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Description of Statistical Computation

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 01:34:59 -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/t1258620251mtdfp2jitw02lhg.htm/, Retrieved Fri, 26 Apr 2024 13:55:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57654, Retrieved Fri, 26 Apr 2024 13:55:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact192

Tree of Dependent Computations

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] [] [2009-11-18 16:30:48] [74be16979710d4c4e7c6647856088456]
-   P       [Multiple Regression] [] [2009-11-19 07:41:31] [74be16979710d4c4e7c6647856088456]
-   PD          [Multiple Regression] [] [2009-11-19 08:34:59] [a93df6747c5c78315f2ee9914aea3ec6] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2.057	0	2.058	2.077	2.053	2.085
2.076	0	2.057	2.058	2.077	2.053
2.07	0	2.076	2.057	2.058	2.077
2.062	0	2.07	2.076	2.057	2.058
2.073	0	2.062	2.07	2.076	2.057
2.061	0	2.073	2.062	2.07	2.076
2.094	0	2.061	2.073	2.062	2.07
2.067	0	2.094	2.061	2.073	2.062
2.086	0	2.067	2.094	2.061	2.073
2.276	0	2.086	2.067	2.094	2.061
2.326	0	2.276	2.086	2.067	2.094
2.349	0	2.326	2.276	2.086	2.067
2.52	0	2.349	2.326	2.276	2.086
2.628	0	2.52	2.349	2.326	2.276
2.577	0	2.628	2.52	2.349	2.326
2.698	0	2.577	2.628	2.52	2.349
2.814	0	2.698	2.577	2.628	2.52
2.968	0	2.814	2.698	2.577	2.628
3.041	0	2.968	2.814	2.698	2.577
3.278	0	3.041	2.968	2.814	2.698
3.328	0	3.278	3.041	2.968	2.814
3.5	0	3.328	3.278	3.041	2.968
3.563	0	3.5	3.328	3.278	3.041
3.569	0	3.563	3.5	3.328	3.278
3.69	0	3.569	3.563	3.5	3.328
3.819	0	3.69	3.569	3.563	3.5
3.79	0	3.819	3.69	3.569	3.563
3.956	0	3.79	3.819	3.69	3.569
4.063	0	3.956	3.79	3.819	3.69
4.047	0	4.063	3.956	3.79	3.819
4.029	0	4.047	4.063	3.956	3.79
3.941	0	4.029	4.047	4.063	3.956
4.022	0	3.941	4.029	4.047	4.063
3.879	0	4.022	3.941	4.029	4.047
4.022	0	3.879	4.022	3.941	4.029
4.028	0	4.022	3.879	4.022	3.941
4.091	0	4.028	4.022	3.879	4.022
3.987	0	4.091	4.028	4.022	3.879
4.01	0	3.987	4.091	4.028	4.022
4.007	0	4.01	3.987	4.091	4.028
4.191	0	4.007	4.01	3.987	4.091
4.299	0	4.191	4.007	4.01	3.987
4.273	0	4.299	4.191	4.007	4.01
3.82	0	4.273	4.299	4.191	4.007
3.15	1	3.82	4.273	4.299	4.191
2.486	1	3.15	3.82	4.273	4.299
1.812	1	2.486	3.15	3.82	4.273
1.257	1	1.812	2.486	3.15	3.82
1.062	1	1.257	1.812	2.486	3.15
0.842	1	1.062	1.257	1.812	2.486
0.782	1	0.842	1.062	1.257	1.812
0.698	1	0.782	0.842	1.062	1.257
0.358	1	0.698	0.782	0.842	1.062
0.347	1	0.358	0.698	0.782	0.842
0.363	1	0.347	0.358	0.698	0.782
0.359	1	0.363	0.347	0.358	0.698
0.355	1	0.359	0.363	0.347	0.358

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57654&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]9 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=57654&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57654&T=0

As an alternative you can also use a QR Code:  
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 time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
intb[t] = + 0.210659035938168 -0.25603237044698X[t] + 1.39418813246313`Yt-1`[t] -0.279777174124115`Yt-2`[t] -0.344875777009029`Yt-3`[t] + 0.169566453810560`Yt-4`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
intb[t] =  +  0.210659035938168 -0.25603237044698X[t] +  1.39418813246313`Yt-1`[t] -0.279777174124115`Yt-2`[t] -0.344875777009029`Yt-3`[t] +  0.169566453810560`Yt-4`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57654&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]intb[t] =  +  0.210659035938168 -0.25603237044698X[t] +  1.39418813246313`Yt-1`[t] -0.279777174124115`Yt-2`[t] -0.344875777009029`Yt-3`[t] +  0.169566453810560`Yt-4`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57654&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57654&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
intb[t] = + 0.210659035938168 -0.25603237044698X[t] + 1.39418813246313`Yt-1`[t] -0.279777174124115`Yt-2`[t] -0.344875777009029`Yt-3`[t] + 0.169566453810560`Yt-4`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.2106590359381680.0698033.01790.0039660.001983
X-0.256032370446980.080161-3.1940.0024070.001203
`Yt-1`1.394188132463130.1500289.292900
`Yt-2`-0.2797771741241150.244782-1.1430.258390.129195
`Yt-3`-0.3448757770090290.242766-1.42060.1615160.080758
`Yt-4`0.1695664538105600.1341841.26370.2120870.106044

\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) & 0.210659035938168 & 0.069803 & 3.0179 & 0.003966 & 0.001983 \tabularnewline
X & -0.25603237044698 & 0.080161 & -3.194 & 0.002407 & 0.001203 \tabularnewline
`Yt-1` & 1.39418813246313 & 0.150028 & 9.2929 & 0 & 0 \tabularnewline
`Yt-2` & -0.279777174124115 & 0.244782 & -1.143 & 0.25839 & 0.129195 \tabularnewline
`Yt-3` & -0.344875777009029 & 0.242766 & -1.4206 & 0.161516 & 0.080758 \tabularnewline
`Yt-4` & 0.169566453810560 & 0.134184 & 1.2637 & 0.212087 & 0.106044 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57654&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]0.210659035938168[/C][C]0.069803[/C][C]3.0179[/C][C]0.003966[/C][C]0.001983[/C][/ROW]
[ROW][C]X[/C][C]-0.25603237044698[/C][C]0.080161[/C][C]-3.194[/C][C]0.002407[/C][C]0.001203[/C][/ROW]
[ROW][C]`Yt-1`[/C][C]1.39418813246313[/C][C]0.150028[/C][C]9.2929[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Yt-2`[/C][C]-0.279777174124115[/C][C]0.244782[/C][C]-1.143[/C][C]0.25839[/C][C]0.129195[/C][/ROW]
[ROW][C]`Yt-3`[/C][C]-0.344875777009029[/C][C]0.242766[/C][C]-1.4206[/C][C]0.161516[/C][C]0.080758[/C][/ROW]
[ROW][C]`Yt-4`[/C][C]0.169566453810560[/C][C]0.134184[/C][C]1.2637[/C][C]0.212087[/C][C]0.106044[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57654&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57654&T=2

As an alternative you can also use a QR Code:  
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)0.2106590359381680.0698033.01790.0039660.001983
X-0.256032370446980.080161-3.1940.0024070.001203
`Yt-1`1.394188132463130.1500289.292900
`Yt-2`-0.2797771741241150.244782-1.1430.258390.129195
`Yt-3`-0.3448757770090290.242766-1.42060.1615160.080758
`Yt-4`0.1695664538105600.1341841.26370.2120870.106044






Multiple Linear Regression - Regression Statistics
Multiple R0.995246444713004
R-squared0.990515485713875
Adjusted R-squared0.989585631372098
F-TEST (value)1065.23725406385
F-TEST (DF numerator)5
F-TEST (DF denominator)51
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.126170925631754
Sum Squared Residuals0.811874226213457

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.995246444713004 \tabularnewline
R-squared & 0.990515485713875 \tabularnewline
Adjusted R-squared & 0.989585631372098 \tabularnewline
F-TEST (value) & 1065.23725406385 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 51 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.126170925631754 \tabularnewline
Sum Squared Residuals & 0.811874226213457 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57654&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.995246444713004[/C][/ROW]
[ROW][C]R-squared[/C][C]0.990515485713875[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.989585631372098[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1065.23725406385[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]51[/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]0.126170925631754[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.811874226213457[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57654&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57654&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.995246444713004
R-squared0.990515485713875
Adjusted R-squared0.989585631372098
F-TEST (value)1065.23725406385
F-TEST (DF numerator)5
F-TEST (DF denominator)51
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.126170925631754
Sum Squared Residuals0.811874226213457






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12.0572.14431710788699-0.0873171078869878
22.0762.13453554089273-0.0585355408927277
32.072.17192712723827-0.101927127238274
42.0622.15536934528975-0.0933693452897483
52.0732.13917229705781-0.0661722970578056
62.0612.16203760119235-0.101037601192348
72.0942.14397140218063-0.049971402180634
82.0672.18818677146382-0.121186771463823
92.0862.14731478545725-0.0613147854572474
102.2762.167942645588370.108057354411627
112.3262.442429963403-0.116429963403002
122.3492.44785077292652-0.0988507729265203
132.522.403623606257650.116376393742348
142.6282.65056873927755-0.0225687392775473
152.5772.75384534062766-0.176845340627663
162.6982.597452081635740.100547918364263
172.8142.772166761228740.0418332387712635
182.9682.935941388164440.0320586118355568
193.0413.06781435020294-0.0268143502029370
203.2783.107016349835660.170983650164338
213.3283.383574042501-0.055574042500999
223.53.387913561021910.112086438978093
233.5633.544367853076390.0186321469236089
243.5693.60702349217487-0.0380234921748724
253.693.546922348044810.143077651955193
263.8193.721378705131950.0976212948680517
273.793.87598936807869-0.0859893680786856
283.9563.758754086480010.197245913519985
294.0633.974331420195410.0886685798045928
304.0474.10894200953918-0.0619420095391831
314.0293.994532035644490.0344679643555126
323.9413.96515940723872-0.0241594072387241
334.0223.871168463706080.150831536293924
343.8794.01221279448371-0.133212794483706
354.0223.817478812645630.204521187354371
364.0284.013999065614550.0140009343854544
374.0914.045408177380520.0455918226194789
383.9874.05799812767375-0.0709981276737537
394.013.917555348160620.092444651839375
404.0073.958008726087480.0489912739125212
414.1913.993941054084240.197058945915761
424.2994.225743947912320.0732560520876786
434.2734.32977192194817-0.0567719219481741
443.824.19934125336763-0.379341253367634
453.153.31296950902625-0.162969509026250
462.4862.53288246736795-0.0468824673679502
471.8121.94641225326160-0.134412253261603
481.2571.34675466261973-0.0897546626197302
491.0620.8769380563432650.185061943656735
500.8420.880201850525712-0.0382018505257123
510.7820.705155276709720.0768447232902804
520.6980.6561963617211370.041803638278863
530.3580.598678401490608-0.240678401490608
540.3470.1315436458617870.215456354138213
550.3630.2301273936470170.132872606352983
560.3590.3585261347447750.000473865255225039
570.3550.2946139866804450.0603860133195546

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2.057 & 2.14431710788699 & -0.0873171078869878 \tabularnewline
2 & 2.076 & 2.13453554089273 & -0.0585355408927277 \tabularnewline
3 & 2.07 & 2.17192712723827 & -0.101927127238274 \tabularnewline
4 & 2.062 & 2.15536934528975 & -0.0933693452897483 \tabularnewline
5 & 2.073 & 2.13917229705781 & -0.0661722970578056 \tabularnewline
6 & 2.061 & 2.16203760119235 & -0.101037601192348 \tabularnewline
7 & 2.094 & 2.14397140218063 & -0.049971402180634 \tabularnewline
8 & 2.067 & 2.18818677146382 & -0.121186771463823 \tabularnewline
9 & 2.086 & 2.14731478545725 & -0.0613147854572474 \tabularnewline
10 & 2.276 & 2.16794264558837 & 0.108057354411627 \tabularnewline
11 & 2.326 & 2.442429963403 & -0.116429963403002 \tabularnewline
12 & 2.349 & 2.44785077292652 & -0.0988507729265203 \tabularnewline
13 & 2.52 & 2.40362360625765 & 0.116376393742348 \tabularnewline
14 & 2.628 & 2.65056873927755 & -0.0225687392775473 \tabularnewline
15 & 2.577 & 2.75384534062766 & -0.176845340627663 \tabularnewline
16 & 2.698 & 2.59745208163574 & 0.100547918364263 \tabularnewline
17 & 2.814 & 2.77216676122874 & 0.0418332387712635 \tabularnewline
18 & 2.968 & 2.93594138816444 & 0.0320586118355568 \tabularnewline
19 & 3.041 & 3.06781435020294 & -0.0268143502029370 \tabularnewline
20 & 3.278 & 3.10701634983566 & 0.170983650164338 \tabularnewline
21 & 3.328 & 3.383574042501 & -0.055574042500999 \tabularnewline
22 & 3.5 & 3.38791356102191 & 0.112086438978093 \tabularnewline
23 & 3.563 & 3.54436785307639 & 0.0186321469236089 \tabularnewline
24 & 3.569 & 3.60702349217487 & -0.0380234921748724 \tabularnewline
25 & 3.69 & 3.54692234804481 & 0.143077651955193 \tabularnewline
26 & 3.819 & 3.72137870513195 & 0.0976212948680517 \tabularnewline
27 & 3.79 & 3.87598936807869 & -0.0859893680786856 \tabularnewline
28 & 3.956 & 3.75875408648001 & 0.197245913519985 \tabularnewline
29 & 4.063 & 3.97433142019541 & 0.0886685798045928 \tabularnewline
30 & 4.047 & 4.10894200953918 & -0.0619420095391831 \tabularnewline
31 & 4.029 & 3.99453203564449 & 0.0344679643555126 \tabularnewline
32 & 3.941 & 3.96515940723872 & -0.0241594072387241 \tabularnewline
33 & 4.022 & 3.87116846370608 & 0.150831536293924 \tabularnewline
34 & 3.879 & 4.01221279448371 & -0.133212794483706 \tabularnewline
35 & 4.022 & 3.81747881264563 & 0.204521187354371 \tabularnewline
36 & 4.028 & 4.01399906561455 & 0.0140009343854544 \tabularnewline
37 & 4.091 & 4.04540817738052 & 0.0455918226194789 \tabularnewline
38 & 3.987 & 4.05799812767375 & -0.0709981276737537 \tabularnewline
39 & 4.01 & 3.91755534816062 & 0.092444651839375 \tabularnewline
40 & 4.007 & 3.95800872608748 & 0.0489912739125212 \tabularnewline
41 & 4.191 & 3.99394105408424 & 0.197058945915761 \tabularnewline
42 & 4.299 & 4.22574394791232 & 0.0732560520876786 \tabularnewline
43 & 4.273 & 4.32977192194817 & -0.0567719219481741 \tabularnewline
44 & 3.82 & 4.19934125336763 & -0.379341253367634 \tabularnewline
45 & 3.15 & 3.31296950902625 & -0.162969509026250 \tabularnewline
46 & 2.486 & 2.53288246736795 & -0.0468824673679502 \tabularnewline
47 & 1.812 & 1.94641225326160 & -0.134412253261603 \tabularnewline
48 & 1.257 & 1.34675466261973 & -0.0897546626197302 \tabularnewline
49 & 1.062 & 0.876938056343265 & 0.185061943656735 \tabularnewline
50 & 0.842 & 0.880201850525712 & -0.0382018505257123 \tabularnewline
51 & 0.782 & 0.70515527670972 & 0.0768447232902804 \tabularnewline
52 & 0.698 & 0.656196361721137 & 0.041803638278863 \tabularnewline
53 & 0.358 & 0.598678401490608 & -0.240678401490608 \tabularnewline
54 & 0.347 & 0.131543645861787 & 0.215456354138213 \tabularnewline
55 & 0.363 & 0.230127393647017 & 0.132872606352983 \tabularnewline
56 & 0.359 & 0.358526134744775 & 0.000473865255225039 \tabularnewline
57 & 0.355 & 0.294613986680445 & 0.0603860133195546 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57654&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]2.057[/C][C]2.14431710788699[/C][C]-0.0873171078869878[/C][/ROW]
[ROW][C]2[/C][C]2.076[/C][C]2.13453554089273[/C][C]-0.0585355408927277[/C][/ROW]
[ROW][C]3[/C][C]2.07[/C][C]2.17192712723827[/C][C]-0.101927127238274[/C][/ROW]
[ROW][C]4[/C][C]2.062[/C][C]2.15536934528975[/C][C]-0.0933693452897483[/C][/ROW]
[ROW][C]5[/C][C]2.073[/C][C]2.13917229705781[/C][C]-0.0661722970578056[/C][/ROW]
[ROW][C]6[/C][C]2.061[/C][C]2.16203760119235[/C][C]-0.101037601192348[/C][/ROW]
[ROW][C]7[/C][C]2.094[/C][C]2.14397140218063[/C][C]-0.049971402180634[/C][/ROW]
[ROW][C]8[/C][C]2.067[/C][C]2.18818677146382[/C][C]-0.121186771463823[/C][/ROW]
[ROW][C]9[/C][C]2.086[/C][C]2.14731478545725[/C][C]-0.0613147854572474[/C][/ROW]
[ROW][C]10[/C][C]2.276[/C][C]2.16794264558837[/C][C]0.108057354411627[/C][/ROW]
[ROW][C]11[/C][C]2.326[/C][C]2.442429963403[/C][C]-0.116429963403002[/C][/ROW]
[ROW][C]12[/C][C]2.349[/C][C]2.44785077292652[/C][C]-0.0988507729265203[/C][/ROW]
[ROW][C]13[/C][C]2.52[/C][C]2.40362360625765[/C][C]0.116376393742348[/C][/ROW]
[ROW][C]14[/C][C]2.628[/C][C]2.65056873927755[/C][C]-0.0225687392775473[/C][/ROW]
[ROW][C]15[/C][C]2.577[/C][C]2.75384534062766[/C][C]-0.176845340627663[/C][/ROW]
[ROW][C]16[/C][C]2.698[/C][C]2.59745208163574[/C][C]0.100547918364263[/C][/ROW]
[ROW][C]17[/C][C]2.814[/C][C]2.77216676122874[/C][C]0.0418332387712635[/C][/ROW]
[ROW][C]18[/C][C]2.968[/C][C]2.93594138816444[/C][C]0.0320586118355568[/C][/ROW]
[ROW][C]19[/C][C]3.041[/C][C]3.06781435020294[/C][C]-0.0268143502029370[/C][/ROW]
[ROW][C]20[/C][C]3.278[/C][C]3.10701634983566[/C][C]0.170983650164338[/C][/ROW]
[ROW][C]21[/C][C]3.328[/C][C]3.383574042501[/C][C]-0.055574042500999[/C][/ROW]
[ROW][C]22[/C][C]3.5[/C][C]3.38791356102191[/C][C]0.112086438978093[/C][/ROW]
[ROW][C]23[/C][C]3.563[/C][C]3.54436785307639[/C][C]0.0186321469236089[/C][/ROW]
[ROW][C]24[/C][C]3.569[/C][C]3.60702349217487[/C][C]-0.0380234921748724[/C][/ROW]
[ROW][C]25[/C][C]3.69[/C][C]3.54692234804481[/C][C]0.143077651955193[/C][/ROW]
[ROW][C]26[/C][C]3.819[/C][C]3.72137870513195[/C][C]0.0976212948680517[/C][/ROW]
[ROW][C]27[/C][C]3.79[/C][C]3.87598936807869[/C][C]-0.0859893680786856[/C][/ROW]
[ROW][C]28[/C][C]3.956[/C][C]3.75875408648001[/C][C]0.197245913519985[/C][/ROW]
[ROW][C]29[/C][C]4.063[/C][C]3.97433142019541[/C][C]0.0886685798045928[/C][/ROW]
[ROW][C]30[/C][C]4.047[/C][C]4.10894200953918[/C][C]-0.0619420095391831[/C][/ROW]
[ROW][C]31[/C][C]4.029[/C][C]3.99453203564449[/C][C]0.0344679643555126[/C][/ROW]
[ROW][C]32[/C][C]3.941[/C][C]3.96515940723872[/C][C]-0.0241594072387241[/C][/ROW]
[ROW][C]33[/C][C]4.022[/C][C]3.87116846370608[/C][C]0.150831536293924[/C][/ROW]
[ROW][C]34[/C][C]3.879[/C][C]4.01221279448371[/C][C]-0.133212794483706[/C][/ROW]
[ROW][C]35[/C][C]4.022[/C][C]3.81747881264563[/C][C]0.204521187354371[/C][/ROW]
[ROW][C]36[/C][C]4.028[/C][C]4.01399906561455[/C][C]0.0140009343854544[/C][/ROW]
[ROW][C]37[/C][C]4.091[/C][C]4.04540817738052[/C][C]0.0455918226194789[/C][/ROW]
[ROW][C]38[/C][C]3.987[/C][C]4.05799812767375[/C][C]-0.0709981276737537[/C][/ROW]
[ROW][C]39[/C][C]4.01[/C][C]3.91755534816062[/C][C]0.092444651839375[/C][/ROW]
[ROW][C]40[/C][C]4.007[/C][C]3.95800872608748[/C][C]0.0489912739125212[/C][/ROW]
[ROW][C]41[/C][C]4.191[/C][C]3.99394105408424[/C][C]0.197058945915761[/C][/ROW]
[ROW][C]42[/C][C]4.299[/C][C]4.22574394791232[/C][C]0.0732560520876786[/C][/ROW]
[ROW][C]43[/C][C]4.273[/C][C]4.32977192194817[/C][C]-0.0567719219481741[/C][/ROW]
[ROW][C]44[/C][C]3.82[/C][C]4.19934125336763[/C][C]-0.379341253367634[/C][/ROW]
[ROW][C]45[/C][C]3.15[/C][C]3.31296950902625[/C][C]-0.162969509026250[/C][/ROW]
[ROW][C]46[/C][C]2.486[/C][C]2.53288246736795[/C][C]-0.0468824673679502[/C][/ROW]
[ROW][C]47[/C][C]1.812[/C][C]1.94641225326160[/C][C]-0.134412253261603[/C][/ROW]
[ROW][C]48[/C][C]1.257[/C][C]1.34675466261973[/C][C]-0.0897546626197302[/C][/ROW]
[ROW][C]49[/C][C]1.062[/C][C]0.876938056343265[/C][C]0.185061943656735[/C][/ROW]
[ROW][C]50[/C][C]0.842[/C][C]0.880201850525712[/C][C]-0.0382018505257123[/C][/ROW]
[ROW][C]51[/C][C]0.782[/C][C]0.70515527670972[/C][C]0.0768447232902804[/C][/ROW]
[ROW][C]52[/C][C]0.698[/C][C]0.656196361721137[/C][C]0.041803638278863[/C][/ROW]
[ROW][C]53[/C][C]0.358[/C][C]0.598678401490608[/C][C]-0.240678401490608[/C][/ROW]
[ROW][C]54[/C][C]0.347[/C][C]0.131543645861787[/C][C]0.215456354138213[/C][/ROW]
[ROW][C]55[/C][C]0.363[/C][C]0.230127393647017[/C][C]0.132872606352983[/C][/ROW]
[ROW][C]56[/C][C]0.359[/C][C]0.358526134744775[/C][C]0.000473865255225039[/C][/ROW]
[ROW][C]57[/C][C]0.355[/C][C]0.294613986680445[/C][C]0.0603860133195546[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57654&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57654&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12.0572.14431710788699-0.0873171078869878
22.0762.13453554089273-0.0585355408927277
32.072.17192712723827-0.101927127238274
42.0622.15536934528975-0.0933693452897483
52.0732.13917229705781-0.0661722970578056
62.0612.16203760119235-0.101037601192348
72.0942.14397140218063-0.049971402180634
82.0672.18818677146382-0.121186771463823
92.0862.14731478545725-0.0613147854572474
102.2762.167942645588370.108057354411627
112.3262.442429963403-0.116429963403002
122.3492.44785077292652-0.0988507729265203
132.522.403623606257650.116376393742348
142.6282.65056873927755-0.0225687392775473
152.5772.75384534062766-0.176845340627663
162.6982.597452081635740.100547918364263
172.8142.772166761228740.0418332387712635
182.9682.935941388164440.0320586118355568
193.0413.06781435020294-0.0268143502029370
203.2783.107016349835660.170983650164338
213.3283.383574042501-0.055574042500999
223.53.387913561021910.112086438978093
233.5633.544367853076390.0186321469236089
243.5693.60702349217487-0.0380234921748724
253.693.546922348044810.143077651955193
263.8193.721378705131950.0976212948680517
273.793.87598936807869-0.0859893680786856
283.9563.758754086480010.197245913519985
294.0633.974331420195410.0886685798045928
304.0474.10894200953918-0.0619420095391831
314.0293.994532035644490.0344679643555126
323.9413.96515940723872-0.0241594072387241
334.0223.871168463706080.150831536293924
343.8794.01221279448371-0.133212794483706
354.0223.817478812645630.204521187354371
364.0284.013999065614550.0140009343854544
374.0914.045408177380520.0455918226194789
383.9874.05799812767375-0.0709981276737537
394.013.917555348160620.092444651839375
404.0073.958008726087480.0489912739125212
414.1913.993941054084240.197058945915761
424.2994.225743947912320.0732560520876786
434.2734.32977192194817-0.0567719219481741
443.824.19934125336763-0.379341253367634
453.153.31296950902625-0.162969509026250
462.4862.53288246736795-0.0468824673679502
471.8121.94641225326160-0.134412253261603
481.2571.34675466261973-0.0897546626197302
491.0620.8769380563432650.185061943656735
500.8420.880201850525712-0.0382018505257123
510.7820.705155276709720.0768447232902804
520.6980.6561963617211370.041803638278863
530.3580.598678401490608-0.240678401490608
540.3470.1315436458617870.215456354138213
550.3630.2301273936470170.132872606352983
560.3590.3585261347447750.000473865255225039
570.3550.2946139866804450.0603860133195546






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.003660166748679970.007320333497359930.99633983325132
100.05109231409252980.1021846281850600.94890768590747
110.01984628238842110.03969256477684210.980153717611579
120.01756756027880590.03513512055761180.982432439721194
130.02799699101713150.05599398203426310.972003008982868
140.01290683122298320.02581366244596640.987093168777017
150.01261252150014690.02522504300029390.987387478499853
160.006509873754254390.01301974750850880.993490126245746
170.002918468391163630.005836936782327260.997081531608836
180.01656228735975780.03312457471951560.983437712640242
190.01449262981447390.02898525962894780.985507370185526
200.01368850944681150.02737701889362300.986311490553188
210.02384079112625780.04768158225251560.976159208873742
220.01326655915405140.02653311830810280.986733440845949
230.01804115569311740.03608231138623480.981958844306883
240.02057016011312590.04114032022625190.979429839886874
250.01176677182742740.02353354365485480.988233228172573
260.007415777820126230.01483155564025250.992584222179874
270.007097294087645040.01419458817529010.992902705912355
280.00542736771645350.0108547354329070.994572632283546
290.003394844254474250.00678968850894850.996605155745526
300.002106127929077170.004212255858154330.997893872070923
310.002692639849238670.005385279698477330.997307360150761
320.002975262665677140.005950525331354290.997024737334323
330.002917429850498570.005834859700997140.997082570149501
340.003572787213553580.007145574427107170.996427212786446
350.00648272577942750.0129654515588550.993517274220572
360.003457216384705010.006914432769410030.996542783615295
370.002138265780850270.004276531561700540.99786173421915
380.002602150461071670.005204300922143340.997397849538928
390.001455755071673000.002911510143346000.998544244928327
400.0006933645674996670.001386729134999330.9993066354325
410.004548645560769250.00909729112153850.99545135443923
420.01238714978351200.02477429956702400.987612850216488
430.08434166466193570.1686833293238710.915658335338064
440.3073556139540840.6147112279081680.692644386045916
450.294641303992770.589282607985540.70535869600723
460.3915469894688560.7830939789377120.608453010531144
470.3840121231636790.7680242463273580.615987876836321
480.2551337984317940.5102675968635870.744866201568206

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.00366016674867997 & 0.00732033349735993 & 0.99633983325132 \tabularnewline
10 & 0.0510923140925298 & 0.102184628185060 & 0.94890768590747 \tabularnewline
11 & 0.0198462823884211 & 0.0396925647768421 & 0.980153717611579 \tabularnewline
12 & 0.0175675602788059 & 0.0351351205576118 & 0.982432439721194 \tabularnewline
13 & 0.0279969910171315 & 0.0559939820342631 & 0.972003008982868 \tabularnewline
14 & 0.0129068312229832 & 0.0258136624459664 & 0.987093168777017 \tabularnewline
15 & 0.0126125215001469 & 0.0252250430002939 & 0.987387478499853 \tabularnewline
16 & 0.00650987375425439 & 0.0130197475085088 & 0.993490126245746 \tabularnewline
17 & 0.00291846839116363 & 0.00583693678232726 & 0.997081531608836 \tabularnewline
18 & 0.0165622873597578 & 0.0331245747195156 & 0.983437712640242 \tabularnewline
19 & 0.0144926298144739 & 0.0289852596289478 & 0.985507370185526 \tabularnewline
20 & 0.0136885094468115 & 0.0273770188936230 & 0.986311490553188 \tabularnewline
21 & 0.0238407911262578 & 0.0476815822525156 & 0.976159208873742 \tabularnewline
22 & 0.0132665591540514 & 0.0265331183081028 & 0.986733440845949 \tabularnewline
23 & 0.0180411556931174 & 0.0360823113862348 & 0.981958844306883 \tabularnewline
24 & 0.0205701601131259 & 0.0411403202262519 & 0.979429839886874 \tabularnewline
25 & 0.0117667718274274 & 0.0235335436548548 & 0.988233228172573 \tabularnewline
26 & 0.00741577782012623 & 0.0148315556402525 & 0.992584222179874 \tabularnewline
27 & 0.00709729408764504 & 0.0141945881752901 & 0.992902705912355 \tabularnewline
28 & 0.0054273677164535 & 0.010854735432907 & 0.994572632283546 \tabularnewline
29 & 0.00339484425447425 & 0.0067896885089485 & 0.996605155745526 \tabularnewline
30 & 0.00210612792907717 & 0.00421225585815433 & 0.997893872070923 \tabularnewline
31 & 0.00269263984923867 & 0.00538527969847733 & 0.997307360150761 \tabularnewline
32 & 0.00297526266567714 & 0.00595052533135429 & 0.997024737334323 \tabularnewline
33 & 0.00291742985049857 & 0.00583485970099714 & 0.997082570149501 \tabularnewline
34 & 0.00357278721355358 & 0.00714557442710717 & 0.996427212786446 \tabularnewline
35 & 0.0064827257794275 & 0.012965451558855 & 0.993517274220572 \tabularnewline
36 & 0.00345721638470501 & 0.00691443276941003 & 0.996542783615295 \tabularnewline
37 & 0.00213826578085027 & 0.00427653156170054 & 0.99786173421915 \tabularnewline
38 & 0.00260215046107167 & 0.00520430092214334 & 0.997397849538928 \tabularnewline
39 & 0.00145575507167300 & 0.00291151014334600 & 0.998544244928327 \tabularnewline
40 & 0.000693364567499667 & 0.00138672913499933 & 0.9993066354325 \tabularnewline
41 & 0.00454864556076925 & 0.0090972911215385 & 0.99545135443923 \tabularnewline
42 & 0.0123871497835120 & 0.0247742995670240 & 0.987612850216488 \tabularnewline
43 & 0.0843416646619357 & 0.168683329323871 & 0.915658335338064 \tabularnewline
44 & 0.307355613954084 & 0.614711227908168 & 0.692644386045916 \tabularnewline
45 & 0.29464130399277 & 0.58928260798554 & 0.70535869600723 \tabularnewline
46 & 0.391546989468856 & 0.783093978937712 & 0.608453010531144 \tabularnewline
47 & 0.384012123163679 & 0.768024246327358 & 0.615987876836321 \tabularnewline
48 & 0.255133798431794 & 0.510267596863587 & 0.744866201568206 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57654&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]9[/C][C]0.00366016674867997[/C][C]0.00732033349735993[/C][C]0.99633983325132[/C][/ROW]
[ROW][C]10[/C][C]0.0510923140925298[/C][C]0.102184628185060[/C][C]0.94890768590747[/C][/ROW]
[ROW][C]11[/C][C]0.0198462823884211[/C][C]0.0396925647768421[/C][C]0.980153717611579[/C][/ROW]
[ROW][C]12[/C][C]0.0175675602788059[/C][C]0.0351351205576118[/C][C]0.982432439721194[/C][/ROW]
[ROW][C]13[/C][C]0.0279969910171315[/C][C]0.0559939820342631[/C][C]0.972003008982868[/C][/ROW]
[ROW][C]14[/C][C]0.0129068312229832[/C][C]0.0258136624459664[/C][C]0.987093168777017[/C][/ROW]
[ROW][C]15[/C][C]0.0126125215001469[/C][C]0.0252250430002939[/C][C]0.987387478499853[/C][/ROW]
[ROW][C]16[/C][C]0.00650987375425439[/C][C]0.0130197475085088[/C][C]0.993490126245746[/C][/ROW]
[ROW][C]17[/C][C]0.00291846839116363[/C][C]0.00583693678232726[/C][C]0.997081531608836[/C][/ROW]
[ROW][C]18[/C][C]0.0165622873597578[/C][C]0.0331245747195156[/C][C]0.983437712640242[/C][/ROW]
[ROW][C]19[/C][C]0.0144926298144739[/C][C]0.0289852596289478[/C][C]0.985507370185526[/C][/ROW]
[ROW][C]20[/C][C]0.0136885094468115[/C][C]0.0273770188936230[/C][C]0.986311490553188[/C][/ROW]
[ROW][C]21[/C][C]0.0238407911262578[/C][C]0.0476815822525156[/C][C]0.976159208873742[/C][/ROW]
[ROW][C]22[/C][C]0.0132665591540514[/C][C]0.0265331183081028[/C][C]0.986733440845949[/C][/ROW]
[ROW][C]23[/C][C]0.0180411556931174[/C][C]0.0360823113862348[/C][C]0.981958844306883[/C][/ROW]
[ROW][C]24[/C][C]0.0205701601131259[/C][C]0.0411403202262519[/C][C]0.979429839886874[/C][/ROW]
[ROW][C]25[/C][C]0.0117667718274274[/C][C]0.0235335436548548[/C][C]0.988233228172573[/C][/ROW]
[ROW][C]26[/C][C]0.00741577782012623[/C][C]0.0148315556402525[/C][C]0.992584222179874[/C][/ROW]
[ROW][C]27[/C][C]0.00709729408764504[/C][C]0.0141945881752901[/C][C]0.992902705912355[/C][/ROW]
[ROW][C]28[/C][C]0.0054273677164535[/C][C]0.010854735432907[/C][C]0.994572632283546[/C][/ROW]
[ROW][C]29[/C][C]0.00339484425447425[/C][C]0.0067896885089485[/C][C]0.996605155745526[/C][/ROW]
[ROW][C]30[/C][C]0.00210612792907717[/C][C]0.00421225585815433[/C][C]0.997893872070923[/C][/ROW]
[ROW][C]31[/C][C]0.00269263984923867[/C][C]0.00538527969847733[/C][C]0.997307360150761[/C][/ROW]
[ROW][C]32[/C][C]0.00297526266567714[/C][C]0.00595052533135429[/C][C]0.997024737334323[/C][/ROW]
[ROW][C]33[/C][C]0.00291742985049857[/C][C]0.00583485970099714[/C][C]0.997082570149501[/C][/ROW]
[ROW][C]34[/C][C]0.00357278721355358[/C][C]0.00714557442710717[/C][C]0.996427212786446[/C][/ROW]
[ROW][C]35[/C][C]0.0064827257794275[/C][C]0.012965451558855[/C][C]0.993517274220572[/C][/ROW]
[ROW][C]36[/C][C]0.00345721638470501[/C][C]0.00691443276941003[/C][C]0.996542783615295[/C][/ROW]
[ROW][C]37[/C][C]0.00213826578085027[/C][C]0.00427653156170054[/C][C]0.99786173421915[/C][/ROW]
[ROW][C]38[/C][C]0.00260215046107167[/C][C]0.00520430092214334[/C][C]0.997397849538928[/C][/ROW]
[ROW][C]39[/C][C]0.00145575507167300[/C][C]0.00291151014334600[/C][C]0.998544244928327[/C][/ROW]
[ROW][C]40[/C][C]0.000693364567499667[/C][C]0.00138672913499933[/C][C]0.9993066354325[/C][/ROW]
[ROW][C]41[/C][C]0.00454864556076925[/C][C]0.0090972911215385[/C][C]0.99545135443923[/C][/ROW]
[ROW][C]42[/C][C]0.0123871497835120[/C][C]0.0247742995670240[/C][C]0.987612850216488[/C][/ROW]
[ROW][C]43[/C][C]0.0843416646619357[/C][C]0.168683329323871[/C][C]0.915658335338064[/C][/ROW]
[ROW][C]44[/C][C]0.307355613954084[/C][C]0.614711227908168[/C][C]0.692644386045916[/C][/ROW]
[ROW][C]45[/C][C]0.29464130399277[/C][C]0.58928260798554[/C][C]0.70535869600723[/C][/ROW]
[ROW][C]46[/C][C]0.391546989468856[/C][C]0.783093978937712[/C][C]0.608453010531144[/C][/ROW]
[ROW][C]47[/C][C]0.384012123163679[/C][C]0.768024246327358[/C][C]0.615987876836321[/C][/ROW]
[ROW][C]48[/C][C]0.255133798431794[/C][C]0.510267596863587[/C][C]0.744866201568206[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57654&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57654&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.003660166748679970.007320333497359930.99633983325132
100.05109231409252980.1021846281850600.94890768590747
110.01984628238842110.03969256477684210.980153717611579
120.01756756027880590.03513512055761180.982432439721194
130.02799699101713150.05599398203426310.972003008982868
140.01290683122298320.02581366244596640.987093168777017
150.01261252150014690.02522504300029390.987387478499853
160.006509873754254390.01301974750850880.993490126245746
170.002918468391163630.005836936782327260.997081531608836
180.01656228735975780.03312457471951560.983437712640242
190.01449262981447390.02898525962894780.985507370185526
200.01368850944681150.02737701889362300.986311490553188
210.02384079112625780.04768158225251560.976159208873742
220.01326655915405140.02653311830810280.986733440845949
230.01804115569311740.03608231138623480.981958844306883
240.02057016011312590.04114032022625190.979429839886874
250.01176677182742740.02353354365485480.988233228172573
260.007415777820126230.01483155564025250.992584222179874
270.007097294087645040.01419458817529010.992902705912355
280.00542736771645350.0108547354329070.994572632283546
290.003394844254474250.00678968850894850.996605155745526
300.002106127929077170.004212255858154330.997893872070923
310.002692639849238670.005385279698477330.997307360150761
320.002975262665677140.005950525331354290.997024737334323
330.002917429850498570.005834859700997140.997082570149501
340.003572787213553580.007145574427107170.996427212786446
350.00648272577942750.0129654515588550.993517274220572
360.003457216384705010.006914432769410030.996542783615295
370.002138265780850270.004276531561700540.99786173421915
380.002602150461071670.005204300922143340.997397849538928
390.001455755071673000.002911510143346000.998544244928327
400.0006933645674996670.001386729134999330.9993066354325
410.004548645560769250.00909729112153850.99545135443923
420.01238714978351200.02477429956702400.987612850216488
430.08434166466193570.1686833293238710.915658335338064
440.3073556139540840.6147112279081680.692644386045916
450.294641303992770.589282607985540.70535869600723
460.3915469894688560.7830939789377120.608453010531144
470.3840121231636790.7680242463273580.615987876836321
480.2551337984317940.5102675968635870.744866201568206






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level140.35NOK
5% type I error level320.8NOK
10% type I error level330.825NOK

\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 & 14 & 0.35 & NOK \tabularnewline
5% type I error level & 32 & 0.8 & NOK \tabularnewline
10% type I error level & 33 & 0.825 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57654&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]14[/C][C]0.35[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]32[/C][C]0.8[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]33[/C][C]0.825[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57654&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57654&T=6

As an alternative you can also use a QR Code:  
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 level140.35NOK
5% type I error level320.8NOK
10% type I error level330.825NOK


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

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')
}