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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 computationTue, 21 Dec 2010 19:11:59 +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/21/t12929586212kzzlyxhijj6d1x.htm/, Retrieved Fri, 17 May 2024 01:43:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113847, Retrieved Fri, 17 May 2024 01:43:18 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact99

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [sleep in mammals] [2010-12-21 19:11:59] [531024149246456e4f6d79ace2e85c12] [Current]
-         [Multiple Regression] [] [2010-12-21 19:34:40] [f47feae0308dca73181bb669fbad1c56]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-999,00	-999,00	38,60	6654,00	5712,00	645,00	3,00	5,00	3,00	3,30
6,30	2,00	4,50	1,00	6600,00	42,00	3,00	1,00	3,00	8,30
-999,00	-999,00	14,00	3,39	44,50	60,00	1,00	1,00	1,00	12,50
-999,00	-999,00	-999,00	0,92	5,70	25,00	5,00	2,00	3,00	16,50
2,10	1,80	69,00	2547,00	4603,00	624,00	3,00	5,00	4,00	3,90
9,10	0,70	27,00	10,55	179,50	180,00	4,00	4,00	4,00	9,80
15,80	3,90	19,00	0,02	0,30	35,00	1,00	1,00	1,00	19,70
5,20	1,00	30,40	160,00	169,00	392,00	4,00	5,00	4,00	6,20
10,90	3,60	28,00	3,30	25,60	63,00	1,00	2,00	1,00	14,50
8,30	1,40	50,00	52,16	440,00	230,00	1,00	1,00	1,00	9,70
11,00	1,50	7,00	0,43	6,40	112,00	5,00	4,00	4,00	12,50
3,20	0,70	30,00	465,00	423,00	281,00	5,00	5,00	5,00	3,90
7,60	2,70	-999,00	0,55	2,40	-999,00	2,00	1,00	2,00	10,30
-999,00	-999,00	40,00	187,10	419,00	365,00	5,00	5,00	5,00	3,10
6,30	2,10	3,50	0,08	1,20	42,00	1,00	1,00	1,00	8,40
8,60	0,00	50,00	3,00	25,00	28,00	2,00	2,00	2,00	8,60
6,60	4,10	6,00	0,79	3500,00	42,00	2,00	2,00	2,00	10,70
9,50	1,20	10,40	0,20	5,00	120,00	2,00	2,00	2,00	10,70
4,80	1,30	34,00	1,41	17,50	-999,00	1,00	2,00	1,00	6,10
12,00	6,10	7,00	60,00	81,00	-999,00	1,00	1,00	1,00	18,10
-999,00	0,30	28,00	529,00	680,00	400,00	5,00	5,00	5,00	-999,00
3,30	0,50	20,00	27,66	115,00	148,00	5,00	5,00	5,00	3,80
11,00	3,40	3,90	0,12	1,00	16,00	3,00	1,00	2,00	14,40
-999,00	-999,00	39,30	207,00	406,00	252,00	1,00	4,00	1,00	12,00
4,70	1,50	41,00	85,00	325,00	310,00	1,00	3,00	1,00	6,20
-999,00	-999,00	16,20	36,33	119,50	63,00	1,00	1,00	1,00	13,00
10,40	3,40	9,00	0,10	4,00	28,00	5,00	1,00	3,00	13,80
7,40	0,80	7,60	1,04	5,50	68,00	5,00	3,00	4,00	8,20
2,10	0,80	46,00	521,00	655,00	336,00	5,00	5,00	5,00	2,90
-999,00	-999,00	22,40	100,00	157,00	100,00	1,00	1,00	1,00	10,80
-999,00	-999,00	16,30	35,00	56,00	33,00	3,00	5,00	4,00	-999,00
7,70	1,40	2,60	0,01	0,14	21,50	5,00	2,00	4,00	9,10
17,90	2,00	24,00	0,01	0,25	50,00	1,00	1,00	1,00	19,90
6,10	1,90	100,00	62,00	1320,00	267,00	1,00	1,00	1,00	8,00
8,20	2,40	-999,00	0,12	3,00	30,00	2,00	1,00	1,00	10,60
8,40	2,80	-999,00	1,35	8,10	45,00	3,00	1,00	3,00	11,20
11,90	1,30	3,20	0,02	0,40	19,00	4,00	1,00	3,00	13,20
10,80	2,00	2,00	0,05	0,33	30,00	4,00	1,00	3,00	12,80
13,80	5,60	5,00	1,70	6,30	12,00	2,00	1,00	1,00	19,40
14,30	3,10	6,50	3,50	10,80	120,00	2,00	1,00	1,00	17,40
-999,00	1,00	23,60	250,00	490,00	440,00	5,00	5,00	5,00	-999,00
15,20	1,80	12,00	0,48	15,50	140,00	2,00	2,00	2,00	17,00
10,00	0,90	20,20	10,00	115,00	170,00	4,00	4,00	4,00	10,90
11,90	1,80	13,00	1,62	11,40	17,00	2,00	1,00	2,00	13,70
6,50	1,90	27,00	192,00	180,00	115,00	4,00	4,00	4,00	8,40
7,50	0,90	18,00	2,50	12,10	31,00	5,00	5,00	5,00	8,40
-999,00	-999,00	13,70	4,29	39,20	63,00	2,00	2,00	2,00	12,50
10,60	2,60	4,70	0,28	1,90	21,00	3,00	1,00	3,00	13,20
7,40	2,40	9,80	4,24	50,40	52,00	1,00	1,00	1,00	9,80
8,40	1,20	29,00	6,80	179,00	164,00	2,00	3,00	2,00	9,60
5,70	0,90	7,00	0,75	12,30	225,00	2,00	2,00	2,00	6,60
4,90	0,50	6,00	3,60	21,00	225,00	3,00	2,00	3,00	5,40
-999,00	-999,00	17,00	14,83	98,20	150,00	5,00	5,00	5,00	2,60
3,20	0,60	20,00	55,50	175,00	151,00	5,00	5,00	5,00	3,80
-999,00	-999,00	12,70	1,40	12,50	90,00	2,00	2,00	2,00	11,00
8,10	2,20	3,50	0,06	1,00	-999,00	3,00	1,00	2,00	10,30
11,00	2,30	4,50	0,90	2,60	60,00	2,00	1,00	2,00	13,30
4,90	0,50	7,50	2,00	12,30	200,00	3,00	1,00	3,00	5,40
13,20	2,60	2,30	0,10	2,50	46,00	3,00	2,00	2,00	15,80
9,70	0,60	24,00	4,19	58,00	210,00	4,00	3,00	4,00	10,30
12,80	6,60	3,00	3,50	3,90	14,00	2,00	1,00	1,00	19,40
-999,00	-999,00	13,00	4,05	17,00	38,00	3,00	1,00	1,00	-999,00

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113847&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]5 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=113847&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113847&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 time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Multiple Linear Regression - Estimated Regression Equation
TS[t] = + 34.3623416950708 + 0.997337133245564SWS[t] -0.826836682048261PS[t] -0.0603303939819822L[t] + 0.0318126126570365Wb[t] -0.00602942557266054Wbr[t] + 0.0505446567493215Tg[t] -36.4418346082895P[t] -22.2453149809266S[t] + 45.6701835467499D[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TS[t] =  +  34.3623416950708 +  0.997337133245564SWS[t] -0.826836682048261PS[t] -0.0603303939819822L[t] +  0.0318126126570365Wb[t] -0.00602942557266054Wbr[t] +  0.0505446567493215Tg[t] -36.4418346082895P[t] -22.2453149809266S[t] +  45.6701835467499D[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113847&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TS[t] =  +  34.3623416950708 +  0.997337133245564SWS[t] -0.826836682048261PS[t] -0.0603303939819822L[t] +  0.0318126126570365Wb[t] -0.00602942557266054Wbr[t] +  0.0505446567493215Tg[t] -36.4418346082895P[t] -22.2453149809266S[t] +  45.6701835467499D[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113847&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113847&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
TS[t] = + 34.3623416950708 + 0.997337133245564SWS[t] -0.826836682048261PS[t] -0.0603303939819822L[t] + 0.0318126126570365Wb[t] -0.00602942557266054Wbr[t] + 0.0505446567493215Tg[t] -36.4418346082895P[t] -22.2453149809266S[t] + 45.6701835467499D[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)34.362341695070855.2133150.62240.5364270.268214
SWS0.9973371332455640.1347957.398900
PS-0.8268366820482610.142513-5.801800
L-0.06033039398198220.096716-0.62380.5354920.267746
Wb0.03181261265703650.0368170.86410.3915150.195758
Wbr-0.006029425572660540.024183-0.24930.8040910.402046
Tg0.05054465674932150.0853040.59250.556070.278035
P-36.441834608289543.88123-0.83050.4100730.205036
S-22.245314980926629.432912-0.75580.4531810.22659
D45.670183546749958.5064650.78060.4385770.219288

\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) & 34.3623416950708 & 55.213315 & 0.6224 & 0.536427 & 0.268214 \tabularnewline
SWS & 0.997337133245564 & 0.134795 & 7.3989 & 0 & 0 \tabularnewline
PS & -0.826836682048261 & 0.142513 & -5.8018 & 0 & 0 \tabularnewline
L & -0.0603303939819822 & 0.096716 & -0.6238 & 0.535492 & 0.267746 \tabularnewline
Wb & 0.0318126126570365 & 0.036817 & 0.8641 & 0.391515 & 0.195758 \tabularnewline
Wbr & -0.00602942557266054 & 0.024183 & -0.2493 & 0.804091 & 0.402046 \tabularnewline
Tg & 0.0505446567493215 & 0.085304 & 0.5925 & 0.55607 & 0.278035 \tabularnewline
P & -36.4418346082895 & 43.88123 & -0.8305 & 0.410073 & 0.205036 \tabularnewline
S & -22.2453149809266 & 29.432912 & -0.7558 & 0.453181 & 0.22659 \tabularnewline
D & 45.6701835467499 & 58.506465 & 0.7806 & 0.438577 & 0.219288 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113847&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]34.3623416950708[/C][C]55.213315[/C][C]0.6224[/C][C]0.536427[/C][C]0.268214[/C][/ROW]
[ROW][C]SWS[/C][C]0.997337133245564[/C][C]0.134795[/C][C]7.3989[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]PS[/C][C]-0.826836682048261[/C][C]0.142513[/C][C]-5.8018[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]L[/C][C]-0.0603303939819822[/C][C]0.096716[/C][C]-0.6238[/C][C]0.535492[/C][C]0.267746[/C][/ROW]
[ROW][C]Wb[/C][C]0.0318126126570365[/C][C]0.036817[/C][C]0.8641[/C][C]0.391515[/C][C]0.195758[/C][/ROW]
[ROW][C]Wbr[/C][C]-0.00602942557266054[/C][C]0.024183[/C][C]-0.2493[/C][C]0.804091[/C][C]0.402046[/C][/ROW]
[ROW][C]Tg[/C][C]0.0505446567493215[/C][C]0.085304[/C][C]0.5925[/C][C]0.55607[/C][C]0.278035[/C][/ROW]
[ROW][C]P[/C][C]-36.4418346082895[/C][C]43.88123[/C][C]-0.8305[/C][C]0.410073[/C][C]0.205036[/C][/ROW]
[ROW][C]S[/C][C]-22.2453149809266[/C][C]29.432912[/C][C]-0.7558[/C][C]0.453181[/C][C]0.22659[/C][/ROW]
[ROW][C]D[/C][C]45.6701835467499[/C][C]58.506465[/C][C]0.7806[/C][C]0.438577[/C][C]0.219288[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113847&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113847&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)34.362341695070855.2133150.62240.5364270.268214
SWS0.9973371332455640.1347957.398900
PS-0.8268366820482610.142513-5.801800
L-0.06033039398198220.096716-0.62380.5354920.267746
Wb0.03181261265703650.0368170.86410.3915150.195758
Wbr-0.006029425572660540.024183-0.24930.8040910.402046
Tg0.05054465674932150.0853040.59250.556070.278035
P-36.441834608289543.88123-0.83050.4100730.205036
S-22.245314980926629.432912-0.75580.4531810.22659
D45.670183546749958.5064650.78060.4385770.219288






Multiple Linear Regression - Regression Statistics
Multiple R0.758466158770888
R-squared0.575270914000666
Adjusted R-squared0.501760110654627
F-TEST (value)7.82566490659452
F-TEST (DF numerator)9
F-TEST (DF denominator)52
p-value3.50571223939333e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation176.519122961762
Sum Squared Residuals1620268.04010186

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.758466158770888 \tabularnewline
R-squared & 0.575270914000666 \tabularnewline
Adjusted R-squared & 0.501760110654627 \tabularnewline
F-TEST (value) & 7.82566490659452 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 52 \tabularnewline
p-value & 3.50571223939333e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 176.519122961762 \tabularnewline
Sum Squared Residuals & 1620268.04010186 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113847&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.758466158770888[/C][/ROW]
[ROW][C]R-squared[/C][C]0.575270914000666[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.501760110654627[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.82566490659452[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]52[/C][/ROW]
[ROW][C]p-value[/C][C]3.50571223939333e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]176.519122961762[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1620268.04010186[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113847&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113847&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 R0.758466158770888
R-squared0.575270914000666
Adjusted R-squared0.501760110654627
F-TEST (value)7.82566490659452
F-TEST (DF numerator)9
F-TEST (DF denominator)52
p-value3.50571223939333e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation176.519122961762
Sum Squared Residuals1620268.04010186






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13.3-11.995540995794315.2955409957943
28.36.520616748525681.77938325147432
312.5-146.956985885365159.456985885365
416.5-164.128281529472180.628281529472
53.977.7474462580329-73.8474462580329
69.8-2.4860814890898712.2860814890899
719.734.5002522230462-14.8002522230462
86.2-13.541014472864319.7410144728643
914.58.440114039500656.05988596049935
109.736.0808524811404-26.3808524811404
1112.5-39.203123742627251.7031237426272
123.9-3.474240772921957.37424077292195
1310.345.7000055732159-35.4000055732159
143.1-181.591044795955184.691044795955
158.427.7992714626102-19.3992714626102
168.615.2489418445866-6.64894184458662
1710.7-7.7961600275720818.4961600275721
1810.722.2250464646873-11.5250464646873
196.1-49.793613427040855.8936134270408
2018.1-21.226334071352439.3263340713524
21-999-996.052861259797-2.94713874020323
223.8-21.384140074076525.1841400740765
2314.42.8625677889181911.5374322110818
2412-201.216986981437213.216986981437
256.2-5.7582186023051511.9582186023051
2613-146.342378238904159.342378238904
2713.8-24.669193818145638.4691938181456
288.2-22.20476772057430.404767720574
292.9-2.456645894259195.35664589425919
3010.8-143.046868792969153.846868792969
31-999-172.378571731787-826.621428268213
329.1-2.2054774478034611.3054774478035
3319.938.6221511252355-18.7221511252355
34827.3340666522057-19.3340666522057
3510.652.8694300272936-42.2694300272936
3611.2108.403244560721-97.2032445607211
3713.214.9191788197142-1.71917881971419
3812.813.873086430901-1.07308643090101
3919.4-5.6425119648752425.0425119648752
4017.42.3217059325276215.0782940674724
41-999-1002.074535006243.07453500624458
421728.2737291826044-11.2737291826044
4310.9-1.4776442815189812.3776442815190
4413.741.0114954729491-27.3114954729491
458.4-3.5873808888591711.9873808888592
468.4-23.499100281995431.8991002819954
4712.5-159.743631532462172.243631532462
4813.248.9994083315792-35.7994083315792
499.828.6093491204509-18.8093491204509
509.6-0.85467660001068410.4546766000107
516.624.1690107914671-17.5690107914671
525.433.0287650337228-27.6287650337228
532.6-194.616665994192197.216665994192
543.8-20.891025883345724.6910258833457
5511-158.249548194037169.249548194037
5610.3-50.31810907876460.618109078764
5713.342.4168561649980-29.1168561649979
585.453.9215238271743-48.5215238271743
5915.8-14.923948210990930.7239482109909
6010.322.6678973149941-12.3678973149941
6119.4-7.173202354549426.5732023545494
62-999-220.705501628845-778.294498371155

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3.3 & -11.9955409957943 & 15.2955409957943 \tabularnewline
2 & 8.3 & 6.52061674852568 & 1.77938325147432 \tabularnewline
3 & 12.5 & -146.956985885365 & 159.456985885365 \tabularnewline
4 & 16.5 & -164.128281529472 & 180.628281529472 \tabularnewline
5 & 3.9 & 77.7474462580329 & -73.8474462580329 \tabularnewline
6 & 9.8 & -2.48608148908987 & 12.2860814890899 \tabularnewline
7 & 19.7 & 34.5002522230462 & -14.8002522230462 \tabularnewline
8 & 6.2 & -13.5410144728643 & 19.7410144728643 \tabularnewline
9 & 14.5 & 8.44011403950065 & 6.05988596049935 \tabularnewline
10 & 9.7 & 36.0808524811404 & -26.3808524811404 \tabularnewline
11 & 12.5 & -39.2031237426272 & 51.7031237426272 \tabularnewline
12 & 3.9 & -3.47424077292195 & 7.37424077292195 \tabularnewline
13 & 10.3 & 45.7000055732159 & -35.4000055732159 \tabularnewline
14 & 3.1 & -181.591044795955 & 184.691044795955 \tabularnewline
15 & 8.4 & 27.7992714626102 & -19.3992714626102 \tabularnewline
16 & 8.6 & 15.2489418445866 & -6.64894184458662 \tabularnewline
17 & 10.7 & -7.79616002757208 & 18.4961600275721 \tabularnewline
18 & 10.7 & 22.2250464646873 & -11.5250464646873 \tabularnewline
19 & 6.1 & -49.7936134270408 & 55.8936134270408 \tabularnewline
20 & 18.1 & -21.2263340713524 & 39.3263340713524 \tabularnewline
21 & -999 & -996.052861259797 & -2.94713874020323 \tabularnewline
22 & 3.8 & -21.3841400740765 & 25.1841400740765 \tabularnewline
23 & 14.4 & 2.86256778891819 & 11.5374322110818 \tabularnewline
24 & 12 & -201.216986981437 & 213.216986981437 \tabularnewline
25 & 6.2 & -5.75821860230515 & 11.9582186023051 \tabularnewline
26 & 13 & -146.342378238904 & 159.342378238904 \tabularnewline
27 & 13.8 & -24.6691938181456 & 38.4691938181456 \tabularnewline
28 & 8.2 & -22.204767720574 & 30.404767720574 \tabularnewline
29 & 2.9 & -2.45664589425919 & 5.35664589425919 \tabularnewline
30 & 10.8 & -143.046868792969 & 153.846868792969 \tabularnewline
31 & -999 & -172.378571731787 & -826.621428268213 \tabularnewline
32 & 9.1 & -2.20547744780346 & 11.3054774478035 \tabularnewline
33 & 19.9 & 38.6221511252355 & -18.7221511252355 \tabularnewline
34 & 8 & 27.3340666522057 & -19.3340666522057 \tabularnewline
35 & 10.6 & 52.8694300272936 & -42.2694300272936 \tabularnewline
36 & 11.2 & 108.403244560721 & -97.2032445607211 \tabularnewline
37 & 13.2 & 14.9191788197142 & -1.71917881971419 \tabularnewline
38 & 12.8 & 13.873086430901 & -1.07308643090101 \tabularnewline
39 & 19.4 & -5.64251196487524 & 25.0425119648752 \tabularnewline
40 & 17.4 & 2.32170593252762 & 15.0782940674724 \tabularnewline
41 & -999 & -1002.07453500624 & 3.07453500624458 \tabularnewline
42 & 17 & 28.2737291826044 & -11.2737291826044 \tabularnewline
43 & 10.9 & -1.47764428151898 & 12.3776442815190 \tabularnewline
44 & 13.7 & 41.0114954729491 & -27.3114954729491 \tabularnewline
45 & 8.4 & -3.58738088885917 & 11.9873808888592 \tabularnewline
46 & 8.4 & -23.4991002819954 & 31.8991002819954 \tabularnewline
47 & 12.5 & -159.743631532462 & 172.243631532462 \tabularnewline
48 & 13.2 & 48.9994083315792 & -35.7994083315792 \tabularnewline
49 & 9.8 & 28.6093491204509 & -18.8093491204509 \tabularnewline
50 & 9.6 & -0.854676600010684 & 10.4546766000107 \tabularnewline
51 & 6.6 & 24.1690107914671 & -17.5690107914671 \tabularnewline
52 & 5.4 & 33.0287650337228 & -27.6287650337228 \tabularnewline
53 & 2.6 & -194.616665994192 & 197.216665994192 \tabularnewline
54 & 3.8 & -20.8910258833457 & 24.6910258833457 \tabularnewline
55 & 11 & -158.249548194037 & 169.249548194037 \tabularnewline
56 & 10.3 & -50.318109078764 & 60.618109078764 \tabularnewline
57 & 13.3 & 42.4168561649980 & -29.1168561649979 \tabularnewline
58 & 5.4 & 53.9215238271743 & -48.5215238271743 \tabularnewline
59 & 15.8 & -14.9239482109909 & 30.7239482109909 \tabularnewline
60 & 10.3 & 22.6678973149941 & -12.3678973149941 \tabularnewline
61 & 19.4 & -7.1732023545494 & 26.5732023545494 \tabularnewline
62 & -999 & -220.705501628845 & -778.294498371155 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113847&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]3.3[/C][C]-11.9955409957943[/C][C]15.2955409957943[/C][/ROW]
[ROW][C]2[/C][C]8.3[/C][C]6.52061674852568[/C][C]1.77938325147432[/C][/ROW]
[ROW][C]3[/C][C]12.5[/C][C]-146.956985885365[/C][C]159.456985885365[/C][/ROW]
[ROW][C]4[/C][C]16.5[/C][C]-164.128281529472[/C][C]180.628281529472[/C][/ROW]
[ROW][C]5[/C][C]3.9[/C][C]77.7474462580329[/C][C]-73.8474462580329[/C][/ROW]
[ROW][C]6[/C][C]9.8[/C][C]-2.48608148908987[/C][C]12.2860814890899[/C][/ROW]
[ROW][C]7[/C][C]19.7[/C][C]34.5002522230462[/C][C]-14.8002522230462[/C][/ROW]
[ROW][C]8[/C][C]6.2[/C][C]-13.5410144728643[/C][C]19.7410144728643[/C][/ROW]
[ROW][C]9[/C][C]14.5[/C][C]8.44011403950065[/C][C]6.05988596049935[/C][/ROW]
[ROW][C]10[/C][C]9.7[/C][C]36.0808524811404[/C][C]-26.3808524811404[/C][/ROW]
[ROW][C]11[/C][C]12.5[/C][C]-39.2031237426272[/C][C]51.7031237426272[/C][/ROW]
[ROW][C]12[/C][C]3.9[/C][C]-3.47424077292195[/C][C]7.37424077292195[/C][/ROW]
[ROW][C]13[/C][C]10.3[/C][C]45.7000055732159[/C][C]-35.4000055732159[/C][/ROW]
[ROW][C]14[/C][C]3.1[/C][C]-181.591044795955[/C][C]184.691044795955[/C][/ROW]
[ROW][C]15[/C][C]8.4[/C][C]27.7992714626102[/C][C]-19.3992714626102[/C][/ROW]
[ROW][C]16[/C][C]8.6[/C][C]15.2489418445866[/C][C]-6.64894184458662[/C][/ROW]
[ROW][C]17[/C][C]10.7[/C][C]-7.79616002757208[/C][C]18.4961600275721[/C][/ROW]
[ROW][C]18[/C][C]10.7[/C][C]22.2250464646873[/C][C]-11.5250464646873[/C][/ROW]
[ROW][C]19[/C][C]6.1[/C][C]-49.7936134270408[/C][C]55.8936134270408[/C][/ROW]
[ROW][C]20[/C][C]18.1[/C][C]-21.2263340713524[/C][C]39.3263340713524[/C][/ROW]
[ROW][C]21[/C][C]-999[/C][C]-996.052861259797[/C][C]-2.94713874020323[/C][/ROW]
[ROW][C]22[/C][C]3.8[/C][C]-21.3841400740765[/C][C]25.1841400740765[/C][/ROW]
[ROW][C]23[/C][C]14.4[/C][C]2.86256778891819[/C][C]11.5374322110818[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]-201.216986981437[/C][C]213.216986981437[/C][/ROW]
[ROW][C]25[/C][C]6.2[/C][C]-5.75821860230515[/C][C]11.9582186023051[/C][/ROW]
[ROW][C]26[/C][C]13[/C][C]-146.342378238904[/C][C]159.342378238904[/C][/ROW]
[ROW][C]27[/C][C]13.8[/C][C]-24.6691938181456[/C][C]38.4691938181456[/C][/ROW]
[ROW][C]28[/C][C]8.2[/C][C]-22.204767720574[/C][C]30.404767720574[/C][/ROW]
[ROW][C]29[/C][C]2.9[/C][C]-2.45664589425919[/C][C]5.35664589425919[/C][/ROW]
[ROW][C]30[/C][C]10.8[/C][C]-143.046868792969[/C][C]153.846868792969[/C][/ROW]
[ROW][C]31[/C][C]-999[/C][C]-172.378571731787[/C][C]-826.621428268213[/C][/ROW]
[ROW][C]32[/C][C]9.1[/C][C]-2.20547744780346[/C][C]11.3054774478035[/C][/ROW]
[ROW][C]33[/C][C]19.9[/C][C]38.6221511252355[/C][C]-18.7221511252355[/C][/ROW]
[ROW][C]34[/C][C]8[/C][C]27.3340666522057[/C][C]-19.3340666522057[/C][/ROW]
[ROW][C]35[/C][C]10.6[/C][C]52.8694300272936[/C][C]-42.2694300272936[/C][/ROW]
[ROW][C]36[/C][C]11.2[/C][C]108.403244560721[/C][C]-97.2032445607211[/C][/ROW]
[ROW][C]37[/C][C]13.2[/C][C]14.9191788197142[/C][C]-1.71917881971419[/C][/ROW]
[ROW][C]38[/C][C]12.8[/C][C]13.873086430901[/C][C]-1.07308643090101[/C][/ROW]
[ROW][C]39[/C][C]19.4[/C][C]-5.64251196487524[/C][C]25.0425119648752[/C][/ROW]
[ROW][C]40[/C][C]17.4[/C][C]2.32170593252762[/C][C]15.0782940674724[/C][/ROW]
[ROW][C]41[/C][C]-999[/C][C]-1002.07453500624[/C][C]3.07453500624458[/C][/ROW]
[ROW][C]42[/C][C]17[/C][C]28.2737291826044[/C][C]-11.2737291826044[/C][/ROW]
[ROW][C]43[/C][C]10.9[/C][C]-1.47764428151898[/C][C]12.3776442815190[/C][/ROW]
[ROW][C]44[/C][C]13.7[/C][C]41.0114954729491[/C][C]-27.3114954729491[/C][/ROW]
[ROW][C]45[/C][C]8.4[/C][C]-3.58738088885917[/C][C]11.9873808888592[/C][/ROW]
[ROW][C]46[/C][C]8.4[/C][C]-23.4991002819954[/C][C]31.8991002819954[/C][/ROW]
[ROW][C]47[/C][C]12.5[/C][C]-159.743631532462[/C][C]172.243631532462[/C][/ROW]
[ROW][C]48[/C][C]13.2[/C][C]48.9994083315792[/C][C]-35.7994083315792[/C][/ROW]
[ROW][C]49[/C][C]9.8[/C][C]28.6093491204509[/C][C]-18.8093491204509[/C][/ROW]
[ROW][C]50[/C][C]9.6[/C][C]-0.854676600010684[/C][C]10.4546766000107[/C][/ROW]
[ROW][C]51[/C][C]6.6[/C][C]24.1690107914671[/C][C]-17.5690107914671[/C][/ROW]
[ROW][C]52[/C][C]5.4[/C][C]33.0287650337228[/C][C]-27.6287650337228[/C][/ROW]
[ROW][C]53[/C][C]2.6[/C][C]-194.616665994192[/C][C]197.216665994192[/C][/ROW]
[ROW][C]54[/C][C]3.8[/C][C]-20.8910258833457[/C][C]24.6910258833457[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]-158.249548194037[/C][C]169.249548194037[/C][/ROW]
[ROW][C]56[/C][C]10.3[/C][C]-50.318109078764[/C][C]60.618109078764[/C][/ROW]
[ROW][C]57[/C][C]13.3[/C][C]42.4168561649980[/C][C]-29.1168561649979[/C][/ROW]
[ROW][C]58[/C][C]5.4[/C][C]53.9215238271743[/C][C]-48.5215238271743[/C][/ROW]
[ROW][C]59[/C][C]15.8[/C][C]-14.9239482109909[/C][C]30.7239482109909[/C][/ROW]
[ROW][C]60[/C][C]10.3[/C][C]22.6678973149941[/C][C]-12.3678973149941[/C][/ROW]
[ROW][C]61[/C][C]19.4[/C][C]-7.1732023545494[/C][C]26.5732023545494[/C][/ROW]
[ROW][C]62[/C][C]-999[/C][C]-220.705501628845[/C][C]-778.294498371155[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113847&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113847&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
13.3-11.995540995794315.2955409957943
28.36.520616748525681.77938325147432
312.5-146.956985885365159.456985885365
416.5-164.128281529472180.628281529472
53.977.7474462580329-73.8474462580329
69.8-2.4860814890898712.2860814890899
719.734.5002522230462-14.8002522230462
86.2-13.541014472864319.7410144728643
914.58.440114039500656.05988596049935
109.736.0808524811404-26.3808524811404
1112.5-39.203123742627251.7031237426272
123.9-3.474240772921957.37424077292195
1310.345.7000055732159-35.4000055732159
143.1-181.591044795955184.691044795955
158.427.7992714626102-19.3992714626102
168.615.2489418445866-6.64894184458662
1710.7-7.7961600275720818.4961600275721
1810.722.2250464646873-11.5250464646873
196.1-49.793613427040855.8936134270408
2018.1-21.226334071352439.3263340713524
21-999-996.052861259797-2.94713874020323
223.8-21.384140074076525.1841400740765
2314.42.8625677889181911.5374322110818
2412-201.216986981437213.216986981437
256.2-5.7582186023051511.9582186023051
2613-146.342378238904159.342378238904
2713.8-24.669193818145638.4691938181456
288.2-22.20476772057430.404767720574
292.9-2.456645894259195.35664589425919
3010.8-143.046868792969153.846868792969
31-999-172.378571731787-826.621428268213
329.1-2.2054774478034611.3054774478035
3319.938.6221511252355-18.7221511252355
34827.3340666522057-19.3340666522057
3510.652.8694300272936-42.2694300272936
3611.2108.403244560721-97.2032445607211
3713.214.9191788197142-1.71917881971419
3812.813.873086430901-1.07308643090101
3919.4-5.6425119648752425.0425119648752
4017.42.3217059325276215.0782940674724
41-999-1002.074535006243.07453500624458
421728.2737291826044-11.2737291826044
4310.9-1.4776442815189812.3776442815190
4413.741.0114954729491-27.3114954729491
458.4-3.5873808888591711.9873808888592
468.4-23.499100281995431.8991002819954
4712.5-159.743631532462172.243631532462
4813.248.9994083315792-35.7994083315792
499.828.6093491204509-18.8093491204509
509.6-0.85467660001068410.4546766000107
516.624.1690107914671-17.5690107914671
525.433.0287650337228-27.6287650337228
532.6-194.616665994192197.216665994192
543.8-20.891025883345724.6910258833457
5511-158.249548194037169.249548194037
5610.3-50.31810907876460.618109078764
5713.342.4168561649980-29.1168561649979
585.453.9215238271743-48.5215238271743
5915.8-14.923948210990930.7239482109909
6010.322.6678973149941-12.3678973149941
6119.4-7.173202354549426.5732023545494
62-999-220.705501628845-778.294498371155






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
134.16801205372821e-068.33602410745642e-060.999995831987946
141.25546308987227e-072.51092617974454e-070.99999987445369
151.84722947368898e-093.69445894737795e-090.99999999815277
164.68965879079796e-119.37931758159592e-110.999999999953103
176.29727962316913e-131.25945592463383e-120.99999999999937
188.10391050064924e-151.62078210012985e-140.999999999999992
199.28543729192027e-171.85708745838405e-161
208.75316005089924e-171.75063201017985e-161
211.46987941491156e-182.93975882982313e-181
222.27548396957985e-204.5509679391597e-201
233.73571810586561e-227.47143621173122e-221
248.0697960900013e-241.61395921800026e-231
251.77787424560019e-253.55574849120039e-251
264.18166621544073e-278.36333243088147e-271
275.5588729728587e-291.11177459457174e-281
288.34998549637248e-311.66999709927450e-301
291.20029360022974e-322.40058720045949e-321
303.20543568152003e-346.41087136304007e-341
310.7589994747587710.4820010504824580.241000525241229
320.7097993325861270.5804013348277470.290200667413873
330.6424400652989080.7151198694021840.357559934701092
340.5866516020156340.8266967959687320.413348397984366
350.5559664156531540.8880671686936920.444033584346846
360.6770872833940270.6458254332119470.322912716605973
370.6575735215883330.6848529568233350.342426478411667
380.6968144015262960.6063711969474090.303185598473704
390.6895709222594430.6208581554811140.310429077740557
400.7811626847114380.4376746305771250.218837315288562
410.7313582569507960.5372834860984070.268641743049204
420.7239224468308770.5521551063382460.276077553169123
430.6613950956279860.6772098087440290.338604904372014
440.5728948412990780.8542103174018440.427105158700922
450.4597945656546610.9195891313093220.540205434345339
460.4408107722285640.8816215444571270.559189227771436
470.3838696144644370.7677392289288740.616130385535563
480.2614018519523030.5228037039046060.738598148047697
490.1557611118197170.3115222236394350.844238888180283

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 4.16801205372821e-06 & 8.33602410745642e-06 & 0.999995831987946 \tabularnewline
14 & 1.25546308987227e-07 & 2.51092617974454e-07 & 0.99999987445369 \tabularnewline
15 & 1.84722947368898e-09 & 3.69445894737795e-09 & 0.99999999815277 \tabularnewline
16 & 4.68965879079796e-11 & 9.37931758159592e-11 & 0.999999999953103 \tabularnewline
17 & 6.29727962316913e-13 & 1.25945592463383e-12 & 0.99999999999937 \tabularnewline
18 & 8.10391050064924e-15 & 1.62078210012985e-14 & 0.999999999999992 \tabularnewline
19 & 9.28543729192027e-17 & 1.85708745838405e-16 & 1 \tabularnewline
20 & 8.75316005089924e-17 & 1.75063201017985e-16 & 1 \tabularnewline
21 & 1.46987941491156e-18 & 2.93975882982313e-18 & 1 \tabularnewline
22 & 2.27548396957985e-20 & 4.5509679391597e-20 & 1 \tabularnewline
23 & 3.73571810586561e-22 & 7.47143621173122e-22 & 1 \tabularnewline
24 & 8.0697960900013e-24 & 1.61395921800026e-23 & 1 \tabularnewline
25 & 1.77787424560019e-25 & 3.55574849120039e-25 & 1 \tabularnewline
26 & 4.18166621544073e-27 & 8.36333243088147e-27 & 1 \tabularnewline
27 & 5.5588729728587e-29 & 1.11177459457174e-28 & 1 \tabularnewline
28 & 8.34998549637248e-31 & 1.66999709927450e-30 & 1 \tabularnewline
29 & 1.20029360022974e-32 & 2.40058720045949e-32 & 1 \tabularnewline
30 & 3.20543568152003e-34 & 6.41087136304007e-34 & 1 \tabularnewline
31 & 0.758999474758771 & 0.482001050482458 & 0.241000525241229 \tabularnewline
32 & 0.709799332586127 & 0.580401334827747 & 0.290200667413873 \tabularnewline
33 & 0.642440065298908 & 0.715119869402184 & 0.357559934701092 \tabularnewline
34 & 0.586651602015634 & 0.826696795968732 & 0.413348397984366 \tabularnewline
35 & 0.555966415653154 & 0.888067168693692 & 0.444033584346846 \tabularnewline
36 & 0.677087283394027 & 0.645825433211947 & 0.322912716605973 \tabularnewline
37 & 0.657573521588333 & 0.684852956823335 & 0.342426478411667 \tabularnewline
38 & 0.696814401526296 & 0.606371196947409 & 0.303185598473704 \tabularnewline
39 & 0.689570922259443 & 0.620858155481114 & 0.310429077740557 \tabularnewline
40 & 0.781162684711438 & 0.437674630577125 & 0.218837315288562 \tabularnewline
41 & 0.731358256950796 & 0.537283486098407 & 0.268641743049204 \tabularnewline
42 & 0.723922446830877 & 0.552155106338246 & 0.276077553169123 \tabularnewline
43 & 0.661395095627986 & 0.677209808744029 & 0.338604904372014 \tabularnewline
44 & 0.572894841299078 & 0.854210317401844 & 0.427105158700922 \tabularnewline
45 & 0.459794565654661 & 0.919589131309322 & 0.540205434345339 \tabularnewline
46 & 0.440810772228564 & 0.881621544457127 & 0.559189227771436 \tabularnewline
47 & 0.383869614464437 & 0.767739228928874 & 0.616130385535563 \tabularnewline
48 & 0.261401851952303 & 0.522803703904606 & 0.738598148047697 \tabularnewline
49 & 0.155761111819717 & 0.311522223639435 & 0.844238888180283 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113847&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]13[/C][C]4.16801205372821e-06[/C][C]8.33602410745642e-06[/C][C]0.999995831987946[/C][/ROW]
[ROW][C]14[/C][C]1.25546308987227e-07[/C][C]2.51092617974454e-07[/C][C]0.99999987445369[/C][/ROW]
[ROW][C]15[/C][C]1.84722947368898e-09[/C][C]3.69445894737795e-09[/C][C]0.99999999815277[/C][/ROW]
[ROW][C]16[/C][C]4.68965879079796e-11[/C][C]9.37931758159592e-11[/C][C]0.999999999953103[/C][/ROW]
[ROW][C]17[/C][C]6.29727962316913e-13[/C][C]1.25945592463383e-12[/C][C]0.99999999999937[/C][/ROW]
[ROW][C]18[/C][C]8.10391050064924e-15[/C][C]1.62078210012985e-14[/C][C]0.999999999999992[/C][/ROW]
[ROW][C]19[/C][C]9.28543729192027e-17[/C][C]1.85708745838405e-16[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]8.75316005089924e-17[/C][C]1.75063201017985e-16[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]1.46987941491156e-18[/C][C]2.93975882982313e-18[/C][C]1[/C][/ROW]
[ROW][C]22[/C][C]2.27548396957985e-20[/C][C]4.5509679391597e-20[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]3.73571810586561e-22[/C][C]7.47143621173122e-22[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]8.0697960900013e-24[/C][C]1.61395921800026e-23[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]1.77787424560019e-25[/C][C]3.55574849120039e-25[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]4.18166621544073e-27[/C][C]8.36333243088147e-27[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]5.5588729728587e-29[/C][C]1.11177459457174e-28[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]8.34998549637248e-31[/C][C]1.66999709927450e-30[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]1.20029360022974e-32[/C][C]2.40058720045949e-32[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]3.20543568152003e-34[/C][C]6.41087136304007e-34[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]0.758999474758771[/C][C]0.482001050482458[/C][C]0.241000525241229[/C][/ROW]
[ROW][C]32[/C][C]0.709799332586127[/C][C]0.580401334827747[/C][C]0.290200667413873[/C][/ROW]
[ROW][C]33[/C][C]0.642440065298908[/C][C]0.715119869402184[/C][C]0.357559934701092[/C][/ROW]
[ROW][C]34[/C][C]0.586651602015634[/C][C]0.826696795968732[/C][C]0.413348397984366[/C][/ROW]
[ROW][C]35[/C][C]0.555966415653154[/C][C]0.888067168693692[/C][C]0.444033584346846[/C][/ROW]
[ROW][C]36[/C][C]0.677087283394027[/C][C]0.645825433211947[/C][C]0.322912716605973[/C][/ROW]
[ROW][C]37[/C][C]0.657573521588333[/C][C]0.684852956823335[/C][C]0.342426478411667[/C][/ROW]
[ROW][C]38[/C][C]0.696814401526296[/C][C]0.606371196947409[/C][C]0.303185598473704[/C][/ROW]
[ROW][C]39[/C][C]0.689570922259443[/C][C]0.620858155481114[/C][C]0.310429077740557[/C][/ROW]
[ROW][C]40[/C][C]0.781162684711438[/C][C]0.437674630577125[/C][C]0.218837315288562[/C][/ROW]
[ROW][C]41[/C][C]0.731358256950796[/C][C]0.537283486098407[/C][C]0.268641743049204[/C][/ROW]
[ROW][C]42[/C][C]0.723922446830877[/C][C]0.552155106338246[/C][C]0.276077553169123[/C][/ROW]
[ROW][C]43[/C][C]0.661395095627986[/C][C]0.677209808744029[/C][C]0.338604904372014[/C][/ROW]
[ROW][C]44[/C][C]0.572894841299078[/C][C]0.854210317401844[/C][C]0.427105158700922[/C][/ROW]
[ROW][C]45[/C][C]0.459794565654661[/C][C]0.919589131309322[/C][C]0.540205434345339[/C][/ROW]
[ROW][C]46[/C][C]0.440810772228564[/C][C]0.881621544457127[/C][C]0.559189227771436[/C][/ROW]
[ROW][C]47[/C][C]0.383869614464437[/C][C]0.767739228928874[/C][C]0.616130385535563[/C][/ROW]
[ROW][C]48[/C][C]0.261401851952303[/C][C]0.522803703904606[/C][C]0.738598148047697[/C][/ROW]
[ROW][C]49[/C][C]0.155761111819717[/C][C]0.311522223639435[/C][C]0.844238888180283[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113847&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113847&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
134.16801205372821e-068.33602410745642e-060.999995831987946
141.25546308987227e-072.51092617974454e-070.99999987445369
151.84722947368898e-093.69445894737795e-090.99999999815277
164.68965879079796e-119.37931758159592e-110.999999999953103
176.29727962316913e-131.25945592463383e-120.99999999999937
188.10391050064924e-151.62078210012985e-140.999999999999992
199.28543729192027e-171.85708745838405e-161
208.75316005089924e-171.75063201017985e-161
211.46987941491156e-182.93975882982313e-181
222.27548396957985e-204.5509679391597e-201
233.73571810586561e-227.47143621173122e-221
248.0697960900013e-241.61395921800026e-231
251.77787424560019e-253.55574849120039e-251
264.18166621544073e-278.36333243088147e-271
275.5588729728587e-291.11177459457174e-281
288.34998549637248e-311.66999709927450e-301
291.20029360022974e-322.40058720045949e-321
303.20543568152003e-346.41087136304007e-341
310.7589994747587710.4820010504824580.241000525241229
320.7097993325861270.5804013348277470.290200667413873
330.6424400652989080.7151198694021840.357559934701092
340.5866516020156340.8266967959687320.413348397984366
350.5559664156531540.8880671686936920.444033584346846
360.6770872833940270.6458254332119470.322912716605973
370.6575735215883330.6848529568233350.342426478411667
380.6968144015262960.6063711969474090.303185598473704
390.6895709222594430.6208581554811140.310429077740557
400.7811626847114380.4376746305771250.218837315288562
410.7313582569507960.5372834860984070.268641743049204
420.7239224468308770.5521551063382460.276077553169123
430.6613950956279860.6772098087440290.338604904372014
440.5728948412990780.8542103174018440.427105158700922
450.4597945656546610.9195891313093220.540205434345339
460.4408107722285640.8816215444571270.559189227771436
470.3838696144644370.7677392289288740.616130385535563
480.2614018519523030.5228037039046060.738598148047697
490.1557611118197170.3115222236394350.844238888180283






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level180.486486486486487NOK
5% type I error level180.486486486486487NOK
10% type I error level180.486486486486487NOK

\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 & 18 & 0.486486486486487 & NOK \tabularnewline
5% type I error level & 18 & 0.486486486486487 & NOK \tabularnewline
10% type I error level & 18 & 0.486486486486487 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113847&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]18[/C][C]0.486486486486487[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]18[/C][C]0.486486486486487[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]18[/C][C]0.486486486486487[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113847&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113847&T=6

As an alternative you can also use a QR Code:  
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 level180.486486486486487NOK
5% type I error level180.486486486486487NOK
10% type I error level180.486486486486487NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 10 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 10 ; 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')
}