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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 21 Nov 2010 13:53:20 +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/Nov/21/t129034778188teu6cc167s7f7.htm/, Retrieved Thu, 02 May 2024 11:02:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98353, Retrieved Thu, 02 May 2024 11:02:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact172

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [Meervoudige regre...] [2010-11-21 13:53:20] [b6992a7b26e556359948e164e4227eba] [Current]
-    D      [Multiple Regression] [Meervoudige regre...] [2010-11-21 14:47:58] [6bc4f9343b7ea3ef5a59412d1f72bb2b]
-    D        [Multiple Regression] [Meervoudige regre...] [2010-11-21 15:35:45] [6bc4f9343b7ea3ef5a59412d1f72bb2b]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
15	10	77	46	15	12	13	6	11	6	4	15	16
9	20	63	37	12	7	11	4	26	5	4	23	24
12	16	73	45	15	13	14	6	26	20	10	26	22
15	10	76	46	12	11	12	5	15	12	6	19	21
17	8	90	55	14	16	12	5	10	11	5	19	23
14	14	67	40	8	10	6	4	21	12	8	16	23
9	19	69	43	11	15	10	5	27	11	9	23	21
11	23	54	33	4	4	10	2	21	13	8	19	22
13	9	54	33	13	7	12	5	21	9	11	24	20
16	12	76	47	19	15	15	6	22	14	6	19	12
16	14	75	44	10	5	13	6	29	12	8	25	23
15	13	76	47	15	16	18	8	29	18	11	23	23
10	11	80	49	6	15	11	6	29	9	5	31	30
16	11	89	55	7	13	12	3	30	15	10	29	22
12	10	73	43	14	13	13	6	19	12	7	18	21
15	12	74	46	16	15	14	6	19	12	7	17	21
13	18	78	51	16	15	16	7	22	12	13	22	15
18	12	76	47	14	10	16	8	18	15	10	21	22
13	10	69	42	15	17	16	6	28	11	8	24	24
17	15	74	42	14	14	15	7	17	13	6	22	23
14	15	82	48	12	9	13	4	18	10	8	16	15
13	12	77	45	9	6	8	4	20	17	7	22	24
13	9	84	51	12	11	14	2	16	13	5	21	24
15	11	75	46	14	13	15	6	17	17	9	25	21
15	16	79	47	14	10	16	6	25	15	11	22	21
13	17	79	47	10	4	13	6	22	13	11	24	18
13	11	88	55	16	15	15	7	31	17	9	25	19
16	13	57	36	10	8	11	4	38	21	7	29	29
14	9	69	42	8	10	14	3	18	12	6	19	20
18	11	86	51	12	8	13	5	20	15	6	25	24
9	20	66	40	8	9	12	4	23	8	5	19	27
16	8	54	33	13	14	14	6	12	15	4	27	28
16	12	85	52	11	5	13	3	20	16	10	25	24
17	10	79	49	12	7	12	3	15	9	8	23	29
13	11	84	50	16	16	14	6	21	13	6	24	24
17	13	70	43	16	14	15	6	20	11	4	25	25
15	13	54	33	13	16	16	6	30	9	9	23	14
14	13	70	44	14	15	15	8	22	15	10	22	22
10	15	54	33	5	4	5	2	33	9	6	32	24
13	12	69	41	14	12	15	6	25	15	9	22	24
11	13	68	40	13	8	8	4	20	14	10	18	24
11	14	66	42	15	12	16	6	21	14	13	19	21
15	9	67	42	11	12	14	5	16	12	8	16	21
15	9	71	45	15	13	13	6	23	15	10	23	21
12	15	54	33	16	14	14	6	25	11	5	17	15
17	10	76	46	13	14	14	5	18	11	8	17	26
15	13	77	47	11	15	12	6	33	9	6	28	22
16	8	71	44	12	14	13	7	18	8	9	24	24
14	15	69	44	12	11	15	5	18	13	9	21	13
17	13	73	46	10	13	15	6	13	12	7	14	19
10	24	46	30	8	4	13	6	24	24	20	21	10
11	11	66	42	9	8	10	4	19	11	8	20	28
15	13	77	46	12	13	13	5	20	11	8	25	25
15	12	77	46	14	15	14	6	21	16	7	20	24
7	22	70	43	12	15	13	6	18	12	7	17	22
17	11	86	52	11	8	13	4	29	18	10	26	30
14	15	38	11	14	17	18	6	13	12	5	17	22
18	7	66	41	7	12	12	4	26	14	8	17	24
14	14	75	45	16	13	14	7	22	16	9	24	23
14	10	64	41	11	7	13	6	28	24	20	30	20
9	9	80	47	16	16	16	6	28	13	6	25	22
14	12	86	53	13	11	15	6	23	11	10	15	22
11	16	54	35	11	10	14	5	22	14	11	25	19
16	13	74	45	13	14	13	6	28	12	7	20	22
17	11	88	54	14	19	12	6	31	21	12	32	26
12	11	63	36	10	8	9	5	15	11	8	14	12
15	13	81	48	15	15	15	8	15	6	6	20	25
15	10	74	45	11	8	12	6	22	14	9	25	23
16	11	80	47	6	6	11	2	17	16	5	25	23
16	9	80	49	11	7	13	2	25	18	11	35	17
11	13	60	38	12	16	13	4	32	9	6	29	26
12	14	62	46	12	10	15	6	23	13	10	25	27
14	14	63	42	8	8	14	5	20	17	8	21	23
15	11	89	54	9	9	12	4	20	11	7	21	20
17	10	76	45	10	8	16	4	28	16	8	24	24
19	11	81	53	16	14	14	6	20	11	9	26	22
15	12	72	44	15	14	13	5	20	11	8	24	26
16	14	84	51	14	14	12	6	23	11	10	20	29
14	14	76	46	12	15	13	7	20	20	13	24	20
16	21	76	46	12	7	12	6	21	10	7	18	17
15	13	72	44	12	12	13	4	14	12	7	17	16
17	11	81	48	8	7	10	3	31	11	8	22	24
12	12	72	44	16	12	15	8	21	14	9	22	24
18	12	78	47	11	6	9	4	18	12	9	22	19
13	11	79	47	12	10	13	4	26	12	8	24	29
14	14	52	31	9	12	13	5	25	12	7	32	25
14	13	67	44	14	13	13	5	9	10	6	19	25
14	13	74	42	15	14	15	7	18	12	8	21	24
12	12	73	41	8	8	13	4	19	10	8	23	29
14	14	69	43	12	14	14	5	29	7	4	26	22
12	12	67	41	10	10	11	5	31	10	8	18	23
15	12	76	47	16	14	15	8	24	13	10	19	15
11	18	63	37	8	10	15	2	19	13	8	27	21
15	11	84	54	9	6	12	5	19	9	7	21	23
14	15	90	55	8	9	15	4	22	14	10	20	20
15	13	75	45	11	11	14	5	31	14	9	21	25
16	11	76	47	16	16	16	7	20	12	8	20	28
14	22	53	37	5	8	12	3	26	18	5	29	18
18	10	87	53	15	16	11	5	17	17	8	30	25
14	11	78	46	15	16	13	6	16	15	9	23	24
13	15	54	33	12	14	12	5	9	8	11	29	23
14	14	58	36	12	12	12	6	19	8	7	19	25
14	11	80	49	16	16	16	7	22	12	8	26	27
17	10	74	44	12	15	13	6	15	10	4	22	24
12	14	56	37	10	11	12	6	25	18	16	26	24
16	14	82	53	12	6	14	5	30	15	9	27	26
10	15	67	42	11	16	14	6	24	11	12	24	26
13	11	75	45	16	16	15	6	20	10	8	26	23
15	10	69	40	7	8	12	3	12	7	4	22	28
16	10	72	44	9	11	11	4	31	17	11	23	20
14	12	54	33	11	13	11	4	25	7	8	25	23
13	15	54	33	6	9	11	4	23	14	12	19	24
17	10	71	43	14	15	13	6	23	12	8	20	21
14	12	53	32	11	11	12	6	26	15	6	25	25
16	15	54	33	11	12	12	4	14	13	8	14	16
12	11	69	42	16	8	14	4	28	16	14	27	22
16	10	30	0	7	7	12	4	19	11	10	21	27
8	20	53	32	8	10	12	4	21	7	5	21	24
9	19	68	41	10	9	12	4	18	15	8	14	17
13	17	69	44	14	13	13	5	29	18	12	21	21
19	8	54	33	9	11	11	4	16	11	11	23	21
11	17	66	42	13	12	13	7	22	13	8	18	19
15	11	79	46	13	5	12	3	15	11	8	20	25
11	13	67	44	12	12	14	5	21	13	9	19	24
15	9	74	45	11	14	15	5	17	12	6	15	21
16	10	86	53	10	15	15	6	17	11	5	23	26
15	13	63	38	12	14	13	5	33	11	8	26	25
12	16	69	43	14	13	16	6	17	13	7	21	25
16	12	73	43	11	14	17	6	20	8	4	13	13
15	14	69	42	13	14	13	3	17	12	9	24	25
13	11	71	42	14	15	14	6	16	9	5	17	23
14	13	77	47	13	13	13	5	18	14	9	21	26
11	15	74	44	16	14	16	8	32	18	12	28	22
15	14	82	49	13	11	13	6	22	15	6	22	20
14	14	54	33	9	11	13	3	29	11	6	27	24
13	10	80	47	14	8	14	4	23	17	7	25	21
15	8	76	47	15	12	16	7	17	12	9	21	24

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 10 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98353&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98353&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98353&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 17.3893058309824 -0.369421768925105Depression[t] + 0.0673020022887946Belonging[t] -0.0489287737012476BelongingFinal[t] -0.0937609856736327Popularity[t] -0.0384462079358146KnowingPeople[t] + 0.0656628197343761Liked[t] + 0.130635340805913Celebrity[t] -0.0539467717320201ConcernOverMistakes[t] + 0.097263567422785ParentalExpectations[t] -0.0906357963563662ParentalCriticism[t] + 0.043090642762466PersonalStandards[t] -0.0673752186150563Organization[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Happiness[t] =  +  17.3893058309824 -0.369421768925105Depression[t] +  0.0673020022887946Belonging[t] -0.0489287737012476BelongingFinal[t] -0.0937609856736327Popularity[t] -0.0384462079358146KnowingPeople[t] +  0.0656628197343761Liked[t] +  0.130635340805913Celebrity[t] -0.0539467717320201ConcernOverMistakes[t] +  0.097263567422785ParentalExpectations[t] -0.0906357963563662ParentalCriticism[t] +  0.043090642762466PersonalStandards[t] -0.0673752186150563Organization[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98353&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Happiness[t] =  +  17.3893058309824 -0.369421768925105Depression[t] +  0.0673020022887946Belonging[t] -0.0489287737012476BelongingFinal[t] -0.0937609856736327Popularity[t] -0.0384462079358146KnowingPeople[t] +  0.0656628197343761Liked[t] +  0.130635340805913Celebrity[t] -0.0539467717320201ConcernOverMistakes[t] +  0.097263567422785ParentalExpectations[t] -0.0906357963563662ParentalCriticism[t] +  0.043090642762466PersonalStandards[t] -0.0673752186150563Organization[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98353&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98353&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 17.3893058309824 -0.369421768925105Depression[t] + 0.0673020022887946Belonging[t] -0.0489287737012476BelongingFinal[t] -0.0937609856736327Popularity[t] -0.0384462079358146KnowingPeople[t] + 0.0656628197343761Liked[t] + 0.130635340805913Celebrity[t] -0.0539467717320201ConcernOverMistakes[t] + 0.097263567422785ParentalExpectations[t] -0.0906357963563662ParentalCriticism[t] + 0.043090642762466PersonalStandards[t] -0.0673752186150563Organization[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)17.38930583098242.513856.917400
Depression-0.3694217689251050.060753-6.080700
Belonging0.06730200228879460.0534921.25820.2106920.105346
BelongingFinal-0.04892877370124760.075322-0.64960.5171540.258577
Popularity-0.09376098567363270.090697-1.03380.303250.151625
KnowingPeople-0.03844620793581460.069841-0.55050.582980.29149
Liked0.06566281973437610.106270.61790.5377840.268892
Celebrity0.1306353408059130.1810690.72150.4719810.235991
ConcernOverMistakes-0.05394677173202010.03594-1.5010.1358880.067944
ParentalExpectations0.0972635674227850.0642341.51420.132520.06626
ParentalCriticism-0.09063579635636620.083458-1.0860.2795850.139792
PersonalStandards0.0430906427624660.0513960.83840.4034130.201707
Organization-0.06737521861505630.051073-1.31920.1895320.094766

\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) & 17.3893058309824 & 2.51385 & 6.9174 & 0 & 0 \tabularnewline
Depression & -0.369421768925105 & 0.060753 & -6.0807 & 0 & 0 \tabularnewline
Belonging & 0.0673020022887946 & 0.053492 & 1.2582 & 0.210692 & 0.105346 \tabularnewline
BelongingFinal & -0.0489287737012476 & 0.075322 & -0.6496 & 0.517154 & 0.258577 \tabularnewline
Popularity & -0.0937609856736327 & 0.090697 & -1.0338 & 0.30325 & 0.151625 \tabularnewline
KnowingPeople & -0.0384462079358146 & 0.069841 & -0.5505 & 0.58298 & 0.29149 \tabularnewline
Liked & 0.0656628197343761 & 0.10627 & 0.6179 & 0.537784 & 0.268892 \tabularnewline
Celebrity & 0.130635340805913 & 0.181069 & 0.7215 & 0.471981 & 0.235991 \tabularnewline
ConcernOverMistakes & -0.0539467717320201 & 0.03594 & -1.501 & 0.135888 & 0.067944 \tabularnewline
ParentalExpectations & 0.097263567422785 & 0.064234 & 1.5142 & 0.13252 & 0.06626 \tabularnewline
ParentalCriticism & -0.0906357963563662 & 0.083458 & -1.086 & 0.279585 & 0.139792 \tabularnewline
PersonalStandards & 0.043090642762466 & 0.051396 & 0.8384 & 0.403413 & 0.201707 \tabularnewline
Organization & -0.0673752186150563 & 0.051073 & -1.3192 & 0.189532 & 0.094766 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98353&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]17.3893058309824[/C][C]2.51385[/C][C]6.9174[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Depression[/C][C]-0.369421768925105[/C][C]0.060753[/C][C]-6.0807[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0673020022887946[/C][C]0.053492[/C][C]1.2582[/C][C]0.210692[/C][C]0.105346[/C][/ROW]
[ROW][C]BelongingFinal[/C][C]-0.0489287737012476[/C][C]0.075322[/C][C]-0.6496[/C][C]0.517154[/C][C]0.258577[/C][/ROW]
[ROW][C]Popularity[/C][C]-0.0937609856736327[/C][C]0.090697[/C][C]-1.0338[/C][C]0.30325[/C][C]0.151625[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]-0.0384462079358146[/C][C]0.069841[/C][C]-0.5505[/C][C]0.58298[/C][C]0.29149[/C][/ROW]
[ROW][C]Liked[/C][C]0.0656628197343761[/C][C]0.10627[/C][C]0.6179[/C][C]0.537784[/C][C]0.268892[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.130635340805913[/C][C]0.181069[/C][C]0.7215[/C][C]0.471981[/C][C]0.235991[/C][/ROW]
[ROW][C]ConcernOverMistakes[/C][C]-0.0539467717320201[/C][C]0.03594[/C][C]-1.501[/C][C]0.135888[/C][C]0.067944[/C][/ROW]
[ROW][C]ParentalExpectations[/C][C]0.097263567422785[/C][C]0.064234[/C][C]1.5142[/C][C]0.13252[/C][C]0.06626[/C][/ROW]
[ROW][C]ParentalCriticism[/C][C]-0.0906357963563662[/C][C]0.083458[/C][C]-1.086[/C][C]0.279585[/C][C]0.139792[/C][/ROW]
[ROW][C]PersonalStandards[/C][C]0.043090642762466[/C][C]0.051396[/C][C]0.8384[/C][C]0.403413[/C][C]0.201707[/C][/ROW]
[ROW][C]Organization[/C][C]-0.0673752186150563[/C][C]0.051073[/C][C]-1.3192[/C][C]0.189532[/C][C]0.094766[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98353&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98353&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)17.38930583098242.513856.917400
Depression-0.3694217689251050.060753-6.080700
Belonging0.06730200228879460.0534921.25820.2106920.105346
BelongingFinal-0.04892877370124760.075322-0.64960.5171540.258577
Popularity-0.09376098567363270.090697-1.03380.303250.151625
KnowingPeople-0.03844620793581460.069841-0.55050.582980.29149
Liked0.06566281973437610.106270.61790.5377840.268892
Celebrity0.1306353408059130.1810690.72150.4719810.235991
ConcernOverMistakes-0.05394677173202010.03594-1.5010.1358880.067944
ParentalExpectations0.0972635674227850.0642341.51420.132520.06626
ParentalCriticism-0.09063579635636620.083458-1.0860.2795850.139792
PersonalStandards0.0430906427624660.0513960.83840.4034130.201707
Organization-0.06737521861505630.051073-1.31920.1895320.094766






Multiple Linear Regression - Regression Statistics
Multiple R0.598500396625606
R-squared0.358202724761007
Adjusted R-squared0.296093311028202
F-TEST (value)5.76728555677554
F-TEST (DF numerator)12
F-TEST (DF denominator)124
p-value7.55891645942697e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.98907390262861
Sum Squared Residuals490.595458774659

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.598500396625606 \tabularnewline
R-squared & 0.358202724761007 \tabularnewline
Adjusted R-squared & 0.296093311028202 \tabularnewline
F-TEST (value) & 5.76728555677554 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 124 \tabularnewline
p-value & 7.55891645942697e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.98907390262861 \tabularnewline
Sum Squared Residuals & 490.595458774659 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98353&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.598500396625606[/C][/ROW]
[ROW][C]R-squared[/C][C]0.358202724761007[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.296093311028202[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.76728555677554[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]124[/C][/ROW]
[ROW][C]p-value[/C][C]7.55891645942697e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.98907390262861[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]490.595458774659[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98353&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98353&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.598500396625606
R-squared0.358202724761007
Adjusted R-squared0.296093311028202
F-TEST (value)5.76728555677554
F-TEST (DF numerator)12
F-TEST (DF denominator)124
p-value7.55891645942697e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.98907390262861
Sum Squared Residuals490.595458774659






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11515.5922580224144-0.592258022414358
2910.3763471898270-1.37634718982703
31213.2610841337057-1.26108413370568
41515.6703962274125-0.670396227412501
51716.65971147333180.340288526668162
61413.00046707611620.999532923883771
7910.9857534804041-1.9857534804041
81110.04425904390500.955740956094975
91314.4694499681551-1.46944996815505
101614.82341268466831.17658731533166
111614.02502909871191.97497090128810
121514.23833084338630.761669156613684
131014.8514507853627-4.85145078536272
141615.34976050568740.650239494312601
151215.1976467889243-3.19764678892434
161514.13747672201220.8625232779878
171312.12152066155360.87847933844641
181815.17431872236152.82568127763846
191314.3098080866189-1.30980808661894
201714.02368853369072.97631146630934
211413.87850208456590.121497915434081
221315.1811052970486-2.18110529704858
231316.0910179849196-3.09101798491963
241515.6619426301443-0.661942630144332
251513.27946963481271.7205303651873
261313.5744007187145-0.574400718714475
271315.3422262831563-2.34222628315628
281613.31510828809342.68489171190657
291415.9534990870273-1.9534990870273
301815.98882710074882.01117289925119
31910.7837882163600-1.78378821635997
321616.1255323657905-0.125532365790519
331615.18572386570020.81427613429977
341714.77832859045492.2216714095451
351315.1252718748917-2.12527187489165
361713.94566361025963.05433638974035
371513.09594495682811.90405504317186
381414.1172809572284-0.117280957228351
391012.1478420918796-2.14784209187965
401314.2143000310942-1.21430003109421
411113.2626123550771-2.26261235507708
421113.0253576083747-2.02535760837472
431515.4519651950179-0.451965195017864
441515.2603954602792-0.260395460279236
451212.5222703421012-0.522270342101192
461714.72918940346842.27081059653161
471513.70872361676341.29127638323656
481615.57270776277090.427292237229146
491413.93571827097400.0642817290259524
501714.73503323978092.26496676021906
511010.3428067365893-0.342806736589346
521114.0354719963516-3.03547199635159
531514.05897729010770.941022709892318
541514.73521169889520.264788301104821
55710.6167900207868-3.61679002078684
561714.98558987424212.01441012575788
571413.12970425128690.870295748713117
581815.78154287114312.21845712885694
591413.93521814071910.0647818592809266
601415.2895859629806-1.2895859629806
61915.6732330420276-6.67323304202762
621414.3648153945076-0.364815394507583
631112.5319859520448-1.53198595204482
641613.64742583609482.35257416390516
651715.04442324610641.95557675389359
661215.1335968952848-3.13359689528478
671514.14461844130090.855381558699093
681515.5932417098202-0.593241709820164
691616.3140717474382-0.31407174743823
701616.6334281632169-0.633428163216929
711112.5046700257125-1.50467002571253
721212.7739898806394-0.773989880639433
731414.1219496567694-0.121949656769382
741515.7079336124191-0.707933612419097
751715.67400373682941.32599626317064
761914.66191593336554.33808406663445
771513.75955156865711.24044843134293
781612.9269640143053.07303598569499
791414.5226103599700-0.522610359970031
801611.50874361188924.49125638811078
811514.50136751280320.498632487196845
821714.46132214048052.53867785951953
831214.4616919466267-2.46169194662671
841814.80587080703473.19412919296535
851314.3291907616437-1.32919076164374
861413.28046623889450.719533761105549
871413.71517150104230.284828498957662
881414.2258232926222-0.225823292622172
891214.4414756042756-2.44147560427561
901413.05832670452830.941673295471697
911213.3139957417549-1.31399574175488
921514.63458657907940.365413420920584
931112.543957626866-1.54395762686600
941515.2746915466298-0.274691546629762
951414.4082683371557-0.408268337155737
961513.64499612632091.35500387367912
971614.32915132404611.67084867595388
981411.35397754820192.64602245179807
991815.93068891711022.06931108288978
1001415.0945360835772-1.09453608357724
1011312.64098213605600.359017863944032
1021412.59777195296051.40222804703946
1031414.7185273175246-0.718527317524611
1041715.59005382934381.40994617065617
1051212.8424499485455-0.842449948545538
1061613.79610721492732.20389278507266
1071012.3287532128499-2.32875321284993
1081314.5643015234240-1.56430152342403
1091515.3301671929391-0.330167192939076
1101614.99375885942511.00624114057488
1111412.82428937878461.17571062121543
1121312.43889008692890.561109913071106
1131714.76590877071562.23409122928439
1141413.98042499054770.0195750094522665
1151612.99475845318663.00524154681336
1161214.0065703068552-2.00657030685520
1171614.32351826574911.67648173425089
118810.5607830856912-2.56078308569116
119912.1883336587029-3.18833365870288
1201311.88312891650831.11687108349165
1211915.21762781961933.78237218038072
1221112.4318895916441-1.43188959164413
1231514.87358065384990.126419346150068
1241113.4466997710899-2.44669977108987
1251515.8492974720167-0.849297472016677
1261616.0832409374489-0.0832409374488868
1271512.81151584998492.18848415001507
1281212.9738247901357-0.973824790135663
1291615.11674616185080.88325383814922
1301513.07875416595591.92124583404405
1311314.6048003186335-1.60480031863350
1321413.98559819044390.0144018095560894
1331113.3938277336665-2.39382773366646
1341514.66308246625440.336917533745566
1351412.72389536920181.27610463079824
1361316.0418341374656-3.04183413746564
1371516.0687584843007-1.06875848430071

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 15 & 15.5922580224144 & -0.592258022414358 \tabularnewline
2 & 9 & 10.3763471898270 & -1.37634718982703 \tabularnewline
3 & 12 & 13.2610841337057 & -1.26108413370568 \tabularnewline
4 & 15 & 15.6703962274125 & -0.670396227412501 \tabularnewline
5 & 17 & 16.6597114733318 & 0.340288526668162 \tabularnewline
6 & 14 & 13.0004670761162 & 0.999532923883771 \tabularnewline
7 & 9 & 10.9857534804041 & -1.9857534804041 \tabularnewline
8 & 11 & 10.0442590439050 & 0.955740956094975 \tabularnewline
9 & 13 & 14.4694499681551 & -1.46944996815505 \tabularnewline
10 & 16 & 14.8234126846683 & 1.17658731533166 \tabularnewline
11 & 16 & 14.0250290987119 & 1.97497090128810 \tabularnewline
12 & 15 & 14.2383308433863 & 0.761669156613684 \tabularnewline
13 & 10 & 14.8514507853627 & -4.85145078536272 \tabularnewline
14 & 16 & 15.3497605056874 & 0.650239494312601 \tabularnewline
15 & 12 & 15.1976467889243 & -3.19764678892434 \tabularnewline
16 & 15 & 14.1374767220122 & 0.8625232779878 \tabularnewline
17 & 13 & 12.1215206615536 & 0.87847933844641 \tabularnewline
18 & 18 & 15.1743187223615 & 2.82568127763846 \tabularnewline
19 & 13 & 14.3098080866189 & -1.30980808661894 \tabularnewline
20 & 17 & 14.0236885336907 & 2.97631146630934 \tabularnewline
21 & 14 & 13.8785020845659 & 0.121497915434081 \tabularnewline
22 & 13 & 15.1811052970486 & -2.18110529704858 \tabularnewline
23 & 13 & 16.0910179849196 & -3.09101798491963 \tabularnewline
24 & 15 & 15.6619426301443 & -0.661942630144332 \tabularnewline
25 & 15 & 13.2794696348127 & 1.7205303651873 \tabularnewline
26 & 13 & 13.5744007187145 & -0.574400718714475 \tabularnewline
27 & 13 & 15.3422262831563 & -2.34222628315628 \tabularnewline
28 & 16 & 13.3151082880934 & 2.68489171190657 \tabularnewline
29 & 14 & 15.9534990870273 & -1.9534990870273 \tabularnewline
30 & 18 & 15.9888271007488 & 2.01117289925119 \tabularnewline
31 & 9 & 10.7837882163600 & -1.78378821635997 \tabularnewline
32 & 16 & 16.1255323657905 & -0.125532365790519 \tabularnewline
33 & 16 & 15.1857238657002 & 0.81427613429977 \tabularnewline
34 & 17 & 14.7783285904549 & 2.2216714095451 \tabularnewline
35 & 13 & 15.1252718748917 & -2.12527187489165 \tabularnewline
36 & 17 & 13.9456636102596 & 3.05433638974035 \tabularnewline
37 & 15 & 13.0959449568281 & 1.90405504317186 \tabularnewline
38 & 14 & 14.1172809572284 & -0.117280957228351 \tabularnewline
39 & 10 & 12.1478420918796 & -2.14784209187965 \tabularnewline
40 & 13 & 14.2143000310942 & -1.21430003109421 \tabularnewline
41 & 11 & 13.2626123550771 & -2.26261235507708 \tabularnewline
42 & 11 & 13.0253576083747 & -2.02535760837472 \tabularnewline
43 & 15 & 15.4519651950179 & -0.451965195017864 \tabularnewline
44 & 15 & 15.2603954602792 & -0.260395460279236 \tabularnewline
45 & 12 & 12.5222703421012 & -0.522270342101192 \tabularnewline
46 & 17 & 14.7291894034684 & 2.27081059653161 \tabularnewline
47 & 15 & 13.7087236167634 & 1.29127638323656 \tabularnewline
48 & 16 & 15.5727077627709 & 0.427292237229146 \tabularnewline
49 & 14 & 13.9357182709740 & 0.0642817290259524 \tabularnewline
50 & 17 & 14.7350332397809 & 2.26496676021906 \tabularnewline
51 & 10 & 10.3428067365893 & -0.342806736589346 \tabularnewline
52 & 11 & 14.0354719963516 & -3.03547199635159 \tabularnewline
53 & 15 & 14.0589772901077 & 0.941022709892318 \tabularnewline
54 & 15 & 14.7352116988952 & 0.264788301104821 \tabularnewline
55 & 7 & 10.6167900207868 & -3.61679002078684 \tabularnewline
56 & 17 & 14.9855898742421 & 2.01441012575788 \tabularnewline
57 & 14 & 13.1297042512869 & 0.870295748713117 \tabularnewline
58 & 18 & 15.7815428711431 & 2.21845712885694 \tabularnewline
59 & 14 & 13.9352181407191 & 0.0647818592809266 \tabularnewline
60 & 14 & 15.2895859629806 & -1.2895859629806 \tabularnewline
61 & 9 & 15.6732330420276 & -6.67323304202762 \tabularnewline
62 & 14 & 14.3648153945076 & -0.364815394507583 \tabularnewline
63 & 11 & 12.5319859520448 & -1.53198595204482 \tabularnewline
64 & 16 & 13.6474258360948 & 2.35257416390516 \tabularnewline
65 & 17 & 15.0444232461064 & 1.95557675389359 \tabularnewline
66 & 12 & 15.1335968952848 & -3.13359689528478 \tabularnewline
67 & 15 & 14.1446184413009 & 0.855381558699093 \tabularnewline
68 & 15 & 15.5932417098202 & -0.593241709820164 \tabularnewline
69 & 16 & 16.3140717474382 & -0.31407174743823 \tabularnewline
70 & 16 & 16.6334281632169 & -0.633428163216929 \tabularnewline
71 & 11 & 12.5046700257125 & -1.50467002571253 \tabularnewline
72 & 12 & 12.7739898806394 & -0.773989880639433 \tabularnewline
73 & 14 & 14.1219496567694 & -0.121949656769382 \tabularnewline
74 & 15 & 15.7079336124191 & -0.707933612419097 \tabularnewline
75 & 17 & 15.6740037368294 & 1.32599626317064 \tabularnewline
76 & 19 & 14.6619159333655 & 4.33808406663445 \tabularnewline
77 & 15 & 13.7595515686571 & 1.24044843134293 \tabularnewline
78 & 16 & 12.926964014305 & 3.07303598569499 \tabularnewline
79 & 14 & 14.5226103599700 & -0.522610359970031 \tabularnewline
80 & 16 & 11.5087436118892 & 4.49125638811078 \tabularnewline
81 & 15 & 14.5013675128032 & 0.498632487196845 \tabularnewline
82 & 17 & 14.4613221404805 & 2.53867785951953 \tabularnewline
83 & 12 & 14.4616919466267 & -2.46169194662671 \tabularnewline
84 & 18 & 14.8058708070347 & 3.19412919296535 \tabularnewline
85 & 13 & 14.3291907616437 & -1.32919076164374 \tabularnewline
86 & 14 & 13.2804662388945 & 0.719533761105549 \tabularnewline
87 & 14 & 13.7151715010423 & 0.284828498957662 \tabularnewline
88 & 14 & 14.2258232926222 & -0.225823292622172 \tabularnewline
89 & 12 & 14.4414756042756 & -2.44147560427561 \tabularnewline
90 & 14 & 13.0583267045283 & 0.941673295471697 \tabularnewline
91 & 12 & 13.3139957417549 & -1.31399574175488 \tabularnewline
92 & 15 & 14.6345865790794 & 0.365413420920584 \tabularnewline
93 & 11 & 12.543957626866 & -1.54395762686600 \tabularnewline
94 & 15 & 15.2746915466298 & -0.274691546629762 \tabularnewline
95 & 14 & 14.4082683371557 & -0.408268337155737 \tabularnewline
96 & 15 & 13.6449961263209 & 1.35500387367912 \tabularnewline
97 & 16 & 14.3291513240461 & 1.67084867595388 \tabularnewline
98 & 14 & 11.3539775482019 & 2.64602245179807 \tabularnewline
99 & 18 & 15.9306889171102 & 2.06931108288978 \tabularnewline
100 & 14 & 15.0945360835772 & -1.09453608357724 \tabularnewline
101 & 13 & 12.6409821360560 & 0.359017863944032 \tabularnewline
102 & 14 & 12.5977719529605 & 1.40222804703946 \tabularnewline
103 & 14 & 14.7185273175246 & -0.718527317524611 \tabularnewline
104 & 17 & 15.5900538293438 & 1.40994617065617 \tabularnewline
105 & 12 & 12.8424499485455 & -0.842449948545538 \tabularnewline
106 & 16 & 13.7961072149273 & 2.20389278507266 \tabularnewline
107 & 10 & 12.3287532128499 & -2.32875321284993 \tabularnewline
108 & 13 & 14.5643015234240 & -1.56430152342403 \tabularnewline
109 & 15 & 15.3301671929391 & -0.330167192939076 \tabularnewline
110 & 16 & 14.9937588594251 & 1.00624114057488 \tabularnewline
111 & 14 & 12.8242893787846 & 1.17571062121543 \tabularnewline
112 & 13 & 12.4388900869289 & 0.561109913071106 \tabularnewline
113 & 17 & 14.7659087707156 & 2.23409122928439 \tabularnewline
114 & 14 & 13.9804249905477 & 0.0195750094522665 \tabularnewline
115 & 16 & 12.9947584531866 & 3.00524154681336 \tabularnewline
116 & 12 & 14.0065703068552 & -2.00657030685520 \tabularnewline
117 & 16 & 14.3235182657491 & 1.67648173425089 \tabularnewline
118 & 8 & 10.5607830856912 & -2.56078308569116 \tabularnewline
119 & 9 & 12.1883336587029 & -3.18833365870288 \tabularnewline
120 & 13 & 11.8831289165083 & 1.11687108349165 \tabularnewline
121 & 19 & 15.2176278196193 & 3.78237218038072 \tabularnewline
122 & 11 & 12.4318895916441 & -1.43188959164413 \tabularnewline
123 & 15 & 14.8735806538499 & 0.126419346150068 \tabularnewline
124 & 11 & 13.4466997710899 & -2.44669977108987 \tabularnewline
125 & 15 & 15.8492974720167 & -0.849297472016677 \tabularnewline
126 & 16 & 16.0832409374489 & -0.0832409374488868 \tabularnewline
127 & 15 & 12.8115158499849 & 2.18848415001507 \tabularnewline
128 & 12 & 12.9738247901357 & -0.973824790135663 \tabularnewline
129 & 16 & 15.1167461618508 & 0.88325383814922 \tabularnewline
130 & 15 & 13.0787541659559 & 1.92124583404405 \tabularnewline
131 & 13 & 14.6048003186335 & -1.60480031863350 \tabularnewline
132 & 14 & 13.9855981904439 & 0.0144018095560894 \tabularnewline
133 & 11 & 13.3938277336665 & -2.39382773366646 \tabularnewline
134 & 15 & 14.6630824662544 & 0.336917533745566 \tabularnewline
135 & 14 & 12.7238953692018 & 1.27610463079824 \tabularnewline
136 & 13 & 16.0418341374656 & -3.04183413746564 \tabularnewline
137 & 15 & 16.0687584843007 & -1.06875848430071 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98353&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]15[/C][C]15.5922580224144[/C][C]-0.592258022414358[/C][/ROW]
[ROW][C]2[/C][C]9[/C][C]10.3763471898270[/C][C]-1.37634718982703[/C][/ROW]
[ROW][C]3[/C][C]12[/C][C]13.2610841337057[/C][C]-1.26108413370568[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]15.6703962274125[/C][C]-0.670396227412501[/C][/ROW]
[ROW][C]5[/C][C]17[/C][C]16.6597114733318[/C][C]0.340288526668162[/C][/ROW]
[ROW][C]6[/C][C]14[/C][C]13.0004670761162[/C][C]0.999532923883771[/C][/ROW]
[ROW][C]7[/C][C]9[/C][C]10.9857534804041[/C][C]-1.9857534804041[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]10.0442590439050[/C][C]0.955740956094975[/C][/ROW]
[ROW][C]9[/C][C]13[/C][C]14.4694499681551[/C][C]-1.46944996815505[/C][/ROW]
[ROW][C]10[/C][C]16[/C][C]14.8234126846683[/C][C]1.17658731533166[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]14.0250290987119[/C][C]1.97497090128810[/C][/ROW]
[ROW][C]12[/C][C]15[/C][C]14.2383308433863[/C][C]0.761669156613684[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]14.8514507853627[/C][C]-4.85145078536272[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.3497605056874[/C][C]0.650239494312601[/C][/ROW]
[ROW][C]15[/C][C]12[/C][C]15.1976467889243[/C][C]-3.19764678892434[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]14.1374767220122[/C][C]0.8625232779878[/C][/ROW]
[ROW][C]17[/C][C]13[/C][C]12.1215206615536[/C][C]0.87847933844641[/C][/ROW]
[ROW][C]18[/C][C]18[/C][C]15.1743187223615[/C][C]2.82568127763846[/C][/ROW]
[ROW][C]19[/C][C]13[/C][C]14.3098080866189[/C][C]-1.30980808661894[/C][/ROW]
[ROW][C]20[/C][C]17[/C][C]14.0236885336907[/C][C]2.97631146630934[/C][/ROW]
[ROW][C]21[/C][C]14[/C][C]13.8785020845659[/C][C]0.121497915434081[/C][/ROW]
[ROW][C]22[/C][C]13[/C][C]15.1811052970486[/C][C]-2.18110529704858[/C][/ROW]
[ROW][C]23[/C][C]13[/C][C]16.0910179849196[/C][C]-3.09101798491963[/C][/ROW]
[ROW][C]24[/C][C]15[/C][C]15.6619426301443[/C][C]-0.661942630144332[/C][/ROW]
[ROW][C]25[/C][C]15[/C][C]13.2794696348127[/C][C]1.7205303651873[/C][/ROW]
[ROW][C]26[/C][C]13[/C][C]13.5744007187145[/C][C]-0.574400718714475[/C][/ROW]
[ROW][C]27[/C][C]13[/C][C]15.3422262831563[/C][C]-2.34222628315628[/C][/ROW]
[ROW][C]28[/C][C]16[/C][C]13.3151082880934[/C][C]2.68489171190657[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]15.9534990870273[/C][C]-1.9534990870273[/C][/ROW]
[ROW][C]30[/C][C]18[/C][C]15.9888271007488[/C][C]2.01117289925119[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]10.7837882163600[/C][C]-1.78378821635997[/C][/ROW]
[ROW][C]32[/C][C]16[/C][C]16.1255323657905[/C][C]-0.125532365790519[/C][/ROW]
[ROW][C]33[/C][C]16[/C][C]15.1857238657002[/C][C]0.81427613429977[/C][/ROW]
[ROW][C]34[/C][C]17[/C][C]14.7783285904549[/C][C]2.2216714095451[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]15.1252718748917[/C][C]-2.12527187489165[/C][/ROW]
[ROW][C]36[/C][C]17[/C][C]13.9456636102596[/C][C]3.05433638974035[/C][/ROW]
[ROW][C]37[/C][C]15[/C][C]13.0959449568281[/C][C]1.90405504317186[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]14.1172809572284[/C][C]-0.117280957228351[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]12.1478420918796[/C][C]-2.14784209187965[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]14.2143000310942[/C][C]-1.21430003109421[/C][/ROW]
[ROW][C]41[/C][C]11[/C][C]13.2626123550771[/C][C]-2.26261235507708[/C][/ROW]
[ROW][C]42[/C][C]11[/C][C]13.0253576083747[/C][C]-2.02535760837472[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]15.4519651950179[/C][C]-0.451965195017864[/C][/ROW]
[ROW][C]44[/C][C]15[/C][C]15.2603954602792[/C][C]-0.260395460279236[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]12.5222703421012[/C][C]-0.522270342101192[/C][/ROW]
[ROW][C]46[/C][C]17[/C][C]14.7291894034684[/C][C]2.27081059653161[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]13.7087236167634[/C][C]1.29127638323656[/C][/ROW]
[ROW][C]48[/C][C]16[/C][C]15.5727077627709[/C][C]0.427292237229146[/C][/ROW]
[ROW][C]49[/C][C]14[/C][C]13.9357182709740[/C][C]0.0642817290259524[/C][/ROW]
[ROW][C]50[/C][C]17[/C][C]14.7350332397809[/C][C]2.26496676021906[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]10.3428067365893[/C][C]-0.342806736589346[/C][/ROW]
[ROW][C]52[/C][C]11[/C][C]14.0354719963516[/C][C]-3.03547199635159[/C][/ROW]
[ROW][C]53[/C][C]15[/C][C]14.0589772901077[/C][C]0.941022709892318[/C][/ROW]
[ROW][C]54[/C][C]15[/C][C]14.7352116988952[/C][C]0.264788301104821[/C][/ROW]
[ROW][C]55[/C][C]7[/C][C]10.6167900207868[/C][C]-3.61679002078684[/C][/ROW]
[ROW][C]56[/C][C]17[/C][C]14.9855898742421[/C][C]2.01441012575788[/C][/ROW]
[ROW][C]57[/C][C]14[/C][C]13.1297042512869[/C][C]0.870295748713117[/C][/ROW]
[ROW][C]58[/C][C]18[/C][C]15.7815428711431[/C][C]2.21845712885694[/C][/ROW]
[ROW][C]59[/C][C]14[/C][C]13.9352181407191[/C][C]0.0647818592809266[/C][/ROW]
[ROW][C]60[/C][C]14[/C][C]15.2895859629806[/C][C]-1.2895859629806[/C][/ROW]
[ROW][C]61[/C][C]9[/C][C]15.6732330420276[/C][C]-6.67323304202762[/C][/ROW]
[ROW][C]62[/C][C]14[/C][C]14.3648153945076[/C][C]-0.364815394507583[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]12.5319859520448[/C][C]-1.53198595204482[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]13.6474258360948[/C][C]2.35257416390516[/C][/ROW]
[ROW][C]65[/C][C]17[/C][C]15.0444232461064[/C][C]1.95557675389359[/C][/ROW]
[ROW][C]66[/C][C]12[/C][C]15.1335968952848[/C][C]-3.13359689528478[/C][/ROW]
[ROW][C]67[/C][C]15[/C][C]14.1446184413009[/C][C]0.855381558699093[/C][/ROW]
[ROW][C]68[/C][C]15[/C][C]15.5932417098202[/C][C]-0.593241709820164[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]16.3140717474382[/C][C]-0.31407174743823[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]16.6334281632169[/C][C]-0.633428163216929[/C][/ROW]
[ROW][C]71[/C][C]11[/C][C]12.5046700257125[/C][C]-1.50467002571253[/C][/ROW]
[ROW][C]72[/C][C]12[/C][C]12.7739898806394[/C][C]-0.773989880639433[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]14.1219496567694[/C][C]-0.121949656769382[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]15.7079336124191[/C][C]-0.707933612419097[/C][/ROW]
[ROW][C]75[/C][C]17[/C][C]15.6740037368294[/C][C]1.32599626317064[/C][/ROW]
[ROW][C]76[/C][C]19[/C][C]14.6619159333655[/C][C]4.33808406663445[/C][/ROW]
[ROW][C]77[/C][C]15[/C][C]13.7595515686571[/C][C]1.24044843134293[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]12.926964014305[/C][C]3.07303598569499[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]14.5226103599700[/C][C]-0.522610359970031[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]11.5087436118892[/C][C]4.49125638811078[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]14.5013675128032[/C][C]0.498632487196845[/C][/ROW]
[ROW][C]82[/C][C]17[/C][C]14.4613221404805[/C][C]2.53867785951953[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]14.4616919466267[/C][C]-2.46169194662671[/C][/ROW]
[ROW][C]84[/C][C]18[/C][C]14.8058708070347[/C][C]3.19412919296535[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]14.3291907616437[/C][C]-1.32919076164374[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]13.2804662388945[/C][C]0.719533761105549[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]13.7151715010423[/C][C]0.284828498957662[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]14.2258232926222[/C][C]-0.225823292622172[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]14.4414756042756[/C][C]-2.44147560427561[/C][/ROW]
[ROW][C]90[/C][C]14[/C][C]13.0583267045283[/C][C]0.941673295471697[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.3139957417549[/C][C]-1.31399574175488[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]14.6345865790794[/C][C]0.365413420920584[/C][/ROW]
[ROW][C]93[/C][C]11[/C][C]12.543957626866[/C][C]-1.54395762686600[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.2746915466298[/C][C]-0.274691546629762[/C][/ROW]
[ROW][C]95[/C][C]14[/C][C]14.4082683371557[/C][C]-0.408268337155737[/C][/ROW]
[ROW][C]96[/C][C]15[/C][C]13.6449961263209[/C][C]1.35500387367912[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]14.3291513240461[/C][C]1.67084867595388[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]11.3539775482019[/C][C]2.64602245179807[/C][/ROW]
[ROW][C]99[/C][C]18[/C][C]15.9306889171102[/C][C]2.06931108288978[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]15.0945360835772[/C][C]-1.09453608357724[/C][/ROW]
[ROW][C]101[/C][C]13[/C][C]12.6409821360560[/C][C]0.359017863944032[/C][/ROW]
[ROW][C]102[/C][C]14[/C][C]12.5977719529605[/C][C]1.40222804703946[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]14.7185273175246[/C][C]-0.718527317524611[/C][/ROW]
[ROW][C]104[/C][C]17[/C][C]15.5900538293438[/C][C]1.40994617065617[/C][/ROW]
[ROW][C]105[/C][C]12[/C][C]12.8424499485455[/C][C]-0.842449948545538[/C][/ROW]
[ROW][C]106[/C][C]16[/C][C]13.7961072149273[/C][C]2.20389278507266[/C][/ROW]
[ROW][C]107[/C][C]10[/C][C]12.3287532128499[/C][C]-2.32875321284993[/C][/ROW]
[ROW][C]108[/C][C]13[/C][C]14.5643015234240[/C][C]-1.56430152342403[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]15.3301671929391[/C][C]-0.330167192939076[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]14.9937588594251[/C][C]1.00624114057488[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]12.8242893787846[/C][C]1.17571062121543[/C][/ROW]
[ROW][C]112[/C][C]13[/C][C]12.4388900869289[/C][C]0.561109913071106[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.7659087707156[/C][C]2.23409122928439[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]13.9804249905477[/C][C]0.0195750094522665[/C][/ROW]
[ROW][C]115[/C][C]16[/C][C]12.9947584531866[/C][C]3.00524154681336[/C][/ROW]
[ROW][C]116[/C][C]12[/C][C]14.0065703068552[/C][C]-2.00657030685520[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]14.3235182657491[/C][C]1.67648173425089[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]10.5607830856912[/C][C]-2.56078308569116[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]12.1883336587029[/C][C]-3.18833365870288[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]11.8831289165083[/C][C]1.11687108349165[/C][/ROW]
[ROW][C]121[/C][C]19[/C][C]15.2176278196193[/C][C]3.78237218038072[/C][/ROW]
[ROW][C]122[/C][C]11[/C][C]12.4318895916441[/C][C]-1.43188959164413[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]14.8735806538499[/C][C]0.126419346150068[/C][/ROW]
[ROW][C]124[/C][C]11[/C][C]13.4466997710899[/C][C]-2.44669977108987[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.8492974720167[/C][C]-0.849297472016677[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.0832409374489[/C][C]-0.0832409374488868[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]12.8115158499849[/C][C]2.18848415001507[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]12.9738247901357[/C][C]-0.973824790135663[/C][/ROW]
[ROW][C]129[/C][C]16[/C][C]15.1167461618508[/C][C]0.88325383814922[/C][/ROW]
[ROW][C]130[/C][C]15[/C][C]13.0787541659559[/C][C]1.92124583404405[/C][/ROW]
[ROW][C]131[/C][C]13[/C][C]14.6048003186335[/C][C]-1.60480031863350[/C][/ROW]
[ROW][C]132[/C][C]14[/C][C]13.9855981904439[/C][C]0.0144018095560894[/C][/ROW]
[ROW][C]133[/C][C]11[/C][C]13.3938277336665[/C][C]-2.39382773366646[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]14.6630824662544[/C][C]0.336917533745566[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]12.7238953692018[/C][C]1.27610463079824[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]16.0418341374656[/C][C]-3.04183413746564[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]16.0687584843007[/C][C]-1.06875848430071[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98353&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98353&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11515.5922580224144-0.592258022414358
2910.3763471898270-1.37634718982703
31213.2610841337057-1.26108413370568
41515.6703962274125-0.670396227412501
51716.65971147333180.340288526668162
61413.00046707611620.999532923883771
7910.9857534804041-1.9857534804041
81110.04425904390500.955740956094975
91314.4694499681551-1.46944996815505
101614.82341268466831.17658731533166
111614.02502909871191.97497090128810
121514.23833084338630.761669156613684
131014.8514507853627-4.85145078536272
141615.34976050568740.650239494312601
151215.1976467889243-3.19764678892434
161514.13747672201220.8625232779878
171312.12152066155360.87847933844641
181815.17431872236152.82568127763846
191314.3098080866189-1.30980808661894
201714.02368853369072.97631146630934
211413.87850208456590.121497915434081
221315.1811052970486-2.18110529704858
231316.0910179849196-3.09101798491963
241515.6619426301443-0.661942630144332
251513.27946963481271.7205303651873
261313.5744007187145-0.574400718714475
271315.3422262831563-2.34222628315628
281613.31510828809342.68489171190657
291415.9534990870273-1.9534990870273
301815.98882710074882.01117289925119
31910.7837882163600-1.78378821635997
321616.1255323657905-0.125532365790519
331615.18572386570020.81427613429977
341714.77832859045492.2216714095451
351315.1252718748917-2.12527187489165
361713.94566361025963.05433638974035
371513.09594495682811.90405504317186
381414.1172809572284-0.117280957228351
391012.1478420918796-2.14784209187965
401314.2143000310942-1.21430003109421
411113.2626123550771-2.26261235507708
421113.0253576083747-2.02535760837472
431515.4519651950179-0.451965195017864
441515.2603954602792-0.260395460279236
451212.5222703421012-0.522270342101192
461714.72918940346842.27081059653161
471513.70872361676341.29127638323656
481615.57270776277090.427292237229146
491413.93571827097400.0642817290259524
501714.73503323978092.26496676021906
511010.3428067365893-0.342806736589346
521114.0354719963516-3.03547199635159
531514.05897729010770.941022709892318
541514.73521169889520.264788301104821
55710.6167900207868-3.61679002078684
561714.98558987424212.01441012575788
571413.12970425128690.870295748713117
581815.78154287114312.21845712885694
591413.93521814071910.0647818592809266
601415.2895859629806-1.2895859629806
61915.6732330420276-6.67323304202762
621414.3648153945076-0.364815394507583
631112.5319859520448-1.53198595204482
641613.64742583609482.35257416390516
651715.04442324610641.95557675389359
661215.1335968952848-3.13359689528478
671514.14461844130090.855381558699093
681515.5932417098202-0.593241709820164
691616.3140717474382-0.31407174743823
701616.6334281632169-0.633428163216929
711112.5046700257125-1.50467002571253
721212.7739898806394-0.773989880639433
731414.1219496567694-0.121949656769382
741515.7079336124191-0.707933612419097
751715.67400373682941.32599626317064
761914.66191593336554.33808406663445
771513.75955156865711.24044843134293
781612.9269640143053.07303598569499
791414.5226103599700-0.522610359970031
801611.50874361188924.49125638811078
811514.50136751280320.498632487196845
821714.46132214048052.53867785951953
831214.4616919466267-2.46169194662671
841814.80587080703473.19412919296535
851314.3291907616437-1.32919076164374
861413.28046623889450.719533761105549
871413.71517150104230.284828498957662
881414.2258232926222-0.225823292622172
891214.4414756042756-2.44147560427561
901413.05832670452830.941673295471697
911213.3139957417549-1.31399574175488
921514.63458657907940.365413420920584
931112.543957626866-1.54395762686600
941515.2746915466298-0.274691546629762
951414.4082683371557-0.408268337155737
961513.64499612632091.35500387367912
971614.32915132404611.67084867595388
981411.35397754820192.64602245179807
991815.93068891711022.06931108288978
1001415.0945360835772-1.09453608357724
1011312.64098213605600.359017863944032
1021412.59777195296051.40222804703946
1031414.7185273175246-0.718527317524611
1041715.59005382934381.40994617065617
1051212.8424499485455-0.842449948545538
1061613.79610721492732.20389278507266
1071012.3287532128499-2.32875321284993
1081314.5643015234240-1.56430152342403
1091515.3301671929391-0.330167192939076
1101614.99375885942511.00624114057488
1111412.82428937878461.17571062121543
1121312.43889008692890.561109913071106
1131714.76590877071562.23409122928439
1141413.98042499054770.0195750094522665
1151612.99475845318663.00524154681336
1161214.0065703068552-2.00657030685520
1171614.32351826574911.67648173425089
118810.5607830856912-2.56078308569116
119912.1883336587029-3.18833365870288
1201311.88312891650831.11687108349165
1211915.21762781961933.78237218038072
1221112.4318895916441-1.43188959164413
1231514.87358065384990.126419346150068
1241113.4466997710899-2.44669977108987
1251515.8492974720167-0.849297472016677
1261616.0832409374489-0.0832409374488868
1271512.81151584998492.18848415001507
1281212.9738247901357-0.973824790135663
1291615.11674616185080.88325383814922
1301513.07875416595591.92124583404405
1311314.6048003186335-1.60480031863350
1321413.98559819044390.0144018095560894
1331113.3938277336665-2.39382773366646
1341514.66308246625440.336917533745566
1351412.72389536920181.27610463079824
1361316.0418341374656-3.04183413746564
1371516.0687584843007-1.06875848430071






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.1352253417518210.2704506835036420.864774658248179
170.1884891193235220.3769782386470450.811510880676478
180.1241621701370850.2483243402741690.875837829862915
190.1304891203062530.2609782406125060.869510879693747
200.575667921168350.84866415766330.42433207883165
210.6482155230081880.7035689539836240.351784476991812
220.6766241340406490.6467517319187020.323375865959351
230.6783719089626230.6432561820747550.321628091037377
240.5965193253830090.8069613492339820.403480674616991
250.5199800295865430.9600399408269140.480019970413457
260.531152701110090.937694597779820.46884729888991
270.5146949172847930.9706101654304140.485305082715207
280.6699882688637480.6600234622725030.330011731136252
290.650559409515690.698881180968620.34944059048431
300.6625761905351910.6748476189296180.337423809464809
310.6432599624312070.7134800751375860.356740037568793
320.5778542819058080.8442914361883840.422145718094192
330.5151584176825420.9696831646349160.484841582317458
340.5424805551758640.9150388896482730.457519444824136
350.517507810884880.964984378230240.48249218911512
360.6159385020009040.7681229959981910.384061497999096
370.6884050923644360.6231898152711270.311594907635564
380.6339818389472920.7320363221054150.366018161052708
390.6137169871113020.7725660257773960.386283012888698
400.5889397002817410.8221205994365190.411060299718259
410.5883445330037220.8233109339925550.411655466996277
420.614177249626950.77164550074610.38582275037305
430.5554332965824560.8891334068350880.444566703417544
440.496029442436640.992058884873280.50397055756336
450.445976813443240.891953626886480.55402318655676
460.5079056284900390.9841887430199230.492094371509961
470.516302972763820.967394054472360.48369702723618
480.4724743178875390.9449486357750780.527525682112461
490.4167024373105550.833404874621110.583297562689445
500.4164219321355890.8328438642711790.58357806786441
510.3708105334708630.7416210669417250.629189466529137
520.4508850094205930.9017700188411860.549114990579407
530.416890857905620.833781715811240.58310914209438
540.3635592363786110.7271184727572230.636440763621389
550.4944881313805270.9889762627610540.505511868619473
560.4833550722578850.966710144515770.516644927742115
570.4659593337573220.9319186675146440.534040666242678
580.4756158083858500.9512316167717010.52438419161415
590.4218139243012540.8436278486025090.578186075698746
600.3800292881071410.7600585762142820.619970711892859
610.8569980761636610.2860038476726780.143001923836339
620.8282798683385030.3434402633229940.171720131661497
630.8052311796657070.3895376406685860.194768820334293
640.8121863922031460.3756272155937080.187813607796854
650.8257579089546120.3484841820907770.174242091045388
660.9038261710879780.1923476578240440.0961738289120219
670.8832218174971430.2335563650057150.116778182502857
680.8618076826470770.2763846347058450.138192317352923
690.8342332387443650.3315335225112700.165766761255635
700.8108221743630540.3783556512738920.189177825636946
710.8151187519106610.3697624961786770.184881248089339
720.7827044587957380.4345910824085250.217295541204262
730.747302944016250.5053941119675010.252697055983750
740.7305506276716960.5388987446566080.269449372328304
750.7417462202861540.5165075594276930.258253779713846
760.8777341188934780.2445317622130450.122265881106522
770.8580257771341510.2839484457316970.141974222865849
780.8923740440914450.2152519118171090.107625955908554
790.8666789880219750.2666420239560510.133321011978025
800.9617370449185860.0765259101628270.0382629550814135
810.9499889938079430.1000220123841130.0500110061920566
820.949122152098660.1017556958026790.0508778479013397
830.9504839723888820.09903205522223510.0495160276111176
840.964360406586880.0712791868262420.035639593413121
850.9570647708166360.08587045836672710.0429352291833635
860.9454571401182240.1090857197635530.0545428598817765
870.9278108220486750.1443783559026500.0721891779513251
880.9101494250473850.1797011499052290.0898505749526147
890.9110007157643730.1779985684712540.088999284235627
900.8872636928582060.2254726142835870.112736307141793
910.892884796976060.2142304060478800.107115203023940
920.8703031998130040.2593936003739920.129696800186996
930.8480171177817840.3039657644364310.151982882218216
940.8096520755675230.3806958488649540.190347924432477
950.7759129892348090.4481740215303820.224087010765191
960.7594595899965650.4810808200068690.240540410003435
970.8073053068887740.3853893862224520.192694693111226
980.81176186048020.37647627903960.1882381395198
990.7875532020922480.4248935958155030.212446797907752
1000.7514372576913040.4971254846173920.248562742308696
1010.7008321199429470.5983357601141070.299167880057053
1020.6682075382066280.6635849235867440.331792461793372
1030.6092915560996950.781416887800610.390708443900305
1040.5605245679344330.8789508641311340.439475432065567
1050.4988153159540680.9976306319081360.501184684045932
1060.7985693151850510.4028613696298980.201430684814949
1070.8149650846914820.3700698306170370.185034915308518
1080.8474995306156630.3050009387686740.152500469384337
1090.7934566289662560.4130867420674890.206543371033744
1100.747421336349240.5051573273015210.252578663650760
1110.7551056405430610.4897887189138770.244894359456939
1120.6909496864707730.6181006270584530.309050313529227
1130.6179279405848890.7641441188302220.382072059415111
1140.53074161877650.9385167624470.4692583812235
1150.5479550923653350.904089815269330.452044907634665
1160.5787925165208920.8424149669582160.421207483479108
1170.5859750484921050.828049903015790.414024951507895
1180.6042581134745720.7914837730508560.395741886525428
1190.6759334835825280.6481330328349440.324066516417472
1200.679250666819180.6414986663616410.320749333180821
1210.5402030707759810.9195938584480390.459796929224019

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.135225341751821 & 0.270450683503642 & 0.864774658248179 \tabularnewline
17 & 0.188489119323522 & 0.376978238647045 & 0.811510880676478 \tabularnewline
18 & 0.124162170137085 & 0.248324340274169 & 0.875837829862915 \tabularnewline
19 & 0.130489120306253 & 0.260978240612506 & 0.869510879693747 \tabularnewline
20 & 0.57566792116835 & 0.8486641576633 & 0.42433207883165 \tabularnewline
21 & 0.648215523008188 & 0.703568953983624 & 0.351784476991812 \tabularnewline
22 & 0.676624134040649 & 0.646751731918702 & 0.323375865959351 \tabularnewline
23 & 0.678371908962623 & 0.643256182074755 & 0.321628091037377 \tabularnewline
24 & 0.596519325383009 & 0.806961349233982 & 0.403480674616991 \tabularnewline
25 & 0.519980029586543 & 0.960039940826914 & 0.480019970413457 \tabularnewline
26 & 0.53115270111009 & 0.93769459777982 & 0.46884729888991 \tabularnewline
27 & 0.514694917284793 & 0.970610165430414 & 0.485305082715207 \tabularnewline
28 & 0.669988268863748 & 0.660023462272503 & 0.330011731136252 \tabularnewline
29 & 0.65055940951569 & 0.69888118096862 & 0.34944059048431 \tabularnewline
30 & 0.662576190535191 & 0.674847618929618 & 0.337423809464809 \tabularnewline
31 & 0.643259962431207 & 0.713480075137586 & 0.356740037568793 \tabularnewline
32 & 0.577854281905808 & 0.844291436188384 & 0.422145718094192 \tabularnewline
33 & 0.515158417682542 & 0.969683164634916 & 0.484841582317458 \tabularnewline
34 & 0.542480555175864 & 0.915038889648273 & 0.457519444824136 \tabularnewline
35 & 0.51750781088488 & 0.96498437823024 & 0.48249218911512 \tabularnewline
36 & 0.615938502000904 & 0.768122995998191 & 0.384061497999096 \tabularnewline
37 & 0.688405092364436 & 0.623189815271127 & 0.311594907635564 \tabularnewline
38 & 0.633981838947292 & 0.732036322105415 & 0.366018161052708 \tabularnewline
39 & 0.613716987111302 & 0.772566025777396 & 0.386283012888698 \tabularnewline
40 & 0.588939700281741 & 0.822120599436519 & 0.411060299718259 \tabularnewline
41 & 0.588344533003722 & 0.823310933992555 & 0.411655466996277 \tabularnewline
42 & 0.61417724962695 & 0.7716455007461 & 0.38582275037305 \tabularnewline
43 & 0.555433296582456 & 0.889133406835088 & 0.444566703417544 \tabularnewline
44 & 0.49602944243664 & 0.99205888487328 & 0.50397055756336 \tabularnewline
45 & 0.44597681344324 & 0.89195362688648 & 0.55402318655676 \tabularnewline
46 & 0.507905628490039 & 0.984188743019923 & 0.492094371509961 \tabularnewline
47 & 0.51630297276382 & 0.96739405447236 & 0.48369702723618 \tabularnewline
48 & 0.472474317887539 & 0.944948635775078 & 0.527525682112461 \tabularnewline
49 & 0.416702437310555 & 0.83340487462111 & 0.583297562689445 \tabularnewline
50 & 0.416421932135589 & 0.832843864271179 & 0.58357806786441 \tabularnewline
51 & 0.370810533470863 & 0.741621066941725 & 0.629189466529137 \tabularnewline
52 & 0.450885009420593 & 0.901770018841186 & 0.549114990579407 \tabularnewline
53 & 0.41689085790562 & 0.83378171581124 & 0.58310914209438 \tabularnewline
54 & 0.363559236378611 & 0.727118472757223 & 0.636440763621389 \tabularnewline
55 & 0.494488131380527 & 0.988976262761054 & 0.505511868619473 \tabularnewline
56 & 0.483355072257885 & 0.96671014451577 & 0.516644927742115 \tabularnewline
57 & 0.465959333757322 & 0.931918667514644 & 0.534040666242678 \tabularnewline
58 & 0.475615808385850 & 0.951231616771701 & 0.52438419161415 \tabularnewline
59 & 0.421813924301254 & 0.843627848602509 & 0.578186075698746 \tabularnewline
60 & 0.380029288107141 & 0.760058576214282 & 0.619970711892859 \tabularnewline
61 & 0.856998076163661 & 0.286003847672678 & 0.143001923836339 \tabularnewline
62 & 0.828279868338503 & 0.343440263322994 & 0.171720131661497 \tabularnewline
63 & 0.805231179665707 & 0.389537640668586 & 0.194768820334293 \tabularnewline
64 & 0.812186392203146 & 0.375627215593708 & 0.187813607796854 \tabularnewline
65 & 0.825757908954612 & 0.348484182090777 & 0.174242091045388 \tabularnewline
66 & 0.903826171087978 & 0.192347657824044 & 0.0961738289120219 \tabularnewline
67 & 0.883221817497143 & 0.233556365005715 & 0.116778182502857 \tabularnewline
68 & 0.861807682647077 & 0.276384634705845 & 0.138192317352923 \tabularnewline
69 & 0.834233238744365 & 0.331533522511270 & 0.165766761255635 \tabularnewline
70 & 0.810822174363054 & 0.378355651273892 & 0.189177825636946 \tabularnewline
71 & 0.815118751910661 & 0.369762496178677 & 0.184881248089339 \tabularnewline
72 & 0.782704458795738 & 0.434591082408525 & 0.217295541204262 \tabularnewline
73 & 0.74730294401625 & 0.505394111967501 & 0.252697055983750 \tabularnewline
74 & 0.730550627671696 & 0.538898744656608 & 0.269449372328304 \tabularnewline
75 & 0.741746220286154 & 0.516507559427693 & 0.258253779713846 \tabularnewline
76 & 0.877734118893478 & 0.244531762213045 & 0.122265881106522 \tabularnewline
77 & 0.858025777134151 & 0.283948445731697 & 0.141974222865849 \tabularnewline
78 & 0.892374044091445 & 0.215251911817109 & 0.107625955908554 \tabularnewline
79 & 0.866678988021975 & 0.266642023956051 & 0.133321011978025 \tabularnewline
80 & 0.961737044918586 & 0.076525910162827 & 0.0382629550814135 \tabularnewline
81 & 0.949988993807943 & 0.100022012384113 & 0.0500110061920566 \tabularnewline
82 & 0.94912215209866 & 0.101755695802679 & 0.0508778479013397 \tabularnewline
83 & 0.950483972388882 & 0.0990320552222351 & 0.0495160276111176 \tabularnewline
84 & 0.96436040658688 & 0.071279186826242 & 0.035639593413121 \tabularnewline
85 & 0.957064770816636 & 0.0858704583667271 & 0.0429352291833635 \tabularnewline
86 & 0.945457140118224 & 0.109085719763553 & 0.0545428598817765 \tabularnewline
87 & 0.927810822048675 & 0.144378355902650 & 0.0721891779513251 \tabularnewline
88 & 0.910149425047385 & 0.179701149905229 & 0.0898505749526147 \tabularnewline
89 & 0.911000715764373 & 0.177998568471254 & 0.088999284235627 \tabularnewline
90 & 0.887263692858206 & 0.225472614283587 & 0.112736307141793 \tabularnewline
91 & 0.89288479697606 & 0.214230406047880 & 0.107115203023940 \tabularnewline
92 & 0.870303199813004 & 0.259393600373992 & 0.129696800186996 \tabularnewline
93 & 0.848017117781784 & 0.303965764436431 & 0.151982882218216 \tabularnewline
94 & 0.809652075567523 & 0.380695848864954 & 0.190347924432477 \tabularnewline
95 & 0.775912989234809 & 0.448174021530382 & 0.224087010765191 \tabularnewline
96 & 0.759459589996565 & 0.481080820006869 & 0.240540410003435 \tabularnewline
97 & 0.807305306888774 & 0.385389386222452 & 0.192694693111226 \tabularnewline
98 & 0.8117618604802 & 0.3764762790396 & 0.1882381395198 \tabularnewline
99 & 0.787553202092248 & 0.424893595815503 & 0.212446797907752 \tabularnewline
100 & 0.751437257691304 & 0.497125484617392 & 0.248562742308696 \tabularnewline
101 & 0.700832119942947 & 0.598335760114107 & 0.299167880057053 \tabularnewline
102 & 0.668207538206628 & 0.663584923586744 & 0.331792461793372 \tabularnewline
103 & 0.609291556099695 & 0.78141688780061 & 0.390708443900305 \tabularnewline
104 & 0.560524567934433 & 0.878950864131134 & 0.439475432065567 \tabularnewline
105 & 0.498815315954068 & 0.997630631908136 & 0.501184684045932 \tabularnewline
106 & 0.798569315185051 & 0.402861369629898 & 0.201430684814949 \tabularnewline
107 & 0.814965084691482 & 0.370069830617037 & 0.185034915308518 \tabularnewline
108 & 0.847499530615663 & 0.305000938768674 & 0.152500469384337 \tabularnewline
109 & 0.793456628966256 & 0.413086742067489 & 0.206543371033744 \tabularnewline
110 & 0.74742133634924 & 0.505157327301521 & 0.252578663650760 \tabularnewline
111 & 0.755105640543061 & 0.489788718913877 & 0.244894359456939 \tabularnewline
112 & 0.690949686470773 & 0.618100627058453 & 0.309050313529227 \tabularnewline
113 & 0.617927940584889 & 0.764144118830222 & 0.382072059415111 \tabularnewline
114 & 0.5307416187765 & 0.938516762447 & 0.4692583812235 \tabularnewline
115 & 0.547955092365335 & 0.90408981526933 & 0.452044907634665 \tabularnewline
116 & 0.578792516520892 & 0.842414966958216 & 0.421207483479108 \tabularnewline
117 & 0.585975048492105 & 0.82804990301579 & 0.414024951507895 \tabularnewline
118 & 0.604258113474572 & 0.791483773050856 & 0.395741886525428 \tabularnewline
119 & 0.675933483582528 & 0.648133032834944 & 0.324066516417472 \tabularnewline
120 & 0.67925066681918 & 0.641498666361641 & 0.320749333180821 \tabularnewline
121 & 0.540203070775981 & 0.919593858448039 & 0.459796929224019 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98353&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]0.135225341751821[/C][C]0.270450683503642[/C][C]0.864774658248179[/C][/ROW]
[ROW][C]17[/C][C]0.188489119323522[/C][C]0.376978238647045[/C][C]0.811510880676478[/C][/ROW]
[ROW][C]18[/C][C]0.124162170137085[/C][C]0.248324340274169[/C][C]0.875837829862915[/C][/ROW]
[ROW][C]19[/C][C]0.130489120306253[/C][C]0.260978240612506[/C][C]0.869510879693747[/C][/ROW]
[ROW][C]20[/C][C]0.57566792116835[/C][C]0.8486641576633[/C][C]0.42433207883165[/C][/ROW]
[ROW][C]21[/C][C]0.648215523008188[/C][C]0.703568953983624[/C][C]0.351784476991812[/C][/ROW]
[ROW][C]22[/C][C]0.676624134040649[/C][C]0.646751731918702[/C][C]0.323375865959351[/C][/ROW]
[ROW][C]23[/C][C]0.678371908962623[/C][C]0.643256182074755[/C][C]0.321628091037377[/C][/ROW]
[ROW][C]24[/C][C]0.596519325383009[/C][C]0.806961349233982[/C][C]0.403480674616991[/C][/ROW]
[ROW][C]25[/C][C]0.519980029586543[/C][C]0.960039940826914[/C][C]0.480019970413457[/C][/ROW]
[ROW][C]26[/C][C]0.53115270111009[/C][C]0.93769459777982[/C][C]0.46884729888991[/C][/ROW]
[ROW][C]27[/C][C]0.514694917284793[/C][C]0.970610165430414[/C][C]0.485305082715207[/C][/ROW]
[ROW][C]28[/C][C]0.669988268863748[/C][C]0.660023462272503[/C][C]0.330011731136252[/C][/ROW]
[ROW][C]29[/C][C]0.65055940951569[/C][C]0.69888118096862[/C][C]0.34944059048431[/C][/ROW]
[ROW][C]30[/C][C]0.662576190535191[/C][C]0.674847618929618[/C][C]0.337423809464809[/C][/ROW]
[ROW][C]31[/C][C]0.643259962431207[/C][C]0.713480075137586[/C][C]0.356740037568793[/C][/ROW]
[ROW][C]32[/C][C]0.577854281905808[/C][C]0.844291436188384[/C][C]0.422145718094192[/C][/ROW]
[ROW][C]33[/C][C]0.515158417682542[/C][C]0.969683164634916[/C][C]0.484841582317458[/C][/ROW]
[ROW][C]34[/C][C]0.542480555175864[/C][C]0.915038889648273[/C][C]0.457519444824136[/C][/ROW]
[ROW][C]35[/C][C]0.51750781088488[/C][C]0.96498437823024[/C][C]0.48249218911512[/C][/ROW]
[ROW][C]36[/C][C]0.615938502000904[/C][C]0.768122995998191[/C][C]0.384061497999096[/C][/ROW]
[ROW][C]37[/C][C]0.688405092364436[/C][C]0.623189815271127[/C][C]0.311594907635564[/C][/ROW]
[ROW][C]38[/C][C]0.633981838947292[/C][C]0.732036322105415[/C][C]0.366018161052708[/C][/ROW]
[ROW][C]39[/C][C]0.613716987111302[/C][C]0.772566025777396[/C][C]0.386283012888698[/C][/ROW]
[ROW][C]40[/C][C]0.588939700281741[/C][C]0.822120599436519[/C][C]0.411060299718259[/C][/ROW]
[ROW][C]41[/C][C]0.588344533003722[/C][C]0.823310933992555[/C][C]0.411655466996277[/C][/ROW]
[ROW][C]42[/C][C]0.61417724962695[/C][C]0.7716455007461[/C][C]0.38582275037305[/C][/ROW]
[ROW][C]43[/C][C]0.555433296582456[/C][C]0.889133406835088[/C][C]0.444566703417544[/C][/ROW]
[ROW][C]44[/C][C]0.49602944243664[/C][C]0.99205888487328[/C][C]0.50397055756336[/C][/ROW]
[ROW][C]45[/C][C]0.44597681344324[/C][C]0.89195362688648[/C][C]0.55402318655676[/C][/ROW]
[ROW][C]46[/C][C]0.507905628490039[/C][C]0.984188743019923[/C][C]0.492094371509961[/C][/ROW]
[ROW][C]47[/C][C]0.51630297276382[/C][C]0.96739405447236[/C][C]0.48369702723618[/C][/ROW]
[ROW][C]48[/C][C]0.472474317887539[/C][C]0.944948635775078[/C][C]0.527525682112461[/C][/ROW]
[ROW][C]49[/C][C]0.416702437310555[/C][C]0.83340487462111[/C][C]0.583297562689445[/C][/ROW]
[ROW][C]50[/C][C]0.416421932135589[/C][C]0.832843864271179[/C][C]0.58357806786441[/C][/ROW]
[ROW][C]51[/C][C]0.370810533470863[/C][C]0.741621066941725[/C][C]0.629189466529137[/C][/ROW]
[ROW][C]52[/C][C]0.450885009420593[/C][C]0.901770018841186[/C][C]0.549114990579407[/C][/ROW]
[ROW][C]53[/C][C]0.41689085790562[/C][C]0.83378171581124[/C][C]0.58310914209438[/C][/ROW]
[ROW][C]54[/C][C]0.363559236378611[/C][C]0.727118472757223[/C][C]0.636440763621389[/C][/ROW]
[ROW][C]55[/C][C]0.494488131380527[/C][C]0.988976262761054[/C][C]0.505511868619473[/C][/ROW]
[ROW][C]56[/C][C]0.483355072257885[/C][C]0.96671014451577[/C][C]0.516644927742115[/C][/ROW]
[ROW][C]57[/C][C]0.465959333757322[/C][C]0.931918667514644[/C][C]0.534040666242678[/C][/ROW]
[ROW][C]58[/C][C]0.475615808385850[/C][C]0.951231616771701[/C][C]0.52438419161415[/C][/ROW]
[ROW][C]59[/C][C]0.421813924301254[/C][C]0.843627848602509[/C][C]0.578186075698746[/C][/ROW]
[ROW][C]60[/C][C]0.380029288107141[/C][C]0.760058576214282[/C][C]0.619970711892859[/C][/ROW]
[ROW][C]61[/C][C]0.856998076163661[/C][C]0.286003847672678[/C][C]0.143001923836339[/C][/ROW]
[ROW][C]62[/C][C]0.828279868338503[/C][C]0.343440263322994[/C][C]0.171720131661497[/C][/ROW]
[ROW][C]63[/C][C]0.805231179665707[/C][C]0.389537640668586[/C][C]0.194768820334293[/C][/ROW]
[ROW][C]64[/C][C]0.812186392203146[/C][C]0.375627215593708[/C][C]0.187813607796854[/C][/ROW]
[ROW][C]65[/C][C]0.825757908954612[/C][C]0.348484182090777[/C][C]0.174242091045388[/C][/ROW]
[ROW][C]66[/C][C]0.903826171087978[/C][C]0.192347657824044[/C][C]0.0961738289120219[/C][/ROW]
[ROW][C]67[/C][C]0.883221817497143[/C][C]0.233556365005715[/C][C]0.116778182502857[/C][/ROW]
[ROW][C]68[/C][C]0.861807682647077[/C][C]0.276384634705845[/C][C]0.138192317352923[/C][/ROW]
[ROW][C]69[/C][C]0.834233238744365[/C][C]0.331533522511270[/C][C]0.165766761255635[/C][/ROW]
[ROW][C]70[/C][C]0.810822174363054[/C][C]0.378355651273892[/C][C]0.189177825636946[/C][/ROW]
[ROW][C]71[/C][C]0.815118751910661[/C][C]0.369762496178677[/C][C]0.184881248089339[/C][/ROW]
[ROW][C]72[/C][C]0.782704458795738[/C][C]0.434591082408525[/C][C]0.217295541204262[/C][/ROW]
[ROW][C]73[/C][C]0.74730294401625[/C][C]0.505394111967501[/C][C]0.252697055983750[/C][/ROW]
[ROW][C]74[/C][C]0.730550627671696[/C][C]0.538898744656608[/C][C]0.269449372328304[/C][/ROW]
[ROW][C]75[/C][C]0.741746220286154[/C][C]0.516507559427693[/C][C]0.258253779713846[/C][/ROW]
[ROW][C]76[/C][C]0.877734118893478[/C][C]0.244531762213045[/C][C]0.122265881106522[/C][/ROW]
[ROW][C]77[/C][C]0.858025777134151[/C][C]0.283948445731697[/C][C]0.141974222865849[/C][/ROW]
[ROW][C]78[/C][C]0.892374044091445[/C][C]0.215251911817109[/C][C]0.107625955908554[/C][/ROW]
[ROW][C]79[/C][C]0.866678988021975[/C][C]0.266642023956051[/C][C]0.133321011978025[/C][/ROW]
[ROW][C]80[/C][C]0.961737044918586[/C][C]0.076525910162827[/C][C]0.0382629550814135[/C][/ROW]
[ROW][C]81[/C][C]0.949988993807943[/C][C]0.100022012384113[/C][C]0.0500110061920566[/C][/ROW]
[ROW][C]82[/C][C]0.94912215209866[/C][C]0.101755695802679[/C][C]0.0508778479013397[/C][/ROW]
[ROW][C]83[/C][C]0.950483972388882[/C][C]0.0990320552222351[/C][C]0.0495160276111176[/C][/ROW]
[ROW][C]84[/C][C]0.96436040658688[/C][C]0.071279186826242[/C][C]0.035639593413121[/C][/ROW]
[ROW][C]85[/C][C]0.957064770816636[/C][C]0.0858704583667271[/C][C]0.0429352291833635[/C][/ROW]
[ROW][C]86[/C][C]0.945457140118224[/C][C]0.109085719763553[/C][C]0.0545428598817765[/C][/ROW]
[ROW][C]87[/C][C]0.927810822048675[/C][C]0.144378355902650[/C][C]0.0721891779513251[/C][/ROW]
[ROW][C]88[/C][C]0.910149425047385[/C][C]0.179701149905229[/C][C]0.0898505749526147[/C][/ROW]
[ROW][C]89[/C][C]0.911000715764373[/C][C]0.177998568471254[/C][C]0.088999284235627[/C][/ROW]
[ROW][C]90[/C][C]0.887263692858206[/C][C]0.225472614283587[/C][C]0.112736307141793[/C][/ROW]
[ROW][C]91[/C][C]0.89288479697606[/C][C]0.214230406047880[/C][C]0.107115203023940[/C][/ROW]
[ROW][C]92[/C][C]0.870303199813004[/C][C]0.259393600373992[/C][C]0.129696800186996[/C][/ROW]
[ROW][C]93[/C][C]0.848017117781784[/C][C]0.303965764436431[/C][C]0.151982882218216[/C][/ROW]
[ROW][C]94[/C][C]0.809652075567523[/C][C]0.380695848864954[/C][C]0.190347924432477[/C][/ROW]
[ROW][C]95[/C][C]0.775912989234809[/C][C]0.448174021530382[/C][C]0.224087010765191[/C][/ROW]
[ROW][C]96[/C][C]0.759459589996565[/C][C]0.481080820006869[/C][C]0.240540410003435[/C][/ROW]
[ROW][C]97[/C][C]0.807305306888774[/C][C]0.385389386222452[/C][C]0.192694693111226[/C][/ROW]
[ROW][C]98[/C][C]0.8117618604802[/C][C]0.3764762790396[/C][C]0.1882381395198[/C][/ROW]
[ROW][C]99[/C][C]0.787553202092248[/C][C]0.424893595815503[/C][C]0.212446797907752[/C][/ROW]
[ROW][C]100[/C][C]0.751437257691304[/C][C]0.497125484617392[/C][C]0.248562742308696[/C][/ROW]
[ROW][C]101[/C][C]0.700832119942947[/C][C]0.598335760114107[/C][C]0.299167880057053[/C][/ROW]
[ROW][C]102[/C][C]0.668207538206628[/C][C]0.663584923586744[/C][C]0.331792461793372[/C][/ROW]
[ROW][C]103[/C][C]0.609291556099695[/C][C]0.78141688780061[/C][C]0.390708443900305[/C][/ROW]
[ROW][C]104[/C][C]0.560524567934433[/C][C]0.878950864131134[/C][C]0.439475432065567[/C][/ROW]
[ROW][C]105[/C][C]0.498815315954068[/C][C]0.997630631908136[/C][C]0.501184684045932[/C][/ROW]
[ROW][C]106[/C][C]0.798569315185051[/C][C]0.402861369629898[/C][C]0.201430684814949[/C][/ROW]
[ROW][C]107[/C][C]0.814965084691482[/C][C]0.370069830617037[/C][C]0.185034915308518[/C][/ROW]
[ROW][C]108[/C][C]0.847499530615663[/C][C]0.305000938768674[/C][C]0.152500469384337[/C][/ROW]
[ROW][C]109[/C][C]0.793456628966256[/C][C]0.413086742067489[/C][C]0.206543371033744[/C][/ROW]
[ROW][C]110[/C][C]0.74742133634924[/C][C]0.505157327301521[/C][C]0.252578663650760[/C][/ROW]
[ROW][C]111[/C][C]0.755105640543061[/C][C]0.489788718913877[/C][C]0.244894359456939[/C][/ROW]
[ROW][C]112[/C][C]0.690949686470773[/C][C]0.618100627058453[/C][C]0.309050313529227[/C][/ROW]
[ROW][C]113[/C][C]0.617927940584889[/C][C]0.764144118830222[/C][C]0.382072059415111[/C][/ROW]
[ROW][C]114[/C][C]0.5307416187765[/C][C]0.938516762447[/C][C]0.4692583812235[/C][/ROW]
[ROW][C]115[/C][C]0.547955092365335[/C][C]0.90408981526933[/C][C]0.452044907634665[/C][/ROW]
[ROW][C]116[/C][C]0.578792516520892[/C][C]0.842414966958216[/C][C]0.421207483479108[/C][/ROW]
[ROW][C]117[/C][C]0.585975048492105[/C][C]0.82804990301579[/C][C]0.414024951507895[/C][/ROW]
[ROW][C]118[/C][C]0.604258113474572[/C][C]0.791483773050856[/C][C]0.395741886525428[/C][/ROW]
[ROW][C]119[/C][C]0.675933483582528[/C][C]0.648133032834944[/C][C]0.324066516417472[/C][/ROW]
[ROW][C]120[/C][C]0.67925066681918[/C][C]0.641498666361641[/C][C]0.320749333180821[/C][/ROW]
[ROW][C]121[/C][C]0.540203070775981[/C][C]0.919593858448039[/C][C]0.459796929224019[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98353&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98353&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.1352253417518210.2704506835036420.864774658248179
170.1884891193235220.3769782386470450.811510880676478
180.1241621701370850.2483243402741690.875837829862915
190.1304891203062530.2609782406125060.869510879693747
200.575667921168350.84866415766330.42433207883165
210.6482155230081880.7035689539836240.351784476991812
220.6766241340406490.6467517319187020.323375865959351
230.6783719089626230.6432561820747550.321628091037377
240.5965193253830090.8069613492339820.403480674616991
250.5199800295865430.9600399408269140.480019970413457
260.531152701110090.937694597779820.46884729888991
270.5146949172847930.9706101654304140.485305082715207
280.6699882688637480.6600234622725030.330011731136252
290.650559409515690.698881180968620.34944059048431
300.6625761905351910.6748476189296180.337423809464809
310.6432599624312070.7134800751375860.356740037568793
320.5778542819058080.8442914361883840.422145718094192
330.5151584176825420.9696831646349160.484841582317458
340.5424805551758640.9150388896482730.457519444824136
350.517507810884880.964984378230240.48249218911512
360.6159385020009040.7681229959981910.384061497999096
370.6884050923644360.6231898152711270.311594907635564
380.6339818389472920.7320363221054150.366018161052708
390.6137169871113020.7725660257773960.386283012888698
400.5889397002817410.8221205994365190.411060299718259
410.5883445330037220.8233109339925550.411655466996277
420.614177249626950.77164550074610.38582275037305
430.5554332965824560.8891334068350880.444566703417544
440.496029442436640.992058884873280.50397055756336
450.445976813443240.891953626886480.55402318655676
460.5079056284900390.9841887430199230.492094371509961
470.516302972763820.967394054472360.48369702723618
480.4724743178875390.9449486357750780.527525682112461
490.4167024373105550.833404874621110.583297562689445
500.4164219321355890.8328438642711790.58357806786441
510.3708105334708630.7416210669417250.629189466529137
520.4508850094205930.9017700188411860.549114990579407
530.416890857905620.833781715811240.58310914209438
540.3635592363786110.7271184727572230.636440763621389
550.4944881313805270.9889762627610540.505511868619473
560.4833550722578850.966710144515770.516644927742115
570.4659593337573220.9319186675146440.534040666242678
580.4756158083858500.9512316167717010.52438419161415
590.4218139243012540.8436278486025090.578186075698746
600.3800292881071410.7600585762142820.619970711892859
610.8569980761636610.2860038476726780.143001923836339
620.8282798683385030.3434402633229940.171720131661497
630.8052311796657070.3895376406685860.194768820334293
640.8121863922031460.3756272155937080.187813607796854
650.8257579089546120.3484841820907770.174242091045388
660.9038261710879780.1923476578240440.0961738289120219
670.8832218174971430.2335563650057150.116778182502857
680.8618076826470770.2763846347058450.138192317352923
690.8342332387443650.3315335225112700.165766761255635
700.8108221743630540.3783556512738920.189177825636946
710.8151187519106610.3697624961786770.184881248089339
720.7827044587957380.4345910824085250.217295541204262
730.747302944016250.5053941119675010.252697055983750
740.7305506276716960.5388987446566080.269449372328304
750.7417462202861540.5165075594276930.258253779713846
760.8777341188934780.2445317622130450.122265881106522
770.8580257771341510.2839484457316970.141974222865849
780.8923740440914450.2152519118171090.107625955908554
790.8666789880219750.2666420239560510.133321011978025
800.9617370449185860.0765259101628270.0382629550814135
810.9499889938079430.1000220123841130.0500110061920566
820.949122152098660.1017556958026790.0508778479013397
830.9504839723888820.09903205522223510.0495160276111176
840.964360406586880.0712791868262420.035639593413121
850.9570647708166360.08587045836672710.0429352291833635
860.9454571401182240.1090857197635530.0545428598817765
870.9278108220486750.1443783559026500.0721891779513251
880.9101494250473850.1797011499052290.0898505749526147
890.9110007157643730.1779985684712540.088999284235627
900.8872636928582060.2254726142835870.112736307141793
910.892884796976060.2142304060478800.107115203023940
920.8703031998130040.2593936003739920.129696800186996
930.8480171177817840.3039657644364310.151982882218216
940.8096520755675230.3806958488649540.190347924432477
950.7759129892348090.4481740215303820.224087010765191
960.7594595899965650.4810808200068690.240540410003435
970.8073053068887740.3853893862224520.192694693111226
980.81176186048020.37647627903960.1882381395198
990.7875532020922480.4248935958155030.212446797907752
1000.7514372576913040.4971254846173920.248562742308696
1010.7008321199429470.5983357601141070.299167880057053
1020.6682075382066280.6635849235867440.331792461793372
1030.6092915560996950.781416887800610.390708443900305
1040.5605245679344330.8789508641311340.439475432065567
1050.4988153159540680.9976306319081360.501184684045932
1060.7985693151850510.4028613696298980.201430684814949
1070.8149650846914820.3700698306170370.185034915308518
1080.8474995306156630.3050009387686740.152500469384337
1090.7934566289662560.4130867420674890.206543371033744
1100.747421336349240.5051573273015210.252578663650760
1110.7551056405430610.4897887189138770.244894359456939
1120.6909496864707730.6181006270584530.309050313529227
1130.6179279405848890.7641441188302220.382072059415111
1140.53074161877650.9385167624470.4692583812235
1150.5479550923653350.904089815269330.452044907634665
1160.5787925165208920.8424149669582160.421207483479108
1170.5859750484921050.828049903015790.414024951507895
1180.6042581134745720.7914837730508560.395741886525428
1190.6759334835825280.6481330328349440.324066516417472
1200.679250666819180.6414986663616410.320749333180821
1210.5402030707759810.9195938584480390.459796929224019






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level40.0377358490566038OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 4 & 0.0377358490566038 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98353&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.0377358490566038[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98353&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98353&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level40.0377358490566038OK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}