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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 01 Dec 2010 15:05:12 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/01/t1291215806v5qz9c6ybn1nsly.htm/, Retrieved Fri, 29 Mar 2024 09:42:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=104048, Retrieved Fri, 29 Mar 2024 09:42:14 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact217
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]
-           [Multiple Regression] [] [2010-12-01 15:05:12] [6d73806852b9f5b8ac8b27fc8f7b83c4] [Current]
-             [Multiple Regression] [] [2010-12-03 17:31:11] [5e7b9ab9ddedd2d2f5ce6c303ba3ebe3]
- R PD        [Multiple Regression] [multiple regressi...] [2011-11-24 10:39:28] [ca5872d1d4d784184b94263c274137e3]
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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 time12 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 12 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104048&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]12 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104048&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104048&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 time12 seconds
R Server'George Udny Yule' @ 72.249.76.132







Multiple Linear Regression - Estimated Regression Equation
ParentalExpectations[t] = + 7.56408311336285 -4.02282154930859Gender[t] -0.0497128514879975ConcernoverMistakes[t] + 0.0139238377293544`C*G`[t] -0.0584300461850058Doubtsaboutactions[t] + 0.0411688955628657`D*G`[t] + 0.554368493058405`PE*G`[t] + 0.762594333796532ParentalCriticism[t] -0.413361375350402`PC*G`[t] + 0.229602388869078PersonalStandards[t] -0.11794509827969`PS*G`[t] -0.205802809085591Organization[t] + 0.101854058940112`O*G`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ParentalExpectations[t] =  +  7.56408311336285 -4.02282154930859Gender[t] -0.0497128514879975ConcernoverMistakes[t] +  0.0139238377293544`C*G`[t] -0.0584300461850058Doubtsaboutactions[t] +  0.0411688955628657`D*G`[t] +  0.554368493058405`PE*G`[t] +  0.762594333796532ParentalCriticism[t] -0.413361375350402`PC*G`[t] +  0.229602388869078PersonalStandards[t] -0.11794509827969`PS*G`[t] -0.205802809085591Organization[t] +  0.101854058940112`O*G`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104048&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ParentalExpectations[t] =  +  7.56408311336285 -4.02282154930859Gender[t] -0.0497128514879975ConcernoverMistakes[t] +  0.0139238377293544`C*G`[t] -0.0584300461850058Doubtsaboutactions[t] +  0.0411688955628657`D*G`[t] +  0.554368493058405`PE*G`[t] +  0.762594333796532ParentalCriticism[t] -0.413361375350402`PC*G`[t] +  0.229602388869078PersonalStandards[t] -0.11794509827969`PS*G`[t] -0.205802809085591Organization[t] +  0.101854058940112`O*G`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104048&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104048&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] = + 7.56408311336285 -4.02282154930859Gender[t] -0.0497128514879975ConcernoverMistakes[t] + 0.0139238377293544`C*G`[t] -0.0584300461850058Doubtsaboutactions[t] + 0.0411688955628657`D*G`[t] + 0.554368493058405`PE*G`[t] + 0.762594333796532ParentalCriticism[t] -0.413361375350402`PC*G`[t] + 0.229602388869078PersonalStandards[t] -0.11794509827969`PS*G`[t] -0.205802809085591Organization[t] + 0.101854058940112`O*G`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.564083113362851.5634694.8383e-062e-06
Gender-4.022821549308590.950195-4.23374e-052e-05
ConcernoverMistakes-0.04971285148799750.04408-1.12780.2612550.130628
`C*G`0.01392383772935440.0270930.51390.6080830.304042
Doubtsaboutactions-0.05843004618500580.075433-0.77460.4398330.219917
`D*G`0.04116889556286570.0473290.86990.3858080.192904
`PE*G`0.5543684930584050.01288243.034200
ParentalCriticism0.7625943337965320.0785689.706100
`PC*G`-0.4133613753504020.047198-8.757900
PersonalStandards0.2296023888690780.0578573.96840.0001135.7e-05
`PS*G`-0.117945098279690.034576-3.41110.0008370.000418
Organization-0.2058028090855910.054165-3.79950.0002120.000106
`O*G`0.1018540589401120.0332743.06110.0026260.001313

\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) & 7.56408311336285 & 1.563469 & 4.838 & 3e-06 & 2e-06 \tabularnewline
Gender & -4.02282154930859 & 0.950195 & -4.2337 & 4e-05 & 2e-05 \tabularnewline
ConcernoverMistakes & -0.0497128514879975 & 0.04408 & -1.1278 & 0.261255 & 0.130628 \tabularnewline
`C*G` & 0.0139238377293544 & 0.027093 & 0.5139 & 0.608083 & 0.304042 \tabularnewline
Doubtsaboutactions & -0.0584300461850058 & 0.075433 & -0.7746 & 0.439833 & 0.219917 \tabularnewline
`D*G` & 0.0411688955628657 & 0.047329 & 0.8699 & 0.385808 & 0.192904 \tabularnewline
`PE*G` & 0.554368493058405 & 0.012882 & 43.0342 & 0 & 0 \tabularnewline
ParentalCriticism & 0.762594333796532 & 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 & -0.205802809085591 & 0.054165 & -3.7995 & 0.000212 & 0.000106 \tabularnewline
`O*G` & 0.101854058940112 & 0.033274 & 3.0611 & 0.002626 & 0.001313 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104048&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]7.56408311336285[/C][C]1.563469[/C][C]4.838[/C][C]3e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]Gender[/C][C]-4.02282154930859[/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]0.0139238377293544[/C][C]0.027093[/C][C]0.5139[/C][C]0.608083[/C][C]0.304042[/C][/ROW]
[ROW][C]Doubtsaboutactions[/C][C]-0.0584300461850058[/C][C]0.075433[/C][C]-0.7746[/C][C]0.439833[/C][C]0.219917[/C][/ROW]
[ROW][C]`D*G`[/C][C]0.0411688955628657[/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]0.762594333796532[/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]-0.205802809085591[/C][C]0.054165[/C][C]-3.7995[/C][C]0.000212[/C][C]0.000106[/C][/ROW]
[ROW][C]`O*G`[/C][C]0.101854058940112[/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=104048&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104048&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)7.564083113362851.5634694.8383e-062e-06
Gender-4.022821549308590.950195-4.23374e-052e-05
ConcernoverMistakes-0.04971285148799750.04408-1.12780.2612550.130628
`C*G`0.01392383772935440.0270930.51390.6080830.304042
Doubtsaboutactions-0.05843004618500580.075433-0.77460.4398330.219917
`D*G`0.04116889556286570.0473290.86990.3858080.192904
`PE*G`0.5543684930584050.01288243.034200
ParentalCriticism0.7625943337965320.0785689.706100
`PC*G`-0.4133613753504020.047198-8.757900
PersonalStandards0.2296023888690780.0578573.96840.0001135.7e-05
`PS*G`-0.117945098279690.034576-3.41110.0008370.000418
Organization-0.2058028090855910.054165-3.79950.0002120.000106
`O*G`0.1018540589401120.0332743.06110.0026260.001313







Multiple Linear Regression - Regression Statistics
Multiple R0.979951191393236
R-squared0.960304337513023
Adjusted R-squared0.957041680322313
F-TEST (value)294.331975865330
F-TEST (DF numerator)12
F-TEST (DF denominator)146
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.714124047186037
Sum Squared Residuals74.4560805963274

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.979951191393236 \tabularnewline
R-squared & 0.960304337513023 \tabularnewline
Adjusted R-squared & 0.957041680322313 \tabularnewline
F-TEST (value) & 294.331975865330 \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 & 0.714124047186037 \tabularnewline
Sum Squared Residuals & 74.4560805963274 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104048&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]0.960304337513023[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.957041680322313[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]294.331975865330[/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]0.714124047186037[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]74.4560805963274[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104048&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104048&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-squared0.960304337513023
Adjusted R-squared0.957041680322313
F-TEST (value)294.331975865330
F-TEST (DF numerator)12
F-TEST (DF denominator)146
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.714124047186037
Sum Squared Residuals74.4560805963274







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11110.54958070773970.450419292260260
279.5317176936113-2.5317176936113
31715.79840944003111.20159055996895
41010.7581413749384-0.758141374938443
51213.0359227943400-1.03592279434002
61211.33502510226600.664974897734024
71111.0502446508432-0.0502446508432135
81110.73366856177330.266331438226719
91211.32923496908730.67076503091273
101312.14048114093610.859518859063858
111414.0723922113957-0.0723922113957462
121616.0907124941696-0.0907124941696067
131110.62122528196740.378774718032598
14109.854077020315750.145922979684245
151111.9877159723979-0.987715972397948
161515.4978078449037-0.49780784490372
17911.1442403731213-2.14424037312126
181112.0740713104147-1.07407131041468
191717.4834239412261-0.483423941226097
201717.1319307640624-0.131930764062352
211111.9423849133973-0.942384913397281
221818.2354633273534-0.235463327353353
231413.79963254704850.200367452951524
241010.1467046042424-0.146704604242418
251110.86465886903160.135341130968438
261515.2188660316108-0.218866031610784
271515.3351227077666-0.335122707766567
281312.94112515493270.0588748450673461
291614.90049914151381.09950085848621
301313.2042023778971-0.204202377897087
3198.608674375862440.391325624137565
321818.0717900238226-0.0717900238226058
331815.51265614124162.48734385875843
341211.76544036238720.23455963761279
351715.95336749424581.04663250575424
3699.73994282533566-0.739942825335656
37910.0945972746802-1.09459727468024
381210.80971412098821.19028587901178
391817.19783932601920.80216067398083
401211.88412414763180.115875852368246
411818.3087161840174-0.308716184017430
421413.90341944598640.096580554013632
431515.3583326939882-0.358332693988225
441616.5756783300809-0.575678330080885
451011.5026866827912-1.50268668279115
461111.3236366995712-0.323636699571206
471414.1334254325684-0.133425432568411
48910.2815454848804-1.28154548488042
491212.1776652544908-0.177665254490788
501714.95386174169092.04613825830906
5154.424748454196960.575251545803043
521211.94953649842740.0504635015726222
531211.92762293417840.0723770658216422
5465.689445622511210.310554377488794
552424.1940683193530-0.194068319352962
561211.49789905243800.502100947561969
571212.1240506408088-0.124050640808757
581414.2785506948979-0.278550694897936
5976.731074229242930.268925770757075
601313.3499528417808-0.349952841780756
611212.6540727377824-0.654072737782425
621313.1259791121347-0.125979112134686
631412.62913607718681.37086392281317
6487.527227693747860.472772306252141
651110.58564540397190.414354596028115
6699.39190224187301-0.391902241873014
671111.1473204132457-0.147320413245734
681313.5383971172275-0.538397117227497
691010.1566179309609-0.156617930960873
701110.81763872816690.182361271833136
711211.78158260019910.218417399800924
7298.66768347215730.332316527842702
731514.90732069896950.0926793010304781
741818.3020217735624-0.302021773562364
751515.2018369603855-0.201836960385467
761212.0420103843867-0.0420103843866668
771313.4133002319974-0.413300231997438
781413.44850438596490.551495614035051
79109.75887151357920.241128486420798
801312.99473219959080.00526780040917663
811312.90879419091550.0912058090844854
821110.88091594011450.119084059885525
831313.0811661890698-0.081166189069839
841615.34627981854330.653720181456747
8587.723478190994640.276521809005361
861616.4371206292828-0.43712062928278
871110.96005794142470.0399420585752663
88910.0995542833964-1.09955428339636
891616.8190670212398-0.819067021239801
901212.0471427886204-0.0471427886204004
911414.1367790178915-0.136779017891527
9287.734711431620690.265288568379308
9399.0215986035023-0.0215986035023019
941515.3425422885411-0.342542288541094
951110.68439342885660.315606571143370
962121.3379608954175-0.337960895417492
971413.98389428875570.0161057112443153
981816.61029293269271.38970706730728
991211.62988005041850.370119949581526
1001312.61675081031590.383249189684101
1011515.0557809459990-0.0557809459990443
1021211.56998273884060.430017261159416
1031919.5617535527112-0.56175355271117
1041515.1277722604393-0.127772260439273
1051111.2854732045681-0.285473204568122
1061110.58804101508870.411958984911257
107109.740213931577920.259786068422081
1081312.83025343078880.169746569211176
1091514.83550669007320.164493309926800
1101212.2632081527268-0.263208152726758
1111212.1535711603963-0.153571160396323
1121615.90427469311480.0957253068851773
11398.33095235808610.669047641913901
1141818.1353349731023-0.135334973102288
11587.399058882371690.600941117628312
1161311.63353643981441.36646356018559
1171717.3954286994524-0.395428699452431
11898.838024303513370.161975696486628
1191515.1718137246818-0.171813724681842
12087.848531424181560.151468575818437
12176.442414132978910.557585867021086
1221212.1823892781054-0.182389278105418
1231414.9313746103498-0.931374610349844
12467.92237291987548-1.92237291987548
12588.91839571134878-0.918395711348782
1261717.4994012580397-0.499401258039671
1271010.0239761692677-0.0239761692676851
1281111.5046331936122-0.504633193612202
1291414.1226004162867-0.122600416286716
1301110.59882686691800.401173133082016
1311312.81994774375700.180052256243035
1321211.86316546896180.136834531038244
1331110.48716637020460.512833629795364
13498.835806387511980.16419361248802
1351211.94138244493710.0586175550628829
1362017.58226874599472.41773125400534
1371211.46299754397730.537002456022705
1381314.0332818951934-1.03328189519336
1391211.88336551261170.116634487388332
1401211.64962910270980.350370897290232
14198.424725032197910.575274967802092
1421515.0195032480584-0.0195032480583685
1432424.0354877283835-0.0354877283835392
14478.00755235831579-1.00755235831579
1451716.11236942229060.887630577709371
1461110.85816677824020.141833221759800
1471717.1301679489920-0.130167948992033
1481111.4786431655151-0.478643165515068
1491212.3740446850354-0.374044685035397
1501414.0234733525441-0.0234733525441383
1511110.65787559742550.342124402574511
1521616.3143234741159-0.314323474115905
1532121.5420338421294-0.542033842129365
1541413.32921307871190.67078692128807
1552020.4923907824139-0.492390782413934
1561313.2272551833787-0.22725518337873
1571110.72282305365040.277176946349599
1581515.1535624733354-0.153562473335444
1591918.09555136793990.90444863206013

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 11 & 10.5495807077397 & 0.450419292260260 \tabularnewline
2 & 7 & 9.5317176936113 & -2.5317176936113 \tabularnewline
3 & 17 & 15.7984094400311 & 1.20159055996895 \tabularnewline
4 & 10 & 10.7581413749384 & -0.758141374938443 \tabularnewline
5 & 12 & 13.0359227943400 & -1.03592279434002 \tabularnewline
6 & 12 & 11.3350251022660 & 0.664974897734024 \tabularnewline
7 & 11 & 11.0502446508432 & -0.0502446508432135 \tabularnewline
8 & 11 & 10.7336685617733 & 0.266331438226719 \tabularnewline
9 & 12 & 11.3292349690873 & 0.67076503091273 \tabularnewline
10 & 13 & 12.1404811409361 & 0.859518859063858 \tabularnewline
11 & 14 & 14.0723922113957 & -0.0723922113957462 \tabularnewline
12 & 16 & 16.0907124941696 & -0.0907124941696067 \tabularnewline
13 & 11 & 10.6212252819674 & 0.378774718032598 \tabularnewline
14 & 10 & 9.85407702031575 & 0.145922979684245 \tabularnewline
15 & 11 & 11.9877159723979 & -0.987715972397948 \tabularnewline
16 & 15 & 15.4978078449037 & -0.49780784490372 \tabularnewline
17 & 9 & 11.1442403731213 & -2.14424037312126 \tabularnewline
18 & 11 & 12.0740713104147 & -1.07407131041468 \tabularnewline
19 & 17 & 17.4834239412261 & -0.483423941226097 \tabularnewline
20 & 17 & 17.1319307640624 & -0.131930764062352 \tabularnewline
21 & 11 & 11.9423849133973 & -0.942384913397281 \tabularnewline
22 & 18 & 18.2354633273534 & -0.235463327353353 \tabularnewline
23 & 14 & 13.7996325470485 & 0.200367452951524 \tabularnewline
24 & 10 & 10.1467046042424 & -0.146704604242418 \tabularnewline
25 & 11 & 10.8646588690316 & 0.135341130968438 \tabularnewline
26 & 15 & 15.2188660316108 & -0.218866031610784 \tabularnewline
27 & 15 & 15.3351227077666 & -0.335122707766567 \tabularnewline
28 & 13 & 12.9411251549327 & 0.0588748450673461 \tabularnewline
29 & 16 & 14.9004991415138 & 1.09950085848621 \tabularnewline
30 & 13 & 13.2042023778971 & -0.204202377897087 \tabularnewline
31 & 9 & 8.60867437586244 & 0.391325624137565 \tabularnewline
32 & 18 & 18.0717900238226 & -0.0717900238226058 \tabularnewline
33 & 18 & 15.5126561412416 & 2.48734385875843 \tabularnewline
34 & 12 & 11.7654403623872 & 0.23455963761279 \tabularnewline
35 & 17 & 15.9533674942458 & 1.04663250575424 \tabularnewline
36 & 9 & 9.73994282533566 & -0.739942825335656 \tabularnewline
37 & 9 & 10.0945972746802 & -1.09459727468024 \tabularnewline
38 & 12 & 10.8097141209882 & 1.19028587901178 \tabularnewline
39 & 18 & 17.1978393260192 & 0.80216067398083 \tabularnewline
40 & 12 & 11.8841241476318 & 0.115875852368246 \tabularnewline
41 & 18 & 18.3087161840174 & -0.308716184017430 \tabularnewline
42 & 14 & 13.9034194459864 & 0.096580554013632 \tabularnewline
43 & 15 & 15.3583326939882 & -0.358332693988225 \tabularnewline
44 & 16 & 16.5756783300809 & -0.575678330080885 \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 & -0.177665254490788 \tabularnewline
50 & 17 & 14.9538617416909 & 2.04613825830906 \tabularnewline
51 & 5 & 4.42474845419696 & 0.575251545803043 \tabularnewline
52 & 12 & 11.9495364984274 & 0.0504635015726222 \tabularnewline
53 & 12 & 11.9276229341784 & 0.0723770658216422 \tabularnewline
54 & 6 & 5.68944562251121 & 0.310554377488794 \tabularnewline
55 & 24 & 24.1940683193530 & -0.194068319352962 \tabularnewline
56 & 12 & 11.4978990524380 & 0.502100947561969 \tabularnewline
57 & 12 & 12.1240506408088 & -0.124050640808757 \tabularnewline
58 & 14 & 14.2785506948979 & -0.278550694897936 \tabularnewline
59 & 7 & 6.73107422924293 & 0.268925770757075 \tabularnewline
60 & 13 & 13.3499528417808 & -0.349952841780756 \tabularnewline
61 & 12 & 12.6540727377824 & -0.654072737782425 \tabularnewline
62 & 13 & 13.1259791121347 & -0.125979112134686 \tabularnewline
63 & 14 & 12.6291360771868 & 1.37086392281317 \tabularnewline
64 & 8 & 7.52722769374786 & 0.472772306252141 \tabularnewline
65 & 11 & 10.5856454039719 & 0.414354596028115 \tabularnewline
66 & 9 & 9.39190224187301 & -0.391902241873014 \tabularnewline
67 & 11 & 11.1473204132457 & -0.147320413245734 \tabularnewline
68 & 13 & 13.5383971172275 & -0.538397117227497 \tabularnewline
69 & 10 & 10.1566179309609 & -0.156617930960873 \tabularnewline
70 & 11 & 10.8176387281669 & 0.182361271833136 \tabularnewline
71 & 12 & 11.7815826001991 & 0.218417399800924 \tabularnewline
72 & 9 & 8.6676834721573 & 0.332316527842702 \tabularnewline
73 & 15 & 14.9073206989695 & 0.0926793010304781 \tabularnewline
74 & 18 & 18.3020217735624 & -0.302021773562364 \tabularnewline
75 & 15 & 15.2018369603855 & -0.201836960385467 \tabularnewline
76 & 12 & 12.0420103843867 & -0.0420103843866668 \tabularnewline
77 & 13 & 13.4133002319974 & -0.413300231997438 \tabularnewline
78 & 14 & 13.4485043859649 & 0.551495614035051 \tabularnewline
79 & 10 & 9.7588715135792 & 0.241128486420798 \tabularnewline
80 & 13 & 12.9947321995908 & 0.00526780040917663 \tabularnewline
81 & 13 & 12.9087941909155 & 0.0912058090844854 \tabularnewline
82 & 11 & 10.8809159401145 & 0.119084059885525 \tabularnewline
83 & 13 & 13.0811661890698 & -0.081166189069839 \tabularnewline
84 & 16 & 15.3462798185433 & 0.653720181456747 \tabularnewline
85 & 8 & 7.72347819099464 & 0.276521809005361 \tabularnewline
86 & 16 & 16.4371206292828 & -0.43712062928278 \tabularnewline
87 & 11 & 10.9600579414247 & 0.0399420585752663 \tabularnewline
88 & 9 & 10.0995542833964 & -1.09955428339636 \tabularnewline
89 & 16 & 16.8190670212398 & -0.819067021239801 \tabularnewline
90 & 12 & 12.0471427886204 & -0.0471427886204004 \tabularnewline
91 & 14 & 14.1367790178915 & -0.136779017891527 \tabularnewline
92 & 8 & 7.73471143162069 & 0.265288568379308 \tabularnewline
93 & 9 & 9.0215986035023 & -0.0215986035023019 \tabularnewline
94 & 15 & 15.3425422885411 & -0.342542288541094 \tabularnewline
95 & 11 & 10.6843934288566 & 0.315606571143370 \tabularnewline
96 & 21 & 21.3379608954175 & -0.337960895417492 \tabularnewline
97 & 14 & 13.9838942887557 & 0.0161057112443153 \tabularnewline
98 & 18 & 16.6102929326927 & 1.38970706730728 \tabularnewline
99 & 12 & 11.6298800504185 & 0.370119949581526 \tabularnewline
100 & 13 & 12.6167508103159 & 0.383249189684101 \tabularnewline
101 & 15 & 15.0557809459990 & -0.0557809459990443 \tabularnewline
102 & 12 & 11.5699827388406 & 0.430017261159416 \tabularnewline
103 & 19 & 19.5617535527112 & -0.56175355271117 \tabularnewline
104 & 15 & 15.1277722604393 & -0.127772260439273 \tabularnewline
105 & 11 & 11.2854732045681 & -0.285473204568122 \tabularnewline
106 & 11 & 10.5880410150887 & 0.411958984911257 \tabularnewline
107 & 10 & 9.74021393157792 & 0.259786068422081 \tabularnewline
108 & 13 & 12.8302534307888 & 0.169746569211176 \tabularnewline
109 & 15 & 14.8355066900732 & 0.164493309926800 \tabularnewline
110 & 12 & 12.2632081527268 & -0.263208152726758 \tabularnewline
111 & 12 & 12.1535711603963 & -0.153571160396323 \tabularnewline
112 & 16 & 15.9042746931148 & 0.0957253068851773 \tabularnewline
113 & 9 & 8.3309523580861 & 0.669047641913901 \tabularnewline
114 & 18 & 18.1353349731023 & -0.135334973102288 \tabularnewline
115 & 8 & 7.39905888237169 & 0.600941117628312 \tabularnewline
116 & 13 & 11.6335364398144 & 1.36646356018559 \tabularnewline
117 & 17 & 17.3954286994524 & -0.395428699452431 \tabularnewline
118 & 9 & 8.83802430351337 & 0.161975696486628 \tabularnewline
119 & 15 & 15.1718137246818 & -0.171813724681842 \tabularnewline
120 & 8 & 7.84853142418156 & 0.151468575818437 \tabularnewline
121 & 7 & 6.44241413297891 & 0.557585867021086 \tabularnewline
122 & 12 & 12.1823892781054 & -0.182389278105418 \tabularnewline
123 & 14 & 14.9313746103498 & -0.931374610349844 \tabularnewline
124 & 6 & 7.92237291987548 & -1.92237291987548 \tabularnewline
125 & 8 & 8.91839571134878 & -0.918395711348782 \tabularnewline
126 & 17 & 17.4994012580397 & -0.499401258039671 \tabularnewline
127 & 10 & 10.0239761692677 & -0.0239761692676851 \tabularnewline
128 & 11 & 11.5046331936122 & -0.504633193612202 \tabularnewline
129 & 14 & 14.1226004162867 & -0.122600416286716 \tabularnewline
130 & 11 & 10.5988268669180 & 0.401173133082016 \tabularnewline
131 & 13 & 12.8199477437570 & 0.180052256243035 \tabularnewline
132 & 12 & 11.8631654689618 & 0.136834531038244 \tabularnewline
133 & 11 & 10.4871663702046 & 0.512833629795364 \tabularnewline
134 & 9 & 8.83580638751198 & 0.16419361248802 \tabularnewline
135 & 12 & 11.9413824449371 & 0.0586175550628829 \tabularnewline
136 & 20 & 17.5822687459947 & 2.41773125400534 \tabularnewline
137 & 12 & 11.4629975439773 & 0.537002456022705 \tabularnewline
138 & 13 & 14.0332818951934 & -1.03328189519336 \tabularnewline
139 & 12 & 11.8833655126117 & 0.116634487388332 \tabularnewline
140 & 12 & 11.6496291027098 & 0.350370897290232 \tabularnewline
141 & 9 & 8.42472503219791 & 0.575274967802092 \tabularnewline
142 & 15 & 15.0195032480584 & -0.0195032480583685 \tabularnewline
143 & 24 & 24.0354877283835 & -0.0354877283835392 \tabularnewline
144 & 7 & 8.00755235831579 & -1.00755235831579 \tabularnewline
145 & 17 & 16.1123694222906 & 0.887630577709371 \tabularnewline
146 & 11 & 10.8581667782402 & 0.141833221759800 \tabularnewline
147 & 17 & 17.1301679489920 & -0.130167948992033 \tabularnewline
148 & 11 & 11.4786431655151 & -0.478643165515068 \tabularnewline
149 & 12 & 12.3740446850354 & -0.374044685035397 \tabularnewline
150 & 14 & 14.0234733525441 & -0.0234733525441383 \tabularnewline
151 & 11 & 10.6578755974255 & 0.342124402574511 \tabularnewline
152 & 16 & 16.3143234741159 & -0.314323474115905 \tabularnewline
153 & 21 & 21.5420338421294 & -0.542033842129365 \tabularnewline
154 & 14 & 13.3292130787119 & 0.67078692128807 \tabularnewline
155 & 20 & 20.4923907824139 & -0.492390782413934 \tabularnewline
156 & 13 & 13.2272551833787 & -0.22725518337873 \tabularnewline
157 & 11 & 10.7228230536504 & 0.277176946349599 \tabularnewline
158 & 15 & 15.1535624733354 & -0.153562473335444 \tabularnewline
159 & 19 & 18.0955513679399 & 0.90444863206013 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104048&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]0.450419292260260[/C][/ROW]
[ROW][C]2[/C][C]7[/C][C]9.5317176936113[/C][C]-2.5317176936113[/C][/ROW]
[ROW][C]3[/C][C]17[/C][C]15.7984094400311[/C][C]1.20159055996895[/C][/ROW]
[ROW][C]4[/C][C]10[/C][C]10.7581413749384[/C][C]-0.758141374938443[/C][/ROW]
[ROW][C]5[/C][C]12[/C][C]13.0359227943400[/C][C]-1.03592279434002[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]11.3350251022660[/C][C]0.664974897734024[/C][/ROW]
[ROW][C]7[/C][C]11[/C][C]11.0502446508432[/C][C]-0.0502446508432135[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]10.7336685617733[/C][C]0.266331438226719[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]11.3292349690873[/C][C]0.67076503091273[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.1404811409361[/C][C]0.859518859063858[/C][/ROW]
[ROW][C]11[/C][C]14[/C][C]14.0723922113957[/C][C]-0.0723922113957462[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.0907124941696[/C][C]-0.0907124941696067[/C][/ROW]
[ROW][C]13[/C][C]11[/C][C]10.6212252819674[/C][C]0.378774718032598[/C][/ROW]
[ROW][C]14[/C][C]10[/C][C]9.85407702031575[/C][C]0.145922979684245[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.9877159723979[/C][C]-0.987715972397948[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.4978078449037[/C][C]-0.49780784490372[/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]-0.483423941226097[/C][/ROW]
[ROW][C]20[/C][C]17[/C][C]17.1319307640624[/C][C]-0.131930764062352[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]11.9423849133973[/C][C]-0.942384913397281[/C][/ROW]
[ROW][C]22[/C][C]18[/C][C]18.2354633273534[/C][C]-0.235463327353353[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]13.7996325470485[/C][C]0.200367452951524[/C][/ROW]
[ROW][C]24[/C][C]10[/C][C]10.1467046042424[/C][C]-0.146704604242418[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]10.8646588690316[/C][C]0.135341130968438[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]15.2188660316108[/C][C]-0.218866031610784[/C][/ROW]
[ROW][C]27[/C][C]15[/C][C]15.3351227077666[/C][C]-0.335122707766567[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]12.9411251549327[/C][C]0.0588748450673461[/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]-0.204202377897087[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]8.60867437586244[/C][C]0.391325624137565[/C][/ROW]
[ROW][C]32[/C][C]18[/C][C]18.0717900238226[/C][C]-0.0717900238226058[/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]0.23455963761279[/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]-1.09459727468024[/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]0.80216067398083[/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]-0.308716184017430[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]13.9034194459864[/C][C]0.096580554013632[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]15.3583326939882[/C][C]-0.358332693988225[/C][/ROW]
[ROW][C]44[/C][C]16[/C][C]16.5756783300809[/C][C]-0.575678330080885[/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]-0.177665254490788[/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]0.575251545803043[/C][/ROW]
[ROW][C]52[/C][C]12[/C][C]11.9495364984274[/C][C]0.0504635015726222[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]11.9276229341784[/C][C]0.0723770658216422[/C][/ROW]
[ROW][C]54[/C][C]6[/C][C]5.68944562251121[/C][C]0.310554377488794[/C][/ROW]
[ROW][C]55[/C][C]24[/C][C]24.1940683193530[/C][C]-0.194068319352962[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]11.4978990524380[/C][C]0.502100947561969[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]12.1240506408088[/C][C]-0.124050640808757[/C][/ROW]
[ROW][C]58[/C][C]14[/C][C]14.2785506948979[/C][C]-0.278550694897936[/C][/ROW]
[ROW][C]59[/C][C]7[/C][C]6.73107422924293[/C][C]0.268925770757075[/C][/ROW]
[ROW][C]60[/C][C]13[/C][C]13.3499528417808[/C][C]-0.349952841780756[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]12.6540727377824[/C][C]-0.654072737782425[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]13.1259791121347[/C][C]-0.125979112134686[/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]0.472772306252141[/C][/ROW]
[ROW][C]65[/C][C]11[/C][C]10.5856454039719[/C][C]0.414354596028115[/C][/ROW]
[ROW][C]66[/C][C]9[/C][C]9.39190224187301[/C][C]-0.391902241873014[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]11.1473204132457[/C][C]-0.147320413245734[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]13.5383971172275[/C][C]-0.538397117227497[/C][/ROW]
[ROW][C]69[/C][C]10[/C][C]10.1566179309609[/C][C]-0.156617930960873[/C][/ROW]
[ROW][C]70[/C][C]11[/C][C]10.8176387281669[/C][C]0.182361271833136[/C][/ROW]
[ROW][C]71[/C][C]12[/C][C]11.7815826001991[/C][C]0.218417399800924[/C][/ROW]
[ROW][C]72[/C][C]9[/C][C]8.6676834721573[/C][C]0.332316527842702[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]14.9073206989695[/C][C]0.0926793010304781[/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]-0.201836960385467[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]12.0420103843867[/C][C]-0.0420103843866668[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]13.4133002319974[/C][C]-0.413300231997438[/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]0.241128486420798[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]12.9947321995908[/C][C]0.00526780040917663[/C][/ROW]
[ROW][C]81[/C][C]13[/C][C]12.9087941909155[/C][C]0.0912058090844854[/C][/ROW]
[ROW][C]82[/C][C]11[/C][C]10.8809159401145[/C][C]0.119084059885525[/C][/ROW]
[ROW][C]83[/C][C]13[/C][C]13.0811661890698[/C][C]-0.081166189069839[/C][/ROW]
[ROW][C]84[/C][C]16[/C][C]15.3462798185433[/C][C]0.653720181456747[/C][/ROW]
[ROW][C]85[/C][C]8[/C][C]7.72347819099464[/C][C]0.276521809005361[/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]0.0399420585752663[/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]-0.819067021239801[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]12.0471427886204[/C][C]-0.0471427886204004[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]14.1367790178915[/C][C]-0.136779017891527[/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]-0.0215986035023019[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.3425422885411[/C][C]-0.342542288541094[/C][/ROW]
[ROW][C]95[/C][C]11[/C][C]10.6843934288566[/C][C]0.315606571143370[/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]0.0161057112443153[/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]0.370119949581526[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]12.6167508103159[/C][C]0.383249189684101[/C][/ROW]
[ROW][C]101[/C][C]15[/C][C]15.0557809459990[/C][C]-0.0557809459990443[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]11.5699827388406[/C][C]0.430017261159416[/C][/ROW]
[ROW][C]103[/C][C]19[/C][C]19.5617535527112[/C][C]-0.56175355271117[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]15.1277722604393[/C][C]-0.127772260439273[/C][/ROW]
[ROW][C]105[/C][C]11[/C][C]11.2854732045681[/C][C]-0.285473204568122[/C][/ROW]
[ROW][C]106[/C][C]11[/C][C]10.5880410150887[/C][C]0.411958984911257[/C][/ROW]
[ROW][C]107[/C][C]10[/C][C]9.74021393157792[/C][C]0.259786068422081[/C][/ROW]
[ROW][C]108[/C][C]13[/C][C]12.8302534307888[/C][C]0.169746569211176[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]14.8355066900732[/C][C]0.164493309926800[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.2632081527268[/C][C]-0.263208152726758[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]12.1535711603963[/C][C]-0.153571160396323[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.9042746931148[/C][C]0.0957253068851773[/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]-0.135334973102288[/C][/ROW]
[ROW][C]115[/C][C]8[/C][C]7.39905888237169[/C][C]0.600941117628312[/C][/ROW]
[ROW][C]116[/C][C]13[/C][C]11.6335364398144[/C][C]1.36646356018559[/C][/ROW]
[ROW][C]117[/C][C]17[/C][C]17.3954286994524[/C][C]-0.395428699452431[/C][/ROW]
[ROW][C]118[/C][C]9[/C][C]8.83802430351337[/C][C]0.161975696486628[/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]6.44241413297891[/C][C]0.557585867021086[/C][/ROW]
[ROW][C]122[/C][C]12[/C][C]12.1823892781054[/C][C]-0.182389278105418[/C][/ROW]
[ROW][C]123[/C][C]14[/C][C]14.9313746103498[/C][C]-0.931374610349844[/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]-0.918395711348782[/C][/ROW]
[ROW][C]126[/C][C]17[/C][C]17.4994012580397[/C][C]-0.499401258039671[/C][/ROW]
[ROW][C]127[/C][C]10[/C][C]10.0239761692677[/C][C]-0.0239761692676851[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]11.5046331936122[/C][C]-0.504633193612202[/C][/ROW]
[ROW][C]129[/C][C]14[/C][C]14.1226004162867[/C][C]-0.122600416286716[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]10.5988268669180[/C][C]0.401173133082016[/C][/ROW]
[ROW][C]131[/C][C]13[/C][C]12.8199477437570[/C][C]0.180052256243035[/C][/ROW]
[ROW][C]132[/C][C]12[/C][C]11.8631654689618[/C][C]0.136834531038244[/C][/ROW]
[ROW][C]133[/C][C]11[/C][C]10.4871663702046[/C][C]0.512833629795364[/C][/ROW]
[ROW][C]134[/C][C]9[/C][C]8.83580638751198[/C][C]0.16419361248802[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]11.9413824449371[/C][C]0.0586175550628829[/C][/ROW]
[ROW][C]136[/C][C]20[/C][C]17.5822687459947[/C][C]2.41773125400534[/C][/ROW]
[ROW][C]137[/C][C]12[/C][C]11.4629975439773[/C][C]0.537002456022705[/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]0.116634487388332[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]11.6496291027098[/C][C]0.350370897290232[/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]-0.0195032480583685[/C][/ROW]
[ROW][C]143[/C][C]24[/C][C]24.0354877283835[/C][C]-0.0354877283835392[/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]0.887630577709371[/C][/ROW]
[ROW][C]146[/C][C]11[/C][C]10.8581667782402[/C][C]0.141833221759800[/C][/ROW]
[ROW][C]147[/C][C]17[/C][C]17.1301679489920[/C][C]-0.130167948992033[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]11.4786431655151[/C][C]-0.478643165515068[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]12.3740446850354[/C][C]-0.374044685035397[/C][/ROW]
[ROW][C]150[/C][C]14[/C][C]14.0234733525441[/C][C]-0.0234733525441383[/C][/ROW]
[ROW][C]151[/C][C]11[/C][C]10.6578755974255[/C][C]0.342124402574511[/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]-0.542033842129365[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]13.3292130787119[/C][C]0.67078692128807[/C][/ROW]
[ROW][C]155[/C][C]20[/C][C]20.4923907824139[/C][C]-0.492390782413934[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]13.2272551833787[/C][C]-0.22725518337873[/C][/ROW]
[ROW][C]157[/C][C]11[/C][C]10.7228230536504[/C][C]0.277176946349599[/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]0.90444863206013[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104048&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104048&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
11110.54958070773970.450419292260260
279.5317176936113-2.5317176936113
31715.79840944003111.20159055996895
41010.7581413749384-0.758141374938443
51213.0359227943400-1.03592279434002
61211.33502510226600.664974897734024
71111.0502446508432-0.0502446508432135
81110.73366856177330.266331438226719
91211.32923496908730.67076503091273
101312.14048114093610.859518859063858
111414.0723922113957-0.0723922113957462
121616.0907124941696-0.0907124941696067
131110.62122528196740.378774718032598
14109.854077020315750.145922979684245
151111.9877159723979-0.987715972397948
161515.4978078449037-0.49780784490372
17911.1442403731213-2.14424037312126
181112.0740713104147-1.07407131041468
191717.4834239412261-0.483423941226097
201717.1319307640624-0.131930764062352
211111.9423849133973-0.942384913397281
221818.2354633273534-0.235463327353353
231413.79963254704850.200367452951524
241010.1467046042424-0.146704604242418
251110.86465886903160.135341130968438
261515.2188660316108-0.218866031610784
271515.3351227077666-0.335122707766567
281312.94112515493270.0588748450673461
291614.90049914151381.09950085848621
301313.2042023778971-0.204202377897087
3198.608674375862440.391325624137565
321818.0717900238226-0.0717900238226058
331815.51265614124162.48734385875843
341211.76544036238720.23455963761279
351715.95336749424581.04663250575424
3699.73994282533566-0.739942825335656
37910.0945972746802-1.09459727468024
381210.80971412098821.19028587901178
391817.19783932601920.80216067398083
401211.88412414763180.115875852368246
411818.3087161840174-0.308716184017430
421413.90341944598640.096580554013632
431515.3583326939882-0.358332693988225
441616.5756783300809-0.575678330080885
451011.5026866827912-1.50268668279115
461111.3236366995712-0.323636699571206
471414.1334254325684-0.133425432568411
48910.2815454848804-1.28154548488042
491212.1776652544908-0.177665254490788
501714.95386174169092.04613825830906
5154.424748454196960.575251545803043
521211.94953649842740.0504635015726222
531211.92762293417840.0723770658216422
5465.689445622511210.310554377488794
552424.1940683193530-0.194068319352962
561211.49789905243800.502100947561969
571212.1240506408088-0.124050640808757
581414.2785506948979-0.278550694897936
5976.731074229242930.268925770757075
601313.3499528417808-0.349952841780756
611212.6540727377824-0.654072737782425
621313.1259791121347-0.125979112134686
631412.62913607718681.37086392281317
6487.527227693747860.472772306252141
651110.58564540397190.414354596028115
6699.39190224187301-0.391902241873014
671111.1473204132457-0.147320413245734
681313.5383971172275-0.538397117227497
691010.1566179309609-0.156617930960873
701110.81763872816690.182361271833136
711211.78158260019910.218417399800924
7298.66768347215730.332316527842702
731514.90732069896950.0926793010304781
741818.3020217735624-0.302021773562364
751515.2018369603855-0.201836960385467
761212.0420103843867-0.0420103843866668
771313.4133002319974-0.413300231997438
781413.44850438596490.551495614035051
79109.75887151357920.241128486420798
801312.99473219959080.00526780040917663
811312.90879419091550.0912058090844854
821110.88091594011450.119084059885525
831313.0811661890698-0.081166189069839
841615.34627981854330.653720181456747
8587.723478190994640.276521809005361
861616.4371206292828-0.43712062928278
871110.96005794142470.0399420585752663
88910.0995542833964-1.09955428339636
891616.8190670212398-0.819067021239801
901212.0471427886204-0.0471427886204004
911414.1367790178915-0.136779017891527
9287.734711431620690.265288568379308
9399.0215986035023-0.0215986035023019
941515.3425422885411-0.342542288541094
951110.68439342885660.315606571143370
962121.3379608954175-0.337960895417492
971413.98389428875570.0161057112443153
981816.61029293269271.38970706730728
991211.62988005041850.370119949581526
1001312.61675081031590.383249189684101
1011515.0557809459990-0.0557809459990443
1021211.56998273884060.430017261159416
1031919.5617535527112-0.56175355271117
1041515.1277722604393-0.127772260439273
1051111.2854732045681-0.285473204568122
1061110.58804101508870.411958984911257
107109.740213931577920.259786068422081
1081312.83025343078880.169746569211176
1091514.83550669007320.164493309926800
1101212.2632081527268-0.263208152726758
1111212.1535711603963-0.153571160396323
1121615.90427469311480.0957253068851773
11398.33095235808610.669047641913901
1141818.1353349731023-0.135334973102288
11587.399058882371690.600941117628312
1161311.63353643981441.36646356018559
1171717.3954286994524-0.395428699452431
11898.838024303513370.161975696486628
1191515.1718137246818-0.171813724681842
12087.848531424181560.151468575818437
12176.442414132978910.557585867021086
1221212.1823892781054-0.182389278105418
1231414.9313746103498-0.931374610349844
12467.92237291987548-1.92237291987548
12588.91839571134878-0.918395711348782
1261717.4994012580397-0.499401258039671
1271010.0239761692677-0.0239761692676851
1281111.5046331936122-0.504633193612202
1291414.1226004162867-0.122600416286716
1301110.59882686691800.401173133082016
1311312.81994774375700.180052256243035
1321211.86316546896180.136834531038244
1331110.48716637020460.512833629795364
13498.835806387511980.16419361248802
1351211.94138244493710.0586175550628829
1362017.58226874599472.41773125400534
1371211.46299754397730.537002456022705
1381314.0332818951934-1.03328189519336
1391211.88336551261170.116634487388332
1401211.64962910270980.350370897290232
14198.424725032197910.575274967802092
1421515.0195032480584-0.0195032480583685
1432424.0354877283835-0.0354877283835392
14478.00755235831579-1.00755235831579
1451716.11236942229060.887630577709371
1461110.85816677824020.141833221759800
1471717.1301679489920-0.130167948992033
1481111.4786431655151-0.478643165515068
1491212.3740446850354-0.374044685035397
1501414.0234733525441-0.0234733525441383
1511110.65787559742550.342124402574511
1521616.3143234741159-0.314323474115905
1532121.5420338421294-0.542033842129365
1541413.32921307871190.67078692128807
1552020.4923907824139-0.492390782413934
1561313.2272551833787-0.22725518337873
1571110.72282305365040.277176946349599
1581515.1535624733354-0.153562473335444
1591918.09555136793990.90444863206013







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.7899628453100630.4200743093798750.210037154689937
170.7087775791094770.5824448417810450.291222420890523
180.6181369523007010.7637260953985980.381863047699299
190.9247172436571550.1505655126856900.0752827563428449
200.8792575959519040.2414848080961930.120742404048096
210.890213577334210.2195728453315800.109786422665790
220.9041426752556250.191714649488750.095857324744375
230.8596296797959540.2807406404080930.140370320204046
240.8894692663229350.221061467354130.110530733677065
250.888297739688360.2234045206232810.111702260311641
260.866669190769680.2666616184606400.133330809230320
270.8208806304502030.3582387390995950.179119369549797
280.7660818031673150.4678363936653690.233918196832685
290.8716475785843880.2567048428312240.128352421415612
300.8333240087447150.333351982510570.166675991255285
310.8885977515739130.2228044968521740.111402248426087
320.8706720438203130.2586559123593740.129327956179687
330.9936267797588230.01274644048235480.0063732202411774
340.9962973727920850.00740525441583080.0037026272079154
350.9980370102571050.003925979485789510.00196298974289476
360.9989773846692560.002045230661488970.00102261533074448
370.999225365576490.001549268847020890.000774634423510447
380.9992644958578070.001471008284385590.000735504142192795
390.9996759156571680.0006481686856637580.000324084342831879
400.9995348674551170.0009302650897668780.000465132544883439
410.999462582779840.001074834440319030.000537417220159516
420.9991468690312770.001706261937445680.00085313096872284
430.9988853848776450.002229230244709300.00111461512235465
440.9986899272577590.002620145484483090.00131007274224155
450.9997050814996050.0005898370007904240.000294918500395212
460.9995701910633370.0008596178733256620.000429808936662831
470.9993246140419030.001350771916193310.000675385958096657
480.9998023267318750.0003953465362509290.000197673268125465
490.9997518022793120.0004963954413759930.000248197720687996
500.9999875893661972.4821267605259e-051.24106338026295e-05
510.999990801267171.83974656618052e-059.19873283090262e-06
520.9999840744700753.18510598492757e-051.59255299246378e-05
530.999972345235585.53095288409733e-052.76547644204867e-05
540.9999646837233437.0632553314277e-053.53162766571385e-05
550.9999405669304820.0001188661390370815.94330695185407e-05
560.9999220494548150.0001559010903704847.79505451852422e-05
570.9998741989800850.0002516020398302350.000125801019915117
580.9998069354319010.0003861291361977520.000193064568098876
590.9997221550829530.0005556898340940680.000277844917047034
600.99962557155090.0007488568981979790.000374428449098989
610.999587481647390.0008250367052205290.000412518352610264
620.9993642540454530.001271491909093150.000635745954546577
630.999815054204490.0003698915910185480.000184945795509274
640.9997713768598540.0004572462802911660.000228623140145583
650.9997624191173520.000475161765296940.00023758088264847
660.9997285336635330.0005429326729345040.000271466336467252
670.999870235051290.0002595298974180450.000129764948709022
680.9998417333572760.0003165332854472560.000158266642723628
690.9998849570733820.0002300858532366310.000115042926618315
700.9998205899887330.0003588200225335930.000179410011266797
710.9997289630125940.0005420739748118230.000271036987405911
720.9996163461068140.0007673077863717210.000383653893185860
730.9994062349152860.001187530169427840.000593765084713918
740.9991728640466380.001654271906723360.00082713595336168
750.9987850065943130.002429986811374250.00121499340568713
760.9985270942339880.002945811532023590.00147290576601179
770.9980744488066110.003851102386777730.00192555119338886
780.9979014380308190.004197123938362440.00209856196918122
790.9969950052840420.006009989431915950.00300499471595797
800.9956409080857970.008718183828406390.00435909191420319
810.9937806702191020.01243865956179580.00621932978089792
820.9920070884440880.01598582311182380.00799291155591189
830.9888757772253080.02224844554938500.0111242227746925
840.9887446834923780.02251063301524350.0112553165076217
850.9848352160180260.03032956796394780.0151647839819739
860.9814339401875610.03713211962487760.0185660598124388
870.9749862494898130.05002750102037480.0250137505101874
880.978588998488310.04282200302338120.0214110015116906
890.9885135136205030.02297297275899400.0114864863794970
900.9840990886853080.03180182262938310.0159009113146916
910.978660077284350.04267984543129930.0213399227156497
920.9721576114129780.05568477717404440.0278423885870222
930.9639193064635070.07216138707298620.0360806935364931
940.9541914714937660.09161705701246880.0458085285062344
950.9423135918176230.1153728163647550.0576864081823774
960.928154027998230.1436919440035390.0718459720017693
970.9086516811692880.1826966376614240.091348318830712
980.936380272954890.1272394540902200.0636197270451098
990.9216145698430080.1567708603139850.0783854301569925
1000.9034383832435670.1931232335128650.0965616167564326
1010.8787995120410830.2424009759178350.121200487958917
1020.8654604820018060.2690790359963880.134539517998194
1030.8462128127458940.3075743745082110.153787187254106
1040.8127071853433390.3745856293133230.187292814656661
1050.7860633299136470.4278733401727070.213936670086353
1060.7465907528148140.5068184943703730.253409247185186
1070.7074576384322180.5850847231355640.292542361567782
1080.6622635397469540.6754729205060930.337736460253046
1090.6134203929030740.7731592141938520.386579607096926
1100.5644486830284460.8711026339431080.435551316971554
1110.5107945763827370.9784108472345250.489205423617263
1120.4568452608431230.9136905216862450.543154739156877
1130.440743114243530.881486228487060.55925688575647
1140.3915859260709250.783171852141850.608414073929075
1150.3679316320662020.7358632641324040.632068367933798
1160.5604105801212110.8791788397575780.439589419878789
1170.533362251403750.93327549719250.46663774859625
1180.4747681101537090.9495362203074190.525231889846291
1190.4153185301403550.8306370602807110.584681469859645
1200.3659621976185860.7319243952371720.634037802381414
1210.3258788284924060.6517576569848120.674121171507594
1220.2981127250729290.5962254501458590.701887274927071
1230.3050381353234410.6100762706468820.69496186467656
1240.6100258340400730.7799483319198530.389974165959927
1250.5841931299750030.8316137400499930.415806870024997
1260.5387894557996550.922421088400690.461210544200345
1270.4698291469661260.9396582939322530.530170853033874
1280.4935460108850450.987092021770090.506453989114955
1290.4217281711577860.8434563423155710.578271828842214
1300.3836725779686080.7673451559372150.616327422031392
1310.3140078404963550.628015680992710.685992159503645
1320.2490583913491760.4981167826983520.750941608650824
1330.1978786618145290.3957573236290580.802121338185471
1340.1500273175322340.3000546350644680.849972682467766
1350.1125492836864490.2250985673728980.88745071631355
1360.6352727336115460.7294545327769090.364727266388454
1370.7484534021088470.5030931957823060.251546597891153
1380.7235065790942210.5529868418115580.276493420905779
1390.6289477224504980.7421045550990030.371052277549501
1400.5147411623557790.9705176752884430.485258837644221
1410.39388252694710.78776505389420.6061174730529
1420.3401855165921250.680371033184250.659814483407875
1430.9950880593138450.00982388137231070.00491194068615535

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.789962845310063 & 0.420074309379875 & 0.210037154689937 \tabularnewline
17 & 0.708777579109477 & 0.582444841781045 & 0.291222420890523 \tabularnewline
18 & 0.618136952300701 & 0.763726095398598 & 0.381863047699299 \tabularnewline
19 & 0.924717243657155 & 0.150565512685690 & 0.0752827563428449 \tabularnewline
20 & 0.879257595951904 & 0.241484808096193 & 0.120742404048096 \tabularnewline
21 & 0.89021357733421 & 0.219572845331580 & 0.109786422665790 \tabularnewline
22 & 0.904142675255625 & 0.19171464948875 & 0.095857324744375 \tabularnewline
23 & 0.859629679795954 & 0.280740640408093 & 0.140370320204046 \tabularnewline
24 & 0.889469266322935 & 0.22106146735413 & 0.110530733677065 \tabularnewline
25 & 0.88829773968836 & 0.223404520623281 & 0.111702260311641 \tabularnewline
26 & 0.86666919076968 & 0.266661618460640 & 0.133330809230320 \tabularnewline
27 & 0.820880630450203 & 0.358238739099595 & 0.179119369549797 \tabularnewline
28 & 0.766081803167315 & 0.467836393665369 & 0.233918196832685 \tabularnewline
29 & 0.871647578584388 & 0.256704842831224 & 0.128352421415612 \tabularnewline
30 & 0.833324008744715 & 0.33335198251057 & 0.166675991255285 \tabularnewline
31 & 0.888597751573913 & 0.222804496852174 & 0.111402248426087 \tabularnewline
32 & 0.870672043820313 & 0.258655912359374 & 0.129327956179687 \tabularnewline
33 & 0.993626779758823 & 0.0127464404823548 & 0.0063732202411774 \tabularnewline
34 & 0.996297372792085 & 0.0074052544158308 & 0.0037026272079154 \tabularnewline
35 & 0.998037010257105 & 0.00392597948578951 & 0.00196298974289476 \tabularnewline
36 & 0.998977384669256 & 0.00204523066148897 & 0.00102261533074448 \tabularnewline
37 & 0.99922536557649 & 0.00154926884702089 & 0.000774634423510447 \tabularnewline
38 & 0.999264495857807 & 0.00147100828438559 & 0.000735504142192795 \tabularnewline
39 & 0.999675915657168 & 0.000648168685663758 & 0.000324084342831879 \tabularnewline
40 & 0.999534867455117 & 0.000930265089766878 & 0.000465132544883439 \tabularnewline
41 & 0.99946258277984 & 0.00107483444031903 & 0.000537417220159516 \tabularnewline
42 & 0.999146869031277 & 0.00170626193744568 & 0.00085313096872284 \tabularnewline
43 & 0.998885384877645 & 0.00222923024470930 & 0.00111461512235465 \tabularnewline
44 & 0.998689927257759 & 0.00262014548448309 & 0.00131007274224155 \tabularnewline
45 & 0.999705081499605 & 0.000589837000790424 & 0.000294918500395212 \tabularnewline
46 & 0.999570191063337 & 0.000859617873325662 & 0.000429808936662831 \tabularnewline
47 & 0.999324614041903 & 0.00135077191619331 & 0.000675385958096657 \tabularnewline
48 & 0.999802326731875 & 0.000395346536250929 & 0.000197673268125465 \tabularnewline
49 & 0.999751802279312 & 0.000496395441375993 & 0.000248197720687996 \tabularnewline
50 & 0.999987589366197 & 2.4821267605259e-05 & 1.24106338026295e-05 \tabularnewline
51 & 0.99999080126717 & 1.83974656618052e-05 & 9.19873283090262e-06 \tabularnewline
52 & 0.999984074470075 & 3.18510598492757e-05 & 1.59255299246378e-05 \tabularnewline
53 & 0.99997234523558 & 5.53095288409733e-05 & 2.76547644204867e-05 \tabularnewline
54 & 0.999964683723343 & 7.0632553314277e-05 & 3.53162766571385e-05 \tabularnewline
55 & 0.999940566930482 & 0.000118866139037081 & 5.94330695185407e-05 \tabularnewline
56 & 0.999922049454815 & 0.000155901090370484 & 7.79505451852422e-05 \tabularnewline
57 & 0.999874198980085 & 0.000251602039830235 & 0.000125801019915117 \tabularnewline
58 & 0.999806935431901 & 0.000386129136197752 & 0.000193064568098876 \tabularnewline
59 & 0.999722155082953 & 0.000555689834094068 & 0.000277844917047034 \tabularnewline
60 & 0.9996255715509 & 0.000748856898197979 & 0.000374428449098989 \tabularnewline
61 & 0.99958748164739 & 0.000825036705220529 & 0.000412518352610264 \tabularnewline
62 & 0.999364254045453 & 0.00127149190909315 & 0.000635745954546577 \tabularnewline
63 & 0.99981505420449 & 0.000369891591018548 & 0.000184945795509274 \tabularnewline
64 & 0.999771376859854 & 0.000457246280291166 & 0.000228623140145583 \tabularnewline
65 & 0.999762419117352 & 0.00047516176529694 & 0.00023758088264847 \tabularnewline
66 & 0.999728533663533 & 0.000542932672934504 & 0.000271466336467252 \tabularnewline
67 & 0.99987023505129 & 0.000259529897418045 & 0.000129764948709022 \tabularnewline
68 & 0.999841733357276 & 0.000316533285447256 & 0.000158266642723628 \tabularnewline
69 & 0.999884957073382 & 0.000230085853236631 & 0.000115042926618315 \tabularnewline
70 & 0.999820589988733 & 0.000358820022533593 & 0.000179410011266797 \tabularnewline
71 & 0.999728963012594 & 0.000542073974811823 & 0.000271036987405911 \tabularnewline
72 & 0.999616346106814 & 0.000767307786371721 & 0.000383653893185860 \tabularnewline
73 & 0.999406234915286 & 0.00118753016942784 & 0.000593765084713918 \tabularnewline
74 & 0.999172864046638 & 0.00165427190672336 & 0.00082713595336168 \tabularnewline
75 & 0.998785006594313 & 0.00242998681137425 & 0.00121499340568713 \tabularnewline
76 & 0.998527094233988 & 0.00294581153202359 & 0.00147290576601179 \tabularnewline
77 & 0.998074448806611 & 0.00385110238677773 & 0.00192555119338886 \tabularnewline
78 & 0.997901438030819 & 0.00419712393836244 & 0.00209856196918122 \tabularnewline
79 & 0.996995005284042 & 0.00600998943191595 & 0.00300499471595797 \tabularnewline
80 & 0.995640908085797 & 0.00871818382840639 & 0.00435909191420319 \tabularnewline
81 & 0.993780670219102 & 0.0124386595617958 & 0.00621932978089792 \tabularnewline
82 & 0.992007088444088 & 0.0159858231118238 & 0.00799291155591189 \tabularnewline
83 & 0.988875777225308 & 0.0222484455493850 & 0.0111242227746925 \tabularnewline
84 & 0.988744683492378 & 0.0225106330152435 & 0.0112553165076217 \tabularnewline
85 & 0.984835216018026 & 0.0303295679639478 & 0.0151647839819739 \tabularnewline
86 & 0.981433940187561 & 0.0371321196248776 & 0.0185660598124388 \tabularnewline
87 & 0.974986249489813 & 0.0500275010203748 & 0.0250137505101874 \tabularnewline
88 & 0.97858899848831 & 0.0428220030233812 & 0.0214110015116906 \tabularnewline
89 & 0.988513513620503 & 0.0229729727589940 & 0.0114864863794970 \tabularnewline
90 & 0.984099088685308 & 0.0318018226293831 & 0.0159009113146916 \tabularnewline
91 & 0.97866007728435 & 0.0426798454312993 & 0.0213399227156497 \tabularnewline
92 & 0.972157611412978 & 0.0556847771740444 & 0.0278423885870222 \tabularnewline
93 & 0.963919306463507 & 0.0721613870729862 & 0.0360806935364931 \tabularnewline
94 & 0.954191471493766 & 0.0916170570124688 & 0.0458085285062344 \tabularnewline
95 & 0.942313591817623 & 0.115372816364755 & 0.0576864081823774 \tabularnewline
96 & 0.92815402799823 & 0.143691944003539 & 0.0718459720017693 \tabularnewline
97 & 0.908651681169288 & 0.182696637661424 & 0.091348318830712 \tabularnewline
98 & 0.93638027295489 & 0.127239454090220 & 0.0636197270451098 \tabularnewline
99 & 0.921614569843008 & 0.156770860313985 & 0.0783854301569925 \tabularnewline
100 & 0.903438383243567 & 0.193123233512865 & 0.0965616167564326 \tabularnewline
101 & 0.878799512041083 & 0.242400975917835 & 0.121200487958917 \tabularnewline
102 & 0.865460482001806 & 0.269079035996388 & 0.134539517998194 \tabularnewline
103 & 0.846212812745894 & 0.307574374508211 & 0.153787187254106 \tabularnewline
104 & 0.812707185343339 & 0.374585629313323 & 0.187292814656661 \tabularnewline
105 & 0.786063329913647 & 0.427873340172707 & 0.213936670086353 \tabularnewline
106 & 0.746590752814814 & 0.506818494370373 & 0.253409247185186 \tabularnewline
107 & 0.707457638432218 & 0.585084723135564 & 0.292542361567782 \tabularnewline
108 & 0.662263539746954 & 0.675472920506093 & 0.337736460253046 \tabularnewline
109 & 0.613420392903074 & 0.773159214193852 & 0.386579607096926 \tabularnewline
110 & 0.564448683028446 & 0.871102633943108 & 0.435551316971554 \tabularnewline
111 & 0.510794576382737 & 0.978410847234525 & 0.489205423617263 \tabularnewline
112 & 0.456845260843123 & 0.913690521686245 & 0.543154739156877 \tabularnewline
113 & 0.44074311424353 & 0.88148622848706 & 0.55925688575647 \tabularnewline
114 & 0.391585926070925 & 0.78317185214185 & 0.608414073929075 \tabularnewline
115 & 0.367931632066202 & 0.735863264132404 & 0.632068367933798 \tabularnewline
116 & 0.560410580121211 & 0.879178839757578 & 0.439589419878789 \tabularnewline
117 & 0.53336225140375 & 0.9332754971925 & 0.46663774859625 \tabularnewline
118 & 0.474768110153709 & 0.949536220307419 & 0.525231889846291 \tabularnewline
119 & 0.415318530140355 & 0.830637060280711 & 0.584681469859645 \tabularnewline
120 & 0.365962197618586 & 0.731924395237172 & 0.634037802381414 \tabularnewline
121 & 0.325878828492406 & 0.651757656984812 & 0.674121171507594 \tabularnewline
122 & 0.298112725072929 & 0.596225450145859 & 0.701887274927071 \tabularnewline
123 & 0.305038135323441 & 0.610076270646882 & 0.69496186467656 \tabularnewline
124 & 0.610025834040073 & 0.779948331919853 & 0.389974165959927 \tabularnewline
125 & 0.584193129975003 & 0.831613740049993 & 0.415806870024997 \tabularnewline
126 & 0.538789455799655 & 0.92242108840069 & 0.461210544200345 \tabularnewline
127 & 0.469829146966126 & 0.939658293932253 & 0.530170853033874 \tabularnewline
128 & 0.493546010885045 & 0.98709202177009 & 0.506453989114955 \tabularnewline
129 & 0.421728171157786 & 0.843456342315571 & 0.578271828842214 \tabularnewline
130 & 0.383672577968608 & 0.767345155937215 & 0.616327422031392 \tabularnewline
131 & 0.314007840496355 & 0.62801568099271 & 0.685992159503645 \tabularnewline
132 & 0.249058391349176 & 0.498116782698352 & 0.750941608650824 \tabularnewline
133 & 0.197878661814529 & 0.395757323629058 & 0.802121338185471 \tabularnewline
134 & 0.150027317532234 & 0.300054635064468 & 0.849972682467766 \tabularnewline
135 & 0.112549283686449 & 0.225098567372898 & 0.88745071631355 \tabularnewline
136 & 0.635272733611546 & 0.729454532776909 & 0.364727266388454 \tabularnewline
137 & 0.748453402108847 & 0.503093195782306 & 0.251546597891153 \tabularnewline
138 & 0.723506579094221 & 0.552986841811558 & 0.276493420905779 \tabularnewline
139 & 0.628947722450498 & 0.742104555099003 & 0.371052277549501 \tabularnewline
140 & 0.514741162355779 & 0.970517675288443 & 0.485258837644221 \tabularnewline
141 & 0.3938825269471 & 0.7877650538942 & 0.6061174730529 \tabularnewline
142 & 0.340185516592125 & 0.68037103318425 & 0.659814483407875 \tabularnewline
143 & 0.995088059313845 & 0.0098238813723107 & 0.00491194068615535 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104048&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]0.789962845310063[/C][C]0.420074309379875[/C][C]0.210037154689937[/C][/ROW]
[ROW][C]17[/C][C]0.708777579109477[/C][C]0.582444841781045[/C][C]0.291222420890523[/C][/ROW]
[ROW][C]18[/C][C]0.618136952300701[/C][C]0.763726095398598[/C][C]0.381863047699299[/C][/ROW]
[ROW][C]19[/C][C]0.924717243657155[/C][C]0.150565512685690[/C][C]0.0752827563428449[/C][/ROW]
[ROW][C]20[/C][C]0.879257595951904[/C][C]0.241484808096193[/C][C]0.120742404048096[/C][/ROW]
[ROW][C]21[/C][C]0.89021357733421[/C][C]0.219572845331580[/C][C]0.109786422665790[/C][/ROW]
[ROW][C]22[/C][C]0.904142675255625[/C][C]0.19171464948875[/C][C]0.095857324744375[/C][/ROW]
[ROW][C]23[/C][C]0.859629679795954[/C][C]0.280740640408093[/C][C]0.140370320204046[/C][/ROW]
[ROW][C]24[/C][C]0.889469266322935[/C][C]0.22106146735413[/C][C]0.110530733677065[/C][/ROW]
[ROW][C]25[/C][C]0.88829773968836[/C][C]0.223404520623281[/C][C]0.111702260311641[/C][/ROW]
[ROW][C]26[/C][C]0.86666919076968[/C][C]0.266661618460640[/C][C]0.133330809230320[/C][/ROW]
[ROW][C]27[/C][C]0.820880630450203[/C][C]0.358238739099595[/C][C]0.179119369549797[/C][/ROW]
[ROW][C]28[/C][C]0.766081803167315[/C][C]0.467836393665369[/C][C]0.233918196832685[/C][/ROW]
[ROW][C]29[/C][C]0.871647578584388[/C][C]0.256704842831224[/C][C]0.128352421415612[/C][/ROW]
[ROW][C]30[/C][C]0.833324008744715[/C][C]0.33335198251057[/C][C]0.166675991255285[/C][/ROW]
[ROW][C]31[/C][C]0.888597751573913[/C][C]0.222804496852174[/C][C]0.111402248426087[/C][/ROW]
[ROW][C]32[/C][C]0.870672043820313[/C][C]0.258655912359374[/C][C]0.129327956179687[/C][/ROW]
[ROW][C]33[/C][C]0.993626779758823[/C][C]0.0127464404823548[/C][C]0.0063732202411774[/C][/ROW]
[ROW][C]34[/C][C]0.996297372792085[/C][C]0.0074052544158308[/C][C]0.0037026272079154[/C][/ROW]
[ROW][C]35[/C][C]0.998037010257105[/C][C]0.00392597948578951[/C][C]0.00196298974289476[/C][/ROW]
[ROW][C]36[/C][C]0.998977384669256[/C][C]0.00204523066148897[/C][C]0.00102261533074448[/C][/ROW]
[ROW][C]37[/C][C]0.99922536557649[/C][C]0.00154926884702089[/C][C]0.000774634423510447[/C][/ROW]
[ROW][C]38[/C][C]0.999264495857807[/C][C]0.00147100828438559[/C][C]0.000735504142192795[/C][/ROW]
[ROW][C]39[/C][C]0.999675915657168[/C][C]0.000648168685663758[/C][C]0.000324084342831879[/C][/ROW]
[ROW][C]40[/C][C]0.999534867455117[/C][C]0.000930265089766878[/C][C]0.000465132544883439[/C][/ROW]
[ROW][C]41[/C][C]0.99946258277984[/C][C]0.00107483444031903[/C][C]0.000537417220159516[/C][/ROW]
[ROW][C]42[/C][C]0.999146869031277[/C][C]0.00170626193744568[/C][C]0.00085313096872284[/C][/ROW]
[ROW][C]43[/C][C]0.998885384877645[/C][C]0.00222923024470930[/C][C]0.00111461512235465[/C][/ROW]
[ROW][C]44[/C][C]0.998689927257759[/C][C]0.00262014548448309[/C][C]0.00131007274224155[/C][/ROW]
[ROW][C]45[/C][C]0.999705081499605[/C][C]0.000589837000790424[/C][C]0.000294918500395212[/C][/ROW]
[ROW][C]46[/C][C]0.999570191063337[/C][C]0.000859617873325662[/C][C]0.000429808936662831[/C][/ROW]
[ROW][C]47[/C][C]0.999324614041903[/C][C]0.00135077191619331[/C][C]0.000675385958096657[/C][/ROW]
[ROW][C]48[/C][C]0.999802326731875[/C][C]0.000395346536250929[/C][C]0.000197673268125465[/C][/ROW]
[ROW][C]49[/C][C]0.999751802279312[/C][C]0.000496395441375993[/C][C]0.000248197720687996[/C][/ROW]
[ROW][C]50[/C][C]0.999987589366197[/C][C]2.4821267605259e-05[/C][C]1.24106338026295e-05[/C][/ROW]
[ROW][C]51[/C][C]0.99999080126717[/C][C]1.83974656618052e-05[/C][C]9.19873283090262e-06[/C][/ROW]
[ROW][C]52[/C][C]0.999984074470075[/C][C]3.18510598492757e-05[/C][C]1.59255299246378e-05[/C][/ROW]
[ROW][C]53[/C][C]0.99997234523558[/C][C]5.53095288409733e-05[/C][C]2.76547644204867e-05[/C][/ROW]
[ROW][C]54[/C][C]0.999964683723343[/C][C]7.0632553314277e-05[/C][C]3.53162766571385e-05[/C][/ROW]
[ROW][C]55[/C][C]0.999940566930482[/C][C]0.000118866139037081[/C][C]5.94330695185407e-05[/C][/ROW]
[ROW][C]56[/C][C]0.999922049454815[/C][C]0.000155901090370484[/C][C]7.79505451852422e-05[/C][/ROW]
[ROW][C]57[/C][C]0.999874198980085[/C][C]0.000251602039830235[/C][C]0.000125801019915117[/C][/ROW]
[ROW][C]58[/C][C]0.999806935431901[/C][C]0.000386129136197752[/C][C]0.000193064568098876[/C][/ROW]
[ROW][C]59[/C][C]0.999722155082953[/C][C]0.000555689834094068[/C][C]0.000277844917047034[/C][/ROW]
[ROW][C]60[/C][C]0.9996255715509[/C][C]0.000748856898197979[/C][C]0.000374428449098989[/C][/ROW]
[ROW][C]61[/C][C]0.99958748164739[/C][C]0.000825036705220529[/C][C]0.000412518352610264[/C][/ROW]
[ROW][C]62[/C][C]0.999364254045453[/C][C]0.00127149190909315[/C][C]0.000635745954546577[/C][/ROW]
[ROW][C]63[/C][C]0.99981505420449[/C][C]0.000369891591018548[/C][C]0.000184945795509274[/C][/ROW]
[ROW][C]64[/C][C]0.999771376859854[/C][C]0.000457246280291166[/C][C]0.000228623140145583[/C][/ROW]
[ROW][C]65[/C][C]0.999762419117352[/C][C]0.00047516176529694[/C][C]0.00023758088264847[/C][/ROW]
[ROW][C]66[/C][C]0.999728533663533[/C][C]0.000542932672934504[/C][C]0.000271466336467252[/C][/ROW]
[ROW][C]67[/C][C]0.99987023505129[/C][C]0.000259529897418045[/C][C]0.000129764948709022[/C][/ROW]
[ROW][C]68[/C][C]0.999841733357276[/C][C]0.000316533285447256[/C][C]0.000158266642723628[/C][/ROW]
[ROW][C]69[/C][C]0.999884957073382[/C][C]0.000230085853236631[/C][C]0.000115042926618315[/C][/ROW]
[ROW][C]70[/C][C]0.999820589988733[/C][C]0.000358820022533593[/C][C]0.000179410011266797[/C][/ROW]
[ROW][C]71[/C][C]0.999728963012594[/C][C]0.000542073974811823[/C][C]0.000271036987405911[/C][/ROW]
[ROW][C]72[/C][C]0.999616346106814[/C][C]0.000767307786371721[/C][C]0.000383653893185860[/C][/ROW]
[ROW][C]73[/C][C]0.999406234915286[/C][C]0.00118753016942784[/C][C]0.000593765084713918[/C][/ROW]
[ROW][C]74[/C][C]0.999172864046638[/C][C]0.00165427190672336[/C][C]0.00082713595336168[/C][/ROW]
[ROW][C]75[/C][C]0.998785006594313[/C][C]0.00242998681137425[/C][C]0.00121499340568713[/C][/ROW]
[ROW][C]76[/C][C]0.998527094233988[/C][C]0.00294581153202359[/C][C]0.00147290576601179[/C][/ROW]
[ROW][C]77[/C][C]0.998074448806611[/C][C]0.00385110238677773[/C][C]0.00192555119338886[/C][/ROW]
[ROW][C]78[/C][C]0.997901438030819[/C][C]0.00419712393836244[/C][C]0.00209856196918122[/C][/ROW]
[ROW][C]79[/C][C]0.996995005284042[/C][C]0.00600998943191595[/C][C]0.00300499471595797[/C][/ROW]
[ROW][C]80[/C][C]0.995640908085797[/C][C]0.00871818382840639[/C][C]0.00435909191420319[/C][/ROW]
[ROW][C]81[/C][C]0.993780670219102[/C][C]0.0124386595617958[/C][C]0.00621932978089792[/C][/ROW]
[ROW][C]82[/C][C]0.992007088444088[/C][C]0.0159858231118238[/C][C]0.00799291155591189[/C][/ROW]
[ROW][C]83[/C][C]0.988875777225308[/C][C]0.0222484455493850[/C][C]0.0111242227746925[/C][/ROW]
[ROW][C]84[/C][C]0.988744683492378[/C][C]0.0225106330152435[/C][C]0.0112553165076217[/C][/ROW]
[ROW][C]85[/C][C]0.984835216018026[/C][C]0.0303295679639478[/C][C]0.0151647839819739[/C][/ROW]
[ROW][C]86[/C][C]0.981433940187561[/C][C]0.0371321196248776[/C][C]0.0185660598124388[/C][/ROW]
[ROW][C]87[/C][C]0.974986249489813[/C][C]0.0500275010203748[/C][C]0.0250137505101874[/C][/ROW]
[ROW][C]88[/C][C]0.97858899848831[/C][C]0.0428220030233812[/C][C]0.0214110015116906[/C][/ROW]
[ROW][C]89[/C][C]0.988513513620503[/C][C]0.0229729727589940[/C][C]0.0114864863794970[/C][/ROW]
[ROW][C]90[/C][C]0.984099088685308[/C][C]0.0318018226293831[/C][C]0.0159009113146916[/C][/ROW]
[ROW][C]91[/C][C]0.97866007728435[/C][C]0.0426798454312993[/C][C]0.0213399227156497[/C][/ROW]
[ROW][C]92[/C][C]0.972157611412978[/C][C]0.0556847771740444[/C][C]0.0278423885870222[/C][/ROW]
[ROW][C]93[/C][C]0.963919306463507[/C][C]0.0721613870729862[/C][C]0.0360806935364931[/C][/ROW]
[ROW][C]94[/C][C]0.954191471493766[/C][C]0.0916170570124688[/C][C]0.0458085285062344[/C][/ROW]
[ROW][C]95[/C][C]0.942313591817623[/C][C]0.115372816364755[/C][C]0.0576864081823774[/C][/ROW]
[ROW][C]96[/C][C]0.92815402799823[/C][C]0.143691944003539[/C][C]0.0718459720017693[/C][/ROW]
[ROW][C]97[/C][C]0.908651681169288[/C][C]0.182696637661424[/C][C]0.091348318830712[/C][/ROW]
[ROW][C]98[/C][C]0.93638027295489[/C][C]0.127239454090220[/C][C]0.0636197270451098[/C][/ROW]
[ROW][C]99[/C][C]0.921614569843008[/C][C]0.156770860313985[/C][C]0.0783854301569925[/C][/ROW]
[ROW][C]100[/C][C]0.903438383243567[/C][C]0.193123233512865[/C][C]0.0965616167564326[/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]0.269079035996388[/C][C]0.134539517998194[/C][/ROW]
[ROW][C]103[/C][C]0.846212812745894[/C][C]0.307574374508211[/C][C]0.153787187254106[/C][/ROW]
[ROW][C]104[/C][C]0.812707185343339[/C][C]0.374585629313323[/C][C]0.187292814656661[/C][/ROW]
[ROW][C]105[/C][C]0.786063329913647[/C][C]0.427873340172707[/C][C]0.213936670086353[/C][/ROW]
[ROW][C]106[/C][C]0.746590752814814[/C][C]0.506818494370373[/C][C]0.253409247185186[/C][/ROW]
[ROW][C]107[/C][C]0.707457638432218[/C][C]0.585084723135564[/C][C]0.292542361567782[/C][/ROW]
[ROW][C]108[/C][C]0.662263539746954[/C][C]0.675472920506093[/C][C]0.337736460253046[/C][/ROW]
[ROW][C]109[/C][C]0.613420392903074[/C][C]0.773159214193852[/C][C]0.386579607096926[/C][/ROW]
[ROW][C]110[/C][C]0.564448683028446[/C][C]0.871102633943108[/C][C]0.435551316971554[/C][/ROW]
[ROW][C]111[/C][C]0.510794576382737[/C][C]0.978410847234525[/C][C]0.489205423617263[/C][/ROW]
[ROW][C]112[/C][C]0.456845260843123[/C][C]0.913690521686245[/C][C]0.543154739156877[/C][/ROW]
[ROW][C]113[/C][C]0.44074311424353[/C][C]0.88148622848706[/C][C]0.55925688575647[/C][/ROW]
[ROW][C]114[/C][C]0.391585926070925[/C][C]0.78317185214185[/C][C]0.608414073929075[/C][/ROW]
[ROW][C]115[/C][C]0.367931632066202[/C][C]0.735863264132404[/C][C]0.632068367933798[/C][/ROW]
[ROW][C]116[/C][C]0.560410580121211[/C][C]0.879178839757578[/C][C]0.439589419878789[/C][/ROW]
[ROW][C]117[/C][C]0.53336225140375[/C][C]0.9332754971925[/C][C]0.46663774859625[/C][/ROW]
[ROW][C]118[/C][C]0.474768110153709[/C][C]0.949536220307419[/C][C]0.525231889846291[/C][/ROW]
[ROW][C]119[/C][C]0.415318530140355[/C][C]0.830637060280711[/C][C]0.584681469859645[/C][/ROW]
[ROW][C]120[/C][C]0.365962197618586[/C][C]0.731924395237172[/C][C]0.634037802381414[/C][/ROW]
[ROW][C]121[/C][C]0.325878828492406[/C][C]0.651757656984812[/C][C]0.674121171507594[/C][/ROW]
[ROW][C]122[/C][C]0.298112725072929[/C][C]0.596225450145859[/C][C]0.701887274927071[/C][/ROW]
[ROW][C]123[/C][C]0.305038135323441[/C][C]0.610076270646882[/C][C]0.69496186467656[/C][/ROW]
[ROW][C]124[/C][C]0.610025834040073[/C][C]0.779948331919853[/C][C]0.389974165959927[/C][/ROW]
[ROW][C]125[/C][C]0.584193129975003[/C][C]0.831613740049993[/C][C]0.415806870024997[/C][/ROW]
[ROW][C]126[/C][C]0.538789455799655[/C][C]0.92242108840069[/C][C]0.461210544200345[/C][/ROW]
[ROW][C]127[/C][C]0.469829146966126[/C][C]0.939658293932253[/C][C]0.530170853033874[/C][/ROW]
[ROW][C]128[/C][C]0.493546010885045[/C][C]0.98709202177009[/C][C]0.506453989114955[/C][/ROW]
[ROW][C]129[/C][C]0.421728171157786[/C][C]0.843456342315571[/C][C]0.578271828842214[/C][/ROW]
[ROW][C]130[/C][C]0.383672577968608[/C][C]0.767345155937215[/C][C]0.616327422031392[/C][/ROW]
[ROW][C]131[/C][C]0.314007840496355[/C][C]0.62801568099271[/C][C]0.685992159503645[/C][/ROW]
[ROW][C]132[/C][C]0.249058391349176[/C][C]0.498116782698352[/C][C]0.750941608650824[/C][/ROW]
[ROW][C]133[/C][C]0.197878661814529[/C][C]0.395757323629058[/C][C]0.802121338185471[/C][/ROW]
[ROW][C]134[/C][C]0.150027317532234[/C][C]0.300054635064468[/C][C]0.849972682467766[/C][/ROW]
[ROW][C]135[/C][C]0.112549283686449[/C][C]0.225098567372898[/C][C]0.88745071631355[/C][/ROW]
[ROW][C]136[/C][C]0.635272733611546[/C][C]0.729454532776909[/C][C]0.364727266388454[/C][/ROW]
[ROW][C]137[/C][C]0.748453402108847[/C][C]0.503093195782306[/C][C]0.251546597891153[/C][/ROW]
[ROW][C]138[/C][C]0.723506579094221[/C][C]0.552986841811558[/C][C]0.276493420905779[/C][/ROW]
[ROW][C]139[/C][C]0.628947722450498[/C][C]0.742104555099003[/C][C]0.371052277549501[/C][/ROW]
[ROW][C]140[/C][C]0.514741162355779[/C][C]0.970517675288443[/C][C]0.485258837644221[/C][/ROW]
[ROW][C]141[/C][C]0.3938825269471[/C][C]0.7877650538942[/C][C]0.6061174730529[/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]0.0098238813723107[/C][C]0.00491194068615535[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104048&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104048&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
160.7899628453100630.4200743093798750.210037154689937
170.7087775791094770.5824448417810450.291222420890523
180.6181369523007010.7637260953985980.381863047699299
190.9247172436571550.1505655126856900.0752827563428449
200.8792575959519040.2414848080961930.120742404048096
210.890213577334210.2195728453315800.109786422665790
220.9041426752556250.191714649488750.095857324744375
230.8596296797959540.2807406404080930.140370320204046
240.8894692663229350.221061467354130.110530733677065
250.888297739688360.2234045206232810.111702260311641
260.866669190769680.2666616184606400.133330809230320
270.8208806304502030.3582387390995950.179119369549797
280.7660818031673150.4678363936653690.233918196832685
290.8716475785843880.2567048428312240.128352421415612
300.8333240087447150.333351982510570.166675991255285
310.8885977515739130.2228044968521740.111402248426087
320.8706720438203130.2586559123593740.129327956179687
330.9936267797588230.01274644048235480.0063732202411774
340.9962973727920850.00740525441583080.0037026272079154
350.9980370102571050.003925979485789510.00196298974289476
360.9989773846692560.002045230661488970.00102261533074448
370.999225365576490.001549268847020890.000774634423510447
380.9992644958578070.001471008284385590.000735504142192795
390.9996759156571680.0006481686856637580.000324084342831879
400.9995348674551170.0009302650897668780.000465132544883439
410.999462582779840.001074834440319030.000537417220159516
420.9991468690312770.001706261937445680.00085313096872284
430.9988853848776450.002229230244709300.00111461512235465
440.9986899272577590.002620145484483090.00131007274224155
450.9997050814996050.0005898370007904240.000294918500395212
460.9995701910633370.0008596178733256620.000429808936662831
470.9993246140419030.001350771916193310.000675385958096657
480.9998023267318750.0003953465362509290.000197673268125465
490.9997518022793120.0004963954413759930.000248197720687996
500.9999875893661972.4821267605259e-051.24106338026295e-05
510.999990801267171.83974656618052e-059.19873283090262e-06
520.9999840744700753.18510598492757e-051.59255299246378e-05
530.999972345235585.53095288409733e-052.76547644204867e-05
540.9999646837233437.0632553314277e-053.53162766571385e-05
550.9999405669304820.0001188661390370815.94330695185407e-05
560.9999220494548150.0001559010903704847.79505451852422e-05
570.9998741989800850.0002516020398302350.000125801019915117
580.9998069354319010.0003861291361977520.000193064568098876
590.9997221550829530.0005556898340940680.000277844917047034
600.99962557155090.0007488568981979790.000374428449098989
610.999587481647390.0008250367052205290.000412518352610264
620.9993642540454530.001271491909093150.000635745954546577
630.999815054204490.0003698915910185480.000184945795509274
640.9997713768598540.0004572462802911660.000228623140145583
650.9997624191173520.000475161765296940.00023758088264847
660.9997285336635330.0005429326729345040.000271466336467252
670.999870235051290.0002595298974180450.000129764948709022
680.9998417333572760.0003165332854472560.000158266642723628
690.9998849570733820.0002300858532366310.000115042926618315
700.9998205899887330.0003588200225335930.000179410011266797
710.9997289630125940.0005420739748118230.000271036987405911
720.9996163461068140.0007673077863717210.000383653893185860
730.9994062349152860.001187530169427840.000593765084713918
740.9991728640466380.001654271906723360.00082713595336168
750.9987850065943130.002429986811374250.00121499340568713
760.9985270942339880.002945811532023590.00147290576601179
770.9980744488066110.003851102386777730.00192555119338886
780.9979014380308190.004197123938362440.00209856196918122
790.9969950052840420.006009989431915950.00300499471595797
800.9956409080857970.008718183828406390.00435909191420319
810.9937806702191020.01243865956179580.00621932978089792
820.9920070884440880.01598582311182380.00799291155591189
830.9888757772253080.02224844554938500.0111242227746925
840.9887446834923780.02251063301524350.0112553165076217
850.9848352160180260.03032956796394780.0151647839819739
860.9814339401875610.03713211962487760.0185660598124388
870.9749862494898130.05002750102037480.0250137505101874
880.978588998488310.04282200302338120.0214110015116906
890.9885135136205030.02297297275899400.0114864863794970
900.9840990886853080.03180182262938310.0159009113146916
910.978660077284350.04267984543129930.0213399227156497
920.9721576114129780.05568477717404440.0278423885870222
930.9639193064635070.07216138707298620.0360806935364931
940.9541914714937660.09161705701246880.0458085285062344
950.9423135918176230.1153728163647550.0576864081823774
960.928154027998230.1436919440035390.0718459720017693
970.9086516811692880.1826966376614240.091348318830712
980.936380272954890.1272394540902200.0636197270451098
990.9216145698430080.1567708603139850.0783854301569925
1000.9034383832435670.1931232335128650.0965616167564326
1010.8787995120410830.2424009759178350.121200487958917
1020.8654604820018060.2690790359963880.134539517998194
1030.8462128127458940.3075743745082110.153787187254106
1040.8127071853433390.3745856293133230.187292814656661
1050.7860633299136470.4278733401727070.213936670086353
1060.7465907528148140.5068184943703730.253409247185186
1070.7074576384322180.5850847231355640.292542361567782
1080.6622635397469540.6754729205060930.337736460253046
1090.6134203929030740.7731592141938520.386579607096926
1100.5644486830284460.8711026339431080.435551316971554
1110.5107945763827370.9784108472345250.489205423617263
1120.4568452608431230.9136905216862450.543154739156877
1130.440743114243530.881486228487060.55925688575647
1140.3915859260709250.783171852141850.608414073929075
1150.3679316320662020.7358632641324040.632068367933798
1160.5604105801212110.8791788397575780.439589419878789
1170.533362251403750.93327549719250.46663774859625
1180.4747681101537090.9495362203074190.525231889846291
1190.4153185301403550.8306370602807110.584681469859645
1200.3659621976185860.7319243952371720.634037802381414
1210.3258788284924060.6517576569848120.674121171507594
1220.2981127250729290.5962254501458590.701887274927071
1230.3050381353234410.6100762706468820.69496186467656
1240.6100258340400730.7799483319198530.389974165959927
1250.5841931299750030.8316137400499930.415806870024997
1260.5387894557996550.922421088400690.461210544200345
1270.4698291469661260.9396582939322530.530170853033874
1280.4935460108850450.987092021770090.506453989114955
1290.4217281711577860.8434563423155710.578271828842214
1300.3836725779686080.7673451559372150.616327422031392
1310.3140078404963550.628015680992710.685992159503645
1320.2490583913491760.4981167826983520.750941608650824
1330.1978786618145290.3957573236290580.802121338185471
1340.1500273175322340.3000546350644680.849972682467766
1350.1125492836864490.2250985673728980.88745071631355
1360.6352727336115460.7294545327769090.364727266388454
1370.7484534021088470.5030931957823060.251546597891153
1380.7235065790942210.5529868418115580.276493420905779
1390.6289477224504980.7421045550990030.371052277549501
1400.5147411623557790.9705176752884430.485258837644221
1410.39388252694710.78776505389420.6061174730529
1420.3401855165921250.680371033184250.659814483407875
1430.9950880593138450.00982388137231070.00491194068615535







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=104048&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=104048&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104048&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 ;
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')
}