.01190943370727710.994045283146362
150.003166383739294210.006332767478588420.996833616260706
160.00176401810700230.00352803621400460.998235981892998
170.0009120135356135620.001824027071227120.999087986464386
180.0004782010951843380.0009564021903686760.999521798904816
190.0002505426529216970.0005010853058433940.999749457347078
200.0001383589710214440.0002767179420428890.999861641028979
217.9696226487747e-050.0001593924529754940.999920303773512
225.63625522299001e-050.0001127251044598000.99994363744777
230.0001147398754283320.0002294797508566640.999885260124572
240.0003181223693476020.0006362447386952040.999681877630652
250.001487127688070630.002974255376141250.99851287231193
260.001779649080751360.003559298161502730.99822035091925
270.002513538503933750.00502707700786750.997486461496066
280.004445936157693310.008891872315386620.995554063842307
290.006763805728475150.01352761145695030.993236194271525
300.01242750028651960.02485500057303910.98757249971348
310.03426131785978340.06852263571956680.965738682140217
320.07181497764461720.1436299552892340.928185022355383
330.1609109137999110.3218218275998220.839089086200089
340.3890761070858190.7781522141716390.610923892914181
350.4017262156543620.8034524313087250.598273784345638
360.4283816993166740.8567633986333480.571618300683326
370.4500076583425560.9000153166851130.549992341657444
380.4722223095673260.9444446191346530.527777690432674
390.5080017504594050.9839964990811910.491998249540596
400.5161588984797030.9676822030405940.483841101520297
410.5204483620361580.9591032759276830.479551637963842
420.5239023197565930.9521953604868140.476097680243407
430.5368748086757840.9262503826484320.463125191324216
440.5336029349987360.9327941300025290.466397065001264
450.5906877247032330.8186245505935350.409312275296767
460.6434415305556710.7131169388886590.356558469444329
470.5676389279131080.8647221441737840.432361072086892
480.5453434450626470.9093131098747060.454656554937353
490.5558521151323580.8882957697352840.444147884867642
500.6310055352916050.737988929416790.368994464708395
510.7566523862462760.4866952275074480.243347613753724
520.7949695360868680.4100609278262630.205030463913132
530.7585770435208980.4828459129582040.241422956479102
540.6825581569478250.634883686104350.317441843052175
550.5464316040470980.9071367919058040.453568395952902

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0267686202906719 & 0.0535372405813437 & 0.973231379709328 \tabularnewline
6 & 0.00724331702714891 & 0.0144866340542978 & 0.992756682972851 \tabularnewline
7 & 0.0024757133828431 & 0.0049514267656862 & 0.997524286617157 \tabularnewline
8 & 0.00225408579966423 & 0.00450817159932846 & 0.997745914200336 \tabularnewline
9 & 0.00258189808369808 & 0.00516379616739616 & 0.997418101916302 \tabularnewline
10 & 0.00286671662914624 & 0.00573343325829248 & 0.997133283370854 \tabularnewline
11 & 0.00306784674628177 & 0.00613569349256355 & 0.996932153253718 \tabularnewline
12 & 0.00500169567999027 & 0.0100033913599805 & 0.99499830432001 \tabularnewline
13 & 0.00734191981545878 & 0.0146838396309176 & 0.992658080184541 \tabularnewline
14 & 0.00595471685363854 & 0.0119094337072771 & 0.994045283146362 \tabularnewline
15 & 0.00316638373929421 & 0.00633276747858842 & 0.996833616260706 \tabularnewline
16 & 0.0017640181070023 & 0.0035280362140046 & 0.998235981892998 \tabularnewline
17 & 0.000912013535613562 & 0.00182402707122712 & 0.999087986464386 \tabularnewline
18 & 0.000478201095184338 & 0.000956402190368676 & 0.999521798904816 \tabularnewline
19 & 0.000250542652921697 & 0.000501085305843394 & 0.999749457347078 \tabularnewline
20 & 0.000138358971021444 & 0.000276717942042889 & 0.999861641028979 \tabularnewline
21 & 7.9696226487747e-05 & 0.000159392452975494 & 0.999920303773512 \tabularnewline
22 & 5.63625522299001e-05 & 0.000112725104459800 & 0.99994363744777 \tabularnewline
23 & 0.000114739875428332 & 0.000229479750856664 & 0.999885260124572 \tabularnewline
24 & 0.000318122369347602 & 0.000636244738695204 & 0.999681877630652 \tabularnewline
25 & 0.00148712768807063 & 0.00297425537614125 & 0.99851287231193 \tabularnewline
26 & 0.00177964908075136 & 0.00355929816150273 & 0.99822035091925 \tabularnewline
27 & 0.00251353850393375 & 0.0050270770078675 & 0.997486461496066 \tabularnewline
28 & 0.00444593615769331 & 0.00889187231538662 & 0.995554063842307 \tabularnewline
29 & 0.00676380572847515 & 0.0135276114569503 & 0.993236194271525 \tabularnewline
30 & 0.0124275002865196 & 0.0248550005730391 & 0.98757249971348 \tabularnewline
31 & 0.0342613178597834 & 0.0685226357195668 & 0.965738682140217 \tabularnewline
32 & 0.0718149776446172 & 0.143629955289234 & 0.928185022355383 \tabularnewline
33 & 0.160910913799911 & 0.321821827599822 & 0.839089086200089 \tabularnewline
34 & 0.389076107085819 & 0.778152214171639 & 0.610923892914181 \tabularnewline
35 & 0.401726215654362 & 0.803452431308725 & 0.598273784345638 \tabularnewline
36 & 0.428381699316674 & 0.856763398633348 & 0.571618300683326 \tabularnewline
37 & 0.450007658342556 & 0.900015316685113 & 0.549992341657444 \tabularnewline
38 & 0.472222309567326 & 0.944444619134653 & 0.527777690432674 \tabularnewline
39 & 0.508001750459405 & 0.983996499081191 & 0.491998249540596 \tabularnewline
40 & 0.516158898479703 & 0.967682203040594 & 0.483841101520297 \tabularnewline
41 & 0.520448362036158 & 0.959103275927683 & 0.479551637963842 \tabularnewline
42 & 0.523902319756593 & 0.952195360486814 & 0.476097680243407 \tabularnewline
43 & 0.536874808675784 & 0.926250382648432 & 0.463125191324216 \tabularnewline
44 & 0.533602934998736 & 0.932794130002529 & 0.466397065001264 \tabularnewline
45 & 0.590687724703233 & 0.818624550593535 & 0.409312275296767 \tabularnewline
46 & 0.643441530555671 & 0.713116938888659 & 0.356558469444329 \tabularnewline
47 & 0.567638927913108 & 0.864722144173784 & 0.432361072086892 \tabularnewline
48 & 0.545343445062647 & 0.909313109874706 & 0.454656554937353 \tabularnewline
49 & 0.555852115132358 & 0.888295769735284 & 0.444147884867642 \tabularnewline
50 & 0.631005535291605 & 0.73798892941679 & 0.368994464708395 \tabularnewline
51 & 0.756652386246276 & 0.486695227507448 & 0.243347613753724 \tabularnewline
52 & 0.794969536086868 & 0.410060927826263 & 0.205030463913132 \tabularnewline
53 & 0.758577043520898 & 0.482845912958204 & 0.241422956479102 \tabularnewline
54 & 0.682558156947825 & 0.63488368610435 & 0.317441843052175 \tabularnewline
55 & 0.546431604047098 & 0.907136791905804 & 0.453568395952902 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57974&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]0.0267686202906719[/C][C]0.0535372405813437[/C][C]0.973231379709328[/C][/ROW]
[ROW][C]6[/C][C]0.00724331702714891[/C][C]0.0144866340542978[/C][C]0.992756682972851[/C][/ROW]
[ROW][C]7[/C][C]0.0024757133828431[/C][C]0.0049514267656862[/C][C]0.997524286617157[/C][/ROW]
[ROW][C]8[/C][C]0.00225408579966423[/C][C]0.00450817159932846[/C][C]0.997745914200336[/C][/ROW]
[ROW][C]9[/C][C]0.00258189808369808[/C][C]0.00516379616739616[/C][C]0.997418101916302[/C][/ROW]
[ROW][C]10[/C][C]0.00286671662914624[/C][C]0.00573343325829248[/C][C]0.997133283370854[/C][/ROW]
[ROW][C]11[/C][C]0.00306784674628177[/C][C]0.00613569349256355[/C][C]0.996932153253718[/C][/ROW]
[ROW][C]12[/C][C]0.00500169567999027[/C][C]0.0100033913599805[/C][C]0.99499830432001[/C][/ROW]
[ROW][C]13[/C][C]0.00734191981545878[/C][C]0.0146838396309176[/C][C]0.992658080184541[/C][/ROW]
[ROW][C]14[/C][C]0.00595471685363854[/C][C]0.0119094337072771[/C][C]0.994045283146362[/C][/ROW]
[ROW][C]15[/C][C]0.00316638373929421[/C][C]0.00633276747858842[/C][C]0.996833616260706[/C][/ROW]
[ROW][C]16[/C][C]0.0017640181070023[/C][C]0.0035280362140046[/C][C]0.998235981892998[/C][/ROW]
[ROW][C]17[/C][C]0.000912013535613562[/C][C]0.00182402707122712[/C][C]0.999087986464386[/C][/ROW]
[ROW][C]18[/C][C]0.000478201095184338[/C][C]0.000956402190368676[/C][C]0.999521798904816[/C][/ROW]
[ROW][C]19[/C][C]0.000250542652921697[/C][C]0.000501085305843394[/C][C]0.999749457347078[/C][/ROW]
[ROW][C]20[/C][C]0.000138358971021444[/C][C]0.000276717942042889[/C][C]0.999861641028979[/C][/ROW]
[ROW][C]21[/C][C]7.9696226487747e-05[/C][C]0.000159392452975494[/C][C]0.999920303773512[/C][/ROW]
[ROW][C]22[/C][C]5.63625522299001e-05[/C][C]0.000112725104459800[/C][C]0.99994363744777[/C][/ROW]
[ROW][C]23[/C][C]0.000114739875428332[/C][C]0.000229479750856664[/C][C]0.999885260124572[/C][/ROW]
[ROW][C]24[/C][C]0.000318122369347602[/C][C]0.000636244738695204[/C][C]0.999681877630652[/C][/ROW]
[ROW][C]25[/C][C]0.00148712768807063[/C][C]0.00297425537614125[/C][C]0.99851287231193[/C][/ROW]
[ROW][C]26[/C][C]0.00177964908075136[/C][C]0.00355929816150273[/C][C]0.99822035091925[/C][/ROW]
[ROW][C]27[/C][C]0.00251353850393375[/C][C]0.0050270770078675[/C][C]0.997486461496066[/C][/ROW]
[ROW][C]28[/C][C]0.00444593615769331[/C][C]0.00889187231538662[/C][C]0.995554063842307[/C][/ROW]
[ROW][C]29[/C][C]0.00676380572847515[/C][C]0.0135276114569503[/C][C]0.993236194271525[/C][/ROW]
[ROW][C]30[/C][C]0.0124275002865196[/C][C]0.0248550005730391[/C][C]0.98757249971348[/C][/ROW]
[ROW][C]31[/C][C]0.0342613178597834[/C][C]0.0685226357195668[/C][C]0.965738682140217[/C][/ROW]
[ROW][C]32[/C][C]0.0718149776446172[/C][C]0.143629955289234[/C][C]0.928185022355383[/C][/ROW]
[ROW][C]33[/C][C]0.160910913799911[/C][C]0.321821827599822[/C][C]0.839089086200089[/C][/ROW]
[ROW][C]34[/C][C]0.389076107085819[/C][C]0.778152214171639[/C][C]0.610923892914181[/C][/ROW]
[ROW][C]35[/C][C]0.401726215654362[/C][C]0.803452431308725[/C][C]0.598273784345638[/C][/ROW]
[ROW][C]36[/C][C]0.428381699316674[/C][C]0.856763398633348[/C][C]0.571618300683326[/C][/ROW]
[ROW][C]37[/C][C]0.450007658342556[/C][C]0.900015316685113[/C][C]0.549992341657444[/C][/ROW]
[ROW][C]38[/C][C]0.472222309567326[/C][C]0.944444619134653[/C][C]0.527777690432674[/C][/ROW]
[ROW][C]39[/C][C]0.508001750459405[/C][C]0.983996499081191[/C][C]0.491998249540596[/C][/ROW]
[ROW][C]40[/C][C]0.516158898479703[/C][C]0.967682203040594[/C][C]0.483841101520297[/C][/ROW]
[ROW][C]41[/C][C]0.520448362036158[/C][C]0.959103275927683[/C][C]0.479551637963842[/C][/ROW]
[ROW][C]42[/C][C]0.523902319756593[/C][C]0.952195360486814[/C][C]0.476097680243407[/C][/ROW]
[ROW][C]43[/C][C]0.536874808675784[/C][C]0.926250382648432[/C][C]0.463125191324216[/C][/ROW]
[ROW][C]44[/C][C]0.533602934998736[/C][C]0.932794130002529[/C][C]0.466397065001264[/C][/ROW]
[ROW][C]45[/C][C]0.590687724703233[/C][C]0.818624550593535[/C][C]0.409312275296767[/C][/ROW]
[ROW][C]46[/C][C]0.643441530555671[/C][C]0.713116938888659[/C][C]0.356558469444329[/C][/ROW]
[ROW][C]47[/C][C]0.567638927913108[/C][C]0.864722144173784[/C][C]0.432361072086892[/C][/ROW]
[ROW][C]48[/C][C]0.545343445062647[/C][C]0.909313109874706[/C][C]0.454656554937353[/C][/ROW]
[ROW][C]49[/C][C]0.555852115132358[/C][C]0.888295769735284[/C][C]0.444147884867642[/C][/ROW]
[ROW][C]50[/C][C]0.631005535291605[/C][C]0.73798892941679[/C][C]0.368994464708395[/C][/ROW]
[ROW][C]51[/C][C]0.756652386246276[/C][C]0.486695227507448[/C][C]0.243347613753724[/C][/ROW]
[ROW][C]52[/C][C]0.794969536086868[/C][C]0.410060927826263[/C][C]0.205030463913132[/C][/ROW]
[ROW][C]53[/C][C]0.758577043520898[/C][C]0.482845912958204[/C][C]0.241422956479102[/C][/ROW]
[ROW][C]54[/C][C]0.682558156947825[/C][C]0.63488368610435[/C][C]0.317441843052175[/C][/ROW]
[ROW][C]55[/C][C]0.546431604047098[/C][C]0.907136791905804[/C][C]0.453568395952902[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57974&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57974&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
50.02676862029067190.05353724058134370.973231379709328
60.007243317027148910.01448663405429780.992756682972851
70.00247571338284310.00495142676568620.997524286617157
80.002254085799664230.004508171599328460.997745914200336
90.002581898083698080.005163796167396160.997418101916302
100.002866716629146240.005733433258292480.997133283370854
110.003067846746281770.006135693492563550.996932153253718
120.005001695679990270.01000339135998050.99499830432001
130.007341919815458780.01468383963091760.992658080184541
140.005954716853638540.01190943370727710.994045283146362
150.003166383739294210.006332767478588420.996833616260706
160.00176401810700230.00352803621400460.998235981892998
170.0009120135356135620.001824027071227120.999087986464386
180.0004782010951843380.0009564021903686760.999521798904816
190.0002505426529216970.0005010853058433940.999749457347078
200.0001383589710214440.0002767179420428890.999861641028979
217.9696226487747e-050.0001593924529754940.999920303773512
225.63625522299001e-050.0001127251044598000.99994363744777
230.0001147398754283320.0002294797508566640.999885260124572
240.0003181223693476020.0006362447386952040.999681877630652
250.001487127688070630.002974255376141250.99851287231193
260.001779649080751360.003559298161502730.99822035091925
270.002513538503933750.00502707700786750.997486461496066
280.004445936157693310.008891872315386620.995554063842307
290.006763805728475150.01352761145695030.993236194271525
300.01242750028651960.02485500057303910.98757249971348
310.03426131785978340.06852263571956680.965738682140217
320.07181497764461720.1436299552892340.928185022355383
330.1609109137999110.3218218275998220.839089086200089
340.3890761070858190.7781522141716390.610923892914181
350.4017262156543620.8034524313087250.598273784345638
360.4283816993166740.8567633986333480.571618300683326
370.4500076583425560.9000153166851130.549992341657444
380.4722223095673260.9444446191346530.527777690432674
390.5080017504594050.9839964990811910.491998249540596
400.5161588984797030.9676822030405940.483841101520297
410.5204483620361580.9591032759276830.479551637963842
420.5239023197565930.9521953604868140.476097680243407
430.5368748086757840.9262503826484320.463125191324216
440.5336029349987360.9327941300025290.466397065001264
450.5906877247032330.8186245505935350.409312275296767
460.6434415305556710.7131169388886590.356558469444329
470.5676389279131080.8647221441737840.432361072086892
480.5453434450626470.9093131098747060.454656554937353
490.5558521151323580.8882957697352840.444147884867642
500.6310055352916050.737988929416790.368994464708395
510.7566523862462760.4866952275074480.243347613753724
520.7949695360868680.4100609278262630.205030463913132
530.7585770435208980.4828459129582040.241422956479102
540.6825581569478250.634883686104350.317441843052175
550.5464316040470980.9071367919058040.453568395952902







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level190.372549019607843NOK
5% type I error level250.490196078431373NOK
10% type I error level270.529411764705882NOK

\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 & 25 & 0.490196078431373 & NOK \tabularnewline
10% type I error level & 27 & 0.529411764705882 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57974&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]25[/C][C]0.490196078431373[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]27[/C][C]0.529411764705882[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57974&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57974&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 level250.490196078431373NOK
10% type I error level270.529411764705882NOK



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')
}