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Author's title

Author*The author of this computation has been verified*
R Software Module--
Title produced by softwareMultiple Regression
Date of computationThu, 24 Nov 2011 13:58:20 -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/24/t1322161119xg5bnj5uj0a9qrc.htm/, Retrieved Fri, 29 Mar 2024 15:04:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147153, Retrieved Fri, 29 Mar 2024 15:04:50 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact104
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:55:05] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [Two way anova FMPS] [2010-11-22 18:59:20] [95e8426e0df851c9330605aa1e892ab5]
- RM        [Multiple Regression] [] [2011-11-24 18:58:20] [9fcdc23b96f67ca1860b0ed8ec932927] [Current]
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Dataseries X:
2	24	48	14	28	11	22	12	24	24	48	26	52
1	25	25	11	11	7	7	8	8	25	25	23	23
1	17	17	6	6	17	17	8	8	30	30	25	25
1	18	18	12	12	10	10	8	8	19	19	23	23
1	18	18	8	8	12	12	9	9	22	22	19	19
1	16	16	10	10	12	12	7	7	22	22	29	29
2	20	40	10	20	11	22	4	8	25	50	25	50
2	16	32	11	22	11	22	11	22	23	46	21	42
1	18	18	16	16	12	12	7	7	17	17	22	22
1	17	17	11	11	13	13	7	7	21	21	25	25
1	23	23	13	13	14	14	12	12	19	19	24	24
2	30	60	12	24	16	32	10	20	19	38	18	36
2	23	46	8	16	11	22	10	20	15	30	22	44
2	18	36	12	24	10	20	8	16	16	32	15	30
1	15	15	11	11	11	11	8	8	23	23	22	22
2	12	24	4	8	15	30	4	8	27	54	28	56
1	21	21	9	9	9	9	9	9	22	22	20	20
1	15	15	8	8	11	11	8	8	14	14	12	12
2	20	40	8	16	17	34	7	14	22	44	24	48
2	31	62	14	28	17	34	11	22	23	46	20	40
1	27	27	15	15	11	11	9	9	23	23	21	21
2	34	68	16	32	18	36	11	22	21	42	20	40
2	21	42	9	18	14	28	13	26	19	38	21	42
1	31	31	14	14	10	10	8	8	18	18	23	23
2	19	38	11	22	11	22	8	16	20	40	28	56
2	16	32	8	16	15	30	9	18	23	46	24	48
2	20	40	9	18	15	30	6	12	25	50	24	48
2	21	42	9	18	13	26	9	18	19	38	24	48
1	22	22	9	9	16	16	9	9	24	24	23	23
2	17	34	9	18	13	26	6	12	22	44	23	46
2	24	48	10	20	9	18	6	12	25	50	29	58
2	25	50	16	32	18	36	16	32	26	52	24	48
1	26	26	11	11	18	18	5	5	29	29	18	18
2	25	50	8	16	12	24	7	14	32	64	25	50
1	17	17	9	9	17	17	9	9	25	25	21	21
1	32	32	16	16	9	9	6	6	29	29	26	26
1	33	33	11	11	9	9	6	6	28	28	22	22
1	13	13	16	16	12	12	5	5	17	17	22	22
1	32	32	12	12	18	18	12	12	28	28	22	22
2	25	50	12	24	12	24	7	14	29	58	23	46
2	29	58	14	28	18	36	10	20	26	52	30	60
1	22	22	9	9	14	14	9	9	25	25	23	23
2	18	36	10	20	15	30	8	16	14	28	17	34
2	17	34	9	18	16	32	5	10	25	50	23	46
1	20	20	10	10	10	10	8	8	26	26	23	23
1	15	15	12	12	11	11	8	8	20	20	25	25
2	20	40	14	28	14	28	10	20	18	36	24	48
1	33	33	14	14	9	9	6	6	32	32	24	24
1	29	29	10	10	12	12	8	8	25	25	23	23
1	23	23	14	14	17	17	7	7	25	25	21	21
2	26	52	16	32	5	10	4	8	23	46	24	48
2	18	36	9	18	12	24	8	16	21	42	24	48
2	20	40	10	20	12	24	8	16	20	40	28	56
2	11	22	6	12	6	12	4	8	15	30	16	32
2	28	56	8	16	24	48	20	40	30	60	20	40
1	26	26	13	13	12	12	8	8	24	24	29	29
1	22	22	10	10	12	12	8	8	26	26	27	27
2	17	34	8	16	14	28	6	12	24	48	22	44
2	12	24	7	14	7	14	4	8	22	44	28	56
2	14	28	15	30	13	26	8	16	14	28	16	32
1	17	17	9	9	12	12	9	9	24	24	25	25
2	21	42	10	20	13	26	6	12	24	48	24	48
1	19	19	12	12	14	14	7	7	24	24	28	28
2	18	36	13	26	8	16	9	18	24	48	24	48
1	10	10	10	10	11	11	5	5	19	19	23	23
1	29	29	11	11	9	9	5	5	31	31	30	30
1	31	31	8	8	11	11	8	8	22	22	24	24
1	19	19	9	9	13	13	8	8	27	27	21	21
1	9	9	13	13	10	10	6	6	19	19	25	25
2	20	40	11	22	11	22	8	16	25	50	25	50
2	28	56	8	16	12	24	7	14	20	40	22	44
2	19	38	9	18	9	18	7	14	21	42	23	46
2	30	60	9	18	15	30	9	18	27	54	26	52
2	29	58	15	30	18	36	11	22	23	46	23	46
2	26	52	9	18	15	30	6	12	25	50	25	50
1	23	23	10	10	12	12	8	8	20	20	21	21
2	13	26	14	28	13	26	6	12	21	42	25	50
1	21	21	12	12	14	14	9	9	22	22	24	24
2	19	38	12	24	10	20	8	16	23	46	29	58
2	28	56	11	22	13	26	6	12	25	50	22	44
2	23	46	14	28	13	26	10	20	25	50	27	54
1	18	18	6	6	11	11	8	8	17	17	26	26
2	21	42	12	24	13	26	8	16	19	38	22	44
1	20	20	8	8	16	16	10	10	25	25	24	24
2	23	46	14	28	8	16	5	10	19	38	27	54
2	21	42	11	22	16	32	7	14	20	40	24	48
2	21	42	10	20	11	22	5	10	26	52	24	48
1	15	15	14	14	9	9	8	8	23	23	29	29
1	28	28	12	12	16	16	14	14	27	27	22	22
2	19	38	10	20	12	24	7	14	17	34	21	42
2	26	52	14	28	14	28	8	16	17	34	24	48
2	10	20	5	10	8	16	6	12	19	38	24	48
1	16	16	11	11	9	9	5	5	17	17	23	23
2	22	44	10	20	15	30	6	12	22	44	20	40
2	19	38	9	18	11	22	10	20	21	42	27	54
2	31	62	10	20	21	42	12	24	32	64	26	52
2	31	62	16	32	14	28	9	18	21	42	25	50
1	29	29	13	13	18	18	12	12	21	21	21	21
1	19	19	9	9	12	12	7	7	18	18	21	21
1	22	22	10	10	13	13	8	8	18	18	19	19
2	23	46	10	20	15	30	10	20	23	46	21	42
1	15	15	7	7	12	12	6	6	19	19	21	21
2	20	40	9	18	19	38	10	20	20	40	16	32
2	18	36	8	16	15	30	10	20	21	42	22	44
1	23	23	14	14	11	11	10	10	20	20	29	29
1	25	25	14	14	11	11	5	5	17	17	15	15
2	21	42	8	16	10	20	7	14	18	36	17	34
2	24	48	9	18	13	26	10	20	19	38	15	30
1	25	25	14	14	15	15	11	11	22	22	21	21
2	17	34	14	28	12	24	6	12	15	30	21	42
2	13	26	8	16	12	24	7	14	14	28	19	38
2	28	56	8	16	16	32	12	24	18	36	24	48
2	21	42	8	16	9	18	11	22	24	48	20	40
2	25	50	7	14	18	36	11	22	35	70	17	34
2	9	18	6	12	8	16	11	22	29	58	23	46
1	16	16	8	8	13	13	5	5	21	21	24	24
2	19	38	6	12	17	34	8	16	25	50	14	28
2	17	34	11	22	9	18	6	12	20	40	19	38
2	25	50	14	28	15	30	9	18	22	44	24	48
2	20	40	11	22	8	16	4	8	13	26	13	26
2	29	58	11	22	7	14	4	8	26	52	22	44
1	14	14	11	11	12	12	7	7	17	17	16	16
1	22	22	14	14	14	14	11	11	25	25	19	19
1	15	15	8	8	6	6	6	6	20	20	25	25
1	19	19	20	20	8	8	7	7	19	19	25	25
2	20	40	11	22	17	34	8	16	21	42	23	46
2	15	30	8	16	10	20	4	8	22	44	24	48
1	20	20	11	11	11	11	8	8	24	24	26	26
2	18	36	10	20	14	28	9	18	21	42	26	52
2	33	66	14	28	11	22	8	16	26	52	25	50
2	22	44	11	22	13	26	11	22	24	48	18	36
1	16	16	9	9	12	12	8	8	16	16	21	21
1	17	17	9	9	11	11	5	5	23	23	26	26
1	16	16	8	8	9	9	4	4	18	18	23	23
2	21	42	10	20	12	24	8	16	16	32	23	46
1	26	26	13	13	20	20	10	10	26	26	22	22
1	18	18	13	13	12	12	6	6	19	19	20	20
1	18	18	12	12	13	13	9	9	21	21	13	13
2	17	34	8	16	12	24	9	18	21	42	24	48
2	22	44	13	26	12	24	13	26	22	44	15	30
2	30	60	14	28	9	18	9	18	23	46	14	28
1	30	30	12	12	15	15	10	10	29	29	22	22
1	24	24	14	14	24	24	20	20	21	21	10	10
1	21	21	15	15	7	7	5	5	21	21	24	24
1	21	21	13	13	17	17	11	11	23	23	22	22
2	29	58	16	32	11	22	6	12	27	54	24	48
2	31	62	9	18	17	34	9	18	25	50	19	38
1	20	20	9	9	11	11	7	7	21	21	20	20
1	16	16	9	9	12	12	9	9	10	10	13	13
1	22	22	8	8	14	14	10	10	20	20	20	20
2	20	40	7	14	11	22	9	18	26	52	22	44
2	28	56	16	32	16	32	8	16	24	48	24	48
2	38	76	11	22	21	42	7	14	29	58	29	58
1	22	22	9	9	14	14	6	6	19	19	12	12
2	20	40	11	22	20	40	13	26	24	48	20	40
2	17	34	9	18	13	26	6	12	19	38	21	42
2	28	56	14	28	11	22	8	16	24	48	24	48
2	22	44	13	26	15	30	10	20	22	44	22	44
1	31	31	16	16	19	19	16	16	17	17	20	20




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 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 & 7 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147153&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]7 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=147153&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147153&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 time7 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
ParentalExpectations[t] = + 7.56408311336291 -4.02282154930862Gender[t] -0.0497128514879975ConcernoverMistakes[t] + 0.0139238377293545`C*G`[t] -0.0584300461850065Doubtsaboutactions[t] + 0.0411688955628661`D*G`[t] + 0.554368493058405`PE*G`[t] + 0.762594333796531ParentalCriticism[t] -0.413361375350402`PC*G`[t] + 0.229602388869078PersonalStandards[t] -0.11794509827969`PS*G`[t] -0.205802809085592Organization[t] + 0.101854058940113`O*G`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ParentalExpectations[t] =  +  7.56408311336291 -4.02282154930862Gender[t] -0.0497128514879975ConcernoverMistakes[t] +  0.0139238377293545`C*G`[t] -0.0584300461850065Doubtsaboutactions[t] +  0.0411688955628661`D*G`[t] +  0.554368493058405`PE*G`[t] +  0.762594333796531ParentalCriticism[t] -0.413361375350402`PC*G`[t] +  0.229602388869078PersonalStandards[t] -0.11794509827969`PS*G`[t] -0.205802809085592Organization[t] +  0.101854058940113`O*G`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147153&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ParentalExpectations[t] =  +  7.56408311336291 -4.02282154930862Gender[t] -0.0497128514879975ConcernoverMistakes[t] +  0.0139238377293545`C*G`[t] -0.0584300461850065Doubtsaboutactions[t] +  0.0411688955628661`D*G`[t] +  0.554368493058405`PE*G`[t] +  0.762594333796531ParentalCriticism[t] -0.413361375350402`PC*G`[t] +  0.229602388869078PersonalStandards[t] -0.11794509827969`PS*G`[t] -0.205802809085592Organization[t] +  0.101854058940113`O*G`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147153&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147153&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
ParentalExpectations[t] = + 7.56408311336291 -4.02282154930862Gender[t] -0.0497128514879975ConcernoverMistakes[t] + 0.0139238377293545`C*G`[t] -0.0584300461850065Doubtsaboutactions[t] + 0.0411688955628661`D*G`[t] + 0.554368493058405`PE*G`[t] + 0.762594333796531ParentalCriticism[t] -0.413361375350402`PC*G`[t] + 0.229602388869078PersonalStandards[t] -0.11794509827969`PS*G`[t] -0.205802809085592Organization[t] + 0.101854058940113`O*G`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.564083113362911.5634694.8383e-062e-06
Gender-4.022821549308620.950195-4.23374e-052e-05
ConcernoverMistakes-0.04971285148799750.04408-1.12780.2612550.130628
`C*G`0.01392383772935450.0270930.51390.6080830.304042
Doubtsaboutactions-0.05843004618500650.075433-0.77460.4398330.219917
`D*G`0.04116889556286610.0473290.86990.3858080.192904
`PE*G`0.5543684930584050.01288243.034200
ParentalCriticism0.7625943337965310.0785689.706100
`PC*G`-0.4133613753504020.047198-8.757900
PersonalStandards0.2296023888690780.0578573.96840.0001135.7e-05
`PS*G`-0.117945098279690.034576-3.41110.0008370.000418
Organization-0.2058028090855920.054165-3.79950.0002120.000106
`O*G`0.1018540589401130.0332743.06110.0026260.001313

\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) & 7.56408311336291 & 1.563469 & 4.838 & 3e-06 & 2e-06 \tabularnewline
Gender & -4.02282154930862 & 0.950195 & -4.2337 & 4e-05 & 2e-05 \tabularnewline
ConcernoverMistakes & -0.0497128514879975 & 0.04408 & -1.1278 & 0.261255 & 0.130628 \tabularnewline
`C*G` & 0.0139238377293545 & 0.027093 & 0.5139 & 0.608083 & 0.304042 \tabularnewline
Doubtsaboutactions & -0.0584300461850065 & 0.075433 & -0.7746 & 0.439833 & 0.219917 \tabularnewline
`D*G` & 0.0411688955628661 & 0.047329 & 0.8699 & 0.385808 & 0.192904 \tabularnewline
`PE*G` & 0.554368493058405 & 0.012882 & 43.0342 & 0 & 0 \tabularnewline
ParentalCriticism & 0.762594333796531 & 0.078568 & 9.7061 & 0 & 0 \tabularnewline
`PC*G` & -0.413361375350402 & 0.047198 & -8.7579 & 0 & 0 \tabularnewline
PersonalStandards & 0.229602388869078 & 0.057857 & 3.9684 & 0.000113 & 5.7e-05 \tabularnewline
`PS*G` & -0.11794509827969 & 0.034576 & -3.4111 & 0.000837 & 0.000418 \tabularnewline
Organization & -0.205802809085592 & 0.054165 & -3.7995 & 0.000212 & 0.000106 \tabularnewline
`O*G` & 0.101854058940113 & 0.033274 & 3.0611 & 0.002626 & 0.001313 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147153&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]7.56408311336291[/C][C]1.563469[/C][C]4.838[/C][C]3e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]Gender[/C][C]-4.02282154930862[/C][C]0.950195[/C][C]-4.2337[/C][C]4e-05[/C][C]2e-05[/C][/ROW]
[ROW][C]ConcernoverMistakes[/C][C]-0.0497128514879975[/C][C]0.04408[/C][C]-1.1278[/C][C]0.261255[/C][C]0.130628[/C][/ROW]
[ROW][C]`C*G`[/C][C]0.0139238377293545[/C][C]0.027093[/C][C]0.5139[/C][C]0.608083[/C][C]0.304042[/C][/ROW]
[ROW][C]Doubtsaboutactions[/C][C]-0.0584300461850065[/C][C]0.075433[/C][C]-0.7746[/C][C]0.439833[/C][C]0.219917[/C][/ROW]
[ROW][C]`D*G`[/C][C]0.0411688955628661[/C][C]0.047329[/C][C]0.8699[/C][C]0.385808[/C][C]0.192904[/C][/ROW]
[ROW][C]`PE*G`[/C][C]0.554368493058405[/C][C]0.012882[/C][C]43.0342[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]ParentalCriticism[/C][C]0.762594333796531[/C][C]0.078568[/C][C]9.7061[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`PC*G`[/C][C]-0.413361375350402[/C][C]0.047198[/C][C]-8.7579[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]PersonalStandards[/C][C]0.229602388869078[/C][C]0.057857[/C][C]3.9684[/C][C]0.000113[/C][C]5.7e-05[/C][/ROW]
[ROW][C]`PS*G`[/C][C]-0.11794509827969[/C][C]0.034576[/C][C]-3.4111[/C][C]0.000837[/C][C]0.000418[/C][/ROW]
[ROW][C]Organization[/C][C]-0.205802809085592[/C][C]0.054165[/C][C]-3.7995[/C][C]0.000212[/C][C]0.000106[/C][/ROW]
[ROW][C]`O*G`[/C][C]0.101854058940113[/C][C]0.033274[/C][C]3.0611[/C][C]0.002626[/C][C]0.001313[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147153&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147153&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)7.564083113362911.5634694.8383e-062e-06
Gender-4.022821549308620.950195-4.23374e-052e-05
ConcernoverMistakes-0.04971285148799750.04408-1.12780.2612550.130628
`C*G`0.01392383772935450.0270930.51390.6080830.304042
Doubtsaboutactions-0.05843004618500650.075433-0.77460.4398330.219917
`D*G`0.04116889556286610.0473290.86990.3858080.192904
`PE*G`0.5543684930584050.01288243.034200
ParentalCriticism0.7625943337965310.0785689.706100
`PC*G`-0.4133613753504020.047198-8.757900
PersonalStandards0.2296023888690780.0578573.96840.0001135.7e-05
`PS*G`-0.117945098279690.034576-3.41110.0008370.000418
Organization-0.2058028090855920.054165-3.79950.0002120.000106
`O*G`0.1018540589401130.0332743.06110.0026260.001313







Multiple Linear Regression - Regression Statistics
Multiple R0.979951191393236
R-squared0.960304337513024
Adjusted R-squared0.957041680322313
F-TEST (value)294.331975865332
F-TEST (DF numerator)12
F-TEST (DF denominator)146
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.714124047186035
Sum Squared Residuals74.4560805963268

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.979951191393236 \tabularnewline
R-squared & 0.960304337513024 \tabularnewline
Adjusted R-squared & 0.957041680322313 \tabularnewline
F-TEST (value) & 294.331975865332 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 146 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.714124047186035 \tabularnewline
Sum Squared Residuals & 74.4560805963268 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147153&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.979951191393236[/C][/ROW]
[ROW][C]R-squared[/C][C]0.960304337513024[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.957041680322313[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]294.331975865332[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]146[/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]0.714124047186035[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]74.4560805963268[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147153&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147153&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 R0.979951191393236
R-squared0.960304337513024
Adjusted R-squared0.957041680322313
F-TEST (value)294.331975865332
F-TEST (DF numerator)12
F-TEST (DF denominator)146
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.714124047186035
Sum Squared Residuals74.4560805963268







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11110.54958070773970.450419292260252
279.5317176936112-2.5317176936112
31715.79840944003111.20159055996893
41010.7581413749384-0.758141374938445
51213.03592279434-1.03592279434003
61211.3350251022660.664974897734023
71111.0502446508432-0.0502446508432265
81110.73366856177330.266331438226724
91211.32923496908730.670765030912731
101312.14048114093610.859518859063871
111414.0723922113957-0.072392211395747
121616.0907124941696-0.0907124941695982
131110.62122528196740.378774718032596
14109.854077020315750.145922979684246
151111.987715972398-0.98771597239795
161515.4978078449037-0.497807844903716
17911.1442403731213-2.14424037312126
181112.0740713104147-1.07407131041468
191717.4834239412261-0.483423941226096
201717.1319307640624-0.131930764062353
211111.9423849133973-0.942384913397282
221818.2354633273534-0.235463327353355
231413.79963254704850.200367452951523
241010.1467046042424-0.146704604242417
251110.86465886903160.135341130968437
261515.2188660316108-0.218866031610781
271515.3351227077666-0.335122707766565
281312.94112515493270.0588748450673462
291614.90049914151381.09950085848621
301313.2042023778971-0.204202377897086
3198.608674375862440.391325624137562
321818.0717900238226-0.0717900238226042
331815.51265614124162.48734385875843
341211.76544036238720.234559637612792
351715.95336749424581.04663250575424
3699.73994282533566-0.739942825335656
37910.0945972746802-1.09459727468025
381210.80971412098821.19028587901178
391817.19783932601920.802160673980827
401211.88412414763180.115875852368246
411818.3087161840174-0.30871618401743
421413.90341944598640.0965805540136296
431515.3583326939882-0.358332693988224
441616.5756783300809-0.575678330080881
451011.5026866827912-1.50268668279115
461111.3236366995712-0.323636699571206
471414.1334254325684-0.133425432568411
48910.2815454848804-1.28154548488042
491212.1776652544908-0.17766525449079
501714.95386174169092.04613825830906
5154.424748454196960.575251545803035
521211.94953649842740.0504635015726229
531211.92762293417840.0723770658216414
5465.68944562251120.310554377488797
552424.194068319353-0.194068319352963
561211.4978990524380.50210094756197
571212.1240506408088-0.124050640808759
581414.2785506948979-0.278550694897932
5976.731074229242920.268925770757076
601313.3499528417808-0.349952841780751
611212.6540727377824-0.654072737782427
621313.1259791121347-0.125979112134685
631412.62913607718681.37086392281317
6487.527227693747860.472772306252142
651110.58564540397190.414354596028113
6699.39190224187301-0.391902241873015
671111.1473204132457-0.147320413245737
681313.5383971172275-0.5383971172275
691010.1566179309609-0.156617930960872
701110.81763872816690.182361271833137
711211.78158260019910.218417399800923
7298.66768347215730.332316527842703
731514.90732069896950.0926793010304768
741818.3020217735624-0.302021773562364
751515.2018369603855-0.201836960385468
761212.0420103843867-0.0420103843866683
771313.4133002319974-0.413300231997436
781413.44850438596490.551495614035051
79109.75887151357920.241128486420797
801312.99473219959080.00526780040917705
811312.90879419091550.0912058090844853
821110.88091594011450.119084059885523
831313.0811661890698-0.0811661890698386
841615.34627981854330.653720181456743
8587.723478190994640.276521809005359
861616.4371206292828-0.43712062928278
871110.96005794142470.039942058575268
88910.0995542833964-1.09955428339636
891616.8190670212398-0.819067021239804
901212.0471427886204-0.0471427886204003
911414.1367790178915-0.136779017891529
9287.734711431620690.265288568379308
9399.0215986035023-0.0215986035023028
941515.3425422885411-0.342542288541093
951110.68439342885660.315606571143369
962121.3379608954175-0.337960895417492
971413.98389428875570.0161057112443132
981816.61029293269271.38970706730728
991211.62988005041850.370119949581524
1001312.61675081031590.383249189684099
1011515.055780945999-0.0557809459990431
1021211.56998273884060.430017261159413
1031919.5617535527112-0.561753552711171
1041515.1277722604393-0.127772260439272
1051111.2854732045681-0.28547320456812
1061110.58804101508870.411958984911256
107109.740213931577920.259786068422082
1081312.83025343078880.169746569211177
1091514.83550669007320.164493309926801
1101212.2632081527268-0.263208152726757
1111212.1535711603963-0.153571160396321
1121615.90427469311480.0957253068851745
11398.33095235808610.669047641913901
1141818.1353349731023-0.135334973102286
11587.399058882371680.600941117628316
1161311.63353643981441.36646356018558
1171717.3954286994524-0.395428699452427
11898.838024303513370.161975696486629
1191515.1718137246818-0.171813724681842
12087.848531424181560.151468575818437
12176.442414132978920.557585867021083
1221212.1823892781054-0.182389278105422
1231414.9313746103498-0.931374610349846
12467.92237291987548-1.92237291987548
12588.91839571134878-0.918395711348779
1261717.4994012580397-0.499401258039669
1271010.0239761692677-0.0239761692676849
1281111.5046331936122-0.504633193612203
1291414.1226004162867-0.122600416286715
1301110.5988268669180.401173133082012
1311312.8199477437570.180052256243036
1321211.86316546896180.136834531038241
1331110.48716637020460.512833629795362
13498.835806387511980.164193612488016
1351211.94138244493710.0586175550628822
1362017.58226874599472.41773125400533
1371211.46299754397730.537002456022704
1381314.0332818951934-1.03328189519336
1391211.88336551261170.116634487388333
1401211.64962910270980.350370897290233
14198.424725032197910.575274967802092
1421515.0195032480584-0.0195032480583712
1432424.0354877283835-0.0354877283835396
14478.00755235831579-1.00755235831579
1451716.11236942229060.88763057770937
1461110.85816677824020.141833221759797
1471717.130167948992-0.130167948992032
1481111.4786431655151-0.478643165515071
1491212.3740446850354-0.374044685035401
1501414.0234733525441-0.0234733525441407
1511110.65787559742550.342124402574513
1521616.3143234741159-0.314323474115905
1532121.5420338421294-0.542033842129366
1541413.32921307871190.670786921288065
1552020.4923907824139-0.492390782413933
1561313.2272551833787-0.227255183378728
1571110.72282305365040.277176946349598
1581515.1535624733354-0.153562473335444
1591918.09555136793990.904448632060132

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 11 & 10.5495807077397 & 0.450419292260252 \tabularnewline
2 & 7 & 9.5317176936112 & -2.5317176936112 \tabularnewline
3 & 17 & 15.7984094400311 & 1.20159055996893 \tabularnewline
4 & 10 & 10.7581413749384 & -0.758141374938445 \tabularnewline
5 & 12 & 13.03592279434 & -1.03592279434003 \tabularnewline
6 & 12 & 11.335025102266 & 0.664974897734023 \tabularnewline
7 & 11 & 11.0502446508432 & -0.0502446508432265 \tabularnewline
8 & 11 & 10.7336685617733 & 0.266331438226724 \tabularnewline
9 & 12 & 11.3292349690873 & 0.670765030912731 \tabularnewline
10 & 13 & 12.1404811409361 & 0.859518859063871 \tabularnewline
11 & 14 & 14.0723922113957 & -0.072392211395747 \tabularnewline
12 & 16 & 16.0907124941696 & -0.0907124941695982 \tabularnewline
13 & 11 & 10.6212252819674 & 0.378774718032596 \tabularnewline
14 & 10 & 9.85407702031575 & 0.145922979684246 \tabularnewline
15 & 11 & 11.987715972398 & -0.98771597239795 \tabularnewline
16 & 15 & 15.4978078449037 & -0.497807844903716 \tabularnewline
17 & 9 & 11.1442403731213 & -2.14424037312126 \tabularnewline
18 & 11 & 12.0740713104147 & -1.07407131041468 \tabularnewline
19 & 17 & 17.4834239412261 & -0.483423941226096 \tabularnewline
20 & 17 & 17.1319307640624 & -0.131930764062353 \tabularnewline
21 & 11 & 11.9423849133973 & -0.942384913397282 \tabularnewline
22 & 18 & 18.2354633273534 & -0.235463327353355 \tabularnewline
23 & 14 & 13.7996325470485 & 0.200367452951523 \tabularnewline
24 & 10 & 10.1467046042424 & -0.146704604242417 \tabularnewline
25 & 11 & 10.8646588690316 & 0.135341130968437 \tabularnewline
26 & 15 & 15.2188660316108 & -0.218866031610781 \tabularnewline
27 & 15 & 15.3351227077666 & -0.335122707766565 \tabularnewline
28 & 13 & 12.9411251549327 & 0.0588748450673462 \tabularnewline
29 & 16 & 14.9004991415138 & 1.09950085848621 \tabularnewline
30 & 13 & 13.2042023778971 & -0.204202377897086 \tabularnewline
31 & 9 & 8.60867437586244 & 0.391325624137562 \tabularnewline
32 & 18 & 18.0717900238226 & -0.0717900238226042 \tabularnewline
33 & 18 & 15.5126561412416 & 2.48734385875843 \tabularnewline
34 & 12 & 11.7654403623872 & 0.234559637612792 \tabularnewline
35 & 17 & 15.9533674942458 & 1.04663250575424 \tabularnewline
36 & 9 & 9.73994282533566 & -0.739942825335656 \tabularnewline
37 & 9 & 10.0945972746802 & -1.09459727468025 \tabularnewline
38 & 12 & 10.8097141209882 & 1.19028587901178 \tabularnewline
39 & 18 & 17.1978393260192 & 0.802160673980827 \tabularnewline
40 & 12 & 11.8841241476318 & 0.115875852368246 \tabularnewline
41 & 18 & 18.3087161840174 & -0.30871618401743 \tabularnewline
42 & 14 & 13.9034194459864 & 0.0965805540136296 \tabularnewline
43 & 15 & 15.3583326939882 & -0.358332693988224 \tabularnewline
44 & 16 & 16.5756783300809 & -0.575678330080881 \tabularnewline
45 & 10 & 11.5026866827912 & -1.50268668279115 \tabularnewline
46 & 11 & 11.3236366995712 & -0.323636699571206 \tabularnewline
47 & 14 & 14.1334254325684 & -0.133425432568411 \tabularnewline
48 & 9 & 10.2815454848804 & -1.28154548488042 \tabularnewline
49 & 12 & 12.1776652544908 & -0.17766525449079 \tabularnewline
50 & 17 & 14.9538617416909 & 2.04613825830906 \tabularnewline
51 & 5 & 4.42474845419696 & 0.575251545803035 \tabularnewline
52 & 12 & 11.9495364984274 & 0.0504635015726229 \tabularnewline
53 & 12 & 11.9276229341784 & 0.0723770658216414 \tabularnewline
54 & 6 & 5.6894456225112 & 0.310554377488797 \tabularnewline
55 & 24 & 24.194068319353 & -0.194068319352963 \tabularnewline
56 & 12 & 11.497899052438 & 0.50210094756197 \tabularnewline
57 & 12 & 12.1240506408088 & -0.124050640808759 \tabularnewline
58 & 14 & 14.2785506948979 & -0.278550694897932 \tabularnewline
59 & 7 & 6.73107422924292 & 0.268925770757076 \tabularnewline
60 & 13 & 13.3499528417808 & -0.349952841780751 \tabularnewline
61 & 12 & 12.6540727377824 & -0.654072737782427 \tabularnewline
62 & 13 & 13.1259791121347 & -0.125979112134685 \tabularnewline
63 & 14 & 12.6291360771868 & 1.37086392281317 \tabularnewline
64 & 8 & 7.52722769374786 & 0.472772306252142 \tabularnewline
65 & 11 & 10.5856454039719 & 0.414354596028113 \tabularnewline
66 & 9 & 9.39190224187301 & -0.391902241873015 \tabularnewline
67 & 11 & 11.1473204132457 & -0.147320413245737 \tabularnewline
68 & 13 & 13.5383971172275 & -0.5383971172275 \tabularnewline
69 & 10 & 10.1566179309609 & -0.156617930960872 \tabularnewline
70 & 11 & 10.8176387281669 & 0.182361271833137 \tabularnewline
71 & 12 & 11.7815826001991 & 0.218417399800923 \tabularnewline
72 & 9 & 8.6676834721573 & 0.332316527842703 \tabularnewline
73 & 15 & 14.9073206989695 & 0.0926793010304768 \tabularnewline
74 & 18 & 18.3020217735624 & -0.302021773562364 \tabularnewline
75 & 15 & 15.2018369603855 & -0.201836960385468 \tabularnewline
76 & 12 & 12.0420103843867 & -0.0420103843866683 \tabularnewline
77 & 13 & 13.4133002319974 & -0.413300231997436 \tabularnewline
78 & 14 & 13.4485043859649 & 0.551495614035051 \tabularnewline
79 & 10 & 9.7588715135792 & 0.241128486420797 \tabularnewline
80 & 13 & 12.9947321995908 & 0.00526780040917705 \tabularnewline
81 & 13 & 12.9087941909155 & 0.0912058090844853 \tabularnewline
82 & 11 & 10.8809159401145 & 0.119084059885523 \tabularnewline
83 & 13 & 13.0811661890698 & -0.0811661890698386 \tabularnewline
84 & 16 & 15.3462798185433 & 0.653720181456743 \tabularnewline
85 & 8 & 7.72347819099464 & 0.276521809005359 \tabularnewline
86 & 16 & 16.4371206292828 & -0.43712062928278 \tabularnewline
87 & 11 & 10.9600579414247 & 0.039942058575268 \tabularnewline
88 & 9 & 10.0995542833964 & -1.09955428339636 \tabularnewline
89 & 16 & 16.8190670212398 & -0.819067021239804 \tabularnewline
90 & 12 & 12.0471427886204 & -0.0471427886204003 \tabularnewline
91 & 14 & 14.1367790178915 & -0.136779017891529 \tabularnewline
92 & 8 & 7.73471143162069 & 0.265288568379308 \tabularnewline
93 & 9 & 9.0215986035023 & -0.0215986035023028 \tabularnewline
94 & 15 & 15.3425422885411 & -0.342542288541093 \tabularnewline
95 & 11 & 10.6843934288566 & 0.315606571143369 \tabularnewline
96 & 21 & 21.3379608954175 & -0.337960895417492 \tabularnewline
97 & 14 & 13.9838942887557 & 0.0161057112443132 \tabularnewline
98 & 18 & 16.6102929326927 & 1.38970706730728 \tabularnewline
99 & 12 & 11.6298800504185 & 0.370119949581524 \tabularnewline
100 & 13 & 12.6167508103159 & 0.383249189684099 \tabularnewline
101 & 15 & 15.055780945999 & -0.0557809459990431 \tabularnewline
102 & 12 & 11.5699827388406 & 0.430017261159413 \tabularnewline
103 & 19 & 19.5617535527112 & -0.561753552711171 \tabularnewline
104 & 15 & 15.1277722604393 & -0.127772260439272 \tabularnewline
105 & 11 & 11.2854732045681 & -0.28547320456812 \tabularnewline
106 & 11 & 10.5880410150887 & 0.411958984911256 \tabularnewline
107 & 10 & 9.74021393157792 & 0.259786068422082 \tabularnewline
108 & 13 & 12.8302534307888 & 0.169746569211177 \tabularnewline
109 & 15 & 14.8355066900732 & 0.164493309926801 \tabularnewline
110 & 12 & 12.2632081527268 & -0.263208152726757 \tabularnewline
111 & 12 & 12.1535711603963 & -0.153571160396321 \tabularnewline
112 & 16 & 15.9042746931148 & 0.0957253068851745 \tabularnewline
113 & 9 & 8.3309523580861 & 0.669047641913901 \tabularnewline
114 & 18 & 18.1353349731023 & -0.135334973102286 \tabularnewline
115 & 8 & 7.39905888237168 & 0.600941117628316 \tabularnewline
116 & 13 & 11.6335364398144 & 1.36646356018558 \tabularnewline
117 & 17 & 17.3954286994524 & -0.395428699452427 \tabularnewline
118 & 9 & 8.83802430351337 & 0.161975696486629 \tabularnewline
119 & 15 & 15.1718137246818 & -0.171813724681842 \tabularnewline
120 & 8 & 7.84853142418156 & 0.151468575818437 \tabularnewline
121 & 7 & 6.44241413297892 & 0.557585867021083 \tabularnewline
122 & 12 & 12.1823892781054 & -0.182389278105422 \tabularnewline
123 & 14 & 14.9313746103498 & -0.931374610349846 \tabularnewline
124 & 6 & 7.92237291987548 & -1.92237291987548 \tabularnewline
125 & 8 & 8.91839571134878 & -0.918395711348779 \tabularnewline
126 & 17 & 17.4994012580397 & -0.499401258039669 \tabularnewline
127 & 10 & 10.0239761692677 & -0.0239761692676849 \tabularnewline
128 & 11 & 11.5046331936122 & -0.504633193612203 \tabularnewline
129 & 14 & 14.1226004162867 & -0.122600416286715 \tabularnewline
130 & 11 & 10.598826866918 & 0.401173133082012 \tabularnewline
131 & 13 & 12.819947743757 & 0.180052256243036 \tabularnewline
132 & 12 & 11.8631654689618 & 0.136834531038241 \tabularnewline
133 & 11 & 10.4871663702046 & 0.512833629795362 \tabularnewline
134 & 9 & 8.83580638751198 & 0.164193612488016 \tabularnewline
135 & 12 & 11.9413824449371 & 0.0586175550628822 \tabularnewline
136 & 20 & 17.5822687459947 & 2.41773125400533 \tabularnewline
137 & 12 & 11.4629975439773 & 0.537002456022704 \tabularnewline
138 & 13 & 14.0332818951934 & -1.03328189519336 \tabularnewline
139 & 12 & 11.8833655126117 & 0.116634487388333 \tabularnewline
140 & 12 & 11.6496291027098 & 0.350370897290233 \tabularnewline
141 & 9 & 8.42472503219791 & 0.575274967802092 \tabularnewline
142 & 15 & 15.0195032480584 & -0.0195032480583712 \tabularnewline
143 & 24 & 24.0354877283835 & -0.0354877283835396 \tabularnewline
144 & 7 & 8.00755235831579 & -1.00755235831579 \tabularnewline
145 & 17 & 16.1123694222906 & 0.88763057770937 \tabularnewline
146 & 11 & 10.8581667782402 & 0.141833221759797 \tabularnewline
147 & 17 & 17.130167948992 & -0.130167948992032 \tabularnewline
148 & 11 & 11.4786431655151 & -0.478643165515071 \tabularnewline
149 & 12 & 12.3740446850354 & -0.374044685035401 \tabularnewline
150 & 14 & 14.0234733525441 & -0.0234733525441407 \tabularnewline
151 & 11 & 10.6578755974255 & 0.342124402574513 \tabularnewline
152 & 16 & 16.3143234741159 & -0.314323474115905 \tabularnewline
153 & 21 & 21.5420338421294 & -0.542033842129366 \tabularnewline
154 & 14 & 13.3292130787119 & 0.670786921288065 \tabularnewline
155 & 20 & 20.4923907824139 & -0.492390782413933 \tabularnewline
156 & 13 & 13.2272551833787 & -0.227255183378728 \tabularnewline
157 & 11 & 10.7228230536504 & 0.277176946349598 \tabularnewline
158 & 15 & 15.1535624733354 & -0.153562473335444 \tabularnewline
159 & 19 & 18.0955513679399 & 0.904448632060132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147153&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]11[/C][C]10.5495807077397[/C][C]0.450419292260252[/C][/ROW]
[ROW][C]2[/C][C]7[/C][C]9.5317176936112[/C][C]-2.5317176936112[/C][/ROW]
[ROW][C]3[/C][C]17[/C][C]15.7984094400311[/C][C]1.20159055996893[/C][/ROW]
[ROW][C]4[/C][C]10[/C][C]10.7581413749384[/C][C]-0.758141374938445[/C][/ROW]
[ROW][C]5[/C][C]12[/C][C]13.03592279434[/C][C]-1.03592279434003[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]11.335025102266[/C][C]0.664974897734023[/C][/ROW]
[ROW][C]7[/C][C]11[/C][C]11.0502446508432[/C][C]-0.0502446508432265[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]10.7336685617733[/C][C]0.266331438226724[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]11.3292349690873[/C][C]0.670765030912731[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.1404811409361[/C][C]0.859518859063871[/C][/ROW]
[ROW][C]11[/C][C]14[/C][C]14.0723922113957[/C][C]-0.072392211395747[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.0907124941696[/C][C]-0.0907124941695982[/C][/ROW]
[ROW][C]13[/C][C]11[/C][C]10.6212252819674[/C][C]0.378774718032596[/C][/ROW]
[ROW][C]14[/C][C]10[/C][C]9.85407702031575[/C][C]0.145922979684246[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.987715972398[/C][C]-0.98771597239795[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.4978078449037[/C][C]-0.497807844903716[/C][/ROW]
[ROW][C]17[/C][C]9[/C][C]11.1442403731213[/C][C]-2.14424037312126[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]12.0740713104147[/C][C]-1.07407131041468[/C][/ROW]
[ROW][C]19[/C][C]17[/C][C]17.4834239412261[/C][C]-0.483423941226096[/C][/ROW]
[ROW][C]20[/C][C]17[/C][C]17.1319307640624[/C][C]-0.131930764062353[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]11.9423849133973[/C][C]-0.942384913397282[/C][/ROW]
[ROW][C]22[/C][C]18[/C][C]18.2354633273534[/C][C]-0.235463327353355[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]13.7996325470485[/C][C]0.200367452951523[/C][/ROW]
[ROW][C]24[/C][C]10[/C][C]10.1467046042424[/C][C]-0.146704604242417[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]10.8646588690316[/C][C]0.135341130968437[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]15.2188660316108[/C][C]-0.218866031610781[/C][/ROW]
[ROW][C]27[/C][C]15[/C][C]15.3351227077666[/C][C]-0.335122707766565[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]12.9411251549327[/C][C]0.0588748450673462[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]14.9004991415138[/C][C]1.09950085848621[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]13.2042023778971[/C][C]-0.204202377897086[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]8.60867437586244[/C][C]0.391325624137562[/C][/ROW]
[ROW][C]32[/C][C]18[/C][C]18.0717900238226[/C][C]-0.0717900238226042[/C][/ROW]
[ROW][C]33[/C][C]18[/C][C]15.5126561412416[/C][C]2.48734385875843[/C][/ROW]
[ROW][C]34[/C][C]12[/C][C]11.7654403623872[/C][C]0.234559637612792[/C][/ROW]
[ROW][C]35[/C][C]17[/C][C]15.9533674942458[/C][C]1.04663250575424[/C][/ROW]
[ROW][C]36[/C][C]9[/C][C]9.73994282533566[/C][C]-0.739942825335656[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]10.0945972746802[/C][C]-1.09459727468025[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]10.8097141209882[/C][C]1.19028587901178[/C][/ROW]
[ROW][C]39[/C][C]18[/C][C]17.1978393260192[/C][C]0.802160673980827[/C][/ROW]
[ROW][C]40[/C][C]12[/C][C]11.8841241476318[/C][C]0.115875852368246[/C][/ROW]
[ROW][C]41[/C][C]18[/C][C]18.3087161840174[/C][C]-0.30871618401743[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]13.9034194459864[/C][C]0.0965805540136296[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]15.3583326939882[/C][C]-0.358332693988224[/C][/ROW]
[ROW][C]44[/C][C]16[/C][C]16.5756783300809[/C][C]-0.575678330080881[/C][/ROW]
[ROW][C]45[/C][C]10[/C][C]11.5026866827912[/C][C]-1.50268668279115[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]11.3236366995712[/C][C]-0.323636699571206[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]14.1334254325684[/C][C]-0.133425432568411[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]10.2815454848804[/C][C]-1.28154548488042[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]12.1776652544908[/C][C]-0.17766525449079[/C][/ROW]
[ROW][C]50[/C][C]17[/C][C]14.9538617416909[/C][C]2.04613825830906[/C][/ROW]
[ROW][C]51[/C][C]5[/C][C]4.42474845419696[/C][C]0.575251545803035[/C][/ROW]
[ROW][C]52[/C][C]12[/C][C]11.9495364984274[/C][C]0.0504635015726229[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]11.9276229341784[/C][C]0.0723770658216414[/C][/ROW]
[ROW][C]54[/C][C]6[/C][C]5.6894456225112[/C][C]0.310554377488797[/C][/ROW]
[ROW][C]55[/C][C]24[/C][C]24.194068319353[/C][C]-0.194068319352963[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]11.497899052438[/C][C]0.50210094756197[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]12.1240506408088[/C][C]-0.124050640808759[/C][/ROW]
[ROW][C]58[/C][C]14[/C][C]14.2785506948979[/C][C]-0.278550694897932[/C][/ROW]
[ROW][C]59[/C][C]7[/C][C]6.73107422924292[/C][C]0.268925770757076[/C][/ROW]
[ROW][C]60[/C][C]13[/C][C]13.3499528417808[/C][C]-0.349952841780751[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]12.6540727377824[/C][C]-0.654072737782427[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]13.1259791121347[/C][C]-0.125979112134685[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]12.6291360771868[/C][C]1.37086392281317[/C][/ROW]
[ROW][C]64[/C][C]8[/C][C]7.52722769374786[/C][C]0.472772306252142[/C][/ROW]
[ROW][C]65[/C][C]11[/C][C]10.5856454039719[/C][C]0.414354596028113[/C][/ROW]
[ROW][C]66[/C][C]9[/C][C]9.39190224187301[/C][C]-0.391902241873015[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]11.1473204132457[/C][C]-0.147320413245737[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]13.5383971172275[/C][C]-0.5383971172275[/C][/ROW]
[ROW][C]69[/C][C]10[/C][C]10.1566179309609[/C][C]-0.156617930960872[/C][/ROW]
[ROW][C]70[/C][C]11[/C][C]10.8176387281669[/C][C]0.182361271833137[/C][/ROW]
[ROW][C]71[/C][C]12[/C][C]11.7815826001991[/C][C]0.218417399800923[/C][/ROW]
[ROW][C]72[/C][C]9[/C][C]8.6676834721573[/C][C]0.332316527842703[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]14.9073206989695[/C][C]0.0926793010304768[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]18.3020217735624[/C][C]-0.302021773562364[/C][/ROW]
[ROW][C]75[/C][C]15[/C][C]15.2018369603855[/C][C]-0.201836960385468[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]12.0420103843867[/C][C]-0.0420103843866683[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]13.4133002319974[/C][C]-0.413300231997436[/C][/ROW]
[ROW][C]78[/C][C]14[/C][C]13.4485043859649[/C][C]0.551495614035051[/C][/ROW]
[ROW][C]79[/C][C]10[/C][C]9.7588715135792[/C][C]0.241128486420797[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]12.9947321995908[/C][C]0.00526780040917705[/C][/ROW]
[ROW][C]81[/C][C]13[/C][C]12.9087941909155[/C][C]0.0912058090844853[/C][/ROW]
[ROW][C]82[/C][C]11[/C][C]10.8809159401145[/C][C]0.119084059885523[/C][/ROW]
[ROW][C]83[/C][C]13[/C][C]13.0811661890698[/C][C]-0.0811661890698386[/C][/ROW]
[ROW][C]84[/C][C]16[/C][C]15.3462798185433[/C][C]0.653720181456743[/C][/ROW]
[ROW][C]85[/C][C]8[/C][C]7.72347819099464[/C][C]0.276521809005359[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]16.4371206292828[/C][C]-0.43712062928278[/C][/ROW]
[ROW][C]87[/C][C]11[/C][C]10.9600579414247[/C][C]0.039942058575268[/C][/ROW]
[ROW][C]88[/C][C]9[/C][C]10.0995542833964[/C][C]-1.09955428339636[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]16.8190670212398[/C][C]-0.819067021239804[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]12.0471427886204[/C][C]-0.0471427886204003[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]14.1367790178915[/C][C]-0.136779017891529[/C][/ROW]
[ROW][C]92[/C][C]8[/C][C]7.73471143162069[/C][C]0.265288568379308[/C][/ROW]
[ROW][C]93[/C][C]9[/C][C]9.0215986035023[/C][C]-0.0215986035023028[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.3425422885411[/C][C]-0.342542288541093[/C][/ROW]
[ROW][C]95[/C][C]11[/C][C]10.6843934288566[/C][C]0.315606571143369[/C][/ROW]
[ROW][C]96[/C][C]21[/C][C]21.3379608954175[/C][C]-0.337960895417492[/C][/ROW]
[ROW][C]97[/C][C]14[/C][C]13.9838942887557[/C][C]0.0161057112443132[/C][/ROW]
[ROW][C]98[/C][C]18[/C][C]16.6102929326927[/C][C]1.38970706730728[/C][/ROW]
[ROW][C]99[/C][C]12[/C][C]11.6298800504185[/C][C]0.370119949581524[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]12.6167508103159[/C][C]0.383249189684099[/C][/ROW]
[ROW][C]101[/C][C]15[/C][C]15.055780945999[/C][C]-0.0557809459990431[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]11.5699827388406[/C][C]0.430017261159413[/C][/ROW]
[ROW][C]103[/C][C]19[/C][C]19.5617535527112[/C][C]-0.561753552711171[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]15.1277722604393[/C][C]-0.127772260439272[/C][/ROW]
[ROW][C]105[/C][C]11[/C][C]11.2854732045681[/C][C]-0.28547320456812[/C][/ROW]
[ROW][C]106[/C][C]11[/C][C]10.5880410150887[/C][C]0.411958984911256[/C][/ROW]
[ROW][C]107[/C][C]10[/C][C]9.74021393157792[/C][C]0.259786068422082[/C][/ROW]
[ROW][C]108[/C][C]13[/C][C]12.8302534307888[/C][C]0.169746569211177[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]14.8355066900732[/C][C]0.164493309926801[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.2632081527268[/C][C]-0.263208152726757[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]12.1535711603963[/C][C]-0.153571160396321[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.9042746931148[/C][C]0.0957253068851745[/C][/ROW]
[ROW][C]113[/C][C]9[/C][C]8.3309523580861[/C][C]0.669047641913901[/C][/ROW]
[ROW][C]114[/C][C]18[/C][C]18.1353349731023[/C][C]-0.135334973102286[/C][/ROW]
[ROW][C]115[/C][C]8[/C][C]7.39905888237168[/C][C]0.600941117628316[/C][/ROW]
[ROW][C]116[/C][C]13[/C][C]11.6335364398144[/C][C]1.36646356018558[/C][/ROW]
[ROW][C]117[/C][C]17[/C][C]17.3954286994524[/C][C]-0.395428699452427[/C][/ROW]
[ROW][C]118[/C][C]9[/C][C]8.83802430351337[/C][C]0.161975696486629[/C][/ROW]
[ROW][C]119[/C][C]15[/C][C]15.1718137246818[/C][C]-0.171813724681842[/C][/ROW]
[ROW][C]120[/C][C]8[/C][C]7.84853142418156[/C][C]0.151468575818437[/C][/ROW]
[ROW][C]121[/C][C]7[/C][C]6.44241413297892[/C][C]0.557585867021083[/C][/ROW]
[ROW][C]122[/C][C]12[/C][C]12.1823892781054[/C][C]-0.182389278105422[/C][/ROW]
[ROW][C]123[/C][C]14[/C][C]14.9313746103498[/C][C]-0.931374610349846[/C][/ROW]
[ROW][C]124[/C][C]6[/C][C]7.92237291987548[/C][C]-1.92237291987548[/C][/ROW]
[ROW][C]125[/C][C]8[/C][C]8.91839571134878[/C][C]-0.918395711348779[/C][/ROW]
[ROW][C]126[/C][C]17[/C][C]17.4994012580397[/C][C]-0.499401258039669[/C][/ROW]
[ROW][C]127[/C][C]10[/C][C]10.0239761692677[/C][C]-0.0239761692676849[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]11.5046331936122[/C][C]-0.504633193612203[/C][/ROW]
[ROW][C]129[/C][C]14[/C][C]14.1226004162867[/C][C]-0.122600416286715[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]10.598826866918[/C][C]0.401173133082012[/C][/ROW]
[ROW][C]131[/C][C]13[/C][C]12.819947743757[/C][C]0.180052256243036[/C][/ROW]
[ROW][C]132[/C][C]12[/C][C]11.8631654689618[/C][C]0.136834531038241[/C][/ROW]
[ROW][C]133[/C][C]11[/C][C]10.4871663702046[/C][C]0.512833629795362[/C][/ROW]
[ROW][C]134[/C][C]9[/C][C]8.83580638751198[/C][C]0.164193612488016[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]11.9413824449371[/C][C]0.0586175550628822[/C][/ROW]
[ROW][C]136[/C][C]20[/C][C]17.5822687459947[/C][C]2.41773125400533[/C][/ROW]
[ROW][C]137[/C][C]12[/C][C]11.4629975439773[/C][C]0.537002456022704[/C][/ROW]
[ROW][C]138[/C][C]13[/C][C]14.0332818951934[/C][C]-1.03328189519336[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]11.8833655126117[/C][C]0.116634487388333[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]11.6496291027098[/C][C]0.350370897290233[/C][/ROW]
[ROW][C]141[/C][C]9[/C][C]8.42472503219791[/C][C]0.575274967802092[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]15.0195032480584[/C][C]-0.0195032480583712[/C][/ROW]
[ROW][C]143[/C][C]24[/C][C]24.0354877283835[/C][C]-0.0354877283835396[/C][/ROW]
[ROW][C]144[/C][C]7[/C][C]8.00755235831579[/C][C]-1.00755235831579[/C][/ROW]
[ROW][C]145[/C][C]17[/C][C]16.1123694222906[/C][C]0.88763057770937[/C][/ROW]
[ROW][C]146[/C][C]11[/C][C]10.8581667782402[/C][C]0.141833221759797[/C][/ROW]
[ROW][C]147[/C][C]17[/C][C]17.130167948992[/C][C]-0.130167948992032[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]11.4786431655151[/C][C]-0.478643165515071[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]12.3740446850354[/C][C]-0.374044685035401[/C][/ROW]
[ROW][C]150[/C][C]14[/C][C]14.0234733525441[/C][C]-0.0234733525441407[/C][/ROW]
[ROW][C]151[/C][C]11[/C][C]10.6578755974255[/C][C]0.342124402574513[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]16.3143234741159[/C][C]-0.314323474115905[/C][/ROW]
[ROW][C]153[/C][C]21[/C][C]21.5420338421294[/C][C]-0.542033842129366[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]13.3292130787119[/C][C]0.670786921288065[/C][/ROW]
[ROW][C]155[/C][C]20[/C][C]20.4923907824139[/C][C]-0.492390782413933[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]13.2272551833787[/C][C]-0.227255183378728[/C][/ROW]
[ROW][C]157[/C][C]11[/C][C]10.7228230536504[/C][C]0.277176946349598[/C][/ROW]
[ROW][C]158[/C][C]15[/C][C]15.1535624733354[/C][C]-0.153562473335444[/C][/ROW]
[ROW][C]159[/C][C]19[/C][C]18.0955513679399[/C][C]0.904448632060132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147153&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147153&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
11110.54958070773970.450419292260252
279.5317176936112-2.5317176936112
31715.79840944003111.20159055996893
41010.7581413749384-0.758141374938445
51213.03592279434-1.03592279434003
61211.3350251022660.664974897734023
71111.0502446508432-0.0502446508432265
81110.73366856177330.266331438226724
91211.32923496908730.670765030912731
101312.14048114093610.859518859063871
111414.0723922113957-0.072392211395747
121616.0907124941696-0.0907124941695982
131110.62122528196740.378774718032596
14109.854077020315750.145922979684246
151111.987715972398-0.98771597239795
161515.4978078449037-0.497807844903716
17911.1442403731213-2.14424037312126
181112.0740713104147-1.07407131041468
191717.4834239412261-0.483423941226096
201717.1319307640624-0.131930764062353
211111.9423849133973-0.942384913397282
221818.2354633273534-0.235463327353355
231413.79963254704850.200367452951523
241010.1467046042424-0.146704604242417
251110.86465886903160.135341130968437
261515.2188660316108-0.218866031610781
271515.3351227077666-0.335122707766565
281312.94112515493270.0588748450673462
291614.90049914151381.09950085848621
301313.2042023778971-0.204202377897086
3198.608674375862440.391325624137562
321818.0717900238226-0.0717900238226042
331815.51265614124162.48734385875843
341211.76544036238720.234559637612792
351715.95336749424581.04663250575424
3699.73994282533566-0.739942825335656
37910.0945972746802-1.09459727468025
381210.80971412098821.19028587901178
391817.19783932601920.802160673980827
401211.88412414763180.115875852368246
411818.3087161840174-0.30871618401743
421413.90341944598640.0965805540136296
431515.3583326939882-0.358332693988224
441616.5756783300809-0.575678330080881
451011.5026866827912-1.50268668279115
461111.3236366995712-0.323636699571206
471414.1334254325684-0.133425432568411
48910.2815454848804-1.28154548488042
491212.1776652544908-0.17766525449079
501714.95386174169092.04613825830906
5154.424748454196960.575251545803035
521211.94953649842740.0504635015726229
531211.92762293417840.0723770658216414
5465.68944562251120.310554377488797
552424.194068319353-0.194068319352963
561211.4978990524380.50210094756197
571212.1240506408088-0.124050640808759
581414.2785506948979-0.278550694897932
5976.731074229242920.268925770757076
601313.3499528417808-0.349952841780751
611212.6540727377824-0.654072737782427
621313.1259791121347-0.125979112134685
631412.62913607718681.37086392281317
6487.527227693747860.472772306252142
651110.58564540397190.414354596028113
6699.39190224187301-0.391902241873015
671111.1473204132457-0.147320413245737
681313.5383971172275-0.5383971172275
691010.1566179309609-0.156617930960872
701110.81763872816690.182361271833137
711211.78158260019910.218417399800923
7298.66768347215730.332316527842703
731514.90732069896950.0926793010304768
741818.3020217735624-0.302021773562364
751515.2018369603855-0.201836960385468
761212.0420103843867-0.0420103843866683
771313.4133002319974-0.413300231997436
781413.44850438596490.551495614035051
79109.75887151357920.241128486420797
801312.99473219959080.00526780040917705
811312.90879419091550.0912058090844853
821110.88091594011450.119084059885523
831313.0811661890698-0.0811661890698386
841615.34627981854330.653720181456743
8587.723478190994640.276521809005359
861616.4371206292828-0.43712062928278
871110.96005794142470.039942058575268
88910.0995542833964-1.09955428339636
891616.8190670212398-0.819067021239804
901212.0471427886204-0.0471427886204003
911414.1367790178915-0.136779017891529
9287.734711431620690.265288568379308
9399.0215986035023-0.0215986035023028
941515.3425422885411-0.342542288541093
951110.68439342885660.315606571143369
962121.3379608954175-0.337960895417492
971413.98389428875570.0161057112443132
981816.61029293269271.38970706730728
991211.62988005041850.370119949581524
1001312.61675081031590.383249189684099
1011515.055780945999-0.0557809459990431
1021211.56998273884060.430017261159413
1031919.5617535527112-0.561753552711171
1041515.1277722604393-0.127772260439272
1051111.2854732045681-0.28547320456812
1061110.58804101508870.411958984911256
107109.740213931577920.259786068422082
1081312.83025343078880.169746569211177
1091514.83550669007320.164493309926801
1101212.2632081527268-0.263208152726757
1111212.1535711603963-0.153571160396321
1121615.90427469311480.0957253068851745
11398.33095235808610.669047641913901
1141818.1353349731023-0.135334973102286
11587.399058882371680.600941117628316
1161311.63353643981441.36646356018558
1171717.3954286994524-0.395428699452427
11898.838024303513370.161975696486629
1191515.1718137246818-0.171813724681842
12087.848531424181560.151468575818437
12176.442414132978920.557585867021083
1221212.1823892781054-0.182389278105422
1231414.9313746103498-0.931374610349846
12467.92237291987548-1.92237291987548
12588.91839571134878-0.918395711348779
1261717.4994012580397-0.499401258039669
1271010.0239761692677-0.0239761692676849
1281111.5046331936122-0.504633193612203
1291414.1226004162867-0.122600416286715
1301110.5988268669180.401173133082012
1311312.8199477437570.180052256243036
1321211.86316546896180.136834531038241
1331110.48716637020460.512833629795362
13498.835806387511980.164193612488016
1351211.94138244493710.0586175550628822
1362017.58226874599472.41773125400533
1371211.46299754397730.537002456022704
1381314.0332818951934-1.03328189519336
1391211.88336551261170.116634487388333
1401211.64962910270980.350370897290233
14198.424725032197910.575274967802092
1421515.0195032480584-0.0195032480583712
1432424.0354877283835-0.0354877283835396
14478.00755235831579-1.00755235831579
1451716.11236942229060.88763057770937
1461110.85816677824020.141833221759797
1471717.130167948992-0.130167948992032
1481111.4786431655151-0.478643165515071
1491212.3740446850354-0.374044685035401
1501414.0234733525441-0.0234733525441407
1511110.65787559742550.342124402574513
1521616.3143234741159-0.314323474115905
1532121.5420338421294-0.542033842129366
1541413.32921307871190.670786921288065
1552020.4923907824139-0.492390782413933
1561313.2272551833787-0.227255183378728
1571110.72282305365040.277176946349598
1581515.1535624733354-0.153562473335444
1591918.09555136793990.904448632060132







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.7899628453100620.4200743093798760.210037154689938
170.7087775791094810.5824448417810390.291222420890519
180.6181369523007010.7637260953985990.381863047699299
190.9247172436571530.1505655126856940.0752827563428469
200.8792575959519060.2414848080961880.120742404048094
210.8902135773342110.2195728453315780.109786422665789
220.9041426752556250.191714649488750.0958573247443749
230.8596296797959550.280740640408090.140370320204045
240.8894692663229340.2210614673541320.110530733677066
250.8882977396883590.2234045206232830.111702260311641
260.8666691907696770.2666616184606460.133330809230323
270.8208806304502030.3582387390995950.179119369549797
280.766081803167320.467836393665360.23391819683268
290.8716475785843870.2567048428312260.128352421415613
300.8333240087447130.3333519825105750.166675991255287
310.8885977515739110.2228044968521780.111402248426089
320.8706720438203170.2586559123593650.129327956179683
330.9936267797588230.01274644048235490.00637322024117747
340.9962973727920850.007405254415829470.00370262720791473
350.9980370102571050.003925979485789890.00196298974289494
360.9989773846692560.002045230661488560.00102261533074428
370.999225365576490.001549268847020720.000774634423510361
380.9992644958578070.001471008284385660.000735504142192829
390.9996759156571680.0006481686856637930.000324084342831896
400.9995348674551170.0009302650897669320.000465132544883466
410.999462582779840.001074834440319060.000537417220159531
420.9991468690312770.001706261937445750.000853130968722873
430.9988853848776450.002229230244709090.00111461512235455
440.9986899272577590.002620145484482940.00131007274224147
450.9997050814996050.0005898370007904590.00029491850039523
460.9995701910633370.0008596178733256710.000429808936662836
470.9993246140419030.001350771916193510.000675385958096757
480.9998023267318750.0003953465362509190.00019767326812546
490.9997518022793120.0004963954413760270.000248197720688014
500.9999875893661972.48212676052584e-051.24106338026292e-05
510.9999908012671691.83974656618058e-059.19873283090289e-06
520.9999840744700753.18510598492752e-051.59255299246376e-05
530.999972345235585.53095288409749e-052.76547644204875e-05
540.9999646837233437.06325533142758e-053.53162766571379e-05
550.9999405669304820.0001188661390370945.9433069518547e-05
560.9999220494548150.0001559010903704877.79505451852435e-05
570.9998741989800850.0002516020398302540.000125801019915127
580.9998069354319010.0003861291361977360.000193064568098868
590.9997221550829530.0005556898340940790.00027784491704704
600.9996255715509010.0007488568981979170.000374428449098958
610.999587481647390.0008250367052205020.000412518352610251
620.9993642540454540.001271491909092840.00063574595454642
630.9998150542044910.0003698915910185850.000184945795509293
640.9997713768598540.0004572462802912020.000228623140145601
650.9997624191173520.0004751617652969310.000237580882648465
660.9997285336635330.0005429326729345120.000271466336467256
670.9998702350512910.0002595298974180280.000129764948709014
680.9998417333572760.0003165332854472650.000158266642723632
690.9998849570733820.0002300858532366180.000115042926618309
700.9998205899887330.0003588200225336040.000179410011266802
710.9997289630125940.000542073974811770.000271036987405885
720.9996163461068140.0007673077863716850.000383653893185843
730.9994062349152860.001187530169427870.000593765084713935
740.9991728640466380.001654271906723380.000827135953361689
750.9987850065943130.002429986811374290.00121499340568714
760.9985270942339880.002945811532023460.00147290576601173
770.9980744488066110.003851102386777530.00192555119338876
780.9979014380308190.004197123938362530.00209856196918126
790.9969950052840420.006009989431915880.00300499471595794
800.9956409080857970.008718183828406350.00435909191420317
810.9937806702191030.01243865956179480.00621932978089739
820.9920070884440880.01598582311182450.00799291155591225
830.9888757772253080.02224844554938420.0111242227746921
840.9887446834923780.02251063301524330.0112553165076217
850.9848352160180260.03032956796394730.0151647839819737
860.9814339401875610.03713211962487730.0185660598124387
870.9749862494898130.05002750102037460.0250137505101873
880.978588998488310.04282200302338080.0214110015116904
890.9885135136205030.02297297275899420.0114864863794971
900.9840990886853080.03180182262938350.0159009113146917
910.9786600772843490.04267984543130210.021339922715651
920.9721576114129780.0556847771740450.0278423885870225
930.9639193064635060.07216138707298850.0360806935364943
940.9541914714937650.09161705701247060.0458085285062353
950.9423135918176220.1153728163647560.0576864081823778
960.9281540279982310.1436919440035380.0718459720017689
970.9086516811692880.1826966376614240.0913483188307118
980.9363802729548920.1272394540902150.0636197270451077
990.9216145698430050.1567708603139890.0783854301569945
1000.9034383832435720.1931232335128550.0965616167564276
1010.8787995120410830.2424009759178350.121200487958917
1020.8654604820018060.2690790359963870.134539517998193
1030.8462128127458940.3075743745082130.153787187254106
1040.8127071853433490.3745856293133010.187292814656651
1050.7860633299136550.427873340172690.213936670086345
1060.7465907528148230.5068184943703540.253409247185177
1070.7074576384322080.5850847231355830.292542361567792
1080.6622635397469540.6754729205060920.337736460253046
1090.613420392903070.7731592141938590.38657960709693
1100.5644486830284460.8711026339431080.435551316971554
1110.5107945763827290.9784108472345420.489205423617271
1120.4568452608431250.913690521686250.543154739156875
1130.4407431142435290.8814862284870580.559256885756471
1140.391585926070930.7831718521418610.60841407392907
1150.3679316320662130.7358632641324260.632068367933787
1160.560410580121220.879178839757560.43958941987878
1170.5333622514037450.9332754971925110.466637748596255
1180.4747681101537170.9495362203074340.525231889846283
1190.4153185301403530.8306370602807070.584681469859647
1200.3659621976185850.7319243952371710.634037802381415
1210.3258788284924110.6517576569848220.674121171507589
1220.2981127250729260.5962254501458520.701887274927074
1230.3050381353234390.6100762706468770.694961864676561
1240.6100258340400730.7799483319198540.389974165959927
1250.5841931299750.8316137400499990.415806870025
1260.5387894557996610.9224210884006780.461210544200339
1270.469829146966130.939658293932260.53017085303387
1280.4935460108850510.9870920217701030.506453989114949
1290.421728171157790.843456342315580.57827182884221
1300.383672577968610.7673451559372190.61632742203139
1310.3140078404963480.6280156809926960.685992159503652
1320.2490583913491750.4981167826983490.750941608650825
1330.197878661814530.3957573236290590.80212133818547
1340.1500273175322320.3000546350644640.849972682467768
1350.1125492836864530.2250985673729060.887450716313547
1360.6352727336115380.7294545327769230.364727266388462
1370.7484534021088530.5030931957822940.251546597891147
1380.7235065790942250.5529868418115490.276493420905775
1390.62894772245050.7421045550989990.3710522775495
1400.514741162355780.9705176752884390.48525883764422
1410.3938825269470960.7877650538941920.606117473052904
1420.3401855165921250.680371033184250.659814483407875
1430.9950880593138450.009823881372310930.00491194068615547

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.789962845310062 & 0.420074309379876 & 0.210037154689938 \tabularnewline
17 & 0.708777579109481 & 0.582444841781039 & 0.291222420890519 \tabularnewline
18 & 0.618136952300701 & 0.763726095398599 & 0.381863047699299 \tabularnewline
19 & 0.924717243657153 & 0.150565512685694 & 0.0752827563428469 \tabularnewline
20 & 0.879257595951906 & 0.241484808096188 & 0.120742404048094 \tabularnewline
21 & 0.890213577334211 & 0.219572845331578 & 0.109786422665789 \tabularnewline
22 & 0.904142675255625 & 0.19171464948875 & 0.0958573247443749 \tabularnewline
23 & 0.859629679795955 & 0.28074064040809 & 0.140370320204045 \tabularnewline
24 & 0.889469266322934 & 0.221061467354132 & 0.110530733677066 \tabularnewline
25 & 0.888297739688359 & 0.223404520623283 & 0.111702260311641 \tabularnewline
26 & 0.866669190769677 & 0.266661618460646 & 0.133330809230323 \tabularnewline
27 & 0.820880630450203 & 0.358238739099595 & 0.179119369549797 \tabularnewline
28 & 0.76608180316732 & 0.46783639366536 & 0.23391819683268 \tabularnewline
29 & 0.871647578584387 & 0.256704842831226 & 0.128352421415613 \tabularnewline
30 & 0.833324008744713 & 0.333351982510575 & 0.166675991255287 \tabularnewline
31 & 0.888597751573911 & 0.222804496852178 & 0.111402248426089 \tabularnewline
32 & 0.870672043820317 & 0.258655912359365 & 0.129327956179683 \tabularnewline
33 & 0.993626779758823 & 0.0127464404823549 & 0.00637322024117747 \tabularnewline
34 & 0.996297372792085 & 0.00740525441582947 & 0.00370262720791473 \tabularnewline
35 & 0.998037010257105 & 0.00392597948578989 & 0.00196298974289494 \tabularnewline
36 & 0.998977384669256 & 0.00204523066148856 & 0.00102261533074428 \tabularnewline
37 & 0.99922536557649 & 0.00154926884702072 & 0.000774634423510361 \tabularnewline
38 & 0.999264495857807 & 0.00147100828438566 & 0.000735504142192829 \tabularnewline
39 & 0.999675915657168 & 0.000648168685663793 & 0.000324084342831896 \tabularnewline
40 & 0.999534867455117 & 0.000930265089766932 & 0.000465132544883466 \tabularnewline
41 & 0.99946258277984 & 0.00107483444031906 & 0.000537417220159531 \tabularnewline
42 & 0.999146869031277 & 0.00170626193744575 & 0.000853130968722873 \tabularnewline
43 & 0.998885384877645 & 0.00222923024470909 & 0.00111461512235455 \tabularnewline
44 & 0.998689927257759 & 0.00262014548448294 & 0.00131007274224147 \tabularnewline
45 & 0.999705081499605 & 0.000589837000790459 & 0.00029491850039523 \tabularnewline
46 & 0.999570191063337 & 0.000859617873325671 & 0.000429808936662836 \tabularnewline
47 & 0.999324614041903 & 0.00135077191619351 & 0.000675385958096757 \tabularnewline
48 & 0.999802326731875 & 0.000395346536250919 & 0.00019767326812546 \tabularnewline
49 & 0.999751802279312 & 0.000496395441376027 & 0.000248197720688014 \tabularnewline
50 & 0.999987589366197 & 2.48212676052584e-05 & 1.24106338026292e-05 \tabularnewline
51 & 0.999990801267169 & 1.83974656618058e-05 & 9.19873283090289e-06 \tabularnewline
52 & 0.999984074470075 & 3.18510598492752e-05 & 1.59255299246376e-05 \tabularnewline
53 & 0.99997234523558 & 5.53095288409749e-05 & 2.76547644204875e-05 \tabularnewline
54 & 0.999964683723343 & 7.06325533142758e-05 & 3.53162766571379e-05 \tabularnewline
55 & 0.999940566930482 & 0.000118866139037094 & 5.9433069518547e-05 \tabularnewline
56 & 0.999922049454815 & 0.000155901090370487 & 7.79505451852435e-05 \tabularnewline
57 & 0.999874198980085 & 0.000251602039830254 & 0.000125801019915127 \tabularnewline
58 & 0.999806935431901 & 0.000386129136197736 & 0.000193064568098868 \tabularnewline
59 & 0.999722155082953 & 0.000555689834094079 & 0.00027784491704704 \tabularnewline
60 & 0.999625571550901 & 0.000748856898197917 & 0.000374428449098958 \tabularnewline
61 & 0.99958748164739 & 0.000825036705220502 & 0.000412518352610251 \tabularnewline
62 & 0.999364254045454 & 0.00127149190909284 & 0.00063574595454642 \tabularnewline
63 & 0.999815054204491 & 0.000369891591018585 & 0.000184945795509293 \tabularnewline
64 & 0.999771376859854 & 0.000457246280291202 & 0.000228623140145601 \tabularnewline
65 & 0.999762419117352 & 0.000475161765296931 & 0.000237580882648465 \tabularnewline
66 & 0.999728533663533 & 0.000542932672934512 & 0.000271466336467256 \tabularnewline
67 & 0.999870235051291 & 0.000259529897418028 & 0.000129764948709014 \tabularnewline
68 & 0.999841733357276 & 0.000316533285447265 & 0.000158266642723632 \tabularnewline
69 & 0.999884957073382 & 0.000230085853236618 & 0.000115042926618309 \tabularnewline
70 & 0.999820589988733 & 0.000358820022533604 & 0.000179410011266802 \tabularnewline
71 & 0.999728963012594 & 0.00054207397481177 & 0.000271036987405885 \tabularnewline
72 & 0.999616346106814 & 0.000767307786371685 & 0.000383653893185843 \tabularnewline
73 & 0.999406234915286 & 0.00118753016942787 & 0.000593765084713935 \tabularnewline
74 & 0.999172864046638 & 0.00165427190672338 & 0.000827135953361689 \tabularnewline
75 & 0.998785006594313 & 0.00242998681137429 & 0.00121499340568714 \tabularnewline
76 & 0.998527094233988 & 0.00294581153202346 & 0.00147290576601173 \tabularnewline
77 & 0.998074448806611 & 0.00385110238677753 & 0.00192555119338876 \tabularnewline
78 & 0.997901438030819 & 0.00419712393836253 & 0.00209856196918126 \tabularnewline
79 & 0.996995005284042 & 0.00600998943191588 & 0.00300499471595794 \tabularnewline
80 & 0.995640908085797 & 0.00871818382840635 & 0.00435909191420317 \tabularnewline
81 & 0.993780670219103 & 0.0124386595617948 & 0.00621932978089739 \tabularnewline
82 & 0.992007088444088 & 0.0159858231118245 & 0.00799291155591225 \tabularnewline
83 & 0.988875777225308 & 0.0222484455493842 & 0.0111242227746921 \tabularnewline
84 & 0.988744683492378 & 0.0225106330152433 & 0.0112553165076217 \tabularnewline
85 & 0.984835216018026 & 0.0303295679639473 & 0.0151647839819737 \tabularnewline
86 & 0.981433940187561 & 0.0371321196248773 & 0.0185660598124387 \tabularnewline
87 & 0.974986249489813 & 0.0500275010203746 & 0.0250137505101873 \tabularnewline
88 & 0.97858899848831 & 0.0428220030233808 & 0.0214110015116904 \tabularnewline
89 & 0.988513513620503 & 0.0229729727589942 & 0.0114864863794971 \tabularnewline
90 & 0.984099088685308 & 0.0318018226293835 & 0.0159009113146917 \tabularnewline
91 & 0.978660077284349 & 0.0426798454313021 & 0.021339922715651 \tabularnewline
92 & 0.972157611412978 & 0.055684777174045 & 0.0278423885870225 \tabularnewline
93 & 0.963919306463506 & 0.0721613870729885 & 0.0360806935364943 \tabularnewline
94 & 0.954191471493765 & 0.0916170570124706 & 0.0458085285062353 \tabularnewline
95 & 0.942313591817622 & 0.115372816364756 & 0.0576864081823778 \tabularnewline
96 & 0.928154027998231 & 0.143691944003538 & 0.0718459720017689 \tabularnewline
97 & 0.908651681169288 & 0.182696637661424 & 0.0913483188307118 \tabularnewline
98 & 0.936380272954892 & 0.127239454090215 & 0.0636197270451077 \tabularnewline
99 & 0.921614569843005 & 0.156770860313989 & 0.0783854301569945 \tabularnewline
100 & 0.903438383243572 & 0.193123233512855 & 0.0965616167564276 \tabularnewline
101 & 0.878799512041083 & 0.242400975917835 & 0.121200487958917 \tabularnewline
102 & 0.865460482001806 & 0.269079035996387 & 0.134539517998193 \tabularnewline
103 & 0.846212812745894 & 0.307574374508213 & 0.153787187254106 \tabularnewline
104 & 0.812707185343349 & 0.374585629313301 & 0.187292814656651 \tabularnewline
105 & 0.786063329913655 & 0.42787334017269 & 0.213936670086345 \tabularnewline
106 & 0.746590752814823 & 0.506818494370354 & 0.253409247185177 \tabularnewline
107 & 0.707457638432208 & 0.585084723135583 & 0.292542361567792 \tabularnewline
108 & 0.662263539746954 & 0.675472920506092 & 0.337736460253046 \tabularnewline
109 & 0.61342039290307 & 0.773159214193859 & 0.38657960709693 \tabularnewline
110 & 0.564448683028446 & 0.871102633943108 & 0.435551316971554 \tabularnewline
111 & 0.510794576382729 & 0.978410847234542 & 0.489205423617271 \tabularnewline
112 & 0.456845260843125 & 0.91369052168625 & 0.543154739156875 \tabularnewline
113 & 0.440743114243529 & 0.881486228487058 & 0.559256885756471 \tabularnewline
114 & 0.39158592607093 & 0.783171852141861 & 0.60841407392907 \tabularnewline
115 & 0.367931632066213 & 0.735863264132426 & 0.632068367933787 \tabularnewline
116 & 0.56041058012122 & 0.87917883975756 & 0.43958941987878 \tabularnewline
117 & 0.533362251403745 & 0.933275497192511 & 0.466637748596255 \tabularnewline
118 & 0.474768110153717 & 0.949536220307434 & 0.525231889846283 \tabularnewline
119 & 0.415318530140353 & 0.830637060280707 & 0.584681469859647 \tabularnewline
120 & 0.365962197618585 & 0.731924395237171 & 0.634037802381415 \tabularnewline
121 & 0.325878828492411 & 0.651757656984822 & 0.674121171507589 \tabularnewline
122 & 0.298112725072926 & 0.596225450145852 & 0.701887274927074 \tabularnewline
123 & 0.305038135323439 & 0.610076270646877 & 0.694961864676561 \tabularnewline
124 & 0.610025834040073 & 0.779948331919854 & 0.389974165959927 \tabularnewline
125 & 0.584193129975 & 0.831613740049999 & 0.415806870025 \tabularnewline
126 & 0.538789455799661 & 0.922421088400678 & 0.461210544200339 \tabularnewline
127 & 0.46982914696613 & 0.93965829393226 & 0.53017085303387 \tabularnewline
128 & 0.493546010885051 & 0.987092021770103 & 0.506453989114949 \tabularnewline
129 & 0.42172817115779 & 0.84345634231558 & 0.57827182884221 \tabularnewline
130 & 0.38367257796861 & 0.767345155937219 & 0.61632742203139 \tabularnewline
131 & 0.314007840496348 & 0.628015680992696 & 0.685992159503652 \tabularnewline
132 & 0.249058391349175 & 0.498116782698349 & 0.750941608650825 \tabularnewline
133 & 0.19787866181453 & 0.395757323629059 & 0.80212133818547 \tabularnewline
134 & 0.150027317532232 & 0.300054635064464 & 0.849972682467768 \tabularnewline
135 & 0.112549283686453 & 0.225098567372906 & 0.887450716313547 \tabularnewline
136 & 0.635272733611538 & 0.729454532776923 & 0.364727266388462 \tabularnewline
137 & 0.748453402108853 & 0.503093195782294 & 0.251546597891147 \tabularnewline
138 & 0.723506579094225 & 0.552986841811549 & 0.276493420905775 \tabularnewline
139 & 0.6289477224505 & 0.742104555098999 & 0.3710522775495 \tabularnewline
140 & 0.51474116235578 & 0.970517675288439 & 0.48525883764422 \tabularnewline
141 & 0.393882526947096 & 0.787765053894192 & 0.606117473052904 \tabularnewline
142 & 0.340185516592125 & 0.68037103318425 & 0.659814483407875 \tabularnewline
143 & 0.995088059313845 & 0.00982388137231093 & 0.00491194068615547 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147153&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]16[/C][C]0.789962845310062[/C][C]0.420074309379876[/C][C]0.210037154689938[/C][/ROW]
[ROW][C]17[/C][C]0.708777579109481[/C][C]0.582444841781039[/C][C]0.291222420890519[/C][/ROW]
[ROW][C]18[/C][C]0.618136952300701[/C][C]0.763726095398599[/C][C]0.381863047699299[/C][/ROW]
[ROW][C]19[/C][C]0.924717243657153[/C][C]0.150565512685694[/C][C]0.0752827563428469[/C][/ROW]
[ROW][C]20[/C][C]0.879257595951906[/C][C]0.241484808096188[/C][C]0.120742404048094[/C][/ROW]
[ROW][C]21[/C][C]0.890213577334211[/C][C]0.219572845331578[/C][C]0.109786422665789[/C][/ROW]
[ROW][C]22[/C][C]0.904142675255625[/C][C]0.19171464948875[/C][C]0.0958573247443749[/C][/ROW]
[ROW][C]23[/C][C]0.859629679795955[/C][C]0.28074064040809[/C][C]0.140370320204045[/C][/ROW]
[ROW][C]24[/C][C]0.889469266322934[/C][C]0.221061467354132[/C][C]0.110530733677066[/C][/ROW]
[ROW][C]25[/C][C]0.888297739688359[/C][C]0.223404520623283[/C][C]0.111702260311641[/C][/ROW]
[ROW][C]26[/C][C]0.866669190769677[/C][C]0.266661618460646[/C][C]0.133330809230323[/C][/ROW]
[ROW][C]27[/C][C]0.820880630450203[/C][C]0.358238739099595[/C][C]0.179119369549797[/C][/ROW]
[ROW][C]28[/C][C]0.76608180316732[/C][C]0.46783639366536[/C][C]0.23391819683268[/C][/ROW]
[ROW][C]29[/C][C]0.871647578584387[/C][C]0.256704842831226[/C][C]0.128352421415613[/C][/ROW]
[ROW][C]30[/C][C]0.833324008744713[/C][C]0.333351982510575[/C][C]0.166675991255287[/C][/ROW]
[ROW][C]31[/C][C]0.888597751573911[/C][C]0.222804496852178[/C][C]0.111402248426089[/C][/ROW]
[ROW][C]32[/C][C]0.870672043820317[/C][C]0.258655912359365[/C][C]0.129327956179683[/C][/ROW]
[ROW][C]33[/C][C]0.993626779758823[/C][C]0.0127464404823549[/C][C]0.00637322024117747[/C][/ROW]
[ROW][C]34[/C][C]0.996297372792085[/C][C]0.00740525441582947[/C][C]0.00370262720791473[/C][/ROW]
[ROW][C]35[/C][C]0.998037010257105[/C][C]0.00392597948578989[/C][C]0.00196298974289494[/C][/ROW]
[ROW][C]36[/C][C]0.998977384669256[/C][C]0.00204523066148856[/C][C]0.00102261533074428[/C][/ROW]
[ROW][C]37[/C][C]0.99922536557649[/C][C]0.00154926884702072[/C][C]0.000774634423510361[/C][/ROW]
[ROW][C]38[/C][C]0.999264495857807[/C][C]0.00147100828438566[/C][C]0.000735504142192829[/C][/ROW]
[ROW][C]39[/C][C]0.999675915657168[/C][C]0.000648168685663793[/C][C]0.000324084342831896[/C][/ROW]
[ROW][C]40[/C][C]0.999534867455117[/C][C]0.000930265089766932[/C][C]0.000465132544883466[/C][/ROW]
[ROW][C]41[/C][C]0.99946258277984[/C][C]0.00107483444031906[/C][C]0.000537417220159531[/C][/ROW]
[ROW][C]42[/C][C]0.999146869031277[/C][C]0.00170626193744575[/C][C]0.000853130968722873[/C][/ROW]
[ROW][C]43[/C][C]0.998885384877645[/C][C]0.00222923024470909[/C][C]0.00111461512235455[/C][/ROW]
[ROW][C]44[/C][C]0.998689927257759[/C][C]0.00262014548448294[/C][C]0.00131007274224147[/C][/ROW]
[ROW][C]45[/C][C]0.999705081499605[/C][C]0.000589837000790459[/C][C]0.00029491850039523[/C][/ROW]
[ROW][C]46[/C][C]0.999570191063337[/C][C]0.000859617873325671[/C][C]0.000429808936662836[/C][/ROW]
[ROW][C]47[/C][C]0.999324614041903[/C][C]0.00135077191619351[/C][C]0.000675385958096757[/C][/ROW]
[ROW][C]48[/C][C]0.999802326731875[/C][C]0.000395346536250919[/C][C]0.00019767326812546[/C][/ROW]
[ROW][C]49[/C][C]0.999751802279312[/C][C]0.000496395441376027[/C][C]0.000248197720688014[/C][/ROW]
[ROW][C]50[/C][C]0.999987589366197[/C][C]2.48212676052584e-05[/C][C]1.24106338026292e-05[/C][/ROW]
[ROW][C]51[/C][C]0.999990801267169[/C][C]1.83974656618058e-05[/C][C]9.19873283090289e-06[/C][/ROW]
[ROW][C]52[/C][C]0.999984074470075[/C][C]3.18510598492752e-05[/C][C]1.59255299246376e-05[/C][/ROW]
[ROW][C]53[/C][C]0.99997234523558[/C][C]5.53095288409749e-05[/C][C]2.76547644204875e-05[/C][/ROW]
[ROW][C]54[/C][C]0.999964683723343[/C][C]7.06325533142758e-05[/C][C]3.53162766571379e-05[/C][/ROW]
[ROW][C]55[/C][C]0.999940566930482[/C][C]0.000118866139037094[/C][C]5.9433069518547e-05[/C][/ROW]
[ROW][C]56[/C][C]0.999922049454815[/C][C]0.000155901090370487[/C][C]7.79505451852435e-05[/C][/ROW]
[ROW][C]57[/C][C]0.999874198980085[/C][C]0.000251602039830254[/C][C]0.000125801019915127[/C][/ROW]
[ROW][C]58[/C][C]0.999806935431901[/C][C]0.000386129136197736[/C][C]0.000193064568098868[/C][/ROW]
[ROW][C]59[/C][C]0.999722155082953[/C][C]0.000555689834094079[/C][C]0.00027784491704704[/C][/ROW]
[ROW][C]60[/C][C]0.999625571550901[/C][C]0.000748856898197917[/C][C]0.000374428449098958[/C][/ROW]
[ROW][C]61[/C][C]0.99958748164739[/C][C]0.000825036705220502[/C][C]0.000412518352610251[/C][/ROW]
[ROW][C]62[/C][C]0.999364254045454[/C][C]0.00127149190909284[/C][C]0.00063574595454642[/C][/ROW]
[ROW][C]63[/C][C]0.999815054204491[/C][C]0.000369891591018585[/C][C]0.000184945795509293[/C][/ROW]
[ROW][C]64[/C][C]0.999771376859854[/C][C]0.000457246280291202[/C][C]0.000228623140145601[/C][/ROW]
[ROW][C]65[/C][C]0.999762419117352[/C][C]0.000475161765296931[/C][C]0.000237580882648465[/C][/ROW]
[ROW][C]66[/C][C]0.999728533663533[/C][C]0.000542932672934512[/C][C]0.000271466336467256[/C][/ROW]
[ROW][C]67[/C][C]0.999870235051291[/C][C]0.000259529897418028[/C][C]0.000129764948709014[/C][/ROW]
[ROW][C]68[/C][C]0.999841733357276[/C][C]0.000316533285447265[/C][C]0.000158266642723632[/C][/ROW]
[ROW][C]69[/C][C]0.999884957073382[/C][C]0.000230085853236618[/C][C]0.000115042926618309[/C][/ROW]
[ROW][C]70[/C][C]0.999820589988733[/C][C]0.000358820022533604[/C][C]0.000179410011266802[/C][/ROW]
[ROW][C]71[/C][C]0.999728963012594[/C][C]0.00054207397481177[/C][C]0.000271036987405885[/C][/ROW]
[ROW][C]72[/C][C]0.999616346106814[/C][C]0.000767307786371685[/C][C]0.000383653893185843[/C][/ROW]
[ROW][C]73[/C][C]0.999406234915286[/C][C]0.00118753016942787[/C][C]0.000593765084713935[/C][/ROW]
[ROW][C]74[/C][C]0.999172864046638[/C][C]0.00165427190672338[/C][C]0.000827135953361689[/C][/ROW]
[ROW][C]75[/C][C]0.998785006594313[/C][C]0.00242998681137429[/C][C]0.00121499340568714[/C][/ROW]
[ROW][C]76[/C][C]0.998527094233988[/C][C]0.00294581153202346[/C][C]0.00147290576601173[/C][/ROW]
[ROW][C]77[/C][C]0.998074448806611[/C][C]0.00385110238677753[/C][C]0.00192555119338876[/C][/ROW]
[ROW][C]78[/C][C]0.997901438030819[/C][C]0.00419712393836253[/C][C]0.00209856196918126[/C][/ROW]
[ROW][C]79[/C][C]0.996995005284042[/C][C]0.00600998943191588[/C][C]0.00300499471595794[/C][/ROW]
[ROW][C]80[/C][C]0.995640908085797[/C][C]0.00871818382840635[/C][C]0.00435909191420317[/C][/ROW]
[ROW][C]81[/C][C]0.993780670219103[/C][C]0.0124386595617948[/C][C]0.00621932978089739[/C][/ROW]
[ROW][C]82[/C][C]0.992007088444088[/C][C]0.0159858231118245[/C][C]0.00799291155591225[/C][/ROW]
[ROW][C]83[/C][C]0.988875777225308[/C][C]0.0222484455493842[/C][C]0.0111242227746921[/C][/ROW]
[ROW][C]84[/C][C]0.988744683492378[/C][C]0.0225106330152433[/C][C]0.0112553165076217[/C][/ROW]
[ROW][C]85[/C][C]0.984835216018026[/C][C]0.0303295679639473[/C][C]0.0151647839819737[/C][/ROW]
[ROW][C]86[/C][C]0.981433940187561[/C][C]0.0371321196248773[/C][C]0.0185660598124387[/C][/ROW]
[ROW][C]87[/C][C]0.974986249489813[/C][C]0.0500275010203746[/C][C]0.0250137505101873[/C][/ROW]
[ROW][C]88[/C][C]0.97858899848831[/C][C]0.0428220030233808[/C][C]0.0214110015116904[/C][/ROW]
[ROW][C]89[/C][C]0.988513513620503[/C][C]0.0229729727589942[/C][C]0.0114864863794971[/C][/ROW]
[ROW][C]90[/C][C]0.984099088685308[/C][C]0.0318018226293835[/C][C]0.0159009113146917[/C][/ROW]
[ROW][C]91[/C][C]0.978660077284349[/C][C]0.0426798454313021[/C][C]0.021339922715651[/C][/ROW]
[ROW][C]92[/C][C]0.972157611412978[/C][C]0.055684777174045[/C][C]0.0278423885870225[/C][/ROW]
[ROW][C]93[/C][C]0.963919306463506[/C][C]0.0721613870729885[/C][C]0.0360806935364943[/C][/ROW]
[ROW][C]94[/C][C]0.954191471493765[/C][C]0.0916170570124706[/C][C]0.0458085285062353[/C][/ROW]
[ROW][C]95[/C][C]0.942313591817622[/C][C]0.115372816364756[/C][C]0.0576864081823778[/C][/ROW]
[ROW][C]96[/C][C]0.928154027998231[/C][C]0.143691944003538[/C][C]0.0718459720017689[/C][/ROW]
[ROW][C]97[/C][C]0.908651681169288[/C][C]0.182696637661424[/C][C]0.0913483188307118[/C][/ROW]
[ROW][C]98[/C][C]0.936380272954892[/C][C]0.127239454090215[/C][C]0.0636197270451077[/C][/ROW]
[ROW][C]99[/C][C]0.921614569843005[/C][C]0.156770860313989[/C][C]0.0783854301569945[/C][/ROW]
[ROW][C]100[/C][C]0.903438383243572[/C][C]0.193123233512855[/C][C]0.0965616167564276[/C][/ROW]
[ROW][C]101[/C][C]0.878799512041083[/C][C]0.242400975917835[/C][C]0.121200487958917[/C][/ROW]
[ROW][C]102[/C][C]0.865460482001806[/C][C]0.269079035996387[/C][C]0.134539517998193[/C][/ROW]
[ROW][C]103[/C][C]0.846212812745894[/C][C]0.307574374508213[/C][C]0.153787187254106[/C][/ROW]
[ROW][C]104[/C][C]0.812707185343349[/C][C]0.374585629313301[/C][C]0.187292814656651[/C][/ROW]
[ROW][C]105[/C][C]0.786063329913655[/C][C]0.42787334017269[/C][C]0.213936670086345[/C][/ROW]
[ROW][C]106[/C][C]0.746590752814823[/C][C]0.506818494370354[/C][C]0.253409247185177[/C][/ROW]
[ROW][C]107[/C][C]0.707457638432208[/C][C]0.585084723135583[/C][C]0.292542361567792[/C][/ROW]
[ROW][C]108[/C][C]0.662263539746954[/C][C]0.675472920506092[/C][C]0.337736460253046[/C][/ROW]
[ROW][C]109[/C][C]0.61342039290307[/C][C]0.773159214193859[/C][C]0.38657960709693[/C][/ROW]
[ROW][C]110[/C][C]0.564448683028446[/C][C]0.871102633943108[/C][C]0.435551316971554[/C][/ROW]
[ROW][C]111[/C][C]0.510794576382729[/C][C]0.978410847234542[/C][C]0.489205423617271[/C][/ROW]
[ROW][C]112[/C][C]0.456845260843125[/C][C]0.91369052168625[/C][C]0.543154739156875[/C][/ROW]
[ROW][C]113[/C][C]0.440743114243529[/C][C]0.881486228487058[/C][C]0.559256885756471[/C][/ROW]
[ROW][C]114[/C][C]0.39158592607093[/C][C]0.783171852141861[/C][C]0.60841407392907[/C][/ROW]
[ROW][C]115[/C][C]0.367931632066213[/C][C]0.735863264132426[/C][C]0.632068367933787[/C][/ROW]
[ROW][C]116[/C][C]0.56041058012122[/C][C]0.87917883975756[/C][C]0.43958941987878[/C][/ROW]
[ROW][C]117[/C][C]0.533362251403745[/C][C]0.933275497192511[/C][C]0.466637748596255[/C][/ROW]
[ROW][C]118[/C][C]0.474768110153717[/C][C]0.949536220307434[/C][C]0.525231889846283[/C][/ROW]
[ROW][C]119[/C][C]0.415318530140353[/C][C]0.830637060280707[/C][C]0.584681469859647[/C][/ROW]
[ROW][C]120[/C][C]0.365962197618585[/C][C]0.731924395237171[/C][C]0.634037802381415[/C][/ROW]
[ROW][C]121[/C][C]0.325878828492411[/C][C]0.651757656984822[/C][C]0.674121171507589[/C][/ROW]
[ROW][C]122[/C][C]0.298112725072926[/C][C]0.596225450145852[/C][C]0.701887274927074[/C][/ROW]
[ROW][C]123[/C][C]0.305038135323439[/C][C]0.610076270646877[/C][C]0.694961864676561[/C][/ROW]
[ROW][C]124[/C][C]0.610025834040073[/C][C]0.779948331919854[/C][C]0.389974165959927[/C][/ROW]
[ROW][C]125[/C][C]0.584193129975[/C][C]0.831613740049999[/C][C]0.415806870025[/C][/ROW]
[ROW][C]126[/C][C]0.538789455799661[/C][C]0.922421088400678[/C][C]0.461210544200339[/C][/ROW]
[ROW][C]127[/C][C]0.46982914696613[/C][C]0.93965829393226[/C][C]0.53017085303387[/C][/ROW]
[ROW][C]128[/C][C]0.493546010885051[/C][C]0.987092021770103[/C][C]0.506453989114949[/C][/ROW]
[ROW][C]129[/C][C]0.42172817115779[/C][C]0.84345634231558[/C][C]0.57827182884221[/C][/ROW]
[ROW][C]130[/C][C]0.38367257796861[/C][C]0.767345155937219[/C][C]0.61632742203139[/C][/ROW]
[ROW][C]131[/C][C]0.314007840496348[/C][C]0.628015680992696[/C][C]0.685992159503652[/C][/ROW]
[ROW][C]132[/C][C]0.249058391349175[/C][C]0.498116782698349[/C][C]0.750941608650825[/C][/ROW]
[ROW][C]133[/C][C]0.19787866181453[/C][C]0.395757323629059[/C][C]0.80212133818547[/C][/ROW]
[ROW][C]134[/C][C]0.150027317532232[/C][C]0.300054635064464[/C][C]0.849972682467768[/C][/ROW]
[ROW][C]135[/C][C]0.112549283686453[/C][C]0.225098567372906[/C][C]0.887450716313547[/C][/ROW]
[ROW][C]136[/C][C]0.635272733611538[/C][C]0.729454532776923[/C][C]0.364727266388462[/C][/ROW]
[ROW][C]137[/C][C]0.748453402108853[/C][C]0.503093195782294[/C][C]0.251546597891147[/C][/ROW]
[ROW][C]138[/C][C]0.723506579094225[/C][C]0.552986841811549[/C][C]0.276493420905775[/C][/ROW]
[ROW][C]139[/C][C]0.6289477224505[/C][C]0.742104555098999[/C][C]0.3710522775495[/C][/ROW]
[ROW][C]140[/C][C]0.51474116235578[/C][C]0.970517675288439[/C][C]0.48525883764422[/C][/ROW]
[ROW][C]141[/C][C]0.393882526947096[/C][C]0.787765053894192[/C][C]0.606117473052904[/C][/ROW]
[ROW][C]142[/C][C]0.340185516592125[/C][C]0.68037103318425[/C][C]0.659814483407875[/C][/ROW]
[ROW][C]143[/C][C]0.995088059313845[/C][C]0.00982388137231093[/C][C]0.00491194068615547[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147153&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147153&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
160.7899628453100620.4200743093798760.210037154689938
170.7087775791094810.5824448417810390.291222420890519
180.6181369523007010.7637260953985990.381863047699299
190.9247172436571530.1505655126856940.0752827563428469
200.8792575959519060.2414848080961880.120742404048094
210.8902135773342110.2195728453315780.109786422665789
220.9041426752556250.191714649488750.0958573247443749
230.8596296797959550.280740640408090.140370320204045
240.8894692663229340.2210614673541320.110530733677066
250.8882977396883590.2234045206232830.111702260311641
260.8666691907696770.2666616184606460.133330809230323
270.8208806304502030.3582387390995950.179119369549797
280.766081803167320.467836393665360.23391819683268
290.8716475785843870.2567048428312260.128352421415613
300.8333240087447130.3333519825105750.166675991255287
310.8885977515739110.2228044968521780.111402248426089
320.8706720438203170.2586559123593650.129327956179683
330.9936267797588230.01274644048235490.00637322024117747
340.9962973727920850.007405254415829470.00370262720791473
350.9980370102571050.003925979485789890.00196298974289494
360.9989773846692560.002045230661488560.00102261533074428
370.999225365576490.001549268847020720.000774634423510361
380.9992644958578070.001471008284385660.000735504142192829
390.9996759156571680.0006481686856637930.000324084342831896
400.9995348674551170.0009302650897669320.000465132544883466
410.999462582779840.001074834440319060.000537417220159531
420.9991468690312770.001706261937445750.000853130968722873
430.9988853848776450.002229230244709090.00111461512235455
440.9986899272577590.002620145484482940.00131007274224147
450.9997050814996050.0005898370007904590.00029491850039523
460.9995701910633370.0008596178733256710.000429808936662836
470.9993246140419030.001350771916193510.000675385958096757
480.9998023267318750.0003953465362509190.00019767326812546
490.9997518022793120.0004963954413760270.000248197720688014
500.9999875893661972.48212676052584e-051.24106338026292e-05
510.9999908012671691.83974656618058e-059.19873283090289e-06
520.9999840744700753.18510598492752e-051.59255299246376e-05
530.999972345235585.53095288409749e-052.76547644204875e-05
540.9999646837233437.06325533142758e-053.53162766571379e-05
550.9999405669304820.0001188661390370945.9433069518547e-05
560.9999220494548150.0001559010903704877.79505451852435e-05
570.9998741989800850.0002516020398302540.000125801019915127
580.9998069354319010.0003861291361977360.000193064568098868
590.9997221550829530.0005556898340940790.00027784491704704
600.9996255715509010.0007488568981979170.000374428449098958
610.999587481647390.0008250367052205020.000412518352610251
620.9993642540454540.001271491909092840.00063574595454642
630.9998150542044910.0003698915910185850.000184945795509293
640.9997713768598540.0004572462802912020.000228623140145601
650.9997624191173520.0004751617652969310.000237580882648465
660.9997285336635330.0005429326729345120.000271466336467256
670.9998702350512910.0002595298974180280.000129764948709014
680.9998417333572760.0003165332854472650.000158266642723632
690.9998849570733820.0002300858532366180.000115042926618309
700.9998205899887330.0003588200225336040.000179410011266802
710.9997289630125940.000542073974811770.000271036987405885
720.9996163461068140.0007673077863716850.000383653893185843
730.9994062349152860.001187530169427870.000593765084713935
740.9991728640466380.001654271906723380.000827135953361689
750.9987850065943130.002429986811374290.00121499340568714
760.9985270942339880.002945811532023460.00147290576601173
770.9980744488066110.003851102386777530.00192555119338876
780.9979014380308190.004197123938362530.00209856196918126
790.9969950052840420.006009989431915880.00300499471595794
800.9956409080857970.008718183828406350.00435909191420317
810.9937806702191030.01243865956179480.00621932978089739
820.9920070884440880.01598582311182450.00799291155591225
830.9888757772253080.02224844554938420.0111242227746921
840.9887446834923780.02251063301524330.0112553165076217
850.9848352160180260.03032956796394730.0151647839819737
860.9814339401875610.03713211962487730.0185660598124387
870.9749862494898130.05002750102037460.0250137505101873
880.978588998488310.04282200302338080.0214110015116904
890.9885135136205030.02297297275899420.0114864863794971
900.9840990886853080.03180182262938350.0159009113146917
910.9786600772843490.04267984543130210.021339922715651
920.9721576114129780.0556847771740450.0278423885870225
930.9639193064635060.07216138707298850.0360806935364943
940.9541914714937650.09161705701247060.0458085285062353
950.9423135918176220.1153728163647560.0576864081823778
960.9281540279982310.1436919440035380.0718459720017689
970.9086516811692880.1826966376614240.0913483188307118
980.9363802729548920.1272394540902150.0636197270451077
990.9216145698430050.1567708603139890.0783854301569945
1000.9034383832435720.1931232335128550.0965616167564276
1010.8787995120410830.2424009759178350.121200487958917
1020.8654604820018060.2690790359963870.134539517998193
1030.8462128127458940.3075743745082130.153787187254106
1040.8127071853433490.3745856293133010.187292814656651
1050.7860633299136550.427873340172690.213936670086345
1060.7465907528148230.5068184943703540.253409247185177
1070.7074576384322080.5850847231355830.292542361567792
1080.6622635397469540.6754729205060920.337736460253046
1090.613420392903070.7731592141938590.38657960709693
1100.5644486830284460.8711026339431080.435551316971554
1110.5107945763827290.9784108472345420.489205423617271
1120.4568452608431250.913690521686250.543154739156875
1130.4407431142435290.8814862284870580.559256885756471
1140.391585926070930.7831718521418610.60841407392907
1150.3679316320662130.7358632641324260.632068367933787
1160.560410580121220.879178839757560.43958941987878
1170.5333622514037450.9332754971925110.466637748596255
1180.4747681101537170.9495362203074340.525231889846283
1190.4153185301403530.8306370602807070.584681469859647
1200.3659621976185850.7319243952371710.634037802381415
1210.3258788284924110.6517576569848220.674121171507589
1220.2981127250729260.5962254501458520.701887274927074
1230.3050381353234390.6100762706468770.694961864676561
1240.6100258340400730.7799483319198540.389974165959927
1250.5841931299750.8316137400499990.415806870025
1260.5387894557996610.9224210884006780.461210544200339
1270.469829146966130.939658293932260.53017085303387
1280.4935460108850510.9870920217701030.506453989114949
1290.421728171157790.843456342315580.57827182884221
1300.383672577968610.7673451559372190.61632742203139
1310.3140078404963480.6280156809926960.685992159503652
1320.2490583913491750.4981167826983490.750941608650825
1330.197878661814530.3957573236290590.80212133818547
1340.1500273175322320.3000546350644640.849972682467768
1350.1125492836864530.2250985673729060.887450716313547
1360.6352727336115380.7294545327769230.364727266388462
1370.7484534021088530.5030931957822940.251546597891147
1380.7235065790942250.5529868418115490.276493420905775
1390.62894772245050.7421045550989990.3710522775495
1400.514741162355780.9705176752884390.48525883764422
1410.3938825269470960.7877650538941920.606117473052904
1420.3401855165921250.680371033184250.659814483407875
1430.9950880593138450.009823881372310930.00491194068615547







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level480.375NOK
5% type I error level590.4609375NOK
10% type I error level630.4921875NOK

\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 & 48 & 0.375 & NOK \tabularnewline
5% type I error level & 59 & 0.4609375 & NOK \tabularnewline
10% type I error level & 63 & 0.4921875 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147153&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]48[/C][C]0.375[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]59[/C][C]0.4609375[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]63[/C][C]0.4921875[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147153&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147153&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 level480.375NOK
5% type I error level590.4609375NOK
10% type I error level630.4921875NOK



Parameters (Session):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')
}