## Free Statistics

of Irreproducible Research!

Author's title
Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 24 Nov 2009 13:45:36 -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/24/t1259095580oo7ab03s8tdojwv.htm/, Retrieved Wed, 06 Dec 2023 06:56:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=59273, Retrieved Wed, 06 Dec 2023 06:56:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact138
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]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [] [2009-11-20 13:13:49] [badc6a9acdc45286bea7f74742e15a21]
-   PD      [Multiple Regression] [] [2009-11-20 13:28:34] [2c5be225250d91402426bbbf07a5e2b3]
-   PD          [Multiple Regression] [4e multiple ] [2009-11-24 20:45:36] [244731fa3e7e6c85774b8c0902c58f85] [Current]
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Dataseries X:
2,40	2,00	1,70	1,00	1,20	1,40
2,00	2,00	2,40	1,70	1,00	1,20
2,10	2,00	2,00	2,40	1,70	1,00
2,00	2,00	2,10	2,00	2,40	1,70
1,80	2,00	2,00	2,10	2,00	2,40
2,70	2,00	1,80	2,00	2,10	2,00
2,30	2,00	2,70	1,80	2,00	2,10
1,90	2,00	2,30	2,70	1,80	2,00
2,00	2,00	1,90	2,30	2,70	1,80
2,30	2,00	2,00	1,90	2,30	2,70
2,80	2,00	2,30	2,00	1,90	2,30
2,40	2,00	2,80	2,30	2,00	1,90
2,30	2,00	2,40	2,80	2,30	2,00
2,70	2,00	2,30	2,40	2,80	2,30
2,70	2,00	2,70	2,30	2,40	2,80
2,90	2,00	2,70	2,70	2,30	2,40
3,00	2,00	2,90	2,70	2,70	2,30
2,20	2,00	3,00	2,90	2,70	2,70
2,30	2,00	2,20	3,00	2,90	2,70
2,80	2,21	2,30	2,20	3,00	2,90
2,80	2,25	2,80	2,30	2,20	3,00
2,80	2,25	2,80	2,80	2,30	2,20
2,20	2,45	2,80	2,80	2,80	2,30
2,60	2,50	2,20	2,80	2,80	2,80
2,80	2,50	2,60	2,20	2,80	2,80
2,50	2,64	2,80	2,60	2,20	2,80
2,40	2,75	2,50	2,80	2,60	2,20
2,30	2,93	2,40	2,50	2,80	2,60
1,90	3,00	2,30	2,40	2,50	2,80
1,70	3,17	1,90	2,30	2,40	2,50
2,00	3,25	1,70	1,90	2,30	2,40
2,10	3,39	2,00	1,70	1,90	2,30
1,70	3,50	2,10	2,00	1,70	1,90
1,80	3,50	1,70	2,10	2,00	1,70
1,80	3,65	1,80	1,70	2,10	2,00
1,80	3,75	1,80	1,80	1,70	2,10
1,30	3,75	1,80	1,80	1,80	1,70
1,30	3,90	1,30	1,80	1,80	1,80
1,30	4,00	1,30	1,30	1,80	1,80
1,20	4,00	1,30	1,30	1,30	1,80
1,40	4,00	1,20	1,30	1,30	1,30
2,20	4,00	1,40	1,20	1,30	1,30
2,90	4,00	2,20	1,40	1,20	1,30
3,10	4,00	2,90	2,20	1,40	1,20
3,50	4,00	3,10	2,90	2,20	1,40
3,60	4,00	3,50	3,10	2,90	2,20
4,40	4,00	3,60	3,50	3,10	2,90
4,10	4,00	4,40	3,60	3,50	3,10
5,10	4,00	4,10	4,40	3,60	3,50
5,80	4,00	5,10	4,10	4,40	3,60
5,90	4,18	5,80	5,10	4,10	4,40
5,40	4,25	5,90	5,80	5,10	4,10
5,50	4,25	5,40	5,90	5,80	5,10
4,80	3,97	5,50	5,40	5,90	5,80
3,20	3,42	4,80	5,50	5,40	5,90
2,70	2,75	3,20	4,80	5,50	5,40

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59273&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59273&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59273&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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135

 Multiple Linear Regression - Estimated Regression Equation X1[t] = + 0.185036980311549 + 0.073579736810377X2[t] + 1.11584285808511X3[t] -0.256942291868568X4[t] + 0.278431112422916X5[t] -0.293078735356719X6[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
X1[t] =  +  0.185036980311549 +  0.073579736810377X2[t] +  1.11584285808511X3[t] -0.256942291868568X4[t] +  0.278431112422916X5[t] -0.293078735356719X6[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59273&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]X1[t] =  +  0.185036980311549 +  0.073579736810377X2[t] +  1.11584285808511X3[t] -0.256942291868568X4[t] +  0.278431112422916X5[t] -0.293078735356719X6[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59273&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59273&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 X1[t] = + 0.185036980311549 + 0.073579736810377X2[t] + 1.11584285808511X3[t] -0.256942291868568X4[t] + 0.278431112422916X5[t] -0.293078735356719X6[t] + e[t]

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 0.185036980311549 0.231657 0.7988 0.428211 0.214106 X2 0.073579736810377 0.07253 1.0145 0.315243 0.157621 X3 1.11584285808511 0.13542 8.2399 0 0 X4 -0.256942291868568 0.207051 -1.241 0.220411 0.110206 X5 0.278431112422916 0.211157 1.3186 0.193312 0.096656 X6 -0.293078735356719 0.156309 -1.875 0.06664 0.03332

\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) & 0.185036980311549 & 0.231657 & 0.7988 & 0.428211 & 0.214106 \tabularnewline
X2 & 0.073579736810377 & 0.07253 & 1.0145 & 0.315243 & 0.157621 \tabularnewline
X3 & 1.11584285808511 & 0.13542 & 8.2399 & 0 & 0 \tabularnewline
X4 & -0.256942291868568 & 0.207051 & -1.241 & 0.220411 & 0.110206 \tabularnewline
X5 & 0.278431112422916 & 0.211157 & 1.3186 & 0.193312 & 0.096656 \tabularnewline
X6 & -0.293078735356719 & 0.156309 & -1.875 & 0.06664 & 0.03332 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59273&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]0.185036980311549[/C][C]0.231657[/C][C]0.7988[/C][C]0.428211[/C][C]0.214106[/C][/ROW]
[ROW][C]X2[/C][C]0.073579736810377[/C][C]0.07253[/C][C]1.0145[/C][C]0.315243[/C][C]0.157621[/C][/ROW]
[ROW][C]X3[/C][C]1.11584285808511[/C][C]0.13542[/C][C]8.2399[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X4[/C][C]-0.256942291868568[/C][C]0.207051[/C][C]-1.241[/C][C]0.220411[/C][C]0.110206[/C][/ROW]
[ROW][C]X5[/C][C]0.278431112422916[/C][C]0.211157[/C][C]1.3186[/C][C]0.193312[/C][C]0.096656[/C][/ROW]
[ROW][C]X6[/C][C]-0.293078735356719[/C][C]0.156309[/C][C]-1.875[/C][C]0.06664[/C][C]0.03332[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59273&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59273&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 Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 0.185036980311549 0.231657 0.7988 0.428211 0.214106 X2 0.073579736810377 0.07253 1.0145 0.315243 0.157621 X3 1.11584285808511 0.13542 8.2399 0 0 X4 -0.256942291868568 0.207051 -1.241 0.220411 0.110206 X5 0.278431112422916 0.211157 1.3186 0.193312 0.096656 X6 -0.293078735356719 0.156309 -1.875 0.06664 0.03332

 Multiple Linear Regression - Regression Statistics Multiple R 0.930382067608297 R-squared 0.86561079172709 Adjusted R-squared 0.8521718708998 F-TEST (value) 64.4107367586582 F-TEST (DF numerator) 5 F-TEST (DF denominator) 50 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.442858093411831 Sum Squared Residuals 9.8061645450181

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.930382067608297 \tabularnewline
R-squared & 0.86561079172709 \tabularnewline
F-TEST (value) & 64.4107367586582 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 50 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.442858093411831 \tabularnewline
Sum Squared Residuals & 9.8061645450181 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59273&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.930382067608297[/C][/ROW]
[ROW][C]R-squared[/C][C]0.86561079172709[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]64.4107367586582[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]50[/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]0.442858093411831[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]9.8061645450181[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59273&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59273&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 R 0.930382067608297 R-squared 0.86561079172709 Adjusted R-squared 0.8521718708998 F-TEST (value) 64.4107367586582 F-TEST (DF numerator) 5 F-TEST (DF denominator) 50 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.442858093411831 Sum Squared Residuals 9.8061645450181

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 2.4 1.89599412621650 0.504005873783495 2 2 2.50015404715485 -0.500154047154846 3 2.1 2.12747482538019 -0.0274748253801892 4 2 2.33158269188247 -0.331582691882465 5 1.8 1.87777661716823 -0.077776617168228 6 2.7 1.82537688012304 0.874623119876957 7 2.3 2.82387292599539 -0.523872925995388 8 1.9 2.11990937113072 -0.219909371130724 9 2 2.08555289289608 -0.0855528928960763 10 2.3 1.9247707886618 0.375229211338199 11 2.8 2.23968846607400 0.560311533926003 12 2.4 2.86560181294096 -0.465601812940959 13 2.3 2.34501498396384 -0.0450149839638354 14 2.7 2.38749955050719 0.312500449492806 15 2.7 2.60161911028057 0.0983808897194326 16 2.9 2.58823057643354 0.311769423566464 17 3 2.95207946655540 0.0479205334446046 18 2.2 2.89504379984750 -0.695043799847505 19 2.3 2.03236150667715 0.267638493322853 20 2.8 2.33417873488164 0.465821265118362 21 2.8 2.61729636073574 0.182703639264255 22 2.8 2.75113131432913 0.0488686856708722 23 2.2 2.87575494436699 -0.675754944366989 24 2.6 2.06338884867809 0.536611151321915 25 2.8 2.66389136703327 0.136108632966732 26 2.5 2.62752551760257 -0.127525517602565 27 2.4 2.53669765903566 -0.136697659035659 28 2.3 2.45389514175548 -0.153895141755482 29 1.9 2.23101058591234 -0.331010585912336 30 1.7 1.88295673648764 -0.182956736487639 31 2 1.76991622285626 0.230083777143744 32 2.1 2.08429413037546 0.0157058696245409 33 1.7 2.18843477133064 -0.488434771330645 34 1.8 1.85854847970796 -0.0585484797079644 35 1.8 2.02386613342073 -0.223866133420735 36 1.8 1.86484955941008 -0.0648495594100774 37 1.3 2.00992416479506 -0.709924164795057 38 1.3 1.43373182273839 -0.133731822738388 39 1.3 1.56956094235371 -0.26956094235371 40 1.2 1.43034538614225 -0.230345386142252 41 1.4 1.4653004680121 -0.0653004680121008 42 2.2 1.71416326881598 0.485836731184022 43 2.9 2.52760598566806 0.372394014331942 44 3.1 3.18813624885303 -0.0881362488530329 45 3.5 3.39557435902905 0.104425640970954 46 3.6 3.75096183430004 -0.15096183430004 47 4.4 3.610300311096 0.789699688903997 48 4.1 4.53003706627505 -0.430037066275055 49 5.1 3.90034199245427 1.19965800754573 50 5.8 5.28670455450261 0.513295445497391 51 5.9 5.50610429390723 0.393895706092768 52 5.4 5.8093342900144 -0.409334290014404 53 5.5 5.12754167512432 0.372458324875683 54 4.8 5.16968277705279 -0.369682777052794 55 3.2 4.15390626221353 -0.953906262213525 56 2.7 2.67350134884305 0.0264986511569475

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2.4 & 1.89599412621650 & 0.504005873783495 \tabularnewline
2 & 2 & 2.50015404715485 & -0.500154047154846 \tabularnewline
3 & 2.1 & 2.12747482538019 & -0.0274748253801892 \tabularnewline
4 & 2 & 2.33158269188247 & -0.331582691882465 \tabularnewline
5 & 1.8 & 1.87777661716823 & -0.077776617168228 \tabularnewline
6 & 2.7 & 1.82537688012304 & 0.874623119876957 \tabularnewline
7 & 2.3 & 2.82387292599539 & -0.523872925995388 \tabularnewline
8 & 1.9 & 2.11990937113072 & -0.219909371130724 \tabularnewline
9 & 2 & 2.08555289289608 & -0.0855528928960763 \tabularnewline
10 & 2.3 & 1.9247707886618 & 0.375229211338199 \tabularnewline
11 & 2.8 & 2.23968846607400 & 0.560311533926003 \tabularnewline
12 & 2.4 & 2.86560181294096 & -0.465601812940959 \tabularnewline
13 & 2.3 & 2.34501498396384 & -0.0450149839638354 \tabularnewline
14 & 2.7 & 2.38749955050719 & 0.312500449492806 \tabularnewline
15 & 2.7 & 2.60161911028057 & 0.0983808897194326 \tabularnewline
16 & 2.9 & 2.58823057643354 & 0.311769423566464 \tabularnewline
17 & 3 & 2.95207946655540 & 0.0479205334446046 \tabularnewline
18 & 2.2 & 2.89504379984750 & -0.695043799847505 \tabularnewline
19 & 2.3 & 2.03236150667715 & 0.267638493322853 \tabularnewline
20 & 2.8 & 2.33417873488164 & 0.465821265118362 \tabularnewline
21 & 2.8 & 2.61729636073574 & 0.182703639264255 \tabularnewline
22 & 2.8 & 2.75113131432913 & 0.0488686856708722 \tabularnewline
23 & 2.2 & 2.87575494436699 & -0.675754944366989 \tabularnewline
24 & 2.6 & 2.06338884867809 & 0.536611151321915 \tabularnewline
25 & 2.8 & 2.66389136703327 & 0.136108632966732 \tabularnewline
26 & 2.5 & 2.62752551760257 & -0.127525517602565 \tabularnewline
27 & 2.4 & 2.53669765903566 & -0.136697659035659 \tabularnewline
28 & 2.3 & 2.45389514175548 & -0.153895141755482 \tabularnewline
29 & 1.9 & 2.23101058591234 & -0.331010585912336 \tabularnewline
30 & 1.7 & 1.88295673648764 & -0.182956736487639 \tabularnewline
31 & 2 & 1.76991622285626 & 0.230083777143744 \tabularnewline
32 & 2.1 & 2.08429413037546 & 0.0157058696245409 \tabularnewline
33 & 1.7 & 2.18843477133064 & -0.488434771330645 \tabularnewline
34 & 1.8 & 1.85854847970796 & -0.0585484797079644 \tabularnewline
35 & 1.8 & 2.02386613342073 & -0.223866133420735 \tabularnewline
36 & 1.8 & 1.86484955941008 & -0.0648495594100774 \tabularnewline
37 & 1.3 & 2.00992416479506 & -0.709924164795057 \tabularnewline
38 & 1.3 & 1.43373182273839 & -0.133731822738388 \tabularnewline
39 & 1.3 & 1.56956094235371 & -0.26956094235371 \tabularnewline
40 & 1.2 & 1.43034538614225 & -0.230345386142252 \tabularnewline
41 & 1.4 & 1.4653004680121 & -0.0653004680121008 \tabularnewline
42 & 2.2 & 1.71416326881598 & 0.485836731184022 \tabularnewline
43 & 2.9 & 2.52760598566806 & 0.372394014331942 \tabularnewline
44 & 3.1 & 3.18813624885303 & -0.0881362488530329 \tabularnewline
45 & 3.5 & 3.39557435902905 & 0.104425640970954 \tabularnewline
46 & 3.6 & 3.75096183430004 & -0.15096183430004 \tabularnewline
47 & 4.4 & 3.610300311096 & 0.789699688903997 \tabularnewline
48 & 4.1 & 4.53003706627505 & -0.430037066275055 \tabularnewline
49 & 5.1 & 3.90034199245427 & 1.19965800754573 \tabularnewline
50 & 5.8 & 5.28670455450261 & 0.513295445497391 \tabularnewline
51 & 5.9 & 5.50610429390723 & 0.393895706092768 \tabularnewline
52 & 5.4 & 5.8093342900144 & -0.409334290014404 \tabularnewline
53 & 5.5 & 5.12754167512432 & 0.372458324875683 \tabularnewline
54 & 4.8 & 5.16968277705279 & -0.369682777052794 \tabularnewline
55 & 3.2 & 4.15390626221353 & -0.953906262213525 \tabularnewline
56 & 2.7 & 2.67350134884305 & 0.0264986511569475 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59273&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]2.4[/C][C]1.89599412621650[/C][C]0.504005873783495[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]2.50015404715485[/C][C]-0.500154047154846[/C][/ROW]
[ROW][C]3[/C][C]2.1[/C][C]2.12747482538019[/C][C]-0.0274748253801892[/C][/ROW]
[ROW][C]4[/C][C]2[/C][C]2.33158269188247[/C][C]-0.331582691882465[/C][/ROW]
[ROW][C]5[/C][C]1.8[/C][C]1.87777661716823[/C][C]-0.077776617168228[/C][/ROW]
[ROW][C]6[/C][C]2.7[/C][C]1.82537688012304[/C][C]0.874623119876957[/C][/ROW]
[ROW][C]7[/C][C]2.3[/C][C]2.82387292599539[/C][C]-0.523872925995388[/C][/ROW]
[ROW][C]8[/C][C]1.9[/C][C]2.11990937113072[/C][C]-0.219909371130724[/C][/ROW]
[ROW][C]9[/C][C]2[/C][C]2.08555289289608[/C][C]-0.0855528928960763[/C][/ROW]
[ROW][C]10[/C][C]2.3[/C][C]1.9247707886618[/C][C]0.375229211338199[/C][/ROW]
[ROW][C]11[/C][C]2.8[/C][C]2.23968846607400[/C][C]0.560311533926003[/C][/ROW]
[ROW][C]12[/C][C]2.4[/C][C]2.86560181294096[/C][C]-0.465601812940959[/C][/ROW]
[ROW][C]13[/C][C]2.3[/C][C]2.34501498396384[/C][C]-0.0450149839638354[/C][/ROW]
[ROW][C]14[/C][C]2.7[/C][C]2.38749955050719[/C][C]0.312500449492806[/C][/ROW]
[ROW][C]15[/C][C]2.7[/C][C]2.60161911028057[/C][C]0.0983808897194326[/C][/ROW]
[ROW][C]16[/C][C]2.9[/C][C]2.58823057643354[/C][C]0.311769423566464[/C][/ROW]
[ROW][C]17[/C][C]3[/C][C]2.95207946655540[/C][C]0.0479205334446046[/C][/ROW]
[ROW][C]18[/C][C]2.2[/C][C]2.89504379984750[/C][C]-0.695043799847505[/C][/ROW]
[ROW][C]19[/C][C]2.3[/C][C]2.03236150667715[/C][C]0.267638493322853[/C][/ROW]
[ROW][C]20[/C][C]2.8[/C][C]2.33417873488164[/C][C]0.465821265118362[/C][/ROW]
[ROW][C]21[/C][C]2.8[/C][C]2.61729636073574[/C][C]0.182703639264255[/C][/ROW]
[ROW][C]22[/C][C]2.8[/C][C]2.75113131432913[/C][C]0.0488686856708722[/C][/ROW]
[ROW][C]23[/C][C]2.2[/C][C]2.87575494436699[/C][C]-0.675754944366989[/C][/ROW]
[ROW][C]24[/C][C]2.6[/C][C]2.06338884867809[/C][C]0.536611151321915[/C][/ROW]
[ROW][C]25[/C][C]2.8[/C][C]2.66389136703327[/C][C]0.136108632966732[/C][/ROW]
[ROW][C]26[/C][C]2.5[/C][C]2.62752551760257[/C][C]-0.127525517602565[/C][/ROW]
[ROW][C]27[/C][C]2.4[/C][C]2.53669765903566[/C][C]-0.136697659035659[/C][/ROW]
[ROW][C]28[/C][C]2.3[/C][C]2.45389514175548[/C][C]-0.153895141755482[/C][/ROW]
[ROW][C]29[/C][C]1.9[/C][C]2.23101058591234[/C][C]-0.331010585912336[/C][/ROW]
[ROW][C]30[/C][C]1.7[/C][C]1.88295673648764[/C][C]-0.182956736487639[/C][/ROW]
[ROW][C]31[/C][C]2[/C][C]1.76991622285626[/C][C]0.230083777143744[/C][/ROW]
[ROW][C]32[/C][C]2.1[/C][C]2.08429413037546[/C][C]0.0157058696245409[/C][/ROW]
[ROW][C]33[/C][C]1.7[/C][C]2.18843477133064[/C][C]-0.488434771330645[/C][/ROW]
[ROW][C]34[/C][C]1.8[/C][C]1.85854847970796[/C][C]-0.0585484797079644[/C][/ROW]
[ROW][C]35[/C][C]1.8[/C][C]2.02386613342073[/C][C]-0.223866133420735[/C][/ROW]
[ROW][C]36[/C][C]1.8[/C][C]1.86484955941008[/C][C]-0.0648495594100774[/C][/ROW]
[ROW][C]37[/C][C]1.3[/C][C]2.00992416479506[/C][C]-0.709924164795057[/C][/ROW]
[ROW][C]38[/C][C]1.3[/C][C]1.43373182273839[/C][C]-0.133731822738388[/C][/ROW]
[ROW][C]39[/C][C]1.3[/C][C]1.56956094235371[/C][C]-0.26956094235371[/C][/ROW]
[ROW][C]40[/C][C]1.2[/C][C]1.43034538614225[/C][C]-0.230345386142252[/C][/ROW]
[ROW][C]41[/C][C]1.4[/C][C]1.4653004680121[/C][C]-0.0653004680121008[/C][/ROW]
[ROW][C]42[/C][C]2.2[/C][C]1.71416326881598[/C][C]0.485836731184022[/C][/ROW]
[ROW][C]43[/C][C]2.9[/C][C]2.52760598566806[/C][C]0.372394014331942[/C][/ROW]
[ROW][C]44[/C][C]3.1[/C][C]3.18813624885303[/C][C]-0.0881362488530329[/C][/ROW]
[ROW][C]45[/C][C]3.5[/C][C]3.39557435902905[/C][C]0.104425640970954[/C][/ROW]
[ROW][C]46[/C][C]3.6[/C][C]3.75096183430004[/C][C]-0.15096183430004[/C][/ROW]
[ROW][C]47[/C][C]4.4[/C][C]3.610300311096[/C][C]0.789699688903997[/C][/ROW]
[ROW][C]48[/C][C]4.1[/C][C]4.53003706627505[/C][C]-0.430037066275055[/C][/ROW]
[ROW][C]49[/C][C]5.1[/C][C]3.90034199245427[/C][C]1.19965800754573[/C][/ROW]
[ROW][C]50[/C][C]5.8[/C][C]5.28670455450261[/C][C]0.513295445497391[/C][/ROW]
[ROW][C]51[/C][C]5.9[/C][C]5.50610429390723[/C][C]0.393895706092768[/C][/ROW]
[ROW][C]52[/C][C]5.4[/C][C]5.8093342900144[/C][C]-0.409334290014404[/C][/ROW]
[ROW][C]53[/C][C]5.5[/C][C]5.12754167512432[/C][C]0.372458324875683[/C][/ROW]
[ROW][C]54[/C][C]4.8[/C][C]5.16968277705279[/C][C]-0.369682777052794[/C][/ROW]
[ROW][C]55[/C][C]3.2[/C][C]4.15390626221353[/C][C]-0.953906262213525[/C][/ROW]
[ROW][C]56[/C][C]2.7[/C][C]2.67350134884305[/C][C]0.0264986511569475[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59273&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59273&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 Index Actuals InterpolationForecast ResidualsPrediction Error 1 2.4 1.89599412621650 0.504005873783495 2 2 2.50015404715485 -0.500154047154846 3 2.1 2.12747482538019 -0.0274748253801892 4 2 2.33158269188247 -0.331582691882465 5 1.8 1.87777661716823 -0.077776617168228 6 2.7 1.82537688012304 0.874623119876957 7 2.3 2.82387292599539 -0.523872925995388 8 1.9 2.11990937113072 -0.219909371130724 9 2 2.08555289289608 -0.0855528928960763 10 2.3 1.9247707886618 0.375229211338199 11 2.8 2.23968846607400 0.560311533926003 12 2.4 2.86560181294096 -0.465601812940959 13 2.3 2.34501498396384 -0.0450149839638354 14 2.7 2.38749955050719 0.312500449492806 15 2.7 2.60161911028057 0.0983808897194326 16 2.9 2.58823057643354 0.311769423566464 17 3 2.95207946655540 0.0479205334446046 18 2.2 2.89504379984750 -0.695043799847505 19 2.3 2.03236150667715 0.267638493322853 20 2.8 2.33417873488164 0.465821265118362 21 2.8 2.61729636073574 0.182703639264255 22 2.8 2.75113131432913 0.0488686856708722 23 2.2 2.87575494436699 -0.675754944366989 24 2.6 2.06338884867809 0.536611151321915 25 2.8 2.66389136703327 0.136108632966732 26 2.5 2.62752551760257 -0.127525517602565 27 2.4 2.53669765903566 -0.136697659035659 28 2.3 2.45389514175548 -0.153895141755482 29 1.9 2.23101058591234 -0.331010585912336 30 1.7 1.88295673648764 -0.182956736487639 31 2 1.76991622285626 0.230083777143744 32 2.1 2.08429413037546 0.0157058696245409 33 1.7 2.18843477133064 -0.488434771330645 34 1.8 1.85854847970796 -0.0585484797079644 35 1.8 2.02386613342073 -0.223866133420735 36 1.8 1.86484955941008 -0.0648495594100774 37 1.3 2.00992416479506 -0.709924164795057 38 1.3 1.43373182273839 -0.133731822738388 39 1.3 1.56956094235371 -0.26956094235371 40 1.2 1.43034538614225 -0.230345386142252 41 1.4 1.4653004680121 -0.0653004680121008 42 2.2 1.71416326881598 0.485836731184022 43 2.9 2.52760598566806 0.372394014331942 44 3.1 3.18813624885303 -0.0881362488530329 45 3.5 3.39557435902905 0.104425640970954 46 3.6 3.75096183430004 -0.15096183430004 47 4.4 3.610300311096 0.789699688903997 48 4.1 4.53003706627505 -0.430037066275055 49 5.1 3.90034199245427 1.19965800754573 50 5.8 5.28670455450261 0.513295445497391 51 5.9 5.50610429390723 0.393895706092768 52 5.4 5.8093342900144 -0.409334290014404 53 5.5 5.12754167512432 0.372458324875683 54 4.8 5.16968277705279 -0.369682777052794 55 3.2 4.15390626221353 -0.953906262213525 56 2.7 2.67350134884305 0.0264986511569475

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.496974768679411 0.993949537358823 0.503025231320589 10 0.33525812504257 0.67051625008514 0.66474187495743 11 0.483334929740901 0.966669859481802 0.516665070259099 12 0.405026140458623 0.810052280917246 0.594973859541377 13 0.313067085638713 0.626134171277425 0.686932914361287 14 0.291702957379306 0.583405914758612 0.708297042620694 15 0.216466296204152 0.432932592408305 0.783533703795848 16 0.238570409634258 0.477140819268516 0.761429590365742 17 0.210069613222579 0.420139226445159 0.78993038677742 18 0.258276415672042 0.516552831344084 0.741723584327958 19 0.19107972013187 0.38215944026374 0.80892027986813 20 0.159432753429131 0.318865506858263 0.840567246570869 21 0.113735253324280 0.227470506648560 0.88626474667572 22 0.0782806326040034 0.156561265208007 0.921719367395997 23 0.115178083700688 0.230356167401376 0.884821916299312 24 0.104946813795048 0.209893627590096 0.895053186204952 25 0.079964605751412 0.159929211502824 0.920035394248588 26 0.0547237146614571 0.109447429322914 0.945276285338543 27 0.0341851063691204 0.0683702127382408 0.96581489363088 28 0.0233631030411595 0.046726206082319 0.97663689695884 29 0.0233175188109267 0.0466350376218534 0.976682481189073 30 0.0161936102537599 0.0323872205075198 0.98380638974624 31 0.0125892433320088 0.0251784866640176 0.987410756667991 32 0.00848421394679435 0.0169684278935887 0.991515786053206 33 0.00565132118505816 0.0113026423701163 0.994348678814942 34 0.00318867771880137 0.00637735543760275 0.996811322281199 35 0.00160284528968789 0.00320569057937579 0.998397154710312 36 0.00078229157771132 0.00156458315542264 0.999217708422289 37 0.00129873383441236 0.00259746766882473 0.998701266165588 38 0.000693543337269381 0.00138708667453876 0.99930645666273 39 0.00040240760556238 0.00080481521112476 0.999597592394438 40 0.000294766327560781 0.000589532655121561 0.99970523367244 41 0.000322367248992252 0.000644734497984504 0.999677632751008 42 0.000831085179947803 0.00166217035989561 0.999168914820052 43 0.00235246909875911 0.00470493819751822 0.997647530901241 44 0.00317737231068529 0.00635474462137059 0.996822627689315 45 0.00395303077700117 0.00790606155400233 0.996046969222999 46 0.00898323892181925 0.0179664778436385 0.991016761078181 47 0.00822416335619052 0.0164483267123810 0.99177583664381

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.496974768679411 & 0.993949537358823 & 0.503025231320589 \tabularnewline
10 & 0.33525812504257 & 0.67051625008514 & 0.66474187495743 \tabularnewline
11 & 0.483334929740901 & 0.966669859481802 & 0.516665070259099 \tabularnewline
12 & 0.405026140458623 & 0.810052280917246 & 0.594973859541377 \tabularnewline
13 & 0.313067085638713 & 0.626134171277425 & 0.686932914361287 \tabularnewline
14 & 0.291702957379306 & 0.583405914758612 & 0.708297042620694 \tabularnewline
15 & 0.216466296204152 & 0.432932592408305 & 0.783533703795848 \tabularnewline
16 & 0.238570409634258 & 0.477140819268516 & 0.761429590365742 \tabularnewline
17 & 0.210069613222579 & 0.420139226445159 & 0.78993038677742 \tabularnewline
18 & 0.258276415672042 & 0.516552831344084 & 0.741723584327958 \tabularnewline
19 & 0.19107972013187 & 0.38215944026374 & 0.80892027986813 \tabularnewline
20 & 0.159432753429131 & 0.318865506858263 & 0.840567246570869 \tabularnewline
21 & 0.113735253324280 & 0.227470506648560 & 0.88626474667572 \tabularnewline
22 & 0.0782806326040034 & 0.156561265208007 & 0.921719367395997 \tabularnewline
23 & 0.115178083700688 & 0.230356167401376 & 0.884821916299312 \tabularnewline
24 & 0.104946813795048 & 0.209893627590096 & 0.895053186204952 \tabularnewline
25 & 0.079964605751412 & 0.159929211502824 & 0.920035394248588 \tabularnewline
26 & 0.0547237146614571 & 0.109447429322914 & 0.945276285338543 \tabularnewline
27 & 0.0341851063691204 & 0.0683702127382408 & 0.96581489363088 \tabularnewline
28 & 0.0233631030411595 & 0.046726206082319 & 0.97663689695884 \tabularnewline
29 & 0.0233175188109267 & 0.0466350376218534 & 0.976682481189073 \tabularnewline
30 & 0.0161936102537599 & 0.0323872205075198 & 0.98380638974624 \tabularnewline
31 & 0.0125892433320088 & 0.0251784866640176 & 0.987410756667991 \tabularnewline
32 & 0.00848421394679435 & 0.0169684278935887 & 0.991515786053206 \tabularnewline
33 & 0.00565132118505816 & 0.0113026423701163 & 0.994348678814942 \tabularnewline
34 & 0.00318867771880137 & 0.00637735543760275 & 0.996811322281199 \tabularnewline
35 & 0.00160284528968789 & 0.00320569057937579 & 0.998397154710312 \tabularnewline
36 & 0.00078229157771132 & 0.00156458315542264 & 0.999217708422289 \tabularnewline
37 & 0.00129873383441236 & 0.00259746766882473 & 0.998701266165588 \tabularnewline
38 & 0.000693543337269381 & 0.00138708667453876 & 0.99930645666273 \tabularnewline
39 & 0.00040240760556238 & 0.00080481521112476 & 0.999597592394438 \tabularnewline
40 & 0.000294766327560781 & 0.000589532655121561 & 0.99970523367244 \tabularnewline
41 & 0.000322367248992252 & 0.000644734497984504 & 0.999677632751008 \tabularnewline
42 & 0.000831085179947803 & 0.00166217035989561 & 0.999168914820052 \tabularnewline
43 & 0.00235246909875911 & 0.00470493819751822 & 0.997647530901241 \tabularnewline
44 & 0.00317737231068529 & 0.00635474462137059 & 0.996822627689315 \tabularnewline
45 & 0.00395303077700117 & 0.00790606155400233 & 0.996046969222999 \tabularnewline
46 & 0.00898323892181925 & 0.0179664778436385 & 0.991016761078181 \tabularnewline
47 & 0.00822416335619052 & 0.0164483267123810 & 0.99177583664381 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59273&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]0.496974768679411[/C][C]0.993949537358823[/C][C]0.503025231320589[/C][/ROW]
[ROW][C]10[/C][C]0.33525812504257[/C][C]0.67051625008514[/C][C]0.66474187495743[/C][/ROW]
[ROW][C]11[/C][C]0.483334929740901[/C][C]0.966669859481802[/C][C]0.516665070259099[/C][/ROW]
[ROW][C]12[/C][C]0.405026140458623[/C][C]0.810052280917246[/C][C]0.594973859541377[/C][/ROW]
[ROW][C]13[/C][C]0.313067085638713[/C][C]0.626134171277425[/C][C]0.686932914361287[/C][/ROW]
[ROW][C]14[/C][C]0.291702957379306[/C][C]0.583405914758612[/C][C]0.708297042620694[/C][/ROW]
[ROW][C]15[/C][C]0.216466296204152[/C][C]0.432932592408305[/C][C]0.783533703795848[/C][/ROW]
[ROW][C]16[/C][C]0.238570409634258[/C][C]0.477140819268516[/C][C]0.761429590365742[/C][/ROW]
[ROW][C]17[/C][C]0.210069613222579[/C][C]0.420139226445159[/C][C]0.78993038677742[/C][/ROW]
[ROW][C]18[/C][C]0.258276415672042[/C][C]0.516552831344084[/C][C]0.741723584327958[/C][/ROW]
[ROW][C]19[/C][C]0.19107972013187[/C][C]0.38215944026374[/C][C]0.80892027986813[/C][/ROW]
[ROW][C]20[/C][C]0.159432753429131[/C][C]0.318865506858263[/C][C]0.840567246570869[/C][/ROW]
[ROW][C]21[/C][C]0.113735253324280[/C][C]0.227470506648560[/C][C]0.88626474667572[/C][/ROW]
[ROW][C]22[/C][C]0.0782806326040034[/C][C]0.156561265208007[/C][C]0.921719367395997[/C][/ROW]
[ROW][C]23[/C][C]0.115178083700688[/C][C]0.230356167401376[/C][C]0.884821916299312[/C][/ROW]
[ROW][C]24[/C][C]0.104946813795048[/C][C]0.209893627590096[/C][C]0.895053186204952[/C][/ROW]
[ROW][C]25[/C][C]0.079964605751412[/C][C]0.159929211502824[/C][C]0.920035394248588[/C][/ROW]
[ROW][C]26[/C][C]0.0547237146614571[/C][C]0.109447429322914[/C][C]0.945276285338543[/C][/ROW]
[ROW][C]27[/C][C]0.0341851063691204[/C][C]0.0683702127382408[/C][C]0.96581489363088[/C][/ROW]
[ROW][C]28[/C][C]0.0233631030411595[/C][C]0.046726206082319[/C][C]0.97663689695884[/C][/ROW]
[ROW][C]29[/C][C]0.0233175188109267[/C][C]0.0466350376218534[/C][C]0.976682481189073[/C][/ROW]
[ROW][C]30[/C][C]0.0161936102537599[/C][C]0.0323872205075198[/C][C]0.98380638974624[/C][/ROW]
[ROW][C]31[/C][C]0.0125892433320088[/C][C]0.0251784866640176[/C][C]0.987410756667991[/C][/ROW]
[ROW][C]32[/C][C]0.00848421394679435[/C][C]0.0169684278935887[/C][C]0.991515786053206[/C][/ROW]
[ROW][C]33[/C][C]0.00565132118505816[/C][C]0.0113026423701163[/C][C]0.994348678814942[/C][/ROW]
[ROW][C]34[/C][C]0.00318867771880137[/C][C]0.00637735543760275[/C][C]0.996811322281199[/C][/ROW]
[ROW][C]35[/C][C]0.00160284528968789[/C][C]0.00320569057937579[/C][C]0.998397154710312[/C][/ROW]
[ROW][C]36[/C][C]0.00078229157771132[/C][C]0.00156458315542264[/C][C]0.999217708422289[/C][/ROW]
[ROW][C]37[/C][C]0.00129873383441236[/C][C]0.00259746766882473[/C][C]0.998701266165588[/C][/ROW]
[ROW][C]38[/C][C]0.000693543337269381[/C][C]0.00138708667453876[/C][C]0.99930645666273[/C][/ROW]
[ROW][C]39[/C][C]0.00040240760556238[/C][C]0.00080481521112476[/C][C]0.999597592394438[/C][/ROW]
[ROW][C]40[/C][C]0.000294766327560781[/C][C]0.000589532655121561[/C][C]0.99970523367244[/C][/ROW]
[ROW][C]41[/C][C]0.000322367248992252[/C][C]0.000644734497984504[/C][C]0.999677632751008[/C][/ROW]
[ROW][C]42[/C][C]0.000831085179947803[/C][C]0.00166217035989561[/C][C]0.999168914820052[/C][/ROW]
[ROW][C]43[/C][C]0.00235246909875911[/C][C]0.00470493819751822[/C][C]0.997647530901241[/C][/ROW]
[ROW][C]44[/C][C]0.00317737231068529[/C][C]0.00635474462137059[/C][C]0.996822627689315[/C][/ROW]
[ROW][C]45[/C][C]0.00395303077700117[/C][C]0.00790606155400233[/C][C]0.996046969222999[/C][/ROW]
[ROW][C]46[/C][C]0.00898323892181925[/C][C]0.0179664778436385[/C][C]0.991016761078181[/C][/ROW]
[ROW][C]47[/C][C]0.00822416335619052[/C][C]0.0164483267123810[/C][C]0.99177583664381[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59273&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59273&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-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.496974768679411 0.993949537358823 0.503025231320589 10 0.33525812504257 0.67051625008514 0.66474187495743 11 0.483334929740901 0.966669859481802 0.516665070259099 12 0.405026140458623 0.810052280917246 0.594973859541377 13 0.313067085638713 0.626134171277425 0.686932914361287 14 0.291702957379306 0.583405914758612 0.708297042620694 15 0.216466296204152 0.432932592408305 0.783533703795848 16 0.238570409634258 0.477140819268516 0.761429590365742 17 0.210069613222579 0.420139226445159 0.78993038677742 18 0.258276415672042 0.516552831344084 0.741723584327958 19 0.19107972013187 0.38215944026374 0.80892027986813 20 0.159432753429131 0.318865506858263 0.840567246570869 21 0.113735253324280 0.227470506648560 0.88626474667572 22 0.0782806326040034 0.156561265208007 0.921719367395997 23 0.115178083700688 0.230356167401376 0.884821916299312 24 0.104946813795048 0.209893627590096 0.895053186204952 25 0.079964605751412 0.159929211502824 0.920035394248588 26 0.0547237146614571 0.109447429322914 0.945276285338543 27 0.0341851063691204 0.0683702127382408 0.96581489363088 28 0.0233631030411595 0.046726206082319 0.97663689695884 29 0.0233175188109267 0.0466350376218534 0.976682481189073 30 0.0161936102537599 0.0323872205075198 0.98380638974624 31 0.0125892433320088 0.0251784866640176 0.987410756667991 32 0.00848421394679435 0.0169684278935887 0.991515786053206 33 0.00565132118505816 0.0113026423701163 0.994348678814942 34 0.00318867771880137 0.00637735543760275 0.996811322281199 35 0.00160284528968789 0.00320569057937579 0.998397154710312 36 0.00078229157771132 0.00156458315542264 0.999217708422289 37 0.00129873383441236 0.00259746766882473 0.998701266165588 38 0.000693543337269381 0.00138708667453876 0.99930645666273 39 0.00040240760556238 0.00080481521112476 0.999597592394438 40 0.000294766327560781 0.000589532655121561 0.99970523367244 41 0.000322367248992252 0.000644734497984504 0.999677632751008 42 0.000831085179947803 0.00166217035989561 0.999168914820052 43 0.00235246909875911 0.00470493819751822 0.997647530901241 44 0.00317737231068529 0.00635474462137059 0.996822627689315 45 0.00395303077700117 0.00790606155400233 0.996046969222999 46 0.00898323892181925 0.0179664778436385 0.991016761078181 47 0.00822416335619052 0.0164483267123810 0.99177583664381

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 12 0.307692307692308 NOK 5% type I error level 20 0.512820512820513 NOK 10% type I error level 21 0.538461538461538 NOK

\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 & 12 & 0.307692307692308 & NOK \tabularnewline
5% type I error level & 20 & 0.512820512820513 & NOK \tabularnewline
10% type I error level & 21 & 0.538461538461538 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59273&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]12[/C][C]0.307692307692308[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]20[/C][C]0.512820512820513[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]21[/C][C]0.538461538461538[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59273&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59273&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 tests OK/NOK 1% type I error level 12 0.307692307692308 NOK 5% type I error level 20 0.512820512820513 NOK 10% type I error level 21 0.538461538461538 NOK

library(lattice)library(lmtest)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt testpar1 <- 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 <- x1if (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'}xk <- length(x[1,])df <- as.data.frame(x)(mylm <- lm(df))(mysum <- summary(mylm))if (n > n25) {kp3 <- k + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)zplot(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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')}}myeq <- paste(myeq, ' + e[t]')a<-table.row.start(a)a<-table.element(a, myeq)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable1.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Variable',header=TRUE)a<-table.element(a,'Parameter',header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'T-STATH0: parameter = 0',header=TRUE)a<-table.element(a,'2-tail p-value',header=TRUE)a<-table.element(a,'1-tail p-value',header=TRUE)a<-table.row.end(a)for (i in 1:k){a<-table.row.start(a)a<-table.element(a,rownames(mysum$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$sigma)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Sum Squared Residuals',1,TRUE)a<-table.element(a, sum(myerror*myerror))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable3.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,x[i])a<-table.element(a,x[i]-mysum$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')}