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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 21 Nov 2011 13:09:29 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/21/t1321898989djreciv8ajn2377.htm/, Retrieved Fri, 29 Mar 2024 10:09:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145877, Retrieved Fri, 29 Mar 2024 10:09:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact107
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [WS7 Tutorial Acco...] [2011-11-21 16:40:16] [9d4f280afcb4ecc352d7c6f913a0a151]
- R  D  [Multiple Regression] [WS7 Tutorial Acco...] [2011-11-21 16:45:28] [9d4f280afcb4ecc352d7c6f913a0a151]
-    D    [Multiple Regression] [WS7 Tutorial Acco...] [2011-11-21 16:59:15] [9d4f280afcb4ecc352d7c6f913a0a151]
- R PD        [Multiple Regression] [WS7 Tutorial Acco...] [2011-11-21 18:09:29] [2a6d487209befbc7c5ce02a41ecac161] [Current]
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Dataseries X:
20	1	14	3	1	1
14	1	8	3	0	1
18	0	12	6	1	1
12	1	7	2	0	1
16	0	10	1	1	0
13	0	7	2	0	0
22	1	16	8	1	1
16	1	11	1	1	0
20	0	14	4	1	1
10	0	6	0	0	0
22	0	16	4	1	0
17	1	11	2	0	1
21	0	16	1	1	1
18	1	12	2	1	1
13	0	7	3	0	0
17	0	13	1	1	0
17	1	11	2	1	1
19	1	15	6	1	0
12	1	7	0	0	1
14	1	9	1	0	1
13	0	7	3	0	1
20	1	14	5	1	1
20	1	15	0	1	1
13	1	7	1	0	1
21	1	15	3	1	1
21	1	17	6	1	1
19	1	15	5	1	0
18	1	14	4	1	0
20	0	14	4	0	0
14	1	8	4	1	1
14	0	8	0	0	1
20	1	14	3	1	0
21	1	14	5	1	1
14	0	8	3	0	0
16	1	11	1	1	1
21	1	16	5	1	1
16	1	10	5	1	1
14	1	8	0	0	1
19	1	14	3	1	1
22	1	16	6	1	0
19	0	13	3	1	1
11	1	5	1	0	0
13	1	8	2	0	1
16	1	10	2	0	0
14	0	8	2	0	1
19	1	13	4	1	1
21	1	15	4	1	1
12	0	6	0	0	1
17	0	12	3	1	1
21	1	16	6	0	1
11	1	5	3	1	0
19	0	15	1	1	1
18	0	12	4	1	0
14	0	8	3	0	1
19	0	13	3	1	1
20	1	14	3	1	1
18	0	12	2	1	1
22	0	16	6	1	1
16	1	10	5	1	1
20	0	15	5	1	0
14	0	8	2	0	1
22	1	16	4	1	1
25	0	19	2	1	1
20	0	14	5	1	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145877&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145877&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145877&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Income[t] = + 5.97386995948922 -0.226789724375297Change[t] + 0.919863609515728Size[t] + 0.11669319227812Complex[t] + 0.107922912000111Big4[t] + 0.38889288282041Product[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Income[t] =  +  5.97386995948922 -0.226789724375297Change[t] +  0.919863609515728Size[t] +  0.11669319227812Complex[t] +  0.107922912000111Big4[t] +  0.38889288282041Product[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145877&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Income[t] =  +  5.97386995948922 -0.226789724375297Change[t] +  0.919863609515728Size[t] +  0.11669319227812Complex[t] +  0.107922912000111Big4[t] +  0.38889288282041Product[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145877&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145877&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
Income[t] = + 5.97386995948922 -0.226789724375297Change[t] + 0.919863609515728Size[t] + 0.11669319227812Complex[t] + 0.107922912000111Big4[t] + 0.38889288282041Product[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.973869959489220.33744417.703300
Change-0.2267897243752970.176982-1.28140.2051430.102571
Size0.9198636095157280.03632625.322600
Complex0.116693192278120.0550022.12160.0381520.019076
Big40.1079229120001110.2510280.42990.6688440.334422
Product0.388892882820410.1876052.07290.0426310.021316

\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) & 5.97386995948922 & 0.337444 & 17.7033 & 0 & 0 \tabularnewline
Change & -0.226789724375297 & 0.176982 & -1.2814 & 0.205143 & 0.102571 \tabularnewline
Size & 0.919863609515728 & 0.036326 & 25.3226 & 0 & 0 \tabularnewline
Complex & 0.11669319227812 & 0.055002 & 2.1216 & 0.038152 & 0.019076 \tabularnewline
Big4 & 0.107922912000111 & 0.251028 & 0.4299 & 0.668844 & 0.334422 \tabularnewline
Product & 0.38889288282041 & 0.187605 & 2.0729 & 0.042631 & 0.021316 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145877&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]5.97386995948922[/C][C]0.337444[/C][C]17.7033[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Change[/C][C]-0.226789724375297[/C][C]0.176982[/C][C]-1.2814[/C][C]0.205143[/C][C]0.102571[/C][/ROW]
[ROW][C]Size[/C][C]0.919863609515728[/C][C]0.036326[/C][C]25.3226[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Complex[/C][C]0.11669319227812[/C][C]0.055002[/C][C]2.1216[/C][C]0.038152[/C][C]0.019076[/C][/ROW]
[ROW][C]Big4[/C][C]0.107922912000111[/C][C]0.251028[/C][C]0.4299[/C][C]0.668844[/C][C]0.334422[/C][/ROW]
[ROW][C]Product[/C][C]0.38889288282041[/C][C]0.187605[/C][C]2.0729[/C][C]0.042631[/C][C]0.021316[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145877&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145877&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.973869959489220.33744417.703300
Change-0.2267897243752970.176982-1.28140.2051430.102571
Size0.9198636095157280.03632625.322600
Complex0.116693192278120.0550022.12160.0381520.019076
Big40.1079229120001110.2510280.42990.6688440.334422
Product0.388892882820410.1876052.07290.0426310.021316







Multiple Linear Regression - Regression Statistics
Multiple R0.982641681321665
R-squared0.965584673870669
Adjusted R-squared0.962617835411244
F-TEST (value)325.459133375859
F-TEST (DF numerator)5
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.67670637370448
Sum Squared Residuals26.5600279403115

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.982641681321665 \tabularnewline
R-squared & 0.965584673870669 \tabularnewline
Adjusted R-squared & 0.962617835411244 \tabularnewline
F-TEST (value) & 325.459133375859 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.67670637370448 \tabularnewline
Sum Squared Residuals & 26.5600279403115 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145877&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.982641681321665[/C][/ROW]
[ROW][C]R-squared[/C][C]0.965584673870669[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.962617835411244[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]325.459133375859[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/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]0.67670637370448[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]26.5600279403115[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145877&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145877&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.982641681321665
R-squared0.965584673870669
Adjusted R-squared0.962617835411244
F-TEST (value)325.459133375859
F-TEST (DF numerator)5
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.67670637370448
Sum Squared Residuals26.5600279403115







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12019.4720661399890.527933860011005
21413.84496157089450.155038429105482
31818.2092082221672-0.2092082221672
41212.8084047691007-0.80840476910067
51615.39712215892470.602877841075269
61312.64630161065560.353698389344443
72221.89525932041110.104740679588946
81616.0901960440652-0.0901960440651619
92019.81554905664240.184450943357586
101011.4930516165836-1.49305161658359
112221.26638339285350.733616607146541
121716.48785920716360.512140792836418
132121.3051966988395-0.305196698839508
141817.51564572867940.484354271320579
151312.76299480293370.237005197066323
161718.1567129874719-1.15671298747191
171716.59578211916370.404217880836307
181920.3531164435187-1.35311644351868
191212.5750183845444-0.575018384544429
201414.531438795854-0.531438795854006
211313.1518876857541-0.151887685754087
222019.70545252454520.294547475454762
232020.0418501726704-0.0418501726703632
241312.69171157682250.308288423177451
252120.39192974950470.608070250495275
262122.5817365453705-1.58173654537054
271920.2364232512406-1.23642325124056
281819.1998664494467-1.19986644944671
292019.31873326182190.681266738178107
301414.0695776751728-0.0695776751727499
311413.72167171843550.278328281564546
322019.08317325716860.916826742831413
332119.70545252454521.29454747545476
341413.68285841244940.317141587550595
351616.4790889268856-0.479088926885572
362121.5451797435767-0.545179743576693
371616.0259980864823-0.0259980864823262
381413.49488199406020.505118005939843
391919.472066139989-0.472066139988997
402221.27298005303440.727019946965597
411918.77899225484860.221007745151434
421110.46309147497070.536908525029316
431313.7282683786164-0.728268378616398
441615.17910271482740.820897285172556
451413.95505810299170.0449418970083054
461918.66889572275140.331104277248611
472120.50862294178280.491377058217155
481211.8819444994040.118055500596002
491717.8591286453328-0.859128645332838
502121.5539500238547-0.553950023854703
511110.8044007715270.195599228472965
521920.3853330893238-1.38533308932378
531817.58692895479050.413071045209452
541414.0717512952698-0.0717512952698151
551918.77899225484860.221007745151434
562019.4720661399890.527933860011003
571817.74243545305470.257564546945283
582221.88866266023010.11133733976989
591616.0259980864823-0.0259980864823262
602020.4632129756159-0.463212975615852
611413.95505810299170.0449418970083054
622221.42848655129860.571513448701428
632524.18148071966480.818519280335188
642019.54334936610010.456650633899876

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 20 & 19.472066139989 & 0.527933860011005 \tabularnewline
2 & 14 & 13.8449615708945 & 0.155038429105482 \tabularnewline
3 & 18 & 18.2092082221672 & -0.2092082221672 \tabularnewline
4 & 12 & 12.8084047691007 & -0.80840476910067 \tabularnewline
5 & 16 & 15.3971221589247 & 0.602877841075269 \tabularnewline
6 & 13 & 12.6463016106556 & 0.353698389344443 \tabularnewline
7 & 22 & 21.8952593204111 & 0.104740679588946 \tabularnewline
8 & 16 & 16.0901960440652 & -0.0901960440651619 \tabularnewline
9 & 20 & 19.8155490566424 & 0.184450943357586 \tabularnewline
10 & 10 & 11.4930516165836 & -1.49305161658359 \tabularnewline
11 & 22 & 21.2663833928535 & 0.733616607146541 \tabularnewline
12 & 17 & 16.4878592071636 & 0.512140792836418 \tabularnewline
13 & 21 & 21.3051966988395 & -0.305196698839508 \tabularnewline
14 & 18 & 17.5156457286794 & 0.484354271320579 \tabularnewline
15 & 13 & 12.7629948029337 & 0.237005197066323 \tabularnewline
16 & 17 & 18.1567129874719 & -1.15671298747191 \tabularnewline
17 & 17 & 16.5957821191637 & 0.404217880836307 \tabularnewline
18 & 19 & 20.3531164435187 & -1.35311644351868 \tabularnewline
19 & 12 & 12.5750183845444 & -0.575018384544429 \tabularnewline
20 & 14 & 14.531438795854 & -0.531438795854006 \tabularnewline
21 & 13 & 13.1518876857541 & -0.151887685754087 \tabularnewline
22 & 20 & 19.7054525245452 & 0.294547475454762 \tabularnewline
23 & 20 & 20.0418501726704 & -0.0418501726703632 \tabularnewline
24 & 13 & 12.6917115768225 & 0.308288423177451 \tabularnewline
25 & 21 & 20.3919297495047 & 0.608070250495275 \tabularnewline
26 & 21 & 22.5817365453705 & -1.58173654537054 \tabularnewline
27 & 19 & 20.2364232512406 & -1.23642325124056 \tabularnewline
28 & 18 & 19.1998664494467 & -1.19986644944671 \tabularnewline
29 & 20 & 19.3187332618219 & 0.681266738178107 \tabularnewline
30 & 14 & 14.0695776751728 & -0.0695776751727499 \tabularnewline
31 & 14 & 13.7216717184355 & 0.278328281564546 \tabularnewline
32 & 20 & 19.0831732571686 & 0.916826742831413 \tabularnewline
33 & 21 & 19.7054525245452 & 1.29454747545476 \tabularnewline
34 & 14 & 13.6828584124494 & 0.317141587550595 \tabularnewline
35 & 16 & 16.4790889268856 & -0.479088926885572 \tabularnewline
36 & 21 & 21.5451797435767 & -0.545179743576693 \tabularnewline
37 & 16 & 16.0259980864823 & -0.0259980864823262 \tabularnewline
38 & 14 & 13.4948819940602 & 0.505118005939843 \tabularnewline
39 & 19 & 19.472066139989 & -0.472066139988997 \tabularnewline
40 & 22 & 21.2729800530344 & 0.727019946965597 \tabularnewline
41 & 19 & 18.7789922548486 & 0.221007745151434 \tabularnewline
42 & 11 & 10.4630914749707 & 0.536908525029316 \tabularnewline
43 & 13 & 13.7282683786164 & -0.728268378616398 \tabularnewline
44 & 16 & 15.1791027148274 & 0.820897285172556 \tabularnewline
45 & 14 & 13.9550581029917 & 0.0449418970083054 \tabularnewline
46 & 19 & 18.6688957227514 & 0.331104277248611 \tabularnewline
47 & 21 & 20.5086229417828 & 0.491377058217155 \tabularnewline
48 & 12 & 11.881944499404 & 0.118055500596002 \tabularnewline
49 & 17 & 17.8591286453328 & -0.859128645332838 \tabularnewline
50 & 21 & 21.5539500238547 & -0.553950023854703 \tabularnewline
51 & 11 & 10.804400771527 & 0.195599228472965 \tabularnewline
52 & 19 & 20.3853330893238 & -1.38533308932378 \tabularnewline
53 & 18 & 17.5869289547905 & 0.413071045209452 \tabularnewline
54 & 14 & 14.0717512952698 & -0.0717512952698151 \tabularnewline
55 & 19 & 18.7789922548486 & 0.221007745151434 \tabularnewline
56 & 20 & 19.472066139989 & 0.527933860011003 \tabularnewline
57 & 18 & 17.7424354530547 & 0.257564546945283 \tabularnewline
58 & 22 & 21.8886626602301 & 0.11133733976989 \tabularnewline
59 & 16 & 16.0259980864823 & -0.0259980864823262 \tabularnewline
60 & 20 & 20.4632129756159 & -0.463212975615852 \tabularnewline
61 & 14 & 13.9550581029917 & 0.0449418970083054 \tabularnewline
62 & 22 & 21.4284865512986 & 0.571513448701428 \tabularnewline
63 & 25 & 24.1814807196648 & 0.818519280335188 \tabularnewline
64 & 20 & 19.5433493661001 & 0.456650633899876 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145877&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]20[/C][C]19.472066139989[/C][C]0.527933860011005[/C][/ROW]
[ROW][C]2[/C][C]14[/C][C]13.8449615708945[/C][C]0.155038429105482[/C][/ROW]
[ROW][C]3[/C][C]18[/C][C]18.2092082221672[/C][C]-0.2092082221672[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]12.8084047691007[/C][C]-0.80840476910067[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]15.3971221589247[/C][C]0.602877841075269[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]12.6463016106556[/C][C]0.353698389344443[/C][/ROW]
[ROW][C]7[/C][C]22[/C][C]21.8952593204111[/C][C]0.104740679588946[/C][/ROW]
[ROW][C]8[/C][C]16[/C][C]16.0901960440652[/C][C]-0.0901960440651619[/C][/ROW]
[ROW][C]9[/C][C]20[/C][C]19.8155490566424[/C][C]0.184450943357586[/C][/ROW]
[ROW][C]10[/C][C]10[/C][C]11.4930516165836[/C][C]-1.49305161658359[/C][/ROW]
[ROW][C]11[/C][C]22[/C][C]21.2663833928535[/C][C]0.733616607146541[/C][/ROW]
[ROW][C]12[/C][C]17[/C][C]16.4878592071636[/C][C]0.512140792836418[/C][/ROW]
[ROW][C]13[/C][C]21[/C][C]21.3051966988395[/C][C]-0.305196698839508[/C][/ROW]
[ROW][C]14[/C][C]18[/C][C]17.5156457286794[/C][C]0.484354271320579[/C][/ROW]
[ROW][C]15[/C][C]13[/C][C]12.7629948029337[/C][C]0.237005197066323[/C][/ROW]
[ROW][C]16[/C][C]17[/C][C]18.1567129874719[/C][C]-1.15671298747191[/C][/ROW]
[ROW][C]17[/C][C]17[/C][C]16.5957821191637[/C][C]0.404217880836307[/C][/ROW]
[ROW][C]18[/C][C]19[/C][C]20.3531164435187[/C][C]-1.35311644351868[/C][/ROW]
[ROW][C]19[/C][C]12[/C][C]12.5750183845444[/C][C]-0.575018384544429[/C][/ROW]
[ROW][C]20[/C][C]14[/C][C]14.531438795854[/C][C]-0.531438795854006[/C][/ROW]
[ROW][C]21[/C][C]13[/C][C]13.1518876857541[/C][C]-0.151887685754087[/C][/ROW]
[ROW][C]22[/C][C]20[/C][C]19.7054525245452[/C][C]0.294547475454762[/C][/ROW]
[ROW][C]23[/C][C]20[/C][C]20.0418501726704[/C][C]-0.0418501726703632[/C][/ROW]
[ROW][C]24[/C][C]13[/C][C]12.6917115768225[/C][C]0.308288423177451[/C][/ROW]
[ROW][C]25[/C][C]21[/C][C]20.3919297495047[/C][C]0.608070250495275[/C][/ROW]
[ROW][C]26[/C][C]21[/C][C]22.5817365453705[/C][C]-1.58173654537054[/C][/ROW]
[ROW][C]27[/C][C]19[/C][C]20.2364232512406[/C][C]-1.23642325124056[/C][/ROW]
[ROW][C]28[/C][C]18[/C][C]19.1998664494467[/C][C]-1.19986644944671[/C][/ROW]
[ROW][C]29[/C][C]20[/C][C]19.3187332618219[/C][C]0.681266738178107[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.0695776751728[/C][C]-0.0695776751727499[/C][/ROW]
[ROW][C]31[/C][C]14[/C][C]13.7216717184355[/C][C]0.278328281564546[/C][/ROW]
[ROW][C]32[/C][C]20[/C][C]19.0831732571686[/C][C]0.916826742831413[/C][/ROW]
[ROW][C]33[/C][C]21[/C][C]19.7054525245452[/C][C]1.29454747545476[/C][/ROW]
[ROW][C]34[/C][C]14[/C][C]13.6828584124494[/C][C]0.317141587550595[/C][/ROW]
[ROW][C]35[/C][C]16[/C][C]16.4790889268856[/C][C]-0.479088926885572[/C][/ROW]
[ROW][C]36[/C][C]21[/C][C]21.5451797435767[/C][C]-0.545179743576693[/C][/ROW]
[ROW][C]37[/C][C]16[/C][C]16.0259980864823[/C][C]-0.0259980864823262[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]13.4948819940602[/C][C]0.505118005939843[/C][/ROW]
[ROW][C]39[/C][C]19[/C][C]19.472066139989[/C][C]-0.472066139988997[/C][/ROW]
[ROW][C]40[/C][C]22[/C][C]21.2729800530344[/C][C]0.727019946965597[/C][/ROW]
[ROW][C]41[/C][C]19[/C][C]18.7789922548486[/C][C]0.221007745151434[/C][/ROW]
[ROW][C]42[/C][C]11[/C][C]10.4630914749707[/C][C]0.536908525029316[/C][/ROW]
[ROW][C]43[/C][C]13[/C][C]13.7282683786164[/C][C]-0.728268378616398[/C][/ROW]
[ROW][C]44[/C][C]16[/C][C]15.1791027148274[/C][C]0.820897285172556[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]13.9550581029917[/C][C]0.0449418970083054[/C][/ROW]
[ROW][C]46[/C][C]19[/C][C]18.6688957227514[/C][C]0.331104277248611[/C][/ROW]
[ROW][C]47[/C][C]21[/C][C]20.5086229417828[/C][C]0.491377058217155[/C][/ROW]
[ROW][C]48[/C][C]12[/C][C]11.881944499404[/C][C]0.118055500596002[/C][/ROW]
[ROW][C]49[/C][C]17[/C][C]17.8591286453328[/C][C]-0.859128645332838[/C][/ROW]
[ROW][C]50[/C][C]21[/C][C]21.5539500238547[/C][C]-0.553950023854703[/C][/ROW]
[ROW][C]51[/C][C]11[/C][C]10.804400771527[/C][C]0.195599228472965[/C][/ROW]
[ROW][C]52[/C][C]19[/C][C]20.3853330893238[/C][C]-1.38533308932378[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]17.5869289547905[/C][C]0.413071045209452[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]14.0717512952698[/C][C]-0.0717512952698151[/C][/ROW]
[ROW][C]55[/C][C]19[/C][C]18.7789922548486[/C][C]0.221007745151434[/C][/ROW]
[ROW][C]56[/C][C]20[/C][C]19.472066139989[/C][C]0.527933860011003[/C][/ROW]
[ROW][C]57[/C][C]18[/C][C]17.7424354530547[/C][C]0.257564546945283[/C][/ROW]
[ROW][C]58[/C][C]22[/C][C]21.8886626602301[/C][C]0.11133733976989[/C][/ROW]
[ROW][C]59[/C][C]16[/C][C]16.0259980864823[/C][C]-0.0259980864823262[/C][/ROW]
[ROW][C]60[/C][C]20[/C][C]20.4632129756159[/C][C]-0.463212975615852[/C][/ROW]
[ROW][C]61[/C][C]14[/C][C]13.9550581029917[/C][C]0.0449418970083054[/C][/ROW]
[ROW][C]62[/C][C]22[/C][C]21.4284865512986[/C][C]0.571513448701428[/C][/ROW]
[ROW][C]63[/C][C]25[/C][C]24.1814807196648[/C][C]0.818519280335188[/C][/ROW]
[ROW][C]64[/C][C]20[/C][C]19.5433493661001[/C][C]0.456650633899876[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145877&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145877&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12019.4720661399890.527933860011005
21413.84496157089450.155038429105482
31818.2092082221672-0.2092082221672
41212.8084047691007-0.80840476910067
51615.39712215892470.602877841075269
61312.64630161065560.353698389344443
72221.89525932041110.104740679588946
81616.0901960440652-0.0901960440651619
92019.81554905664240.184450943357586
101011.4930516165836-1.49305161658359
112221.26638339285350.733616607146541
121716.48785920716360.512140792836418
132121.3051966988395-0.305196698839508
141817.51564572867940.484354271320579
151312.76299480293370.237005197066323
161718.1567129874719-1.15671298747191
171716.59578211916370.404217880836307
181920.3531164435187-1.35311644351868
191212.5750183845444-0.575018384544429
201414.531438795854-0.531438795854006
211313.1518876857541-0.151887685754087
222019.70545252454520.294547475454762
232020.0418501726704-0.0418501726703632
241312.69171157682250.308288423177451
252120.39192974950470.608070250495275
262122.5817365453705-1.58173654537054
271920.2364232512406-1.23642325124056
281819.1998664494467-1.19986644944671
292019.31873326182190.681266738178107
301414.0695776751728-0.0695776751727499
311413.72167171843550.278328281564546
322019.08317325716860.916826742831413
332119.70545252454521.29454747545476
341413.68285841244940.317141587550595
351616.4790889268856-0.479088926885572
362121.5451797435767-0.545179743576693
371616.0259980864823-0.0259980864823262
381413.49488199406020.505118005939843
391919.472066139989-0.472066139988997
402221.27298005303440.727019946965597
411918.77899225484860.221007745151434
421110.46309147497070.536908525029316
431313.7282683786164-0.728268378616398
441615.17910271482740.820897285172556
451413.95505810299170.0449418970083054
461918.66889572275140.331104277248611
472120.50862294178280.491377058217155
481211.8819444994040.118055500596002
491717.8591286453328-0.859128645332838
502121.5539500238547-0.553950023854703
511110.8044007715270.195599228472965
521920.3853330893238-1.38533308932378
531817.58692895479050.413071045209452
541414.0717512952698-0.0717512952698151
551918.77899225484860.221007745151434
562019.4720661399890.527933860011003
571817.74243545305470.257564546945283
582221.88866266023010.11133733976989
591616.0259980864823-0.0259980864823262
602020.4632129756159-0.463212975615852
611413.95505810299170.0449418970083054
622221.42848655129860.571513448701428
632524.18148071966480.818519280335188
642019.54334936610010.456650633899876







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.284402440544910.568804881089820.71559755945509
100.7534129904120120.4931740191759750.246587009587987
110.6444383038525890.7111233922948220.355561696147411
120.5524698868838510.8950602262322980.447530113116149
130.539468904110150.92106219177970.46053109588985
140.4707157269322310.9414314538644630.529284273067769
150.398222435491960.7964448709839210.60177756450804
160.6120887468383520.7758225063232960.387911253161648
170.5436890351540650.9126219296918690.456310964845935
180.7985761854965720.4028476290068570.201423814503428
190.7692552720795870.4614894558408260.230744727920413
200.727341116725750.5453177665484990.27265888327425
210.6531769335621920.6936461328756150.346823066437808
220.582622643697720.834754712604560.41737735630228
230.49935494111260.99870988222520.5006450588874
240.4423004157205740.8846008314411490.557699584279426
250.4131929117080070.8263858234160130.586807088291993
260.7099156514117090.5801686971765830.290084348588291
270.8120522558833280.3758954882333430.187947744116672
280.9225836522357310.1548326955285370.0774163477642685
290.9378991388290670.1242017223418660.0621008611709332
300.910075055155050.1798498896898990.0899249448449495
310.8806156090804030.2387687818391940.119384390919597
320.9119448343216650.176110331356670.0880551656783348
330.9721369714860870.05572605702782680.0278630285139134
340.9598770112929610.08024597741407830.0401229887070391
350.9545216714083370.09095665718332690.0454783285916634
360.9512213154970370.0975573690059250.0487786845029625
370.9263683282515130.1472633434969740.0736316717484872
380.9078899182520270.1842201634959460.0921100817479728
390.9034719323483320.1930561353033360.0965280676516681
400.8909316116432240.2181367767135530.109068388356776
410.8516964189393270.2966071621213470.148303581060673
420.8133182498629940.3733635002740120.186681750137006
430.8478739587956910.3042520824086180.152126041204309
440.8216818509076650.356636298184670.178318149092335
450.7583909942332590.4832180115334830.241609005766741
460.6844057494120140.6311885011759730.315594250587986
470.6160166051462960.7679667897074090.383983394853704
480.5317545467179990.9364909065640020.468245453282001
490.5882172732058140.8235654535883730.411782726794186
500.6062016728079570.7875966543840850.393798327192043
510.4962110184255920.9924220368511850.503788981574408
520.9753332467326890.04933350653462230.0246667532673111
530.964264317721130.07147136455774090.0357356822788705
540.9114744316780060.1770511366439880.088525568321994
550.8030086142501840.3939827714996310.196991385749816

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.28440244054491 & 0.56880488108982 & 0.71559755945509 \tabularnewline
10 & 0.753412990412012 & 0.493174019175975 & 0.246587009587987 \tabularnewline
11 & 0.644438303852589 & 0.711123392294822 & 0.355561696147411 \tabularnewline
12 & 0.552469886883851 & 0.895060226232298 & 0.447530113116149 \tabularnewline
13 & 0.53946890411015 & 0.9210621917797 & 0.46053109588985 \tabularnewline
14 & 0.470715726932231 & 0.941431453864463 & 0.529284273067769 \tabularnewline
15 & 0.39822243549196 & 0.796444870983921 & 0.60177756450804 \tabularnewline
16 & 0.612088746838352 & 0.775822506323296 & 0.387911253161648 \tabularnewline
17 & 0.543689035154065 & 0.912621929691869 & 0.456310964845935 \tabularnewline
18 & 0.798576185496572 & 0.402847629006857 & 0.201423814503428 \tabularnewline
19 & 0.769255272079587 & 0.461489455840826 & 0.230744727920413 \tabularnewline
20 & 0.72734111672575 & 0.545317766548499 & 0.27265888327425 \tabularnewline
21 & 0.653176933562192 & 0.693646132875615 & 0.346823066437808 \tabularnewline
22 & 0.58262264369772 & 0.83475471260456 & 0.41737735630228 \tabularnewline
23 & 0.4993549411126 & 0.9987098822252 & 0.5006450588874 \tabularnewline
24 & 0.442300415720574 & 0.884600831441149 & 0.557699584279426 \tabularnewline
25 & 0.413192911708007 & 0.826385823416013 & 0.586807088291993 \tabularnewline
26 & 0.709915651411709 & 0.580168697176583 & 0.290084348588291 \tabularnewline
27 & 0.812052255883328 & 0.375895488233343 & 0.187947744116672 \tabularnewline
28 & 0.922583652235731 & 0.154832695528537 & 0.0774163477642685 \tabularnewline
29 & 0.937899138829067 & 0.124201722341866 & 0.0621008611709332 \tabularnewline
30 & 0.91007505515505 & 0.179849889689899 & 0.0899249448449495 \tabularnewline
31 & 0.880615609080403 & 0.238768781839194 & 0.119384390919597 \tabularnewline
32 & 0.911944834321665 & 0.17611033135667 & 0.0880551656783348 \tabularnewline
33 & 0.972136971486087 & 0.0557260570278268 & 0.0278630285139134 \tabularnewline
34 & 0.959877011292961 & 0.0802459774140783 & 0.0401229887070391 \tabularnewline
35 & 0.954521671408337 & 0.0909566571833269 & 0.0454783285916634 \tabularnewline
36 & 0.951221315497037 & 0.097557369005925 & 0.0487786845029625 \tabularnewline
37 & 0.926368328251513 & 0.147263343496974 & 0.0736316717484872 \tabularnewline
38 & 0.907889918252027 & 0.184220163495946 & 0.0921100817479728 \tabularnewline
39 & 0.903471932348332 & 0.193056135303336 & 0.0965280676516681 \tabularnewline
40 & 0.890931611643224 & 0.218136776713553 & 0.109068388356776 \tabularnewline
41 & 0.851696418939327 & 0.296607162121347 & 0.148303581060673 \tabularnewline
42 & 0.813318249862994 & 0.373363500274012 & 0.186681750137006 \tabularnewline
43 & 0.847873958795691 & 0.304252082408618 & 0.152126041204309 \tabularnewline
44 & 0.821681850907665 & 0.35663629818467 & 0.178318149092335 \tabularnewline
45 & 0.758390994233259 & 0.483218011533483 & 0.241609005766741 \tabularnewline
46 & 0.684405749412014 & 0.631188501175973 & 0.315594250587986 \tabularnewline
47 & 0.616016605146296 & 0.767966789707409 & 0.383983394853704 \tabularnewline
48 & 0.531754546717999 & 0.936490906564002 & 0.468245453282001 \tabularnewline
49 & 0.588217273205814 & 0.823565453588373 & 0.411782726794186 \tabularnewline
50 & 0.606201672807957 & 0.787596654384085 & 0.393798327192043 \tabularnewline
51 & 0.496211018425592 & 0.992422036851185 & 0.503788981574408 \tabularnewline
52 & 0.975333246732689 & 0.0493335065346223 & 0.0246667532673111 \tabularnewline
53 & 0.96426431772113 & 0.0714713645577409 & 0.0357356822788705 \tabularnewline
54 & 0.911474431678006 & 0.177051136643988 & 0.088525568321994 \tabularnewline
55 & 0.803008614250184 & 0.393982771499631 & 0.196991385749816 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145877&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]0.28440244054491[/C][C]0.56880488108982[/C][C]0.71559755945509[/C][/ROW]
[ROW][C]10[/C][C]0.753412990412012[/C][C]0.493174019175975[/C][C]0.246587009587987[/C][/ROW]
[ROW][C]11[/C][C]0.644438303852589[/C][C]0.711123392294822[/C][C]0.355561696147411[/C][/ROW]
[ROW][C]12[/C][C]0.552469886883851[/C][C]0.895060226232298[/C][C]0.447530113116149[/C][/ROW]
[ROW][C]13[/C][C]0.53946890411015[/C][C]0.9210621917797[/C][C]0.46053109588985[/C][/ROW]
[ROW][C]14[/C][C]0.470715726932231[/C][C]0.941431453864463[/C][C]0.529284273067769[/C][/ROW]
[ROW][C]15[/C][C]0.39822243549196[/C][C]0.796444870983921[/C][C]0.60177756450804[/C][/ROW]
[ROW][C]16[/C][C]0.612088746838352[/C][C]0.775822506323296[/C][C]0.387911253161648[/C][/ROW]
[ROW][C]17[/C][C]0.543689035154065[/C][C]0.912621929691869[/C][C]0.456310964845935[/C][/ROW]
[ROW][C]18[/C][C]0.798576185496572[/C][C]0.402847629006857[/C][C]0.201423814503428[/C][/ROW]
[ROW][C]19[/C][C]0.769255272079587[/C][C]0.461489455840826[/C][C]0.230744727920413[/C][/ROW]
[ROW][C]20[/C][C]0.72734111672575[/C][C]0.545317766548499[/C][C]0.27265888327425[/C][/ROW]
[ROW][C]21[/C][C]0.653176933562192[/C][C]0.693646132875615[/C][C]0.346823066437808[/C][/ROW]
[ROW][C]22[/C][C]0.58262264369772[/C][C]0.83475471260456[/C][C]0.41737735630228[/C][/ROW]
[ROW][C]23[/C][C]0.4993549411126[/C][C]0.9987098822252[/C][C]0.5006450588874[/C][/ROW]
[ROW][C]24[/C][C]0.442300415720574[/C][C]0.884600831441149[/C][C]0.557699584279426[/C][/ROW]
[ROW][C]25[/C][C]0.413192911708007[/C][C]0.826385823416013[/C][C]0.586807088291993[/C][/ROW]
[ROW][C]26[/C][C]0.709915651411709[/C][C]0.580168697176583[/C][C]0.290084348588291[/C][/ROW]
[ROW][C]27[/C][C]0.812052255883328[/C][C]0.375895488233343[/C][C]0.187947744116672[/C][/ROW]
[ROW][C]28[/C][C]0.922583652235731[/C][C]0.154832695528537[/C][C]0.0774163477642685[/C][/ROW]
[ROW][C]29[/C][C]0.937899138829067[/C][C]0.124201722341866[/C][C]0.0621008611709332[/C][/ROW]
[ROW][C]30[/C][C]0.91007505515505[/C][C]0.179849889689899[/C][C]0.0899249448449495[/C][/ROW]
[ROW][C]31[/C][C]0.880615609080403[/C][C]0.238768781839194[/C][C]0.119384390919597[/C][/ROW]
[ROW][C]32[/C][C]0.911944834321665[/C][C]0.17611033135667[/C][C]0.0880551656783348[/C][/ROW]
[ROW][C]33[/C][C]0.972136971486087[/C][C]0.0557260570278268[/C][C]0.0278630285139134[/C][/ROW]
[ROW][C]34[/C][C]0.959877011292961[/C][C]0.0802459774140783[/C][C]0.0401229887070391[/C][/ROW]
[ROW][C]35[/C][C]0.954521671408337[/C][C]0.0909566571833269[/C][C]0.0454783285916634[/C][/ROW]
[ROW][C]36[/C][C]0.951221315497037[/C][C]0.097557369005925[/C][C]0.0487786845029625[/C][/ROW]
[ROW][C]37[/C][C]0.926368328251513[/C][C]0.147263343496974[/C][C]0.0736316717484872[/C][/ROW]
[ROW][C]38[/C][C]0.907889918252027[/C][C]0.184220163495946[/C][C]0.0921100817479728[/C][/ROW]
[ROW][C]39[/C][C]0.903471932348332[/C][C]0.193056135303336[/C][C]0.0965280676516681[/C][/ROW]
[ROW][C]40[/C][C]0.890931611643224[/C][C]0.218136776713553[/C][C]0.109068388356776[/C][/ROW]
[ROW][C]41[/C][C]0.851696418939327[/C][C]0.296607162121347[/C][C]0.148303581060673[/C][/ROW]
[ROW][C]42[/C][C]0.813318249862994[/C][C]0.373363500274012[/C][C]0.186681750137006[/C][/ROW]
[ROW][C]43[/C][C]0.847873958795691[/C][C]0.304252082408618[/C][C]0.152126041204309[/C][/ROW]
[ROW][C]44[/C][C]0.821681850907665[/C][C]0.35663629818467[/C][C]0.178318149092335[/C][/ROW]
[ROW][C]45[/C][C]0.758390994233259[/C][C]0.483218011533483[/C][C]0.241609005766741[/C][/ROW]
[ROW][C]46[/C][C]0.684405749412014[/C][C]0.631188501175973[/C][C]0.315594250587986[/C][/ROW]
[ROW][C]47[/C][C]0.616016605146296[/C][C]0.767966789707409[/C][C]0.383983394853704[/C][/ROW]
[ROW][C]48[/C][C]0.531754546717999[/C][C]0.936490906564002[/C][C]0.468245453282001[/C][/ROW]
[ROW][C]49[/C][C]0.588217273205814[/C][C]0.823565453588373[/C][C]0.411782726794186[/C][/ROW]
[ROW][C]50[/C][C]0.606201672807957[/C][C]0.787596654384085[/C][C]0.393798327192043[/C][/ROW]
[ROW][C]51[/C][C]0.496211018425592[/C][C]0.992422036851185[/C][C]0.503788981574408[/C][/ROW]
[ROW][C]52[/C][C]0.975333246732689[/C][C]0.0493335065346223[/C][C]0.0246667532673111[/C][/ROW]
[ROW][C]53[/C][C]0.96426431772113[/C][C]0.0714713645577409[/C][C]0.0357356822788705[/C][/ROW]
[ROW][C]54[/C][C]0.911474431678006[/C][C]0.177051136643988[/C][C]0.088525568321994[/C][/ROW]
[ROW][C]55[/C][C]0.803008614250184[/C][C]0.393982771499631[/C][C]0.196991385749816[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145877&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145877&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.284402440544910.568804881089820.71559755945509
100.7534129904120120.4931740191759750.246587009587987
110.6444383038525890.7111233922948220.355561696147411
120.5524698868838510.8950602262322980.447530113116149
130.539468904110150.92106219177970.46053109588985
140.4707157269322310.9414314538644630.529284273067769
150.398222435491960.7964448709839210.60177756450804
160.6120887468383520.7758225063232960.387911253161648
170.5436890351540650.9126219296918690.456310964845935
180.7985761854965720.4028476290068570.201423814503428
190.7692552720795870.4614894558408260.230744727920413
200.727341116725750.5453177665484990.27265888327425
210.6531769335621920.6936461328756150.346823066437808
220.582622643697720.834754712604560.41737735630228
230.49935494111260.99870988222520.5006450588874
240.4423004157205740.8846008314411490.557699584279426
250.4131929117080070.8263858234160130.586807088291993
260.7099156514117090.5801686971765830.290084348588291
270.8120522558833280.3758954882333430.187947744116672
280.9225836522357310.1548326955285370.0774163477642685
290.9378991388290670.1242017223418660.0621008611709332
300.910075055155050.1798498896898990.0899249448449495
310.8806156090804030.2387687818391940.119384390919597
320.9119448343216650.176110331356670.0880551656783348
330.9721369714860870.05572605702782680.0278630285139134
340.9598770112929610.08024597741407830.0401229887070391
350.9545216714083370.09095665718332690.0454783285916634
360.9512213154970370.0975573690059250.0487786845029625
370.9263683282515130.1472633434969740.0736316717484872
380.9078899182520270.1842201634959460.0921100817479728
390.9034719323483320.1930561353033360.0965280676516681
400.8909316116432240.2181367767135530.109068388356776
410.8516964189393270.2966071621213470.148303581060673
420.8133182498629940.3733635002740120.186681750137006
430.8478739587956910.3042520824086180.152126041204309
440.8216818509076650.356636298184670.178318149092335
450.7583909942332590.4832180115334830.241609005766741
460.6844057494120140.6311885011759730.315594250587986
470.6160166051462960.7679667897074090.383983394853704
480.5317545467179990.9364909065640020.468245453282001
490.5882172732058140.8235654535883730.411782726794186
500.6062016728079570.7875966543840850.393798327192043
510.4962110184255920.9924220368511850.503788981574408
520.9753332467326890.04933350653462230.0246667532673111
530.964264317721130.07147136455774090.0357356822788705
540.9114744316780060.1770511366439880.088525568321994
550.8030086142501840.3939827714996310.196991385749816







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0212765957446809OK
10% type I error level60.127659574468085NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.0212765957446809 & OK \tabularnewline
10% type I error level & 6 & 0.127659574468085 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145877&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.0212765957446809[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]6[/C][C]0.127659574468085[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145877&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145877&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0212765957446809OK
10% type I error level60.127659574468085NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}