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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 computationWed, 22 Dec 2010 18:41:35 +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/22/t1293043192u1lok62b4c93q4t.htm/, Retrieved Sun, 05 May 2024 21:10:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114476, Retrieved Sun, 05 May 2024 21:10:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact93

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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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
6.30	2.00	4.50	1.00	6600.00	42.00	3.00	1.00	3.00
-999.00	-999.00	14.00	3.39	44.50	60.00	1.00	1.00	1.00
-999.00	-999.00	-999.00	0.92	5.70	25.00	5.00	2.00	3.00
2.10	1.80	69.00	2547.00	4603.00	624.00	3.00	5.00	4.00
9.10	0.70	27.00	10.55	179.50	180.00	4.00	4.00	4.00
15.80	3.90	19.00	0.02	0.30	35.00	1.00	1.00	1.00
5.20	1.00	30.40	160.00	169.00	392.00	4.00	5.00	4.00
10.90	3.60	28.00	3.30	25.60	63.00	1.00	2.00	1.00
8.30	1.40	50.00	52.16	440.00	230.00	1.00	1.00	1.00
11.00	1.50	7.00	0.43	6.40	112.00	5.00	4.00	4.00
3.20	0.70	30.00	465.00	423.00	281.00	5.00	5.00	5.00
7.60	2.70	-999.00	0.55	2.40	-999.00	2.00	1.00	2.00
-999.00	-999.00	40.00	187.10	419.00	365.00	5.00	5.00	5.00
6.30	2.10	3.50	0.08	1.20	42.00	1.00	1.00	1.00
8.60	0.00	50.00	3.00	25.00	28.00	2.00	2.00	2.00
6.60	4.10	6.00	0.79	3500.00	42.00	2.00	2.00	2.00
9.50	1.20	10.40	0.20	5.00	120.00	2.00	2.00	2.00
4.80	1.30	34.00	1.41	17.50	-999.00	1.00	2.00	1.00
12.00	6.10	7.00	60.00	81.00	-999.00	1.00	1.00	1.00
-999.00	0.30	28.00	529.00	680.00	400.00	5.00	5.00	5.00
3.30	0.50	20.00	27.66	115.00	148.00	5.00	5.00	5.00
11.00	3.40	3.90	0.12	1.00	16.00	3.00	1.00	2.00
-999.00	-999.00	39.30	207.00	406.00	252.00	1.00	4.00	1.00
4.70	1.50	41.00	85.00	325.00	310.00	1.00	3.00	1.00
-999.00	-999.00	16.20	36.33	119.50	63.00	1.00	1.00	1.00
10.40	3.40	9.00	0.10	4.00	28.00	5.00	1.00	3.00
7.40	0.80	7.60	1.04	5.50	68.00	5.00	3.00	4.00
2.10	0.80	46.00	521.00	655.00	336.00	5.00	5.00	5.00
-999.00	-999.00	22.40	100.00	157.00	100.00	1.00	1.00	1.00
-999.00	-999.00	16.30	35.00	56.00	33.00	3.00	5.00	4.00
7.70	1.40	2.60	0.01	0.14	21.50	5.00	2.00	4.00
17.90	2.00	24.00	0.01	0.25	50.00	1.00	1.00	1.00
6.10	1.90	100.00	62.00	1320.00	267.00	1.00	1.00	1.00
8.20	2.40	-999.00	0.12	3.00	30.00	2.00	1.00	1.00
8.40	2.80	-999.00	1.35	8.10	45.00	3.00	1.00	3.00
11.90	1.30	3.20	0.02	0.40	19.00	4.00	1.00	3.00
10.80	2.00	2.00	0.05	0.33	30.00	4.00	1.00	3.00
13.80	5.60	5.00	1.70	6.30	12.00	2.00	1.00	1.00
14.30	3.10	6.50	3.50	10.80	120.00	2.00	1.00	1.00
-999.00	1.00	23.60	250.00	490.00	440.00	5.00	5.00	5.00
15.20	1.80	12.00	0.48	15.50	140.00	2.00	2.00	2.00
10.00	0.90	20.20	10.00	115.00	170.00	4.00	4.00	4.00
11.90	1.80	13.00	1.62	11.40	17.00	2.00	1.00	2.00
6.50	1.90	27.00	192.00	180.00	115.00	4.00	4.00	4.00
7.50	0.90	18.00	2.50	12.10	31.00	5.00	5.00	5.00
-999.00	-999.00	13.70	4.29	39.20	63.00	2.00	2.00	2.00
10.60	2.60	4.70	0.28	1.90	21.00	3.00	1.00	3.00
7.40	2.40	9.80	4.24	50.40	52.00	1.00	1.00	1.00
8.40	1.20	29.00	6.80	179.00	164.00	2.00	3.00	2.00
5.70	0.90	7.00	0.75	12.30	225.00	2.00	2.00	2.00
4.90	0.50	6.00	3.60	21.00	225.00	3.00	2.00	3.00
-999.00	-999.00	17.00	14.83	98.20	150.00	5.00	5.00	5.00
3.20	0.60	20.00	55.50	175.00	151.00	5.00	5.00	5.00
-999.00	-999.00	12.70	1.40	12.50	90.00	2.00	2.00	2.00
8.10	2.20	3.50	0.06	1.00	-999.00	3.00	1.00	2.00
11.00	2.30	4.50	0.90	2.60	60.00	2.00	1.00	2.00
4.90	0.50	7.50	2.00	12.30	200.00	3.00	1.00	3.00
13.20	2.60	2.30	0.10	2.50	46.00	3.00	2.00	2.00
9.70	0.60	24.00	4.19	58.00	210.00	4.00	3.00	4.00
12.80	6.60	3.00	3.50	3.90	14.00	2.00	1.00	1.00
-999.00	-999.00	13.00	4.05	17.00	38.00	3.00	1.00	1.00

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114476&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114476&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114476&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 time7 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
SWS[t] = + 58.5962033954784 + 0.955258558887444PS[t] + 0.0198987966065785L[t] + 0.00797442355260723Wb[t] + 0.0026217940522085Wbr[t] -0.067620433809013Tg[t] -2.79466247616151P[t] -22.0902142737359S[t] -11.4834724299415D[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SWS[t] =  +  58.5962033954784 +  0.955258558887444PS[t] +  0.0198987966065785L[t] +  0.00797442355260723Wb[t] +  0.0026217940522085Wbr[t] -0.067620433809013Tg[t] -2.79466247616151P[t] -22.0902142737359S[t] -11.4834724299415D[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114476&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]SWS[t] =  +  58.5962033954784 +  0.955258558887444PS[t] +  0.0198987966065785L[t] +  0.00797442355260723Wb[t] +  0.0026217940522085Wbr[t] -0.067620433809013Tg[t] -2.79466247616151P[t] -22.0902142737359S[t] -11.4834724299415D[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114476&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114476&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
SWS[t] = + 58.5962033954784 + 0.955258558887444PS[t] + 0.0198987966065785L[t] + 0.00797442355260723Wb[t] + 0.0026217940522085Wbr[t] -0.067620433809013Tg[t] -2.79466247616151P[t] -22.0902142737359S[t] -11.4834724299415D[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)58.596203395478455.6857071.05230.2974510.148726
PS0.9552585588874440.06223515.349300
L0.01989879660657850.0985190.2020.8407060.420353
Wb0.007974423552607230.0375020.21260.8324230.416211
Wbr0.00262179405220850.0246410.10640.9156660.457833
Tg-0.0676204338090130.086431-0.78240.4374810.21874
P-2.7946624761615144.714931-0.06250.95040.4752
S-22.090214273735929.839333-0.74030.4623820.231191
D-11.483472429941559.599358-0.19270.8479480.423974

\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) & 58.5962033954784 & 55.685707 & 1.0523 & 0.297451 & 0.148726 \tabularnewline
PS & 0.955258558887444 & 0.062235 & 15.3493 & 0 & 0 \tabularnewline
L & 0.0198987966065785 & 0.098519 & 0.202 & 0.840706 & 0.420353 \tabularnewline
Wb & 0.00797442355260723 & 0.037502 & 0.2126 & 0.832423 & 0.416211 \tabularnewline
Wbr & 0.0026217940522085 & 0.024641 & 0.1064 & 0.915666 & 0.457833 \tabularnewline
Tg & -0.067620433809013 & 0.086431 & -0.7824 & 0.437481 & 0.21874 \tabularnewline
P & -2.79466247616151 & 44.714931 & -0.0625 & 0.9504 & 0.4752 \tabularnewline
S & -22.0902142737359 & 29.839333 & -0.7403 & 0.462382 & 0.231191 \tabularnewline
D & -11.4834724299415 & 59.599358 & -0.1927 & 0.847948 & 0.423974 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114476&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]58.5962033954784[/C][C]55.685707[/C][C]1.0523[/C][C]0.297451[/C][C]0.148726[/C][/ROW]
[ROW][C]PS[/C][C]0.955258558887444[/C][C]0.062235[/C][C]15.3493[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]L[/C][C]0.0198987966065785[/C][C]0.098519[/C][C]0.202[/C][C]0.840706[/C][C]0.420353[/C][/ROW]
[ROW][C]Wb[/C][C]0.00797442355260723[/C][C]0.037502[/C][C]0.2126[/C][C]0.832423[/C][C]0.416211[/C][/ROW]
[ROW][C]Wbr[/C][C]0.0026217940522085[/C][C]0.024641[/C][C]0.1064[/C][C]0.915666[/C][C]0.457833[/C][/ROW]
[ROW][C]Tg[/C][C]-0.067620433809013[/C][C]0.086431[/C][C]-0.7824[/C][C]0.437481[/C][C]0.21874[/C][/ROW]
[ROW][C]P[/C][C]-2.79466247616151[/C][C]44.714931[/C][C]-0.0625[/C][C]0.9504[/C][C]0.4752[/C][/ROW]
[ROW][C]S[/C][C]-22.0902142737359[/C][C]29.839333[/C][C]-0.7403[/C][C]0.462382[/C][C]0.231191[/C][/ROW]
[ROW][C]D[/C][C]-11.4834724299415[/C][C]59.599358[/C][C]-0.1927[/C][C]0.847948[/C][C]0.423974[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114476&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114476&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)58.596203395478455.6857071.05230.2974510.148726
PS0.9552585588874440.06223515.349300
L0.01989879660657850.0985190.2020.8407060.420353
Wb0.007974423552607230.0375020.21260.8324230.416211
Wbr0.00262179405220850.0246410.10640.9156660.457833
Tg-0.0676204338090130.086431-0.78240.4374810.21874
P-2.7946624761615144.714931-0.06250.95040.4752
S-22.090214273735929.839333-0.74030.4623820.231191
D-11.483472429941559.599358-0.19270.8479480.423974






Multiple Linear Regression - Regression Statistics
Multiple R0.918797752642164
R-squared0.844189310260292
Adjusted R-squared0.8206707155826
F-TEST (value)35.8945473498481
F-TEST (DF numerator)8
F-TEST (DF denominator)53
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation179.879442882999
Sum Squared Residuals1714900.54051059

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.918797752642164 \tabularnewline
R-squared & 0.844189310260292 \tabularnewline
Adjusted R-squared & 0.8206707155826 \tabularnewline
F-TEST (value) & 35.8945473498481 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 53 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 179.879442882999 \tabularnewline
Sum Squared Residuals & 1714900.54051059 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114476&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.918797752642164[/C][/ROW]
[ROW][C]R-squared[/C][C]0.844189310260292[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.8206707155826[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]35.8945473498481[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]53[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]179.879442882999[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1714900.54051059[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114476&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114476&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 R0.918797752642164
R-squared0.844189310260292
Adjusted R-squared0.8206707155826
F-TEST (value)35.8945473498481
F-TEST (DF numerator)8
F-TEST (DF denominator)53
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation179.879442882999
Sum Squared Residuals1714900.54051059






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-999-1023.8021573326024.8021573326012
26.310.1434030540876-3.84340305408762
3-999-935.7103858578-63.2896141422008
4-999-1009.8583831106110.8583831106134
52.1-112.896438635618114.996438635618
69.1-97.288180709049106.388180709049
715.823.9656705741970-8.16567057419695
85.2-132.195444711778137.395444711778
910.9-0.032916745624886410.9329167456249
108.310.5769915678138-2.27699156781375
1111-95.652956488009106.652956488009
123.2-136.164113678583139.364113678583
137.658.213511222444-50.613511222444
14-999-1098.8438029537399.8438029537311
156.321.4672688641947-15.1672688641947
168.6-14.949459158559723.5494591585597
176.6-3.762021335763510.3620213357635
189.5-20.886985410935930.3869854109359
194.869.6659738614078-64.8659738614079
201296.4378671076884-84.4378671076884
21-999-143.448682139840-855.55131786016
223.3-131.855682624405135.155682624406
23117.402193024292373.59780697570763
24-999-1011.8892614867712.8892614867712
254.7-39.136261244485843.8362612444858
26-999-935.410157740953-63.5898422590467
2710.4-10.372859807301220.7728598073012
287.4-71.241680056213778.6416800562137
292.1-138.414506997427140.514506997427
30-999-937.182692428374-61.8173075716263
31-999-1061.9572440897562.9572440897535
327.7-45.555720930438253.2557209304382
3317.921.2356559542704-3.33565595427038
346.111.9332517203540-5.83325172035399
358.2-0.1728762304514248.37287623045142
368.4-26.543506959440134.9435069594401
3711.9-9.1011458333124421.0011458333124
3810.8-9.200112462795420.0001124627954
3913.824.1007622691409-10.3007622691409
4014.314.4656092510884-0.165609251088387
41-999-148.295378247145-850.704621752855
4215.2-21.604639201070236.8046392010702
4310-96.7297281254276106.729728125428
4411.98.821128715016793.07887128498321
456.5-90.298272190151796.7982721901517
467.5-124.072205142965131.572205142965
47-999-972.294284505327-26.7057154946735
4810.6-5.1640338621034715.7640338621035
497.421.3651703817397-13.9651703817397
508.4-45.073557794854553.4735577948545
515.7-28.317839407475834.0178394074758
524.9-42.952439818361247.8524398183612
53-999-1086.977907484187.9779074841008
543.2-131.583702475107134.783702475107
55-999-974.232983000037-24.7670169999626
568.174.8821850857198-66.7821850857198
57116.193126196899644.80687380310036
584.9-19.177435190428524.0774351904285
5913.2-17.508905982787130.7089059827871
609.7-77.7510670064387.45106700643
6112.824.8890440238665-12.0890440238665
62-999-939.898796279822-59.1012037201784

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -999 & -1023.80215733260 & 24.8021573326012 \tabularnewline
2 & 6.3 & 10.1434030540876 & -3.84340305408762 \tabularnewline
3 & -999 & -935.7103858578 & -63.2896141422008 \tabularnewline
4 & -999 & -1009.85838311061 & 10.8583831106134 \tabularnewline
5 & 2.1 & -112.896438635618 & 114.996438635618 \tabularnewline
6 & 9.1 & -97.288180709049 & 106.388180709049 \tabularnewline
7 & 15.8 & 23.9656705741970 & -8.16567057419695 \tabularnewline
8 & 5.2 & -132.195444711778 & 137.395444711778 \tabularnewline
9 & 10.9 & -0.0329167456248864 & 10.9329167456249 \tabularnewline
10 & 8.3 & 10.5769915678138 & -2.27699156781375 \tabularnewline
11 & 11 & -95.652956488009 & 106.652956488009 \tabularnewline
12 & 3.2 & -136.164113678583 & 139.364113678583 \tabularnewline
13 & 7.6 & 58.213511222444 & -50.613511222444 \tabularnewline
14 & -999 & -1098.84380295373 & 99.8438029537311 \tabularnewline
15 & 6.3 & 21.4672688641947 & -15.1672688641947 \tabularnewline
16 & 8.6 & -14.9494591585597 & 23.5494591585597 \tabularnewline
17 & 6.6 & -3.7620213357635 & 10.3620213357635 \tabularnewline
18 & 9.5 & -20.8869854109359 & 30.3869854109359 \tabularnewline
19 & 4.8 & 69.6659738614078 & -64.8659738614079 \tabularnewline
20 & 12 & 96.4378671076884 & -84.4378671076884 \tabularnewline
21 & -999 & -143.448682139840 & -855.55131786016 \tabularnewline
22 & 3.3 & -131.855682624405 & 135.155682624406 \tabularnewline
23 & 11 & 7.40219302429237 & 3.59780697570763 \tabularnewline
24 & -999 & -1011.88926148677 & 12.8892614867712 \tabularnewline
25 & 4.7 & -39.1362612444858 & 43.8362612444858 \tabularnewline
26 & -999 & -935.410157740953 & -63.5898422590467 \tabularnewline
27 & 10.4 & -10.3728598073012 & 20.7728598073012 \tabularnewline
28 & 7.4 & -71.2416800562137 & 78.6416800562137 \tabularnewline
29 & 2.1 & -138.414506997427 & 140.514506997427 \tabularnewline
30 & -999 & -937.182692428374 & -61.8173075716263 \tabularnewline
31 & -999 & -1061.95724408975 & 62.9572440897535 \tabularnewline
32 & 7.7 & -45.5557209304382 & 53.2557209304382 \tabularnewline
33 & 17.9 & 21.2356559542704 & -3.33565595427038 \tabularnewline
34 & 6.1 & 11.9332517203540 & -5.83325172035399 \tabularnewline
35 & 8.2 & -0.172876230451424 & 8.37287623045142 \tabularnewline
36 & 8.4 & -26.5435069594401 & 34.9435069594401 \tabularnewline
37 & 11.9 & -9.10114583331244 & 21.0011458333124 \tabularnewline
38 & 10.8 & -9.2001124627954 & 20.0001124627954 \tabularnewline
39 & 13.8 & 24.1007622691409 & -10.3007622691409 \tabularnewline
40 & 14.3 & 14.4656092510884 & -0.165609251088387 \tabularnewline
41 & -999 & -148.295378247145 & -850.704621752855 \tabularnewline
42 & 15.2 & -21.6046392010702 & 36.8046392010702 \tabularnewline
43 & 10 & -96.7297281254276 & 106.729728125428 \tabularnewline
44 & 11.9 & 8.82112871501679 & 3.07887128498321 \tabularnewline
45 & 6.5 & -90.2982721901517 & 96.7982721901517 \tabularnewline
46 & 7.5 & -124.072205142965 & 131.572205142965 \tabularnewline
47 & -999 & -972.294284505327 & -26.7057154946735 \tabularnewline
48 & 10.6 & -5.16403386210347 & 15.7640338621035 \tabularnewline
49 & 7.4 & 21.3651703817397 & -13.9651703817397 \tabularnewline
50 & 8.4 & -45.0735577948545 & 53.4735577948545 \tabularnewline
51 & 5.7 & -28.3178394074758 & 34.0178394074758 \tabularnewline
52 & 4.9 & -42.9524398183612 & 47.8524398183612 \tabularnewline
53 & -999 & -1086.9779074841 & 87.9779074841008 \tabularnewline
54 & 3.2 & -131.583702475107 & 134.783702475107 \tabularnewline
55 & -999 & -974.232983000037 & -24.7670169999626 \tabularnewline
56 & 8.1 & 74.8821850857198 & -66.7821850857198 \tabularnewline
57 & 11 & 6.19312619689964 & 4.80687380310036 \tabularnewline
58 & 4.9 & -19.1774351904285 & 24.0774351904285 \tabularnewline
59 & 13.2 & -17.5089059827871 & 30.7089059827871 \tabularnewline
60 & 9.7 & -77.75106700643 & 87.45106700643 \tabularnewline
61 & 12.8 & 24.8890440238665 & -12.0890440238665 \tabularnewline
62 & -999 & -939.898796279822 & -59.1012037201784 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114476&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]-999[/C][C]-1023.80215733260[/C][C]24.8021573326012[/C][/ROW]
[ROW][C]2[/C][C]6.3[/C][C]10.1434030540876[/C][C]-3.84340305408762[/C][/ROW]
[ROW][C]3[/C][C]-999[/C][C]-935.7103858578[/C][C]-63.2896141422008[/C][/ROW]
[ROW][C]4[/C][C]-999[/C][C]-1009.85838311061[/C][C]10.8583831106134[/C][/ROW]
[ROW][C]5[/C][C]2.1[/C][C]-112.896438635618[/C][C]114.996438635618[/C][/ROW]
[ROW][C]6[/C][C]9.1[/C][C]-97.288180709049[/C][C]106.388180709049[/C][/ROW]
[ROW][C]7[/C][C]15.8[/C][C]23.9656705741970[/C][C]-8.16567057419695[/C][/ROW]
[ROW][C]8[/C][C]5.2[/C][C]-132.195444711778[/C][C]137.395444711778[/C][/ROW]
[ROW][C]9[/C][C]10.9[/C][C]-0.0329167456248864[/C][C]10.9329167456249[/C][/ROW]
[ROW][C]10[/C][C]8.3[/C][C]10.5769915678138[/C][C]-2.27699156781375[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]-95.652956488009[/C][C]106.652956488009[/C][/ROW]
[ROW][C]12[/C][C]3.2[/C][C]-136.164113678583[/C][C]139.364113678583[/C][/ROW]
[ROW][C]13[/C][C]7.6[/C][C]58.213511222444[/C][C]-50.613511222444[/C][/ROW]
[ROW][C]14[/C][C]-999[/C][C]-1098.84380295373[/C][C]99.8438029537311[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]21.4672688641947[/C][C]-15.1672688641947[/C][/ROW]
[ROW][C]16[/C][C]8.6[/C][C]-14.9494591585597[/C][C]23.5494591585597[/C][/ROW]
[ROW][C]17[/C][C]6.6[/C][C]-3.7620213357635[/C][C]10.3620213357635[/C][/ROW]
[ROW][C]18[/C][C]9.5[/C][C]-20.8869854109359[/C][C]30.3869854109359[/C][/ROW]
[ROW][C]19[/C][C]4.8[/C][C]69.6659738614078[/C][C]-64.8659738614079[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]96.4378671076884[/C][C]-84.4378671076884[/C][/ROW]
[ROW][C]21[/C][C]-999[/C][C]-143.448682139840[/C][C]-855.55131786016[/C][/ROW]
[ROW][C]22[/C][C]3.3[/C][C]-131.855682624405[/C][C]135.155682624406[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]7.40219302429237[/C][C]3.59780697570763[/C][/ROW]
[ROW][C]24[/C][C]-999[/C][C]-1011.88926148677[/C][C]12.8892614867712[/C][/ROW]
[ROW][C]25[/C][C]4.7[/C][C]-39.1362612444858[/C][C]43.8362612444858[/C][/ROW]
[ROW][C]26[/C][C]-999[/C][C]-935.410157740953[/C][C]-63.5898422590467[/C][/ROW]
[ROW][C]27[/C][C]10.4[/C][C]-10.3728598073012[/C][C]20.7728598073012[/C][/ROW]
[ROW][C]28[/C][C]7.4[/C][C]-71.2416800562137[/C][C]78.6416800562137[/C][/ROW]
[ROW][C]29[/C][C]2.1[/C][C]-138.414506997427[/C][C]140.514506997427[/C][/ROW]
[ROW][C]30[/C][C]-999[/C][C]-937.182692428374[/C][C]-61.8173075716263[/C][/ROW]
[ROW][C]31[/C][C]-999[/C][C]-1061.95724408975[/C][C]62.9572440897535[/C][/ROW]
[ROW][C]32[/C][C]7.7[/C][C]-45.5557209304382[/C][C]53.2557209304382[/C][/ROW]
[ROW][C]33[/C][C]17.9[/C][C]21.2356559542704[/C][C]-3.33565595427038[/C][/ROW]
[ROW][C]34[/C][C]6.1[/C][C]11.9332517203540[/C][C]-5.83325172035399[/C][/ROW]
[ROW][C]35[/C][C]8.2[/C][C]-0.172876230451424[/C][C]8.37287623045142[/C][/ROW]
[ROW][C]36[/C][C]8.4[/C][C]-26.5435069594401[/C][C]34.9435069594401[/C][/ROW]
[ROW][C]37[/C][C]11.9[/C][C]-9.10114583331244[/C][C]21.0011458333124[/C][/ROW]
[ROW][C]38[/C][C]10.8[/C][C]-9.2001124627954[/C][C]20.0001124627954[/C][/ROW]
[ROW][C]39[/C][C]13.8[/C][C]24.1007622691409[/C][C]-10.3007622691409[/C][/ROW]
[ROW][C]40[/C][C]14.3[/C][C]14.4656092510884[/C][C]-0.165609251088387[/C][/ROW]
[ROW][C]41[/C][C]-999[/C][C]-148.295378247145[/C][C]-850.704621752855[/C][/ROW]
[ROW][C]42[/C][C]15.2[/C][C]-21.6046392010702[/C][C]36.8046392010702[/C][/ROW]
[ROW][C]43[/C][C]10[/C][C]-96.7297281254276[/C][C]106.729728125428[/C][/ROW]
[ROW][C]44[/C][C]11.9[/C][C]8.82112871501679[/C][C]3.07887128498321[/C][/ROW]
[ROW][C]45[/C][C]6.5[/C][C]-90.2982721901517[/C][C]96.7982721901517[/C][/ROW]
[ROW][C]46[/C][C]7.5[/C][C]-124.072205142965[/C][C]131.572205142965[/C][/ROW]
[ROW][C]47[/C][C]-999[/C][C]-972.294284505327[/C][C]-26.7057154946735[/C][/ROW]
[ROW][C]48[/C][C]10.6[/C][C]-5.16403386210347[/C][C]15.7640338621035[/C][/ROW]
[ROW][C]49[/C][C]7.4[/C][C]21.3651703817397[/C][C]-13.9651703817397[/C][/ROW]
[ROW][C]50[/C][C]8.4[/C][C]-45.0735577948545[/C][C]53.4735577948545[/C][/ROW]
[ROW][C]51[/C][C]5.7[/C][C]-28.3178394074758[/C][C]34.0178394074758[/C][/ROW]
[ROW][C]52[/C][C]4.9[/C][C]-42.9524398183612[/C][C]47.8524398183612[/C][/ROW]
[ROW][C]53[/C][C]-999[/C][C]-1086.9779074841[/C][C]87.9779074841008[/C][/ROW]
[ROW][C]54[/C][C]3.2[/C][C]-131.583702475107[/C][C]134.783702475107[/C][/ROW]
[ROW][C]55[/C][C]-999[/C][C]-974.232983000037[/C][C]-24.7670169999626[/C][/ROW]
[ROW][C]56[/C][C]8.1[/C][C]74.8821850857198[/C][C]-66.7821850857198[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]6.19312619689964[/C][C]4.80687380310036[/C][/ROW]
[ROW][C]58[/C][C]4.9[/C][C]-19.1774351904285[/C][C]24.0774351904285[/C][/ROW]
[ROW][C]59[/C][C]13.2[/C][C]-17.5089059827871[/C][C]30.7089059827871[/C][/ROW]
[ROW][C]60[/C][C]9.7[/C][C]-77.75106700643[/C][C]87.45106700643[/C][/ROW]
[ROW][C]61[/C][C]12.8[/C][C]24.8890440238665[/C][C]-12.0890440238665[/C][/ROW]
[ROW][C]62[/C][C]-999[/C][C]-939.898796279822[/C][C]-59.1012037201784[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114476&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114476&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
1-999-1023.8021573326024.8021573326012
26.310.1434030540876-3.84340305408762
3-999-935.7103858578-63.2896141422008
4-999-1009.8583831106110.8583831106134
52.1-112.896438635618114.996438635618
69.1-97.288180709049106.388180709049
715.823.9656705741970-8.16567057419695
85.2-132.195444711778137.395444711778
910.9-0.032916745624886410.9329167456249
108.310.5769915678138-2.27699156781375
1111-95.652956488009106.652956488009
123.2-136.164113678583139.364113678583
137.658.213511222444-50.613511222444
14-999-1098.8438029537399.8438029537311
156.321.4672688641947-15.1672688641947
168.6-14.949459158559723.5494591585597
176.6-3.762021335763510.3620213357635
189.5-20.886985410935930.3869854109359
194.869.6659738614078-64.8659738614079
201296.4378671076884-84.4378671076884
21-999-143.448682139840-855.55131786016
223.3-131.855682624405135.155682624406
23117.402193024292373.59780697570763
24-999-1011.8892614867712.8892614867712
254.7-39.136261244485843.8362612444858
26-999-935.410157740953-63.5898422590467
2710.4-10.372859807301220.7728598073012
287.4-71.241680056213778.6416800562137
292.1-138.414506997427140.514506997427
30-999-937.182692428374-61.8173075716263
31-999-1061.9572440897562.9572440897535
327.7-45.555720930438253.2557209304382
3317.921.2356559542704-3.33565595427038
346.111.9332517203540-5.83325172035399
358.2-0.1728762304514248.37287623045142
368.4-26.543506959440134.9435069594401
3711.9-9.1011458333124421.0011458333124
3810.8-9.200112462795420.0001124627954
3913.824.1007622691409-10.3007622691409
4014.314.4656092510884-0.165609251088387
41-999-148.295378247145-850.704621752855
4215.2-21.604639201070236.8046392010702
4310-96.7297281254276106.729728125428
4411.98.821128715016793.07887128498321
456.5-90.298272190151796.7982721901517
467.5-124.072205142965131.572205142965
47-999-972.294284505327-26.7057154946735
4810.6-5.1640338621034715.7640338621035
497.421.3651703817397-13.9651703817397
508.4-45.073557794854553.4735577948545
515.7-28.317839407475834.0178394074758
524.9-42.952439818361247.8524398183612
53-999-1086.977907484187.9779074841008
543.2-131.583702475107134.783702475107
55-999-974.232983000037-24.7670169999626
568.174.8821850857198-66.7821850857198
57116.193126196899644.80687380310036
584.9-19.177435190428524.0774351904285
5913.2-17.508905982787130.7089059827871
609.7-77.7510670064387.45106700643
6112.824.8890440238665-12.0890440238665
62-999-939.898796279822-59.1012037201784






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
124.99658916386013e-069.99317832772025e-060.999995003410836
139.03486512656496e-081.80697302531299e-070.99999990965135
142.45472832420741e-094.90945664841482e-090.999999997545272
151.02082008681588e-102.04164017363175e-100.999999999897918
161.55627668595619e-123.11255337191239e-120.999999999998444
173.09130025553861e-146.18260051107722e-140.99999999999997
184.42630014991552e-168.85260029983105e-161
191.18497578096273e-172.36995156192546e-171
201.75260334599068e-193.50520669198137e-191
210.9838935643266330.03221287134673310.0161064356733665
220.9794727434912820.04105451301743550.0205272565087178
230.966857697766890.06628460446622040.0331423022331102
240.9526235012185850.09475299756282960.0473764987814148
250.9281154695865760.1437690608268480.0718845304134239
260.8943758318889250.2112483362221500.105624168111075
270.8500860067980140.2998279864039710.149913993201986
280.802517568637950.39496486272410.19748243136205
290.9822009599496490.03559808010070260.0177990400503513
300.9800691799111160.03986164017776840.0199308200888842
310.968254968357540.06349006328492060.0317450316424603
320.950285294294840.09942941141031920.0497147057051596
330.9238990147650260.1522019704699490.0761009852349744
340.9946817230535340.01063655389293290.00531827694646645
350.9901487475386950.01970250492261030.00985125246130514
360.9979374628013150.004125074397370780.00206253719868539
370.995946327568560.008107344862878530.00405367243143927
380.9932876052427660.01342478951446740.00671239475723369
390.986793033950910.02641393209818200.0132069660490910
400.9751566371367480.04968672572650450.0248433628632523
4113.45288085588953e-211.72644042794477e-21
4213.81194452559832e-201.90597226279916e-20
4311.76417004321183e-188.82085021605915e-19
4419.63218265915105e-174.81609132957553e-17
450.9999999999999983.42067378438969e-151.71033689219485e-15
460.999999999999892.19663493610247e-131.09831746805123e-13
470.9999999999900141.99712396035696e-119.98561980178479e-12
480.9999999994034421.19311619498435e-095.96558097492173e-10
490.9999999441394561.11721088787161e-075.58605443935805e-08
500.9999957652978228.46940435602068e-064.23470217801034e-06

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 4.99658916386013e-06 & 9.99317832772025e-06 & 0.999995003410836 \tabularnewline
13 & 9.03486512656496e-08 & 1.80697302531299e-07 & 0.99999990965135 \tabularnewline
14 & 2.45472832420741e-09 & 4.90945664841482e-09 & 0.999999997545272 \tabularnewline
15 & 1.02082008681588e-10 & 2.04164017363175e-10 & 0.999999999897918 \tabularnewline
16 & 1.55627668595619e-12 & 3.11255337191239e-12 & 0.999999999998444 \tabularnewline
17 & 3.09130025553861e-14 & 6.18260051107722e-14 & 0.99999999999997 \tabularnewline
18 & 4.42630014991552e-16 & 8.85260029983105e-16 & 1 \tabularnewline
19 & 1.18497578096273e-17 & 2.36995156192546e-17 & 1 \tabularnewline
20 & 1.75260334599068e-19 & 3.50520669198137e-19 & 1 \tabularnewline
21 & 0.983893564326633 & 0.0322128713467331 & 0.0161064356733665 \tabularnewline
22 & 0.979472743491282 & 0.0410545130174355 & 0.0205272565087178 \tabularnewline
23 & 0.96685769776689 & 0.0662846044662204 & 0.0331423022331102 \tabularnewline
24 & 0.952623501218585 & 0.0947529975628296 & 0.0473764987814148 \tabularnewline
25 & 0.928115469586576 & 0.143769060826848 & 0.0718845304134239 \tabularnewline
26 & 0.894375831888925 & 0.211248336222150 & 0.105624168111075 \tabularnewline
27 & 0.850086006798014 & 0.299827986403971 & 0.149913993201986 \tabularnewline
28 & 0.80251756863795 & 0.3949648627241 & 0.19748243136205 \tabularnewline
29 & 0.982200959949649 & 0.0355980801007026 & 0.0177990400503513 \tabularnewline
30 & 0.980069179911116 & 0.0398616401777684 & 0.0199308200888842 \tabularnewline
31 & 0.96825496835754 & 0.0634900632849206 & 0.0317450316424603 \tabularnewline
32 & 0.95028529429484 & 0.0994294114103192 & 0.0497147057051596 \tabularnewline
33 & 0.923899014765026 & 0.152201970469949 & 0.0761009852349744 \tabularnewline
34 & 0.994681723053534 & 0.0106365538929329 & 0.00531827694646645 \tabularnewline
35 & 0.990148747538695 & 0.0197025049226103 & 0.00985125246130514 \tabularnewline
36 & 0.997937462801315 & 0.00412507439737078 & 0.00206253719868539 \tabularnewline
37 & 0.99594632756856 & 0.00810734486287853 & 0.00405367243143927 \tabularnewline
38 & 0.993287605242766 & 0.0134247895144674 & 0.00671239475723369 \tabularnewline
39 & 0.98679303395091 & 0.0264139320981820 & 0.0132069660490910 \tabularnewline
40 & 0.975156637136748 & 0.0496867257265045 & 0.0248433628632523 \tabularnewline
41 & 1 & 3.45288085588953e-21 & 1.72644042794477e-21 \tabularnewline
42 & 1 & 3.81194452559832e-20 & 1.90597226279916e-20 \tabularnewline
43 & 1 & 1.76417004321183e-18 & 8.82085021605915e-19 \tabularnewline
44 & 1 & 9.63218265915105e-17 & 4.81609132957553e-17 \tabularnewline
45 & 0.999999999999998 & 3.42067378438969e-15 & 1.71033689219485e-15 \tabularnewline
46 & 0.99999999999989 & 2.19663493610247e-13 & 1.09831746805123e-13 \tabularnewline
47 & 0.999999999990014 & 1.99712396035696e-11 & 9.98561980178479e-12 \tabularnewline
48 & 0.999999999403442 & 1.19311619498435e-09 & 5.96558097492173e-10 \tabularnewline
49 & 0.999999944139456 & 1.11721088787161e-07 & 5.58605443935805e-08 \tabularnewline
50 & 0.999995765297822 & 8.46940435602068e-06 & 4.23470217801034e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114476&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]12[/C][C]4.99658916386013e-06[/C][C]9.99317832772025e-06[/C][C]0.999995003410836[/C][/ROW]
[ROW][C]13[/C][C]9.03486512656496e-08[/C][C]1.80697302531299e-07[/C][C]0.99999990965135[/C][/ROW]
[ROW][C]14[/C][C]2.45472832420741e-09[/C][C]4.90945664841482e-09[/C][C]0.999999997545272[/C][/ROW]
[ROW][C]15[/C][C]1.02082008681588e-10[/C][C]2.04164017363175e-10[/C][C]0.999999999897918[/C][/ROW]
[ROW][C]16[/C][C]1.55627668595619e-12[/C][C]3.11255337191239e-12[/C][C]0.999999999998444[/C][/ROW]
[ROW][C]17[/C][C]3.09130025553861e-14[/C][C]6.18260051107722e-14[/C][C]0.99999999999997[/C][/ROW]
[ROW][C]18[/C][C]4.42630014991552e-16[/C][C]8.85260029983105e-16[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]1.18497578096273e-17[/C][C]2.36995156192546e-17[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]1.75260334599068e-19[/C][C]3.50520669198137e-19[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]0.983893564326633[/C][C]0.0322128713467331[/C][C]0.0161064356733665[/C][/ROW]
[ROW][C]22[/C][C]0.979472743491282[/C][C]0.0410545130174355[/C][C]0.0205272565087178[/C][/ROW]
[ROW][C]23[/C][C]0.96685769776689[/C][C]0.0662846044662204[/C][C]0.0331423022331102[/C][/ROW]
[ROW][C]24[/C][C]0.952623501218585[/C][C]0.0947529975628296[/C][C]0.0473764987814148[/C][/ROW]
[ROW][C]25[/C][C]0.928115469586576[/C][C]0.143769060826848[/C][C]0.0718845304134239[/C][/ROW]
[ROW][C]26[/C][C]0.894375831888925[/C][C]0.211248336222150[/C][C]0.105624168111075[/C][/ROW]
[ROW][C]27[/C][C]0.850086006798014[/C][C]0.299827986403971[/C][C]0.149913993201986[/C][/ROW]
[ROW][C]28[/C][C]0.80251756863795[/C][C]0.3949648627241[/C][C]0.19748243136205[/C][/ROW]
[ROW][C]29[/C][C]0.982200959949649[/C][C]0.0355980801007026[/C][C]0.0177990400503513[/C][/ROW]
[ROW][C]30[/C][C]0.980069179911116[/C][C]0.0398616401777684[/C][C]0.0199308200888842[/C][/ROW]
[ROW][C]31[/C][C]0.96825496835754[/C][C]0.0634900632849206[/C][C]0.0317450316424603[/C][/ROW]
[ROW][C]32[/C][C]0.95028529429484[/C][C]0.0994294114103192[/C][C]0.0497147057051596[/C][/ROW]
[ROW][C]33[/C][C]0.923899014765026[/C][C]0.152201970469949[/C][C]0.0761009852349744[/C][/ROW]
[ROW][C]34[/C][C]0.994681723053534[/C][C]0.0106365538929329[/C][C]0.00531827694646645[/C][/ROW]
[ROW][C]35[/C][C]0.990148747538695[/C][C]0.0197025049226103[/C][C]0.00985125246130514[/C][/ROW]
[ROW][C]36[/C][C]0.997937462801315[/C][C]0.00412507439737078[/C][C]0.00206253719868539[/C][/ROW]
[ROW][C]37[/C][C]0.99594632756856[/C][C]0.00810734486287853[/C][C]0.00405367243143927[/C][/ROW]
[ROW][C]38[/C][C]0.993287605242766[/C][C]0.0134247895144674[/C][C]0.00671239475723369[/C][/ROW]
[ROW][C]39[/C][C]0.98679303395091[/C][C]0.0264139320981820[/C][C]0.0132069660490910[/C][/ROW]
[ROW][C]40[/C][C]0.975156637136748[/C][C]0.0496867257265045[/C][C]0.0248433628632523[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]3.45288085588953e-21[/C][C]1.72644042794477e-21[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]3.81194452559832e-20[/C][C]1.90597226279916e-20[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]1.76417004321183e-18[/C][C]8.82085021605915e-19[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]9.63218265915105e-17[/C][C]4.81609132957553e-17[/C][/ROW]
[ROW][C]45[/C][C]0.999999999999998[/C][C]3.42067378438969e-15[/C][C]1.71033689219485e-15[/C][/ROW]
[ROW][C]46[/C][C]0.99999999999989[/C][C]2.19663493610247e-13[/C][C]1.09831746805123e-13[/C][/ROW]
[ROW][C]47[/C][C]0.999999999990014[/C][C]1.99712396035696e-11[/C][C]9.98561980178479e-12[/C][/ROW]
[ROW][C]48[/C][C]0.999999999403442[/C][C]1.19311619498435e-09[/C][C]5.96558097492173e-10[/C][/ROW]
[ROW][C]49[/C][C]0.999999944139456[/C][C]1.11721088787161e-07[/C][C]5.58605443935805e-08[/C][/ROW]
[ROW][C]50[/C][C]0.999995765297822[/C][C]8.46940435602068e-06[/C][C]4.23470217801034e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114476&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114476&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
124.99658916386013e-069.99317832772025e-060.999995003410836
139.03486512656496e-081.80697302531299e-070.99999990965135
142.45472832420741e-094.90945664841482e-090.999999997545272
151.02082008681588e-102.04164017363175e-100.999999999897918
161.55627668595619e-123.11255337191239e-120.999999999998444
173.09130025553861e-146.18260051107722e-140.99999999999997
184.42630014991552e-168.85260029983105e-161
191.18497578096273e-172.36995156192546e-171
201.75260334599068e-193.50520669198137e-191
210.9838935643266330.03221287134673310.0161064356733665
220.9794727434912820.04105451301743550.0205272565087178
230.966857697766890.06628460446622040.0331423022331102
240.9526235012185850.09475299756282960.0473764987814148
250.9281154695865760.1437690608268480.0718845304134239
260.8943758318889250.2112483362221500.105624168111075
270.8500860067980140.2998279864039710.149913993201986
280.802517568637950.39496486272410.19748243136205
290.9822009599496490.03559808010070260.0177990400503513
300.9800691799111160.03986164017776840.0199308200888842
310.968254968357540.06349006328492060.0317450316424603
320.950285294294840.09942941141031920.0497147057051596
330.9238990147650260.1522019704699490.0761009852349744
340.9946817230535340.01063655389293290.00531827694646645
350.9901487475386950.01970250492261030.00985125246130514
360.9979374628013150.004125074397370780.00206253719868539
370.995946327568560.008107344862878530.00405367243143927
380.9932876052427660.01342478951446740.00671239475723369
390.986793033950910.02641393209818200.0132069660490910
400.9751566371367480.04968672572650450.0248433628632523
4113.45288085588953e-211.72644042794477e-21
4213.81194452559832e-201.90597226279916e-20
4311.76417004321183e-188.82085021605915e-19
4419.63218265915105e-174.81609132957553e-17
450.9999999999999983.42067378438969e-151.71033689219485e-15
460.999999999999892.19663493610247e-131.09831746805123e-13
470.9999999999900141.99712396035696e-119.98561980178479e-12
480.9999999994034421.19311619498435e-095.96558097492173e-10
490.9999999441394561.11721088787161e-075.58605443935805e-08
500.9999957652978228.46940435602068e-064.23470217801034e-06






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level210.538461538461538NOK
5% type I error level300.769230769230769NOK
10% type I error level340.871794871794872NOK

\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 & 21 & 0.538461538461538 & NOK \tabularnewline
5% type I error level & 30 & 0.769230769230769 & NOK \tabularnewline
10% type I error level & 34 & 0.871794871794872 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114476&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]21[/C][C]0.538461538461538[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]30[/C][C]0.769230769230769[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]34[/C][C]0.871794871794872[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114476&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114476&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 level210.538461538461538NOK
5% type I error level300.769230769230769NOK
10% type I error level340.871794871794872NOK


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 9
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Figure 10
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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')
}