Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 22 Dec 2012 05:03:53 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/22/t135617065990zu1xocl0gnoyc.htm/, Retrieved Fri, 29 Mar 2024 09:10:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=204481, Retrieved Fri, 29 Mar 2024 09:10:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact117
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2012-12-19 11:00:37] [8fb02a06fa197bf14378f27a41ead604]
- R PD    [Multiple Regression] [] [2012-12-22 10:03:53] [28bcae639b1bed24764a0886ef20f539] [Current]
- RMP       [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2012-12-22 10:35:38] [8fcd082199f7dbedf65d69a953eb5ad7]
Feedback Forum

Post a new message
Dataseries X:
4	0	0	1
0	0	0	0
0	0	0	0
0	0	0	0
0	0	0	0
0	0	1	1
0	0	0	0
4	0	0	0
0	0	0	1
0	0	0	0
4	0	0	0
0	0	0	0
0	0	1	0
4	0	0	0
0	0	1	1
4	0	1	1
4	1	1	0
4	0	0	0
0	0	0	1
4	1	1	1
0	0	1	0
0	0	1	1
0	0	1	1
0	0	1	1
4	0	0	1
0	0	1	0
0	0	0	1
0	0	0	0
0	0	0	1
0	0	1	0
0	0	0	0
0	0	0	0
0	0	1	0
4	0	0	1
0	0	0	0
0	0	0	0
4	0	1	0
0	0	0	1
0	0	1	1
4	0	1	0
0	1	1	1
0	0	0	1
0	0	1	1
4	0	0	0
0	0	1	0
0	0	1	1
0	0	0	0
0	0	0	1
0	0	1	1
0	0	0	0
4	0	0	0
4	1	1	0
0	0	0	1
0	1	0	0
0	0	0	0
4	0	0	1
0	0	1	1
0	0	0	1
0	0	0	1
4	1	1	1
4	0	0	1
0	0	1	0
0	0	0	0
4	0	0	1
0	0	0	0
0	0	0	0
4	1	1	0
0	0	0	0
0	0	0	1
0	0	0	0
0	0	0	0
0	0	0	1
0	0	0	1
0	0	0	0
0	0	0	1
4	0	1	1
0	0	0	1
0	0	1	1
4	1	0	1
4	0	1	0
0	0	0	0
0	0	0	1
0	0	0	0
0	1	0	0
0	0	1	1
0	0	0	0
0	0	0	1
2	0	0	1
0	0	0	0
0	0	0	1
0	0	1	0
2	0	0	0
0	0	1	0
0	0	0	0
2	0	0	0
0	0	0	1
2	0	0	0
0	0	0	0
0	0	0	0
0	0	0	1
0	0	0	1
0	0	0	0
0	0	0	0
0	0	0	0
2	0	0	0
0	0	0	0
0	0	0	0
2	0	0	0
0	0	0	0
0	0	0	0
2	0	1	0
2	0	0	0
0	0	0	0
2	0	0	0
0	0	0	0
0	0	0	0
0	0	0	1
0	0	0	0
0	0	0	0
0	0	0	1
0	0	0	0
0	0	0	0
2	0	0	0
0	0	1	1
0	0	0	1
2	0	0	0
0	0	1	0
0	0	0	1
0	0	0	0
0	0	0	1
0	0	0	0
0	0	0	1
0	0	0	0
0	0	0	0
0	0	0	0
0	0	0	0
0	0	1	1
2	0	1	1
2	0	0	0
0	0	0	0
0	1	0	1
2	0	0	1
0	0	0	0
0	0	1	1
0	0	1	0
2	0	0	1
2	0	0	0
2	0	0	0
0	0	0	0
0	0	1	1
0	0	0	1
0	1	0	0
0	1	1	0
0	0	0	0




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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204481&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204481&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204481&T=0

As an alternative you can also use a QR Code:  

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

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







Multiple Linear Regression - Estimated Regression Equation
CorrectAnalysis[t] = -0.0184516802995404 + 0.041468855866973`Weeks*t`[t] + 0.122212707154103Useful[t] -0.0112665857955531Outcome[t] + 0.000443487529711028t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CorrectAnalysis[t] =  -0.0184516802995404 +  0.041468855866973`Weeks*t`[t] +  0.122212707154103Useful[t] -0.0112665857955531Outcome[t] +  0.000443487529711028t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204481&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CorrectAnalysis[t] =  -0.0184516802995404 +  0.041468855866973`Weeks*t`[t] +  0.122212707154103Useful[t] -0.0112665857955531Outcome[t] +  0.000443487529711028t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204481&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204481&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
CorrectAnalysis[t] = -0.0184516802995404 + 0.041468855866973`Weeks*t`[t] + 0.122212707154103Useful[t] -0.0112665857955531Outcome[t] + 0.000443487529711028t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.01845168029954040.052558-0.35110.7260280.363014
`Weeks*t`0.0414688558669730.0145332.85340.0049410.002471
Useful0.1222127071541030.0488132.50370.0133690.006685
Outcome-0.01126658579555310.043452-0.25930.7957730.397887
t0.0004434875297110280.0004870.91120.363640.18182

\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) & -0.0184516802995404 & 0.052558 & -0.3511 & 0.726028 & 0.363014 \tabularnewline
`Weeks*t` & 0.041468855866973 & 0.014533 & 2.8534 & 0.004941 & 0.002471 \tabularnewline
Useful & 0.122212707154103 & 0.048813 & 2.5037 & 0.013369 & 0.006685 \tabularnewline
Outcome & -0.0112665857955531 & 0.043452 & -0.2593 & 0.795773 & 0.397887 \tabularnewline
t & 0.000443487529711028 & 0.000487 & 0.9112 & 0.36364 & 0.18182 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204481&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]-0.0184516802995404[/C][C]0.052558[/C][C]-0.3511[/C][C]0.726028[/C][C]0.363014[/C][/ROW]
[ROW][C]`Weeks*t`[/C][C]0.041468855866973[/C][C]0.014533[/C][C]2.8534[/C][C]0.004941[/C][C]0.002471[/C][/ROW]
[ROW][C]Useful[/C][C]0.122212707154103[/C][C]0.048813[/C][C]2.5037[/C][C]0.013369[/C][C]0.006685[/C][/ROW]
[ROW][C]Outcome[/C][C]-0.0112665857955531[/C][C]0.043452[/C][C]-0.2593[/C][C]0.795773[/C][C]0.397887[/C][/ROW]
[ROW][C]t[/C][C]0.000443487529711028[/C][C]0.000487[/C][C]0.9112[/C][C]0.36364[/C][C]0.18182[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204481&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204481&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.01845168029954040.052558-0.35110.7260280.363014
`Weeks*t`0.0414688558669730.0145332.85340.0049410.002471
Useful0.1222127071541030.0488132.50370.0133690.006685
Outcome-0.01126658579555310.043452-0.25930.7957730.397887
t0.0004434875297110280.0004870.91120.363640.18182







Multiple Linear Regression - Regression Statistics
Multiple R0.307451975857687
R-squared0.0945267174587956
Adjusted R-squared0.0702187098737969
F-TEST (value)3.88870692623656
F-TEST (DF numerator)4
F-TEST (DF denominator)149
p-value0.00492733686191882
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.259309913754739
Sum Squared Residuals10.019003074352

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.307451975857687 \tabularnewline
R-squared & 0.0945267174587956 \tabularnewline
Adjusted R-squared & 0.0702187098737969 \tabularnewline
F-TEST (value) & 3.88870692623656 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 149 \tabularnewline
p-value & 0.00492733686191882 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.259309913754739 \tabularnewline
Sum Squared Residuals & 10.019003074352 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204481&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.307451975857687[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0945267174587956[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0702187098737969[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.88870692623656[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]149[/C][/ROW]
[ROW][C]p-value[/C][C]0.00492733686191882[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.259309913754739[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]10.019003074352[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204481&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204481&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.307451975857687
R-squared0.0945267174587956
Adjusted R-squared0.0702187098737969
F-TEST (value)3.88870692623656
F-TEST (DF numerator)4
F-TEST (DF denominator)149
p-value0.00492733686191882
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.259309913754739
Sum Squared Residuals10.019003074352







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.13660064490251-0.13660064490251
20-0.01756470524011840.0175647052401184
30-0.01712121771040720.0171212177104072
40-0.01667773018069630.0166777301806963
50-0.01623424265098530.0162342426509853
600.0951553662372761-0.0951553662372761
70-0.01534726759156320.0153472675915632
800.15097164340604-0.15097164340604
90-0.02572687832769420.0257268783276942
100-0.01401680500243010.0140168050024301
1100.152302105995173-0.152302105995173
120-0.0131298299430080.013129829943008
1300.109526364740806-0.109526364740806
1400.153632568584306-0.153632568584306
1500.0991467540046754-0.0991467540046754
1600.265465665002278-0.265465665002278
1710.2771757383275430.722824261672457
1800.15540651870315-0.15540651870315
190-0.0212920030305840.021292003030584
2010.2672396151211220.732760384878878
2100.113074264978495-0.113074264978495
2200.102251166712653-0.102251166712653
2300.102694654242364-0.102694654242364
2400.103138141772075-0.103138141772075
2500.147244345615574-0.147244345615574
2600.11529170262705-0.11529170262705
270-0.01774410279289570.0177441027928957
280-0.006034029467631590.00603402946763159
290-0.01685712773347370.0168571277334737
3000.117065652745894-0.117065652745894
310-0.004703566878498510.00470356687849851
320-0.004260079348787480.00426007934878748
3300.118396115335027-0.118396115335027
3400.151235733382974-0.151235733382974
350-0.00292961675965440.0029296167596544
360-0.002486129229943370.00248612922994337
3700.286045488921763-0.286045488921763
380-0.01286573996607440.0128657399660744
3900.10979045471774-0.10979045471774
4000.287375951510896-0.287375951510896
4110.1106774297771620.889322570222838
420-0.01109178984723030.0110917898472303
4300.111564404836584-0.111564404836584
4400.166937194475637-0.166937194475637
4500.123717965691559-0.123717965691559
4600.112894867425717-0.112894867425717
4700.00239223359687793-0.00239223359687793
480-0.008430864668964160.00843086466896416
4900.11422533001485-0.11422533001485
5000.00372269618601101-0.00372269618601101
5100.170041607183614-0.170041607183614
5210.2926978018674290.707302198132571
530-0.006213427020409020.00621342702040902
5410.005496646304855020.994503353695145
5500.00594013383456615-0.00594013383456615
5600.160992459036616-0.160992459036616
5700.117773230252539-0.117773230252539
580-0.003995989371853880.00399598937185388
590-0.003552501842142850.00355250184214285
6010.2849791163095640.715020883690436
6100.163209896685171-0.163209896685171
6200.131257253696647-0.131257253696647
6300.00948803407225437-0.00948803407225437
6400.164540359274304-0.164540359274304
6500.0103750091316764-0.0103750091316764
6600.0108184966613875-0.0108184966613875
6710.2993501148130940.700649885186906
6800.0117054717208095-0.0117054717208095
6900.000882373454967425-0.000882373454967425
7000.0125924467802316-0.0125924467802316
7100.0130359343099426-0.0130359343099426
7200.00221283604410051-0.00221283604410051
7300.00265632357381153-0.00265632357381153
7400.0143663968990757-0.0143663968990757
7500.00354329863323359-0.00354329863323359
7600.29207491678494-0.29207491678494
7700.00443027369265564-0.00443027369265564
7800.12708646837647-0.12708646837647
7910.171192672219970.82880732778003
8000.305115452699337-0.305115452699337
8100.0174708096070529-0.0174708096070529
8200.00664771134121078-0.00664771134121078
8300.0183577846664749-0.0183577846664749
8410.01880127219618590.981198727803814
8500.130190881084447-0.130190881084447
8600.019688247255608-0.019688247255608
8700.00886514898976592-0.00886514898976592
8800.092246348253423-0.092246348253423
8900.0210187098447411-0.0210187098447411
9000.010195611578899-0.010195611578899
9100.144118392058267-0.144118392058267
9200.10528688416782-0.10528688416782
9300.145005367117689-0.145005367117689
9400.0232361474932962-0.0232361474932962
9500.106617346756953-0.106617346756953
9600.0128565367571652-0.0128565367571652
9700.107504321816375-0.107504321816375
9800.0250100976121403-0.0250100976121403
9900.0254535851418514-0.0254535851418514
10000.0146304868760093-0.0146304868760093
10100.0150739744057203-0.0150739744057203
10200.0267840477309844-0.0267840477309844
10300.0272275352606955-0.0272275352606955
10400.0276710227904065-0.0276710227904065
10500.111052222054064-0.111052222054064
10600.0285579978498286-0.0285579978498286
10700.0290014853795396-0.0290014853795396
10800.112382684643197-0.112382684643197
10900.0298884604389616-0.0298884604389616
11000.0303319479686727-0.0303319479686727
11100.235925854386433-0.235925854386433
11200.114156634762041-0.114156634762041
11300.0316624105578058-0.0316624105578058
11400.115043609821463-0.115043609821463
11500.0325493856172278-0.0325493856172278
11600.0329928731469388-0.0329928731469388
11700.0221697748810967-0.0221697748810967
11800.0338798482063609-0.0338798482063609
11900.0343233357360719-0.0343233357360719
12000.0235002374702298-0.0235002374702298
12100.035210310795494-0.035210310795494
12200.035653798325205-0.035653798325205
12300.119034997588862-0.119034997588862
12400.147486894743177-0.147486894743177
12500.025717675118785-0.025717675118785
12600.120365460177995-0.120365460177995
12700.160083943127864-0.160083943127864
12800.0270481377079181-0.0270481377079181
12900.0387582110331822-0.0387582110331822
13000.0279351127673401-0.0279351127673401
13100.0396451860926042-0.0396451860926042
13200.0288220878267622-0.0288220878267622
13300.0405321611520263-0.0405321611520263
13400.0409756486817373-0.0409756486817373
13500.0414191362114484-0.0414191362114484
13600.0418626237411594-0.0418626237411594
13700.153252232629421-0.153252232629421
13800.236633431893078-0.236633431893078
13900.126130798064239-0.126130798064239
14000.0436365738600035-0.0436365738600035
14110.03281347559416140.967186524405839
14200.116194674857818-0.116194674857818
14300.0449670364491366-0.0449670364491366
14400.156356645337398-0.156356645337398
14500.168066718662662-0.168066718662662
14600.117968624976663-0.117968624976663
14700.129678698301927-0.129678698301927
14800.130122185831638-0.130122185831638
14900.0476279616274028-0.0476279616274028
15000.159017570515664-0.159017570515664
15100.0372483508912717-0.0372483508912717
15210.04895842421653570.951041575783464
15310.171614618900350.82838538109965
15400.0498453992759579-0.0498453992759579

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 0.13660064490251 & -0.13660064490251 \tabularnewline
2 & 0 & -0.0175647052401184 & 0.0175647052401184 \tabularnewline
3 & 0 & -0.0171212177104072 & 0.0171212177104072 \tabularnewline
4 & 0 & -0.0166777301806963 & 0.0166777301806963 \tabularnewline
5 & 0 & -0.0162342426509853 & 0.0162342426509853 \tabularnewline
6 & 0 & 0.0951553662372761 & -0.0951553662372761 \tabularnewline
7 & 0 & -0.0153472675915632 & 0.0153472675915632 \tabularnewline
8 & 0 & 0.15097164340604 & -0.15097164340604 \tabularnewline
9 & 0 & -0.0257268783276942 & 0.0257268783276942 \tabularnewline
10 & 0 & -0.0140168050024301 & 0.0140168050024301 \tabularnewline
11 & 0 & 0.152302105995173 & -0.152302105995173 \tabularnewline
12 & 0 & -0.013129829943008 & 0.013129829943008 \tabularnewline
13 & 0 & 0.109526364740806 & -0.109526364740806 \tabularnewline
14 & 0 & 0.153632568584306 & -0.153632568584306 \tabularnewline
15 & 0 & 0.0991467540046754 & -0.0991467540046754 \tabularnewline
16 & 0 & 0.265465665002278 & -0.265465665002278 \tabularnewline
17 & 1 & 0.277175738327543 & 0.722824261672457 \tabularnewline
18 & 0 & 0.15540651870315 & -0.15540651870315 \tabularnewline
19 & 0 & -0.021292003030584 & 0.021292003030584 \tabularnewline
20 & 1 & 0.267239615121122 & 0.732760384878878 \tabularnewline
21 & 0 & 0.113074264978495 & -0.113074264978495 \tabularnewline
22 & 0 & 0.102251166712653 & -0.102251166712653 \tabularnewline
23 & 0 & 0.102694654242364 & -0.102694654242364 \tabularnewline
24 & 0 & 0.103138141772075 & -0.103138141772075 \tabularnewline
25 & 0 & 0.147244345615574 & -0.147244345615574 \tabularnewline
26 & 0 & 0.11529170262705 & -0.11529170262705 \tabularnewline
27 & 0 & -0.0177441027928957 & 0.0177441027928957 \tabularnewline
28 & 0 & -0.00603402946763159 & 0.00603402946763159 \tabularnewline
29 & 0 & -0.0168571277334737 & 0.0168571277334737 \tabularnewline
30 & 0 & 0.117065652745894 & -0.117065652745894 \tabularnewline
31 & 0 & -0.00470356687849851 & 0.00470356687849851 \tabularnewline
32 & 0 & -0.00426007934878748 & 0.00426007934878748 \tabularnewline
33 & 0 & 0.118396115335027 & -0.118396115335027 \tabularnewline
34 & 0 & 0.151235733382974 & -0.151235733382974 \tabularnewline
35 & 0 & -0.0029296167596544 & 0.0029296167596544 \tabularnewline
36 & 0 & -0.00248612922994337 & 0.00248612922994337 \tabularnewline
37 & 0 & 0.286045488921763 & -0.286045488921763 \tabularnewline
38 & 0 & -0.0128657399660744 & 0.0128657399660744 \tabularnewline
39 & 0 & 0.10979045471774 & -0.10979045471774 \tabularnewline
40 & 0 & 0.287375951510896 & -0.287375951510896 \tabularnewline
41 & 1 & 0.110677429777162 & 0.889322570222838 \tabularnewline
42 & 0 & -0.0110917898472303 & 0.0110917898472303 \tabularnewline
43 & 0 & 0.111564404836584 & -0.111564404836584 \tabularnewline
44 & 0 & 0.166937194475637 & -0.166937194475637 \tabularnewline
45 & 0 & 0.123717965691559 & -0.123717965691559 \tabularnewline
46 & 0 & 0.112894867425717 & -0.112894867425717 \tabularnewline
47 & 0 & 0.00239223359687793 & -0.00239223359687793 \tabularnewline
48 & 0 & -0.00843086466896416 & 0.00843086466896416 \tabularnewline
49 & 0 & 0.11422533001485 & -0.11422533001485 \tabularnewline
50 & 0 & 0.00372269618601101 & -0.00372269618601101 \tabularnewline
51 & 0 & 0.170041607183614 & -0.170041607183614 \tabularnewline
52 & 1 & 0.292697801867429 & 0.707302198132571 \tabularnewline
53 & 0 & -0.00621342702040902 & 0.00621342702040902 \tabularnewline
54 & 1 & 0.00549664630485502 & 0.994503353695145 \tabularnewline
55 & 0 & 0.00594013383456615 & -0.00594013383456615 \tabularnewline
56 & 0 & 0.160992459036616 & -0.160992459036616 \tabularnewline
57 & 0 & 0.117773230252539 & -0.117773230252539 \tabularnewline
58 & 0 & -0.00399598937185388 & 0.00399598937185388 \tabularnewline
59 & 0 & -0.00355250184214285 & 0.00355250184214285 \tabularnewline
60 & 1 & 0.284979116309564 & 0.715020883690436 \tabularnewline
61 & 0 & 0.163209896685171 & -0.163209896685171 \tabularnewline
62 & 0 & 0.131257253696647 & -0.131257253696647 \tabularnewline
63 & 0 & 0.00948803407225437 & -0.00948803407225437 \tabularnewline
64 & 0 & 0.164540359274304 & -0.164540359274304 \tabularnewline
65 & 0 & 0.0103750091316764 & -0.0103750091316764 \tabularnewline
66 & 0 & 0.0108184966613875 & -0.0108184966613875 \tabularnewline
67 & 1 & 0.299350114813094 & 0.700649885186906 \tabularnewline
68 & 0 & 0.0117054717208095 & -0.0117054717208095 \tabularnewline
69 & 0 & 0.000882373454967425 & -0.000882373454967425 \tabularnewline
70 & 0 & 0.0125924467802316 & -0.0125924467802316 \tabularnewline
71 & 0 & 0.0130359343099426 & -0.0130359343099426 \tabularnewline
72 & 0 & 0.00221283604410051 & -0.00221283604410051 \tabularnewline
73 & 0 & 0.00265632357381153 & -0.00265632357381153 \tabularnewline
74 & 0 & 0.0143663968990757 & -0.0143663968990757 \tabularnewline
75 & 0 & 0.00354329863323359 & -0.00354329863323359 \tabularnewline
76 & 0 & 0.29207491678494 & -0.29207491678494 \tabularnewline
77 & 0 & 0.00443027369265564 & -0.00443027369265564 \tabularnewline
78 & 0 & 0.12708646837647 & -0.12708646837647 \tabularnewline
79 & 1 & 0.17119267221997 & 0.82880732778003 \tabularnewline
80 & 0 & 0.305115452699337 & -0.305115452699337 \tabularnewline
81 & 0 & 0.0174708096070529 & -0.0174708096070529 \tabularnewline
82 & 0 & 0.00664771134121078 & -0.00664771134121078 \tabularnewline
83 & 0 & 0.0183577846664749 & -0.0183577846664749 \tabularnewline
84 & 1 & 0.0188012721961859 & 0.981198727803814 \tabularnewline
85 & 0 & 0.130190881084447 & -0.130190881084447 \tabularnewline
86 & 0 & 0.019688247255608 & -0.019688247255608 \tabularnewline
87 & 0 & 0.00886514898976592 & -0.00886514898976592 \tabularnewline
88 & 0 & 0.092246348253423 & -0.092246348253423 \tabularnewline
89 & 0 & 0.0210187098447411 & -0.0210187098447411 \tabularnewline
90 & 0 & 0.010195611578899 & -0.010195611578899 \tabularnewline
91 & 0 & 0.144118392058267 & -0.144118392058267 \tabularnewline
92 & 0 & 0.10528688416782 & -0.10528688416782 \tabularnewline
93 & 0 & 0.145005367117689 & -0.145005367117689 \tabularnewline
94 & 0 & 0.0232361474932962 & -0.0232361474932962 \tabularnewline
95 & 0 & 0.106617346756953 & -0.106617346756953 \tabularnewline
96 & 0 & 0.0128565367571652 & -0.0128565367571652 \tabularnewline
97 & 0 & 0.107504321816375 & -0.107504321816375 \tabularnewline
98 & 0 & 0.0250100976121403 & -0.0250100976121403 \tabularnewline
99 & 0 & 0.0254535851418514 & -0.0254535851418514 \tabularnewline
100 & 0 & 0.0146304868760093 & -0.0146304868760093 \tabularnewline
101 & 0 & 0.0150739744057203 & -0.0150739744057203 \tabularnewline
102 & 0 & 0.0267840477309844 & -0.0267840477309844 \tabularnewline
103 & 0 & 0.0272275352606955 & -0.0272275352606955 \tabularnewline
104 & 0 & 0.0276710227904065 & -0.0276710227904065 \tabularnewline
105 & 0 & 0.111052222054064 & -0.111052222054064 \tabularnewline
106 & 0 & 0.0285579978498286 & -0.0285579978498286 \tabularnewline
107 & 0 & 0.0290014853795396 & -0.0290014853795396 \tabularnewline
108 & 0 & 0.112382684643197 & -0.112382684643197 \tabularnewline
109 & 0 & 0.0298884604389616 & -0.0298884604389616 \tabularnewline
110 & 0 & 0.0303319479686727 & -0.0303319479686727 \tabularnewline
111 & 0 & 0.235925854386433 & -0.235925854386433 \tabularnewline
112 & 0 & 0.114156634762041 & -0.114156634762041 \tabularnewline
113 & 0 & 0.0316624105578058 & -0.0316624105578058 \tabularnewline
114 & 0 & 0.115043609821463 & -0.115043609821463 \tabularnewline
115 & 0 & 0.0325493856172278 & -0.0325493856172278 \tabularnewline
116 & 0 & 0.0329928731469388 & -0.0329928731469388 \tabularnewline
117 & 0 & 0.0221697748810967 & -0.0221697748810967 \tabularnewline
118 & 0 & 0.0338798482063609 & -0.0338798482063609 \tabularnewline
119 & 0 & 0.0343233357360719 & -0.0343233357360719 \tabularnewline
120 & 0 & 0.0235002374702298 & -0.0235002374702298 \tabularnewline
121 & 0 & 0.035210310795494 & -0.035210310795494 \tabularnewline
122 & 0 & 0.035653798325205 & -0.035653798325205 \tabularnewline
123 & 0 & 0.119034997588862 & -0.119034997588862 \tabularnewline
124 & 0 & 0.147486894743177 & -0.147486894743177 \tabularnewline
125 & 0 & 0.025717675118785 & -0.025717675118785 \tabularnewline
126 & 0 & 0.120365460177995 & -0.120365460177995 \tabularnewline
127 & 0 & 0.160083943127864 & -0.160083943127864 \tabularnewline
128 & 0 & 0.0270481377079181 & -0.0270481377079181 \tabularnewline
129 & 0 & 0.0387582110331822 & -0.0387582110331822 \tabularnewline
130 & 0 & 0.0279351127673401 & -0.0279351127673401 \tabularnewline
131 & 0 & 0.0396451860926042 & -0.0396451860926042 \tabularnewline
132 & 0 & 0.0288220878267622 & -0.0288220878267622 \tabularnewline
133 & 0 & 0.0405321611520263 & -0.0405321611520263 \tabularnewline
134 & 0 & 0.0409756486817373 & -0.0409756486817373 \tabularnewline
135 & 0 & 0.0414191362114484 & -0.0414191362114484 \tabularnewline
136 & 0 & 0.0418626237411594 & -0.0418626237411594 \tabularnewline
137 & 0 & 0.153252232629421 & -0.153252232629421 \tabularnewline
138 & 0 & 0.236633431893078 & -0.236633431893078 \tabularnewline
139 & 0 & 0.126130798064239 & -0.126130798064239 \tabularnewline
140 & 0 & 0.0436365738600035 & -0.0436365738600035 \tabularnewline
141 & 1 & 0.0328134755941614 & 0.967186524405839 \tabularnewline
142 & 0 & 0.116194674857818 & -0.116194674857818 \tabularnewline
143 & 0 & 0.0449670364491366 & -0.0449670364491366 \tabularnewline
144 & 0 & 0.156356645337398 & -0.156356645337398 \tabularnewline
145 & 0 & 0.168066718662662 & -0.168066718662662 \tabularnewline
146 & 0 & 0.117968624976663 & -0.117968624976663 \tabularnewline
147 & 0 & 0.129678698301927 & -0.129678698301927 \tabularnewline
148 & 0 & 0.130122185831638 & -0.130122185831638 \tabularnewline
149 & 0 & 0.0476279616274028 & -0.0476279616274028 \tabularnewline
150 & 0 & 0.159017570515664 & -0.159017570515664 \tabularnewline
151 & 0 & 0.0372483508912717 & -0.0372483508912717 \tabularnewline
152 & 1 & 0.0489584242165357 & 0.951041575783464 \tabularnewline
153 & 1 & 0.17161461890035 & 0.82838538109965 \tabularnewline
154 & 0 & 0.0498453992759579 & -0.0498453992759579 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204481&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]0[/C][C]0.13660064490251[/C][C]-0.13660064490251[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]-0.0175647052401184[/C][C]0.0175647052401184[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]-0.0171212177104072[/C][C]0.0171212177104072[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]-0.0166777301806963[/C][C]0.0166777301806963[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]-0.0162342426509853[/C][C]0.0162342426509853[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]0.0951553662372761[/C][C]-0.0951553662372761[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]-0.0153472675915632[/C][C]0.0153472675915632[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.15097164340604[/C][C]-0.15097164340604[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]-0.0257268783276942[/C][C]0.0257268783276942[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]-0.0140168050024301[/C][C]0.0140168050024301[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.152302105995173[/C][C]-0.152302105995173[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]-0.013129829943008[/C][C]0.013129829943008[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.109526364740806[/C][C]-0.109526364740806[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.153632568584306[/C][C]-0.153632568584306[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.0991467540046754[/C][C]-0.0991467540046754[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.265465665002278[/C][C]-0.265465665002278[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.277175738327543[/C][C]0.722824261672457[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.15540651870315[/C][C]-0.15540651870315[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]-0.021292003030584[/C][C]0.021292003030584[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.267239615121122[/C][C]0.732760384878878[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.113074264978495[/C][C]-0.113074264978495[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.102251166712653[/C][C]-0.102251166712653[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.102694654242364[/C][C]-0.102694654242364[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.103138141772075[/C][C]-0.103138141772075[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.147244345615574[/C][C]-0.147244345615574[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.11529170262705[/C][C]-0.11529170262705[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]-0.0177441027928957[/C][C]0.0177441027928957[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]-0.00603402946763159[/C][C]0.00603402946763159[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]-0.0168571277334737[/C][C]0.0168571277334737[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.117065652745894[/C][C]-0.117065652745894[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]-0.00470356687849851[/C][C]0.00470356687849851[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]-0.00426007934878748[/C][C]0.00426007934878748[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.118396115335027[/C][C]-0.118396115335027[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.151235733382974[/C][C]-0.151235733382974[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]-0.0029296167596544[/C][C]0.0029296167596544[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]-0.00248612922994337[/C][C]0.00248612922994337[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.286045488921763[/C][C]-0.286045488921763[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]-0.0128657399660744[/C][C]0.0128657399660744[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.10979045471774[/C][C]-0.10979045471774[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.287375951510896[/C][C]-0.287375951510896[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.110677429777162[/C][C]0.889322570222838[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]-0.0110917898472303[/C][C]0.0110917898472303[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0.111564404836584[/C][C]-0.111564404836584[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.166937194475637[/C][C]-0.166937194475637[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.123717965691559[/C][C]-0.123717965691559[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.112894867425717[/C][C]-0.112894867425717[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.00239223359687793[/C][C]-0.00239223359687793[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]-0.00843086466896416[/C][C]0.00843086466896416[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.11422533001485[/C][C]-0.11422533001485[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.00372269618601101[/C][C]-0.00372269618601101[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.170041607183614[/C][C]-0.170041607183614[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.292697801867429[/C][C]0.707302198132571[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]-0.00621342702040902[/C][C]0.00621342702040902[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]0.00549664630485502[/C][C]0.994503353695145[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.00594013383456615[/C][C]-0.00594013383456615[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.160992459036616[/C][C]-0.160992459036616[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.117773230252539[/C][C]-0.117773230252539[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]-0.00399598937185388[/C][C]0.00399598937185388[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]-0.00355250184214285[/C][C]0.00355250184214285[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.284979116309564[/C][C]0.715020883690436[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.163209896685171[/C][C]-0.163209896685171[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.131257253696647[/C][C]-0.131257253696647[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.00948803407225437[/C][C]-0.00948803407225437[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.164540359274304[/C][C]-0.164540359274304[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.0103750091316764[/C][C]-0.0103750091316764[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.0108184966613875[/C][C]-0.0108184966613875[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.299350114813094[/C][C]0.700649885186906[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.0117054717208095[/C][C]-0.0117054717208095[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0.000882373454967425[/C][C]-0.000882373454967425[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.0125924467802316[/C][C]-0.0125924467802316[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0.0130359343099426[/C][C]-0.0130359343099426[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]0.00221283604410051[/C][C]-0.00221283604410051[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.00265632357381153[/C][C]-0.00265632357381153[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.0143663968990757[/C][C]-0.0143663968990757[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]0.00354329863323359[/C][C]-0.00354329863323359[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0.29207491678494[/C][C]-0.29207491678494[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]0.00443027369265564[/C][C]-0.00443027369265564[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0.12708646837647[/C][C]-0.12708646837647[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.17119267221997[/C][C]0.82880732778003[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0.305115452699337[/C][C]-0.305115452699337[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0.0174708096070529[/C][C]-0.0174708096070529[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]0.00664771134121078[/C][C]-0.00664771134121078[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0.0183577846664749[/C][C]-0.0183577846664749[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0.0188012721961859[/C][C]0.981198727803814[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0.130190881084447[/C][C]-0.130190881084447[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]0.019688247255608[/C][C]-0.019688247255608[/C][/ROW]
[ROW][C]87[/C][C]0[/C][C]0.00886514898976592[/C][C]-0.00886514898976592[/C][/ROW]
[ROW][C]88[/C][C]0[/C][C]0.092246348253423[/C][C]-0.092246348253423[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]0.0210187098447411[/C][C]-0.0210187098447411[/C][/ROW]
[ROW][C]90[/C][C]0[/C][C]0.010195611578899[/C][C]-0.010195611578899[/C][/ROW]
[ROW][C]91[/C][C]0[/C][C]0.144118392058267[/C][C]-0.144118392058267[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]0.10528688416782[/C][C]-0.10528688416782[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]0.145005367117689[/C][C]-0.145005367117689[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]0.0232361474932962[/C][C]-0.0232361474932962[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]0.106617346756953[/C][C]-0.106617346756953[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]0.0128565367571652[/C][C]-0.0128565367571652[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]0.107504321816375[/C][C]-0.107504321816375[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]0.0250100976121403[/C][C]-0.0250100976121403[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]0.0254535851418514[/C][C]-0.0254535851418514[/C][/ROW]
[ROW][C]100[/C][C]0[/C][C]0.0146304868760093[/C][C]-0.0146304868760093[/C][/ROW]
[ROW][C]101[/C][C]0[/C][C]0.0150739744057203[/C][C]-0.0150739744057203[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]0.0267840477309844[/C][C]-0.0267840477309844[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]0.0272275352606955[/C][C]-0.0272275352606955[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]0.0276710227904065[/C][C]-0.0276710227904065[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]0.111052222054064[/C][C]-0.111052222054064[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]0.0285579978498286[/C][C]-0.0285579978498286[/C][/ROW]
[ROW][C]107[/C][C]0[/C][C]0.0290014853795396[/C][C]-0.0290014853795396[/C][/ROW]
[ROW][C]108[/C][C]0[/C][C]0.112382684643197[/C][C]-0.112382684643197[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]0.0298884604389616[/C][C]-0.0298884604389616[/C][/ROW]
[ROW][C]110[/C][C]0[/C][C]0.0303319479686727[/C][C]-0.0303319479686727[/C][/ROW]
[ROW][C]111[/C][C]0[/C][C]0.235925854386433[/C][C]-0.235925854386433[/C][/ROW]
[ROW][C]112[/C][C]0[/C][C]0.114156634762041[/C][C]-0.114156634762041[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]0.0316624105578058[/C][C]-0.0316624105578058[/C][/ROW]
[ROW][C]114[/C][C]0[/C][C]0.115043609821463[/C][C]-0.115043609821463[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]0.0325493856172278[/C][C]-0.0325493856172278[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]0.0329928731469388[/C][C]-0.0329928731469388[/C][/ROW]
[ROW][C]117[/C][C]0[/C][C]0.0221697748810967[/C][C]-0.0221697748810967[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]0.0338798482063609[/C][C]-0.0338798482063609[/C][/ROW]
[ROW][C]119[/C][C]0[/C][C]0.0343233357360719[/C][C]-0.0343233357360719[/C][/ROW]
[ROW][C]120[/C][C]0[/C][C]0.0235002374702298[/C][C]-0.0235002374702298[/C][/ROW]
[ROW][C]121[/C][C]0[/C][C]0.035210310795494[/C][C]-0.035210310795494[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]0.035653798325205[/C][C]-0.035653798325205[/C][/ROW]
[ROW][C]123[/C][C]0[/C][C]0.119034997588862[/C][C]-0.119034997588862[/C][/ROW]
[ROW][C]124[/C][C]0[/C][C]0.147486894743177[/C][C]-0.147486894743177[/C][/ROW]
[ROW][C]125[/C][C]0[/C][C]0.025717675118785[/C][C]-0.025717675118785[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]0.120365460177995[/C][C]-0.120365460177995[/C][/ROW]
[ROW][C]127[/C][C]0[/C][C]0.160083943127864[/C][C]-0.160083943127864[/C][/ROW]
[ROW][C]128[/C][C]0[/C][C]0.0270481377079181[/C][C]-0.0270481377079181[/C][/ROW]
[ROW][C]129[/C][C]0[/C][C]0.0387582110331822[/C][C]-0.0387582110331822[/C][/ROW]
[ROW][C]130[/C][C]0[/C][C]0.0279351127673401[/C][C]-0.0279351127673401[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]0.0396451860926042[/C][C]-0.0396451860926042[/C][/ROW]
[ROW][C]132[/C][C]0[/C][C]0.0288220878267622[/C][C]-0.0288220878267622[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]0.0405321611520263[/C][C]-0.0405321611520263[/C][/ROW]
[ROW][C]134[/C][C]0[/C][C]0.0409756486817373[/C][C]-0.0409756486817373[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]0.0414191362114484[/C][C]-0.0414191362114484[/C][/ROW]
[ROW][C]136[/C][C]0[/C][C]0.0418626237411594[/C][C]-0.0418626237411594[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]0.153252232629421[/C][C]-0.153252232629421[/C][/ROW]
[ROW][C]138[/C][C]0[/C][C]0.236633431893078[/C][C]-0.236633431893078[/C][/ROW]
[ROW][C]139[/C][C]0[/C][C]0.126130798064239[/C][C]-0.126130798064239[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]0.0436365738600035[/C][C]-0.0436365738600035[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]0.0328134755941614[/C][C]0.967186524405839[/C][/ROW]
[ROW][C]142[/C][C]0[/C][C]0.116194674857818[/C][C]-0.116194674857818[/C][/ROW]
[ROW][C]143[/C][C]0[/C][C]0.0449670364491366[/C][C]-0.0449670364491366[/C][/ROW]
[ROW][C]144[/C][C]0[/C][C]0.156356645337398[/C][C]-0.156356645337398[/C][/ROW]
[ROW][C]145[/C][C]0[/C][C]0.168066718662662[/C][C]-0.168066718662662[/C][/ROW]
[ROW][C]146[/C][C]0[/C][C]0.117968624976663[/C][C]-0.117968624976663[/C][/ROW]
[ROW][C]147[/C][C]0[/C][C]0.129678698301927[/C][C]-0.129678698301927[/C][/ROW]
[ROW][C]148[/C][C]0[/C][C]0.130122185831638[/C][C]-0.130122185831638[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]0.0476279616274028[/C][C]-0.0476279616274028[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]0.159017570515664[/C][C]-0.159017570515664[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]0.0372483508912717[/C][C]-0.0372483508912717[/C][/ROW]
[ROW][C]152[/C][C]1[/C][C]0.0489584242165357[/C][C]0.951041575783464[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]0.17161461890035[/C][C]0.82838538109965[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]0.0498453992759579[/C][C]-0.0498453992759579[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204481&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204481&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.13660064490251-0.13660064490251
20-0.01756470524011840.0175647052401184
30-0.01712121771040720.0171212177104072
40-0.01667773018069630.0166777301806963
50-0.01623424265098530.0162342426509853
600.0951553662372761-0.0951553662372761
70-0.01534726759156320.0153472675915632
800.15097164340604-0.15097164340604
90-0.02572687832769420.0257268783276942
100-0.01401680500243010.0140168050024301
1100.152302105995173-0.152302105995173
120-0.0131298299430080.013129829943008
1300.109526364740806-0.109526364740806
1400.153632568584306-0.153632568584306
1500.0991467540046754-0.0991467540046754
1600.265465665002278-0.265465665002278
1710.2771757383275430.722824261672457
1800.15540651870315-0.15540651870315
190-0.0212920030305840.021292003030584
2010.2672396151211220.732760384878878
2100.113074264978495-0.113074264978495
2200.102251166712653-0.102251166712653
2300.102694654242364-0.102694654242364
2400.103138141772075-0.103138141772075
2500.147244345615574-0.147244345615574
2600.11529170262705-0.11529170262705
270-0.01774410279289570.0177441027928957
280-0.006034029467631590.00603402946763159
290-0.01685712773347370.0168571277334737
3000.117065652745894-0.117065652745894
310-0.004703566878498510.00470356687849851
320-0.004260079348787480.00426007934878748
3300.118396115335027-0.118396115335027
3400.151235733382974-0.151235733382974
350-0.00292961675965440.0029296167596544
360-0.002486129229943370.00248612922994337
3700.286045488921763-0.286045488921763
380-0.01286573996607440.0128657399660744
3900.10979045471774-0.10979045471774
4000.287375951510896-0.287375951510896
4110.1106774297771620.889322570222838
420-0.01109178984723030.0110917898472303
4300.111564404836584-0.111564404836584
4400.166937194475637-0.166937194475637
4500.123717965691559-0.123717965691559
4600.112894867425717-0.112894867425717
4700.00239223359687793-0.00239223359687793
480-0.008430864668964160.00843086466896416
4900.11422533001485-0.11422533001485
5000.00372269618601101-0.00372269618601101
5100.170041607183614-0.170041607183614
5210.2926978018674290.707302198132571
530-0.006213427020409020.00621342702040902
5410.005496646304855020.994503353695145
5500.00594013383456615-0.00594013383456615
5600.160992459036616-0.160992459036616
5700.117773230252539-0.117773230252539
580-0.003995989371853880.00399598937185388
590-0.003552501842142850.00355250184214285
6010.2849791163095640.715020883690436
6100.163209896685171-0.163209896685171
6200.131257253696647-0.131257253696647
6300.00948803407225437-0.00948803407225437
6400.164540359274304-0.164540359274304
6500.0103750091316764-0.0103750091316764
6600.0108184966613875-0.0108184966613875
6710.2993501148130940.700649885186906
6800.0117054717208095-0.0117054717208095
6900.000882373454967425-0.000882373454967425
7000.0125924467802316-0.0125924467802316
7100.0130359343099426-0.0130359343099426
7200.00221283604410051-0.00221283604410051
7300.00265632357381153-0.00265632357381153
7400.0143663968990757-0.0143663968990757
7500.00354329863323359-0.00354329863323359
7600.29207491678494-0.29207491678494
7700.00443027369265564-0.00443027369265564
7800.12708646837647-0.12708646837647
7910.171192672219970.82880732778003
8000.305115452699337-0.305115452699337
8100.0174708096070529-0.0174708096070529
8200.00664771134121078-0.00664771134121078
8300.0183577846664749-0.0183577846664749
8410.01880127219618590.981198727803814
8500.130190881084447-0.130190881084447
8600.019688247255608-0.019688247255608
8700.00886514898976592-0.00886514898976592
8800.092246348253423-0.092246348253423
8900.0210187098447411-0.0210187098447411
9000.010195611578899-0.010195611578899
9100.144118392058267-0.144118392058267
9200.10528688416782-0.10528688416782
9300.145005367117689-0.145005367117689
9400.0232361474932962-0.0232361474932962
9500.106617346756953-0.106617346756953
9600.0128565367571652-0.0128565367571652
9700.107504321816375-0.107504321816375
9800.0250100976121403-0.0250100976121403
9900.0254535851418514-0.0254535851418514
10000.0146304868760093-0.0146304868760093
10100.0150739744057203-0.0150739744057203
10200.0267840477309844-0.0267840477309844
10300.0272275352606955-0.0272275352606955
10400.0276710227904065-0.0276710227904065
10500.111052222054064-0.111052222054064
10600.0285579978498286-0.0285579978498286
10700.0290014853795396-0.0290014853795396
10800.112382684643197-0.112382684643197
10900.0298884604389616-0.0298884604389616
11000.0303319479686727-0.0303319479686727
11100.235925854386433-0.235925854386433
11200.114156634762041-0.114156634762041
11300.0316624105578058-0.0316624105578058
11400.115043609821463-0.115043609821463
11500.0325493856172278-0.0325493856172278
11600.0329928731469388-0.0329928731469388
11700.0221697748810967-0.0221697748810967
11800.0338798482063609-0.0338798482063609
11900.0343233357360719-0.0343233357360719
12000.0235002374702298-0.0235002374702298
12100.035210310795494-0.035210310795494
12200.035653798325205-0.035653798325205
12300.119034997588862-0.119034997588862
12400.147486894743177-0.147486894743177
12500.025717675118785-0.025717675118785
12600.120365460177995-0.120365460177995
12700.160083943127864-0.160083943127864
12800.0270481377079181-0.0270481377079181
12900.0387582110331822-0.0387582110331822
13000.0279351127673401-0.0279351127673401
13100.0396451860926042-0.0396451860926042
13200.0288220878267622-0.0288220878267622
13300.0405321611520263-0.0405321611520263
13400.0409756486817373-0.0409756486817373
13500.0414191362114484-0.0414191362114484
13600.0418626237411594-0.0418626237411594
13700.153252232629421-0.153252232629421
13800.236633431893078-0.236633431893078
13900.126130798064239-0.126130798064239
14000.0436365738600035-0.0436365738600035
14110.03281347559416140.967186524405839
14200.116194674857818-0.116194674857818
14300.0449670364491366-0.0449670364491366
14400.156356645337398-0.156356645337398
14500.168066718662662-0.168066718662662
14600.117968624976663-0.117968624976663
14700.129678698301927-0.129678698301927
14800.130122185831638-0.130122185831638
14900.0476279616274028-0.0476279616274028
15000.159017570515664-0.159017570515664
15100.0372483508912717-0.0372483508912717
15210.04895842421653570.951041575783464
15310.171614618900350.82838538109965
15400.0498453992759579-0.0498453992759579







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
8001
9001
10001
11001
12001
13001
14001
15001
16001
170.2294448166596410.4588896333192820.770555183340359
180.181874644253410.363749288506820.81812535574659
190.1587934149899590.3175868299799170.841206585010041
200.5786854921457150.8426290157085690.421314507854285
210.5897706457074060.8204587085851890.410229354292594
220.5457762781906130.9084474436187740.454223721809387
230.4908835820662010.9817671641324020.509116417933799
240.4314685686076790.8629371372153580.568531431392321
250.3716081283351870.7432162566703740.628391871664813
260.3277408622576910.6554817245153820.672259137742309
270.2914971677358870.5829943354717750.708502832264113
280.2405274740126850.481054948025370.759472525987315
290.199044619869990.398089239739980.80095538013001
300.1694960543283630.3389921086567260.830503945671637
310.1334509295300020.2669018590600040.866549070469998
320.1028477717629720.2056955435259440.897152228237028
330.08413743154126590.1682748630825320.915862568458734
340.06879671180503170.1375934236100630.931203288194968
350.05162317887527340.1032463577505470.948376821124727
360.03793303621574150.07586607243148290.962066963784258
370.04618108279591760.09236216559183520.953818917204082
380.03468080164305050.0693616032861010.965319198356949
390.02577622391640390.05155244783280790.974223776083596
400.02659796953654890.05319593907309790.973402030463451
410.3834185153359060.7668370306718120.616581484664094
420.3320251034308960.6640502068617920.667974896569104
430.2994189585503570.5988379171007130.700581041449643
440.2666316876702870.5332633753405750.733368312329713
450.23237540958520.46475081917040.7676245904148
460.2029080400119810.4058160800239620.797091959988019
470.169546264204070.3390925284081410.83045373579593
480.1383607952172250.276721590434450.861639204782775
490.117484057995130.2349681159902590.88251594200487
500.09498759170231940.1899751834046390.905012408297681
510.0810960273880380.1621920547760760.918903972611962
520.2948155898762820.5896311797525630.705184410123718
530.2528519862937730.5057039725875460.747148013706227
540.8135085964178930.3729828071642150.186491403582107
550.7811336261965060.4377327476069880.218866373803494
560.7643650727022740.4712698545954520.235634927297726
570.7392685282689740.5214629434620530.260731471731026
580.6983349861503460.6033300276993070.301665013849654
590.6548420350765370.6903159298469250.345157964923463
600.8618210640733930.2763578718532130.138178935926607
610.8496831415996670.3006337168006650.150316858400333
620.8358398399620970.3283203200758070.164160160037903
630.8054024188755420.3891951622489160.194597581124458
640.7888884605803320.4222230788393370.211111539419668
650.7534970761315680.4930058477368640.246502923868432
660.7151340578660060.5697318842679880.284865942133994
670.9091772618596230.1816454762807540.0908227381403768
680.8890958123077680.2218083753844640.110904187692232
690.8651417188741650.269716562251670.134858281125835
700.8388088212275440.3223823575449130.161191178772457
710.8092395777874570.3815208444250860.190760422212543
720.7755826298033960.4488347403932080.224417370196604
730.7388498832726720.5223002334546550.261150116727328
740.7003656018174050.599268796365190.299634398182595
750.6584208752903430.6831582494193150.341579124709657
760.6739467436905010.6521065126189990.326053256309499
770.6306489201921290.7387021596157420.369351079807871
780.5990342235139930.8019315529720140.400965776486007
790.9495161078831550.1009677842336910.0504838921168454
800.9540481109249850.09190377815003050.0459518890750152
810.9416623548360010.1166752903279970.0583376451639986
820.9266013359103090.1467973281793820.073398664089691
830.9088953205967860.1822093588064280.0911046794032138
840.9991396563558410.001720687288317560.000860343644158782
850.9988303236411690.002339352717661770.00116967635883088
860.9983180598497020.003363880300596090.00168194015029805
870.9975911680386170.004817663922765610.00240883196138281
880.9969090657708320.006181868458335660.00309093422916783
890.9956670519087250.008665896182549720.00433294809127486
900.9940143858282980.01197122834340340.00598561417170171
910.9922936877877430.01541262442451450.00770631221225723
920.9903103999715630.01937920005687320.00968960002843661
930.9874447637601680.02511047247966430.0125552362398321
940.9830393434832540.0339213130334930.0169606565167465
950.9789569811455810.04208603770883870.0210430188544194
960.9724281138487910.05514377230241830.0275718861512091
970.9664266993617930.06714660127641380.0335733006382069
980.9563384029492470.08732319410150510.0436615970507526
990.9438587070036990.1122825859926020.0561412929963009
1000.9295533392499940.1408933215000120.0704466607500062
1010.9129190990243780.1741618019512430.0870809009756216
1020.8917562118461690.2164875763076620.108243788153831
1030.8669563853826580.2660872292346840.133043614617342
1040.8383076658838630.3233846682322730.161692334116137
1050.8142255260723230.3715489478553540.185774473927677
1060.7786138009780580.4427723980438840.221386199021942
1070.7391065598262740.5217868803474520.260893440173726
1080.7074942070411630.5850115859176740.292505792958837
1090.6619097644188230.6761804711623530.338090235581177
1100.6134442462237850.7731115075524310.386555753776215
1110.5843777027454370.8312445945091260.415622297254563
1120.5498362541001380.9003274917997240.450163745899862
1130.4975355067936150.995071013587230.502464493206385
1140.4672744264034240.9345488528068490.532725573596576
1150.4152486840913650.8304973681827310.584751315908635
1160.3641944339364440.7283888678728890.635805566063556
1170.3162742228312120.6325484456624240.683725777168788
1180.2694754250164220.5389508500328430.730524574983578
1190.225970042918530.451940085837060.77402995708147
1200.1872370554086880.3744741108173760.812762944591312
1210.1516441831590090.3032883663180180.848355816840991
1220.1204994959257880.2409989918515760.879500504074212
1230.1043593891602550.2087187783205090.895640610839745
1240.08183889904321790.1636777980864360.918161100956782
1250.06189501633889270.1237900326777850.938104983661107
1260.05395673930912180.1079134786182440.946043260690878
1270.04036676126731010.08073352253462020.95963323873269
1280.02866763271711630.05733526543423260.971332367282884
1290.01987955230328170.03975910460656340.980120447696718
1300.01331818084685810.02663636169371620.986681819153142
1310.008667477069898810.01733495413979760.991332522930101
1320.00544993936364940.01089987872729880.994550060636351
1330.003314778547413550.006629557094827110.996685221452586
1340.00195192777325430.003903855546508610.998048072226746
1350.001120056240905610.002240112481811220.998879943759094
1360.0006407531541014830.001281506308202970.999359246845898
1370.0003705062091823020.0007410124183646040.999629493790818
1380.000206943004110940.000413886008221880.999793056995889
1390.0001033850251034540.0002067700502069090.999896614974897
1406.4977484137261e-050.0001299549682745220.999935022515863
1410.02532494158009950.05064988316019890.974675058419901
1420.02622321254136050.05244642508272110.973776787458639
1430.01703412383626790.03406824767253590.982965876163732
1440.01067353503998170.02134707007996340.989326464960018
1450.005327487390366570.01065497478073310.994672512609633
1460.006229887542063160.01245977508412630.993770112457937

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0 & 0 & 1 \tabularnewline
9 & 0 & 0 & 1 \tabularnewline
10 & 0 & 0 & 1 \tabularnewline
11 & 0 & 0 & 1 \tabularnewline
12 & 0 & 0 & 1 \tabularnewline
13 & 0 & 0 & 1 \tabularnewline
14 & 0 & 0 & 1 \tabularnewline
15 & 0 & 0 & 1 \tabularnewline
16 & 0 & 0 & 1 \tabularnewline
17 & 0.229444816659641 & 0.458889633319282 & 0.770555183340359 \tabularnewline
18 & 0.18187464425341 & 0.36374928850682 & 0.81812535574659 \tabularnewline
19 & 0.158793414989959 & 0.317586829979917 & 0.841206585010041 \tabularnewline
20 & 0.578685492145715 & 0.842629015708569 & 0.421314507854285 \tabularnewline
21 & 0.589770645707406 & 0.820458708585189 & 0.410229354292594 \tabularnewline
22 & 0.545776278190613 & 0.908447443618774 & 0.454223721809387 \tabularnewline
23 & 0.490883582066201 & 0.981767164132402 & 0.509116417933799 \tabularnewline
24 & 0.431468568607679 & 0.862937137215358 & 0.568531431392321 \tabularnewline
25 & 0.371608128335187 & 0.743216256670374 & 0.628391871664813 \tabularnewline
26 & 0.327740862257691 & 0.655481724515382 & 0.672259137742309 \tabularnewline
27 & 0.291497167735887 & 0.582994335471775 & 0.708502832264113 \tabularnewline
28 & 0.240527474012685 & 0.48105494802537 & 0.759472525987315 \tabularnewline
29 & 0.19904461986999 & 0.39808923973998 & 0.80095538013001 \tabularnewline
30 & 0.169496054328363 & 0.338992108656726 & 0.830503945671637 \tabularnewline
31 & 0.133450929530002 & 0.266901859060004 & 0.866549070469998 \tabularnewline
32 & 0.102847771762972 & 0.205695543525944 & 0.897152228237028 \tabularnewline
33 & 0.0841374315412659 & 0.168274863082532 & 0.915862568458734 \tabularnewline
34 & 0.0687967118050317 & 0.137593423610063 & 0.931203288194968 \tabularnewline
35 & 0.0516231788752734 & 0.103246357750547 & 0.948376821124727 \tabularnewline
36 & 0.0379330362157415 & 0.0758660724314829 & 0.962066963784258 \tabularnewline
37 & 0.0461810827959176 & 0.0923621655918352 & 0.953818917204082 \tabularnewline
38 & 0.0346808016430505 & 0.069361603286101 & 0.965319198356949 \tabularnewline
39 & 0.0257762239164039 & 0.0515524478328079 & 0.974223776083596 \tabularnewline
40 & 0.0265979695365489 & 0.0531959390730979 & 0.973402030463451 \tabularnewline
41 & 0.383418515335906 & 0.766837030671812 & 0.616581484664094 \tabularnewline
42 & 0.332025103430896 & 0.664050206861792 & 0.667974896569104 \tabularnewline
43 & 0.299418958550357 & 0.598837917100713 & 0.700581041449643 \tabularnewline
44 & 0.266631687670287 & 0.533263375340575 & 0.733368312329713 \tabularnewline
45 & 0.2323754095852 & 0.4647508191704 & 0.7676245904148 \tabularnewline
46 & 0.202908040011981 & 0.405816080023962 & 0.797091959988019 \tabularnewline
47 & 0.16954626420407 & 0.339092528408141 & 0.83045373579593 \tabularnewline
48 & 0.138360795217225 & 0.27672159043445 & 0.861639204782775 \tabularnewline
49 & 0.11748405799513 & 0.234968115990259 & 0.88251594200487 \tabularnewline
50 & 0.0949875917023194 & 0.189975183404639 & 0.905012408297681 \tabularnewline
51 & 0.081096027388038 & 0.162192054776076 & 0.918903972611962 \tabularnewline
52 & 0.294815589876282 & 0.589631179752563 & 0.705184410123718 \tabularnewline
53 & 0.252851986293773 & 0.505703972587546 & 0.747148013706227 \tabularnewline
54 & 0.813508596417893 & 0.372982807164215 & 0.186491403582107 \tabularnewline
55 & 0.781133626196506 & 0.437732747606988 & 0.218866373803494 \tabularnewline
56 & 0.764365072702274 & 0.471269854595452 & 0.235634927297726 \tabularnewline
57 & 0.739268528268974 & 0.521462943462053 & 0.260731471731026 \tabularnewline
58 & 0.698334986150346 & 0.603330027699307 & 0.301665013849654 \tabularnewline
59 & 0.654842035076537 & 0.690315929846925 & 0.345157964923463 \tabularnewline
60 & 0.861821064073393 & 0.276357871853213 & 0.138178935926607 \tabularnewline
61 & 0.849683141599667 & 0.300633716800665 & 0.150316858400333 \tabularnewline
62 & 0.835839839962097 & 0.328320320075807 & 0.164160160037903 \tabularnewline
63 & 0.805402418875542 & 0.389195162248916 & 0.194597581124458 \tabularnewline
64 & 0.788888460580332 & 0.422223078839337 & 0.211111539419668 \tabularnewline
65 & 0.753497076131568 & 0.493005847736864 & 0.246502923868432 \tabularnewline
66 & 0.715134057866006 & 0.569731884267988 & 0.284865942133994 \tabularnewline
67 & 0.909177261859623 & 0.181645476280754 & 0.0908227381403768 \tabularnewline
68 & 0.889095812307768 & 0.221808375384464 & 0.110904187692232 \tabularnewline
69 & 0.865141718874165 & 0.26971656225167 & 0.134858281125835 \tabularnewline
70 & 0.838808821227544 & 0.322382357544913 & 0.161191178772457 \tabularnewline
71 & 0.809239577787457 & 0.381520844425086 & 0.190760422212543 \tabularnewline
72 & 0.775582629803396 & 0.448834740393208 & 0.224417370196604 \tabularnewline
73 & 0.738849883272672 & 0.522300233454655 & 0.261150116727328 \tabularnewline
74 & 0.700365601817405 & 0.59926879636519 & 0.299634398182595 \tabularnewline
75 & 0.658420875290343 & 0.683158249419315 & 0.341579124709657 \tabularnewline
76 & 0.673946743690501 & 0.652106512618999 & 0.326053256309499 \tabularnewline
77 & 0.630648920192129 & 0.738702159615742 & 0.369351079807871 \tabularnewline
78 & 0.599034223513993 & 0.801931552972014 & 0.400965776486007 \tabularnewline
79 & 0.949516107883155 & 0.100967784233691 & 0.0504838921168454 \tabularnewline
80 & 0.954048110924985 & 0.0919037781500305 & 0.0459518890750152 \tabularnewline
81 & 0.941662354836001 & 0.116675290327997 & 0.0583376451639986 \tabularnewline
82 & 0.926601335910309 & 0.146797328179382 & 0.073398664089691 \tabularnewline
83 & 0.908895320596786 & 0.182209358806428 & 0.0911046794032138 \tabularnewline
84 & 0.999139656355841 & 0.00172068728831756 & 0.000860343644158782 \tabularnewline
85 & 0.998830323641169 & 0.00233935271766177 & 0.00116967635883088 \tabularnewline
86 & 0.998318059849702 & 0.00336388030059609 & 0.00168194015029805 \tabularnewline
87 & 0.997591168038617 & 0.00481766392276561 & 0.00240883196138281 \tabularnewline
88 & 0.996909065770832 & 0.00618186845833566 & 0.00309093422916783 \tabularnewline
89 & 0.995667051908725 & 0.00866589618254972 & 0.00433294809127486 \tabularnewline
90 & 0.994014385828298 & 0.0119712283434034 & 0.00598561417170171 \tabularnewline
91 & 0.992293687787743 & 0.0154126244245145 & 0.00770631221225723 \tabularnewline
92 & 0.990310399971563 & 0.0193792000568732 & 0.00968960002843661 \tabularnewline
93 & 0.987444763760168 & 0.0251104724796643 & 0.0125552362398321 \tabularnewline
94 & 0.983039343483254 & 0.033921313033493 & 0.0169606565167465 \tabularnewline
95 & 0.978956981145581 & 0.0420860377088387 & 0.0210430188544194 \tabularnewline
96 & 0.972428113848791 & 0.0551437723024183 & 0.0275718861512091 \tabularnewline
97 & 0.966426699361793 & 0.0671466012764138 & 0.0335733006382069 \tabularnewline
98 & 0.956338402949247 & 0.0873231941015051 & 0.0436615970507526 \tabularnewline
99 & 0.943858707003699 & 0.112282585992602 & 0.0561412929963009 \tabularnewline
100 & 0.929553339249994 & 0.140893321500012 & 0.0704466607500062 \tabularnewline
101 & 0.912919099024378 & 0.174161801951243 & 0.0870809009756216 \tabularnewline
102 & 0.891756211846169 & 0.216487576307662 & 0.108243788153831 \tabularnewline
103 & 0.866956385382658 & 0.266087229234684 & 0.133043614617342 \tabularnewline
104 & 0.838307665883863 & 0.323384668232273 & 0.161692334116137 \tabularnewline
105 & 0.814225526072323 & 0.371548947855354 & 0.185774473927677 \tabularnewline
106 & 0.778613800978058 & 0.442772398043884 & 0.221386199021942 \tabularnewline
107 & 0.739106559826274 & 0.521786880347452 & 0.260893440173726 \tabularnewline
108 & 0.707494207041163 & 0.585011585917674 & 0.292505792958837 \tabularnewline
109 & 0.661909764418823 & 0.676180471162353 & 0.338090235581177 \tabularnewline
110 & 0.613444246223785 & 0.773111507552431 & 0.386555753776215 \tabularnewline
111 & 0.584377702745437 & 0.831244594509126 & 0.415622297254563 \tabularnewline
112 & 0.549836254100138 & 0.900327491799724 & 0.450163745899862 \tabularnewline
113 & 0.497535506793615 & 0.99507101358723 & 0.502464493206385 \tabularnewline
114 & 0.467274426403424 & 0.934548852806849 & 0.532725573596576 \tabularnewline
115 & 0.415248684091365 & 0.830497368182731 & 0.584751315908635 \tabularnewline
116 & 0.364194433936444 & 0.728388867872889 & 0.635805566063556 \tabularnewline
117 & 0.316274222831212 & 0.632548445662424 & 0.683725777168788 \tabularnewline
118 & 0.269475425016422 & 0.538950850032843 & 0.730524574983578 \tabularnewline
119 & 0.22597004291853 & 0.45194008583706 & 0.77402995708147 \tabularnewline
120 & 0.187237055408688 & 0.374474110817376 & 0.812762944591312 \tabularnewline
121 & 0.151644183159009 & 0.303288366318018 & 0.848355816840991 \tabularnewline
122 & 0.120499495925788 & 0.240998991851576 & 0.879500504074212 \tabularnewline
123 & 0.104359389160255 & 0.208718778320509 & 0.895640610839745 \tabularnewline
124 & 0.0818388990432179 & 0.163677798086436 & 0.918161100956782 \tabularnewline
125 & 0.0618950163388927 & 0.123790032677785 & 0.938104983661107 \tabularnewline
126 & 0.0539567393091218 & 0.107913478618244 & 0.946043260690878 \tabularnewline
127 & 0.0403667612673101 & 0.0807335225346202 & 0.95963323873269 \tabularnewline
128 & 0.0286676327171163 & 0.0573352654342326 & 0.971332367282884 \tabularnewline
129 & 0.0198795523032817 & 0.0397591046065634 & 0.980120447696718 \tabularnewline
130 & 0.0133181808468581 & 0.0266363616937162 & 0.986681819153142 \tabularnewline
131 & 0.00866747706989881 & 0.0173349541397976 & 0.991332522930101 \tabularnewline
132 & 0.0054499393636494 & 0.0108998787272988 & 0.994550060636351 \tabularnewline
133 & 0.00331477854741355 & 0.00662955709482711 & 0.996685221452586 \tabularnewline
134 & 0.0019519277732543 & 0.00390385554650861 & 0.998048072226746 \tabularnewline
135 & 0.00112005624090561 & 0.00224011248181122 & 0.998879943759094 \tabularnewline
136 & 0.000640753154101483 & 0.00128150630820297 & 0.999359246845898 \tabularnewline
137 & 0.000370506209182302 & 0.000741012418364604 & 0.999629493790818 \tabularnewline
138 & 0.00020694300411094 & 0.00041388600822188 & 0.999793056995889 \tabularnewline
139 & 0.000103385025103454 & 0.000206770050206909 & 0.999896614974897 \tabularnewline
140 & 6.4977484137261e-05 & 0.000129954968274522 & 0.999935022515863 \tabularnewline
141 & 0.0253249415800995 & 0.0506498831601989 & 0.974675058419901 \tabularnewline
142 & 0.0262232125413605 & 0.0524464250827211 & 0.973776787458639 \tabularnewline
143 & 0.0170341238362679 & 0.0340682476725359 & 0.982965876163732 \tabularnewline
144 & 0.0106735350399817 & 0.0213470700799634 & 0.989326464960018 \tabularnewline
145 & 0.00532748739036657 & 0.0106549747807331 & 0.994672512609633 \tabularnewline
146 & 0.00622988754206316 & 0.0124597750841263 & 0.993770112457937 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204481&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]8[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]0.229444816659641[/C][C]0.458889633319282[/C][C]0.770555183340359[/C][/ROW]
[ROW][C]18[/C][C]0.18187464425341[/C][C]0.36374928850682[/C][C]0.81812535574659[/C][/ROW]
[ROW][C]19[/C][C]0.158793414989959[/C][C]0.317586829979917[/C][C]0.841206585010041[/C][/ROW]
[ROW][C]20[/C][C]0.578685492145715[/C][C]0.842629015708569[/C][C]0.421314507854285[/C][/ROW]
[ROW][C]21[/C][C]0.589770645707406[/C][C]0.820458708585189[/C][C]0.410229354292594[/C][/ROW]
[ROW][C]22[/C][C]0.545776278190613[/C][C]0.908447443618774[/C][C]0.454223721809387[/C][/ROW]
[ROW][C]23[/C][C]0.490883582066201[/C][C]0.981767164132402[/C][C]0.509116417933799[/C][/ROW]
[ROW][C]24[/C][C]0.431468568607679[/C][C]0.862937137215358[/C][C]0.568531431392321[/C][/ROW]
[ROW][C]25[/C][C]0.371608128335187[/C][C]0.743216256670374[/C][C]0.628391871664813[/C][/ROW]
[ROW][C]26[/C][C]0.327740862257691[/C][C]0.655481724515382[/C][C]0.672259137742309[/C][/ROW]
[ROW][C]27[/C][C]0.291497167735887[/C][C]0.582994335471775[/C][C]0.708502832264113[/C][/ROW]
[ROW][C]28[/C][C]0.240527474012685[/C][C]0.48105494802537[/C][C]0.759472525987315[/C][/ROW]
[ROW][C]29[/C][C]0.19904461986999[/C][C]0.39808923973998[/C][C]0.80095538013001[/C][/ROW]
[ROW][C]30[/C][C]0.169496054328363[/C][C]0.338992108656726[/C][C]0.830503945671637[/C][/ROW]
[ROW][C]31[/C][C]0.133450929530002[/C][C]0.266901859060004[/C][C]0.866549070469998[/C][/ROW]
[ROW][C]32[/C][C]0.102847771762972[/C][C]0.205695543525944[/C][C]0.897152228237028[/C][/ROW]
[ROW][C]33[/C][C]0.0841374315412659[/C][C]0.168274863082532[/C][C]0.915862568458734[/C][/ROW]
[ROW][C]34[/C][C]0.0687967118050317[/C][C]0.137593423610063[/C][C]0.931203288194968[/C][/ROW]
[ROW][C]35[/C][C]0.0516231788752734[/C][C]0.103246357750547[/C][C]0.948376821124727[/C][/ROW]
[ROW][C]36[/C][C]0.0379330362157415[/C][C]0.0758660724314829[/C][C]0.962066963784258[/C][/ROW]
[ROW][C]37[/C][C]0.0461810827959176[/C][C]0.0923621655918352[/C][C]0.953818917204082[/C][/ROW]
[ROW][C]38[/C][C]0.0346808016430505[/C][C]0.069361603286101[/C][C]0.965319198356949[/C][/ROW]
[ROW][C]39[/C][C]0.0257762239164039[/C][C]0.0515524478328079[/C][C]0.974223776083596[/C][/ROW]
[ROW][C]40[/C][C]0.0265979695365489[/C][C]0.0531959390730979[/C][C]0.973402030463451[/C][/ROW]
[ROW][C]41[/C][C]0.383418515335906[/C][C]0.766837030671812[/C][C]0.616581484664094[/C][/ROW]
[ROW][C]42[/C][C]0.332025103430896[/C][C]0.664050206861792[/C][C]0.667974896569104[/C][/ROW]
[ROW][C]43[/C][C]0.299418958550357[/C][C]0.598837917100713[/C][C]0.700581041449643[/C][/ROW]
[ROW][C]44[/C][C]0.266631687670287[/C][C]0.533263375340575[/C][C]0.733368312329713[/C][/ROW]
[ROW][C]45[/C][C]0.2323754095852[/C][C]0.4647508191704[/C][C]0.7676245904148[/C][/ROW]
[ROW][C]46[/C][C]0.202908040011981[/C][C]0.405816080023962[/C][C]0.797091959988019[/C][/ROW]
[ROW][C]47[/C][C]0.16954626420407[/C][C]0.339092528408141[/C][C]0.83045373579593[/C][/ROW]
[ROW][C]48[/C][C]0.138360795217225[/C][C]0.27672159043445[/C][C]0.861639204782775[/C][/ROW]
[ROW][C]49[/C][C]0.11748405799513[/C][C]0.234968115990259[/C][C]0.88251594200487[/C][/ROW]
[ROW][C]50[/C][C]0.0949875917023194[/C][C]0.189975183404639[/C][C]0.905012408297681[/C][/ROW]
[ROW][C]51[/C][C]0.081096027388038[/C][C]0.162192054776076[/C][C]0.918903972611962[/C][/ROW]
[ROW][C]52[/C][C]0.294815589876282[/C][C]0.589631179752563[/C][C]0.705184410123718[/C][/ROW]
[ROW][C]53[/C][C]0.252851986293773[/C][C]0.505703972587546[/C][C]0.747148013706227[/C][/ROW]
[ROW][C]54[/C][C]0.813508596417893[/C][C]0.372982807164215[/C][C]0.186491403582107[/C][/ROW]
[ROW][C]55[/C][C]0.781133626196506[/C][C]0.437732747606988[/C][C]0.218866373803494[/C][/ROW]
[ROW][C]56[/C][C]0.764365072702274[/C][C]0.471269854595452[/C][C]0.235634927297726[/C][/ROW]
[ROW][C]57[/C][C]0.739268528268974[/C][C]0.521462943462053[/C][C]0.260731471731026[/C][/ROW]
[ROW][C]58[/C][C]0.698334986150346[/C][C]0.603330027699307[/C][C]0.301665013849654[/C][/ROW]
[ROW][C]59[/C][C]0.654842035076537[/C][C]0.690315929846925[/C][C]0.345157964923463[/C][/ROW]
[ROW][C]60[/C][C]0.861821064073393[/C][C]0.276357871853213[/C][C]0.138178935926607[/C][/ROW]
[ROW][C]61[/C][C]0.849683141599667[/C][C]0.300633716800665[/C][C]0.150316858400333[/C][/ROW]
[ROW][C]62[/C][C]0.835839839962097[/C][C]0.328320320075807[/C][C]0.164160160037903[/C][/ROW]
[ROW][C]63[/C][C]0.805402418875542[/C][C]0.389195162248916[/C][C]0.194597581124458[/C][/ROW]
[ROW][C]64[/C][C]0.788888460580332[/C][C]0.422223078839337[/C][C]0.211111539419668[/C][/ROW]
[ROW][C]65[/C][C]0.753497076131568[/C][C]0.493005847736864[/C][C]0.246502923868432[/C][/ROW]
[ROW][C]66[/C][C]0.715134057866006[/C][C]0.569731884267988[/C][C]0.284865942133994[/C][/ROW]
[ROW][C]67[/C][C]0.909177261859623[/C][C]0.181645476280754[/C][C]0.0908227381403768[/C][/ROW]
[ROW][C]68[/C][C]0.889095812307768[/C][C]0.221808375384464[/C][C]0.110904187692232[/C][/ROW]
[ROW][C]69[/C][C]0.865141718874165[/C][C]0.26971656225167[/C][C]0.134858281125835[/C][/ROW]
[ROW][C]70[/C][C]0.838808821227544[/C][C]0.322382357544913[/C][C]0.161191178772457[/C][/ROW]
[ROW][C]71[/C][C]0.809239577787457[/C][C]0.381520844425086[/C][C]0.190760422212543[/C][/ROW]
[ROW][C]72[/C][C]0.775582629803396[/C][C]0.448834740393208[/C][C]0.224417370196604[/C][/ROW]
[ROW][C]73[/C][C]0.738849883272672[/C][C]0.522300233454655[/C][C]0.261150116727328[/C][/ROW]
[ROW][C]74[/C][C]0.700365601817405[/C][C]0.59926879636519[/C][C]0.299634398182595[/C][/ROW]
[ROW][C]75[/C][C]0.658420875290343[/C][C]0.683158249419315[/C][C]0.341579124709657[/C][/ROW]
[ROW][C]76[/C][C]0.673946743690501[/C][C]0.652106512618999[/C][C]0.326053256309499[/C][/ROW]
[ROW][C]77[/C][C]0.630648920192129[/C][C]0.738702159615742[/C][C]0.369351079807871[/C][/ROW]
[ROW][C]78[/C][C]0.599034223513993[/C][C]0.801931552972014[/C][C]0.400965776486007[/C][/ROW]
[ROW][C]79[/C][C]0.949516107883155[/C][C]0.100967784233691[/C][C]0.0504838921168454[/C][/ROW]
[ROW][C]80[/C][C]0.954048110924985[/C][C]0.0919037781500305[/C][C]0.0459518890750152[/C][/ROW]
[ROW][C]81[/C][C]0.941662354836001[/C][C]0.116675290327997[/C][C]0.0583376451639986[/C][/ROW]
[ROW][C]82[/C][C]0.926601335910309[/C][C]0.146797328179382[/C][C]0.073398664089691[/C][/ROW]
[ROW][C]83[/C][C]0.908895320596786[/C][C]0.182209358806428[/C][C]0.0911046794032138[/C][/ROW]
[ROW][C]84[/C][C]0.999139656355841[/C][C]0.00172068728831756[/C][C]0.000860343644158782[/C][/ROW]
[ROW][C]85[/C][C]0.998830323641169[/C][C]0.00233935271766177[/C][C]0.00116967635883088[/C][/ROW]
[ROW][C]86[/C][C]0.998318059849702[/C][C]0.00336388030059609[/C][C]0.00168194015029805[/C][/ROW]
[ROW][C]87[/C][C]0.997591168038617[/C][C]0.00481766392276561[/C][C]0.00240883196138281[/C][/ROW]
[ROW][C]88[/C][C]0.996909065770832[/C][C]0.00618186845833566[/C][C]0.00309093422916783[/C][/ROW]
[ROW][C]89[/C][C]0.995667051908725[/C][C]0.00866589618254972[/C][C]0.00433294809127486[/C][/ROW]
[ROW][C]90[/C][C]0.994014385828298[/C][C]0.0119712283434034[/C][C]0.00598561417170171[/C][/ROW]
[ROW][C]91[/C][C]0.992293687787743[/C][C]0.0154126244245145[/C][C]0.00770631221225723[/C][/ROW]
[ROW][C]92[/C][C]0.990310399971563[/C][C]0.0193792000568732[/C][C]0.00968960002843661[/C][/ROW]
[ROW][C]93[/C][C]0.987444763760168[/C][C]0.0251104724796643[/C][C]0.0125552362398321[/C][/ROW]
[ROW][C]94[/C][C]0.983039343483254[/C][C]0.033921313033493[/C][C]0.0169606565167465[/C][/ROW]
[ROW][C]95[/C][C]0.978956981145581[/C][C]0.0420860377088387[/C][C]0.0210430188544194[/C][/ROW]
[ROW][C]96[/C][C]0.972428113848791[/C][C]0.0551437723024183[/C][C]0.0275718861512091[/C][/ROW]
[ROW][C]97[/C][C]0.966426699361793[/C][C]0.0671466012764138[/C][C]0.0335733006382069[/C][/ROW]
[ROW][C]98[/C][C]0.956338402949247[/C][C]0.0873231941015051[/C][C]0.0436615970507526[/C][/ROW]
[ROW][C]99[/C][C]0.943858707003699[/C][C]0.112282585992602[/C][C]0.0561412929963009[/C][/ROW]
[ROW][C]100[/C][C]0.929553339249994[/C][C]0.140893321500012[/C][C]0.0704466607500062[/C][/ROW]
[ROW][C]101[/C][C]0.912919099024378[/C][C]0.174161801951243[/C][C]0.0870809009756216[/C][/ROW]
[ROW][C]102[/C][C]0.891756211846169[/C][C]0.216487576307662[/C][C]0.108243788153831[/C][/ROW]
[ROW][C]103[/C][C]0.866956385382658[/C][C]0.266087229234684[/C][C]0.133043614617342[/C][/ROW]
[ROW][C]104[/C][C]0.838307665883863[/C][C]0.323384668232273[/C][C]0.161692334116137[/C][/ROW]
[ROW][C]105[/C][C]0.814225526072323[/C][C]0.371548947855354[/C][C]0.185774473927677[/C][/ROW]
[ROW][C]106[/C][C]0.778613800978058[/C][C]0.442772398043884[/C][C]0.221386199021942[/C][/ROW]
[ROW][C]107[/C][C]0.739106559826274[/C][C]0.521786880347452[/C][C]0.260893440173726[/C][/ROW]
[ROW][C]108[/C][C]0.707494207041163[/C][C]0.585011585917674[/C][C]0.292505792958837[/C][/ROW]
[ROW][C]109[/C][C]0.661909764418823[/C][C]0.676180471162353[/C][C]0.338090235581177[/C][/ROW]
[ROW][C]110[/C][C]0.613444246223785[/C][C]0.773111507552431[/C][C]0.386555753776215[/C][/ROW]
[ROW][C]111[/C][C]0.584377702745437[/C][C]0.831244594509126[/C][C]0.415622297254563[/C][/ROW]
[ROW][C]112[/C][C]0.549836254100138[/C][C]0.900327491799724[/C][C]0.450163745899862[/C][/ROW]
[ROW][C]113[/C][C]0.497535506793615[/C][C]0.99507101358723[/C][C]0.502464493206385[/C][/ROW]
[ROW][C]114[/C][C]0.467274426403424[/C][C]0.934548852806849[/C][C]0.532725573596576[/C][/ROW]
[ROW][C]115[/C][C]0.415248684091365[/C][C]0.830497368182731[/C][C]0.584751315908635[/C][/ROW]
[ROW][C]116[/C][C]0.364194433936444[/C][C]0.728388867872889[/C][C]0.635805566063556[/C][/ROW]
[ROW][C]117[/C][C]0.316274222831212[/C][C]0.632548445662424[/C][C]0.683725777168788[/C][/ROW]
[ROW][C]118[/C][C]0.269475425016422[/C][C]0.538950850032843[/C][C]0.730524574983578[/C][/ROW]
[ROW][C]119[/C][C]0.22597004291853[/C][C]0.45194008583706[/C][C]0.77402995708147[/C][/ROW]
[ROW][C]120[/C][C]0.187237055408688[/C][C]0.374474110817376[/C][C]0.812762944591312[/C][/ROW]
[ROW][C]121[/C][C]0.151644183159009[/C][C]0.303288366318018[/C][C]0.848355816840991[/C][/ROW]
[ROW][C]122[/C][C]0.120499495925788[/C][C]0.240998991851576[/C][C]0.879500504074212[/C][/ROW]
[ROW][C]123[/C][C]0.104359389160255[/C][C]0.208718778320509[/C][C]0.895640610839745[/C][/ROW]
[ROW][C]124[/C][C]0.0818388990432179[/C][C]0.163677798086436[/C][C]0.918161100956782[/C][/ROW]
[ROW][C]125[/C][C]0.0618950163388927[/C][C]0.123790032677785[/C][C]0.938104983661107[/C][/ROW]
[ROW][C]126[/C][C]0.0539567393091218[/C][C]0.107913478618244[/C][C]0.946043260690878[/C][/ROW]
[ROW][C]127[/C][C]0.0403667612673101[/C][C]0.0807335225346202[/C][C]0.95963323873269[/C][/ROW]
[ROW][C]128[/C][C]0.0286676327171163[/C][C]0.0573352654342326[/C][C]0.971332367282884[/C][/ROW]
[ROW][C]129[/C][C]0.0198795523032817[/C][C]0.0397591046065634[/C][C]0.980120447696718[/C][/ROW]
[ROW][C]130[/C][C]0.0133181808468581[/C][C]0.0266363616937162[/C][C]0.986681819153142[/C][/ROW]
[ROW][C]131[/C][C]0.00866747706989881[/C][C]0.0173349541397976[/C][C]0.991332522930101[/C][/ROW]
[ROW][C]132[/C][C]0.0054499393636494[/C][C]0.0108998787272988[/C][C]0.994550060636351[/C][/ROW]
[ROW][C]133[/C][C]0.00331477854741355[/C][C]0.00662955709482711[/C][C]0.996685221452586[/C][/ROW]
[ROW][C]134[/C][C]0.0019519277732543[/C][C]0.00390385554650861[/C][C]0.998048072226746[/C][/ROW]
[ROW][C]135[/C][C]0.00112005624090561[/C][C]0.00224011248181122[/C][C]0.998879943759094[/C][/ROW]
[ROW][C]136[/C][C]0.000640753154101483[/C][C]0.00128150630820297[/C][C]0.999359246845898[/C][/ROW]
[ROW][C]137[/C][C]0.000370506209182302[/C][C]0.000741012418364604[/C][C]0.999629493790818[/C][/ROW]
[ROW][C]138[/C][C]0.00020694300411094[/C][C]0.00041388600822188[/C][C]0.999793056995889[/C][/ROW]
[ROW][C]139[/C][C]0.000103385025103454[/C][C]0.000206770050206909[/C][C]0.999896614974897[/C][/ROW]
[ROW][C]140[/C][C]6.4977484137261e-05[/C][C]0.000129954968274522[/C][C]0.999935022515863[/C][/ROW]
[ROW][C]141[/C][C]0.0253249415800995[/C][C]0.0506498831601989[/C][C]0.974675058419901[/C][/ROW]
[ROW][C]142[/C][C]0.0262232125413605[/C][C]0.0524464250827211[/C][C]0.973776787458639[/C][/ROW]
[ROW][C]143[/C][C]0.0170341238362679[/C][C]0.0340682476725359[/C][C]0.982965876163732[/C][/ROW]
[ROW][C]144[/C][C]0.0106735350399817[/C][C]0.0213470700799634[/C][C]0.989326464960018[/C][/ROW]
[ROW][C]145[/C][C]0.00532748739036657[/C][C]0.0106549747807331[/C][C]0.994672512609633[/C][/ROW]
[ROW][C]146[/C][C]0.00622988754206316[/C][C]0.0124597750841263[/C][C]0.993770112457937[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204481&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204481&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
8001
9001
10001
11001
12001
13001
14001
15001
16001
170.2294448166596410.4588896333192820.770555183340359
180.181874644253410.363749288506820.81812535574659
190.1587934149899590.3175868299799170.841206585010041
200.5786854921457150.8426290157085690.421314507854285
210.5897706457074060.8204587085851890.410229354292594
220.5457762781906130.9084474436187740.454223721809387
230.4908835820662010.9817671641324020.509116417933799
240.4314685686076790.8629371372153580.568531431392321
250.3716081283351870.7432162566703740.628391871664813
260.3277408622576910.6554817245153820.672259137742309
270.2914971677358870.5829943354717750.708502832264113
280.2405274740126850.481054948025370.759472525987315
290.199044619869990.398089239739980.80095538013001
300.1694960543283630.3389921086567260.830503945671637
310.1334509295300020.2669018590600040.866549070469998
320.1028477717629720.2056955435259440.897152228237028
330.08413743154126590.1682748630825320.915862568458734
340.06879671180503170.1375934236100630.931203288194968
350.05162317887527340.1032463577505470.948376821124727
360.03793303621574150.07586607243148290.962066963784258
370.04618108279591760.09236216559183520.953818917204082
380.03468080164305050.0693616032861010.965319198356949
390.02577622391640390.05155244783280790.974223776083596
400.02659796953654890.05319593907309790.973402030463451
410.3834185153359060.7668370306718120.616581484664094
420.3320251034308960.6640502068617920.667974896569104
430.2994189585503570.5988379171007130.700581041449643
440.2666316876702870.5332633753405750.733368312329713
450.23237540958520.46475081917040.7676245904148
460.2029080400119810.4058160800239620.797091959988019
470.169546264204070.3390925284081410.83045373579593
480.1383607952172250.276721590434450.861639204782775
490.117484057995130.2349681159902590.88251594200487
500.09498759170231940.1899751834046390.905012408297681
510.0810960273880380.1621920547760760.918903972611962
520.2948155898762820.5896311797525630.705184410123718
530.2528519862937730.5057039725875460.747148013706227
540.8135085964178930.3729828071642150.186491403582107
550.7811336261965060.4377327476069880.218866373803494
560.7643650727022740.4712698545954520.235634927297726
570.7392685282689740.5214629434620530.260731471731026
580.6983349861503460.6033300276993070.301665013849654
590.6548420350765370.6903159298469250.345157964923463
600.8618210640733930.2763578718532130.138178935926607
610.8496831415996670.3006337168006650.150316858400333
620.8358398399620970.3283203200758070.164160160037903
630.8054024188755420.3891951622489160.194597581124458
640.7888884605803320.4222230788393370.211111539419668
650.7534970761315680.4930058477368640.246502923868432
660.7151340578660060.5697318842679880.284865942133994
670.9091772618596230.1816454762807540.0908227381403768
680.8890958123077680.2218083753844640.110904187692232
690.8651417188741650.269716562251670.134858281125835
700.8388088212275440.3223823575449130.161191178772457
710.8092395777874570.3815208444250860.190760422212543
720.7755826298033960.4488347403932080.224417370196604
730.7388498832726720.5223002334546550.261150116727328
740.7003656018174050.599268796365190.299634398182595
750.6584208752903430.6831582494193150.341579124709657
760.6739467436905010.6521065126189990.326053256309499
770.6306489201921290.7387021596157420.369351079807871
780.5990342235139930.8019315529720140.400965776486007
790.9495161078831550.1009677842336910.0504838921168454
800.9540481109249850.09190377815003050.0459518890750152
810.9416623548360010.1166752903279970.0583376451639986
820.9266013359103090.1467973281793820.073398664089691
830.9088953205967860.1822093588064280.0911046794032138
840.9991396563558410.001720687288317560.000860343644158782
850.9988303236411690.002339352717661770.00116967635883088
860.9983180598497020.003363880300596090.00168194015029805
870.9975911680386170.004817663922765610.00240883196138281
880.9969090657708320.006181868458335660.00309093422916783
890.9956670519087250.008665896182549720.00433294809127486
900.9940143858282980.01197122834340340.00598561417170171
910.9922936877877430.01541262442451450.00770631221225723
920.9903103999715630.01937920005687320.00968960002843661
930.9874447637601680.02511047247966430.0125552362398321
940.9830393434832540.0339213130334930.0169606565167465
950.9789569811455810.04208603770883870.0210430188544194
960.9724281138487910.05514377230241830.0275718861512091
970.9664266993617930.06714660127641380.0335733006382069
980.9563384029492470.08732319410150510.0436615970507526
990.9438587070036990.1122825859926020.0561412929963009
1000.9295533392499940.1408933215000120.0704466607500062
1010.9129190990243780.1741618019512430.0870809009756216
1020.8917562118461690.2164875763076620.108243788153831
1030.8669563853826580.2660872292346840.133043614617342
1040.8383076658838630.3233846682322730.161692334116137
1050.8142255260723230.3715489478553540.185774473927677
1060.7786138009780580.4427723980438840.221386199021942
1070.7391065598262740.5217868803474520.260893440173726
1080.7074942070411630.5850115859176740.292505792958837
1090.6619097644188230.6761804711623530.338090235581177
1100.6134442462237850.7731115075524310.386555753776215
1110.5843777027454370.8312445945091260.415622297254563
1120.5498362541001380.9003274917997240.450163745899862
1130.4975355067936150.995071013587230.502464493206385
1140.4672744264034240.9345488528068490.532725573596576
1150.4152486840913650.8304973681827310.584751315908635
1160.3641944339364440.7283888678728890.635805566063556
1170.3162742228312120.6325484456624240.683725777168788
1180.2694754250164220.5389508500328430.730524574983578
1190.225970042918530.451940085837060.77402995708147
1200.1872370554086880.3744741108173760.812762944591312
1210.1516441831590090.3032883663180180.848355816840991
1220.1204994959257880.2409989918515760.879500504074212
1230.1043593891602550.2087187783205090.895640610839745
1240.08183889904321790.1636777980864360.918161100956782
1250.06189501633889270.1237900326777850.938104983661107
1260.05395673930912180.1079134786182440.946043260690878
1270.04036676126731010.08073352253462020.95963323873269
1280.02866763271711630.05733526543423260.971332367282884
1290.01987955230328170.03975910460656340.980120447696718
1300.01331818084685810.02663636169371620.986681819153142
1310.008667477069898810.01733495413979760.991332522930101
1320.00544993936364940.01089987872729880.994550060636351
1330.003314778547413550.006629557094827110.996685221452586
1340.00195192777325430.003903855546508610.998048072226746
1350.001120056240905610.002240112481811220.998879943759094
1360.0006407531541014830.001281506308202970.999359246845898
1370.0003705062091823020.0007410124183646040.999629493790818
1380.000206943004110940.000413886008221880.999793056995889
1390.0001033850251034540.0002067700502069090.999896614974897
1406.4977484137261e-050.0001299549682745220.999935022515863
1410.02532494158009950.05064988316019890.974675058419901
1420.02622321254136050.05244642508272110.973776787458639
1430.01703412383626790.03406824767253590.982965876163732
1440.01067353503998170.02134707007996340.989326464960018
1450.005327487390366570.01065497478073310.994672512609633
1460.006229887542063160.01245977508412630.993770112457937







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level230.165467625899281NOK
5% type I error level370.266187050359712NOK
10% type I error level500.359712230215827NOK

\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 & 23 & 0.165467625899281 & NOK \tabularnewline
5% type I error level & 37 & 0.266187050359712 & NOK \tabularnewline
10% type I error level & 50 & 0.359712230215827 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204481&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]23[/C][C]0.165467625899281[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]37[/C][C]0.266187050359712[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]50[/C][C]0.359712230215827[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204481&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204481&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level230.165467625899281NOK
5% type I error level370.266187050359712NOK
10% type I error level500.359712230215827NOK



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}