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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 22 Nov 2011 12:57:16 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/22/t1321984676wj259x9xdcn1wpv.htm/, Retrieved Wed, 24 Apr 2024 07:10:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146344, Retrieved Wed, 24 Apr 2024 07:10:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact74

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:14:55] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [Deterministic tre...] [2011-11-22 17:57:16] [e1c4030d3eb0ab0fcc7a7b48aeaac474] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9	13	38	14
9	16	32	18
9	19	35	11
9	15	33	12
9	14	37	16
9	13	29	18
9	19	31	14
9	15	36	14
9	14	35	15
9	15	38	15
9	16	31	17
9	16	34	19
9	16	35	10
9	16	38	16
9	17	37	18
9	15	33	14
9	15	32	14
9	20	38	17
9	18	38	14
9	16	32	16
9	16	33	18
9	16	31	11
9	19	38	14
9	16	39	12
9	17	32	17
9	17	32	9
9	16	35	16
9	15	37	14
9	16	33	15
9	14	33	11
9	15	31	16
9	12	32	13
9	14	31	17
9	16	37	15
9	14	30	14
9	10	33	16
9	10	31	9
9	14	33	15
9	16	31	17
9	16	33	13
9	16	32	15
9	14	33	16
9	20	32	16
9	14	33	12
9	14	28	15
9	11	35	11
9	14	39	15
9	15	34	15
9	16	38	17
9	14	32	13
9	16	38	16
9	14	30	14
9	12	33	11
9	16	38	12
9	9	32	12
9	14	35	15
9	16	34	16
9	16	34	15
9	15	36	12
9	16	34	12
9	12	28	8
9	16	34	13
9	16	35	11
9	14	35	14
9	16	31	15
9	17	34	9
10	18	37	10
10	18	35	11
10	12	27	12
10	16	40	15
10	10	37	15
10	14	36	14
10	18	38	16
10	18	39	15
10	16	41	15
10	17	27	13
10	16	30	12
10	16	37	17
10	13	31	13
10	16	31	15
10	16	27	13
10	16	36	15
10	15	37	15
10	15	33	16
10	16	34	15
10	14	31	14
10	16	39	15
10	16	34	14
10	15	32	13
10	12	33	7
10	17	36	17
10	16	32	13
10	15	41	15
10	13	28	14
10	16	30	13
10	16	36	16
10	16	35	12
10	16	31	14
10	14	34	17
10	16	36	15
10	16	36	17
10	20	35	12
10	15	37	16
10	16	28	11
10	13	39	15
10	17	32	9
10	16	35	16
10	16	39	15
10	12	35	10
10	16	42	10
10	16	34	15
10	17	33	11
10	13	41	13
10	12	33	14
10	18	34	18
10	14	32	16
10	14	40	14
10	13	40	14
10	16	35	14
10	13	36	14
10	16	37	12
10	13	27	14
10	16	39	15
10	15	38	15
10	16	31	15
10	15	33	13
10	17	32	17
10	15	39	17
10	12	36	19
10	16	33	15
10	10	33	13
10	16	32	9
10	12	37	15
10	14	30	15
10	15	38	15
10	13	29	16
10	15	22	11
10	11	35	14
10	12	35	11
10	11	34	15
10	16	35	13
10	15	34	15
10	17	37	16
10	16	35	14
10	10	23	15
10	18	31	16
10	13	27	16
10	16	36	11
10	13	31	12
10	10	32	9
10	15	39	16
10	16	37	13
10	16	38	16
10	14	39	12
10	10	31	13
10	17	32	13
10	13	37	14
10	15	36	19
10	16	32	13
10	12	38	12
10	13	36	13
11	13	26	10
11	12	26	14
11	17	33	16
11	15	39	10
11	10	30	11
11	14	33	14
11	11	25	12
11	13	38	9
11	16	37	9
11	12	31	11
11	16	37	16
11	12	35	9
11	9	25	13
11	12	28	16
11	15	35	13
11	12	33	9
11	12	30	12
11	14	31	16
11	12	37	11
11	16	36	14
11	11	30	13
11	19	36	15
11	15	32	14
11	8	28	16
11	16	36	13
11	17	34	14
11	12	31	15
11	11	28	13
11	11	36	11
11	14	36	11
11	16	40	14
11	12	33	15
11	16	37	11
11	13	32	15
11	15	38	12
11	16	31	14
11	16	37	14
11	14	33	8
11	16	32	13
11	16	30	9
11	14	30	15
11	11	31	17
11	12	32	13
11	15	34	15
11	15	36	15
11	16	37	14
11	16	36	16
11	11	33	13
11	15	33	16
11	12	33	9
11	12	44	16
11	15	39	11
11	15	32	10
11	16	35	11
11	14	25	15
11	17	35	17
11	14	34	14
11	13	35	8
11	15	39	15
11	13	33	11
11	14	36	16
11	15	32	10
11	12	32	15
11	13	36	9
11	8	36	16
11	14	32	19
11	14	34	12
11	11	33	8
11	12	35	11
11	13	30	14
11	10	38	9
11	16	34	15
11	18	33	13
11	13	32	16
11	11	31	11
11	4	30	12
11	13	27	13
11	16	31	10
11	10	30	11
11	12	32	12
11	12	35	8
11	10	28	12
11	13	33	12
11	15	31	15
11	12	35	11
11	14	35	13
11	10	32	14
11	12	21	10
11	12	20	12
11	11	34	15
11	10	32	13
11	12	34	13
11	16	32	13
11	12	33	12
11	14	33	12
11	16	37	9
11	14	32	9
11	13	34	15
11	4	30	10
11	15	30	14
11	11	38	15
11	11	36	7
11	14	32	14

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146344&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146344&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146344&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 time11 seconds
R Server'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Doorzettingsvermogen[t] = + 1.80788254675192 + 0.815531941461644month[t] + 0.14900725343015Zelfstandig[t] + 0.119922351559431Stressbestendig[t] -0.0184319183358612t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Doorzettingsvermogen[t] =  +  1.80788254675192 +  0.815531941461644month[t] +  0.14900725343015Zelfstandig[t] +  0.119922351559431Stressbestendig[t] -0.0184319183358612t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146344&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Doorzettingsvermogen[t] =  +  1.80788254675192 +  0.815531941461644month[t] +  0.14900725343015Zelfstandig[t] +  0.119922351559431Stressbestendig[t] -0.0184319183358612t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146344&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146344&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
Doorzettingsvermogen[t] = + 1.80788254675192 + 0.815531941461644month[t] + 0.14900725343015Zelfstandig[t] + 0.119922351559431Stressbestendig[t] -0.0184319183358612t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.807882546751924.4713680.40430.6863080.343154
month0.8155319414616440.4804081.69760.0907880.045394
Zelfstandig0.149007253430150.0366064.07066.2e-053.1e-05
Stressbestendig0.1199223515594310.0558072.14890.0325720.016286
t-0.01843191833586120.005001-3.68570.0002780.000139

\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) & 1.80788254675192 & 4.471368 & 0.4043 & 0.686308 & 0.343154 \tabularnewline
month & 0.815531941461644 & 0.480408 & 1.6976 & 0.090788 & 0.045394 \tabularnewline
Zelfstandig & 0.14900725343015 & 0.036606 & 4.0706 & 6.2e-05 & 3.1e-05 \tabularnewline
Stressbestendig & 0.119922351559431 & 0.055807 & 2.1489 & 0.032572 & 0.016286 \tabularnewline
t & -0.0184319183358612 & 0.005001 & -3.6857 & 0.000278 & 0.000139 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146344&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]1.80788254675192[/C][C]4.471368[/C][C]0.4043[/C][C]0.686308[/C][C]0.343154[/C][/ROW]
[ROW][C]month[/C][C]0.815531941461644[/C][C]0.480408[/C][C]1.6976[/C][C]0.090788[/C][C]0.045394[/C][/ROW]
[ROW][C]Zelfstandig[/C][C]0.14900725343015[/C][C]0.036606[/C][C]4.0706[/C][C]6.2e-05[/C][C]3.1e-05[/C][/ROW]
[ROW][C]Stressbestendig[/C][C]0.119922351559431[/C][C]0.055807[/C][C]2.1489[/C][C]0.032572[/C][C]0.016286[/C][/ROW]
[ROW][C]t[/C][C]-0.0184319183358612[/C][C]0.005001[/C][C]-3.6857[/C][C]0.000278[/C][C]0.000139[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146344&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146344&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)1.807882546751924.4713680.40430.6863080.343154
month0.8155319414616440.4804081.69760.0907880.045394
Zelfstandig0.149007253430150.0366064.07066.2e-053.1e-05
Stressbestendig0.1199223515594310.0558072.14890.0325720.016286
t-0.01843191833586120.005001-3.68570.0002780.000139






Multiple Linear Regression - Regression Statistics
Multiple R0.478873129634814
R-squared0.229319474286241
Adjusted R-squared0.217417072344716
F-TEST (value)19.2666552022738
F-TEST (DF numerator)4
F-TEST (DF denominator)259
p-value6.87228052242972e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.17264456512217
Sum Squared Residuals1222.57956124592

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.478873129634814 \tabularnewline
R-squared & 0.229319474286241 \tabularnewline
Adjusted R-squared & 0.217417072344716 \tabularnewline
F-TEST (value) & 19.2666552022738 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 259 \tabularnewline
p-value & 6.87228052242972e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.17264456512217 \tabularnewline
Sum Squared Residuals & 1222.57956124592 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146344&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.478873129634814[/C][/ROW]
[ROW][C]R-squared[/C][C]0.229319474286241[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.217417072344716[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]19.2666552022738[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]259[/C][/ROW]
[ROW][C]p-value[/C][C]6.87228052242972e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.17264456512217[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1222.57956124592[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146344&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146344&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 R0.478873129634814
R-squared0.229319474286241
Adjusted R-squared0.217417072344716
F-TEST (value)19.2666552022738
F-TEST (DF numerator)4
F-TEST (DF denominator)259
p-value6.87228052242972e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.17264456512217
Sum Squared Residuals1222.57956124592






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.4704266537486-3.4704266537486
21616.0376406210696-0.0376406210695608
31915.62677400210813.37322599789186
41515.4302499284714-0.430249928471404
51416.4875364300939-2.48753643009387
61315.5168911874357-2.51689118743567
71915.31678436972243.68321563027762
81516.0433887185373-1.04338871853727
91415.9958718983307-1.99587189833069
101516.4244617402853-1.42446174028528
111615.60282375105720.397176248942768
121616.2712582961307-0.271258296130683
131615.32253246719010.677467532809909
141616.4706564185013-0.470656418501268
151716.54306194985410.456938050145881
161515.4489116115599-0.448911611559932
171515.2814724397939-0.281472439793921
182016.51685109671733.48314890328274
191816.13865212370311.8613478762969
201615.46602138790520.5339786120948
211615.83644142611840.163558573881649
221614.68053854000621.31946145999383
231916.06492445035972.93507554964034
241615.95565508233510.0443449176649187
251715.49378414778531.50621585221468
261714.5159734169742.48402658302599
271615.78401971984460.215980280155378
281515.8237576052502-0.823757605250198
291615.32921902475320.670780975246832
301414.8310977001796-0.831097700179582
311515.1142630327806-0.114263032780576
321214.8850713131966-2.88507131319657
331415.1973215476683-1.19732154766829
341615.83308844679450.166911553205537
351414.6516834028881-0.651683402888119
361015.3201179479616-5.32011794796157
371014.1642150618494-4.16421506184939
381415.1633317597304-1.16333175973042
391615.08673003765310.913269962346882
401614.88662321993981.11337678006017
411614.95902875129271.04097124870732
421415.2095264379464-1.2095264379464
432015.04208726618044.9579127338196
441414.692973195037-0.692973195036956
451414.2892720642286-0.289272064228637
461114.8342015136661-3.8342015136661
471415.8914880152886-1.89148801528857
481515.128019829802-0.128019829801955
491615.94546162830560.0545383716944426
501414.5532967831511-0.55329678315107
511615.78867544007440.211324559925596
521414.3383407911785-0.338340791178478
531214.4071635784548-2.40716357845477
541615.25369027882910.746309721170905
55914.3412148399123-5.34121483991233
561415.1295717365452-1.12957173654522
571615.08205491633860.917945083661365
581614.94370064644331.05629935355666
591514.86351618028950.136483819710511
601614.54706975509331.45293024490667
611213.1549049099388-1.15490490993884
621614.6301282699811.36987173001896
631614.52085890195651.47914109804354
641414.8621940382989-0.862194038298895
651614.36765545780191.63234454219814
661714.07671119039992.92328880960013
671815.44075532537552.55924467462447
681815.24423125173882.7557687482612
691214.1536636575212-2.15366365752117
701616.4320930884556-0.432093088455555
711015.9666394098292-5.96663940982924
721415.6792778865038-1.6792778865038
731816.19870517814711.8012948218529
741816.2093581616821.79064183831804
751616.4889407502064-0.488940750206399
761714.14456258072962.85543741927043
771614.45323007112471.54676992887527
781616.0774606845971-0.077460684597077
791314.6852958394426-1.68529583944259
801614.90670862422561.09329137577441
811614.05240298905031.94759701094973
821615.61488105470460.385118945295381
831515.7454563897989-0.745456389798908
841515.2509178093019-0.250917809301878
851615.26157079283670.738429207163265
861414.676194762651-0.676194762650992
871615.96974322331580.0302567766842358
881615.08635268626970.91364731373028
891514.64998390951410.350016090485872
901214.0610251352518-2.06102513525183
911715.68883849280071.31116150719927
921614.59468815450651.40531184549346
931516.1571662201609-1.1571662201609
941314.0817176556737-1.08171765567365
951614.24137789263871.75862210736134
961615.4767565495620.523243450438006
971614.82962797155831.17037202844174
981614.45501174262071.54498825737934
991415.2433686392535-1.24336863925354
1001615.28310652465910.716893475340882
1011615.50451930944210.495480690557881
1022014.7374683798795.26253162012105
1031515.4967403746411-0.496740374641116
1041613.53763141763672.46236858236325
1051315.6379686932703-2.63796869327026
1061713.85695189156683.14304810843324
1071615.12499819443740.87500180556263
1081615.58267293826270.417327061737321
1091214.3686002484091-2.36860024840906
1101615.39321910408430.606780895915749
1111614.78234091610431.21765908389566
1121714.13521233810062.86478766189939
1131315.5486831503248-2.54868315032481
1141214.4581155561072-2.45811555610718
1151815.06838029743922.93161970256081
1161414.5120891691242-0.512089169124169
1171415.4458705751106-1.44587057511065
1181315.4274386567748-2.42743865677479
1191614.66397047128821.33602952871183
1201314.7945458063825-1.79454580638246
1211614.68527643835791.31472356164211
1221313.4166166888394-0.416616688839388
1231615.30619416322480.693805836775239
1241515.1387549914588-0.138754991458749
1251614.07727229911181.92272770088816
1261514.11701018451740.882989815482586
1271714.42926041898912.57073958101087
1281515.4538792746643-0.453879274664317
1291215.2282702991569-3.22827029915687
1301614.28312721429281.71687278570717
1311014.0248505928381-4.02485059283811
1321613.37772201483442.62227798516563
1331214.8238604730058-2.82386047300585
1341413.76237778065890.237622219341064
1351514.93600388976430.0639961102357237
1361313.6964290421165-0.696429042116494
1371512.03533459197242.96466540802757
1381114.3137640229068-3.31376402290681
1391213.9355650498927-1.93556504989266
1401114.2478152843644-3.24781528436437
1411614.13854591633981.8614540836602
1421514.21095144769260.789048552307353
1431714.75946364120672.24053635879333
1441614.20317251289161.79682748710836
1451012.5165759049534-2.51657590495341
1461813.81012436561824.18987563438182
1471313.1956634335617-0.195663433561721
1481613.91868503830012.08131496169994
1491313.2751392043729-0.275139204372874
1501013.0459474847889-3.04594748478887
1511514.91002280138010.0899771986199214
1521614.23380932150561.76619067849438
1531614.72415171127821.27584828872179
1541414.3750376401348-0.37503764013477
1551013.2844700459171-3.28447004591714
1561713.41504538101143.58495461898857
1571314.2615720813857-1.26157208138575
1581514.69374466741690.306255332583107
1591613.35974962600382.64025037399616
1601214.1154388766895-2.11543887668945
1611313.9189148030527-0.918914803052722
1621312.86617523719870.13382476280129
1631213.3274327251006-1.32743272510057
1641714.59189628389462.40810371610537
1651514.74797377678310.252026223216921
1661013.5083989291353-3.5083989291353
1671414.2967558257682-0.29675582576818
1681112.8464211768723-1.84642117687225
1691314.4053164984501-1.40531649845005
1701614.2378773266841.76212267331596
1711213.5652465908861-1.56524659088614
1721615.04046995092830.959530049071663
1731213.8845670648162-1.88456706481616
174912.8557520184165-3.85575201841652
1751213.6441089150494-1.6441089150494
1761514.30896071604630.691039283953701
1771213.5128248846124-1.51282488461241
1781213.4071382606644-1.40713826066439
1791414.0174030019964-0.0174030019964077
1801214.2934028464443-2.29340284644429
1811614.48573072935661.51426927064343
1821113.4533329388804-2.45333293888038
1831914.56878924424434.43121075575572
1841513.83440596062841.16559403937161
185813.4597897316908-5.45978973169079
1861614.27364878611781.72635121388216
1871714.07712471248112.92287528751889
1881213.7315933854142-1.73159338541423
1891113.0262950036691-2.02629500366905
1901113.9600764096555-2.96007640965553
1911413.94164449131970.0583555086803317
1921614.87900864138271.1209913586173
1931213.9374483005952-1.93744830059522
1941614.03535598974221.96464401025776
1951313.7515772104933-0.751577210493348
1961514.26742175806010.732578241939906
1971613.4457837688322.55421623116796
1981614.32139537107711.67860462892292
1991412.9874003296641.01259967033597
2001613.41957291569522.58042708430482
2011612.62343708426133.37656291573871
2021413.3245392752820.675460724717981
2031113.6949593134952-2.69495931349517
2041213.3458452423517-1.34584524235173
2051513.8652725339951.13472746600496
2061514.14485512251950.855144877480525
2071614.15550810605431.84449189394567
2081614.22791363740721.77208636259282
2091113.4026929041026-2.40269290410258
2101513.7440280404451.25597195955499
2111212.8861396611931-0.886139661193131
2121215.3462439915049-3.34624399150494
2131513.98316404822121.01683595177883
2141512.80175900431482.19824099568517
2151613.35027119782882.64972880217115
2161412.32145615142921.67854384857079
2171714.03294147051372.96705852948629
2181413.50573524406940.494264755930591
2191312.91677646980710.0832235301928893
2201514.33383002610790.666169973892131
2211312.94166518095340.0583348190466184
2221413.96986678070510.0301332192948726
2231512.63587173929212.36412826070792
2241213.2170515787534-1.21705157875337
2251313.0751145647815-0.075114564781525
226813.8961391073617-5.89613910736168
2271413.64144522998350.358554770016486
2281413.08157135759190.918428642408065
2291112.4344427795882-1.4344427795882
2301213.0737924227909-1.07379242279093
2311312.67009129198260.329908708017387
2321013.2441056432908-3.2441056432908
2331613.34917882059092.65082117940908
2341812.9418949457065.05810505429395
2351313.1342228286183-0.134222828618331
2361112.3671718990552-1.36717189905516
237412.3196550788486-8.31965507884858
2381311.97412375178171.0258762482183
2391612.19195379248813.80804620751185
2401012.1444369722816-2.14443697228157
2411212.5439419123654-0.543941912365439
2421212.4928423480823-0.492842348082303
2431011.9110490619731-1.91104906197312
2441312.6376534107880.362346589211995
2451512.68097404027012.31902595972986
2461212.7788817294172-0.778881729417152
2471413.00029451420020.999705485799846
2481012.6547631871333-2.65476318713327
2491210.5175620748281.48243792517197
2501210.58996760618091.41003239381911
2511113.0174042905454-2.01740429054542
2521012.4611131622304-2.4611131622304
2531212.7406957507548-0.740695750754836
2541612.42424932555873.57575067444133
2551212.4349023090935-0.434902309093532
2561412.41647039075771.58352960924233
2571612.63430043146413.36569956853588
2581411.87083224597752.12916775402249
2591312.86994894385850.130051056141469
260411.6558762540049-7.65587625400491
2611512.11713374190682.88286625809322
2621113.4106822025716-2.41068220257155
2631112.1348569648999-1.13485696489994
2641412.35985249375951.64014750624051

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.4704266537486 & -3.4704266537486 \tabularnewline
2 & 16 & 16.0376406210696 & -0.0376406210695608 \tabularnewline
3 & 19 & 15.6267740021081 & 3.37322599789186 \tabularnewline
4 & 15 & 15.4302499284714 & -0.430249928471404 \tabularnewline
5 & 14 & 16.4875364300939 & -2.48753643009387 \tabularnewline
6 & 13 & 15.5168911874357 & -2.51689118743567 \tabularnewline
7 & 19 & 15.3167843697224 & 3.68321563027762 \tabularnewline
8 & 15 & 16.0433887185373 & -1.04338871853727 \tabularnewline
9 & 14 & 15.9958718983307 & -1.99587189833069 \tabularnewline
10 & 15 & 16.4244617402853 & -1.42446174028528 \tabularnewline
11 & 16 & 15.6028237510572 & 0.397176248942768 \tabularnewline
12 & 16 & 16.2712582961307 & -0.271258296130683 \tabularnewline
13 & 16 & 15.3225324671901 & 0.677467532809909 \tabularnewline
14 & 16 & 16.4706564185013 & -0.470656418501268 \tabularnewline
15 & 17 & 16.5430619498541 & 0.456938050145881 \tabularnewline
16 & 15 & 15.4489116115599 & -0.448911611559932 \tabularnewline
17 & 15 & 15.2814724397939 & -0.281472439793921 \tabularnewline
18 & 20 & 16.5168510967173 & 3.48314890328274 \tabularnewline
19 & 18 & 16.1386521237031 & 1.8613478762969 \tabularnewline
20 & 16 & 15.4660213879052 & 0.5339786120948 \tabularnewline
21 & 16 & 15.8364414261184 & 0.163558573881649 \tabularnewline
22 & 16 & 14.6805385400062 & 1.31946145999383 \tabularnewline
23 & 19 & 16.0649244503597 & 2.93507554964034 \tabularnewline
24 & 16 & 15.9556550823351 & 0.0443449176649187 \tabularnewline
25 & 17 & 15.4937841477853 & 1.50621585221468 \tabularnewline
26 & 17 & 14.515973416974 & 2.48402658302599 \tabularnewline
27 & 16 & 15.7840197198446 & 0.215980280155378 \tabularnewline
28 & 15 & 15.8237576052502 & -0.823757605250198 \tabularnewline
29 & 16 & 15.3292190247532 & 0.670780975246832 \tabularnewline
30 & 14 & 14.8310977001796 & -0.831097700179582 \tabularnewline
31 & 15 & 15.1142630327806 & -0.114263032780576 \tabularnewline
32 & 12 & 14.8850713131966 & -2.88507131319657 \tabularnewline
33 & 14 & 15.1973215476683 & -1.19732154766829 \tabularnewline
34 & 16 & 15.8330884467945 & 0.166911553205537 \tabularnewline
35 & 14 & 14.6516834028881 & -0.651683402888119 \tabularnewline
36 & 10 & 15.3201179479616 & -5.32011794796157 \tabularnewline
37 & 10 & 14.1642150618494 & -4.16421506184939 \tabularnewline
38 & 14 & 15.1633317597304 & -1.16333175973042 \tabularnewline
39 & 16 & 15.0867300376531 & 0.913269962346882 \tabularnewline
40 & 16 & 14.8866232199398 & 1.11337678006017 \tabularnewline
41 & 16 & 14.9590287512927 & 1.04097124870732 \tabularnewline
42 & 14 & 15.2095264379464 & -1.2095264379464 \tabularnewline
43 & 20 & 15.0420872661804 & 4.9579127338196 \tabularnewline
44 & 14 & 14.692973195037 & -0.692973195036956 \tabularnewline
45 & 14 & 14.2892720642286 & -0.289272064228637 \tabularnewline
46 & 11 & 14.8342015136661 & -3.8342015136661 \tabularnewline
47 & 14 & 15.8914880152886 & -1.89148801528857 \tabularnewline
48 & 15 & 15.128019829802 & -0.128019829801955 \tabularnewline
49 & 16 & 15.9454616283056 & 0.0545383716944426 \tabularnewline
50 & 14 & 14.5532967831511 & -0.55329678315107 \tabularnewline
51 & 16 & 15.7886754400744 & 0.211324559925596 \tabularnewline
52 & 14 & 14.3383407911785 & -0.338340791178478 \tabularnewline
53 & 12 & 14.4071635784548 & -2.40716357845477 \tabularnewline
54 & 16 & 15.2536902788291 & 0.746309721170905 \tabularnewline
55 & 9 & 14.3412148399123 & -5.34121483991233 \tabularnewline
56 & 14 & 15.1295717365452 & -1.12957173654522 \tabularnewline
57 & 16 & 15.0820549163386 & 0.917945083661365 \tabularnewline
58 & 16 & 14.9437006464433 & 1.05629935355666 \tabularnewline
59 & 15 & 14.8635161802895 & 0.136483819710511 \tabularnewline
60 & 16 & 14.5470697550933 & 1.45293024490667 \tabularnewline
61 & 12 & 13.1549049099388 & -1.15490490993884 \tabularnewline
62 & 16 & 14.630128269981 & 1.36987173001896 \tabularnewline
63 & 16 & 14.5208589019565 & 1.47914109804354 \tabularnewline
64 & 14 & 14.8621940382989 & -0.862194038298895 \tabularnewline
65 & 16 & 14.3676554578019 & 1.63234454219814 \tabularnewline
66 & 17 & 14.0767111903999 & 2.92328880960013 \tabularnewline
67 & 18 & 15.4407553253755 & 2.55924467462447 \tabularnewline
68 & 18 & 15.2442312517388 & 2.7557687482612 \tabularnewline
69 & 12 & 14.1536636575212 & -2.15366365752117 \tabularnewline
70 & 16 & 16.4320930884556 & -0.432093088455555 \tabularnewline
71 & 10 & 15.9666394098292 & -5.96663940982924 \tabularnewline
72 & 14 & 15.6792778865038 & -1.6792778865038 \tabularnewline
73 & 18 & 16.1987051781471 & 1.8012948218529 \tabularnewline
74 & 18 & 16.209358161682 & 1.79064183831804 \tabularnewline
75 & 16 & 16.4889407502064 & -0.488940750206399 \tabularnewline
76 & 17 & 14.1445625807296 & 2.85543741927043 \tabularnewline
77 & 16 & 14.4532300711247 & 1.54676992887527 \tabularnewline
78 & 16 & 16.0774606845971 & -0.077460684597077 \tabularnewline
79 & 13 & 14.6852958394426 & -1.68529583944259 \tabularnewline
80 & 16 & 14.9067086242256 & 1.09329137577441 \tabularnewline
81 & 16 & 14.0524029890503 & 1.94759701094973 \tabularnewline
82 & 16 & 15.6148810547046 & 0.385118945295381 \tabularnewline
83 & 15 & 15.7454563897989 & -0.745456389798908 \tabularnewline
84 & 15 & 15.2509178093019 & -0.250917809301878 \tabularnewline
85 & 16 & 15.2615707928367 & 0.738429207163265 \tabularnewline
86 & 14 & 14.676194762651 & -0.676194762650992 \tabularnewline
87 & 16 & 15.9697432233158 & 0.0302567766842358 \tabularnewline
88 & 16 & 15.0863526862697 & 0.91364731373028 \tabularnewline
89 & 15 & 14.6499839095141 & 0.350016090485872 \tabularnewline
90 & 12 & 14.0610251352518 & -2.06102513525183 \tabularnewline
91 & 17 & 15.6888384928007 & 1.31116150719927 \tabularnewline
92 & 16 & 14.5946881545065 & 1.40531184549346 \tabularnewline
93 & 15 & 16.1571662201609 & -1.1571662201609 \tabularnewline
94 & 13 & 14.0817176556737 & -1.08171765567365 \tabularnewline
95 & 16 & 14.2413778926387 & 1.75862210736134 \tabularnewline
96 & 16 & 15.476756549562 & 0.523243450438006 \tabularnewline
97 & 16 & 14.8296279715583 & 1.17037202844174 \tabularnewline
98 & 16 & 14.4550117426207 & 1.54498825737934 \tabularnewline
99 & 14 & 15.2433686392535 & -1.24336863925354 \tabularnewline
100 & 16 & 15.2831065246591 & 0.716893475340882 \tabularnewline
101 & 16 & 15.5045193094421 & 0.495480690557881 \tabularnewline
102 & 20 & 14.737468379879 & 5.26253162012105 \tabularnewline
103 & 15 & 15.4967403746411 & -0.496740374641116 \tabularnewline
104 & 16 & 13.5376314176367 & 2.46236858236325 \tabularnewline
105 & 13 & 15.6379686932703 & -2.63796869327026 \tabularnewline
106 & 17 & 13.8569518915668 & 3.14304810843324 \tabularnewline
107 & 16 & 15.1249981944374 & 0.87500180556263 \tabularnewline
108 & 16 & 15.5826729382627 & 0.417327061737321 \tabularnewline
109 & 12 & 14.3686002484091 & -2.36860024840906 \tabularnewline
110 & 16 & 15.3932191040843 & 0.606780895915749 \tabularnewline
111 & 16 & 14.7823409161043 & 1.21765908389566 \tabularnewline
112 & 17 & 14.1352123381006 & 2.86478766189939 \tabularnewline
113 & 13 & 15.5486831503248 & -2.54868315032481 \tabularnewline
114 & 12 & 14.4581155561072 & -2.45811555610718 \tabularnewline
115 & 18 & 15.0683802974392 & 2.93161970256081 \tabularnewline
116 & 14 & 14.5120891691242 & -0.512089169124169 \tabularnewline
117 & 14 & 15.4458705751106 & -1.44587057511065 \tabularnewline
118 & 13 & 15.4274386567748 & -2.42743865677479 \tabularnewline
119 & 16 & 14.6639704712882 & 1.33602952871183 \tabularnewline
120 & 13 & 14.7945458063825 & -1.79454580638246 \tabularnewline
121 & 16 & 14.6852764383579 & 1.31472356164211 \tabularnewline
122 & 13 & 13.4166166888394 & -0.416616688839388 \tabularnewline
123 & 16 & 15.3061941632248 & 0.693805836775239 \tabularnewline
124 & 15 & 15.1387549914588 & -0.138754991458749 \tabularnewline
125 & 16 & 14.0772722991118 & 1.92272770088816 \tabularnewline
126 & 15 & 14.1170101845174 & 0.882989815482586 \tabularnewline
127 & 17 & 14.4292604189891 & 2.57073958101087 \tabularnewline
128 & 15 & 15.4538792746643 & -0.453879274664317 \tabularnewline
129 & 12 & 15.2282702991569 & -3.22827029915687 \tabularnewline
130 & 16 & 14.2831272142928 & 1.71687278570717 \tabularnewline
131 & 10 & 14.0248505928381 & -4.02485059283811 \tabularnewline
132 & 16 & 13.3777220148344 & 2.62227798516563 \tabularnewline
133 & 12 & 14.8238604730058 & -2.82386047300585 \tabularnewline
134 & 14 & 13.7623777806589 & 0.237622219341064 \tabularnewline
135 & 15 & 14.9360038897643 & 0.0639961102357237 \tabularnewline
136 & 13 & 13.6964290421165 & -0.696429042116494 \tabularnewline
137 & 15 & 12.0353345919724 & 2.96466540802757 \tabularnewline
138 & 11 & 14.3137640229068 & -3.31376402290681 \tabularnewline
139 & 12 & 13.9355650498927 & -1.93556504989266 \tabularnewline
140 & 11 & 14.2478152843644 & -3.24781528436437 \tabularnewline
141 & 16 & 14.1385459163398 & 1.8614540836602 \tabularnewline
142 & 15 & 14.2109514476926 & 0.789048552307353 \tabularnewline
143 & 17 & 14.7594636412067 & 2.24053635879333 \tabularnewline
144 & 16 & 14.2031725128916 & 1.79682748710836 \tabularnewline
145 & 10 & 12.5165759049534 & -2.51657590495341 \tabularnewline
146 & 18 & 13.8101243656182 & 4.18987563438182 \tabularnewline
147 & 13 & 13.1956634335617 & -0.195663433561721 \tabularnewline
148 & 16 & 13.9186850383001 & 2.08131496169994 \tabularnewline
149 & 13 & 13.2751392043729 & -0.275139204372874 \tabularnewline
150 & 10 & 13.0459474847889 & -3.04594748478887 \tabularnewline
151 & 15 & 14.9100228013801 & 0.0899771986199214 \tabularnewline
152 & 16 & 14.2338093215056 & 1.76619067849438 \tabularnewline
153 & 16 & 14.7241517112782 & 1.27584828872179 \tabularnewline
154 & 14 & 14.3750376401348 & -0.37503764013477 \tabularnewline
155 & 10 & 13.2844700459171 & -3.28447004591714 \tabularnewline
156 & 17 & 13.4150453810114 & 3.58495461898857 \tabularnewline
157 & 13 & 14.2615720813857 & -1.26157208138575 \tabularnewline
158 & 15 & 14.6937446674169 & 0.306255332583107 \tabularnewline
159 & 16 & 13.3597496260038 & 2.64025037399616 \tabularnewline
160 & 12 & 14.1154388766895 & -2.11543887668945 \tabularnewline
161 & 13 & 13.9189148030527 & -0.918914803052722 \tabularnewline
162 & 13 & 12.8661752371987 & 0.13382476280129 \tabularnewline
163 & 12 & 13.3274327251006 & -1.32743272510057 \tabularnewline
164 & 17 & 14.5918962838946 & 2.40810371610537 \tabularnewline
165 & 15 & 14.7479737767831 & 0.252026223216921 \tabularnewline
166 & 10 & 13.5083989291353 & -3.5083989291353 \tabularnewline
167 & 14 & 14.2967558257682 & -0.29675582576818 \tabularnewline
168 & 11 & 12.8464211768723 & -1.84642117687225 \tabularnewline
169 & 13 & 14.4053164984501 & -1.40531649845005 \tabularnewline
170 & 16 & 14.237877326684 & 1.76212267331596 \tabularnewline
171 & 12 & 13.5652465908861 & -1.56524659088614 \tabularnewline
172 & 16 & 15.0404699509283 & 0.959530049071663 \tabularnewline
173 & 12 & 13.8845670648162 & -1.88456706481616 \tabularnewline
174 & 9 & 12.8557520184165 & -3.85575201841652 \tabularnewline
175 & 12 & 13.6441089150494 & -1.6441089150494 \tabularnewline
176 & 15 & 14.3089607160463 & 0.691039283953701 \tabularnewline
177 & 12 & 13.5128248846124 & -1.51282488461241 \tabularnewline
178 & 12 & 13.4071382606644 & -1.40713826066439 \tabularnewline
179 & 14 & 14.0174030019964 & -0.0174030019964077 \tabularnewline
180 & 12 & 14.2934028464443 & -2.29340284644429 \tabularnewline
181 & 16 & 14.4857307293566 & 1.51426927064343 \tabularnewline
182 & 11 & 13.4533329388804 & -2.45333293888038 \tabularnewline
183 & 19 & 14.5687892442443 & 4.43121075575572 \tabularnewline
184 & 15 & 13.8344059606284 & 1.16559403937161 \tabularnewline
185 & 8 & 13.4597897316908 & -5.45978973169079 \tabularnewline
186 & 16 & 14.2736487861178 & 1.72635121388216 \tabularnewline
187 & 17 & 14.0771247124811 & 2.92287528751889 \tabularnewline
188 & 12 & 13.7315933854142 & -1.73159338541423 \tabularnewline
189 & 11 & 13.0262950036691 & -2.02629500366905 \tabularnewline
190 & 11 & 13.9600764096555 & -2.96007640965553 \tabularnewline
191 & 14 & 13.9416444913197 & 0.0583555086803317 \tabularnewline
192 & 16 & 14.8790086413827 & 1.1209913586173 \tabularnewline
193 & 12 & 13.9374483005952 & -1.93744830059522 \tabularnewline
194 & 16 & 14.0353559897422 & 1.96464401025776 \tabularnewline
195 & 13 & 13.7515772104933 & -0.751577210493348 \tabularnewline
196 & 15 & 14.2674217580601 & 0.732578241939906 \tabularnewline
197 & 16 & 13.445783768832 & 2.55421623116796 \tabularnewline
198 & 16 & 14.3213953710771 & 1.67860462892292 \tabularnewline
199 & 14 & 12.987400329664 & 1.01259967033597 \tabularnewline
200 & 16 & 13.4195729156952 & 2.58042708430482 \tabularnewline
201 & 16 & 12.6234370842613 & 3.37656291573871 \tabularnewline
202 & 14 & 13.324539275282 & 0.675460724717981 \tabularnewline
203 & 11 & 13.6949593134952 & -2.69495931349517 \tabularnewline
204 & 12 & 13.3458452423517 & -1.34584524235173 \tabularnewline
205 & 15 & 13.865272533995 & 1.13472746600496 \tabularnewline
206 & 15 & 14.1448551225195 & 0.855144877480525 \tabularnewline
207 & 16 & 14.1555081060543 & 1.84449189394567 \tabularnewline
208 & 16 & 14.2279136374072 & 1.77208636259282 \tabularnewline
209 & 11 & 13.4026929041026 & -2.40269290410258 \tabularnewline
210 & 15 & 13.744028040445 & 1.25597195955499 \tabularnewline
211 & 12 & 12.8861396611931 & -0.886139661193131 \tabularnewline
212 & 12 & 15.3462439915049 & -3.34624399150494 \tabularnewline
213 & 15 & 13.9831640482212 & 1.01683595177883 \tabularnewline
214 & 15 & 12.8017590043148 & 2.19824099568517 \tabularnewline
215 & 16 & 13.3502711978288 & 2.64972880217115 \tabularnewline
216 & 14 & 12.3214561514292 & 1.67854384857079 \tabularnewline
217 & 17 & 14.0329414705137 & 2.96705852948629 \tabularnewline
218 & 14 & 13.5057352440694 & 0.494264755930591 \tabularnewline
219 & 13 & 12.9167764698071 & 0.0832235301928893 \tabularnewline
220 & 15 & 14.3338300261079 & 0.666169973892131 \tabularnewline
221 & 13 & 12.9416651809534 & 0.0583348190466184 \tabularnewline
222 & 14 & 13.9698667807051 & 0.0301332192948726 \tabularnewline
223 & 15 & 12.6358717392921 & 2.36412826070792 \tabularnewline
224 & 12 & 13.2170515787534 & -1.21705157875337 \tabularnewline
225 & 13 & 13.0751145647815 & -0.075114564781525 \tabularnewline
226 & 8 & 13.8961391073617 & -5.89613910736168 \tabularnewline
227 & 14 & 13.6414452299835 & 0.358554770016486 \tabularnewline
228 & 14 & 13.0815713575919 & 0.918428642408065 \tabularnewline
229 & 11 & 12.4344427795882 & -1.4344427795882 \tabularnewline
230 & 12 & 13.0737924227909 & -1.07379242279093 \tabularnewline
231 & 13 & 12.6700912919826 & 0.329908708017387 \tabularnewline
232 & 10 & 13.2441056432908 & -3.2441056432908 \tabularnewline
233 & 16 & 13.3491788205909 & 2.65082117940908 \tabularnewline
234 & 18 & 12.941894945706 & 5.05810505429395 \tabularnewline
235 & 13 & 13.1342228286183 & -0.134222828618331 \tabularnewline
236 & 11 & 12.3671718990552 & -1.36717189905516 \tabularnewline
237 & 4 & 12.3196550788486 & -8.31965507884858 \tabularnewline
238 & 13 & 11.9741237517817 & 1.0258762482183 \tabularnewline
239 & 16 & 12.1919537924881 & 3.80804620751185 \tabularnewline
240 & 10 & 12.1444369722816 & -2.14443697228157 \tabularnewline
241 & 12 & 12.5439419123654 & -0.543941912365439 \tabularnewline
242 & 12 & 12.4928423480823 & -0.492842348082303 \tabularnewline
243 & 10 & 11.9110490619731 & -1.91104906197312 \tabularnewline
244 & 13 & 12.637653410788 & 0.362346589211995 \tabularnewline
245 & 15 & 12.6809740402701 & 2.31902595972986 \tabularnewline
246 & 12 & 12.7788817294172 & -0.778881729417152 \tabularnewline
247 & 14 & 13.0002945142002 & 0.999705485799846 \tabularnewline
248 & 10 & 12.6547631871333 & -2.65476318713327 \tabularnewline
249 & 12 & 10.517562074828 & 1.48243792517197 \tabularnewline
250 & 12 & 10.5899676061809 & 1.41003239381911 \tabularnewline
251 & 11 & 13.0174042905454 & -2.01740429054542 \tabularnewline
252 & 10 & 12.4611131622304 & -2.4611131622304 \tabularnewline
253 & 12 & 12.7406957507548 & -0.740695750754836 \tabularnewline
254 & 16 & 12.4242493255587 & 3.57575067444133 \tabularnewline
255 & 12 & 12.4349023090935 & -0.434902309093532 \tabularnewline
256 & 14 & 12.4164703907577 & 1.58352960924233 \tabularnewline
257 & 16 & 12.6343004314641 & 3.36569956853588 \tabularnewline
258 & 14 & 11.8708322459775 & 2.12916775402249 \tabularnewline
259 & 13 & 12.8699489438585 & 0.130051056141469 \tabularnewline
260 & 4 & 11.6558762540049 & -7.65587625400491 \tabularnewline
261 & 15 & 12.1171337419068 & 2.88286625809322 \tabularnewline
262 & 11 & 13.4106822025716 & -2.41068220257155 \tabularnewline
263 & 11 & 12.1348569648999 & -1.13485696489994 \tabularnewline
264 & 14 & 12.3598524937595 & 1.64014750624051 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146344&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]13[/C][C]16.4704266537486[/C][C]-3.4704266537486[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]16.0376406210696[/C][C]-0.0376406210695608[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]15.6267740021081[/C][C]3.37322599789186[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]15.4302499284714[/C][C]-0.430249928471404[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]16.4875364300939[/C][C]-2.48753643009387[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.5168911874357[/C][C]-2.51689118743567[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.3167843697224[/C][C]3.68321563027762[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.0433887185373[/C][C]-1.04338871853727[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.9958718983307[/C][C]-1.99587189833069[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]16.4244617402853[/C][C]-1.42446174028528[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.6028237510572[/C][C]0.397176248942768[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.2712582961307[/C][C]-0.271258296130683[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.3225324671901[/C][C]0.677467532809909[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]16.4706564185013[/C][C]-0.470656418501268[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]16.5430619498541[/C][C]0.456938050145881[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.4489116115599[/C][C]-0.448911611559932[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]15.2814724397939[/C][C]-0.281472439793921[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.5168510967173[/C][C]3.48314890328274[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]16.1386521237031[/C][C]1.8613478762969[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.4660213879052[/C][C]0.5339786120948[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.8364414261184[/C][C]0.163558573881649[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.6805385400062[/C][C]1.31946145999383[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.0649244503597[/C][C]2.93507554964034[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]15.9556550823351[/C][C]0.0443449176649187[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.4937841477853[/C][C]1.50621585221468[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]14.515973416974[/C][C]2.48402658302599[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.7840197198446[/C][C]0.215980280155378[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]15.8237576052502[/C][C]-0.823757605250198[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.3292190247532[/C][C]0.670780975246832[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.8310977001796[/C][C]-0.831097700179582[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.1142630327806[/C][C]-0.114263032780576[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]14.8850713131966[/C][C]-2.88507131319657[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.1973215476683[/C][C]-1.19732154766829[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.8330884467945[/C][C]0.166911553205537[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]14.6516834028881[/C][C]-0.651683402888119[/C][/ROW]
[ROW][C]36[/C][C]10[/C][C]15.3201179479616[/C][C]-5.32011794796157[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]14.1642150618494[/C][C]-4.16421506184939[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.1633317597304[/C][C]-1.16333175973042[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]15.0867300376531[/C][C]0.913269962346882[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.8866232199398[/C][C]1.11337678006017[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]14.9590287512927[/C][C]1.04097124870732[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.2095264379464[/C][C]-1.2095264379464[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]15.0420872661804[/C][C]4.9579127338196[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.692973195037[/C][C]-0.692973195036956[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.2892720642286[/C][C]-0.289272064228637[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]14.8342015136661[/C][C]-3.8342015136661[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]15.8914880152886[/C][C]-1.89148801528857[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.128019829802[/C][C]-0.128019829801955[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.9454616283056[/C][C]0.0545383716944426[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]14.5532967831511[/C][C]-0.55329678315107[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]15.7886754400744[/C][C]0.211324559925596[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.3383407911785[/C][C]-0.338340791178478[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.4071635784548[/C][C]-2.40716357845477[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.2536902788291[/C][C]0.746309721170905[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]14.3412148399123[/C][C]-5.34121483991233[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]15.1295717365452[/C][C]-1.12957173654522[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.0820549163386[/C][C]0.917945083661365[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.9437006464433[/C][C]1.05629935355666[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]14.8635161802895[/C][C]0.136483819710511[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.5470697550933[/C][C]1.45293024490667[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]13.1549049099388[/C][C]-1.15490490993884[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]14.630128269981[/C][C]1.36987173001896[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]14.5208589019565[/C][C]1.47914109804354[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.8621940382989[/C][C]-0.862194038298895[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]14.3676554578019[/C][C]1.63234454219814[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]14.0767111903999[/C][C]2.92328880960013[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]15.4407553253755[/C][C]2.55924467462447[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]15.2442312517388[/C][C]2.7557687482612[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]14.1536636575212[/C][C]-2.15366365752117[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]16.4320930884556[/C][C]-0.432093088455555[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]15.9666394098292[/C][C]-5.96663940982924[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]15.6792778865038[/C][C]-1.6792778865038[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.1987051781471[/C][C]1.8012948218529[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]16.209358161682[/C][C]1.79064183831804[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]16.4889407502064[/C][C]-0.488940750206399[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]14.1445625807296[/C][C]2.85543741927043[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]14.4532300711247[/C][C]1.54676992887527[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]16.0774606845971[/C][C]-0.077460684597077[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.6852958394426[/C][C]-1.68529583944259[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]14.9067086242256[/C][C]1.09329137577441[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]14.0524029890503[/C][C]1.94759701094973[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]15.6148810547046[/C][C]0.385118945295381[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.7454563897989[/C][C]-0.745456389798908[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.2509178093019[/C][C]-0.250917809301878[/C][/ROW]
[ROW][C]85[/C][C]16[/C][C]15.2615707928367[/C][C]0.738429207163265[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.676194762651[/C][C]-0.676194762650992[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]15.9697432233158[/C][C]0.0302567766842358[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.0863526862697[/C][C]0.91364731373028[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.6499839095141[/C][C]0.350016090485872[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]14.0610251352518[/C][C]-2.06102513525183[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]15.6888384928007[/C][C]1.31116150719927[/C][/ROW]
[ROW][C]92[/C][C]16[/C][C]14.5946881545065[/C][C]1.40531184549346[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]16.1571662201609[/C][C]-1.1571662201609[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]14.0817176556737[/C][C]-1.08171765567365[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]14.2413778926387[/C][C]1.75862210736134[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.476756549562[/C][C]0.523243450438006[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]14.8296279715583[/C][C]1.17037202844174[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.4550117426207[/C][C]1.54498825737934[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]15.2433686392535[/C][C]-1.24336863925354[/C][/ROW]
[ROW][C]100[/C][C]16[/C][C]15.2831065246591[/C][C]0.716893475340882[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.5045193094421[/C][C]0.495480690557881[/C][/ROW]
[ROW][C]102[/C][C]20[/C][C]14.737468379879[/C][C]5.26253162012105[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]15.4967403746411[/C][C]-0.496740374641116[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]13.5376314176367[/C][C]2.46236858236325[/C][/ROW]
[ROW][C]105[/C][C]13[/C][C]15.6379686932703[/C][C]-2.63796869327026[/C][/ROW]
[ROW][C]106[/C][C]17[/C][C]13.8569518915668[/C][C]3.14304810843324[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.1249981944374[/C][C]0.87500180556263[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.5826729382627[/C][C]0.417327061737321[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]14.3686002484091[/C][C]-2.36860024840906[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]15.3932191040843[/C][C]0.606780895915749[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.7823409161043[/C][C]1.21765908389566[/C][/ROW]
[ROW][C]112[/C][C]17[/C][C]14.1352123381006[/C][C]2.86478766189939[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]15.5486831503248[/C][C]-2.54868315032481[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]14.4581155561072[/C][C]-2.45811555610718[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]15.0683802974392[/C][C]2.93161970256081[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]14.5120891691242[/C][C]-0.512089169124169[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.4458705751106[/C][C]-1.44587057511065[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]15.4274386567748[/C][C]-2.42743865677479[/C][/ROW]
[ROW][C]119[/C][C]16[/C][C]14.6639704712882[/C][C]1.33602952871183[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]14.7945458063825[/C][C]-1.79454580638246[/C][/ROW]
[ROW][C]121[/C][C]16[/C][C]14.6852764383579[/C][C]1.31472356164211[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]13.4166166888394[/C][C]-0.416616688839388[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]15.3061941632248[/C][C]0.693805836775239[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]15.1387549914588[/C][C]-0.138754991458749[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]14.0772722991118[/C][C]1.92272770088816[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]14.1170101845174[/C][C]0.882989815482586[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]14.4292604189891[/C][C]2.57073958101087[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]15.4538792746643[/C][C]-0.453879274664317[/C][/ROW]
[ROW][C]129[/C][C]12[/C][C]15.2282702991569[/C][C]-3.22827029915687[/C][/ROW]
[ROW][C]130[/C][C]16[/C][C]14.2831272142928[/C][C]1.71687278570717[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]14.0248505928381[/C][C]-4.02485059283811[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]13.3777220148344[/C][C]2.62227798516563[/C][/ROW]
[ROW][C]133[/C][C]12[/C][C]14.8238604730058[/C][C]-2.82386047300585[/C][/ROW]
[ROW][C]134[/C][C]14[/C][C]13.7623777806589[/C][C]0.237622219341064[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]14.9360038897643[/C][C]0.0639961102357237[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]13.6964290421165[/C][C]-0.696429042116494[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]12.0353345919724[/C][C]2.96466540802757[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]14.3137640229068[/C][C]-3.31376402290681[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]13.9355650498927[/C][C]-1.93556504989266[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]14.2478152843644[/C][C]-3.24781528436437[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]14.1385459163398[/C][C]1.8614540836602[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]14.2109514476926[/C][C]0.789048552307353[/C][/ROW]
[ROW][C]143[/C][C]17[/C][C]14.7594636412067[/C][C]2.24053635879333[/C][/ROW]
[ROW][C]144[/C][C]16[/C][C]14.2031725128916[/C][C]1.79682748710836[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]12.5165759049534[/C][C]-2.51657590495341[/C][/ROW]
[ROW][C]146[/C][C]18[/C][C]13.8101243656182[/C][C]4.18987563438182[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]13.1956634335617[/C][C]-0.195663433561721[/C][/ROW]
[ROW][C]148[/C][C]16[/C][C]13.9186850383001[/C][C]2.08131496169994[/C][/ROW]
[ROW][C]149[/C][C]13[/C][C]13.2751392043729[/C][C]-0.275139204372874[/C][/ROW]
[ROW][C]150[/C][C]10[/C][C]13.0459474847889[/C][C]-3.04594748478887[/C][/ROW]
[ROW][C]151[/C][C]15[/C][C]14.9100228013801[/C][C]0.0899771986199214[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]14.2338093215056[/C][C]1.76619067849438[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]14.7241517112782[/C][C]1.27584828872179[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]14.3750376401348[/C][C]-0.37503764013477[/C][/ROW]
[ROW][C]155[/C][C]10[/C][C]13.2844700459171[/C][C]-3.28447004591714[/C][/ROW]
[ROW][C]156[/C][C]17[/C][C]13.4150453810114[/C][C]3.58495461898857[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]14.2615720813857[/C][C]-1.26157208138575[/C][/ROW]
[ROW][C]158[/C][C]15[/C][C]14.6937446674169[/C][C]0.306255332583107[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]13.3597496260038[/C][C]2.64025037399616[/C][/ROW]
[ROW][C]160[/C][C]12[/C][C]14.1154388766895[/C][C]-2.11543887668945[/C][/ROW]
[ROW][C]161[/C][C]13[/C][C]13.9189148030527[/C][C]-0.918914803052722[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.8661752371987[/C][C]0.13382476280129[/C][/ROW]
[ROW][C]163[/C][C]12[/C][C]13.3274327251006[/C][C]-1.32743272510057[/C][/ROW]
[ROW][C]164[/C][C]17[/C][C]14.5918962838946[/C][C]2.40810371610537[/C][/ROW]
[ROW][C]165[/C][C]15[/C][C]14.7479737767831[/C][C]0.252026223216921[/C][/ROW]
[ROW][C]166[/C][C]10[/C][C]13.5083989291353[/C][C]-3.5083989291353[/C][/ROW]
[ROW][C]167[/C][C]14[/C][C]14.2967558257682[/C][C]-0.29675582576818[/C][/ROW]
[ROW][C]168[/C][C]11[/C][C]12.8464211768723[/C][C]-1.84642117687225[/C][/ROW]
[ROW][C]169[/C][C]13[/C][C]14.4053164984501[/C][C]-1.40531649845005[/C][/ROW]
[ROW][C]170[/C][C]16[/C][C]14.237877326684[/C][C]1.76212267331596[/C][/ROW]
[ROW][C]171[/C][C]12[/C][C]13.5652465908861[/C][C]-1.56524659088614[/C][/ROW]
[ROW][C]172[/C][C]16[/C][C]15.0404699509283[/C][C]0.959530049071663[/C][/ROW]
[ROW][C]173[/C][C]12[/C][C]13.8845670648162[/C][C]-1.88456706481616[/C][/ROW]
[ROW][C]174[/C][C]9[/C][C]12.8557520184165[/C][C]-3.85575201841652[/C][/ROW]
[ROW][C]175[/C][C]12[/C][C]13.6441089150494[/C][C]-1.6441089150494[/C][/ROW]
[ROW][C]176[/C][C]15[/C][C]14.3089607160463[/C][C]0.691039283953701[/C][/ROW]
[ROW][C]177[/C][C]12[/C][C]13.5128248846124[/C][C]-1.51282488461241[/C][/ROW]
[ROW][C]178[/C][C]12[/C][C]13.4071382606644[/C][C]-1.40713826066439[/C][/ROW]
[ROW][C]179[/C][C]14[/C][C]14.0174030019964[/C][C]-0.0174030019964077[/C][/ROW]
[ROW][C]180[/C][C]12[/C][C]14.2934028464443[/C][C]-2.29340284644429[/C][/ROW]
[ROW][C]181[/C][C]16[/C][C]14.4857307293566[/C][C]1.51426927064343[/C][/ROW]
[ROW][C]182[/C][C]11[/C][C]13.4533329388804[/C][C]-2.45333293888038[/C][/ROW]
[ROW][C]183[/C][C]19[/C][C]14.5687892442443[/C][C]4.43121075575572[/C][/ROW]
[ROW][C]184[/C][C]15[/C][C]13.8344059606284[/C][C]1.16559403937161[/C][/ROW]
[ROW][C]185[/C][C]8[/C][C]13.4597897316908[/C][C]-5.45978973169079[/C][/ROW]
[ROW][C]186[/C][C]16[/C][C]14.2736487861178[/C][C]1.72635121388216[/C][/ROW]
[ROW][C]187[/C][C]17[/C][C]14.0771247124811[/C][C]2.92287528751889[/C][/ROW]
[ROW][C]188[/C][C]12[/C][C]13.7315933854142[/C][C]-1.73159338541423[/C][/ROW]
[ROW][C]189[/C][C]11[/C][C]13.0262950036691[/C][C]-2.02629500366905[/C][/ROW]
[ROW][C]190[/C][C]11[/C][C]13.9600764096555[/C][C]-2.96007640965553[/C][/ROW]
[ROW][C]191[/C][C]14[/C][C]13.9416444913197[/C][C]0.0583555086803317[/C][/ROW]
[ROW][C]192[/C][C]16[/C][C]14.8790086413827[/C][C]1.1209913586173[/C][/ROW]
[ROW][C]193[/C][C]12[/C][C]13.9374483005952[/C][C]-1.93744830059522[/C][/ROW]
[ROW][C]194[/C][C]16[/C][C]14.0353559897422[/C][C]1.96464401025776[/C][/ROW]
[ROW][C]195[/C][C]13[/C][C]13.7515772104933[/C][C]-0.751577210493348[/C][/ROW]
[ROW][C]196[/C][C]15[/C][C]14.2674217580601[/C][C]0.732578241939906[/C][/ROW]
[ROW][C]197[/C][C]16[/C][C]13.445783768832[/C][C]2.55421623116796[/C][/ROW]
[ROW][C]198[/C][C]16[/C][C]14.3213953710771[/C][C]1.67860462892292[/C][/ROW]
[ROW][C]199[/C][C]14[/C][C]12.987400329664[/C][C]1.01259967033597[/C][/ROW]
[ROW][C]200[/C][C]16[/C][C]13.4195729156952[/C][C]2.58042708430482[/C][/ROW]
[ROW][C]201[/C][C]16[/C][C]12.6234370842613[/C][C]3.37656291573871[/C][/ROW]
[ROW][C]202[/C][C]14[/C][C]13.324539275282[/C][C]0.675460724717981[/C][/ROW]
[ROW][C]203[/C][C]11[/C][C]13.6949593134952[/C][C]-2.69495931349517[/C][/ROW]
[ROW][C]204[/C][C]12[/C][C]13.3458452423517[/C][C]-1.34584524235173[/C][/ROW]
[ROW][C]205[/C][C]15[/C][C]13.865272533995[/C][C]1.13472746600496[/C][/ROW]
[ROW][C]206[/C][C]15[/C][C]14.1448551225195[/C][C]0.855144877480525[/C][/ROW]
[ROW][C]207[/C][C]16[/C][C]14.1555081060543[/C][C]1.84449189394567[/C][/ROW]
[ROW][C]208[/C][C]16[/C][C]14.2279136374072[/C][C]1.77208636259282[/C][/ROW]
[ROW][C]209[/C][C]11[/C][C]13.4026929041026[/C][C]-2.40269290410258[/C][/ROW]
[ROW][C]210[/C][C]15[/C][C]13.744028040445[/C][C]1.25597195955499[/C][/ROW]
[ROW][C]211[/C][C]12[/C][C]12.8861396611931[/C][C]-0.886139661193131[/C][/ROW]
[ROW][C]212[/C][C]12[/C][C]15.3462439915049[/C][C]-3.34624399150494[/C][/ROW]
[ROW][C]213[/C][C]15[/C][C]13.9831640482212[/C][C]1.01683595177883[/C][/ROW]
[ROW][C]214[/C][C]15[/C][C]12.8017590043148[/C][C]2.19824099568517[/C][/ROW]
[ROW][C]215[/C][C]16[/C][C]13.3502711978288[/C][C]2.64972880217115[/C][/ROW]
[ROW][C]216[/C][C]14[/C][C]12.3214561514292[/C][C]1.67854384857079[/C][/ROW]
[ROW][C]217[/C][C]17[/C][C]14.0329414705137[/C][C]2.96705852948629[/C][/ROW]
[ROW][C]218[/C][C]14[/C][C]13.5057352440694[/C][C]0.494264755930591[/C][/ROW]
[ROW][C]219[/C][C]13[/C][C]12.9167764698071[/C][C]0.0832235301928893[/C][/ROW]
[ROW][C]220[/C][C]15[/C][C]14.3338300261079[/C][C]0.666169973892131[/C][/ROW]
[ROW][C]221[/C][C]13[/C][C]12.9416651809534[/C][C]0.0583348190466184[/C][/ROW]
[ROW][C]222[/C][C]14[/C][C]13.9698667807051[/C][C]0.0301332192948726[/C][/ROW]
[ROW][C]223[/C][C]15[/C][C]12.6358717392921[/C][C]2.36412826070792[/C][/ROW]
[ROW][C]224[/C][C]12[/C][C]13.2170515787534[/C][C]-1.21705157875337[/C][/ROW]
[ROW][C]225[/C][C]13[/C][C]13.0751145647815[/C][C]-0.075114564781525[/C][/ROW]
[ROW][C]226[/C][C]8[/C][C]13.8961391073617[/C][C]-5.89613910736168[/C][/ROW]
[ROW][C]227[/C][C]14[/C][C]13.6414452299835[/C][C]0.358554770016486[/C][/ROW]
[ROW][C]228[/C][C]14[/C][C]13.0815713575919[/C][C]0.918428642408065[/C][/ROW]
[ROW][C]229[/C][C]11[/C][C]12.4344427795882[/C][C]-1.4344427795882[/C][/ROW]
[ROW][C]230[/C][C]12[/C][C]13.0737924227909[/C][C]-1.07379242279093[/C][/ROW]
[ROW][C]231[/C][C]13[/C][C]12.6700912919826[/C][C]0.329908708017387[/C][/ROW]
[ROW][C]232[/C][C]10[/C][C]13.2441056432908[/C][C]-3.2441056432908[/C][/ROW]
[ROW][C]233[/C][C]16[/C][C]13.3491788205909[/C][C]2.65082117940908[/C][/ROW]
[ROW][C]234[/C][C]18[/C][C]12.941894945706[/C][C]5.05810505429395[/C][/ROW]
[ROW][C]235[/C][C]13[/C][C]13.1342228286183[/C][C]-0.134222828618331[/C][/ROW]
[ROW][C]236[/C][C]11[/C][C]12.3671718990552[/C][C]-1.36717189905516[/C][/ROW]
[ROW][C]237[/C][C]4[/C][C]12.3196550788486[/C][C]-8.31965507884858[/C][/ROW]
[ROW][C]238[/C][C]13[/C][C]11.9741237517817[/C][C]1.0258762482183[/C][/ROW]
[ROW][C]239[/C][C]16[/C][C]12.1919537924881[/C][C]3.80804620751185[/C][/ROW]
[ROW][C]240[/C][C]10[/C][C]12.1444369722816[/C][C]-2.14443697228157[/C][/ROW]
[ROW][C]241[/C][C]12[/C][C]12.5439419123654[/C][C]-0.543941912365439[/C][/ROW]
[ROW][C]242[/C][C]12[/C][C]12.4928423480823[/C][C]-0.492842348082303[/C][/ROW]
[ROW][C]243[/C][C]10[/C][C]11.9110490619731[/C][C]-1.91104906197312[/C][/ROW]
[ROW][C]244[/C][C]13[/C][C]12.637653410788[/C][C]0.362346589211995[/C][/ROW]
[ROW][C]245[/C][C]15[/C][C]12.6809740402701[/C][C]2.31902595972986[/C][/ROW]
[ROW][C]246[/C][C]12[/C][C]12.7788817294172[/C][C]-0.778881729417152[/C][/ROW]
[ROW][C]247[/C][C]14[/C][C]13.0002945142002[/C][C]0.999705485799846[/C][/ROW]
[ROW][C]248[/C][C]10[/C][C]12.6547631871333[/C][C]-2.65476318713327[/C][/ROW]
[ROW][C]249[/C][C]12[/C][C]10.517562074828[/C][C]1.48243792517197[/C][/ROW]
[ROW][C]250[/C][C]12[/C][C]10.5899676061809[/C][C]1.41003239381911[/C][/ROW]
[ROW][C]251[/C][C]11[/C][C]13.0174042905454[/C][C]-2.01740429054542[/C][/ROW]
[ROW][C]252[/C][C]10[/C][C]12.4611131622304[/C][C]-2.4611131622304[/C][/ROW]
[ROW][C]253[/C][C]12[/C][C]12.7406957507548[/C][C]-0.740695750754836[/C][/ROW]
[ROW][C]254[/C][C]16[/C][C]12.4242493255587[/C][C]3.57575067444133[/C][/ROW]
[ROW][C]255[/C][C]12[/C][C]12.4349023090935[/C][C]-0.434902309093532[/C][/ROW]
[ROW][C]256[/C][C]14[/C][C]12.4164703907577[/C][C]1.58352960924233[/C][/ROW]
[ROW][C]257[/C][C]16[/C][C]12.6343004314641[/C][C]3.36569956853588[/C][/ROW]
[ROW][C]258[/C][C]14[/C][C]11.8708322459775[/C][C]2.12916775402249[/C][/ROW]
[ROW][C]259[/C][C]13[/C][C]12.8699489438585[/C][C]0.130051056141469[/C][/ROW]
[ROW][C]260[/C][C]4[/C][C]11.6558762540049[/C][C]-7.65587625400491[/C][/ROW]
[ROW][C]261[/C][C]15[/C][C]12.1171337419068[/C][C]2.88286625809322[/C][/ROW]
[ROW][C]262[/C][C]11[/C][C]13.4106822025716[/C][C]-2.41068220257155[/C][/ROW]
[ROW][C]263[/C][C]11[/C][C]12.1348569648999[/C][C]-1.13485696489994[/C][/ROW]
[ROW][C]264[/C][C]14[/C][C]12.3598524937595[/C][C]1.64014750624051[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146344&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146344&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
11316.4704266537486-3.4704266537486
21616.0376406210696-0.0376406210695608
31915.62677400210813.37322599789186
41515.4302499284714-0.430249928471404
51416.4875364300939-2.48753643009387
61315.5168911874357-2.51689118743567
71915.31678436972243.68321563027762
81516.0433887185373-1.04338871853727
91415.9958718983307-1.99587189833069
101516.4244617402853-1.42446174028528
111615.60282375105720.397176248942768
121616.2712582961307-0.271258296130683
131615.32253246719010.677467532809909
141616.4706564185013-0.470656418501268
151716.54306194985410.456938050145881
161515.4489116115599-0.448911611559932
171515.2814724397939-0.281472439793921
182016.51685109671733.48314890328274
191816.13865212370311.8613478762969
201615.46602138790520.5339786120948
211615.83644142611840.163558573881649
221614.68053854000621.31946145999383
231916.06492445035972.93507554964034
241615.95565508233510.0443449176649187
251715.49378414778531.50621585221468
261714.5159734169742.48402658302599
271615.78401971984460.215980280155378
281515.8237576052502-0.823757605250198
291615.32921902475320.670780975246832
301414.8310977001796-0.831097700179582
311515.1142630327806-0.114263032780576
321214.8850713131966-2.88507131319657
331415.1973215476683-1.19732154766829
341615.83308844679450.166911553205537
351414.6516834028881-0.651683402888119
361015.3201179479616-5.32011794796157
371014.1642150618494-4.16421506184939
381415.1633317597304-1.16333175973042
391615.08673003765310.913269962346882
401614.88662321993981.11337678006017
411614.95902875129271.04097124870732
421415.2095264379464-1.2095264379464
432015.04208726618044.9579127338196
441414.692973195037-0.692973195036956
451414.2892720642286-0.289272064228637
461114.8342015136661-3.8342015136661
471415.8914880152886-1.89148801528857
481515.128019829802-0.128019829801955
491615.94546162830560.0545383716944426
501414.5532967831511-0.55329678315107
511615.78867544007440.211324559925596
521414.3383407911785-0.338340791178478
531214.4071635784548-2.40716357845477
541615.25369027882910.746309721170905
55914.3412148399123-5.34121483991233
561415.1295717365452-1.12957173654522
571615.08205491633860.917945083661365
581614.94370064644331.05629935355666
591514.86351618028950.136483819710511
601614.54706975509331.45293024490667
611213.1549049099388-1.15490490993884
621614.6301282699811.36987173001896
631614.52085890195651.47914109804354
641414.8621940382989-0.862194038298895
651614.36765545780191.63234454219814
661714.07671119039992.92328880960013
671815.44075532537552.55924467462447
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701616.4320930884556-0.432093088455555
711015.9666394098292-5.96663940982924
721415.6792778865038-1.6792778865038
731816.19870517814711.8012948218529
741816.2093581616821.79064183831804
751616.4889407502064-0.488940750206399
761714.14456258072962.85543741927043
771614.45323007112471.54676992887527
781616.0774606845971-0.077460684597077
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801614.90670862422561.09329137577441
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821615.61488105470460.385118945295381
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861414.676194762651-0.676194762650992
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881615.08635268626970.91364731373028
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901214.0610251352518-2.06102513525183
911715.68883849280071.31116150719927
921614.59468815450651.40531184549346
931516.1571662201609-1.1571662201609
941314.0817176556737-1.08171765567365
951614.24137789263871.75862210736134
961615.4767565495620.523243450438006
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981614.45501174262071.54498825737934
991415.2433686392535-1.24336863925354
1001615.28310652465910.716893475340882
1011615.50451930944210.495480690557881
1022014.7374683798795.26253162012105
1031515.4967403746411-0.496740374641116
1041613.53763141763672.46236858236325
1051315.6379686932703-2.63796869327026
1061713.85695189156683.14304810843324
1071615.12499819443740.87500180556263
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1101615.39321910408430.606780895915749
1111614.78234091610431.21765908389566
1121714.13521233810062.86478766189939
1131315.5486831503248-2.54868315032481
1141214.4581155561072-2.45811555610718
1151815.06838029743922.93161970256081
1161414.5120891691242-0.512089169124169
1171415.4458705751106-1.44587057511065
1181315.4274386567748-2.42743865677479
1191614.66397047128821.33602952871183
1201314.7945458063825-1.79454580638246
1211614.68527643835791.31472356164211
1221313.4166166888394-0.416616688839388
1231615.30619416322480.693805836775239
1241515.1387549914588-0.138754991458749
1251614.07727229911181.92272770088816
1261514.11701018451740.882989815482586
1271714.42926041898912.57073958101087
1281515.4538792746643-0.453879274664317
1291215.2282702991569-3.22827029915687
1301614.28312721429281.71687278570717
1311014.0248505928381-4.02485059283811
1321613.37772201483442.62227798516563
1331214.8238604730058-2.82386047300585
1341413.76237778065890.237622219341064
1351514.93600388976430.0639961102357237
1361313.6964290421165-0.696429042116494
1371512.03533459197242.96466540802757
1381114.3137640229068-3.31376402290681
1391213.9355650498927-1.93556504989266
1401114.2478152843644-3.24781528436437
1411614.13854591633981.8614540836602
1421514.21095144769260.789048552307353
1431714.75946364120672.24053635879333
1441614.20317251289161.79682748710836
1451012.5165759049534-2.51657590495341
1461813.81012436561824.18987563438182
1471313.1956634335617-0.195663433561721
1481613.91868503830012.08131496169994
1491313.2751392043729-0.275139204372874
1501013.0459474847889-3.04594748478887
1511514.91002280138010.0899771986199214
1521614.23380932150561.76619067849438
1531614.72415171127821.27584828872179
1541414.3750376401348-0.37503764013477
1551013.2844700459171-3.28447004591714
1561713.41504538101143.58495461898857
1571314.2615720813857-1.26157208138575
1581514.69374466741690.306255332583107
1591613.35974962600382.64025037399616
1601214.1154388766895-2.11543887668945
1611313.9189148030527-0.918914803052722
1621312.86617523719870.13382476280129
1631213.3274327251006-1.32743272510057
1641714.59189628389462.40810371610537
1651514.74797377678310.252026223216921
1661013.5083989291353-3.5083989291353
1671414.2967558257682-0.29675582576818
1681112.8464211768723-1.84642117687225
1691314.4053164984501-1.40531649845005
1701614.2378773266841.76212267331596
1711213.5652465908861-1.56524659088614
1721615.04046995092830.959530049071663
1731213.8845670648162-1.88456706481616
174912.8557520184165-3.85575201841652
1751213.6441089150494-1.6441089150494
1761514.30896071604630.691039283953701
1771213.5128248846124-1.51282488461241
1781213.4071382606644-1.40713826066439
1791414.0174030019964-0.0174030019964077
1801214.2934028464443-2.29340284644429
1811614.48573072935661.51426927064343
1821113.4533329388804-2.45333293888038
1831914.56878924424434.43121075575572
1841513.83440596062841.16559403937161
185813.4597897316908-5.45978973169079
1861614.27364878611781.72635121388216
1871714.07712471248112.92287528751889
1881213.7315933854142-1.73159338541423
1891113.0262950036691-2.02629500366905
1901113.9600764096555-2.96007640965553
1911413.94164449131970.0583555086803317
1921614.87900864138271.1209913586173
1931213.9374483005952-1.93744830059522
1941614.03535598974221.96464401025776
1951313.7515772104933-0.751577210493348
1961514.26742175806010.732578241939906
1971613.4457837688322.55421623116796
1981614.32139537107711.67860462892292
1991412.9874003296641.01259967033597
2001613.41957291569522.58042708430482
2011612.62343708426133.37656291573871
2021413.3245392752820.675460724717981
2031113.6949593134952-2.69495931349517
2041213.3458452423517-1.34584524235173
2051513.8652725339951.13472746600496
2061514.14485512251950.855144877480525
2071614.15550810605431.84449189394567
2081614.22791363740721.77208636259282
2091113.4026929041026-2.40269290410258
2101513.7440280404451.25597195955499
2111212.8861396611931-0.886139661193131
2121215.3462439915049-3.34624399150494
2131513.98316404822121.01683595177883
2141512.80175900431482.19824099568517
2151613.35027119782882.64972880217115
2161412.32145615142921.67854384857079
2171714.03294147051372.96705852948629
2181413.50573524406940.494264755930591
2191312.91677646980710.0832235301928893
2201514.33383002610790.666169973892131
2211312.94166518095340.0583348190466184
2221413.96986678070510.0301332192948726
2231512.63587173929212.36412826070792
2241213.2170515787534-1.21705157875337
2251313.0751145647815-0.075114564781525
226813.8961391073617-5.89613910736168
2271413.64144522998350.358554770016486
2281413.08157135759190.918428642408065
2291112.4344427795882-1.4344427795882
2301213.0737924227909-1.07379242279093
2311312.67009129198260.329908708017387
2321013.2441056432908-3.2441056432908
2331613.34917882059092.65082117940908
2341812.9418949457065.05810505429395
2351313.1342228286183-0.134222828618331
2361112.3671718990552-1.36717189905516
237412.3196550788486-8.31965507884858
2381311.97412375178171.0258762482183
2391612.19195379248813.80804620751185
2401012.1444369722816-2.14443697228157
2411212.5439419123654-0.543941912365439
2421212.4928423480823-0.492842348082303
2431011.9110490619731-1.91104906197312
2441312.6376534107880.362346589211995
2451512.68097404027012.31902595972986
2461212.7788817294172-0.778881729417152
2471413.00029451420020.999705485799846
2481012.6547631871333-2.65476318713327
2491210.5175620748281.48243792517197
2501210.58996760618091.41003239381911
2511113.0174042905454-2.01740429054542
2521012.4611131622304-2.4611131622304
2531212.7406957507548-0.740695750754836
2541612.42424932555873.57575067444133
2551212.4349023090935-0.434902309093532
2561412.41647039075771.58352960924233
2571612.63430043146413.36569956853588
2581411.87083224597752.12916775402249
2591312.86994894385850.130051056141469
260411.6558762540049-7.65587625400491
2611512.11713374190682.88286625809322
2621113.4106822025716-2.41068220257155
2631112.1348569648999-1.13485696489994
2641412.35985249375951.64014750624051






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.829092950298870.341814099402260.17090704970113
90.7257933480314220.5484133039371550.274206651968578
100.6333247301468040.7333505397063920.366675269853196
110.5190733308377350.961853338324530.480926669162265
120.4989273202104950.997854640420990.501072679789505
130.4499879121087170.8999758242174340.550012087891283
140.3821358173284120.7642716346568240.617864182671588
150.3609036211778190.7218072423556380.639096378822181
160.3370756889408940.6741513778817880.662924311059106
170.2932928096600050.586585619320010.706707190339995
180.4945916438111890.9891832876223780.505408356188811
190.4285553804662270.8571107609324530.571444619533773
200.3626793861028420.7253587722056840.637320613897158
210.2976662343811070.5953324687622130.702333765618893
220.2495400216511930.4990800433023870.750459978348807
230.2270959786305870.4541919572611740.772904021369413
240.2119638908388830.4239277816777650.788036109161117
250.1660370817113570.3320741634227130.833962918288643
260.130373596520.2607471930400010.86962640348
270.1076853046951360.2153706093902730.892314695304864
280.1137042437806650.227408487561330.886295756219335
290.088830732896170.177661465792340.91116926710383
300.1075855505144340.2151711010288680.892414449485566
310.08831647132495930.1766329426499190.91168352867504
320.1687317802563040.3374635605126080.831268219743696
330.1481549343375650.2963098686751290.851845065662435
340.1166755352700070.2333510705400130.883324464729993
350.09621079641495970.1924215928299190.90378920358504
360.2803818196917640.5607636393835280.719618180308236
370.4236460049381850.847292009876370.576353995061815
380.3749278196582250.7498556393164490.625072180341775
390.3595328545517260.7190657091034520.640467145448274
400.3354338861591670.6708677723183340.664566113840833
410.3105539495594950.621107899118990.689446050440505
420.2728578854164620.5457157708329240.727142114583538
430.5001574884949830.9996850230100330.499842511505017
440.4563371431891110.9126742863782220.543662856810889
450.407850976786640.815701953573280.59214902321336
460.4992362968268720.9984725936537450.500763703173128
470.4742607242490370.9485214484980730.525739275750963
480.4281285348934840.8562570697869670.571871465106516
490.3845425461268860.7690850922537720.615457453873114
500.3410636913824470.6821273827648930.658936308617553
510.3023032407999470.6046064815998930.697696759200053
520.2632499086759450.526499817351890.736750091324055
530.2573862433107430.5147724866214870.742613756689257
540.2338317335100240.4676634670200470.766168266489976
550.3930341360373440.7860682720746880.606965863962656
560.3561866060636160.7123732121272330.643813393936384
570.3339488993081850.667897798616370.666051100691815
580.3147629722163910.6295259444327820.685237027783609
590.2820270211813510.5640540423627010.71797297881865
600.2761024663815080.5522049327630170.723897533618492
610.2464891452228790.4929782904457590.75351085477712
620.2343846931152210.4687693862304420.765615306884779
630.2241055515320040.4482111030640080.775894448467996
640.1981058692714120.3962117385428250.801894130728588
650.1894684458542960.3789368917085910.810531554145704
660.2213598585734680.4427197171469360.778640141426532
670.1993032828168960.3986065656337920.800696717183104
680.1807995943624990.3615991887249980.8192004056375
690.2201395454130490.4402790908260980.779860454586951
700.2062717822147420.4125435644294850.793728217785258
710.4495004176569750.899000835313950.550499582343025
720.422515510905660.845031021811320.57748448909434
730.4244673540893850.848934708178770.575532645910615
740.414013866214760.828027732429520.58598613378524
750.3780474549178810.7560949098357610.621952545082119
760.4169026610521050.833805322104210.583097338947895
770.3942592422611130.7885184845222260.605740757738887
780.3570270703615890.7140541407231770.642972929638411
790.3455862499431260.6911724998862520.654413750056874
800.3179506893789940.6359013787579890.682049310621006
810.3061529065104220.6123058130208440.693847093489578
820.2729803616405410.5459607232810820.727019638359459
830.2470499991457520.4940999982915050.752950000854248
840.2183305891678340.4366611783356690.781669410832166
850.192697175328960.385394350657920.80730282467104
860.1712307843729050.3424615687458090.828769215627095
870.1478427709381060.2956855418762130.852157229061894
880.1291319715501760.2582639431003510.870868028449824
890.1100696160582190.2201392321164370.889930383941781
900.1123576343534850.224715268706970.887642365646515
910.09995244600939770.1999048920187950.900047553990602
920.08911845422647430.1782369084529490.910881545773526
930.0797560963778070.1595121927556140.920243903622193
940.07110419101485860.1422083820297170.928895808985141
950.06544546975446130.1308909395089230.934554530245539
960.05453641990212720.1090728398042540.945463580097873
970.04685278054559490.09370556109118980.953147219454405
980.04138621083048720.08277242166097450.958613789169513
990.03716249686365330.07432499372730660.962837503136347
1000.03058744617570480.06117489235140950.969412553824295
1010.02478486288451450.04956972576902910.975215137115485
1020.06132470771001370.1226494154200270.938675292289986
1030.05216568970652050.1043313794130410.94783431029348
1040.05200271436853620.1040054287370720.947997285631464
1050.05988929012058340.1197785802411670.940110709879417
1060.06803094009316590.1360618801863320.931969059906834
1070.05783096623559450.1156619324711890.942169033764406
1080.048111911086950.09622382217390.95188808891305
1090.05369803391351230.1073960678270250.946301966086488
1100.04489464891120580.08978929782241160.955105351088794
1110.0385552143913380.07711042878267590.961444785608662
1120.04220813507409820.08441627014819640.957791864925902
1130.04690193996700570.09380387993401140.953098060032994
1140.05240737662487660.1048147532497530.947592623375123
1150.05886153001715360.1177230600343070.941138469982846
1160.05037820628603040.1007564125720610.94962179371397
1170.04576476189647920.09152952379295830.954235238103521
1180.04834446525617170.09668893051234330.951655534743828
1190.04239935191579340.08479870383158670.957600648084207
1200.0406704195688630.08134083913772590.959329580431137
1210.03564036218867940.07128072437735870.96435963781132
1220.03014185641283710.06028371282567420.969858143587163
1230.02502386748254980.05004773496509950.97497613251745
1240.02024905669677720.04049811339355440.979750943303223
1250.01870393281319190.03740786562638370.981296067186808
1260.01536554385537620.03073108771075250.984634456144624
1270.01610141974415060.03220283948830110.98389858025585
1280.01299322079969730.02598644159939460.987006779200303
1290.01777443791761850.03554887583523690.982225562082382
1300.0159867907781820.03197358155636390.984013209221818
1310.02899202894091120.05798405788182250.971007971059089
1320.03081317914830060.06162635829660130.9691868208517
1330.03580987082646580.07161974165293170.964190129173534
1340.0293959373189160.05879187463783190.970604062681084
1350.02398340280504180.04796680561008350.976016597194958
1360.02011326666378770.04022653332757530.979886733336212
1370.02351519460168390.04703038920336770.976484805398316
1380.03150359759188350.06300719518376710.968496402408116
1390.03050849545578370.06101699091156730.969491504544216
1400.03988439162779290.07976878325558590.960115608372207
1410.03770900325689840.07541800651379680.962290996743102
1420.03171658278887990.06343316557775970.96828341721112
1430.03212456185923440.06424912371846880.967875438140766
1440.02981753417727710.05963506835455410.970182465822723
1450.03350510877255840.06701021754511680.966494891227442
1460.05453132808829240.1090626561765850.945468671911708
1470.04573561468056530.09147122936113050.954264385319435
1480.04505285619596950.0901057123919390.95494714380403
1490.03759456048104880.07518912096209760.962405439518951
1500.04492300742969060.08984601485938120.95507699257031
1510.03729043433015450.0745808686603090.962709565669845
1520.03478141453606910.06956282907213820.96521858546393
1530.03054898828129540.06109797656259080.969451011718705
1540.02497330617312810.04994661234625630.975026693826872
1550.0325295139486880.0650590278973760.967470486051312
1560.04510049545847820.09020099091695640.954899504541522
1570.03933694452550460.07867388905100920.960663055474495
1580.03242604948598010.06485209897196020.96757395051402
1590.03956815030941760.07913630061883510.960431849690582
1600.03591476214302560.07182952428605130.964085237856974
1610.02980352894316420.05960705788632830.970196471056836
1620.02526015828276450.0505203165655290.974739841717235
1630.02261384434930350.0452276886986070.977386155650697
1640.02329124807660780.04658249615321550.976708751923392
1650.01876897135847630.03753794271695260.981231028641524
1660.02598892165732950.05197784331465890.97401107834267
1670.02110050372462970.04220100744925950.97889949627537
1680.01950616066054610.03901232132109230.980493839339454
1690.01708781048277950.0341756209655590.98291218951722
1700.01567416068765380.03134832137530760.984325839312346
1710.01379026780734940.02758053561469870.98620973219265
1720.01130726423869540.02261452847739090.988692735761305
1730.01049017363398880.02098034726797760.98950982636601
1740.01610249100751450.0322049820150290.983897508992486
1750.01441162155652210.02882324311304430.985588378443478
1760.01158333228691330.02316666457382670.988416667713087
1770.01022566838813480.02045133677626960.989774331611865
1780.008892560857920060.01778512171584010.99110743914208
1790.006892402535728650.01378480507145730.993107597464271
1800.00727502242864760.01455004485729520.992724977571352
1810.006194213268213260.01238842653642650.993805786731787
1820.006826791253862560.01365358250772510.993173208746137
1830.0133292331437430.0266584662874860.986670766856257
1840.01099273341022710.02198546682045420.989007266589773
1850.03737971676814580.07475943353629160.962620283231854
1860.03340142171063450.06680284342126890.966598578289366
1870.03689741944712070.07379483889424140.96310258055288
1880.03533063703890030.07066127407780070.9646693629611
1890.03761948567503420.07523897135006840.962380514324966
1900.04796373050093230.09592746100186460.952036269499068
1910.03948451538412640.07896903076825280.960515484615874
1920.03309658445065080.06619316890130150.96690341554935
1930.0343768288911940.0687536577823880.965623171108806
1940.03091148119035510.06182296238071030.969088518809645
1950.02687821816127330.05375643632254660.973121781838727
1960.02150337411345280.04300674822690570.978496625886547
1970.02054960155818620.04109920311637230.979450398441814
1980.01774178748825690.03548357497651370.982258212511743
1990.01407356787646070.02814713575292140.98592643212354
2000.01376176499901830.02752352999803670.986238235000982
2010.01643979895169150.0328795979033830.983560201048309
2020.01278598580702960.02557197161405930.98721401419297
2030.01586096740182170.03172193480364340.984139032598178
2040.01428132269690690.02856264539381370.985718677303093
2050.01127070006150050.02254140012300110.9887292999385
2060.008701617265344850.01740323453068970.991298382734655
2070.00767324844612760.01534649689225520.992326751553872
2080.006659435400368280.01331887080073660.993340564599632
2090.007276971795075570.01455394359015110.992723028204924
2100.005666049871482070.01133209974296410.994333950128518
2110.00448619541095790.008972390821915810.995513804589042
2120.005803691884817430.01160738376963490.994196308115183
2130.004437261828160190.008874523656320370.99556273817184
2140.003979226519510390.007958453039020770.99602077348049
2150.004294190456939120.008588380913878240.99570580954306
2160.003366563713625550.00673312742725110.996633436286374
2170.004078984392311490.008157968784622970.995921015607689
2180.002992262913565250.005984525827130490.997007737086435
2190.002144558958166180.004289117916332350.997855441041834
2200.001648127392469710.003296254784939420.99835187260753
2210.001148886087901250.002297772175802490.998851113912099
2220.000799435238272790.001598870476545580.999200564761727
2230.0009372616483044120.001874523296608820.999062738351696
2240.0006580305184812360.001316061036962470.999341969481519
2250.0004688125378622540.0009376250757245080.999531187462138
2260.002649480374328630.005298960748657250.997350519625671
2270.001818176312594840.003636352625189670.998181823687405
2280.001347608553425330.002695217106850670.998652391446575
2290.0009341238117856650.001868247623571330.999065876188214
2300.0006261752214164820.001252350442832960.999373824778583
2310.0003993719475540890.0007987438951081770.999600628052446
2320.0004512019783106760.0009024039566213530.99954879802169
2330.0004602234802450970.0009204469604901940.999539776519755
2340.002678191556515660.005356383113031320.997321808443484
2350.001835726663316510.003671453326633020.998164273336683
2360.001215117367486130.002430234734972270.998784882632514
2370.05130408247754710.1026081649550940.948695917522453
2380.03827275306739850.0765455061347970.961727246932601
2390.06550736667467660.1310147333493530.934492633325323
2400.05829087017717560.1165817403543510.941709129822824
2410.04215927454964070.08431854909928130.95784072545036
2420.02942467633476330.05884935266952670.970575323665237
2430.027387080970920.05477416194184010.97261291902908
2440.01826153157524690.03652306315049370.981738468424753
2450.01658112145778770.03316224291557540.983418878542212
2460.01065440024105420.02130880048210840.989345599758946
2470.007639546882339270.01527909376467850.99236045311766
2480.007225898609781080.01445179721956220.992774101390219
2490.004302879984536520.008605759969073050.995697120015464
2500.002691722906176840.005383445812353680.997308277093823
2510.00228915778995250.004578315579905010.997710842210047
2520.003094330991467160.006188661982934310.996905669008533
2530.002889888472170960.005779776944341920.997110111527829
2540.001914844251154930.003829688502309860.998085155748845
2550.001059425776520890.002118851553041770.99894057422348
2560.0003751651478586870.0007503302957173740.99962483485214

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.82909295029887 & 0.34181409940226 & 0.17090704970113 \tabularnewline
9 & 0.725793348031422 & 0.548413303937155 & 0.274206651968578 \tabularnewline
10 & 0.633324730146804 & 0.733350539706392 & 0.366675269853196 \tabularnewline
11 & 0.519073330837735 & 0.96185333832453 & 0.480926669162265 \tabularnewline
12 & 0.498927320210495 & 0.99785464042099 & 0.501072679789505 \tabularnewline
13 & 0.449987912108717 & 0.899975824217434 & 0.550012087891283 \tabularnewline
14 & 0.382135817328412 & 0.764271634656824 & 0.617864182671588 \tabularnewline
15 & 0.360903621177819 & 0.721807242355638 & 0.639096378822181 \tabularnewline
16 & 0.337075688940894 & 0.674151377881788 & 0.662924311059106 \tabularnewline
17 & 0.293292809660005 & 0.58658561932001 & 0.706707190339995 \tabularnewline
18 & 0.494591643811189 & 0.989183287622378 & 0.505408356188811 \tabularnewline
19 & 0.428555380466227 & 0.857110760932453 & 0.571444619533773 \tabularnewline
20 & 0.362679386102842 & 0.725358772205684 & 0.637320613897158 \tabularnewline
21 & 0.297666234381107 & 0.595332468762213 & 0.702333765618893 \tabularnewline
22 & 0.249540021651193 & 0.499080043302387 & 0.750459978348807 \tabularnewline
23 & 0.227095978630587 & 0.454191957261174 & 0.772904021369413 \tabularnewline
24 & 0.211963890838883 & 0.423927781677765 & 0.788036109161117 \tabularnewline
25 & 0.166037081711357 & 0.332074163422713 & 0.833962918288643 \tabularnewline
26 & 0.13037359652 & 0.260747193040001 & 0.86962640348 \tabularnewline
27 & 0.107685304695136 & 0.215370609390273 & 0.892314695304864 \tabularnewline
28 & 0.113704243780665 & 0.22740848756133 & 0.886295756219335 \tabularnewline
29 & 0.08883073289617 & 0.17766146579234 & 0.91116926710383 \tabularnewline
30 & 0.107585550514434 & 0.215171101028868 & 0.892414449485566 \tabularnewline
31 & 0.0883164713249593 & 0.176632942649919 & 0.91168352867504 \tabularnewline
32 & 0.168731780256304 & 0.337463560512608 & 0.831268219743696 \tabularnewline
33 & 0.148154934337565 & 0.296309868675129 & 0.851845065662435 \tabularnewline
34 & 0.116675535270007 & 0.233351070540013 & 0.883324464729993 \tabularnewline
35 & 0.0962107964149597 & 0.192421592829919 & 0.90378920358504 \tabularnewline
36 & 0.280381819691764 & 0.560763639383528 & 0.719618180308236 \tabularnewline
37 & 0.423646004938185 & 0.84729200987637 & 0.576353995061815 \tabularnewline
38 & 0.374927819658225 & 0.749855639316449 & 0.625072180341775 \tabularnewline
39 & 0.359532854551726 & 0.719065709103452 & 0.640467145448274 \tabularnewline
40 & 0.335433886159167 & 0.670867772318334 & 0.664566113840833 \tabularnewline
41 & 0.310553949559495 & 0.62110789911899 & 0.689446050440505 \tabularnewline
42 & 0.272857885416462 & 0.545715770832924 & 0.727142114583538 \tabularnewline
43 & 0.500157488494983 & 0.999685023010033 & 0.499842511505017 \tabularnewline
44 & 0.456337143189111 & 0.912674286378222 & 0.543662856810889 \tabularnewline
45 & 0.40785097678664 & 0.81570195357328 & 0.59214902321336 \tabularnewline
46 & 0.499236296826872 & 0.998472593653745 & 0.500763703173128 \tabularnewline
47 & 0.474260724249037 & 0.948521448498073 & 0.525739275750963 \tabularnewline
48 & 0.428128534893484 & 0.856257069786967 & 0.571871465106516 \tabularnewline
49 & 0.384542546126886 & 0.769085092253772 & 0.615457453873114 \tabularnewline
50 & 0.341063691382447 & 0.682127382764893 & 0.658936308617553 \tabularnewline
51 & 0.302303240799947 & 0.604606481599893 & 0.697696759200053 \tabularnewline
52 & 0.263249908675945 & 0.52649981735189 & 0.736750091324055 \tabularnewline
53 & 0.257386243310743 & 0.514772486621487 & 0.742613756689257 \tabularnewline
54 & 0.233831733510024 & 0.467663467020047 & 0.766168266489976 \tabularnewline
55 & 0.393034136037344 & 0.786068272074688 & 0.606965863962656 \tabularnewline
56 & 0.356186606063616 & 0.712373212127233 & 0.643813393936384 \tabularnewline
57 & 0.333948899308185 & 0.66789779861637 & 0.666051100691815 \tabularnewline
58 & 0.314762972216391 & 0.629525944432782 & 0.685237027783609 \tabularnewline
59 & 0.282027021181351 & 0.564054042362701 & 0.71797297881865 \tabularnewline
60 & 0.276102466381508 & 0.552204932763017 & 0.723897533618492 \tabularnewline
61 & 0.246489145222879 & 0.492978290445759 & 0.75351085477712 \tabularnewline
62 & 0.234384693115221 & 0.468769386230442 & 0.765615306884779 \tabularnewline
63 & 0.224105551532004 & 0.448211103064008 & 0.775894448467996 \tabularnewline
64 & 0.198105869271412 & 0.396211738542825 & 0.801894130728588 \tabularnewline
65 & 0.189468445854296 & 0.378936891708591 & 0.810531554145704 \tabularnewline
66 & 0.221359858573468 & 0.442719717146936 & 0.778640141426532 \tabularnewline
67 & 0.199303282816896 & 0.398606565633792 & 0.800696717183104 \tabularnewline
68 & 0.180799594362499 & 0.361599188724998 & 0.8192004056375 \tabularnewline
69 & 0.220139545413049 & 0.440279090826098 & 0.779860454586951 \tabularnewline
70 & 0.206271782214742 & 0.412543564429485 & 0.793728217785258 \tabularnewline
71 & 0.449500417656975 & 0.89900083531395 & 0.550499582343025 \tabularnewline
72 & 0.42251551090566 & 0.84503102181132 & 0.57748448909434 \tabularnewline
73 & 0.424467354089385 & 0.84893470817877 & 0.575532645910615 \tabularnewline
74 & 0.41401386621476 & 0.82802773242952 & 0.58598613378524 \tabularnewline
75 & 0.378047454917881 & 0.756094909835761 & 0.621952545082119 \tabularnewline
76 & 0.416902661052105 & 0.83380532210421 & 0.583097338947895 \tabularnewline
77 & 0.394259242261113 & 0.788518484522226 & 0.605740757738887 \tabularnewline
78 & 0.357027070361589 & 0.714054140723177 & 0.642972929638411 \tabularnewline
79 & 0.345586249943126 & 0.691172499886252 & 0.654413750056874 \tabularnewline
80 & 0.317950689378994 & 0.635901378757989 & 0.682049310621006 \tabularnewline
81 & 0.306152906510422 & 0.612305813020844 & 0.693847093489578 \tabularnewline
82 & 0.272980361640541 & 0.545960723281082 & 0.727019638359459 \tabularnewline
83 & 0.247049999145752 & 0.494099998291505 & 0.752950000854248 \tabularnewline
84 & 0.218330589167834 & 0.436661178335669 & 0.781669410832166 \tabularnewline
85 & 0.19269717532896 & 0.38539435065792 & 0.80730282467104 \tabularnewline
86 & 0.171230784372905 & 0.342461568745809 & 0.828769215627095 \tabularnewline
87 & 0.147842770938106 & 0.295685541876213 & 0.852157229061894 \tabularnewline
88 & 0.129131971550176 & 0.258263943100351 & 0.870868028449824 \tabularnewline
89 & 0.110069616058219 & 0.220139232116437 & 0.889930383941781 \tabularnewline
90 & 0.112357634353485 & 0.22471526870697 & 0.887642365646515 \tabularnewline
91 & 0.0999524460093977 & 0.199904892018795 & 0.900047553990602 \tabularnewline
92 & 0.0891184542264743 & 0.178236908452949 & 0.910881545773526 \tabularnewline
93 & 0.079756096377807 & 0.159512192755614 & 0.920243903622193 \tabularnewline
94 & 0.0711041910148586 & 0.142208382029717 & 0.928895808985141 \tabularnewline
95 & 0.0654454697544613 & 0.130890939508923 & 0.934554530245539 \tabularnewline
96 & 0.0545364199021272 & 0.109072839804254 & 0.945463580097873 \tabularnewline
97 & 0.0468527805455949 & 0.0937055610911898 & 0.953147219454405 \tabularnewline
98 & 0.0413862108304872 & 0.0827724216609745 & 0.958613789169513 \tabularnewline
99 & 0.0371624968636533 & 0.0743249937273066 & 0.962837503136347 \tabularnewline
100 & 0.0305874461757048 & 0.0611748923514095 & 0.969412553824295 \tabularnewline
101 & 0.0247848628845145 & 0.0495697257690291 & 0.975215137115485 \tabularnewline
102 & 0.0613247077100137 & 0.122649415420027 & 0.938675292289986 \tabularnewline
103 & 0.0521656897065205 & 0.104331379413041 & 0.94783431029348 \tabularnewline
104 & 0.0520027143685362 & 0.104005428737072 & 0.947997285631464 \tabularnewline
105 & 0.0598892901205834 & 0.119778580241167 & 0.940110709879417 \tabularnewline
106 & 0.0680309400931659 & 0.136061880186332 & 0.931969059906834 \tabularnewline
107 & 0.0578309662355945 & 0.115661932471189 & 0.942169033764406 \tabularnewline
108 & 0.04811191108695 & 0.0962238221739 & 0.95188808891305 \tabularnewline
109 & 0.0536980339135123 & 0.107396067827025 & 0.946301966086488 \tabularnewline
110 & 0.0448946489112058 & 0.0897892978224116 & 0.955105351088794 \tabularnewline
111 & 0.038555214391338 & 0.0771104287826759 & 0.961444785608662 \tabularnewline
112 & 0.0422081350740982 & 0.0844162701481964 & 0.957791864925902 \tabularnewline
113 & 0.0469019399670057 & 0.0938038799340114 & 0.953098060032994 \tabularnewline
114 & 0.0524073766248766 & 0.104814753249753 & 0.947592623375123 \tabularnewline
115 & 0.0588615300171536 & 0.117723060034307 & 0.941138469982846 \tabularnewline
116 & 0.0503782062860304 & 0.100756412572061 & 0.94962179371397 \tabularnewline
117 & 0.0457647618964792 & 0.0915295237929583 & 0.954235238103521 \tabularnewline
118 & 0.0483444652561717 & 0.0966889305123433 & 0.951655534743828 \tabularnewline
119 & 0.0423993519157934 & 0.0847987038315867 & 0.957600648084207 \tabularnewline
120 & 0.040670419568863 & 0.0813408391377259 & 0.959329580431137 \tabularnewline
121 & 0.0356403621886794 & 0.0712807243773587 & 0.96435963781132 \tabularnewline
122 & 0.0301418564128371 & 0.0602837128256742 & 0.969858143587163 \tabularnewline
123 & 0.0250238674825498 & 0.0500477349650995 & 0.97497613251745 \tabularnewline
124 & 0.0202490566967772 & 0.0404981133935544 & 0.979750943303223 \tabularnewline
125 & 0.0187039328131919 & 0.0374078656263837 & 0.981296067186808 \tabularnewline
126 & 0.0153655438553762 & 0.0307310877107525 & 0.984634456144624 \tabularnewline
127 & 0.0161014197441506 & 0.0322028394883011 & 0.98389858025585 \tabularnewline
128 & 0.0129932207996973 & 0.0259864415993946 & 0.987006779200303 \tabularnewline
129 & 0.0177744379176185 & 0.0355488758352369 & 0.982225562082382 \tabularnewline
130 & 0.015986790778182 & 0.0319735815563639 & 0.984013209221818 \tabularnewline
131 & 0.0289920289409112 & 0.0579840578818225 & 0.971007971059089 \tabularnewline
132 & 0.0308131791483006 & 0.0616263582966013 & 0.9691868208517 \tabularnewline
133 & 0.0358098708264658 & 0.0716197416529317 & 0.964190129173534 \tabularnewline
134 & 0.029395937318916 & 0.0587918746378319 & 0.970604062681084 \tabularnewline
135 & 0.0239834028050418 & 0.0479668056100835 & 0.976016597194958 \tabularnewline
136 & 0.0201132666637877 & 0.0402265333275753 & 0.979886733336212 \tabularnewline
137 & 0.0235151946016839 & 0.0470303892033677 & 0.976484805398316 \tabularnewline
138 & 0.0315035975918835 & 0.0630071951837671 & 0.968496402408116 \tabularnewline
139 & 0.0305084954557837 & 0.0610169909115673 & 0.969491504544216 \tabularnewline
140 & 0.0398843916277929 & 0.0797687832555859 & 0.960115608372207 \tabularnewline
141 & 0.0377090032568984 & 0.0754180065137968 & 0.962290996743102 \tabularnewline
142 & 0.0317165827888799 & 0.0634331655777597 & 0.96828341721112 \tabularnewline
143 & 0.0321245618592344 & 0.0642491237184688 & 0.967875438140766 \tabularnewline
144 & 0.0298175341772771 & 0.0596350683545541 & 0.970182465822723 \tabularnewline
145 & 0.0335051087725584 & 0.0670102175451168 & 0.966494891227442 \tabularnewline
146 & 0.0545313280882924 & 0.109062656176585 & 0.945468671911708 \tabularnewline
147 & 0.0457356146805653 & 0.0914712293611305 & 0.954264385319435 \tabularnewline
148 & 0.0450528561959695 & 0.090105712391939 & 0.95494714380403 \tabularnewline
149 & 0.0375945604810488 & 0.0751891209620976 & 0.962405439518951 \tabularnewline
150 & 0.0449230074296906 & 0.0898460148593812 & 0.95507699257031 \tabularnewline
151 & 0.0372904343301545 & 0.074580868660309 & 0.962709565669845 \tabularnewline
152 & 0.0347814145360691 & 0.0695628290721382 & 0.96521858546393 \tabularnewline
153 & 0.0305489882812954 & 0.0610979765625908 & 0.969451011718705 \tabularnewline
154 & 0.0249733061731281 & 0.0499466123462563 & 0.975026693826872 \tabularnewline
155 & 0.032529513948688 & 0.065059027897376 & 0.967470486051312 \tabularnewline
156 & 0.0451004954584782 & 0.0902009909169564 & 0.954899504541522 \tabularnewline
157 & 0.0393369445255046 & 0.0786738890510092 & 0.960663055474495 \tabularnewline
158 & 0.0324260494859801 & 0.0648520989719602 & 0.96757395051402 \tabularnewline
159 & 0.0395681503094176 & 0.0791363006188351 & 0.960431849690582 \tabularnewline
160 & 0.0359147621430256 & 0.0718295242860513 & 0.964085237856974 \tabularnewline
161 & 0.0298035289431642 & 0.0596070578863283 & 0.970196471056836 \tabularnewline
162 & 0.0252601582827645 & 0.050520316565529 & 0.974739841717235 \tabularnewline
163 & 0.0226138443493035 & 0.045227688698607 & 0.977386155650697 \tabularnewline
164 & 0.0232912480766078 & 0.0465824961532155 & 0.976708751923392 \tabularnewline
165 & 0.0187689713584763 & 0.0375379427169526 & 0.981231028641524 \tabularnewline
166 & 0.0259889216573295 & 0.0519778433146589 & 0.97401107834267 \tabularnewline
167 & 0.0211005037246297 & 0.0422010074492595 & 0.97889949627537 \tabularnewline
168 & 0.0195061606605461 & 0.0390123213210923 & 0.980493839339454 \tabularnewline
169 & 0.0170878104827795 & 0.034175620965559 & 0.98291218951722 \tabularnewline
170 & 0.0156741606876538 & 0.0313483213753076 & 0.984325839312346 \tabularnewline
171 & 0.0137902678073494 & 0.0275805356146987 & 0.98620973219265 \tabularnewline
172 & 0.0113072642386954 & 0.0226145284773909 & 0.988692735761305 \tabularnewline
173 & 0.0104901736339888 & 0.0209803472679776 & 0.98950982636601 \tabularnewline
174 & 0.0161024910075145 & 0.032204982015029 & 0.983897508992486 \tabularnewline
175 & 0.0144116215565221 & 0.0288232431130443 & 0.985588378443478 \tabularnewline
176 & 0.0115833322869133 & 0.0231666645738267 & 0.988416667713087 \tabularnewline
177 & 0.0102256683881348 & 0.0204513367762696 & 0.989774331611865 \tabularnewline
178 & 0.00889256085792006 & 0.0177851217158401 & 0.99110743914208 \tabularnewline
179 & 0.00689240253572865 & 0.0137848050714573 & 0.993107597464271 \tabularnewline
180 & 0.0072750224286476 & 0.0145500448572952 & 0.992724977571352 \tabularnewline
181 & 0.00619421326821326 & 0.0123884265364265 & 0.993805786731787 \tabularnewline
182 & 0.00682679125386256 & 0.0136535825077251 & 0.993173208746137 \tabularnewline
183 & 0.013329233143743 & 0.026658466287486 & 0.986670766856257 \tabularnewline
184 & 0.0109927334102271 & 0.0219854668204542 & 0.989007266589773 \tabularnewline
185 & 0.0373797167681458 & 0.0747594335362916 & 0.962620283231854 \tabularnewline
186 & 0.0334014217106345 & 0.0668028434212689 & 0.966598578289366 \tabularnewline
187 & 0.0368974194471207 & 0.0737948388942414 & 0.96310258055288 \tabularnewline
188 & 0.0353306370389003 & 0.0706612740778007 & 0.9646693629611 \tabularnewline
189 & 0.0376194856750342 & 0.0752389713500684 & 0.962380514324966 \tabularnewline
190 & 0.0479637305009323 & 0.0959274610018646 & 0.952036269499068 \tabularnewline
191 & 0.0394845153841264 & 0.0789690307682528 & 0.960515484615874 \tabularnewline
192 & 0.0330965844506508 & 0.0661931689013015 & 0.96690341554935 \tabularnewline
193 & 0.034376828891194 & 0.068753657782388 & 0.965623171108806 \tabularnewline
194 & 0.0309114811903551 & 0.0618229623807103 & 0.969088518809645 \tabularnewline
195 & 0.0268782181612733 & 0.0537564363225466 & 0.973121781838727 \tabularnewline
196 & 0.0215033741134528 & 0.0430067482269057 & 0.978496625886547 \tabularnewline
197 & 0.0205496015581862 & 0.0410992031163723 & 0.979450398441814 \tabularnewline
198 & 0.0177417874882569 & 0.0354835749765137 & 0.982258212511743 \tabularnewline
199 & 0.0140735678764607 & 0.0281471357529214 & 0.98592643212354 \tabularnewline
200 & 0.0137617649990183 & 0.0275235299980367 & 0.986238235000982 \tabularnewline
201 & 0.0164397989516915 & 0.032879597903383 & 0.983560201048309 \tabularnewline
202 & 0.0127859858070296 & 0.0255719716140593 & 0.98721401419297 \tabularnewline
203 & 0.0158609674018217 & 0.0317219348036434 & 0.984139032598178 \tabularnewline
204 & 0.0142813226969069 & 0.0285626453938137 & 0.985718677303093 \tabularnewline
205 & 0.0112707000615005 & 0.0225414001230011 & 0.9887292999385 \tabularnewline
206 & 0.00870161726534485 & 0.0174032345306897 & 0.991298382734655 \tabularnewline
207 & 0.0076732484461276 & 0.0153464968922552 & 0.992326751553872 \tabularnewline
208 & 0.00665943540036828 & 0.0133188708007366 & 0.993340564599632 \tabularnewline
209 & 0.00727697179507557 & 0.0145539435901511 & 0.992723028204924 \tabularnewline
210 & 0.00566604987148207 & 0.0113320997429641 & 0.994333950128518 \tabularnewline
211 & 0.0044861954109579 & 0.00897239082191581 & 0.995513804589042 \tabularnewline
212 & 0.00580369188481743 & 0.0116073837696349 & 0.994196308115183 \tabularnewline
213 & 0.00443726182816019 & 0.00887452365632037 & 0.99556273817184 \tabularnewline
214 & 0.00397922651951039 & 0.00795845303902077 & 0.99602077348049 \tabularnewline
215 & 0.00429419045693912 & 0.00858838091387824 & 0.99570580954306 \tabularnewline
216 & 0.00336656371362555 & 0.0067331274272511 & 0.996633436286374 \tabularnewline
217 & 0.00407898439231149 & 0.00815796878462297 & 0.995921015607689 \tabularnewline
218 & 0.00299226291356525 & 0.00598452582713049 & 0.997007737086435 \tabularnewline
219 & 0.00214455895816618 & 0.00428911791633235 & 0.997855441041834 \tabularnewline
220 & 0.00164812739246971 & 0.00329625478493942 & 0.99835187260753 \tabularnewline
221 & 0.00114888608790125 & 0.00229777217580249 & 0.998851113912099 \tabularnewline
222 & 0.00079943523827279 & 0.00159887047654558 & 0.999200564761727 \tabularnewline
223 & 0.000937261648304412 & 0.00187452329660882 & 0.999062738351696 \tabularnewline
224 & 0.000658030518481236 & 0.00131606103696247 & 0.999341969481519 \tabularnewline
225 & 0.000468812537862254 & 0.000937625075724508 & 0.999531187462138 \tabularnewline
226 & 0.00264948037432863 & 0.00529896074865725 & 0.997350519625671 \tabularnewline
227 & 0.00181817631259484 & 0.00363635262518967 & 0.998181823687405 \tabularnewline
228 & 0.00134760855342533 & 0.00269521710685067 & 0.998652391446575 \tabularnewline
229 & 0.000934123811785665 & 0.00186824762357133 & 0.999065876188214 \tabularnewline
230 & 0.000626175221416482 & 0.00125235044283296 & 0.999373824778583 \tabularnewline
231 & 0.000399371947554089 & 0.000798743895108177 & 0.999600628052446 \tabularnewline
232 & 0.000451201978310676 & 0.000902403956621353 & 0.99954879802169 \tabularnewline
233 & 0.000460223480245097 & 0.000920446960490194 & 0.999539776519755 \tabularnewline
234 & 0.00267819155651566 & 0.00535638311303132 & 0.997321808443484 \tabularnewline
235 & 0.00183572666331651 & 0.00367145332663302 & 0.998164273336683 \tabularnewline
236 & 0.00121511736748613 & 0.00243023473497227 & 0.998784882632514 \tabularnewline
237 & 0.0513040824775471 & 0.102608164955094 & 0.948695917522453 \tabularnewline
238 & 0.0382727530673985 & 0.076545506134797 & 0.961727246932601 \tabularnewline
239 & 0.0655073666746766 & 0.131014733349353 & 0.934492633325323 \tabularnewline
240 & 0.0582908701771756 & 0.116581740354351 & 0.941709129822824 \tabularnewline
241 & 0.0421592745496407 & 0.0843185490992813 & 0.95784072545036 \tabularnewline
242 & 0.0294246763347633 & 0.0588493526695267 & 0.970575323665237 \tabularnewline
243 & 0.02738708097092 & 0.0547741619418401 & 0.97261291902908 \tabularnewline
244 & 0.0182615315752469 & 0.0365230631504937 & 0.981738468424753 \tabularnewline
245 & 0.0165811214577877 & 0.0331622429155754 & 0.983418878542212 \tabularnewline
246 & 0.0106544002410542 & 0.0213088004821084 & 0.989345599758946 \tabularnewline
247 & 0.00763954688233927 & 0.0152790937646785 & 0.99236045311766 \tabularnewline
248 & 0.00722589860978108 & 0.0144517972195622 & 0.992774101390219 \tabularnewline
249 & 0.00430287998453652 & 0.00860575996907305 & 0.995697120015464 \tabularnewline
250 & 0.00269172290617684 & 0.00538344581235368 & 0.997308277093823 \tabularnewline
251 & 0.0022891577899525 & 0.00457831557990501 & 0.997710842210047 \tabularnewline
252 & 0.00309433099146716 & 0.00618866198293431 & 0.996905669008533 \tabularnewline
253 & 0.00288988847217096 & 0.00577977694434192 & 0.997110111527829 \tabularnewline
254 & 0.00191484425115493 & 0.00382968850230986 & 0.998085155748845 \tabularnewline
255 & 0.00105942577652089 & 0.00211885155304177 & 0.99894057422348 \tabularnewline
256 & 0.000375165147858687 & 0.000750330295717374 & 0.99962483485214 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146344&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]0.82909295029887[/C][C]0.34181409940226[/C][C]0.17090704970113[/C][/ROW]
[ROW][C]9[/C][C]0.725793348031422[/C][C]0.548413303937155[/C][C]0.274206651968578[/C][/ROW]
[ROW][C]10[/C][C]0.633324730146804[/C][C]0.733350539706392[/C][C]0.366675269853196[/C][/ROW]
[ROW][C]11[/C][C]0.519073330837735[/C][C]0.96185333832453[/C][C]0.480926669162265[/C][/ROW]
[ROW][C]12[/C][C]0.498927320210495[/C][C]0.99785464042099[/C][C]0.501072679789505[/C][/ROW]
[ROW][C]13[/C][C]0.449987912108717[/C][C]0.899975824217434[/C][C]0.550012087891283[/C][/ROW]
[ROW][C]14[/C][C]0.382135817328412[/C][C]0.764271634656824[/C][C]0.617864182671588[/C][/ROW]
[ROW][C]15[/C][C]0.360903621177819[/C][C]0.721807242355638[/C][C]0.639096378822181[/C][/ROW]
[ROW][C]16[/C][C]0.337075688940894[/C][C]0.674151377881788[/C][C]0.662924311059106[/C][/ROW]
[ROW][C]17[/C][C]0.293292809660005[/C][C]0.58658561932001[/C][C]0.706707190339995[/C][/ROW]
[ROW][C]18[/C][C]0.494591643811189[/C][C]0.989183287622378[/C][C]0.505408356188811[/C][/ROW]
[ROW][C]19[/C][C]0.428555380466227[/C][C]0.857110760932453[/C][C]0.571444619533773[/C][/ROW]
[ROW][C]20[/C][C]0.362679386102842[/C][C]0.725358772205684[/C][C]0.637320613897158[/C][/ROW]
[ROW][C]21[/C][C]0.297666234381107[/C][C]0.595332468762213[/C][C]0.702333765618893[/C][/ROW]
[ROW][C]22[/C][C]0.249540021651193[/C][C]0.499080043302387[/C][C]0.750459978348807[/C][/ROW]
[ROW][C]23[/C][C]0.227095978630587[/C][C]0.454191957261174[/C][C]0.772904021369413[/C][/ROW]
[ROW][C]24[/C][C]0.211963890838883[/C][C]0.423927781677765[/C][C]0.788036109161117[/C][/ROW]
[ROW][C]25[/C][C]0.166037081711357[/C][C]0.332074163422713[/C][C]0.833962918288643[/C][/ROW]
[ROW][C]26[/C][C]0.13037359652[/C][C]0.260747193040001[/C][C]0.86962640348[/C][/ROW]
[ROW][C]27[/C][C]0.107685304695136[/C][C]0.215370609390273[/C][C]0.892314695304864[/C][/ROW]
[ROW][C]28[/C][C]0.113704243780665[/C][C]0.22740848756133[/C][C]0.886295756219335[/C][/ROW]
[ROW][C]29[/C][C]0.08883073289617[/C][C]0.17766146579234[/C][C]0.91116926710383[/C][/ROW]
[ROW][C]30[/C][C]0.107585550514434[/C][C]0.215171101028868[/C][C]0.892414449485566[/C][/ROW]
[ROW][C]31[/C][C]0.0883164713249593[/C][C]0.176632942649919[/C][C]0.91168352867504[/C][/ROW]
[ROW][C]32[/C][C]0.168731780256304[/C][C]0.337463560512608[/C][C]0.831268219743696[/C][/ROW]
[ROW][C]33[/C][C]0.148154934337565[/C][C]0.296309868675129[/C][C]0.851845065662435[/C][/ROW]
[ROW][C]34[/C][C]0.116675535270007[/C][C]0.233351070540013[/C][C]0.883324464729993[/C][/ROW]
[ROW][C]35[/C][C]0.0962107964149597[/C][C]0.192421592829919[/C][C]0.90378920358504[/C][/ROW]
[ROW][C]36[/C][C]0.280381819691764[/C][C]0.560763639383528[/C][C]0.719618180308236[/C][/ROW]
[ROW][C]37[/C][C]0.423646004938185[/C][C]0.84729200987637[/C][C]0.576353995061815[/C][/ROW]
[ROW][C]38[/C][C]0.374927819658225[/C][C]0.749855639316449[/C][C]0.625072180341775[/C][/ROW]
[ROW][C]39[/C][C]0.359532854551726[/C][C]0.719065709103452[/C][C]0.640467145448274[/C][/ROW]
[ROW][C]40[/C][C]0.335433886159167[/C][C]0.670867772318334[/C][C]0.664566113840833[/C][/ROW]
[ROW][C]41[/C][C]0.310553949559495[/C][C]0.62110789911899[/C][C]0.689446050440505[/C][/ROW]
[ROW][C]42[/C][C]0.272857885416462[/C][C]0.545715770832924[/C][C]0.727142114583538[/C][/ROW]
[ROW][C]43[/C][C]0.500157488494983[/C][C]0.999685023010033[/C][C]0.499842511505017[/C][/ROW]
[ROW][C]44[/C][C]0.456337143189111[/C][C]0.912674286378222[/C][C]0.543662856810889[/C][/ROW]
[ROW][C]45[/C][C]0.40785097678664[/C][C]0.81570195357328[/C][C]0.59214902321336[/C][/ROW]
[ROW][C]46[/C][C]0.499236296826872[/C][C]0.998472593653745[/C][C]0.500763703173128[/C][/ROW]
[ROW][C]47[/C][C]0.474260724249037[/C][C]0.948521448498073[/C][C]0.525739275750963[/C][/ROW]
[ROW][C]48[/C][C]0.428128534893484[/C][C]0.856257069786967[/C][C]0.571871465106516[/C][/ROW]
[ROW][C]49[/C][C]0.384542546126886[/C][C]0.769085092253772[/C][C]0.615457453873114[/C][/ROW]
[ROW][C]50[/C][C]0.341063691382447[/C][C]0.682127382764893[/C][C]0.658936308617553[/C][/ROW]
[ROW][C]51[/C][C]0.302303240799947[/C][C]0.604606481599893[/C][C]0.697696759200053[/C][/ROW]
[ROW][C]52[/C][C]0.263249908675945[/C][C]0.52649981735189[/C][C]0.736750091324055[/C][/ROW]
[ROW][C]53[/C][C]0.257386243310743[/C][C]0.514772486621487[/C][C]0.742613756689257[/C][/ROW]
[ROW][C]54[/C][C]0.233831733510024[/C][C]0.467663467020047[/C][C]0.766168266489976[/C][/ROW]
[ROW][C]55[/C][C]0.393034136037344[/C][C]0.786068272074688[/C][C]0.606965863962656[/C][/ROW]
[ROW][C]56[/C][C]0.356186606063616[/C][C]0.712373212127233[/C][C]0.643813393936384[/C][/ROW]
[ROW][C]57[/C][C]0.333948899308185[/C][C]0.66789779861637[/C][C]0.666051100691815[/C][/ROW]
[ROW][C]58[/C][C]0.314762972216391[/C][C]0.629525944432782[/C][C]0.685237027783609[/C][/ROW]
[ROW][C]59[/C][C]0.282027021181351[/C][C]0.564054042362701[/C][C]0.71797297881865[/C][/ROW]
[ROW][C]60[/C][C]0.276102466381508[/C][C]0.552204932763017[/C][C]0.723897533618492[/C][/ROW]
[ROW][C]61[/C][C]0.246489145222879[/C][C]0.492978290445759[/C][C]0.75351085477712[/C][/ROW]
[ROW][C]62[/C][C]0.234384693115221[/C][C]0.468769386230442[/C][C]0.765615306884779[/C][/ROW]
[ROW][C]63[/C][C]0.224105551532004[/C][C]0.448211103064008[/C][C]0.775894448467996[/C][/ROW]
[ROW][C]64[/C][C]0.198105869271412[/C][C]0.396211738542825[/C][C]0.801894130728588[/C][/ROW]
[ROW][C]65[/C][C]0.189468445854296[/C][C]0.378936891708591[/C][C]0.810531554145704[/C][/ROW]
[ROW][C]66[/C][C]0.221359858573468[/C][C]0.442719717146936[/C][C]0.778640141426532[/C][/ROW]
[ROW][C]67[/C][C]0.199303282816896[/C][C]0.398606565633792[/C][C]0.800696717183104[/C][/ROW]
[ROW][C]68[/C][C]0.180799594362499[/C][C]0.361599188724998[/C][C]0.8192004056375[/C][/ROW]
[ROW][C]69[/C][C]0.220139545413049[/C][C]0.440279090826098[/C][C]0.779860454586951[/C][/ROW]
[ROW][C]70[/C][C]0.206271782214742[/C][C]0.412543564429485[/C][C]0.793728217785258[/C][/ROW]
[ROW][C]71[/C][C]0.449500417656975[/C][C]0.89900083531395[/C][C]0.550499582343025[/C][/ROW]
[ROW][C]72[/C][C]0.42251551090566[/C][C]0.84503102181132[/C][C]0.57748448909434[/C][/ROW]
[ROW][C]73[/C][C]0.424467354089385[/C][C]0.84893470817877[/C][C]0.575532645910615[/C][/ROW]
[ROW][C]74[/C][C]0.41401386621476[/C][C]0.82802773242952[/C][C]0.58598613378524[/C][/ROW]
[ROW][C]75[/C][C]0.378047454917881[/C][C]0.756094909835761[/C][C]0.621952545082119[/C][/ROW]
[ROW][C]76[/C][C]0.416902661052105[/C][C]0.83380532210421[/C][C]0.583097338947895[/C][/ROW]
[ROW][C]77[/C][C]0.394259242261113[/C][C]0.788518484522226[/C][C]0.605740757738887[/C][/ROW]
[ROW][C]78[/C][C]0.357027070361589[/C][C]0.714054140723177[/C][C]0.642972929638411[/C][/ROW]
[ROW][C]79[/C][C]0.345586249943126[/C][C]0.691172499886252[/C][C]0.654413750056874[/C][/ROW]
[ROW][C]80[/C][C]0.317950689378994[/C][C]0.635901378757989[/C][C]0.682049310621006[/C][/ROW]
[ROW][C]81[/C][C]0.306152906510422[/C][C]0.612305813020844[/C][C]0.693847093489578[/C][/ROW]
[ROW][C]82[/C][C]0.272980361640541[/C][C]0.545960723281082[/C][C]0.727019638359459[/C][/ROW]
[ROW][C]83[/C][C]0.247049999145752[/C][C]0.494099998291505[/C][C]0.752950000854248[/C][/ROW]
[ROW][C]84[/C][C]0.218330589167834[/C][C]0.436661178335669[/C][C]0.781669410832166[/C][/ROW]
[ROW][C]85[/C][C]0.19269717532896[/C][C]0.38539435065792[/C][C]0.80730282467104[/C][/ROW]
[ROW][C]86[/C][C]0.171230784372905[/C][C]0.342461568745809[/C][C]0.828769215627095[/C][/ROW]
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[ROW][C]232[/C][C]0.000451201978310676[/C][C]0.000902403956621353[/C][C]0.99954879802169[/C][/ROW]
[ROW][C]233[/C][C]0.000460223480245097[/C][C]0.000920446960490194[/C][C]0.999539776519755[/C][/ROW]
[ROW][C]234[/C][C]0.00267819155651566[/C][C]0.00535638311303132[/C][C]0.997321808443484[/C][/ROW]
[ROW][C]235[/C][C]0.00183572666331651[/C][C]0.00367145332663302[/C][C]0.998164273336683[/C][/ROW]
[ROW][C]236[/C][C]0.00121511736748613[/C][C]0.00243023473497227[/C][C]0.998784882632514[/C][/ROW]
[ROW][C]237[/C][C]0.0513040824775471[/C][C]0.102608164955094[/C][C]0.948695917522453[/C][/ROW]
[ROW][C]238[/C][C]0.0382727530673985[/C][C]0.076545506134797[/C][C]0.961727246932601[/C][/ROW]
[ROW][C]239[/C][C]0.0655073666746766[/C][C]0.131014733349353[/C][C]0.934492633325323[/C][/ROW]
[ROW][C]240[/C][C]0.0582908701771756[/C][C]0.116581740354351[/C][C]0.941709129822824[/C][/ROW]
[ROW][C]241[/C][C]0.0421592745496407[/C][C]0.0843185490992813[/C][C]0.95784072545036[/C][/ROW]
[ROW][C]242[/C][C]0.0294246763347633[/C][C]0.0588493526695267[/C][C]0.970575323665237[/C][/ROW]
[ROW][C]243[/C][C]0.02738708097092[/C][C]0.0547741619418401[/C][C]0.97261291902908[/C][/ROW]
[ROW][C]244[/C][C]0.0182615315752469[/C][C]0.0365230631504937[/C][C]0.981738468424753[/C][/ROW]
[ROW][C]245[/C][C]0.0165811214577877[/C][C]0.0331622429155754[/C][C]0.983418878542212[/C][/ROW]
[ROW][C]246[/C][C]0.0106544002410542[/C][C]0.0213088004821084[/C][C]0.989345599758946[/C][/ROW]
[ROW][C]247[/C][C]0.00763954688233927[/C][C]0.0152790937646785[/C][C]0.99236045311766[/C][/ROW]
[ROW][C]248[/C][C]0.00722589860978108[/C][C]0.0144517972195622[/C][C]0.992774101390219[/C][/ROW]
[ROW][C]249[/C][C]0.00430287998453652[/C][C]0.00860575996907305[/C][C]0.995697120015464[/C][/ROW]
[ROW][C]250[/C][C]0.00269172290617684[/C][C]0.00538344581235368[/C][C]0.997308277093823[/C][/ROW]
[ROW][C]251[/C][C]0.0022891577899525[/C][C]0.00457831557990501[/C][C]0.997710842210047[/C][/ROW]
[ROW][C]252[/C][C]0.00309433099146716[/C][C]0.00618866198293431[/C][C]0.996905669008533[/C][/ROW]
[ROW][C]253[/C][C]0.00288988847217096[/C][C]0.00577977694434192[/C][C]0.997110111527829[/C][/ROW]
[ROW][C]254[/C][C]0.00191484425115493[/C][C]0.00382968850230986[/C][C]0.998085155748845[/C][/ROW]
[ROW][C]255[/C][C]0.00105942577652089[/C][C]0.00211885155304177[/C][C]0.99894057422348[/C][/ROW]
[ROW][C]256[/C][C]0.000375165147858687[/C][C]0.000750330295717374[/C][C]0.99962483485214[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146344&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146344&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
80.829092950298870.341814099402260.17090704970113
90.7257933480314220.5484133039371550.274206651968578
100.6333247301468040.7333505397063920.366675269853196
110.5190733308377350.961853338324530.480926669162265
120.4989273202104950.997854640420990.501072679789505
130.4499879121087170.8999758242174340.550012087891283
140.3821358173284120.7642716346568240.617864182671588
150.3609036211778190.7218072423556380.639096378822181
160.3370756889408940.6741513778817880.662924311059106
170.2932928096600050.586585619320010.706707190339995
180.4945916438111890.9891832876223780.505408356188811
190.4285553804662270.8571107609324530.571444619533773
200.3626793861028420.7253587722056840.637320613897158
210.2976662343811070.5953324687622130.702333765618893
220.2495400216511930.4990800433023870.750459978348807
230.2270959786305870.4541919572611740.772904021369413
240.2119638908388830.4239277816777650.788036109161117
250.1660370817113570.3320741634227130.833962918288643
260.130373596520.2607471930400010.86962640348
270.1076853046951360.2153706093902730.892314695304864
280.1137042437806650.227408487561330.886295756219335
290.088830732896170.177661465792340.91116926710383
300.1075855505144340.2151711010288680.892414449485566
310.08831647132495930.1766329426499190.91168352867504
320.1687317802563040.3374635605126080.831268219743696
330.1481549343375650.2963098686751290.851845065662435
340.1166755352700070.2333510705400130.883324464729993
350.09621079641495970.1924215928299190.90378920358504
360.2803818196917640.5607636393835280.719618180308236
370.4236460049381850.847292009876370.576353995061815
380.3749278196582250.7498556393164490.625072180341775
390.3595328545517260.7190657091034520.640467145448274
400.3354338861591670.6708677723183340.664566113840833
410.3105539495594950.621107899118990.689446050440505
420.2728578854164620.5457157708329240.727142114583538
430.5001574884949830.9996850230100330.499842511505017
440.4563371431891110.9126742863782220.543662856810889
450.407850976786640.815701953573280.59214902321336
460.4992362968268720.9984725936537450.500763703173128
470.4742607242490370.9485214484980730.525739275750963
480.4281285348934840.8562570697869670.571871465106516
490.3845425461268860.7690850922537720.615457453873114
500.3410636913824470.6821273827648930.658936308617553
510.3023032407999470.6046064815998930.697696759200053
520.2632499086759450.526499817351890.736750091324055
530.2573862433107430.5147724866214870.742613756689257
540.2338317335100240.4676634670200470.766168266489976
550.3930341360373440.7860682720746880.606965863962656
560.3561866060636160.7123732121272330.643813393936384
570.3339488993081850.667897798616370.666051100691815
580.3147629722163910.6295259444327820.685237027783609
590.2820270211813510.5640540423627010.71797297881865
600.2761024663815080.5522049327630170.723897533618492
610.2464891452228790.4929782904457590.75351085477712
620.2343846931152210.4687693862304420.765615306884779
630.2241055515320040.4482111030640080.775894448467996
640.1981058692714120.3962117385428250.801894130728588
650.1894684458542960.3789368917085910.810531554145704
660.2213598585734680.4427197171469360.778640141426532
670.1993032828168960.3986065656337920.800696717183104
680.1807995943624990.3615991887249980.8192004056375
690.2201395454130490.4402790908260980.779860454586951
700.2062717822147420.4125435644294850.793728217785258
710.4495004176569750.899000835313950.550499582343025
720.422515510905660.845031021811320.57748448909434
730.4244673540893850.848934708178770.575532645910615
740.414013866214760.828027732429520.58598613378524
750.3780474549178810.7560949098357610.621952545082119
760.4169026610521050.833805322104210.583097338947895
770.3942592422611130.7885184845222260.605740757738887
780.3570270703615890.7140541407231770.642972929638411
790.3455862499431260.6911724998862520.654413750056874
800.3179506893789940.6359013787579890.682049310621006
810.3061529065104220.6123058130208440.693847093489578
820.2729803616405410.5459607232810820.727019638359459
830.2470499991457520.4940999982915050.752950000854248
840.2183305891678340.4366611783356690.781669410832166
850.192697175328960.385394350657920.80730282467104
860.1712307843729050.3424615687458090.828769215627095
870.1478427709381060.2956855418762130.852157229061894
880.1291319715501760.2582639431003510.870868028449824
890.1100696160582190.2201392321164370.889930383941781
900.1123576343534850.224715268706970.887642365646515
910.09995244600939770.1999048920187950.900047553990602
920.08911845422647430.1782369084529490.910881545773526
930.0797560963778070.1595121927556140.920243903622193
940.07110419101485860.1422083820297170.928895808985141
950.06544546975446130.1308909395089230.934554530245539
960.05453641990212720.1090728398042540.945463580097873
970.04685278054559490.09370556109118980.953147219454405
980.04138621083048720.08277242166097450.958613789169513
990.03716249686365330.07432499372730660.962837503136347
1000.03058744617570480.06117489235140950.969412553824295
1010.02478486288451450.04956972576902910.975215137115485
1020.06132470771001370.1226494154200270.938675292289986
1030.05216568970652050.1043313794130410.94783431029348
1040.05200271436853620.1040054287370720.947997285631464
1050.05988929012058340.1197785802411670.940110709879417
1060.06803094009316590.1360618801863320.931969059906834
1070.05783096623559450.1156619324711890.942169033764406
1080.048111911086950.09622382217390.95188808891305
1090.05369803391351230.1073960678270250.946301966086488
1100.04489464891120580.08978929782241160.955105351088794
1110.0385552143913380.07711042878267590.961444785608662
1120.04220813507409820.08441627014819640.957791864925902
1130.04690193996700570.09380387993401140.953098060032994
1140.05240737662487660.1048147532497530.947592623375123
1150.05886153001715360.1177230600343070.941138469982846
1160.05037820628603040.1007564125720610.94962179371397
1170.04576476189647920.09152952379295830.954235238103521
1180.04834446525617170.09668893051234330.951655534743828
1190.04239935191579340.08479870383158670.957600648084207
1200.0406704195688630.08134083913772590.959329580431137
1210.03564036218867940.07128072437735870.96435963781132
1220.03014185641283710.06028371282567420.969858143587163
1230.02502386748254980.05004773496509950.97497613251745
1240.02024905669677720.04049811339355440.979750943303223
1250.01870393281319190.03740786562638370.981296067186808
1260.01536554385537620.03073108771075250.984634456144624
1270.01610141974415060.03220283948830110.98389858025585
1280.01299322079969730.02598644159939460.987006779200303
1290.01777443791761850.03554887583523690.982225562082382
1300.0159867907781820.03197358155636390.984013209221818
1310.02899202894091120.05798405788182250.971007971059089
1320.03081317914830060.06162635829660130.9691868208517
1330.03580987082646580.07161974165293170.964190129173534
1340.0293959373189160.05879187463783190.970604062681084
1350.02398340280504180.04796680561008350.976016597194958
1360.02011326666378770.04022653332757530.979886733336212
1370.02351519460168390.04703038920336770.976484805398316
1380.03150359759188350.06300719518376710.968496402408116
1390.03050849545578370.06101699091156730.969491504544216
1400.03988439162779290.07976878325558590.960115608372207
1410.03770900325689840.07541800651379680.962290996743102
1420.03171658278887990.06343316557775970.96828341721112
1430.03212456185923440.06424912371846880.967875438140766
1440.02981753417727710.05963506835455410.970182465822723
1450.03350510877255840.06701021754511680.966494891227442
1460.05453132808829240.1090626561765850.945468671911708
1470.04573561468056530.09147122936113050.954264385319435
1480.04505285619596950.0901057123919390.95494714380403
1490.03759456048104880.07518912096209760.962405439518951
1500.04492300742969060.08984601485938120.95507699257031
1510.03729043433015450.0745808686603090.962709565669845
1520.03478141453606910.06956282907213820.96521858546393
1530.03054898828129540.06109797656259080.969451011718705
1540.02497330617312810.04994661234625630.975026693826872
1550.0325295139486880.0650590278973760.967470486051312
1560.04510049545847820.09020099091695640.954899504541522
1570.03933694452550460.07867388905100920.960663055474495
1580.03242604948598010.06485209897196020.96757395051402
1590.03956815030941760.07913630061883510.960431849690582
1600.03591476214302560.07182952428605130.964085237856974
1610.02980352894316420.05960705788632830.970196471056836
1620.02526015828276450.0505203165655290.974739841717235
1630.02261384434930350.0452276886986070.977386155650697
1640.02329124807660780.04658249615321550.976708751923392
1650.01876897135847630.03753794271695260.981231028641524
1660.02598892165732950.05197784331465890.97401107834267
1670.02110050372462970.04220100744925950.97889949627537
1680.01950616066054610.03901232132109230.980493839339454
1690.01708781048277950.0341756209655590.98291218951722
1700.01567416068765380.03134832137530760.984325839312346
1710.01379026780734940.02758053561469870.98620973219265
1720.01130726423869540.02261452847739090.988692735761305
1730.01049017363398880.02098034726797760.98950982636601
1740.01610249100751450.0322049820150290.983897508992486
1750.01441162155652210.02882324311304430.985588378443478
1760.01158333228691330.02316666457382670.988416667713087
1770.01022566838813480.02045133677626960.989774331611865
1780.008892560857920060.01778512171584010.99110743914208
1790.006892402535728650.01378480507145730.993107597464271
1800.00727502242864760.01455004485729520.992724977571352
1810.006194213268213260.01238842653642650.993805786731787
1820.006826791253862560.01365358250772510.993173208746137
1830.0133292331437430.0266584662874860.986670766856257
1840.01099273341022710.02198546682045420.989007266589773
1850.03737971676814580.07475943353629160.962620283231854
1860.03340142171063450.06680284342126890.966598578289366
1870.03689741944712070.07379483889424140.96310258055288
1880.03533063703890030.07066127407780070.9646693629611
1890.03761948567503420.07523897135006840.962380514324966
1900.04796373050093230.09592746100186460.952036269499068
1910.03948451538412640.07896903076825280.960515484615874
1920.03309658445065080.06619316890130150.96690341554935
1930.0343768288911940.0687536577823880.965623171108806
1940.03091148119035510.06182296238071030.969088518809645
1950.02687821816127330.05375643632254660.973121781838727
1960.02150337411345280.04300674822690570.978496625886547
1970.02054960155818620.04109920311637230.979450398441814
1980.01774178748825690.03548357497651370.982258212511743
1990.01407356787646070.02814713575292140.98592643212354
2000.01376176499901830.02752352999803670.986238235000982
2010.01643979895169150.0328795979033830.983560201048309
2020.01278598580702960.02557197161405930.98721401419297
2030.01586096740182170.03172193480364340.984139032598178
2040.01428132269690690.02856264539381370.985718677303093
2050.01127070006150050.02254140012300110.9887292999385
2060.008701617265344850.01740323453068970.991298382734655
2070.00767324844612760.01534649689225520.992326751553872
2080.006659435400368280.01331887080073660.993340564599632
2090.007276971795075570.01455394359015110.992723028204924
2100.005666049871482070.01133209974296410.994333950128518
2110.00448619541095790.008972390821915810.995513804589042
2120.005803691884817430.01160738376963490.994196308115183
2130.004437261828160190.008874523656320370.99556273817184
2140.003979226519510390.007958453039020770.99602077348049
2150.004294190456939120.008588380913878240.99570580954306
2160.003366563713625550.00673312742725110.996633436286374
2170.004078984392311490.008157968784622970.995921015607689
2180.002992262913565250.005984525827130490.997007737086435
2190.002144558958166180.004289117916332350.997855441041834
2200.001648127392469710.003296254784939420.99835187260753
2210.001148886087901250.002297772175802490.998851113912099
2220.000799435238272790.001598870476545580.999200564761727
2230.0009372616483044120.001874523296608820.999062738351696
2240.0006580305184812360.001316061036962470.999341969481519
2250.0004688125378622540.0009376250757245080.999531187462138
2260.002649480374328630.005298960748657250.997350519625671
2270.001818176312594840.003636352625189670.998181823687405
2280.001347608553425330.002695217106850670.998652391446575
2290.0009341238117856650.001868247623571330.999065876188214
2300.0006261752214164820.001252350442832960.999373824778583
2310.0003993719475540890.0007987438951081770.999600628052446
2320.0004512019783106760.0009024039566213530.99954879802169
2330.0004602234802450970.0009204469604901940.999539776519755
2340.002678191556515660.005356383113031320.997321808443484
2350.001835726663316510.003671453326633020.998164273336683
2360.001215117367486130.002430234734972270.998784882632514
2370.05130408247754710.1026081649550940.948695917522453
2380.03827275306739850.0765455061347970.961727246932601
2390.06550736667467660.1310147333493530.934492633325323
2400.05829087017717560.1165817403543510.941709129822824
2410.04215927454964070.08431854909928130.95784072545036
2420.02942467633476330.05884935266952670.970575323665237
2430.027387080970920.05477416194184010.97261291902908
2440.01826153157524690.03652306315049370.981738468424753
2450.01658112145778770.03316224291557540.983418878542212
2460.01065440024105420.02130880048210840.989345599758946
2470.007639546882339270.01527909376467850.99236045311766
2480.007225898609781080.01445179721956220.992774101390219
2490.004302879984536520.008605759969073050.995697120015464
2500.002691722906176840.005383445812353680.997308277093823
2510.00228915778995250.004578315579905010.997710842210047
2520.003094330991467160.006188661982934310.996905669008533
2530.002889888472170960.005779776944341920.997110111527829
2540.001914844251154930.003829688502309860.998085155748845
2550.001059425776520890.002118851553041770.99894057422348
2560.0003751651478586870.0007503302957173740.99962483485214






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level330.132530120481928NOK
5% type I error level870.349397590361446NOK
10% type I error level1460.586345381526104NOK

\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 & 33 & 0.132530120481928 & NOK \tabularnewline
5% type I error level & 87 & 0.349397590361446 & NOK \tabularnewline
10% type I error level & 146 & 0.586345381526104 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146344&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]33[/C][C]0.132530120481928[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]87[/C][C]0.349397590361446[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]146[/C][C]0.586345381526104[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146344&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146344&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level330.132530120481928NOK
5% type I error level870.349397590361446NOK
10% type I error level1460.586345381526104NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}