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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 21 Nov 2010 18:23:11 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/21/t1290364140wwnw17bonwxyiob.htm/, Retrieved Sun, 28 Apr 2024 15:54:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98397, Retrieved Sun, 28 Apr 2024 15:54:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact230

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [workshop 7] [2010-11-21 17:50:24] [65eb19f81eab2b6e672eafaed2a27190]
-   PD    [Multiple Regression] [workshop 7] [2010-11-21 18:15:20] [65eb19f81eab2b6e672eafaed2a27190]
F   P         [Multiple Regression] [WS7 Celebrity] [2010-11-21 18:23:11] [8b27277f7b82c0354d659d066108e38e] [Current]
F   PD          [Multiple Regression] [WS7 Celebrity aan...] [2010-11-22 11:35:59] [65eb19f81eab2b6e672eafaed2a27190]
-   PD          [Multiple Regression] [Workshop 7 - Doub...] [2010-11-22 17:46:53] [1429a1a14191a86916b95357f6de790b]
-    D            [Multiple Regression] [Workshop 7 - Doub...] [2010-11-22 18:43:58] [1429a1a14191a86916b95357f6de790b]
-    D              [Multiple Regression] [Workshop 7 - Adju...] [2010-11-23 12:04:06] [1429a1a14191a86916b95357f6de790b]
-    D                [Multiple Regression] [] [2011-11-19 17:50:25] [e21b9c93af4eb9605ecfaf58a559e5ab]
- RM                    [Multiple Regression] [] [2011-11-22 22:42:42] [74be16979710d4c4e7c6647856088456]
-    D              [Multiple Regression] [] [2011-11-19 17:00:50] [e21b9c93af4eb9605ecfaf58a559e5ab]
- RM                  [Multiple Regression] [] [2011-11-22 22:35:20] [74be16979710d4c4e7c6647856088456]
- R                 [Multiple Regression] [Workshop 7 - Doub...] [2011-11-22 19:05:21] [74be16979710d4c4e7c6647856088456]
-   PD              [Multiple Regression] [WS7 Computation1] [2011-11-24 20:39:28] [3dd791303389e75e672968b227170a72]
-   PD              [Multiple Regression] [WS7 Computation 2] [2011-11-24 21:28:34] [3dd791303389e75e672968b227170a72]
Feedback Forum
2010-11-27 08:06:41 [48eb36e2c01435ad7e4ea7854a9d98fe] [reply] 
Naar mijn mening heeft de student hier een goed model ontworpen. Zoals de student correct aangeeft gaat de waarde van de 'adjusted R squared' al zeker in de goede richting.

Maar wat ook heel belangrijk is, zijn de onderliggende voorwaarden. We zien dat deze vrij goed voldaan zijn. Als we ten eerste kijken naar de grafische voorstelling 'residuals' kunnen we zien dat er geen systematische over- of onderschatting is. Hieruit mogen we concluderen dat het gemiddelde van de residu's doorheen de tijd nul is.

Vervolgens zien we, op enkele uitschieters na, inderdaad ook een vrije goede normaalverdeling op de 'normal QQ plot'.

Ten slotte moeten we de autocorrelatiefunctie bekijken. De student schrijft dat deze niet goed is, omdat de eerste autocorrelatiecoëfficiënt buiten het betrouwbaarheidsinterval gelegen is. Maar deze stelt 'lag 0' voor, dus het huidige moment. Met deze waarde dienen we dus geen rekening te houden. We zien dan verder een tweetal autocorrelatiecoëfficiënten die buiten het betrouwbaarheidsinterval gelegen zijn. Maar aangezien het hier gaat om een 95% betrouwbaarheidsinterval vormt dit geen probleem.

We kunnen dus stellen dat er voldaan is aan de onderliggende voorwaarden van dit model.
2010-11-29 21:51:17 [23c3e34d843bca32d327eaf7dc6bdb2b] [reply] 
De student ontwerpt hier een goed model vind ik. De student heeft dan ook de adjusted R², de residu's en een juiste functie beschreven.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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10	14	12	15	6
11	14	14	12	6
11	12	11	14	2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98397&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98397&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98397&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
Celebrity[t] = + 0.423591992490579 -0.0194376966141400FindingFriends[t] + 0.154094039023444Popularity[t] + 0.103356510337141KnowingPeople[t] + 0.147677870542413Liked[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Celebrity[t] =  +  0.423591992490579 -0.0194376966141400FindingFriends[t] +  0.154094039023444Popularity[t] +  0.103356510337141KnowingPeople[t] +  0.147677870542413Liked[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98397&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Celebrity[t] =  +  0.423591992490579 -0.0194376966141400FindingFriends[t] +  0.154094039023444Popularity[t] +  0.103356510337141KnowingPeople[t] +  0.147677870542413Liked[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98397&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98397&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Celebrity[t] = + 0.423591992490579 -0.0194376966141400FindingFriends[t] + 0.154094039023444Popularity[t] + 0.103356510337141KnowingPeople[t] + 0.147677870542413Liked[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.4235919924905790.7057130.60020.5492510.274625
FindingFriends-0.01943769661414000.047694-0.40760.684180.34209
Popularity0.1540940390234440.0383424.01899.2e-054.6e-05
KnowingPeople0.1033565103371410.0308483.35050.0010190.00051
Liked0.1476778705424130.0483843.05220.0026850.001342

\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) & 0.423591992490579 & 0.705713 & 0.6002 & 0.549251 & 0.274625 \tabularnewline
FindingFriends & -0.0194376966141400 & 0.047694 & -0.4076 & 0.68418 & 0.34209 \tabularnewline
Popularity & 0.154094039023444 & 0.038342 & 4.0189 & 9.2e-05 & 4.6e-05 \tabularnewline
KnowingPeople & 0.103356510337141 & 0.030848 & 3.3505 & 0.001019 & 0.00051 \tabularnewline
Liked & 0.147677870542413 & 0.048384 & 3.0522 & 0.002685 & 0.001342 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98397&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]0.423591992490579[/C][C]0.705713[/C][C]0.6002[/C][C]0.549251[/C][C]0.274625[/C][/ROW]
[ROW][C]FindingFriends[/C][C]-0.0194376966141400[/C][C]0.047694[/C][C]-0.4076[/C][C]0.68418[/C][C]0.34209[/C][/ROW]
[ROW][C]Popularity[/C][C]0.154094039023444[/C][C]0.038342[/C][C]4.0189[/C][C]9.2e-05[/C][C]4.6e-05[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.103356510337141[/C][C]0.030848[/C][C]3.3505[/C][C]0.001019[/C][C]0.00051[/C][/ROW]
[ROW][C]Liked[/C][C]0.147677870542413[/C][C]0.048384[/C][C]3.0522[/C][C]0.002685[/C][C]0.001342[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98397&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98397&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.4235919924905790.7057130.60020.5492510.274625
FindingFriends-0.01943769661414000.047694-0.40760.684180.34209
Popularity0.1540940390234440.0383424.01899.2e-054.6e-05
KnowingPeople0.1033565103371410.0308483.35050.0010190.00051
Liked0.1476778705424130.0483843.05220.0026850.001342






Multiple Linear Regression - Regression Statistics
Multiple R0.677446083231663
R-squared0.458933195685922
Adjusted R-squared0.444600300207403
F-TEST (value)32.0195731821073
F-TEST (DF numerator)4
F-TEST (DF denominator)151
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.04372839595306
Sum Squared Residuals164.494713642332

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.677446083231663 \tabularnewline
R-squared & 0.458933195685922 \tabularnewline
Adjusted R-squared & 0.444600300207403 \tabularnewline
F-TEST (value) & 32.0195731821073 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 151 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.04372839595306 \tabularnewline
Sum Squared Residuals & 164.494713642332 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98397&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.677446083231663[/C][/ROW]
[ROW][C]R-squared[/C][C]0.458933195685922[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.444600300207403[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]32.0195731821073[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]151[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.04372839595306[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]164.494713642332[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98397&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98397&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.677446083231663
R-squared0.458933195685922
Adjusted R-squared0.444600300207403
F-TEST (value)32.0195731821073
F-TEST (DF numerator)4
F-TEST (DF denominator)151
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.04372839595306
Sum Squared Residuals164.494713642332






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
135.54092790558286-2.54092790558286
254.786132501150720.213867498849284
366.14374966442514-0.143749664425136
464.593411210113581.40658878988642
554.428177095921570.571822904078433
634.53509812027116-1.53509812027116
786.79421835701611.2057816429839
844.33856417400698-0.338564174006982
945.55394943371597-1.55394943371597
1043.951240138968180.0487598610318215
1165.742632291665790.257367708334207
1264.794430122596281.20556987740372
1355.79934865644749-0.79934865644749
1444.24253508361137-0.242535083611369
1564.489806558561851.51019344143815
1644.85346527272815-0.853465272728152
1764.640336083572841.35966391642716
1866.28028745979897-0.280287459798969
1943.803314127211180.196685872788824
2043.766757519333070.233242480666932
2124.58699504163255-2.58699504163255
2276.183347117942860.816652882057141
2354.931938119474160.0680618805258387
2444.38742024972875-0.387420249728749
2565.817816151557590.182183848442409
2666.6410945194967-0.641094519496695
2775.362139394139341.63786060586066
2855.78579425919598-0.785794259195978
2965.766356562581840.233643437418162
3045.5501865277869-1.55018652778690
3144.24071258069038-0.240712580690376
3275.445146956401841.55485304359816
3376.009815382305280.990184617694725
3444.70978924858383-0.709789248583832
3544.33856417400698-0.338564174006982
3666.54415417764058-0.544154177640585
3765.288201262909820.711798737090177
3853.984421424004881.01557857599512
3965.579803298811150.420196701188854
4076.6918320481830.308167951817002
4165.611103130883310.388896869116691
4234.70130243596722-1.70130243596722
4333.50148326760178-0.501483267601777
4444.24822919180295-0.24822919180295
4564.5037982881991.496201711801
4676.086406776086750.913593223913251
4756.44721383578447-1.44721383578447
4843.320434920851620.679565079148381
4954.756714122043090.243285877956911
5066.21011223449853-0.210112234498526
5164.167874892092401.83212510790760
5264.348021188127641.65197881187237
5355.34723641304169-0.347236413041693
5444.16975634505694-0.169756345056941
5555.59166543426917-0.591665434269169
5655.4257092597877-0.425709259787702
5744.95609972277584-0.956099722775843
5866.73070744141128-0.730707441411277
5924.69126280343019-2.69126280343019
6086.286703628281.71329637172
6133.86798443549108-0.867984435491084
6266.37703861048403-0.377038610484032
6366.14187741220616-0.141877412206162
6466.3374411569663-0.337441156966303
6554.027772582706110.97222741729389
6655.61110313088331-0.611103130883309
6765.207856163944850.792143836055153
6854.689192159294610.310807840705394
6965.765197169906750.234802830093248
7022.07374982907046-0.0737498290704635
7155.88799137685803-0.887991376858033
7255.45700909185986-0.457009091859865
7355.10922356029525-0.109223560295248
7465.959515186004610.0404848139953913
7565.238433935727560.76156606427244
7665.50962807351070.490371926489297
7755.23915599601701-0.239155996017010
7855.38066583929298-0.380665839292981
7945.24199844974002-1.24199844974002
8023.35949950525095-1.35949950525095
8143.764627925153940.235372074846057
8265.580051440025740.419948559974264
8366.07357443912469-0.0735744391246866
8454.679211476801120.320788523198882
8534.48247913862032-1.48247913862032
8665.032442975342730.967557024657273
8743.900254469067290.0997455309327137
8855.48874625631766-0.488746256317664
8986.33744115696631.66255884303370
9044.4530607595127-0.453060759512697
9165.763504908113260.236495091886737
9265.30952041248850.690479587511501
9376.672394351568860.327605648431142
9466.11414209414646-0.114142094146455
9554.967961858233870.0320381417661339
9644.182588682019-0.182588682019002
9763.853518786779082.14648121322092
9833.60392852647842-0.603928526478421
9955.6252840516915-0.625284051691504
10065.250296071185580.749703928814417
10176.795188558520140.204811441479861
10276.42135997068930.578640029310696
10366.72404313171566-0.724043131715656
10434.36084432434413-1.36084432434413
10522.73579592921564-0.735795929215643
10685.724629514884982.27537048511502
10734.67279530832009-1.67279530832009
10886.132609589256561.86739041074344
10934.56302262950192-1.56302262950192
11044.66565707954961-0.665657079549607
11155.23201776724653-0.232017767246529
11275.587378859967261.41262114003274
11364.528681951790131.47131804820987
11465.906458871968130.093541128031866
11576.104874271196850.89512572880315
11666.18976328642389-0.189763286423890
11766.17032558980975-0.17032558980975
11865.075072073754510.92492792624549
11965.96549402210.0345059778999965
12045.3795064466179-1.37950644661789
12145.19955854249928-1.19955854249928
12255.79293248796646-0.792932487966459
12344.14487268146581-0.144872681465809
12465.791962286462420.208037713537580
12566.27368210014689-0.273682100146891
12654.727097351018850.272902648981154
12786.465681330894581.53431866910542
12865.50962807351070.490371926489297
12955.06418013980053-0.0641801398005265
13042.095317119863731.90468288013627
13186.465681330894581.53431866910542
13265.681278356183750.318721643816249
13344.92097803473107-0.920978034731066
13465.526214115656270.473785884343732
13565.912875040449160.087124959550835
13644.88948901148786-0.889489011487857
13765.984931718714140.0150682812858566
13834.38476698717679-1.38476698717679
13966.88364208775964-0.883642087759635
14055.59282482694425-0.592824826944255
14145.63242228046198-1.63242228046198
14265.708043472739420.291956527260581
14346.31158729187113-2.31158729187113
14443.381599665162610.618400334837386
14544.992845521825-0.992845521824998
14664.045080685141121.95491931485888
14754.317434215599350.682565784400647
14865.438482646706220.561517353293779
14966.24238226807473-0.242382268074728
15086.111290439677881.88870956032212
15175.373090278136861.62690972186314
15276.569037841231720.430962158768284
15344.29896672048925-0.298966720489253
15465.841977754859270.158022245140727
15565.586219467292180.413780532707823
15625.26331759931869-3.26331759931869

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3 & 5.54092790558286 & -2.54092790558286 \tabularnewline
2 & 5 & 4.78613250115072 & 0.213867498849284 \tabularnewline
3 & 6 & 6.14374966442514 & -0.143749664425136 \tabularnewline
4 & 6 & 4.59341121011358 & 1.40658878988642 \tabularnewline
5 & 5 & 4.42817709592157 & 0.571822904078433 \tabularnewline
6 & 3 & 4.53509812027116 & -1.53509812027116 \tabularnewline
7 & 8 & 6.7942183570161 & 1.2057816429839 \tabularnewline
8 & 4 & 4.33856417400698 & -0.338564174006982 \tabularnewline
9 & 4 & 5.55394943371597 & -1.55394943371597 \tabularnewline
10 & 4 & 3.95124013896818 & 0.0487598610318215 \tabularnewline
11 & 6 & 5.74263229166579 & 0.257367708334207 \tabularnewline
12 & 6 & 4.79443012259628 & 1.20556987740372 \tabularnewline
13 & 5 & 5.79934865644749 & -0.79934865644749 \tabularnewline
14 & 4 & 4.24253508361137 & -0.242535083611369 \tabularnewline
15 & 6 & 4.48980655856185 & 1.51019344143815 \tabularnewline
16 & 4 & 4.85346527272815 & -0.853465272728152 \tabularnewline
17 & 6 & 4.64033608357284 & 1.35966391642716 \tabularnewline
18 & 6 & 6.28028745979897 & -0.280287459798969 \tabularnewline
19 & 4 & 3.80331412721118 & 0.196685872788824 \tabularnewline
20 & 4 & 3.76675751933307 & 0.233242480666932 \tabularnewline
21 & 2 & 4.58699504163255 & -2.58699504163255 \tabularnewline
22 & 7 & 6.18334711794286 & 0.816652882057141 \tabularnewline
23 & 5 & 4.93193811947416 & 0.0680618805258387 \tabularnewline
24 & 4 & 4.38742024972875 & -0.387420249728749 \tabularnewline
25 & 6 & 5.81781615155759 & 0.182183848442409 \tabularnewline
26 & 6 & 6.6410945194967 & -0.641094519496695 \tabularnewline
27 & 7 & 5.36213939413934 & 1.63786060586066 \tabularnewline
28 & 5 & 5.78579425919598 & -0.785794259195978 \tabularnewline
29 & 6 & 5.76635656258184 & 0.233643437418162 \tabularnewline
30 & 4 & 5.5501865277869 & -1.55018652778690 \tabularnewline
31 & 4 & 4.24071258069038 & -0.240712580690376 \tabularnewline
32 & 7 & 5.44514695640184 & 1.55485304359816 \tabularnewline
33 & 7 & 6.00981538230528 & 0.990184617694725 \tabularnewline
34 & 4 & 4.70978924858383 & -0.709789248583832 \tabularnewline
35 & 4 & 4.33856417400698 & -0.338564174006982 \tabularnewline
36 & 6 & 6.54415417764058 & -0.544154177640585 \tabularnewline
37 & 6 & 5.28820126290982 & 0.711798737090177 \tabularnewline
38 & 5 & 3.98442142400488 & 1.01557857599512 \tabularnewline
39 & 6 & 5.57980329881115 & 0.420196701188854 \tabularnewline
40 & 7 & 6.691832048183 & 0.308167951817002 \tabularnewline
41 & 6 & 5.61110313088331 & 0.388896869116691 \tabularnewline
42 & 3 & 4.70130243596722 & -1.70130243596722 \tabularnewline
43 & 3 & 3.50148326760178 & -0.501483267601777 \tabularnewline
44 & 4 & 4.24822919180295 & -0.24822919180295 \tabularnewline
45 & 6 & 4.503798288199 & 1.496201711801 \tabularnewline
46 & 7 & 6.08640677608675 & 0.913593223913251 \tabularnewline
47 & 5 & 6.44721383578447 & -1.44721383578447 \tabularnewline
48 & 4 & 3.32043492085162 & 0.679565079148381 \tabularnewline
49 & 5 & 4.75671412204309 & 0.243285877956911 \tabularnewline
50 & 6 & 6.21011223449853 & -0.210112234498526 \tabularnewline
51 & 6 & 4.16787489209240 & 1.83212510790760 \tabularnewline
52 & 6 & 4.34802118812764 & 1.65197881187237 \tabularnewline
53 & 5 & 5.34723641304169 & -0.347236413041693 \tabularnewline
54 & 4 & 4.16975634505694 & -0.169756345056941 \tabularnewline
55 & 5 & 5.59166543426917 & -0.591665434269169 \tabularnewline
56 & 5 & 5.4257092597877 & -0.425709259787702 \tabularnewline
57 & 4 & 4.95609972277584 & -0.956099722775843 \tabularnewline
58 & 6 & 6.73070744141128 & -0.730707441411277 \tabularnewline
59 & 2 & 4.69126280343019 & -2.69126280343019 \tabularnewline
60 & 8 & 6.28670362828 & 1.71329637172 \tabularnewline
61 & 3 & 3.86798443549108 & -0.867984435491084 \tabularnewline
62 & 6 & 6.37703861048403 & -0.377038610484032 \tabularnewline
63 & 6 & 6.14187741220616 & -0.141877412206162 \tabularnewline
64 & 6 & 6.3374411569663 & -0.337441156966303 \tabularnewline
65 & 5 & 4.02777258270611 & 0.97222741729389 \tabularnewline
66 & 5 & 5.61110313088331 & -0.611103130883309 \tabularnewline
67 & 6 & 5.20785616394485 & 0.792143836055153 \tabularnewline
68 & 5 & 4.68919215929461 & 0.310807840705394 \tabularnewline
69 & 6 & 5.76519716990675 & 0.234802830093248 \tabularnewline
70 & 2 & 2.07374982907046 & -0.0737498290704635 \tabularnewline
71 & 5 & 5.88799137685803 & -0.887991376858033 \tabularnewline
72 & 5 & 5.45700909185986 & -0.457009091859865 \tabularnewline
73 & 5 & 5.10922356029525 & -0.109223560295248 \tabularnewline
74 & 6 & 5.95951518600461 & 0.0404848139953913 \tabularnewline
75 & 6 & 5.23843393572756 & 0.76156606427244 \tabularnewline
76 & 6 & 5.5096280735107 & 0.490371926489297 \tabularnewline
77 & 5 & 5.23915599601701 & -0.239155996017010 \tabularnewline
78 & 5 & 5.38066583929298 & -0.380665839292981 \tabularnewline
79 & 4 & 5.24199844974002 & -1.24199844974002 \tabularnewline
80 & 2 & 3.35949950525095 & -1.35949950525095 \tabularnewline
81 & 4 & 3.76462792515394 & 0.235372074846057 \tabularnewline
82 & 6 & 5.58005144002574 & 0.419948559974264 \tabularnewline
83 & 6 & 6.07357443912469 & -0.0735744391246866 \tabularnewline
84 & 5 & 4.67921147680112 & 0.320788523198882 \tabularnewline
85 & 3 & 4.48247913862032 & -1.48247913862032 \tabularnewline
86 & 6 & 5.03244297534273 & 0.967557024657273 \tabularnewline
87 & 4 & 3.90025446906729 & 0.0997455309327137 \tabularnewline
88 & 5 & 5.48874625631766 & -0.488746256317664 \tabularnewline
89 & 8 & 6.3374411569663 & 1.66255884303370 \tabularnewline
90 & 4 & 4.4530607595127 & -0.453060759512697 \tabularnewline
91 & 6 & 5.76350490811326 & 0.236495091886737 \tabularnewline
92 & 6 & 5.3095204124885 & 0.690479587511501 \tabularnewline
93 & 7 & 6.67239435156886 & 0.327605648431142 \tabularnewline
94 & 6 & 6.11414209414646 & -0.114142094146455 \tabularnewline
95 & 5 & 4.96796185823387 & 0.0320381417661339 \tabularnewline
96 & 4 & 4.182588682019 & -0.182588682019002 \tabularnewline
97 & 6 & 3.85351878677908 & 2.14648121322092 \tabularnewline
98 & 3 & 3.60392852647842 & -0.603928526478421 \tabularnewline
99 & 5 & 5.6252840516915 & -0.625284051691504 \tabularnewline
100 & 6 & 5.25029607118558 & 0.749703928814417 \tabularnewline
101 & 7 & 6.79518855852014 & 0.204811441479861 \tabularnewline
102 & 7 & 6.4213599706893 & 0.578640029310696 \tabularnewline
103 & 6 & 6.72404313171566 & -0.724043131715656 \tabularnewline
104 & 3 & 4.36084432434413 & -1.36084432434413 \tabularnewline
105 & 2 & 2.73579592921564 & -0.735795929215643 \tabularnewline
106 & 8 & 5.72462951488498 & 2.27537048511502 \tabularnewline
107 & 3 & 4.67279530832009 & -1.67279530832009 \tabularnewline
108 & 8 & 6.13260958925656 & 1.86739041074344 \tabularnewline
109 & 3 & 4.56302262950192 & -1.56302262950192 \tabularnewline
110 & 4 & 4.66565707954961 & -0.665657079549607 \tabularnewline
111 & 5 & 5.23201776724653 & -0.232017767246529 \tabularnewline
112 & 7 & 5.58737885996726 & 1.41262114003274 \tabularnewline
113 & 6 & 4.52868195179013 & 1.47131804820987 \tabularnewline
114 & 6 & 5.90645887196813 & 0.093541128031866 \tabularnewline
115 & 7 & 6.10487427119685 & 0.89512572880315 \tabularnewline
116 & 6 & 6.18976328642389 & -0.189763286423890 \tabularnewline
117 & 6 & 6.17032558980975 & -0.17032558980975 \tabularnewline
118 & 6 & 5.07507207375451 & 0.92492792624549 \tabularnewline
119 & 6 & 5.9654940221 & 0.0345059778999965 \tabularnewline
120 & 4 & 5.3795064466179 & -1.37950644661789 \tabularnewline
121 & 4 & 5.19955854249928 & -1.19955854249928 \tabularnewline
122 & 5 & 5.79293248796646 & -0.792932487966459 \tabularnewline
123 & 4 & 4.14487268146581 & -0.144872681465809 \tabularnewline
124 & 6 & 5.79196228646242 & 0.208037713537580 \tabularnewline
125 & 6 & 6.27368210014689 & -0.273682100146891 \tabularnewline
126 & 5 & 4.72709735101885 & 0.272902648981154 \tabularnewline
127 & 8 & 6.46568133089458 & 1.53431866910542 \tabularnewline
128 & 6 & 5.5096280735107 & 0.490371926489297 \tabularnewline
129 & 5 & 5.06418013980053 & -0.0641801398005265 \tabularnewline
130 & 4 & 2.09531711986373 & 1.90468288013627 \tabularnewline
131 & 8 & 6.46568133089458 & 1.53431866910542 \tabularnewline
132 & 6 & 5.68127835618375 & 0.318721643816249 \tabularnewline
133 & 4 & 4.92097803473107 & -0.920978034731066 \tabularnewline
134 & 6 & 5.52621411565627 & 0.473785884343732 \tabularnewline
135 & 6 & 5.91287504044916 & 0.087124959550835 \tabularnewline
136 & 4 & 4.88948901148786 & -0.889489011487857 \tabularnewline
137 & 6 & 5.98493171871414 & 0.0150682812858566 \tabularnewline
138 & 3 & 4.38476698717679 & -1.38476698717679 \tabularnewline
139 & 6 & 6.88364208775964 & -0.883642087759635 \tabularnewline
140 & 5 & 5.59282482694425 & -0.592824826944255 \tabularnewline
141 & 4 & 5.63242228046198 & -1.63242228046198 \tabularnewline
142 & 6 & 5.70804347273942 & 0.291956527260581 \tabularnewline
143 & 4 & 6.31158729187113 & -2.31158729187113 \tabularnewline
144 & 4 & 3.38159966516261 & 0.618400334837386 \tabularnewline
145 & 4 & 4.992845521825 & -0.992845521824998 \tabularnewline
146 & 6 & 4.04508068514112 & 1.95491931485888 \tabularnewline
147 & 5 & 4.31743421559935 & 0.682565784400647 \tabularnewline
148 & 6 & 5.43848264670622 & 0.561517353293779 \tabularnewline
149 & 6 & 6.24238226807473 & -0.242382268074728 \tabularnewline
150 & 8 & 6.11129043967788 & 1.88870956032212 \tabularnewline
151 & 7 & 5.37309027813686 & 1.62690972186314 \tabularnewline
152 & 7 & 6.56903784123172 & 0.430962158768284 \tabularnewline
153 & 4 & 4.29896672048925 & -0.298966720489253 \tabularnewline
154 & 6 & 5.84197775485927 & 0.158022245140727 \tabularnewline
155 & 6 & 5.58621946729218 & 0.413780532707823 \tabularnewline
156 & 2 & 5.26331759931869 & -3.26331759931869 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98397&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]3[/C][C]5.54092790558286[/C][C]-2.54092790558286[/C][/ROW]
[ROW][C]2[/C][C]5[/C][C]4.78613250115072[/C][C]0.213867498849284[/C][/ROW]
[ROW][C]3[/C][C]6[/C][C]6.14374966442514[/C][C]-0.143749664425136[/C][/ROW]
[ROW][C]4[/C][C]6[/C][C]4.59341121011358[/C][C]1.40658878988642[/C][/ROW]
[ROW][C]5[/C][C]5[/C][C]4.42817709592157[/C][C]0.571822904078433[/C][/ROW]
[ROW][C]6[/C][C]3[/C][C]4.53509812027116[/C][C]-1.53509812027116[/C][/ROW]
[ROW][C]7[/C][C]8[/C][C]6.7942183570161[/C][C]1.2057816429839[/C][/ROW]
[ROW][C]8[/C][C]4[/C][C]4.33856417400698[/C][C]-0.338564174006982[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]5.55394943371597[/C][C]-1.55394943371597[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]3.95124013896818[/C][C]0.0487598610318215[/C][/ROW]
[ROW][C]11[/C][C]6[/C][C]5.74263229166579[/C][C]0.257367708334207[/C][/ROW]
[ROW][C]12[/C][C]6[/C][C]4.79443012259628[/C][C]1.20556987740372[/C][/ROW]
[ROW][C]13[/C][C]5[/C][C]5.79934865644749[/C][C]-0.79934865644749[/C][/ROW]
[ROW][C]14[/C][C]4[/C][C]4.24253508361137[/C][C]-0.242535083611369[/C][/ROW]
[ROW][C]15[/C][C]6[/C][C]4.48980655856185[/C][C]1.51019344143815[/C][/ROW]
[ROW][C]16[/C][C]4[/C][C]4.85346527272815[/C][C]-0.853465272728152[/C][/ROW]
[ROW][C]17[/C][C]6[/C][C]4.64033608357284[/C][C]1.35966391642716[/C][/ROW]
[ROW][C]18[/C][C]6[/C][C]6.28028745979897[/C][C]-0.280287459798969[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]3.80331412721118[/C][C]0.196685872788824[/C][/ROW]
[ROW][C]20[/C][C]4[/C][C]3.76675751933307[/C][C]0.233242480666932[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]4.58699504163255[/C][C]-2.58699504163255[/C][/ROW]
[ROW][C]22[/C][C]7[/C][C]6.18334711794286[/C][C]0.816652882057141[/C][/ROW]
[ROW][C]23[/C][C]5[/C][C]4.93193811947416[/C][C]0.0680618805258387[/C][/ROW]
[ROW][C]24[/C][C]4[/C][C]4.38742024972875[/C][C]-0.387420249728749[/C][/ROW]
[ROW][C]25[/C][C]6[/C][C]5.81781615155759[/C][C]0.182183848442409[/C][/ROW]
[ROW][C]26[/C][C]6[/C][C]6.6410945194967[/C][C]-0.641094519496695[/C][/ROW]
[ROW][C]27[/C][C]7[/C][C]5.36213939413934[/C][C]1.63786060586066[/C][/ROW]
[ROW][C]28[/C][C]5[/C][C]5.78579425919598[/C][C]-0.785794259195978[/C][/ROW]
[ROW][C]29[/C][C]6[/C][C]5.76635656258184[/C][C]0.233643437418162[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]5.5501865277869[/C][C]-1.55018652778690[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]4.24071258069038[/C][C]-0.240712580690376[/C][/ROW]
[ROW][C]32[/C][C]7[/C][C]5.44514695640184[/C][C]1.55485304359816[/C][/ROW]
[ROW][C]33[/C][C]7[/C][C]6.00981538230528[/C][C]0.990184617694725[/C][/ROW]
[ROW][C]34[/C][C]4[/C][C]4.70978924858383[/C][C]-0.709789248583832[/C][/ROW]
[ROW][C]35[/C][C]4[/C][C]4.33856417400698[/C][C]-0.338564174006982[/C][/ROW]
[ROW][C]36[/C][C]6[/C][C]6.54415417764058[/C][C]-0.544154177640585[/C][/ROW]
[ROW][C]37[/C][C]6[/C][C]5.28820126290982[/C][C]0.711798737090177[/C][/ROW]
[ROW][C]38[/C][C]5[/C][C]3.98442142400488[/C][C]1.01557857599512[/C][/ROW]
[ROW][C]39[/C][C]6[/C][C]5.57980329881115[/C][C]0.420196701188854[/C][/ROW]
[ROW][C]40[/C][C]7[/C][C]6.691832048183[/C][C]0.308167951817002[/C][/ROW]
[ROW][C]41[/C][C]6[/C][C]5.61110313088331[/C][C]0.388896869116691[/C][/ROW]
[ROW][C]42[/C][C]3[/C][C]4.70130243596722[/C][C]-1.70130243596722[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]3.50148326760178[/C][C]-0.501483267601777[/C][/ROW]
[ROW][C]44[/C][C]4[/C][C]4.24822919180295[/C][C]-0.24822919180295[/C][/ROW]
[ROW][C]45[/C][C]6[/C][C]4.503798288199[/C][C]1.496201711801[/C][/ROW]
[ROW][C]46[/C][C]7[/C][C]6.08640677608675[/C][C]0.913593223913251[/C][/ROW]
[ROW][C]47[/C][C]5[/C][C]6.44721383578447[/C][C]-1.44721383578447[/C][/ROW]
[ROW][C]48[/C][C]4[/C][C]3.32043492085162[/C][C]0.679565079148381[/C][/ROW]
[ROW][C]49[/C][C]5[/C][C]4.75671412204309[/C][C]0.243285877956911[/C][/ROW]
[ROW][C]50[/C][C]6[/C][C]6.21011223449853[/C][C]-0.210112234498526[/C][/ROW]
[ROW][C]51[/C][C]6[/C][C]4.16787489209240[/C][C]1.83212510790760[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]4.34802118812764[/C][C]1.65197881187237[/C][/ROW]
[ROW][C]53[/C][C]5[/C][C]5.34723641304169[/C][C]-0.347236413041693[/C][/ROW]
[ROW][C]54[/C][C]4[/C][C]4.16975634505694[/C][C]-0.169756345056941[/C][/ROW]
[ROW][C]55[/C][C]5[/C][C]5.59166543426917[/C][C]-0.591665434269169[/C][/ROW]
[ROW][C]56[/C][C]5[/C][C]5.4257092597877[/C][C]-0.425709259787702[/C][/ROW]
[ROW][C]57[/C][C]4[/C][C]4.95609972277584[/C][C]-0.956099722775843[/C][/ROW]
[ROW][C]58[/C][C]6[/C][C]6.73070744141128[/C][C]-0.730707441411277[/C][/ROW]
[ROW][C]59[/C][C]2[/C][C]4.69126280343019[/C][C]-2.69126280343019[/C][/ROW]
[ROW][C]60[/C][C]8[/C][C]6.28670362828[/C][C]1.71329637172[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]3.86798443549108[/C][C]-0.867984435491084[/C][/ROW]
[ROW][C]62[/C][C]6[/C][C]6.37703861048403[/C][C]-0.377038610484032[/C][/ROW]
[ROW][C]63[/C][C]6[/C][C]6.14187741220616[/C][C]-0.141877412206162[/C][/ROW]
[ROW][C]64[/C][C]6[/C][C]6.3374411569663[/C][C]-0.337441156966303[/C][/ROW]
[ROW][C]65[/C][C]5[/C][C]4.02777258270611[/C][C]0.97222741729389[/C][/ROW]
[ROW][C]66[/C][C]5[/C][C]5.61110313088331[/C][C]-0.611103130883309[/C][/ROW]
[ROW][C]67[/C][C]6[/C][C]5.20785616394485[/C][C]0.792143836055153[/C][/ROW]
[ROW][C]68[/C][C]5[/C][C]4.68919215929461[/C][C]0.310807840705394[/C][/ROW]
[ROW][C]69[/C][C]6[/C][C]5.76519716990675[/C][C]0.234802830093248[/C][/ROW]
[ROW][C]70[/C][C]2[/C][C]2.07374982907046[/C][C]-0.0737498290704635[/C][/ROW]
[ROW][C]71[/C][C]5[/C][C]5.88799137685803[/C][C]-0.887991376858033[/C][/ROW]
[ROW][C]72[/C][C]5[/C][C]5.45700909185986[/C][C]-0.457009091859865[/C][/ROW]
[ROW][C]73[/C][C]5[/C][C]5.10922356029525[/C][C]-0.109223560295248[/C][/ROW]
[ROW][C]74[/C][C]6[/C][C]5.95951518600461[/C][C]0.0404848139953913[/C][/ROW]
[ROW][C]75[/C][C]6[/C][C]5.23843393572756[/C][C]0.76156606427244[/C][/ROW]
[ROW][C]76[/C][C]6[/C][C]5.5096280735107[/C][C]0.490371926489297[/C][/ROW]
[ROW][C]77[/C][C]5[/C][C]5.23915599601701[/C][C]-0.239155996017010[/C][/ROW]
[ROW][C]78[/C][C]5[/C][C]5.38066583929298[/C][C]-0.380665839292981[/C][/ROW]
[ROW][C]79[/C][C]4[/C][C]5.24199844974002[/C][C]-1.24199844974002[/C][/ROW]
[ROW][C]80[/C][C]2[/C][C]3.35949950525095[/C][C]-1.35949950525095[/C][/ROW]
[ROW][C]81[/C][C]4[/C][C]3.76462792515394[/C][C]0.235372074846057[/C][/ROW]
[ROW][C]82[/C][C]6[/C][C]5.58005144002574[/C][C]0.419948559974264[/C][/ROW]
[ROW][C]83[/C][C]6[/C][C]6.07357443912469[/C][C]-0.0735744391246866[/C][/ROW]
[ROW][C]84[/C][C]5[/C][C]4.67921147680112[/C][C]0.320788523198882[/C][/ROW]
[ROW][C]85[/C][C]3[/C][C]4.48247913862032[/C][C]-1.48247913862032[/C][/ROW]
[ROW][C]86[/C][C]6[/C][C]5.03244297534273[/C][C]0.967557024657273[/C][/ROW]
[ROW][C]87[/C][C]4[/C][C]3.90025446906729[/C][C]0.0997455309327137[/C][/ROW]
[ROW][C]88[/C][C]5[/C][C]5.48874625631766[/C][C]-0.488746256317664[/C][/ROW]
[ROW][C]89[/C][C]8[/C][C]6.3374411569663[/C][C]1.66255884303370[/C][/ROW]
[ROW][C]90[/C][C]4[/C][C]4.4530607595127[/C][C]-0.453060759512697[/C][/ROW]
[ROW][C]91[/C][C]6[/C][C]5.76350490811326[/C][C]0.236495091886737[/C][/ROW]
[ROW][C]92[/C][C]6[/C][C]5.3095204124885[/C][C]0.690479587511501[/C][/ROW]
[ROW][C]93[/C][C]7[/C][C]6.67239435156886[/C][C]0.327605648431142[/C][/ROW]
[ROW][C]94[/C][C]6[/C][C]6.11414209414646[/C][C]-0.114142094146455[/C][/ROW]
[ROW][C]95[/C][C]5[/C][C]4.96796185823387[/C][C]0.0320381417661339[/C][/ROW]
[ROW][C]96[/C][C]4[/C][C]4.182588682019[/C][C]-0.182588682019002[/C][/ROW]
[ROW][C]97[/C][C]6[/C][C]3.85351878677908[/C][C]2.14648121322092[/C][/ROW]
[ROW][C]98[/C][C]3[/C][C]3.60392852647842[/C][C]-0.603928526478421[/C][/ROW]
[ROW][C]99[/C][C]5[/C][C]5.6252840516915[/C][C]-0.625284051691504[/C][/ROW]
[ROW][C]100[/C][C]6[/C][C]5.25029607118558[/C][C]0.749703928814417[/C][/ROW]
[ROW][C]101[/C][C]7[/C][C]6.79518855852014[/C][C]0.204811441479861[/C][/ROW]
[ROW][C]102[/C][C]7[/C][C]6.4213599706893[/C][C]0.578640029310696[/C][/ROW]
[ROW][C]103[/C][C]6[/C][C]6.72404313171566[/C][C]-0.724043131715656[/C][/ROW]
[ROW][C]104[/C][C]3[/C][C]4.36084432434413[/C][C]-1.36084432434413[/C][/ROW]
[ROW][C]105[/C][C]2[/C][C]2.73579592921564[/C][C]-0.735795929215643[/C][/ROW]
[ROW][C]106[/C][C]8[/C][C]5.72462951488498[/C][C]2.27537048511502[/C][/ROW]
[ROW][C]107[/C][C]3[/C][C]4.67279530832009[/C][C]-1.67279530832009[/C][/ROW]
[ROW][C]108[/C][C]8[/C][C]6.13260958925656[/C][C]1.86739041074344[/C][/ROW]
[ROW][C]109[/C][C]3[/C][C]4.56302262950192[/C][C]-1.56302262950192[/C][/ROW]
[ROW][C]110[/C][C]4[/C][C]4.66565707954961[/C][C]-0.665657079549607[/C][/ROW]
[ROW][C]111[/C][C]5[/C][C]5.23201776724653[/C][C]-0.232017767246529[/C][/ROW]
[ROW][C]112[/C][C]7[/C][C]5.58737885996726[/C][C]1.41262114003274[/C][/ROW]
[ROW][C]113[/C][C]6[/C][C]4.52868195179013[/C][C]1.47131804820987[/C][/ROW]
[ROW][C]114[/C][C]6[/C][C]5.90645887196813[/C][C]0.093541128031866[/C][/ROW]
[ROW][C]115[/C][C]7[/C][C]6.10487427119685[/C][C]0.89512572880315[/C][/ROW]
[ROW][C]116[/C][C]6[/C][C]6.18976328642389[/C][C]-0.189763286423890[/C][/ROW]
[ROW][C]117[/C][C]6[/C][C]6.17032558980975[/C][C]-0.17032558980975[/C][/ROW]
[ROW][C]118[/C][C]6[/C][C]5.07507207375451[/C][C]0.92492792624549[/C][/ROW]
[ROW][C]119[/C][C]6[/C][C]5.9654940221[/C][C]0.0345059778999965[/C][/ROW]
[ROW][C]120[/C][C]4[/C][C]5.3795064466179[/C][C]-1.37950644661789[/C][/ROW]
[ROW][C]121[/C][C]4[/C][C]5.19955854249928[/C][C]-1.19955854249928[/C][/ROW]
[ROW][C]122[/C][C]5[/C][C]5.79293248796646[/C][C]-0.792932487966459[/C][/ROW]
[ROW][C]123[/C][C]4[/C][C]4.14487268146581[/C][C]-0.144872681465809[/C][/ROW]
[ROW][C]124[/C][C]6[/C][C]5.79196228646242[/C][C]0.208037713537580[/C][/ROW]
[ROW][C]125[/C][C]6[/C][C]6.27368210014689[/C][C]-0.273682100146891[/C][/ROW]
[ROW][C]126[/C][C]5[/C][C]4.72709735101885[/C][C]0.272902648981154[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]6.46568133089458[/C][C]1.53431866910542[/C][/ROW]
[ROW][C]128[/C][C]6[/C][C]5.5096280735107[/C][C]0.490371926489297[/C][/ROW]
[ROW][C]129[/C][C]5[/C][C]5.06418013980053[/C][C]-0.0641801398005265[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]2.09531711986373[/C][C]1.90468288013627[/C][/ROW]
[ROW][C]131[/C][C]8[/C][C]6.46568133089458[/C][C]1.53431866910542[/C][/ROW]
[ROW][C]132[/C][C]6[/C][C]5.68127835618375[/C][C]0.318721643816249[/C][/ROW]
[ROW][C]133[/C][C]4[/C][C]4.92097803473107[/C][C]-0.920978034731066[/C][/ROW]
[ROW][C]134[/C][C]6[/C][C]5.52621411565627[/C][C]0.473785884343732[/C][/ROW]
[ROW][C]135[/C][C]6[/C][C]5.91287504044916[/C][C]0.087124959550835[/C][/ROW]
[ROW][C]136[/C][C]4[/C][C]4.88948901148786[/C][C]-0.889489011487857[/C][/ROW]
[ROW][C]137[/C][C]6[/C][C]5.98493171871414[/C][C]0.0150682812858566[/C][/ROW]
[ROW][C]138[/C][C]3[/C][C]4.38476698717679[/C][C]-1.38476698717679[/C][/ROW]
[ROW][C]139[/C][C]6[/C][C]6.88364208775964[/C][C]-0.883642087759635[/C][/ROW]
[ROW][C]140[/C][C]5[/C][C]5.59282482694425[/C][C]-0.592824826944255[/C][/ROW]
[ROW][C]141[/C][C]4[/C][C]5.63242228046198[/C][C]-1.63242228046198[/C][/ROW]
[ROW][C]142[/C][C]6[/C][C]5.70804347273942[/C][C]0.291956527260581[/C][/ROW]
[ROW][C]143[/C][C]4[/C][C]6.31158729187113[/C][C]-2.31158729187113[/C][/ROW]
[ROW][C]144[/C][C]4[/C][C]3.38159966516261[/C][C]0.618400334837386[/C][/ROW]
[ROW][C]145[/C][C]4[/C][C]4.992845521825[/C][C]-0.992845521824998[/C][/ROW]
[ROW][C]146[/C][C]6[/C][C]4.04508068514112[/C][C]1.95491931485888[/C][/ROW]
[ROW][C]147[/C][C]5[/C][C]4.31743421559935[/C][C]0.682565784400647[/C][/ROW]
[ROW][C]148[/C][C]6[/C][C]5.43848264670622[/C][C]0.561517353293779[/C][/ROW]
[ROW][C]149[/C][C]6[/C][C]6.24238226807473[/C][C]-0.242382268074728[/C][/ROW]
[ROW][C]150[/C][C]8[/C][C]6.11129043967788[/C][C]1.88870956032212[/C][/ROW]
[ROW][C]151[/C][C]7[/C][C]5.37309027813686[/C][C]1.62690972186314[/C][/ROW]
[ROW][C]152[/C][C]7[/C][C]6.56903784123172[/C][C]0.430962158768284[/C][/ROW]
[ROW][C]153[/C][C]4[/C][C]4.29896672048925[/C][C]-0.298966720489253[/C][/ROW]
[ROW][C]154[/C][C]6[/C][C]5.84197775485927[/C][C]0.158022245140727[/C][/ROW]
[ROW][C]155[/C][C]6[/C][C]5.58621946729218[/C][C]0.413780532707823[/C][/ROW]
[ROW][C]156[/C][C]2[/C][C]5.26331759931869[/C][C]-3.26331759931869[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98397&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98397&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
135.54092790558286-2.54092790558286
254.786132501150720.213867498849284
366.14374966442514-0.143749664425136
464.593411210113581.40658878988642
554.428177095921570.571822904078433
634.53509812027116-1.53509812027116
786.79421835701611.2057816429839
844.33856417400698-0.338564174006982
945.55394943371597-1.55394943371597
1043.951240138968180.0487598610318215
1165.742632291665790.257367708334207
1264.794430122596281.20556987740372
1355.79934865644749-0.79934865644749
1444.24253508361137-0.242535083611369
1564.489806558561851.51019344143815
1644.85346527272815-0.853465272728152
1764.640336083572841.35966391642716
1866.28028745979897-0.280287459798969
1943.803314127211180.196685872788824
2043.766757519333070.233242480666932
2124.58699504163255-2.58699504163255
2276.183347117942860.816652882057141
2354.931938119474160.0680618805258387
2444.38742024972875-0.387420249728749
2565.817816151557590.182183848442409
2666.6410945194967-0.641094519496695
2775.362139394139341.63786060586066
2855.78579425919598-0.785794259195978
2965.766356562581840.233643437418162
3045.5501865277869-1.55018652778690
3144.24071258069038-0.240712580690376
3275.445146956401841.55485304359816
3376.009815382305280.990184617694725
3444.70978924858383-0.709789248583832
3544.33856417400698-0.338564174006982
3666.54415417764058-0.544154177640585
3765.288201262909820.711798737090177
3853.984421424004881.01557857599512
3965.579803298811150.420196701188854
4076.6918320481830.308167951817002
4165.611103130883310.388896869116691
4234.70130243596722-1.70130243596722
4333.50148326760178-0.501483267601777
4444.24822919180295-0.24822919180295
4564.5037982881991.496201711801
4676.086406776086750.913593223913251
4756.44721383578447-1.44721383578447
4843.320434920851620.679565079148381
4954.756714122043090.243285877956911
5066.21011223449853-0.210112234498526
5164.167874892092401.83212510790760
5264.348021188127641.65197881187237
5355.34723641304169-0.347236413041693
5444.16975634505694-0.169756345056941
5555.59166543426917-0.591665434269169
5655.4257092597877-0.425709259787702
5744.95609972277584-0.956099722775843
5866.73070744141128-0.730707441411277
5924.69126280343019-2.69126280343019
6086.286703628281.71329637172
6133.86798443549108-0.867984435491084
6266.37703861048403-0.377038610484032
6366.14187741220616-0.141877412206162
6466.3374411569663-0.337441156966303
6554.027772582706110.97222741729389
6655.61110313088331-0.611103130883309
6765.207856163944850.792143836055153
6854.689192159294610.310807840705394
6965.765197169906750.234802830093248
7022.07374982907046-0.0737498290704635
7155.88799137685803-0.887991376858033
7255.45700909185986-0.457009091859865
7355.10922356029525-0.109223560295248
7465.959515186004610.0404848139953913
7565.238433935727560.76156606427244
7665.50962807351070.490371926489297
7755.23915599601701-0.239155996017010
7855.38066583929298-0.380665839292981
7945.24199844974002-1.24199844974002
8023.35949950525095-1.35949950525095
8143.764627925153940.235372074846057
8265.580051440025740.419948559974264
8366.07357443912469-0.0735744391246866
8454.679211476801120.320788523198882
8534.48247913862032-1.48247913862032
8665.032442975342730.967557024657273
8743.900254469067290.0997455309327137
8855.48874625631766-0.488746256317664
8986.33744115696631.66255884303370
9044.4530607595127-0.453060759512697
9165.763504908113260.236495091886737
9265.30952041248850.690479587511501
9376.672394351568860.327605648431142
9466.11414209414646-0.114142094146455
9554.967961858233870.0320381417661339
9644.182588682019-0.182588682019002
9763.853518786779082.14648121322092
9833.60392852647842-0.603928526478421
9955.6252840516915-0.625284051691504
10065.250296071185580.749703928814417
10176.795188558520140.204811441479861
10276.42135997068930.578640029310696
10366.72404313171566-0.724043131715656
10434.36084432434413-1.36084432434413
10522.73579592921564-0.735795929215643
10685.724629514884982.27537048511502
10734.67279530832009-1.67279530832009
10886.132609589256561.86739041074344
10934.56302262950192-1.56302262950192
11044.66565707954961-0.665657079549607
11155.23201776724653-0.232017767246529
11275.587378859967261.41262114003274
11364.528681951790131.47131804820987
11465.906458871968130.093541128031866
11576.104874271196850.89512572880315
11666.18976328642389-0.189763286423890
11766.17032558980975-0.17032558980975
11865.075072073754510.92492792624549
11965.96549402210.0345059778999965
12045.3795064466179-1.37950644661789
12145.19955854249928-1.19955854249928
12255.79293248796646-0.792932487966459
12344.14487268146581-0.144872681465809
12465.791962286462420.208037713537580
12566.27368210014689-0.273682100146891
12654.727097351018850.272902648981154
12786.465681330894581.53431866910542
12865.50962807351070.490371926489297
12955.06418013980053-0.0641801398005265
13042.095317119863731.90468288013627
13186.465681330894581.53431866910542
13265.681278356183750.318721643816249
13344.92097803473107-0.920978034731066
13465.526214115656270.473785884343732
13565.912875040449160.087124959550835
13644.88948901148786-0.889489011487857
13765.984931718714140.0150682812858566
13834.38476698717679-1.38476698717679
13966.88364208775964-0.883642087759635
14055.59282482694425-0.592824826944255
14145.63242228046198-1.63242228046198
14265.708043472739420.291956527260581
14346.31158729187113-2.31158729187113
14443.381599665162610.618400334837386
14544.992845521825-0.992845521824998
14664.045080685141121.95491931485888
14754.317434215599350.682565784400647
14865.438482646706220.561517353293779
14966.24238226807473-0.242382268074728
15086.111290439677881.88870956032212
15175.373090278136861.62690972186314
15276.569037841231720.430962158768284
15344.29896672048925-0.298966720489253
15465.841977754859270.158022245140727
15565.586219467292180.413780532707823
15625.26331759931869-3.26331759931869






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.4569425415262570.9138850830525140.543057458473743
90.581608144669770.836783710660460.41839185533023
100.4356314601843820.8712629203687650.564368539815618
110.5707932408418740.8584135183162520.429206759158126
120.769143892972110.4617122140557800.230856107027890
130.850263843176450.2994723136471010.149736156823551
140.7870574733148910.4258850533702180.212942526685109
150.8099990831076890.3800018337846220.190000916892311
160.7484016101014180.5031967797971650.251598389898582
170.7862863479790220.4274273040419560.213713652020978
180.7258965763396370.5482068473207270.274103423660363
190.6598457406054590.6803085187890820.340154259394541
200.6176058744728050.764788251054390.382394125527195
210.9324951936242310.1350096127515380.067504806375769
220.9277161053178190.1445677893643630.0722838946821814
230.9073711623420880.1852576753158240.0926288376579119
240.8780129298005980.2439741403988030.121987070199402
250.8474142922593570.3051714154812850.152585707740643
260.8150414142021850.3699171715956310.184958585797815
270.8356400833139210.3287198333721580.164359916686079
280.811676977462940.376646045074120.18832302253706
290.7705752250551050.458849549889790.229424774944895
300.779527458627270.4409450827454590.220472541372730
310.7446011236254450.510797752749110.255398876374555
320.7952865095481770.4094269809036470.204713490451823
330.792953561618860.4140928767622810.207046438381140
340.7705341080734380.4589317838531240.229465891926562
350.7332804789099270.5334390421801460.266719521090073
360.6900869781233170.6198260437533660.309913021876683
370.6561258956241630.6877482087516740.343874104375837
380.6603923930544050.679215213891190.339607606945595
390.6192231820812450.761553635837510.380776817918755
400.5746023469772310.8507953060455370.425397653022769
410.5345291235434230.9309417529131540.465470876456577
420.5715137879932150.856972424013570.428486212006785
430.5577464606888320.8845070786223360.442253539311168
440.5135295314933480.9729409370133040.486470468506652
450.5761286446431410.8477427107137180.423871355356859
460.5762541304800470.8474917390399060.423745869519953
470.60354944147390.79290111705220.3964505585261
480.5839149096481030.8321701807037950.416085090351897
490.5361285059317990.9277429881364020.463871494068201
500.4899279419712740.9798558839425480.510072058028726
510.5729243301683710.8541513396632580.427075669831629
520.6213087216797240.7573825566405510.378691278320276
530.5803458030421440.8393083939157130.419654196957856
540.5434046011169880.9131907977660240.456595398883012
550.5050766587352070.9898466825295860.494923341264793
560.4643073160879520.9286146321759050.535692683912048
570.4617893652553450.923578730510690.538210634744655
580.4325376901342310.8650753802684630.567462309865769
590.7246774426695720.5506451146608560.275322557330428
600.7933903702370080.4132192595259830.206609629762992
610.7833851823285590.4332296353428820.216614817671441
620.7505992577007450.4988014845985090.249400742299255
630.7121615106453750.575676978709250.287838489354625
640.6744290555151290.6511418889697430.325570944484871
650.6653338715133260.6693322569733490.334666128486674
660.6347315475496650.730536904900670.365268452450335
670.6167390012163880.7665219975672240.383260998783612
680.5772804705411030.8454390589177950.422719529458897
690.5345231435683310.9309537128633380.465476856431669
700.4889531970259020.9779063940518050.511046802974098
710.4751262062375420.9502524124750830.524873793762458
720.4363712467742930.8727424935485860.563628753225707
730.390847361434080.781694722868160.60915263856592
740.347629904742390.695259809484780.65237009525761
750.3271872499029910.6543744998059830.672812750097008
760.2962822661031940.5925645322063880.703717733896806
770.2584583662931450.5169167325862910.741541633706855
780.2267687116495510.4535374232991030.773231288350449
790.2463016887388550.492603377477710.753698311261145
800.2732238152403990.5464476304807980.726776184759601
810.2379084078413890.4758168156827790.76209159215861
820.2072125441119310.4144250882238630.792787455888069
830.1754490533732340.3508981067464690.824550946626766
840.1494671392772250.298934278554450.850532860722775
850.2032271935677750.4064543871355510.796772806432225
860.1981177521003600.3962355042007190.80188224789964
870.1670584008590330.3341168017180660.832941599140967
880.1449154741822610.2898309483645220.855084525817739
890.1882847430758600.3765694861517190.81171525692414
900.1643946535766990.3287893071533990.8356053464233
910.1395424319352280.2790848638704560.860457568064772
920.1331345608095710.2662691216191420.86686543919043
930.1120760191079430.2241520382158870.887923980892056
940.09118448115813830.1823689623162770.908815518841862
950.07328805722521970.1465761144504390.92671194277478
960.05959723263858150.1191944652771630.940402767361418
970.1140648631956020.2281297263912040.885935136804398
980.1046496585648990.2092993171297980.895350341435101
990.09036414368495330.1807282873699070.909635856315047
1000.07878486359201720.1575697271840340.921215136407983
1010.06422268151710450.1284453630342090.935777318482895
1020.05398301611882850.1079660322376570.946016983881172
1030.04487922341693410.08975844683386820.955120776583066
1040.06243903969415380.1248780793883080.937560960305846
1050.05706803648282960.1141360729656590.94293196351717
1060.1134633654613950.2269267309227890.886536634538605
1070.1415979998467680.2831959996935360.858402000153232
1080.2344029793143450.4688059586286890.765597020685655
1090.2620995856002190.5241991712004390.73790041439978
1100.2301401809829350.460280361965870.769859819017065
1110.1934063953198380.3868127906396770.806593604680162
1120.2772159982986290.5544319965972580.722784001701371
1130.2796754459816580.5593508919633150.720324554018342
1140.2375449083144490.4750898166288970.762455091685551
1150.2249067365638640.4498134731277270.775093263436136
1160.1872728955439490.3745457910878980.812727104456051
1170.1546699165509350.309339833101870.845330083449065
1180.1457173751519590.2914347503039170.854282624848041
1190.1176438571801240.2352877143602470.882356142819877
1200.1562356259307210.3124712518614430.843764374069279
1210.1712141703957160.3424283407914310.828785829604284
1220.1538781877537640.3077563755075280.846121812246236
1230.1223338654066320.2446677308132640.877666134593368
1240.0966451694932690.1932903389865380.903354830506731
1250.07712922581684150.1542584516336830.922870774183159
1260.06063891218792510.1212778243758500.939361087812075
1270.08456153404927010.1691230680985400.91543846595073
1280.07208643603214910.1441728720642980.927913563967851
1290.06089306605561730.1217861321112350.939106933944383
1300.0838556492399210.1677112984798420.91614435076008
1310.1184285906448080.2368571812896160.881571409355192
1320.0893306792549320.1786613585098640.910669320745068
1330.07169915036970980.1433983007394200.92830084963029
1340.05202553599359790.1040510719871960.947974464006402
1350.03593903601936230.07187807203872460.964060963980638
1360.0417629859750610.0835259719501220.95823701402494
1370.03137876819164260.06275753638328520.968621231808357
1380.02640894674742530.05281789349485060.973591053252575
1390.03063694858893530.06127389717787060.969363051411065
1400.02537973168652560.05075946337305120.974620268313474
1410.01932017786915440.03864035573830880.980679822130846
1420.01186890310771220.02373780621542450.988131096892288
1430.04267307013607790.08534614027215580.957326929863922
1440.02796650822450430.05593301644900850.972033491775496
1450.04814843274662460.09629686549324910.951851567253375
1460.03343124461142620.06686248922285240.966568755388574
1470.03346119857295490.06692239714590980.966538801427045
1480.02891844151925060.05783688303850120.97108155848075

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.456942541526257 & 0.913885083052514 & 0.543057458473743 \tabularnewline
9 & 0.58160814466977 & 0.83678371066046 & 0.41839185533023 \tabularnewline
10 & 0.435631460184382 & 0.871262920368765 & 0.564368539815618 \tabularnewline
11 & 0.570793240841874 & 0.858413518316252 & 0.429206759158126 \tabularnewline
12 & 0.76914389297211 & 0.461712214055780 & 0.230856107027890 \tabularnewline
13 & 0.85026384317645 & 0.299472313647101 & 0.149736156823551 \tabularnewline
14 & 0.787057473314891 & 0.425885053370218 & 0.212942526685109 \tabularnewline
15 & 0.809999083107689 & 0.380001833784622 & 0.190000916892311 \tabularnewline
16 & 0.748401610101418 & 0.503196779797165 & 0.251598389898582 \tabularnewline
17 & 0.786286347979022 & 0.427427304041956 & 0.213713652020978 \tabularnewline
18 & 0.725896576339637 & 0.548206847320727 & 0.274103423660363 \tabularnewline
19 & 0.659845740605459 & 0.680308518789082 & 0.340154259394541 \tabularnewline
20 & 0.617605874472805 & 0.76478825105439 & 0.382394125527195 \tabularnewline
21 & 0.932495193624231 & 0.135009612751538 & 0.067504806375769 \tabularnewline
22 & 0.927716105317819 & 0.144567789364363 & 0.0722838946821814 \tabularnewline
23 & 0.907371162342088 & 0.185257675315824 & 0.0926288376579119 \tabularnewline
24 & 0.878012929800598 & 0.243974140398803 & 0.121987070199402 \tabularnewline
25 & 0.847414292259357 & 0.305171415481285 & 0.152585707740643 \tabularnewline
26 & 0.815041414202185 & 0.369917171595631 & 0.184958585797815 \tabularnewline
27 & 0.835640083313921 & 0.328719833372158 & 0.164359916686079 \tabularnewline
28 & 0.81167697746294 & 0.37664604507412 & 0.18832302253706 \tabularnewline
29 & 0.770575225055105 & 0.45884954988979 & 0.229424774944895 \tabularnewline
30 & 0.77952745862727 & 0.440945082745459 & 0.220472541372730 \tabularnewline
31 & 0.744601123625445 & 0.51079775274911 & 0.255398876374555 \tabularnewline
32 & 0.795286509548177 & 0.409426980903647 & 0.204713490451823 \tabularnewline
33 & 0.79295356161886 & 0.414092876762281 & 0.207046438381140 \tabularnewline
34 & 0.770534108073438 & 0.458931783853124 & 0.229465891926562 \tabularnewline
35 & 0.733280478909927 & 0.533439042180146 & 0.266719521090073 \tabularnewline
36 & 0.690086978123317 & 0.619826043753366 & 0.309913021876683 \tabularnewline
37 & 0.656125895624163 & 0.687748208751674 & 0.343874104375837 \tabularnewline
38 & 0.660392393054405 & 0.67921521389119 & 0.339607606945595 \tabularnewline
39 & 0.619223182081245 & 0.76155363583751 & 0.380776817918755 \tabularnewline
40 & 0.574602346977231 & 0.850795306045537 & 0.425397653022769 \tabularnewline
41 & 0.534529123543423 & 0.930941752913154 & 0.465470876456577 \tabularnewline
42 & 0.571513787993215 & 0.85697242401357 & 0.428486212006785 \tabularnewline
43 & 0.557746460688832 & 0.884507078622336 & 0.442253539311168 \tabularnewline
44 & 0.513529531493348 & 0.972940937013304 & 0.486470468506652 \tabularnewline
45 & 0.576128644643141 & 0.847742710713718 & 0.423871355356859 \tabularnewline
46 & 0.576254130480047 & 0.847491739039906 & 0.423745869519953 \tabularnewline
47 & 0.6035494414739 & 0.7929011170522 & 0.3964505585261 \tabularnewline
48 & 0.583914909648103 & 0.832170180703795 & 0.416085090351897 \tabularnewline
49 & 0.536128505931799 & 0.927742988136402 & 0.463871494068201 \tabularnewline
50 & 0.489927941971274 & 0.979855883942548 & 0.510072058028726 \tabularnewline
51 & 0.572924330168371 & 0.854151339663258 & 0.427075669831629 \tabularnewline
52 & 0.621308721679724 & 0.757382556640551 & 0.378691278320276 \tabularnewline
53 & 0.580345803042144 & 0.839308393915713 & 0.419654196957856 \tabularnewline
54 & 0.543404601116988 & 0.913190797766024 & 0.456595398883012 \tabularnewline
55 & 0.505076658735207 & 0.989846682529586 & 0.494923341264793 \tabularnewline
56 & 0.464307316087952 & 0.928614632175905 & 0.535692683912048 \tabularnewline
57 & 0.461789365255345 & 0.92357873051069 & 0.538210634744655 \tabularnewline
58 & 0.432537690134231 & 0.865075380268463 & 0.567462309865769 \tabularnewline
59 & 0.724677442669572 & 0.550645114660856 & 0.275322557330428 \tabularnewline
60 & 0.793390370237008 & 0.413219259525983 & 0.206609629762992 \tabularnewline
61 & 0.783385182328559 & 0.433229635342882 & 0.216614817671441 \tabularnewline
62 & 0.750599257700745 & 0.498801484598509 & 0.249400742299255 \tabularnewline
63 & 0.712161510645375 & 0.57567697870925 & 0.287838489354625 \tabularnewline
64 & 0.674429055515129 & 0.651141888969743 & 0.325570944484871 \tabularnewline
65 & 0.665333871513326 & 0.669332256973349 & 0.334666128486674 \tabularnewline
66 & 0.634731547549665 & 0.73053690490067 & 0.365268452450335 \tabularnewline
67 & 0.616739001216388 & 0.766521997567224 & 0.383260998783612 \tabularnewline
68 & 0.577280470541103 & 0.845439058917795 & 0.422719529458897 \tabularnewline
69 & 0.534523143568331 & 0.930953712863338 & 0.465476856431669 \tabularnewline
70 & 0.488953197025902 & 0.977906394051805 & 0.511046802974098 \tabularnewline
71 & 0.475126206237542 & 0.950252412475083 & 0.524873793762458 \tabularnewline
72 & 0.436371246774293 & 0.872742493548586 & 0.563628753225707 \tabularnewline
73 & 0.39084736143408 & 0.78169472286816 & 0.60915263856592 \tabularnewline
74 & 0.34762990474239 & 0.69525980948478 & 0.65237009525761 \tabularnewline
75 & 0.327187249902991 & 0.654374499805983 & 0.672812750097008 \tabularnewline
76 & 0.296282266103194 & 0.592564532206388 & 0.703717733896806 \tabularnewline
77 & 0.258458366293145 & 0.516916732586291 & 0.741541633706855 \tabularnewline
78 & 0.226768711649551 & 0.453537423299103 & 0.773231288350449 \tabularnewline
79 & 0.246301688738855 & 0.49260337747771 & 0.753698311261145 \tabularnewline
80 & 0.273223815240399 & 0.546447630480798 & 0.726776184759601 \tabularnewline
81 & 0.237908407841389 & 0.475816815682779 & 0.76209159215861 \tabularnewline
82 & 0.207212544111931 & 0.414425088223863 & 0.792787455888069 \tabularnewline
83 & 0.175449053373234 & 0.350898106746469 & 0.824550946626766 \tabularnewline
84 & 0.149467139277225 & 0.29893427855445 & 0.850532860722775 \tabularnewline
85 & 0.203227193567775 & 0.406454387135551 & 0.796772806432225 \tabularnewline
86 & 0.198117752100360 & 0.396235504200719 & 0.80188224789964 \tabularnewline
87 & 0.167058400859033 & 0.334116801718066 & 0.832941599140967 \tabularnewline
88 & 0.144915474182261 & 0.289830948364522 & 0.855084525817739 \tabularnewline
89 & 0.188284743075860 & 0.376569486151719 & 0.81171525692414 \tabularnewline
90 & 0.164394653576699 & 0.328789307153399 & 0.8356053464233 \tabularnewline
91 & 0.139542431935228 & 0.279084863870456 & 0.860457568064772 \tabularnewline
92 & 0.133134560809571 & 0.266269121619142 & 0.86686543919043 \tabularnewline
93 & 0.112076019107943 & 0.224152038215887 & 0.887923980892056 \tabularnewline
94 & 0.0911844811581383 & 0.182368962316277 & 0.908815518841862 \tabularnewline
95 & 0.0732880572252197 & 0.146576114450439 & 0.92671194277478 \tabularnewline
96 & 0.0595972326385815 & 0.119194465277163 & 0.940402767361418 \tabularnewline
97 & 0.114064863195602 & 0.228129726391204 & 0.885935136804398 \tabularnewline
98 & 0.104649658564899 & 0.209299317129798 & 0.895350341435101 \tabularnewline
99 & 0.0903641436849533 & 0.180728287369907 & 0.909635856315047 \tabularnewline
100 & 0.0787848635920172 & 0.157569727184034 & 0.921215136407983 \tabularnewline
101 & 0.0642226815171045 & 0.128445363034209 & 0.935777318482895 \tabularnewline
102 & 0.0539830161188285 & 0.107966032237657 & 0.946016983881172 \tabularnewline
103 & 0.0448792234169341 & 0.0897584468338682 & 0.955120776583066 \tabularnewline
104 & 0.0624390396941538 & 0.124878079388308 & 0.937560960305846 \tabularnewline
105 & 0.0570680364828296 & 0.114136072965659 & 0.94293196351717 \tabularnewline
106 & 0.113463365461395 & 0.226926730922789 & 0.886536634538605 \tabularnewline
107 & 0.141597999846768 & 0.283195999693536 & 0.858402000153232 \tabularnewline
108 & 0.234402979314345 & 0.468805958628689 & 0.765597020685655 \tabularnewline
109 & 0.262099585600219 & 0.524199171200439 & 0.73790041439978 \tabularnewline
110 & 0.230140180982935 & 0.46028036196587 & 0.769859819017065 \tabularnewline
111 & 0.193406395319838 & 0.386812790639677 & 0.806593604680162 \tabularnewline
112 & 0.277215998298629 & 0.554431996597258 & 0.722784001701371 \tabularnewline
113 & 0.279675445981658 & 0.559350891963315 & 0.720324554018342 \tabularnewline
114 & 0.237544908314449 & 0.475089816628897 & 0.762455091685551 \tabularnewline
115 & 0.224906736563864 & 0.449813473127727 & 0.775093263436136 \tabularnewline
116 & 0.187272895543949 & 0.374545791087898 & 0.812727104456051 \tabularnewline
117 & 0.154669916550935 & 0.30933983310187 & 0.845330083449065 \tabularnewline
118 & 0.145717375151959 & 0.291434750303917 & 0.854282624848041 \tabularnewline
119 & 0.117643857180124 & 0.235287714360247 & 0.882356142819877 \tabularnewline
120 & 0.156235625930721 & 0.312471251861443 & 0.843764374069279 \tabularnewline
121 & 0.171214170395716 & 0.342428340791431 & 0.828785829604284 \tabularnewline
122 & 0.153878187753764 & 0.307756375507528 & 0.846121812246236 \tabularnewline
123 & 0.122333865406632 & 0.244667730813264 & 0.877666134593368 \tabularnewline
124 & 0.096645169493269 & 0.193290338986538 & 0.903354830506731 \tabularnewline
125 & 0.0771292258168415 & 0.154258451633683 & 0.922870774183159 \tabularnewline
126 & 0.0606389121879251 & 0.121277824375850 & 0.939361087812075 \tabularnewline
127 & 0.0845615340492701 & 0.169123068098540 & 0.91543846595073 \tabularnewline
128 & 0.0720864360321491 & 0.144172872064298 & 0.927913563967851 \tabularnewline
129 & 0.0608930660556173 & 0.121786132111235 & 0.939106933944383 \tabularnewline
130 & 0.083855649239921 & 0.167711298479842 & 0.91614435076008 \tabularnewline
131 & 0.118428590644808 & 0.236857181289616 & 0.881571409355192 \tabularnewline
132 & 0.089330679254932 & 0.178661358509864 & 0.910669320745068 \tabularnewline
133 & 0.0716991503697098 & 0.143398300739420 & 0.92830084963029 \tabularnewline
134 & 0.0520255359935979 & 0.104051071987196 & 0.947974464006402 \tabularnewline
135 & 0.0359390360193623 & 0.0718780720387246 & 0.964060963980638 \tabularnewline
136 & 0.041762985975061 & 0.083525971950122 & 0.95823701402494 \tabularnewline
137 & 0.0313787681916426 & 0.0627575363832852 & 0.968621231808357 \tabularnewline
138 & 0.0264089467474253 & 0.0528178934948506 & 0.973591053252575 \tabularnewline
139 & 0.0306369485889353 & 0.0612738971778706 & 0.969363051411065 \tabularnewline
140 & 0.0253797316865256 & 0.0507594633730512 & 0.974620268313474 \tabularnewline
141 & 0.0193201778691544 & 0.0386403557383088 & 0.980679822130846 \tabularnewline
142 & 0.0118689031077122 & 0.0237378062154245 & 0.988131096892288 \tabularnewline
143 & 0.0426730701360779 & 0.0853461402721558 & 0.957326929863922 \tabularnewline
144 & 0.0279665082245043 & 0.0559330164490085 & 0.972033491775496 \tabularnewline
145 & 0.0481484327466246 & 0.0962968654932491 & 0.951851567253375 \tabularnewline
146 & 0.0334312446114262 & 0.0668624892228524 & 0.966568755388574 \tabularnewline
147 & 0.0334611985729549 & 0.0669223971459098 & 0.966538801427045 \tabularnewline
148 & 0.0289184415192506 & 0.0578368830385012 & 0.97108155848075 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98397&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]0.456942541526257[/C][C]0.913885083052514[/C][C]0.543057458473743[/C][/ROW]
[ROW][C]9[/C][C]0.58160814466977[/C][C]0.83678371066046[/C][C]0.41839185533023[/C][/ROW]
[ROW][C]10[/C][C]0.435631460184382[/C][C]0.871262920368765[/C][C]0.564368539815618[/C][/ROW]
[ROW][C]11[/C][C]0.570793240841874[/C][C]0.858413518316252[/C][C]0.429206759158126[/C][/ROW]
[ROW][C]12[/C][C]0.76914389297211[/C][C]0.461712214055780[/C][C]0.230856107027890[/C][/ROW]
[ROW][C]13[/C][C]0.85026384317645[/C][C]0.299472313647101[/C][C]0.149736156823551[/C][/ROW]
[ROW][C]14[/C][C]0.787057473314891[/C][C]0.425885053370218[/C][C]0.212942526685109[/C][/ROW]
[ROW][C]15[/C][C]0.809999083107689[/C][C]0.380001833784622[/C][C]0.190000916892311[/C][/ROW]
[ROW][C]16[/C][C]0.748401610101418[/C][C]0.503196779797165[/C][C]0.251598389898582[/C][/ROW]
[ROW][C]17[/C][C]0.786286347979022[/C][C]0.427427304041956[/C][C]0.213713652020978[/C][/ROW]
[ROW][C]18[/C][C]0.725896576339637[/C][C]0.548206847320727[/C][C]0.274103423660363[/C][/ROW]
[ROW][C]19[/C][C]0.659845740605459[/C][C]0.680308518789082[/C][C]0.340154259394541[/C][/ROW]
[ROW][C]20[/C][C]0.617605874472805[/C][C]0.76478825105439[/C][C]0.382394125527195[/C][/ROW]
[ROW][C]21[/C][C]0.932495193624231[/C][C]0.135009612751538[/C][C]0.067504806375769[/C][/ROW]
[ROW][C]22[/C][C]0.927716105317819[/C][C]0.144567789364363[/C][C]0.0722838946821814[/C][/ROW]
[ROW][C]23[/C][C]0.907371162342088[/C][C]0.185257675315824[/C][C]0.0926288376579119[/C][/ROW]
[ROW][C]24[/C][C]0.878012929800598[/C][C]0.243974140398803[/C][C]0.121987070199402[/C][/ROW]
[ROW][C]25[/C][C]0.847414292259357[/C][C]0.305171415481285[/C][C]0.152585707740643[/C][/ROW]
[ROW][C]26[/C][C]0.815041414202185[/C][C]0.369917171595631[/C][C]0.184958585797815[/C][/ROW]
[ROW][C]27[/C][C]0.835640083313921[/C][C]0.328719833372158[/C][C]0.164359916686079[/C][/ROW]
[ROW][C]28[/C][C]0.81167697746294[/C][C]0.37664604507412[/C][C]0.18832302253706[/C][/ROW]
[ROW][C]29[/C][C]0.770575225055105[/C][C]0.45884954988979[/C][C]0.229424774944895[/C][/ROW]
[ROW][C]30[/C][C]0.77952745862727[/C][C]0.440945082745459[/C][C]0.220472541372730[/C][/ROW]
[ROW][C]31[/C][C]0.744601123625445[/C][C]0.51079775274911[/C][C]0.255398876374555[/C][/ROW]
[ROW][C]32[/C][C]0.795286509548177[/C][C]0.409426980903647[/C][C]0.204713490451823[/C][/ROW]
[ROW][C]33[/C][C]0.79295356161886[/C][C]0.414092876762281[/C][C]0.207046438381140[/C][/ROW]
[ROW][C]34[/C][C]0.770534108073438[/C][C]0.458931783853124[/C][C]0.229465891926562[/C][/ROW]
[ROW][C]35[/C][C]0.733280478909927[/C][C]0.533439042180146[/C][C]0.266719521090073[/C][/ROW]
[ROW][C]36[/C][C]0.690086978123317[/C][C]0.619826043753366[/C][C]0.309913021876683[/C][/ROW]
[ROW][C]37[/C][C]0.656125895624163[/C][C]0.687748208751674[/C][C]0.343874104375837[/C][/ROW]
[ROW][C]38[/C][C]0.660392393054405[/C][C]0.67921521389119[/C][C]0.339607606945595[/C][/ROW]
[ROW][C]39[/C][C]0.619223182081245[/C][C]0.76155363583751[/C][C]0.380776817918755[/C][/ROW]
[ROW][C]40[/C][C]0.574602346977231[/C][C]0.850795306045537[/C][C]0.425397653022769[/C][/ROW]
[ROW][C]41[/C][C]0.534529123543423[/C][C]0.930941752913154[/C][C]0.465470876456577[/C][/ROW]
[ROW][C]42[/C][C]0.571513787993215[/C][C]0.85697242401357[/C][C]0.428486212006785[/C][/ROW]
[ROW][C]43[/C][C]0.557746460688832[/C][C]0.884507078622336[/C][C]0.442253539311168[/C][/ROW]
[ROW][C]44[/C][C]0.513529531493348[/C][C]0.972940937013304[/C][C]0.486470468506652[/C][/ROW]
[ROW][C]45[/C][C]0.576128644643141[/C][C]0.847742710713718[/C][C]0.423871355356859[/C][/ROW]
[ROW][C]46[/C][C]0.576254130480047[/C][C]0.847491739039906[/C][C]0.423745869519953[/C][/ROW]
[ROW][C]47[/C][C]0.6035494414739[/C][C]0.7929011170522[/C][C]0.3964505585261[/C][/ROW]
[ROW][C]48[/C][C]0.583914909648103[/C][C]0.832170180703795[/C][C]0.416085090351897[/C][/ROW]
[ROW][C]49[/C][C]0.536128505931799[/C][C]0.927742988136402[/C][C]0.463871494068201[/C][/ROW]
[ROW][C]50[/C][C]0.489927941971274[/C][C]0.979855883942548[/C][C]0.510072058028726[/C][/ROW]
[ROW][C]51[/C][C]0.572924330168371[/C][C]0.854151339663258[/C][C]0.427075669831629[/C][/ROW]
[ROW][C]52[/C][C]0.621308721679724[/C][C]0.757382556640551[/C][C]0.378691278320276[/C][/ROW]
[ROW][C]53[/C][C]0.580345803042144[/C][C]0.839308393915713[/C][C]0.419654196957856[/C][/ROW]
[ROW][C]54[/C][C]0.543404601116988[/C][C]0.913190797766024[/C][C]0.456595398883012[/C][/ROW]
[ROW][C]55[/C][C]0.505076658735207[/C][C]0.989846682529586[/C][C]0.494923341264793[/C][/ROW]
[ROW][C]56[/C][C]0.464307316087952[/C][C]0.928614632175905[/C][C]0.535692683912048[/C][/ROW]
[ROW][C]57[/C][C]0.461789365255345[/C][C]0.92357873051069[/C][C]0.538210634744655[/C][/ROW]
[ROW][C]58[/C][C]0.432537690134231[/C][C]0.865075380268463[/C][C]0.567462309865769[/C][/ROW]
[ROW][C]59[/C][C]0.724677442669572[/C][C]0.550645114660856[/C][C]0.275322557330428[/C][/ROW]
[ROW][C]60[/C][C]0.793390370237008[/C][C]0.413219259525983[/C][C]0.206609629762992[/C][/ROW]
[ROW][C]61[/C][C]0.783385182328559[/C][C]0.433229635342882[/C][C]0.216614817671441[/C][/ROW]
[ROW][C]62[/C][C]0.750599257700745[/C][C]0.498801484598509[/C][C]0.249400742299255[/C][/ROW]
[ROW][C]63[/C][C]0.712161510645375[/C][C]0.57567697870925[/C][C]0.287838489354625[/C][/ROW]
[ROW][C]64[/C][C]0.674429055515129[/C][C]0.651141888969743[/C][C]0.325570944484871[/C][/ROW]
[ROW][C]65[/C][C]0.665333871513326[/C][C]0.669332256973349[/C][C]0.334666128486674[/C][/ROW]
[ROW][C]66[/C][C]0.634731547549665[/C][C]0.73053690490067[/C][C]0.365268452450335[/C][/ROW]
[ROW][C]67[/C][C]0.616739001216388[/C][C]0.766521997567224[/C][C]0.383260998783612[/C][/ROW]
[ROW][C]68[/C][C]0.577280470541103[/C][C]0.845439058917795[/C][C]0.422719529458897[/C][/ROW]
[ROW][C]69[/C][C]0.534523143568331[/C][C]0.930953712863338[/C][C]0.465476856431669[/C][/ROW]
[ROW][C]70[/C][C]0.488953197025902[/C][C]0.977906394051805[/C][C]0.511046802974098[/C][/ROW]
[ROW][C]71[/C][C]0.475126206237542[/C][C]0.950252412475083[/C][C]0.524873793762458[/C][/ROW]
[ROW][C]72[/C][C]0.436371246774293[/C][C]0.872742493548586[/C][C]0.563628753225707[/C][/ROW]
[ROW][C]73[/C][C]0.39084736143408[/C][C]0.78169472286816[/C][C]0.60915263856592[/C][/ROW]
[ROW][C]74[/C][C]0.34762990474239[/C][C]0.69525980948478[/C][C]0.65237009525761[/C][/ROW]
[ROW][C]75[/C][C]0.327187249902991[/C][C]0.654374499805983[/C][C]0.672812750097008[/C][/ROW]
[ROW][C]76[/C][C]0.296282266103194[/C][C]0.592564532206388[/C][C]0.703717733896806[/C][/ROW]
[ROW][C]77[/C][C]0.258458366293145[/C][C]0.516916732586291[/C][C]0.741541633706855[/C][/ROW]
[ROW][C]78[/C][C]0.226768711649551[/C][C]0.453537423299103[/C][C]0.773231288350449[/C][/ROW]
[ROW][C]79[/C][C]0.246301688738855[/C][C]0.49260337747771[/C][C]0.753698311261145[/C][/ROW]
[ROW][C]80[/C][C]0.273223815240399[/C][C]0.546447630480798[/C][C]0.726776184759601[/C][/ROW]
[ROW][C]81[/C][C]0.237908407841389[/C][C]0.475816815682779[/C][C]0.76209159215861[/C][/ROW]
[ROW][C]82[/C][C]0.207212544111931[/C][C]0.414425088223863[/C][C]0.792787455888069[/C][/ROW]
[ROW][C]83[/C][C]0.175449053373234[/C][C]0.350898106746469[/C][C]0.824550946626766[/C][/ROW]
[ROW][C]84[/C][C]0.149467139277225[/C][C]0.29893427855445[/C][C]0.850532860722775[/C][/ROW]
[ROW][C]85[/C][C]0.203227193567775[/C][C]0.406454387135551[/C][C]0.796772806432225[/C][/ROW]
[ROW][C]86[/C][C]0.198117752100360[/C][C]0.396235504200719[/C][C]0.80188224789964[/C][/ROW]
[ROW][C]87[/C][C]0.167058400859033[/C][C]0.334116801718066[/C][C]0.832941599140967[/C][/ROW]
[ROW][C]88[/C][C]0.144915474182261[/C][C]0.289830948364522[/C][C]0.855084525817739[/C][/ROW]
[ROW][C]89[/C][C]0.188284743075860[/C][C]0.376569486151719[/C][C]0.81171525692414[/C][/ROW]
[ROW][C]90[/C][C]0.164394653576699[/C][C]0.328789307153399[/C][C]0.8356053464233[/C][/ROW]
[ROW][C]91[/C][C]0.139542431935228[/C][C]0.279084863870456[/C][C]0.860457568064772[/C][/ROW]
[ROW][C]92[/C][C]0.133134560809571[/C][C]0.266269121619142[/C][C]0.86686543919043[/C][/ROW]
[ROW][C]93[/C][C]0.112076019107943[/C][C]0.224152038215887[/C][C]0.887923980892056[/C][/ROW]
[ROW][C]94[/C][C]0.0911844811581383[/C][C]0.182368962316277[/C][C]0.908815518841862[/C][/ROW]
[ROW][C]95[/C][C]0.0732880572252197[/C][C]0.146576114450439[/C][C]0.92671194277478[/C][/ROW]
[ROW][C]96[/C][C]0.0595972326385815[/C][C]0.119194465277163[/C][C]0.940402767361418[/C][/ROW]
[ROW][C]97[/C][C]0.114064863195602[/C][C]0.228129726391204[/C][C]0.885935136804398[/C][/ROW]
[ROW][C]98[/C][C]0.104649658564899[/C][C]0.209299317129798[/C][C]0.895350341435101[/C][/ROW]
[ROW][C]99[/C][C]0.0903641436849533[/C][C]0.180728287369907[/C][C]0.909635856315047[/C][/ROW]
[ROW][C]100[/C][C]0.0787848635920172[/C][C]0.157569727184034[/C][C]0.921215136407983[/C][/ROW]
[ROW][C]101[/C][C]0.0642226815171045[/C][C]0.128445363034209[/C][C]0.935777318482895[/C][/ROW]
[ROW][C]102[/C][C]0.0539830161188285[/C][C]0.107966032237657[/C][C]0.946016983881172[/C][/ROW]
[ROW][C]103[/C][C]0.0448792234169341[/C][C]0.0897584468338682[/C][C]0.955120776583066[/C][/ROW]
[ROW][C]104[/C][C]0.0624390396941538[/C][C]0.124878079388308[/C][C]0.937560960305846[/C][/ROW]
[ROW][C]105[/C][C]0.0570680364828296[/C][C]0.114136072965659[/C][C]0.94293196351717[/C][/ROW]
[ROW][C]106[/C][C]0.113463365461395[/C][C]0.226926730922789[/C][C]0.886536634538605[/C][/ROW]
[ROW][C]107[/C][C]0.141597999846768[/C][C]0.283195999693536[/C][C]0.858402000153232[/C][/ROW]
[ROW][C]108[/C][C]0.234402979314345[/C][C]0.468805958628689[/C][C]0.765597020685655[/C][/ROW]
[ROW][C]109[/C][C]0.262099585600219[/C][C]0.524199171200439[/C][C]0.73790041439978[/C][/ROW]
[ROW][C]110[/C][C]0.230140180982935[/C][C]0.46028036196587[/C][C]0.769859819017065[/C][/ROW]
[ROW][C]111[/C][C]0.193406395319838[/C][C]0.386812790639677[/C][C]0.806593604680162[/C][/ROW]
[ROW][C]112[/C][C]0.277215998298629[/C][C]0.554431996597258[/C][C]0.722784001701371[/C][/ROW]
[ROW][C]113[/C][C]0.279675445981658[/C][C]0.559350891963315[/C][C]0.720324554018342[/C][/ROW]
[ROW][C]114[/C][C]0.237544908314449[/C][C]0.475089816628897[/C][C]0.762455091685551[/C][/ROW]
[ROW][C]115[/C][C]0.224906736563864[/C][C]0.449813473127727[/C][C]0.775093263436136[/C][/ROW]
[ROW][C]116[/C][C]0.187272895543949[/C][C]0.374545791087898[/C][C]0.812727104456051[/C][/ROW]
[ROW][C]117[/C][C]0.154669916550935[/C][C]0.30933983310187[/C][C]0.845330083449065[/C][/ROW]
[ROW][C]118[/C][C]0.145717375151959[/C][C]0.291434750303917[/C][C]0.854282624848041[/C][/ROW]
[ROW][C]119[/C][C]0.117643857180124[/C][C]0.235287714360247[/C][C]0.882356142819877[/C][/ROW]
[ROW][C]120[/C][C]0.156235625930721[/C][C]0.312471251861443[/C][C]0.843764374069279[/C][/ROW]
[ROW][C]121[/C][C]0.171214170395716[/C][C]0.342428340791431[/C][C]0.828785829604284[/C][/ROW]
[ROW][C]122[/C][C]0.153878187753764[/C][C]0.307756375507528[/C][C]0.846121812246236[/C][/ROW]
[ROW][C]123[/C][C]0.122333865406632[/C][C]0.244667730813264[/C][C]0.877666134593368[/C][/ROW]
[ROW][C]124[/C][C]0.096645169493269[/C][C]0.193290338986538[/C][C]0.903354830506731[/C][/ROW]
[ROW][C]125[/C][C]0.0771292258168415[/C][C]0.154258451633683[/C][C]0.922870774183159[/C][/ROW]
[ROW][C]126[/C][C]0.0606389121879251[/C][C]0.121277824375850[/C][C]0.939361087812075[/C][/ROW]
[ROW][C]127[/C][C]0.0845615340492701[/C][C]0.169123068098540[/C][C]0.91543846595073[/C][/ROW]
[ROW][C]128[/C][C]0.0720864360321491[/C][C]0.144172872064298[/C][C]0.927913563967851[/C][/ROW]
[ROW][C]129[/C][C]0.0608930660556173[/C][C]0.121786132111235[/C][C]0.939106933944383[/C][/ROW]
[ROW][C]130[/C][C]0.083855649239921[/C][C]0.167711298479842[/C][C]0.91614435076008[/C][/ROW]
[ROW][C]131[/C][C]0.118428590644808[/C][C]0.236857181289616[/C][C]0.881571409355192[/C][/ROW]
[ROW][C]132[/C][C]0.089330679254932[/C][C]0.178661358509864[/C][C]0.910669320745068[/C][/ROW]
[ROW][C]133[/C][C]0.0716991503697098[/C][C]0.143398300739420[/C][C]0.92830084963029[/C][/ROW]
[ROW][C]134[/C][C]0.0520255359935979[/C][C]0.104051071987196[/C][C]0.947974464006402[/C][/ROW]
[ROW][C]135[/C][C]0.0359390360193623[/C][C]0.0718780720387246[/C][C]0.964060963980638[/C][/ROW]
[ROW][C]136[/C][C]0.041762985975061[/C][C]0.083525971950122[/C][C]0.95823701402494[/C][/ROW]
[ROW][C]137[/C][C]0.0313787681916426[/C][C]0.0627575363832852[/C][C]0.968621231808357[/C][/ROW]
[ROW][C]138[/C][C]0.0264089467474253[/C][C]0.0528178934948506[/C][C]0.973591053252575[/C][/ROW]
[ROW][C]139[/C][C]0.0306369485889353[/C][C]0.0612738971778706[/C][C]0.969363051411065[/C][/ROW]
[ROW][C]140[/C][C]0.0253797316865256[/C][C]0.0507594633730512[/C][C]0.974620268313474[/C][/ROW]
[ROW][C]141[/C][C]0.0193201778691544[/C][C]0.0386403557383088[/C][C]0.980679822130846[/C][/ROW]
[ROW][C]142[/C][C]0.0118689031077122[/C][C]0.0237378062154245[/C][C]0.988131096892288[/C][/ROW]
[ROW][C]143[/C][C]0.0426730701360779[/C][C]0.0853461402721558[/C][C]0.957326929863922[/C][/ROW]
[ROW][C]144[/C][C]0.0279665082245043[/C][C]0.0559330164490085[/C][C]0.972033491775496[/C][/ROW]
[ROW][C]145[/C][C]0.0481484327466246[/C][C]0.0962968654932491[/C][C]0.951851567253375[/C][/ROW]
[ROW][C]146[/C][C]0.0334312446114262[/C][C]0.0668624892228524[/C][C]0.966568755388574[/C][/ROW]
[ROW][C]147[/C][C]0.0334611985729549[/C][C]0.0669223971459098[/C][C]0.966538801427045[/C][/ROW]
[ROW][C]148[/C][C]0.0289184415192506[/C][C]0.0578368830385012[/C][C]0.97108155848075[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98397&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98397&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.4569425415262570.9138850830525140.543057458473743
90.581608144669770.836783710660460.41839185533023
100.4356314601843820.8712629203687650.564368539815618
110.5707932408418740.8584135183162520.429206759158126
120.769143892972110.4617122140557800.230856107027890
130.850263843176450.2994723136471010.149736156823551
140.7870574733148910.4258850533702180.212942526685109
150.8099990831076890.3800018337846220.190000916892311
160.7484016101014180.5031967797971650.251598389898582
170.7862863479790220.4274273040419560.213713652020978
180.7258965763396370.5482068473207270.274103423660363
190.6598457406054590.6803085187890820.340154259394541
200.6176058744728050.764788251054390.382394125527195
210.9324951936242310.1350096127515380.067504806375769
220.9277161053178190.1445677893643630.0722838946821814
230.9073711623420880.1852576753158240.0926288376579119
240.8780129298005980.2439741403988030.121987070199402
250.8474142922593570.3051714154812850.152585707740643
260.8150414142021850.3699171715956310.184958585797815
270.8356400833139210.3287198333721580.164359916686079
280.811676977462940.376646045074120.18832302253706
290.7705752250551050.458849549889790.229424774944895
300.779527458627270.4409450827454590.220472541372730
310.7446011236254450.510797752749110.255398876374555
320.7952865095481770.4094269809036470.204713490451823
330.792953561618860.4140928767622810.207046438381140
340.7705341080734380.4589317838531240.229465891926562
350.7332804789099270.5334390421801460.266719521090073
360.6900869781233170.6198260437533660.309913021876683
370.6561258956241630.6877482087516740.343874104375837
380.6603923930544050.679215213891190.339607606945595
390.6192231820812450.761553635837510.380776817918755
400.5746023469772310.8507953060455370.425397653022769
410.5345291235434230.9309417529131540.465470876456577
420.5715137879932150.856972424013570.428486212006785
430.5577464606888320.8845070786223360.442253539311168
440.5135295314933480.9729409370133040.486470468506652
450.5761286446431410.8477427107137180.423871355356859
460.5762541304800470.8474917390399060.423745869519953
470.60354944147390.79290111705220.3964505585261
480.5839149096481030.8321701807037950.416085090351897
490.5361285059317990.9277429881364020.463871494068201
500.4899279419712740.9798558839425480.510072058028726
510.5729243301683710.8541513396632580.427075669831629
520.6213087216797240.7573825566405510.378691278320276
530.5803458030421440.8393083939157130.419654196957856
540.5434046011169880.9131907977660240.456595398883012
550.5050766587352070.9898466825295860.494923341264793
560.4643073160879520.9286146321759050.535692683912048
570.4617893652553450.923578730510690.538210634744655
580.4325376901342310.8650753802684630.567462309865769
590.7246774426695720.5506451146608560.275322557330428
600.7933903702370080.4132192595259830.206609629762992
610.7833851823285590.4332296353428820.216614817671441
620.7505992577007450.4988014845985090.249400742299255
630.7121615106453750.575676978709250.287838489354625
640.6744290555151290.6511418889697430.325570944484871
650.6653338715133260.6693322569733490.334666128486674
660.6347315475496650.730536904900670.365268452450335
670.6167390012163880.7665219975672240.383260998783612
680.5772804705411030.8454390589177950.422719529458897
690.5345231435683310.9309537128633380.465476856431669
700.4889531970259020.9779063940518050.511046802974098
710.4751262062375420.9502524124750830.524873793762458
720.4363712467742930.8727424935485860.563628753225707
730.390847361434080.781694722868160.60915263856592
740.347629904742390.695259809484780.65237009525761
750.3271872499029910.6543744998059830.672812750097008
760.2962822661031940.5925645322063880.703717733896806
770.2584583662931450.5169167325862910.741541633706855
780.2267687116495510.4535374232991030.773231288350449
790.2463016887388550.492603377477710.753698311261145
800.2732238152403990.5464476304807980.726776184759601
810.2379084078413890.4758168156827790.76209159215861
820.2072125441119310.4144250882238630.792787455888069
830.1754490533732340.3508981067464690.824550946626766
840.1494671392772250.298934278554450.850532860722775
850.2032271935677750.4064543871355510.796772806432225
860.1981177521003600.3962355042007190.80188224789964
870.1670584008590330.3341168017180660.832941599140967
880.1449154741822610.2898309483645220.855084525817739
890.1882847430758600.3765694861517190.81171525692414
900.1643946535766990.3287893071533990.8356053464233
910.1395424319352280.2790848638704560.860457568064772
920.1331345608095710.2662691216191420.86686543919043
930.1120760191079430.2241520382158870.887923980892056
940.09118448115813830.1823689623162770.908815518841862
950.07328805722521970.1465761144504390.92671194277478
960.05959723263858150.1191944652771630.940402767361418
970.1140648631956020.2281297263912040.885935136804398
980.1046496585648990.2092993171297980.895350341435101
990.09036414368495330.1807282873699070.909635856315047
1000.07878486359201720.1575697271840340.921215136407983
1010.06422268151710450.1284453630342090.935777318482895
1020.05398301611882850.1079660322376570.946016983881172
1030.04487922341693410.08975844683386820.955120776583066
1040.06243903969415380.1248780793883080.937560960305846
1050.05706803648282960.1141360729656590.94293196351717
1060.1134633654613950.2269267309227890.886536634538605
1070.1415979998467680.2831959996935360.858402000153232
1080.2344029793143450.4688059586286890.765597020685655
1090.2620995856002190.5241991712004390.73790041439978
1100.2301401809829350.460280361965870.769859819017065
1110.1934063953198380.3868127906396770.806593604680162
1120.2772159982986290.5544319965972580.722784001701371
1130.2796754459816580.5593508919633150.720324554018342
1140.2375449083144490.4750898166288970.762455091685551
1150.2249067365638640.4498134731277270.775093263436136
1160.1872728955439490.3745457910878980.812727104456051
1170.1546699165509350.309339833101870.845330083449065
1180.1457173751519590.2914347503039170.854282624848041
1190.1176438571801240.2352877143602470.882356142819877
1200.1562356259307210.3124712518614430.843764374069279
1210.1712141703957160.3424283407914310.828785829604284
1220.1538781877537640.3077563755075280.846121812246236
1230.1223338654066320.2446677308132640.877666134593368
1240.0966451694932690.1932903389865380.903354830506731
1250.07712922581684150.1542584516336830.922870774183159
1260.06063891218792510.1212778243758500.939361087812075
1270.08456153404927010.1691230680985400.91543846595073
1280.07208643603214910.1441728720642980.927913563967851
1290.06089306605561730.1217861321112350.939106933944383
1300.0838556492399210.1677112984798420.91614435076008
1310.1184285906448080.2368571812896160.881571409355192
1320.0893306792549320.1786613585098640.910669320745068
1330.07169915036970980.1433983007394200.92830084963029
1340.05202553599359790.1040510719871960.947974464006402
1350.03593903601936230.07187807203872460.964060963980638
1360.0417629859750610.0835259719501220.95823701402494
1370.03137876819164260.06275753638328520.968621231808357
1380.02640894674742530.05281789349485060.973591053252575
1390.03063694858893530.06127389717787060.969363051411065
1400.02537973168652560.05075946337305120.974620268313474
1410.01932017786915440.03864035573830880.980679822130846
1420.01186890310771220.02373780621542450.988131096892288
1430.04267307013607790.08534614027215580.957326929863922
1440.02796650822450430.05593301644900850.972033491775496
1450.04814843274662460.09629686549324910.951851567253375
1460.03343124461142620.06686248922285240.966568755388574
1470.03346119857295490.06692239714590980.966538801427045
1480.02891844151925060.05783688303850120.97108155848075






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0141843971631206OK
10% type I error level150.106382978723404NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 2 & 0.0141843971631206 & OK \tabularnewline
10% type I error level & 15 & 0.106382978723404 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98397&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]2[/C][C]0.0141843971631206[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]15[/C][C]0.106382978723404[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98397&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98397&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0141843971631206OK
10% type I error level150.106382978723404NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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