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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 computationThu, 19 Nov 2009 08:22:56 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t1258644239hrohv287gxr2clg.htm/, Retrieved Fri, 29 Mar 2024 05:18:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57758, Retrieved Fri, 29 Mar 2024 05:18:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact134
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 14:03:14] [b98453cac15ba1066b407e146608df68]
-   PD      [Multiple Regression] [Multiple Regression] [2009-11-19 15:22:56] [d45d8d97b86162be82506c3c0ea6e4a6] [Current]
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Dataseries X:
1.4	1.9
1	1.6
-0.8	0
-2.9	-1.3
-0.7	-0.4
-0.7	-0.3
1.5	1.4
3	2.6
3.2	2.8
3.1	2.6
3.9	3.4
1	1.7
1.3	1.2
0.8	0
1.2	0
2.9	1.6
3.9	2.5
4.5	3.2
4.5	3.4
3.3	2.3
2	1.9
1.5	1.7
1	1.9
2.1	3.3
3	3.8
4	4.4
5.1	4.5
4.5	3.5
4.2	3
3.3	2.8
2.7	2.9
1.8	2.6
1.4	2.1
0.5	1.5
-0.4	1.1
0.8	1.5
0.7	1.7
1.9	2.3
2	2.3
1.1	1.9
0.9	2
0.4	1.6
0.7	1.2
2.1	1.9
2.8	2.1
3.9	2.4
3.5	2.9
2	2.5
2	2.3
1.5	2.5
2.5	2.6
3.1	2.4
2.7	2.5
2.8	2.1
2.5	2.2
3	2.7
3.2	3
2.8	3.2
2.4	3
2	2.7
1.8	2.5
1.1	1.6
-1.5	0.1
-3.7	-1.9




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=57758&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=57758&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57758&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
bbp[t] = -0.635828009397949 + 1.25032818779747dnst[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
bbp[t] =  -0.635828009397949 +  1.25032818779747dnst[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57758&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]bbp[t] =  -0.635828009397949 +  1.25032818779747dnst[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57758&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57758&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
bbp[t] = -0.635828009397949 + 1.25032818779747dnst[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.6358280093979490.191327-3.32330.0014960.000748
dnst1.250328187797470.08056115.520300

\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) & -0.635828009397949 & 0.191327 & -3.3233 & 0.001496 & 0.000748 \tabularnewline
dnst & 1.25032818779747 & 0.080561 & 15.5203 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57758&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]-0.635828009397949[/C][C]0.191327[/C][C]-3.3233[/C][C]0.001496[/C][C]0.000748[/C][/ROW]
[ROW][C]dnst[/C][C]1.25032818779747[/C][C]0.080561[/C][C]15.5203[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57758&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57758&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)-0.6358280093979490.191327-3.32330.0014960.000748
dnst1.250328187797470.08056115.520300







Multiple Linear Regression - Regression Statistics
Multiple R0.891794576951301
R-squared0.79529756747975
Adjusted R-squared0.791995915342326
F-TEST (value)240.878667520800
F-TEST (DF numerator)1
F-TEST (DF denominator)62
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.776230952059975
Sum Squared Residuals37.357138438028

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.891794576951301 \tabularnewline
R-squared & 0.79529756747975 \tabularnewline
Adjusted R-squared & 0.791995915342326 \tabularnewline
F-TEST (value) & 240.878667520800 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 62 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.776230952059975 \tabularnewline
Sum Squared Residuals & 37.357138438028 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57758&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.891794576951301[/C][/ROW]
[ROW][C]R-squared[/C][C]0.79529756747975[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.791995915342326[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]240.878667520800[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]62[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.776230952059975[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]37.357138438028[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57758&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57758&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 R0.891794576951301
R-squared0.79529756747975
Adjusted R-squared0.791995915342326
F-TEST (value)240.878667520800
F-TEST (DF numerator)1
F-TEST (DF denominator)62
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.776230952059975
Sum Squared Residuals37.357138438028







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.41.73979554741723-0.339795547417234
211.364697091078-0.364697091077999
3-0.8-0.635828009397951-0.164171990602049
4-2.9-2.26125465353467-0.638745346465333
5-0.7-1.135959284516940.435959284516941
6-0.7-1.010926465737190.310926465737194
71.51.114631453518510.38536854648149
832.615025278875480.384974721124522
93.22.865090916434970.334909083565028
103.12.615025278875480.484974721124523
113.93.615287829113460.284712170886544
1211.48972990985775-0.489729909857752
131.30.8645658159590150.435434184040985
140.8-0.6358280093979521.43582800939795
151.2-0.6358280093979521.83582800939795
162.91.364697091078001.53530290892200
173.92.489992460095731.41000753990427
184.53.365222191553961.13477780844604
194.53.615287829113460.884712170886545
203.32.239926822536241.06007317746376
2121.739795547417250.260204452582754
221.51.489729909857750.0102700901422481
2311.73979554741725-0.739795547417246
242.13.49025501033371-1.39025501033371
2534.11541910423245-1.11541910423244
2644.86561601691093-0.865616016910929
275.14.990648835690680.109351164309324
284.53.74032064789320.759679352106797
294.23.115156553994471.08484344600553
303.32.865090916434970.434909083565028
312.72.99012373521472-0.290123735214719
321.82.61502527887548-0.815025278875477
331.41.98986118497674-0.589861184976741
340.51.23966427229826-0.739664272298257
35-0.40.739532997179268-1.13953299717927
360.81.23966427229826-0.439664272298257
370.71.48972990985775-0.789729909857752
381.92.23992682253624-0.339926822536235
3922.23992682253624-0.239926822536235
401.11.73979554741725-0.639795547417246
410.91.86482836619699-0.964828366196994
420.41.36469709107800-0.964697091078005
430.70.864565815959015-0.164565815959015
442.11.739795547417250.360204452582754
452.81.989861184976740.810138815023259
463.92.364959641315981.53504035868402
473.52.990123735214720.509876264785281
4822.48999246009573-0.48999246009573
4922.23992682253624-0.239926822536235
501.52.48999246009573-0.98999246009573
512.52.61502527887548-0.115025278875478
523.12.364959641315980.735040358684017
532.72.489992460095730.21000753990427
542.81.989861184976740.810138815023259
552.52.114894003756490.385105996243511
5632.740058097655220.259941902344775
573.23.115156553994470.0848434460055337
582.83.36522219155396-0.565222191553962
592.43.11515655399447-0.715156553994467
6022.74005809765522-0.740058097655225
611.82.48999246009573-0.68999246009573
621.11.36469709107800-0.264697091078005
63-1.5-0.510795190618205-0.989204809381795
64-3.7-3.01145156621315-0.68854843378685

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.4 & 1.73979554741723 & -0.339795547417234 \tabularnewline
2 & 1 & 1.364697091078 & -0.364697091077999 \tabularnewline
3 & -0.8 & -0.635828009397951 & -0.164171990602049 \tabularnewline
4 & -2.9 & -2.26125465353467 & -0.638745346465333 \tabularnewline
5 & -0.7 & -1.13595928451694 & 0.435959284516941 \tabularnewline
6 & -0.7 & -1.01092646573719 & 0.310926465737194 \tabularnewline
7 & 1.5 & 1.11463145351851 & 0.38536854648149 \tabularnewline
8 & 3 & 2.61502527887548 & 0.384974721124522 \tabularnewline
9 & 3.2 & 2.86509091643497 & 0.334909083565028 \tabularnewline
10 & 3.1 & 2.61502527887548 & 0.484974721124523 \tabularnewline
11 & 3.9 & 3.61528782911346 & 0.284712170886544 \tabularnewline
12 & 1 & 1.48972990985775 & -0.489729909857752 \tabularnewline
13 & 1.3 & 0.864565815959015 & 0.435434184040985 \tabularnewline
14 & 0.8 & -0.635828009397952 & 1.43582800939795 \tabularnewline
15 & 1.2 & -0.635828009397952 & 1.83582800939795 \tabularnewline
16 & 2.9 & 1.36469709107800 & 1.53530290892200 \tabularnewline
17 & 3.9 & 2.48999246009573 & 1.41000753990427 \tabularnewline
18 & 4.5 & 3.36522219155396 & 1.13477780844604 \tabularnewline
19 & 4.5 & 3.61528782911346 & 0.884712170886545 \tabularnewline
20 & 3.3 & 2.23992682253624 & 1.06007317746376 \tabularnewline
21 & 2 & 1.73979554741725 & 0.260204452582754 \tabularnewline
22 & 1.5 & 1.48972990985775 & 0.0102700901422481 \tabularnewline
23 & 1 & 1.73979554741725 & -0.739795547417246 \tabularnewline
24 & 2.1 & 3.49025501033371 & -1.39025501033371 \tabularnewline
25 & 3 & 4.11541910423245 & -1.11541910423244 \tabularnewline
26 & 4 & 4.86561601691093 & -0.865616016910929 \tabularnewline
27 & 5.1 & 4.99064883569068 & 0.109351164309324 \tabularnewline
28 & 4.5 & 3.7403206478932 & 0.759679352106797 \tabularnewline
29 & 4.2 & 3.11515655399447 & 1.08484344600553 \tabularnewline
30 & 3.3 & 2.86509091643497 & 0.434909083565028 \tabularnewline
31 & 2.7 & 2.99012373521472 & -0.290123735214719 \tabularnewline
32 & 1.8 & 2.61502527887548 & -0.815025278875477 \tabularnewline
33 & 1.4 & 1.98986118497674 & -0.589861184976741 \tabularnewline
34 & 0.5 & 1.23966427229826 & -0.739664272298257 \tabularnewline
35 & -0.4 & 0.739532997179268 & -1.13953299717927 \tabularnewline
36 & 0.8 & 1.23966427229826 & -0.439664272298257 \tabularnewline
37 & 0.7 & 1.48972990985775 & -0.789729909857752 \tabularnewline
38 & 1.9 & 2.23992682253624 & -0.339926822536235 \tabularnewline
39 & 2 & 2.23992682253624 & -0.239926822536235 \tabularnewline
40 & 1.1 & 1.73979554741725 & -0.639795547417246 \tabularnewline
41 & 0.9 & 1.86482836619699 & -0.964828366196994 \tabularnewline
42 & 0.4 & 1.36469709107800 & -0.964697091078005 \tabularnewline
43 & 0.7 & 0.864565815959015 & -0.164565815959015 \tabularnewline
44 & 2.1 & 1.73979554741725 & 0.360204452582754 \tabularnewline
45 & 2.8 & 1.98986118497674 & 0.810138815023259 \tabularnewline
46 & 3.9 & 2.36495964131598 & 1.53504035868402 \tabularnewline
47 & 3.5 & 2.99012373521472 & 0.509876264785281 \tabularnewline
48 & 2 & 2.48999246009573 & -0.48999246009573 \tabularnewline
49 & 2 & 2.23992682253624 & -0.239926822536235 \tabularnewline
50 & 1.5 & 2.48999246009573 & -0.98999246009573 \tabularnewline
51 & 2.5 & 2.61502527887548 & -0.115025278875478 \tabularnewline
52 & 3.1 & 2.36495964131598 & 0.735040358684017 \tabularnewline
53 & 2.7 & 2.48999246009573 & 0.21000753990427 \tabularnewline
54 & 2.8 & 1.98986118497674 & 0.810138815023259 \tabularnewline
55 & 2.5 & 2.11489400375649 & 0.385105996243511 \tabularnewline
56 & 3 & 2.74005809765522 & 0.259941902344775 \tabularnewline
57 & 3.2 & 3.11515655399447 & 0.0848434460055337 \tabularnewline
58 & 2.8 & 3.36522219155396 & -0.565222191553962 \tabularnewline
59 & 2.4 & 3.11515655399447 & -0.715156553994467 \tabularnewline
60 & 2 & 2.74005809765522 & -0.740058097655225 \tabularnewline
61 & 1.8 & 2.48999246009573 & -0.68999246009573 \tabularnewline
62 & 1.1 & 1.36469709107800 & -0.264697091078005 \tabularnewline
63 & -1.5 & -0.510795190618205 & -0.989204809381795 \tabularnewline
64 & -3.7 & -3.01145156621315 & -0.68854843378685 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57758&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]1.4[/C][C]1.73979554741723[/C][C]-0.339795547417234[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]1.364697091078[/C][C]-0.364697091077999[/C][/ROW]
[ROW][C]3[/C][C]-0.8[/C][C]-0.635828009397951[/C][C]-0.164171990602049[/C][/ROW]
[ROW][C]4[/C][C]-2.9[/C][C]-2.26125465353467[/C][C]-0.638745346465333[/C][/ROW]
[ROW][C]5[/C][C]-0.7[/C][C]-1.13595928451694[/C][C]0.435959284516941[/C][/ROW]
[ROW][C]6[/C][C]-0.7[/C][C]-1.01092646573719[/C][C]0.310926465737194[/C][/ROW]
[ROW][C]7[/C][C]1.5[/C][C]1.11463145351851[/C][C]0.38536854648149[/C][/ROW]
[ROW][C]8[/C][C]3[/C][C]2.61502527887548[/C][C]0.384974721124522[/C][/ROW]
[ROW][C]9[/C][C]3.2[/C][C]2.86509091643497[/C][C]0.334909083565028[/C][/ROW]
[ROW][C]10[/C][C]3.1[/C][C]2.61502527887548[/C][C]0.484974721124523[/C][/ROW]
[ROW][C]11[/C][C]3.9[/C][C]3.61528782911346[/C][C]0.284712170886544[/C][/ROW]
[ROW][C]12[/C][C]1[/C][C]1.48972990985775[/C][C]-0.489729909857752[/C][/ROW]
[ROW][C]13[/C][C]1.3[/C][C]0.864565815959015[/C][C]0.435434184040985[/C][/ROW]
[ROW][C]14[/C][C]0.8[/C][C]-0.635828009397952[/C][C]1.43582800939795[/C][/ROW]
[ROW][C]15[/C][C]1.2[/C][C]-0.635828009397952[/C][C]1.83582800939795[/C][/ROW]
[ROW][C]16[/C][C]2.9[/C][C]1.36469709107800[/C][C]1.53530290892200[/C][/ROW]
[ROW][C]17[/C][C]3.9[/C][C]2.48999246009573[/C][C]1.41000753990427[/C][/ROW]
[ROW][C]18[/C][C]4.5[/C][C]3.36522219155396[/C][C]1.13477780844604[/C][/ROW]
[ROW][C]19[/C][C]4.5[/C][C]3.61528782911346[/C][C]0.884712170886545[/C][/ROW]
[ROW][C]20[/C][C]3.3[/C][C]2.23992682253624[/C][C]1.06007317746376[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]1.73979554741725[/C][C]0.260204452582754[/C][/ROW]
[ROW][C]22[/C][C]1.5[/C][C]1.48972990985775[/C][C]0.0102700901422481[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]1.73979554741725[/C][C]-0.739795547417246[/C][/ROW]
[ROW][C]24[/C][C]2.1[/C][C]3.49025501033371[/C][C]-1.39025501033371[/C][/ROW]
[ROW][C]25[/C][C]3[/C][C]4.11541910423245[/C][C]-1.11541910423244[/C][/ROW]
[ROW][C]26[/C][C]4[/C][C]4.86561601691093[/C][C]-0.865616016910929[/C][/ROW]
[ROW][C]27[/C][C]5.1[/C][C]4.99064883569068[/C][C]0.109351164309324[/C][/ROW]
[ROW][C]28[/C][C]4.5[/C][C]3.7403206478932[/C][C]0.759679352106797[/C][/ROW]
[ROW][C]29[/C][C]4.2[/C][C]3.11515655399447[/C][C]1.08484344600553[/C][/ROW]
[ROW][C]30[/C][C]3.3[/C][C]2.86509091643497[/C][C]0.434909083565028[/C][/ROW]
[ROW][C]31[/C][C]2.7[/C][C]2.99012373521472[/C][C]-0.290123735214719[/C][/ROW]
[ROW][C]32[/C][C]1.8[/C][C]2.61502527887548[/C][C]-0.815025278875477[/C][/ROW]
[ROW][C]33[/C][C]1.4[/C][C]1.98986118497674[/C][C]-0.589861184976741[/C][/ROW]
[ROW][C]34[/C][C]0.5[/C][C]1.23966427229826[/C][C]-0.739664272298257[/C][/ROW]
[ROW][C]35[/C][C]-0.4[/C][C]0.739532997179268[/C][C]-1.13953299717927[/C][/ROW]
[ROW][C]36[/C][C]0.8[/C][C]1.23966427229826[/C][C]-0.439664272298257[/C][/ROW]
[ROW][C]37[/C][C]0.7[/C][C]1.48972990985775[/C][C]-0.789729909857752[/C][/ROW]
[ROW][C]38[/C][C]1.9[/C][C]2.23992682253624[/C][C]-0.339926822536235[/C][/ROW]
[ROW][C]39[/C][C]2[/C][C]2.23992682253624[/C][C]-0.239926822536235[/C][/ROW]
[ROW][C]40[/C][C]1.1[/C][C]1.73979554741725[/C][C]-0.639795547417246[/C][/ROW]
[ROW][C]41[/C][C]0.9[/C][C]1.86482836619699[/C][C]-0.964828366196994[/C][/ROW]
[ROW][C]42[/C][C]0.4[/C][C]1.36469709107800[/C][C]-0.964697091078005[/C][/ROW]
[ROW][C]43[/C][C]0.7[/C][C]0.864565815959015[/C][C]-0.164565815959015[/C][/ROW]
[ROW][C]44[/C][C]2.1[/C][C]1.73979554741725[/C][C]0.360204452582754[/C][/ROW]
[ROW][C]45[/C][C]2.8[/C][C]1.98986118497674[/C][C]0.810138815023259[/C][/ROW]
[ROW][C]46[/C][C]3.9[/C][C]2.36495964131598[/C][C]1.53504035868402[/C][/ROW]
[ROW][C]47[/C][C]3.5[/C][C]2.99012373521472[/C][C]0.509876264785281[/C][/ROW]
[ROW][C]48[/C][C]2[/C][C]2.48999246009573[/C][C]-0.48999246009573[/C][/ROW]
[ROW][C]49[/C][C]2[/C][C]2.23992682253624[/C][C]-0.239926822536235[/C][/ROW]
[ROW][C]50[/C][C]1.5[/C][C]2.48999246009573[/C][C]-0.98999246009573[/C][/ROW]
[ROW][C]51[/C][C]2.5[/C][C]2.61502527887548[/C][C]-0.115025278875478[/C][/ROW]
[ROW][C]52[/C][C]3.1[/C][C]2.36495964131598[/C][C]0.735040358684017[/C][/ROW]
[ROW][C]53[/C][C]2.7[/C][C]2.48999246009573[/C][C]0.21000753990427[/C][/ROW]
[ROW][C]54[/C][C]2.8[/C][C]1.98986118497674[/C][C]0.810138815023259[/C][/ROW]
[ROW][C]55[/C][C]2.5[/C][C]2.11489400375649[/C][C]0.385105996243511[/C][/ROW]
[ROW][C]56[/C][C]3[/C][C]2.74005809765522[/C][C]0.259941902344775[/C][/ROW]
[ROW][C]57[/C][C]3.2[/C][C]3.11515655399447[/C][C]0.0848434460055337[/C][/ROW]
[ROW][C]58[/C][C]2.8[/C][C]3.36522219155396[/C][C]-0.565222191553962[/C][/ROW]
[ROW][C]59[/C][C]2.4[/C][C]3.11515655399447[/C][C]-0.715156553994467[/C][/ROW]
[ROW][C]60[/C][C]2[/C][C]2.74005809765522[/C][C]-0.740058097655225[/C][/ROW]
[ROW][C]61[/C][C]1.8[/C][C]2.48999246009573[/C][C]-0.68999246009573[/C][/ROW]
[ROW][C]62[/C][C]1.1[/C][C]1.36469709107800[/C][C]-0.264697091078005[/C][/ROW]
[ROW][C]63[/C][C]-1.5[/C][C]-0.510795190618205[/C][C]-0.989204809381795[/C][/ROW]
[ROW][C]64[/C][C]-3.7[/C][C]-3.01145156621315[/C][C]-0.68854843378685[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57758&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57758&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
11.41.73979554741723-0.339795547417234
211.364697091078-0.364697091077999
3-0.8-0.635828009397951-0.164171990602049
4-2.9-2.26125465353467-0.638745346465333
5-0.7-1.135959284516940.435959284516941
6-0.7-1.010926465737190.310926465737194
71.51.114631453518510.38536854648149
832.615025278875480.384974721124522
93.22.865090916434970.334909083565028
103.12.615025278875480.484974721124523
113.93.615287829113460.284712170886544
1211.48972990985775-0.489729909857752
131.30.8645658159590150.435434184040985
140.8-0.6358280093979521.43582800939795
151.2-0.6358280093979521.83582800939795
162.91.364697091078001.53530290892200
173.92.489992460095731.41000753990427
184.53.365222191553961.13477780844604
194.53.615287829113460.884712170886545
203.32.239926822536241.06007317746376
2121.739795547417250.260204452582754
221.51.489729909857750.0102700901422481
2311.73979554741725-0.739795547417246
242.13.49025501033371-1.39025501033371
2534.11541910423245-1.11541910423244
2644.86561601691093-0.865616016910929
275.14.990648835690680.109351164309324
284.53.74032064789320.759679352106797
294.23.115156553994471.08484344600553
303.32.865090916434970.434909083565028
312.72.99012373521472-0.290123735214719
321.82.61502527887548-0.815025278875477
331.41.98986118497674-0.589861184976741
340.51.23966427229826-0.739664272298257
35-0.40.739532997179268-1.13953299717927
360.81.23966427229826-0.439664272298257
370.71.48972990985775-0.789729909857752
381.92.23992682253624-0.339926822536235
3922.23992682253624-0.239926822536235
401.11.73979554741725-0.639795547417246
410.91.86482836619699-0.964828366196994
420.41.36469709107800-0.964697091078005
430.70.864565815959015-0.164565815959015
442.11.739795547417250.360204452582754
452.81.989861184976740.810138815023259
463.92.364959641315981.53504035868402
473.52.990123735214720.509876264785281
4822.48999246009573-0.48999246009573
4922.23992682253624-0.239926822536235
501.52.48999246009573-0.98999246009573
512.52.61502527887548-0.115025278875478
523.12.364959641315980.735040358684017
532.72.489992460095730.21000753990427
542.81.989861184976740.810138815023259
552.52.114894003756490.385105996243511
5632.740058097655220.259941902344775
573.23.115156553994470.0848434460055337
582.83.36522219155396-0.565222191553962
592.43.11515655399447-0.715156553994467
6022.74005809765522-0.740058097655225
611.82.48999246009573-0.68999246009573
621.11.36469709107800-0.264697091078005
63-1.5-0.510795190618205-0.989204809381795
64-3.7-3.01145156621315-0.68854843378685







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.1988004573516790.3976009147033590.80119954264832
60.1400459632860100.2800919265720210.85995403671399
70.1094785397236910.2189570794473820.890521460276309
80.06662526537772740.1332505307554550.933374734622273
90.03343017732416030.06686035464832070.96656982267584
100.01814676821922020.03629353643844050.98185323178078
110.007956823541298190.01591364708259640.992043176458702
120.009006128798382550.01801225759676510.990993871201618
130.005775140343519860.01155028068703970.99422485965648
140.06752851418675490.1350570283735100.932471485813245
150.3333293066305560.6666586132611110.666670693369444
160.5264627993358450.947074401328310.473537200664155
170.6427920690745260.7144158618509470.357207930925474
180.658095759596950.6838084808060990.341904240403049
190.6276931011910580.7446137976178840.372306898808942
200.652584363246790.6948312735064210.347415636753210
210.5999178735777010.8001642528445980.400082126422299
220.5539428500334490.8921142999331020.446057149966551
230.6316395159791950.736720968041610.368360484020805
240.8596705018374150.2806589963251690.140329498162585
250.922526926879510.1549461462409780.0774730731204892
260.9438188872230770.1123622255538470.0561811127769233
270.9225493944849520.1549012110300960.0774506055150478
280.9161166480495980.1677667039008040.0838833519504018
290.9413892669334030.1172214661331940.0586107330665969
300.9266550804391330.1466898391217330.0733449195608666
310.904933799828920.1901324003421590.0950662001710795
320.9148902606587810.1702194786824390.0851097393412193
330.9039680933091350.1920638133817310.0960319066908655
340.8999724443298940.2000551113402130.100027555670106
350.9269771231536720.1460457536926560.0730228768463281
360.9053606687285740.1892786625428510.0946393312714256
370.9014805212034140.1970389575931730.0985194787965864
380.87057607866970.25884784266060.1294239213303
390.8290355286797670.3419289426404670.170964471320233
400.8062802990080430.3874394019839140.193719700991957
410.8282523751444590.3434952497110820.171747624855541
420.8462943421276880.3074113157446250.153705657872312
430.7944625284225560.4110749431548880.205537471577444
440.7515025409950590.4969949180098820.248497459004941
450.772815418990820.4543691620183610.227184581009180
460.94724753665770.1055049266846000.0527524633423001
470.9416107920987470.1167784158025060.0583892079012528
480.9185671005706620.1628657988586750.0814328994293376
490.877851401947180.2442971961056390.122148598052819
500.9035343936490340.1929312127019320.0964656063509662
510.8528312525773090.2943374948453820.147168747422691
520.8840278554308580.2319442891382850.115972144569142
530.8431676256638560.3136647486722880.156832374336144
540.9395834766671740.1208330466656520.060416523332826
550.9628979036566440.07420419268671230.0371020963433562
560.9803253705777570.03934925884448520.0196746294222426
570.993716535231220.01256692953755920.00628346476877958
580.9785677247954640.04286455040907180.0214322752045359
590.9336729041905250.1326541916189500.0663270958094748

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.198800457351679 & 0.397600914703359 & 0.80119954264832 \tabularnewline
6 & 0.140045963286010 & 0.280091926572021 & 0.85995403671399 \tabularnewline
7 & 0.109478539723691 & 0.218957079447382 & 0.890521460276309 \tabularnewline
8 & 0.0666252653777274 & 0.133250530755455 & 0.933374734622273 \tabularnewline
9 & 0.0334301773241603 & 0.0668603546483207 & 0.96656982267584 \tabularnewline
10 & 0.0181467682192202 & 0.0362935364384405 & 0.98185323178078 \tabularnewline
11 & 0.00795682354129819 & 0.0159136470825964 & 0.992043176458702 \tabularnewline
12 & 0.00900612879838255 & 0.0180122575967651 & 0.990993871201618 \tabularnewline
13 & 0.00577514034351986 & 0.0115502806870397 & 0.99422485965648 \tabularnewline
14 & 0.0675285141867549 & 0.135057028373510 & 0.932471485813245 \tabularnewline
15 & 0.333329306630556 & 0.666658613261111 & 0.666670693369444 \tabularnewline
16 & 0.526462799335845 & 0.94707440132831 & 0.473537200664155 \tabularnewline
17 & 0.642792069074526 & 0.714415861850947 & 0.357207930925474 \tabularnewline
18 & 0.65809575959695 & 0.683808480806099 & 0.341904240403049 \tabularnewline
19 & 0.627693101191058 & 0.744613797617884 & 0.372306898808942 \tabularnewline
20 & 0.65258436324679 & 0.694831273506421 & 0.347415636753210 \tabularnewline
21 & 0.599917873577701 & 0.800164252844598 & 0.400082126422299 \tabularnewline
22 & 0.553942850033449 & 0.892114299933102 & 0.446057149966551 \tabularnewline
23 & 0.631639515979195 & 0.73672096804161 & 0.368360484020805 \tabularnewline
24 & 0.859670501837415 & 0.280658996325169 & 0.140329498162585 \tabularnewline
25 & 0.92252692687951 & 0.154946146240978 & 0.0774730731204892 \tabularnewline
26 & 0.943818887223077 & 0.112362225553847 & 0.0561811127769233 \tabularnewline
27 & 0.922549394484952 & 0.154901211030096 & 0.0774506055150478 \tabularnewline
28 & 0.916116648049598 & 0.167766703900804 & 0.0838833519504018 \tabularnewline
29 & 0.941389266933403 & 0.117221466133194 & 0.0586107330665969 \tabularnewline
30 & 0.926655080439133 & 0.146689839121733 & 0.0733449195608666 \tabularnewline
31 & 0.90493379982892 & 0.190132400342159 & 0.0950662001710795 \tabularnewline
32 & 0.914890260658781 & 0.170219478682439 & 0.0851097393412193 \tabularnewline
33 & 0.903968093309135 & 0.192063813381731 & 0.0960319066908655 \tabularnewline
34 & 0.899972444329894 & 0.200055111340213 & 0.100027555670106 \tabularnewline
35 & 0.926977123153672 & 0.146045753692656 & 0.0730228768463281 \tabularnewline
36 & 0.905360668728574 & 0.189278662542851 & 0.0946393312714256 \tabularnewline
37 & 0.901480521203414 & 0.197038957593173 & 0.0985194787965864 \tabularnewline
38 & 0.8705760786697 & 0.2588478426606 & 0.1294239213303 \tabularnewline
39 & 0.829035528679767 & 0.341928942640467 & 0.170964471320233 \tabularnewline
40 & 0.806280299008043 & 0.387439401983914 & 0.193719700991957 \tabularnewline
41 & 0.828252375144459 & 0.343495249711082 & 0.171747624855541 \tabularnewline
42 & 0.846294342127688 & 0.307411315744625 & 0.153705657872312 \tabularnewline
43 & 0.794462528422556 & 0.411074943154888 & 0.205537471577444 \tabularnewline
44 & 0.751502540995059 & 0.496994918009882 & 0.248497459004941 \tabularnewline
45 & 0.77281541899082 & 0.454369162018361 & 0.227184581009180 \tabularnewline
46 & 0.9472475366577 & 0.105504926684600 & 0.0527524633423001 \tabularnewline
47 & 0.941610792098747 & 0.116778415802506 & 0.0583892079012528 \tabularnewline
48 & 0.918567100570662 & 0.162865798858675 & 0.0814328994293376 \tabularnewline
49 & 0.87785140194718 & 0.244297196105639 & 0.122148598052819 \tabularnewline
50 & 0.903534393649034 & 0.192931212701932 & 0.0964656063509662 \tabularnewline
51 & 0.852831252577309 & 0.294337494845382 & 0.147168747422691 \tabularnewline
52 & 0.884027855430858 & 0.231944289138285 & 0.115972144569142 \tabularnewline
53 & 0.843167625663856 & 0.313664748672288 & 0.156832374336144 \tabularnewline
54 & 0.939583476667174 & 0.120833046665652 & 0.060416523332826 \tabularnewline
55 & 0.962897903656644 & 0.0742041926867123 & 0.0371020963433562 \tabularnewline
56 & 0.980325370577757 & 0.0393492588444852 & 0.0196746294222426 \tabularnewline
57 & 0.99371653523122 & 0.0125669295375592 & 0.00628346476877958 \tabularnewline
58 & 0.978567724795464 & 0.0428645504090718 & 0.0214322752045359 \tabularnewline
59 & 0.933672904190525 & 0.132654191618950 & 0.0663270958094748 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57758&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]0.198800457351679[/C][C]0.397600914703359[/C][C]0.80119954264832[/C][/ROW]
[ROW][C]6[/C][C]0.140045963286010[/C][C]0.280091926572021[/C][C]0.85995403671399[/C][/ROW]
[ROW][C]7[/C][C]0.109478539723691[/C][C]0.218957079447382[/C][C]0.890521460276309[/C][/ROW]
[ROW][C]8[/C][C]0.0666252653777274[/C][C]0.133250530755455[/C][C]0.933374734622273[/C][/ROW]
[ROW][C]9[/C][C]0.0334301773241603[/C][C]0.0668603546483207[/C][C]0.96656982267584[/C][/ROW]
[ROW][C]10[/C][C]0.0181467682192202[/C][C]0.0362935364384405[/C][C]0.98185323178078[/C][/ROW]
[ROW][C]11[/C][C]0.00795682354129819[/C][C]0.0159136470825964[/C][C]0.992043176458702[/C][/ROW]
[ROW][C]12[/C][C]0.00900612879838255[/C][C]0.0180122575967651[/C][C]0.990993871201618[/C][/ROW]
[ROW][C]13[/C][C]0.00577514034351986[/C][C]0.0115502806870397[/C][C]0.99422485965648[/C][/ROW]
[ROW][C]14[/C][C]0.0675285141867549[/C][C]0.135057028373510[/C][C]0.932471485813245[/C][/ROW]
[ROW][C]15[/C][C]0.333329306630556[/C][C]0.666658613261111[/C][C]0.666670693369444[/C][/ROW]
[ROW][C]16[/C][C]0.526462799335845[/C][C]0.94707440132831[/C][C]0.473537200664155[/C][/ROW]
[ROW][C]17[/C][C]0.642792069074526[/C][C]0.714415861850947[/C][C]0.357207930925474[/C][/ROW]
[ROW][C]18[/C][C]0.65809575959695[/C][C]0.683808480806099[/C][C]0.341904240403049[/C][/ROW]
[ROW][C]19[/C][C]0.627693101191058[/C][C]0.744613797617884[/C][C]0.372306898808942[/C][/ROW]
[ROW][C]20[/C][C]0.65258436324679[/C][C]0.694831273506421[/C][C]0.347415636753210[/C][/ROW]
[ROW][C]21[/C][C]0.599917873577701[/C][C]0.800164252844598[/C][C]0.400082126422299[/C][/ROW]
[ROW][C]22[/C][C]0.553942850033449[/C][C]0.892114299933102[/C][C]0.446057149966551[/C][/ROW]
[ROW][C]23[/C][C]0.631639515979195[/C][C]0.73672096804161[/C][C]0.368360484020805[/C][/ROW]
[ROW][C]24[/C][C]0.859670501837415[/C][C]0.280658996325169[/C][C]0.140329498162585[/C][/ROW]
[ROW][C]25[/C][C]0.92252692687951[/C][C]0.154946146240978[/C][C]0.0774730731204892[/C][/ROW]
[ROW][C]26[/C][C]0.943818887223077[/C][C]0.112362225553847[/C][C]0.0561811127769233[/C][/ROW]
[ROW][C]27[/C][C]0.922549394484952[/C][C]0.154901211030096[/C][C]0.0774506055150478[/C][/ROW]
[ROW][C]28[/C][C]0.916116648049598[/C][C]0.167766703900804[/C][C]0.0838833519504018[/C][/ROW]
[ROW][C]29[/C][C]0.941389266933403[/C][C]0.117221466133194[/C][C]0.0586107330665969[/C][/ROW]
[ROW][C]30[/C][C]0.926655080439133[/C][C]0.146689839121733[/C][C]0.0733449195608666[/C][/ROW]
[ROW][C]31[/C][C]0.90493379982892[/C][C]0.190132400342159[/C][C]0.0950662001710795[/C][/ROW]
[ROW][C]32[/C][C]0.914890260658781[/C][C]0.170219478682439[/C][C]0.0851097393412193[/C][/ROW]
[ROW][C]33[/C][C]0.903968093309135[/C][C]0.192063813381731[/C][C]0.0960319066908655[/C][/ROW]
[ROW][C]34[/C][C]0.899972444329894[/C][C]0.200055111340213[/C][C]0.100027555670106[/C][/ROW]
[ROW][C]35[/C][C]0.926977123153672[/C][C]0.146045753692656[/C][C]0.0730228768463281[/C][/ROW]
[ROW][C]36[/C][C]0.905360668728574[/C][C]0.189278662542851[/C][C]0.0946393312714256[/C][/ROW]
[ROW][C]37[/C][C]0.901480521203414[/C][C]0.197038957593173[/C][C]0.0985194787965864[/C][/ROW]
[ROW][C]38[/C][C]0.8705760786697[/C][C]0.2588478426606[/C][C]0.1294239213303[/C][/ROW]
[ROW][C]39[/C][C]0.829035528679767[/C][C]0.341928942640467[/C][C]0.170964471320233[/C][/ROW]
[ROW][C]40[/C][C]0.806280299008043[/C][C]0.387439401983914[/C][C]0.193719700991957[/C][/ROW]
[ROW][C]41[/C][C]0.828252375144459[/C][C]0.343495249711082[/C][C]0.171747624855541[/C][/ROW]
[ROW][C]42[/C][C]0.846294342127688[/C][C]0.307411315744625[/C][C]0.153705657872312[/C][/ROW]
[ROW][C]43[/C][C]0.794462528422556[/C][C]0.411074943154888[/C][C]0.205537471577444[/C][/ROW]
[ROW][C]44[/C][C]0.751502540995059[/C][C]0.496994918009882[/C][C]0.248497459004941[/C][/ROW]
[ROW][C]45[/C][C]0.77281541899082[/C][C]0.454369162018361[/C][C]0.227184581009180[/C][/ROW]
[ROW][C]46[/C][C]0.9472475366577[/C][C]0.105504926684600[/C][C]0.0527524633423001[/C][/ROW]
[ROW][C]47[/C][C]0.941610792098747[/C][C]0.116778415802506[/C][C]0.0583892079012528[/C][/ROW]
[ROW][C]48[/C][C]0.918567100570662[/C][C]0.162865798858675[/C][C]0.0814328994293376[/C][/ROW]
[ROW][C]49[/C][C]0.87785140194718[/C][C]0.244297196105639[/C][C]0.122148598052819[/C][/ROW]
[ROW][C]50[/C][C]0.903534393649034[/C][C]0.192931212701932[/C][C]0.0964656063509662[/C][/ROW]
[ROW][C]51[/C][C]0.852831252577309[/C][C]0.294337494845382[/C][C]0.147168747422691[/C][/ROW]
[ROW][C]52[/C][C]0.884027855430858[/C][C]0.231944289138285[/C][C]0.115972144569142[/C][/ROW]
[ROW][C]53[/C][C]0.843167625663856[/C][C]0.313664748672288[/C][C]0.156832374336144[/C][/ROW]
[ROW][C]54[/C][C]0.939583476667174[/C][C]0.120833046665652[/C][C]0.060416523332826[/C][/ROW]
[ROW][C]55[/C][C]0.962897903656644[/C][C]0.0742041926867123[/C][C]0.0371020963433562[/C][/ROW]
[ROW][C]56[/C][C]0.980325370577757[/C][C]0.0393492588444852[/C][C]0.0196746294222426[/C][/ROW]
[ROW][C]57[/C][C]0.99371653523122[/C][C]0.0125669295375592[/C][C]0.00628346476877958[/C][/ROW]
[ROW][C]58[/C][C]0.978567724795464[/C][C]0.0428645504090718[/C][C]0.0214322752045359[/C][/ROW]
[ROW][C]59[/C][C]0.933672904190525[/C][C]0.132654191618950[/C][C]0.0663270958094748[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57758&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57758&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
50.1988004573516790.3976009147033590.80119954264832
60.1400459632860100.2800919265720210.85995403671399
70.1094785397236910.2189570794473820.890521460276309
80.06662526537772740.1332505307554550.933374734622273
90.03343017732416030.06686035464832070.96656982267584
100.01814676821922020.03629353643844050.98185323178078
110.007956823541298190.01591364708259640.992043176458702
120.009006128798382550.01801225759676510.990993871201618
130.005775140343519860.01155028068703970.99422485965648
140.06752851418675490.1350570283735100.932471485813245
150.3333293066305560.6666586132611110.666670693369444
160.5264627993358450.947074401328310.473537200664155
170.6427920690745260.7144158618509470.357207930925474
180.658095759596950.6838084808060990.341904240403049
190.6276931011910580.7446137976178840.372306898808942
200.652584363246790.6948312735064210.347415636753210
210.5999178735777010.8001642528445980.400082126422299
220.5539428500334490.8921142999331020.446057149966551
230.6316395159791950.736720968041610.368360484020805
240.8596705018374150.2806589963251690.140329498162585
250.922526926879510.1549461462409780.0774730731204892
260.9438188872230770.1123622255538470.0561811127769233
270.9225493944849520.1549012110300960.0774506055150478
280.9161166480495980.1677667039008040.0838833519504018
290.9413892669334030.1172214661331940.0586107330665969
300.9266550804391330.1466898391217330.0733449195608666
310.904933799828920.1901324003421590.0950662001710795
320.9148902606587810.1702194786824390.0851097393412193
330.9039680933091350.1920638133817310.0960319066908655
340.8999724443298940.2000551113402130.100027555670106
350.9269771231536720.1460457536926560.0730228768463281
360.9053606687285740.1892786625428510.0946393312714256
370.9014805212034140.1970389575931730.0985194787965864
380.87057607866970.25884784266060.1294239213303
390.8290355286797670.3419289426404670.170964471320233
400.8062802990080430.3874394019839140.193719700991957
410.8282523751444590.3434952497110820.171747624855541
420.8462943421276880.3074113157446250.153705657872312
430.7944625284225560.4110749431548880.205537471577444
440.7515025409950590.4969949180098820.248497459004941
450.772815418990820.4543691620183610.227184581009180
460.94724753665770.1055049266846000.0527524633423001
470.9416107920987470.1167784158025060.0583892079012528
480.9185671005706620.1628657988586750.0814328994293376
490.877851401947180.2442971961056390.122148598052819
500.9035343936490340.1929312127019320.0964656063509662
510.8528312525773090.2943374948453820.147168747422691
520.8840278554308580.2319442891382850.115972144569142
530.8431676256638560.3136647486722880.156832374336144
540.9395834766671740.1208330466656520.060416523332826
550.9628979036566440.07420419268671230.0371020963433562
560.9803253705777570.03934925884448520.0196746294222426
570.993716535231220.01256692953755920.00628346476877958
580.9785677247954640.04286455040907180.0214322752045359
590.9336729041905250.1326541916189500.0663270958094748







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level70.127272727272727NOK
10% type I error level90.163636363636364NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 7 & 0.127272727272727 & NOK \tabularnewline
10% type I error level & 9 & 0.163636363636364 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57758&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]7[/C][C]0.127272727272727[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]9[/C][C]0.163636363636364[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57758&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57758&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level70.127272727272727NOK
10% type I error level90.163636363636364NOK



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')
}