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Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 14 Dec 2017 15:14:32 +0100

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/14/t1513282181ovyykapwyv0tq3y.htm/, Retrieved Tue, 14 May 2024 19:14:01 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309585, Retrieved Tue, 14 May 2024 19:14:01 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [lnTotDamg limiet] [2017-12-14 14:14:32] [52cce9dbcec2927ac392287242c803b1] [Current]

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12,74310681	0	1	0
14,22097567	1	0	0
11,70724147	1	0	0
11,08214255	1	0	0
9,279306576	0	1	0
9,501740524	1	0	0
9,622516246	1	0	0
11,45550819	0	0	1
10,30895266	0	0	0
10,3306163	1	0	0
10,73280327	0	0	1
11,13118177	0	1	0
10,47022081	1	0	0
10,13863872	1	0	0
9,564512186	0	0	0
10,24821137	1	0	0
9,527411329	1	0	0
9,680344001	1	0	0
9,294589604	1	0	0
10,49127422	1	0	0
13,17464648	0	1	0
10,45843532	0	0	1
NA	0	0	1
9,74689222	0	1	0
9,327412044	0	1	0
11,42894585	1	0	0
NA	1	0	0
10,24569332	1	0	0
9,584246011	1	0	0
10,86885412	0	1	0
9,842462851	1	0	0
9,670798588	1	0	0
11,51292546	0	1	0
11,83598675	0	1	0
11,3707857	0	1	0
NA	1	0	0
10,30895266	1	0	0
9,790262862	1	0	0
9,905984433	0	1	0
10,858999	0	1	0
NA	0	1	0
10,36501788	1	0	0
9,228376734	1	0	0
10,85591733	0	1	0
9,903487553	0	1	0
9,392661929	1	0	0
NA	1	0	0
10,858999	0	1	0
9,396902923	0	1	0
10,16842543	1	0	0
10,58319482	1	0	0
NA	1	0	0
9,952277717	0	1	0
NA	1	0	0
11,90068814	0	1	0
9,947504438	1	0	0
10,84673175	0	0	1
9,822819649	0	1	0
9,452894317	0	1	0
NA	1	0	0
11,82515624	0	1	0
10,72989734	0	0	1
10,13098163	0	1	0
10,74720759	0	1	0
NA	0	1	0
9,702716852	1	0	0
11,40756495	0	1	0
13,88746929	0	1	0
11,19127287	0	0	1
10,64699509	1	0	0
10,63103616	1	0	0
10,08580911	1	0	0
9,903487553	0	1	0
NA	0	1	0
11,15625052	0	1	0
NA	0	1	0
15,05375348	0	1	0
9,269929163	1	0	0
9,294865327	0	1	0
10,60604036	1	0	0
NA	0	1	0
9,436997743	0	1	0
9,812960913	1	0	0
NA	1	0	0
10,74140585	1	0	0
12,59055243	1	0	0
9,453992184	0	1	0
9,320001626	1	0	0
NA	1	0	0
10,29745346	1	0	0
NA	1	0	0
14,80474501	0	1	0
11,2045916	0	1	0
10,04324949	0	1	0
9,248887782	0	1	0
NA	0	1	0
9,320449595	1	0	0
10,05620864	1	0	0
10,04324949	1	0	0
10,1266311	1	0	0
10,90067601	0	1	0
NA	0	0	0
NA	0	0	0
NA	0	1	0
11,29565215	0	1	0
NA	0	1	0
NA	0	1	0
10,58910647	0	1	0
9,25922577	1	0	0
9,826336493	0	1	0
11,36798061	1	0	0
11,15496398	1	0	0
11,0201362	1	0	0
NA	0	0	1
NA	0	1	0
NA	0	1	0
10,62653328	1	0	0
NA	1	0	0
12,21602298	0	1	0
9,798127037	0	1	0
10,2565008	1	0	0
9,537988072	1	0	0
9,433483923	0	1	0
NA	0	1	0
9,438113192	0	0	1
10,1266311	0	1	0
10,71995795	0	1	0
9,805157818	1	0	0
10,16029796	1	0	0
10,51867319	1	0	0
9,826228467	1	0	0
9,770184705	1	0	0
NA	1	0	0
10,78153629	1	0	0
13,36602616	0	1	0
9,480672806	0	1	0
NA	0	1	0
10,26140672	0	0	1
9,838202086	0	0	1
NA	0	0	1
9,6721228	1	0	0
NA	1	0	0
10,00437307	1	0	0
11,35966938	1	0	0
9,779057474	1	0	0
NA	0	0	1
NA	0	1	0
9,265964276	0	1	0
9,98202143	0	0	1
NA	0	1	0
NA	1	0	0
10,11289722	1	0	0
NA	0	1	0
9,903487553	0	1	0
9,33476795	0	0	1
10,94500025	1	0	0
13,72510871	0	1	0
10,83877667	1	0	0
11,46268425	0	1	0
14,2449695	0	1	0
11,22524339	0	0	1
10,26628869	0	0	1
10,00784757	0	0	0
11,86358234	0	1	0
10,89263834	1	0	0
NA	1	0	0
NA	0	1	0
9,73489137	1	0	0
NA	1	0	0
9,823524008	0	1	0
9,52295893	1	0	0
9,944293547	0	1	0
NA	0	1	0
10,12181955	0	0	0
10,34987065	1	0	0
11,72351859	0	1	0
9,724659885	0	1	0
NA	0	1	0
11,75429393	1	0	0
9,256937657	1	0	0
9,545812108	0	1	0
NA	0	1	0
9,817493839	0	1	0
NA	0	1	0
11,15625052	0	1	0
10,3449631	0	1	0
11,06646647	1	0	0
9,585208806	0	1	0
10,42445161	0	0	1
11,28702813	1	0	0
9,92329018	0	1	0
NA	1	0	0
10,01023211	1	0	0
11,93956481	0	1	0
10,52513873	1	0	0
NA	0	0	1
9,334503015	0	1	0
13,5026064	0	1	0
10,9362271	0	1	0
NA	1	0	0
9,852194258	0	1	0
11,11874148	1	0	0
10,25319327	0	1	0
9,825526011	0	1	0
10,82774645	0	0	1
NA	0	1	0
10,2152645	1	0	0
10,03915416	0	0	1
12,45697532	1	0	0
10,40426284	0	1	0
NA	0	1	0
9,998797732	1	0	0
11,28978191	1	0	0
NA	1	0	0
NA	0	1	0
10,59663473	0	1	0
10,74283244	0	1	0
9,231514607	0	1	0
9,80504748	1	0	0
12,42761492	0	1	0
NA	1	0	0
10,30895266	1	0	0
NA	1	0	0
9,83091686	1	0	0
12,73639203	0	1	0
11,63101068	1	0	0
9,718362068	0	0	0
10,07962335	1	0	0
10,13855967	1	0	0
9,214033544	0	0	1
9,215327913	0	1	0
9,600217957	1	0	0
NA	1	0	0
10,35127753	1	0	0
9,976133743	0	0	1
NA	0	0	1
11,13239684	0	1	0
9,558952902	0	0	1
10,20436962	0	0	1
10,41150925	0	1	0
10,99894487	0	0	1
11,62339292	1	0	0
10,01971382	1	0	0
NA	1	0	0
10,9928909	1	0	0
9,292841593	1	0	0
9,897922094	1	0	0
9,239899174	0	1	0
11,13791196	1	0	0
10,30895266	1	0	0
NA	1	0	0
10,84848235	1	0	0
10,09872533	0	0	1
10,51276387	1	0	0
9,243678432	1	0	0
11,91214444	0	1	0
13,23122854	1	0	0
11,56056258	1	0	0
10,06938327	0	1	0
NA	0	1	0
11,527952	0	1	0
10,04324949	0	1	0
10,97976902	0	0	1
NA	1	0	0
12,52452638	0	1	0
9,903487553	0	1	0
11,78676213	1	0	0
10,66918793	1	0	0
9,405084449	1	0	0
NA	1	0	0
9,477845247	1	0	0
NA	1	0	0
14,76182251	0	1	0
11,07131491	0	1	0
9,234349824	1	0	0
9,671555495	0	1	0
9,3615152	1	0	0
9,667448713	1	0	0
12,50617724	0	1	0
9,472704636	1	0	0
NA	1	0	0
10,18814004	1	0	0
9,903487553	0	1	0
11,26983428	1	0	0
NA	1	0	0
9,519147726	0	1	0
NA	1	0	0
11,60823564	1	0	0
NA	1	0	0
NA	1	0	0
9,998161166	1	0	0
13,98267025	0	1	0
10,4206431	0	1	0
9,840654243	1	0	0
NA	1	0	0
NA	0	1	0
12,30371646	1	0	0
9,21949831	1	0	0
9,550448847	0	1	0
NA	1	0	0
9,806701284	0	1	0
10,45024966	0	0	1
11,44344676	1	0	0
9,628063377	1	0	0
NA	0	1	0
11,30713055	0	0	1
NA	0	0	1
10,13950784	0	0	1
10,65815307	1	0	0
NA	0	1	0
NA	0	1	0
9,449357272	0	0	1
12,16837078	0	1	0
10,26660172	1	0	0
9,285726099	0	1	0
NA	0	1	0
10,67572314	1	0	0
11,86870301	1	0	0
9,903487553	1	0	0
NA	1	0	0
10,80529389	0	1	0
NA	1	0	0
NA	1	0	0
9,710084826	0	1	0
9,814000386	1	0	0
10,15366244	1	0	0
9,307013259	1	0	0
9,249561085	0	1	0
10,11455852	1	0	0
9,369052063	0	1	0
NA	1	0	0
10,54534144	1	0	0
10,54534144	1	0	0
9,43986353	0	1	0
11,32989165	1	0	0
10,25765937	0	0	1
9,641018283	1	0	0
9,576926042	1	0	0
10,07361	1	0	0
12,61153775	0	1	0
11,08214255	0	1	0
11,00542763	1	0	0
9,650722072	1	0	0
11,71814127	1	0	0
13,37961459	0	1	0
9,903487553	0	1	0
9,69066555	0	0	1
NA	0	0	1
9,903487553	1	0	0
9,212338375	1	0	0
13,65645621	0	1	0
9,925395805	1	0	0
11,35040654	0	1	0
12,3863421	0	1	0
9,588776808	0	1	0
11,72933439	1	0	0
10,35137338	1	0	0
9,903487553	1	0	0
9,349319399	1	0	0
9,71986515	1	0	0
9,370927437	1	0	0
NA	1	0	0
9,844480372	1	0	0
10,81033383	0	1	0
10,30895266	0	1	0
11,61186541	1	0	0
10,30895266	1	0	0
9,61580548	0	0	1
9,220290703	0	0	1
10,68868962	1	0	0
10,54428825	1	0	0
9,73346999	0	1	0
14,21568302	0	1	0
11,20081408	1	0	0
13,27006761	1	0	0
11,66258608	0	1	0
13,36922346	0	1	0
12,57643893	1	0	0
NA	1	0	0
12,36734079	0	1	0
9,259130536	1	0	0
10,05444743	1	0	0
10,1266311	0	0	1
11,45846928	0	0	1
9,909370216	1	0	0
12,51509293	0	1	0
10,20780548	1	0	0
10,74929144	1	0	0
12,58797908	0	1	0
14,47285282	0	1	0
13,3721473	0	1	0
10,75040677	1	0	0
9,938468521	1	0	0
10,49302269	1	0	0
NA	0	0	1
10,95168336	0	1	0
9,546812609	0	1	0
10,59588445	1	0	0
11,90327694	0	1	0
NA	0	1	0
11,07409487	1	0	0
10,05680932	1	0	0
9,307376334	1	0	0
12,33457579	1	0	0
NA	1	0	0
9,358760377	1	0	0
10,88414179	1	0	0
10,31204787	0	0	1
NA	0	1	0
NA	0	1	0
10,15409062	1	0	0
11,33618829	0	0	1
11,54144489	1	0	0
11,36773783	1	0	0
10,35774282	1	0	0
10,85947965	1	0	0
NA	1	0	0
9,76995616	1	0	0
NA	1	0	0
11,84581988	0	1	0
10,60410675	0	1	0
10,71432888	0	1	0
9,812194294	1	0	0
NA	1	0	0
9,884712397	0	1	0
9,979753908	0	1	0
10,30521234	1	0	0
11,21182037	1	0	0
11,53272809	1	0	0
10,6501755	1	0	0
9,692766521	1	0	0
12,22719793	1	0	0
12,30944118	0	1	0
9,877759404	0	1	0
10,0604913	0	1	0
10,56157734	1	0	0
10,82279373	1	0	0
10,92448058	0	0	1
10,76445583	0	1	0
10,50144455	1	0	0
10,1626544	1	0	0
9,746950695	0	1	0
10,49404814	1	0	0
9,738436007	0	0	1
9,546812609	0	0	0
10,86513399	0	1	0
13,13147573	0	1	0
9,86537023	0	1	0
9,216918687	0	1	0
NA	0	1	0
13,51775121	0	1	0
NA	0	1	0
9,318925682	0	0	1
9,84686429	1	0	0
9,878989921	1	0	0
NA	1	0	0
11,67542689	0	1	0
NA	0	1	0
12,4705539	0	1	0
10,06573394	1	0	0
NA	1	0	0
11,9377225	0	0	1
10,28503558	0	0	0
NA	0	1	0
9,703999491	0	1	0
NA	0	1	0
10,1266311	0	1	0
11,21984217	1	0	0
9,671366322	1	0	0
10,09823163	1	0	0
9,798127037	1	0	0
10,19279331	0	1	0
NA	0	1	0
11,52261834	1	0	0
10,95875731	1	0	0
10,18979368	0	0	1
10,1387968	1	0	0
10,96597167	0	1	0
9,228278517	1	0	0
NA	1	0	0
10,67308639	0	0	1
10,73639668	1	0	0
10,48324205	0	0	1
10,47067461	0	1	0
NA	0	1	0
10,03372603	0	0	1
9,957549511	1	0	0
12,13634736	0	1	0
NA	0	1	0
NA	0	1	0
10,35443578	0	1	0
12,95870969	0	1	0
9,61580548	1	0	0
NA	1	0	0
9,625755811	1	0	0
NA	1	0	0
11,42614679	1	0	0
10,93324982	1	0	0
9,51657429	1	0	0
10,45160896	1	0	0
9,724839241	1	0	0
10,27842472	0	0	1
9,637306012	0	1	0
9,433483923	1	0	0
11,41919703	1	0	0
14,72463125	0	1	0
10,0647557	0	0	1
12,27377335	0	0	0
12,41087298	0	0	0
9,613870275	1	0	0
14,9656882	0	1	0
11,51292546	0	1	0
9,680344001	0	1	0
9,860057995	0	1	0
NA	0	1	0
12,21231313	1	0	0
10,16246146	1	0	0
11,07865957	1	0	0
9,280519207	1	0	0
9,816021527	1	0	0
NA	0	1	0
NA	0	1	0
9,552439604	1	0	0
NA	0	1	0
11,29290204	1	0	0
NA	1	0	0
NA	1	0	0
13,16712448	0	1	0
11,28978191	0	1	0
NA	0	1	0
9,461876998	1	0	0
NA	1	0	0
10,22194128	1	0	0
10,51325312	0	0	1
9,903487553	0	1	0
9,61580548	1	0	0
NA	0	1	0
9,588776808	1	0	0
13,30431987	0	1	0
10,59853293	1	0	0
NA	0	0	0
10,10708124	0	0	0
14,21362068	0	1	0
9,290998275	1	0	0
10,21950195	0	1	0
10,71501759	1	0	0
9,76995616	0	1	0
11,28038793	1	0	0
11,51292546	0	1	0
12,39741588	1	0	0
10,22546235	1	0	0
10,58703884	1	0	0
NA	1	0	0
9,433483923	1	0	0
NA	1	0	0
9,332646496	1	0	0
NA	1	0	0
9,952277717	0	1	0
10,35774282	1	0	0
11,36719424	1	0	0
9,540578934	1	0	0
9,348710041	1	0	0
12,91164235	0	1	0
14,50205348	0	1	0
9,913437883	0	1	0
10,41717907	1	0	0
9,726929356	1	0	0
NA	1	0	0
11,43684324	0	1	0
NA	1	0	0
NA	1	0	0
10,56761778	1	0	0
9,703572127	1	0	0
11,34599683	0	1	0
10,37098805	0	1	0
10,67775378	0	1	0
11,5340997	1	0	0
10,43128828	0	1	0
9,563810185	1	0	0
NA	1	0	0
10,71441777	0	1	0
12,73651559	1	0	0
11,56898439	1	0	0
10,20706758	1	0	0
NA	1	0	0
11,20588387	0	1	0
10,20728901	0	0	1
10,3899795	0	0	0
9,812358619	0	1	0
11,73606902	0	1	0
11,28586174	0	1	0
9,354960062	0	0	1
9,701616136	1	0	0
13,23430539	0	1	0
9,305650552	0	0	1
10,68459982	0	1	0
9,345308232	1	0	0
13,11750962	1	0	0
10,84554349	0	1	0
9,69769264	1	0	0
13,42285012	0	1	0
10,33621108	0	1	0
11,56171563	0	1	0
10,51672535	0	0	1
10,32272406	1	0	0
10,10716282	1	0	0
11,73606902	0	1	0
11,73606902	0	1	0
10,08580911	1	0	0
10,30638269	1	0	0
10,89664673	1	0	0
NA	1	0	0
12,12356022	1	0	0
10,52846295	0	1	0
11,09429913	0	1	0
NA	1	0	0
11,20934434	1	0	0
NA	1	0	0
12,0503307	1	0	0
10,84300641	1	0	0
10,15778087	1	0	0
9,798127037	1	0	0
NA	1	0	0
NA	1	0	0
11,25786546	0	1	0
NA	0	1	0
NA	1	0	0
NA	1	0	0
10,24384509	1	0	0
10,2077686	0	1	0
9,703022392	0	1	0
9,439784036	1	0	0
10,07798693	0	0	1
9,913437883	0	1	0
9,330520532	0	1	0
10,22055851	1	0	0
9,301186055	1	0	0
10,05620864	1	0	0
9,594036922	0	1	0
11,37823912	1	0	0
10,26193093	1	0	0
NA	1	0	0
9,534523115	1	0	0
12,46065672	0	1	0
11,69417144	0	1	0
9,532423871	1	0	0
10,87615862	0	1	0
NA	1	0	0
13,12236338	0	1	0
9,845699529	0	1	0
11,08954585	1	0	0
11,8493977	0	1	0
12,81909378	0	1	0
10,12077398	0	1	0
9,481359384	0	1	0
14,81834589	0	1	0
9,500020447	0	1	0
NA	0	1	0
11,10495723	1	0	0
NA	1	0	0
10,22150482	1	0	0
NA	1	0	0
13,96299564	0	1	0
13,61961246	0	1	0
11,43205158	1	0	0
9,850297724	1	0	0
NA	1	0	0
10,79722588	1	0	0
11,39030575	1	0	0
9,945396956	1	0	0
NA	1	0	0
NA	0	1	0
NA	0	1	0
9,903487553	0	1	0
10,65784742	1	0	0
9,330875174	1	0	0
NA	0	1	0
9,903487553	0	1	0
12,85248193	0	1	0
10,34390144	1	0	0
10,3843693	1	0	0
9,883641924	0	1	0
9,445570584	1	0	0
11,95269538	0	1	0
11,38206479	1	0	0
9,350102314	0	1	0
NA	0	1	0
9,392661929	1	0	0
10,42228135	0	1	0
11,60376202	0	0	0
14,65234241	0	1	0
10,21097225	1	0	0
10,83958091	0	0	1
9,421411342	0	1	0
11,84222921	0	1	0
13,1482884	0	1	0
11,03227334	1	0	0
10,98065487	1	0	0
9,494616651	1	0	0
NA	1	0	0
9,295232839	0	1	0
9,83091686	0	0	1
12,25486281	0	1	0
11,16277207	0	1	0
NA	0	1	0
9,738023113	0	0	1
NA	1	0	0
11,49272276	1	0	0
10,29262001	1	0	0
9,244935017	1	0	0
NA	1	0	0
10,36778745	1	0	0
NA	1	0	0
10,18689801	1	0	0
NA	1	0	0
9,924857578	1	0	0
NA	1	0	0
9,886900749	1	0	0
11,27466828	0	0	1
9,61580548	0	0	1
10,46310334	0	1	0
10,13074264	0	1	0
13,05959609	0	1	0
10,73203937	0	0	1
10,16203686	1	0	0
NA	1	0	0
10,67081413	0	1	0
9,552084403	1	0	0
11,99464766	0	0	1
9,611663581	1	0	0
12,34996085	0	0	0
9,27312736	1	0	0
10,58101335	1	0	0
NA	0	1	0
9,343209045	0	0	1
13,85036889	0	1	0
10,87064249	1	0	0
10,14199251	1	0	0
9,37160852	0	0	1
13,23057183	0	1	0
10,08460005	0	1	0
10,37838544	0	1	0
10,57362196	0	1	0
10,44540478	1	0	0
10,40589787	0	0	1
11,71318517	0	1	0
10,20359214	1	0	0
NA	1	0	0
10,16110982	0	1	0
NA	0	1	0
10,67359577	1	0	0
9,529230372	0	0	1
9,491299899	0	0	1
10,53006749	1	0	0
9,314700387	1	0	0
11,80559508	0	1	0
NA	1	0	0
13,22994702	0	1	0
10,28151294	1	0	0
9,587200223	1	0	0
NA	1	0	0
NA	1	0	0
10,2104942	0	1	0
10,62789112	1	0	0
NA	0	1	0
NA	0	1	0
12,08440771	1	0	0
9,747418367	1	0	0
9,903487553	0	1	0
9,774915272	0	1	0
NA	1	0	0
NA	1	0	0
10,91508846	0	1	0
11,04224961	0	1	0
9,921081861	1	0	0
NA	1	0	0
NA	1	0	0
10,82978802	1	0	0
9,368710708	1	0	0
NA	1	0	0
10,12954685	0	0	1
9,990719738	1	0	0
10,35774282	1	0	0
10,74710006	0	0	1
9,354267541	1	0	0
12,37315365	0	1	0
9,754059039	1	0	0
10,00297083	1	0	0
11,2515607	1	0	0
10,23016195	1	0	0
10,24124468	0	1	0
11,17448329	1	0	0
9,454540666	0	0	1
NA	1	0	0
9,219894585	1	0	0
11,61865391	1	0	0
11,2441829	1	0	0
13,07534933	0	1	0
9,825526011	1	0	0
9,61580548	0	0	0
10,48849257	1	0	0
11,68237284	0	1	0
9,565564264	0	1	0
11,5885801	1	0	0
9,235032985	0	1	0
NA	0	1	0
10,74871184	1	0	0
NA	1	0	0
9,844586445	1	0	0
9,819507939	1	0	0
9,812084729	1	0	0
10,46310334	0	1	0
9,898324246	1	0	0
NA	1	0	0
10,1662748	1	0	0
NA	1	0	0
9,847287503	1	0	0
11,61110425	1	0	0
NA	1	0	0
9,702961291	1	0	0
NA	1	0	0
9,319374133	0	0	1
9,56254534	0	0	1
11,22243947	1	0	0
10,78903038	0	1	0
11,30480598	0	1	0
9,85828096	1	0	0
NA	1	0	0
10,18236851	1	0	0
9,486152272	1	0	0
14,70417107	0	1	0
10,1266311	1	0	0
9,635608107	1	0	0
11,29464507	0	1	0
9,754059039	0	1	0
9,829410349	1	0	0
10,2267298	0	1	0
10,08618404	1	0	0
NA	1	0	0
11,9771625	0	1	0
10,04324949	0	1	0
NA	0	1	0
11,30871406	0	0	0
9,790206866	1	0	0
10,44138345	1	0	0
9,637566972	0	1	0
9,739202358	1	0	0
9,397566548	1	0	0
NA	0	1	0
NA	1	0	0
9,216521231	1	0	0
11,65354791	1	0	0
10,29890232	1	0	0
NA	1	0	0
10,71441777	1	0	0
9,469468637	0	1	0
10,03245228	1	0	0
11,2218376	0	1	0
NA	0	1	0
9,408043031	0	1	0
11,34301458	0	1	0
9,824336114	1	0	0
10,37258452	0	1	0
10,59663473	1	0	0
10,50506754	0	1	0
10,17309475	0	0	1
9,387649387	0	0	1
12,04053151	0	1	0
10,23228758	0	1	0
11,00209984	0	1	0
NA	0	1	0
9,226508953	1	0	0
NA	0	1	0
10,1584398	1	0	0
NA	1	0	0
NA	1	0	0
14,04489539	0	1	0
10,9000302	0	0	1
9,546812609	1	0	0
NA	0	1	0
NA	0	1	0
11,01862914	1	0	0
12,05815252	0	1	0
9,952753794	1	0	0
NA	1	0	0
9,739496952	0	1	0
10,01806546	1	0	0
9,686698767	1	0	0
9,562404703	1	0	0
10,49127422	0	1	0
9,798127037	1	0	0
NA	1	0	0
11,33738089	0	1	0
10,87776413	0	1	0
NA	0	1	0
11,98951983	1	0	0
10,30895266	1	0	0
10,60698103	1	0	0
NA	1	0	0
9,393827915	1	0	0
NA	1	0	0
NA	1	0	0
9,347403022	1	0	0
9,37627845	1	0	0
10,22194128	0	1	0
10,88187006	0	1	0
9,699656312	0	1	0
11,4145185	0	1	0
11,10476172	1	0	0
NA	1	0	0
13,11342354	0	0	0
9,999751823	1	0	0
9,740968623	0	0	1
NA	0	1	0
13,43757112	1	0	0
12,4374264	1	0	0
11,67290768	1	0	0
9,6374365	1	0	0
9,392661929	1	0	0
10,90778916	1	0	0
9,425854896	0	1	0
12,01140722	1	0	0
10,8036489	1	0	0
10,13459927	1	0	0
12,15477935	1	0	0
12,01370075	1	0	0
11,38460334	0	0	1
9,923584254	1	0	0
10,6454249	1	0	0
10,1266311	0	1	0
10,4367887	1	0	0
11,19553991	1	0	0
9,752664663	1	0	0
NA	1	0	0
9,61580548	0	1	0
11,73947122	0	1	0
10,48592661	0	1	0
11,16400608	0	1	0
10,85821023	0	1	0
9,71111566	1	0	0
9,367344121	0	1	0
10,31890299	1	0	0
NA	1	0	0
12,04237655	1	0	0
9,305650552	1	0	0
9,472704636	0	1	0
10,46310334	0	1	0
9,998797732	1	0	0
9,634627234	1	0	0
12,4713864	0	1	0
NA	0	0	1
13,70703408	0	1	0
10,30705085	1	0	0
10,404899	0	1	0
9,245031612	1	0	0
NA	1	0	0
10,64120171	0	1	0
9,287301413	0	1	0
10,90963727	0	1	0
9,903487553	0	1	0
NA	0	1	0
12,20607265	0	1	0
9,998797732	0	1	0
13,72843954	0	1	0
9,904387148	0	1	0
10,1266311	0	0	1
11,05690363	1	0	0
NA	1	0	0
10,46310334	0	1	0
NA	0	1	0
10,9009711	1	0	0
9,820105944	1	0	0
13,01700286	0	1	0
NA	1	0	0
10,01157933	1	0	0
10,99500809	0	1	0
9,975808214	0	1	0
NA	0	0	1
11,02190247	0	1	0
9,903487553	0	1	0
14,97866137	0	1	0
11,56171563	0	1	0
10,30895266	0	1	0
10,12137733	0	0	1
12,07980623	1	0	0
9,45720045	1	0	0
10,46230302	1	0	0
9,803667217	1	0	0
NA	0	1	0
NA	0	1	0
NA	0	1	0
9,792667717	1	0	0
10,23995979	1	0	0
9,232493177	1	0	0
10,92985168	1	0	0
12,59678615	1	0	0
11,26382287	1	0	0
11,99407302	1	0	0
NA	1	0	0
9,92329018	0	1	0
12,61153775	0	1	0
NA	0	1	0
11,17721494	1	0	0
9,913437883	1	0	0
11,50613244	0	1	0
11,01188514	1	0	0
11,23139775	0	0	1
10,99792447	0	1	0
14,10784579	0	1	0
10,11557016	1	0	0
9,429475902	0	1	0
10,10781519	0	0	1
10,25748391	0	1	0
NA	1	0	0
11,93011491	0	1	0
11,92217673	1	0	0
9,73489137	0	0	1
9,903487553	0	1	0
10,73454714	1	0	0
9,299815378	1	0	0
9,225130457	1	0	0
9,53683442	0	1	0
9,71111566	1	0	0
12,16022764	0	1	0
10,33123587	1	0	0
10,29214554	0	0	1
10,78487623	0	0	1
10,61051298	1	0	0
NA	1	0	0
11,04817203	1	0	0
NA	1	0	0
12,2511894	1	0	0
9,962039439	1	0	0
9,903487553	1	0	0
9,609116492	0	1	0
9,472704636	0	1	0
9,42043921	1	0	0
12,12135863	1	0	0
13,85529457	0	1	0
13,82245837	0	1	0
10,08555908	1	0	0
11,58884893	1	0	0
9,712266513	1	0	0
10,89673933	0	1	0
11,69247652	1	0	0
9,936487031	1	0	0
11,39229337	0	0	1
NA	0	0	1
9,323669057	0	1	0
NA	0	1	0
9,798127037	1	0	0
NA	1	0	0
NA	0	0	1
11,67631901	0	1	0
NA	0	0	1
11,8913619	0	1	0
11,10549843	1	0	0
NA	1	0	0
11,69519702	1	0	0
9,5451684	1	0	0
NA	1	0	0
10,31982666	1	0	0
9,395325046	1	0	0
9,75846178	1	0	0
NA	0	1	0
NA	0	1	0
10,07949757	1	0	0
11,18364332	0	0	1
10,91508846	0	1	0
NA	0	1	0
NA	1	0	0
9,321971188	1	0	0
9,680344001	1	0	0
9,792555992	1	0	0
NA	1	0	0
12,15477935	1	0	0
9,387314322	1	0	0
11,72020789	1	0	0
10,31678855	0	1	0
12,99566572	0	1	0
9,649562577	1	0	0
10,16631325	1	0	0
9,675833846	1	0	0
10,0367064	0	0	1
NA	1	0	0
NA	1	0	0
11,25002706	0	1	0
11,25928405	0	1	0
9,248695326	1	0	0
10,89323319	1	0	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
11,05115981	1	0	0
10,47339308	1	0	0
NA	1	0	0
13,92081787	0	1	0
10,59663473	1	0	0
12,43725182	0	1	0
10,68526355	1	0	0
9,829948649	1	0	0
NA	1	0	0
12,2867121	0	1	0
10,22738101	1	0	0
12,52950306	0	0	0
12,22189678	0	1	0
9,791885376	1	0	0
NA	1	0	0
NA	0	1	0
9,303739636	0	0	0
10,30895266	0	1	0
10,30051718	0	0	0
NA	1	0	0
12,22436924	1	0	0
NA	1	0	0
12,37492473	1	0	0
9,546812609	0	1	0
10,76863235	1	0	0
10,46310334	0	1	0
9,392661929	1	0	0
9,60238246	1	0	0
NA	1	0	0
NA	1	0	0
10,91592448	1	0	0
11,82769971	0	1	0
12,45155678	0	1	0
13,54641076	0	1	0
NA	0	1	0
10,09038197	0	1	0
9,23960787	1	0	0
10,86377603	1	0	0
12,22545852	0	1	0
10,30895266	1	0	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
9,878169745	0	1	0
10,02104834	1	0	0
NA	1	0	0
NA	1	0	0
9,453521817	0	0	1
11,74747571	0	1	0
9,541441065	1	0	0
9,870964361	1	0	0
10,80505036	0	1	0
NA	1	0	0
NA	1	0	0
10,38899537	1	0	0
11,73534876	1	0	0
11,5530208	0	0	1
10,50479353	0	1	0
NA	0	1	0
11,46080025	0	1	0
NA	0	1	0
NA	1	0	0
10,81977828	0	1	0
13,44515822	0	1	0
14,33608847	0	1	0
12,28995413	0	1	0
9,442324728	0	1	0
NA	0	1	0
12,04383603	1	0	0
9,798127037	1	0	0
11,03488966	0	0	1
11,00630764	0	1	0
NA	0	1	0
10,63132597	0	1	0
9,90643321	1	0	0
9,90643321	1	0	0
10,19241884	1	0	0
9,452894317	0	0	1
9,740968623	0	0	1
9,251770182	1	0	0
9,99520036	0	1	0
NA	1	0	0
10,87238971	1	0	0
NA	1	0	0
10,30895266	0	0	1
12,71649819	0	1	0
9,287394001	0	1	0
11,30689711	1	0	0
9,489032153	0	1	0
14,73143922	0	1	0
11,19248601	0	0	1
9,470779708	0	1	0
9,61580548	0	1	0
NA	0	1	0
NA	0	1	0
NA	0	1	0
9,517825072	1	0	0
9,61913327	0	1	0
12,36535585	0	1	0
9,336796796	1	0	0
10,58633184	1	0	0
9,825526011	1	0	0
NA	1	0	0
10,48010092	1	0	0
9,571714259	0	0	0
10,1951492	1	0	0
9,663388567	1	0	0
NA	1	0	0
NA	0	0	1
9,296884973	1	0	0
NA	1	0	0
NA	1	0	0
12,85336959	0	1	0
14,55336673	0	1	0
9,814601693	1	0	0
9,792555992	0	1	0
9,302190026	0	1	0
10,75329654	1	0	0
10,23160354	1	0	0
11,33618829	0	1	0
NA	0	1	0
NA	1	0	0
11,33976185	0	0	0
10,89060947	0	1	0
10,48010092	0	1	0
10,52137225	0	0	1
9,8231448	0	1	0
9,697876898	0	1	0
11,75587949	1	0	0
10,09905434	1	0	0
11,70084797	1	0	0
11,12493675	1	0	0
9,819127204	1	0	0
11,46163217	0	0	0
NA	0	0	0
9,939288965	0	1	0
12,61071742	0	1	0
NA	0	0	1
9,568014816	1	0	0
12,23846723	1	0	0
13,31296886	0	1	0
9,228082053	1	0	0
NA	1	0	0
11,28978191	0	1	0
10,30895266	0	1	0
11,24671782	1	0	0
10,1266311	1	0	0
13,90536314	0	1	0
10,8287182	1	0	0
9,546169545	1	0	0
NA	1	0	0
9,632400354	1	0	0
9,460554195	1	0	0
9,952277717	1	0	0
9,593559871	1	0	0
NA	1	0	0
10,80972795	0	1	0
11,02565359	1	0	0
13,83558276	0	0	0
9,549808118	1	0	0
NA	1	0	0
9,560997244	1	0	0
NA	1	0	0
10,82177629	0	1	0
10,4487146	0	1	0
13,03024809	0	1	0
9,230142999	1	0	0
NA	1	0	0
11,6656723	0	1	0
12,49125159	0	1	0
9,319284459	0	0	1
12,21509681	1	0	0
NA	1	0	0
12,3641442	0	0	1
NA	0	1	0
11,34397413	0	1	0
NA	0	1	0
13,92299991	0	1	0
12,40937261	1	0	0
11,13671814	0	1	0
13,83987336	0	1	0
13,81551056	0	1	0
12,12540474	0	1	0
12,32740493	1	0	0
9,883284845	0	0	1
9,601368432	0	1	0
9,395823592	1	0	0
10,32170434	1	0	0
9,278933163	1	0	0
12,06912399	1	0	0
9,392911898	0	0	1
9,433483923	1	0	0
9,757305042	0	1	0
9,812249072	0	0	1
9,349667437	1	0	0
11,61591517	1	0	0
10,54534144	0	1	0
9,491752831	0	0	1
9,989665249	1	0	0
10,26813067	0	1	0
NA	0	1	0
11,19309203	0	1	0
9,92260367	0	0	0
9,222960403	0	0	0
10,08585078	1	0	0
10,56519566	1	0	0
9,392661929	0	1	0
11,49191631	1	0	0
9,259130536	1	0	0
9,649498121	0	0	1
10,84933709	1	0	0
NA	0	1	0
9,91891789	1	0	0
10,36113389	1	0	0
12,56238602	1	0	0
9,952277717	1	0	0
10,19731321	1	0	0
10,46213144	1	0	0
10,28919203	0	1	0
NA	0	1	0
NA	1	0	0
NA	1	0	0
9,995245978	1	0	0
9,449357272	1	0	0
9,483492393	0	0	1
13,05622357	1	0	0
9,27799902	1	0	0
13,75505223	0	0	0
12,88603073	1	0	0
11,02059442	1	0	0
9,26860928	1	0	0
9,413281216	0	0	1
11,11253745	1	0	0
9,839109019	0	0	1
10,51867319	0	1	0
NA	0	1	0
10,1266311	0	1	0
NA	0	1	0
11,65542279	1	0	0
10,36577398	0	0	1
10,98019501	0	1	0
9,772239266	0	1	0
11,39357871	1	0	0
10,59663473	0	1	0
9,998297607	0	0	1
10,48570317	0	1	0
10,14423524	0	0	1
10,59795886	0	1	0
11,12388965	1	0	0
14,51905549	0	1	0
13,63091802	0	1	0
9,786785183	0	1	0
10,01444736	1	0	0
9,367344121	1	0	0
NA	0	1	0
NA	1	0	0
11,81524981	1	0	0
12,33849029	1	0	0
11,91839057	0	1	0
9,58548372	1	0	0
NA	1	0	0
11,52485404	1	0	0
10,01153446	0	0	1
NA	0	0	1
9,45915167	1	0	0
10,1266311	0	0	1
10,07162566	1	0	0
9,575261225	1	0	0
9,749986662	1	0	0
10,49305042	1	0	0
10,57479804	1	0	0
11,00376512	1	0	0
10,1266311	1	0	0
12,23611677	0	1	0
10,90527685	0	1	0
9,597505723	1	0	0
10,1266311	0	1	0
9,400464741	0	1	0
9,76995616	0	1	0
9,713234625	0	0	1
NA	0	1	0
11,77026173	1	0	0
9,963453134	1	0	0
NA	1	0	0
10,87799059	0	1	0
NA	0	1	0
9,760252084	1	0	0
12,64357568	0	0	0
11,65268741	0	1	0
9,928180165	0	0	1
9,641927964	1	0	0
NA	1	0	0
14,2935022	0	1	0
9,952277717	0	1	0
NA	0	1	0
11,33154746	0	1	0
NA	0	1	0
9,612332791	1	0	0
NA	0	1	0
12,64863121	0	1	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
10,66865302	1	0	0
NA	1	0	0
NA	1	0	0
10,29876764	0	0	1
9,392661929	0	0	1
10,31144954	1	0	0
9,392661929	0	0	1
11,75757216	0	0	1
9,71111566	0	0	1
14,51493798	0	1	0
9,787122261	1	0	0
9,433483923	0	1	0
9,919902094	0	1	0
9,948843254	0	1	0
9,576371411	0	1	0
9,693506988	0	0	1
9,400960732	0	0	1
14,98814686	0	1	0
9,469082701	1	0	0
11,51546224	0	1	0
9,629050707	0	1	0
10,55602105	1	0	0
9,579141496	0	1	0
NA	0	1	0
10,82396949	0	0	0
11,90361529	0	1	0
14,25725552	1	0	0
11,19680276	1	0	0
9,71111566	0	1	0
9,581076	0	1	0
11,61005218	0	1	0
10,01717332	0	1	0
10,12262308	0	1	0
11,94794938	1	0	0
11,04509547	1	0	0
10,60323788	0	1	0
NA	1	0	0
NA	1	0	0
9,674388804	1	0	0
12,17087543	1	0	0
NA	1	0	0
9,920836191	0	1	0
10,1266311	0	0	1
NA	0	0	1
10,78931908	0	1	0
NA	1	0	0
NA	1	0	0
10,04081174	0	1	0
13,25549119	0	1	0
9,740968623	0	1	0
9,525151112	1	0	0
11,46643116	0	1	0
11,75717276	0	0	1
11,20525835	0	0	1
11,24373798	0	1	0
10,23935246	1	0	0
11,40275339	0	1	0
11,19643231	1	0	0
NA	1	0	0
11,87894263	1	0	0
11,04469626	1	0	0
10,4487146	0	1	0
NA	1	0	0
10,54534144	0	1	0
12,32620847	0	1	0
11,65789124	0	1	0
11,22632281	1	0	0
11,24127421	0	0	0
NA	0	1	0
11,0656535	1	0	0
9,450773592	0	1	0
12,02791441	0	1	0
9,915910073	0	0	1
10,85096683	0	0	1
10,2561846	1	0	0
9,949511991	1	0	0
9,809176873	0	0	1
9,270494295	1	0	0
9,382190629	1	0	0
11,81815026	1	0	0
11,22791981	0	1	0
9,903487553	0	1	0
11,00209984	1	0	0
9,38806806	1	0	0
10,39479391	1	0	0
9,528503152	0	1	0
12,78755056	1	0	0
10,15225986	1	0	0
9,524055621	0	1	0
9,549808118	1	0	0
10,21178075	0	1	0
14,10441638	0	1	0
9,709720748	0	0	1
NA	0	0	1
10,10389458	1	0	0
9,754465366	0	0	1
9,487972109	1	0	0
NA	1	0	0
12,83232921	0	1	0
10,89942091	0	0	1
11,6977439	0	1	0
NA	1	0	0
9,825526011	0	1	0
9,405084449	0	0	1
NA	1	0	0
NA	1	0	0
12,43243102	0	1	0
12,2730909	0	1	0
10,56035968	0	1	0
10,13658143	0	1	0
10,98613982	1	0	0
10,4421715	1	0	0
9,752664663	1	0	0
11,19353254	1	0	0
10,84933709	0	1	0
9,875396672	1	0	0
NA	1	0	0
NA	1	0	0
12,12725128	1	0	0
13,5164183	0	1	0
10,53539742	1	0	0
9,305650552	1	0	0
9,839748712	1	0	0
9,868999622	1	0	0
9,692766521	1	0	0
11,57803882	1	0	0
9,743084031	1	0	0
NA	1	0	0
11,90017908	1	0	0
14,57238484	0	1	0
NA	1	0	0
11,62500337	0	1	0
12,32436222	0	1	0
10,24558679	1	0	0
12,59351295	0	1	0
12,38640055	0	1	0
10,81977828	0	1	0
10,49127422	1	0	0
9,913437883	1	0	0
10,58405595	1	0	0
9,846070282	0	1	0
12,90917016	0	1	0
10,81679384	0	1	0
NA	1	0	0
10,30011371	1	0	0
11,84730265	0	1	0
11,53517607	0	1	0
10,06556388	1	0	0
9,510444964	1	0	0
NA	1	0	0
10,51254635	0	1	0
10,20032756	1	0	0
9,910959567	0	1	0
12,89798907	0	0	0
14,03302148	0	1	0
NA	0	1	0
NA	0	1	0
9,259130536	1	0	0
12,46914051	0	1	0
9,975808214	0	1	0
9,620859354	1	0	0
10,37464676	0	1	0
9,36777138	1	0	0
NA	1	0	0
9,98681716	0	1	0
14,56835063	0	1	0
11,29009436	0	1	0
9,630759772	0	1	0
11,08374127	0	1	0
9,903487553	0	0	1
NA	0	0	1
9,68427377	1	0	0
NA	1	0	0
NA	0	1	0
9,49912184	0	0	1
10,93310697	1	0	0
9,249561085	1	0	0
9,883284845	0	0	1
10,62132735	0	0	1
12,68585951	1	0	0
11,83632694	1	0	0
14,52917286	0	1	0
11,01246263	0	1	0
NA	0	1	0
9,948317504	1	0	0
NA	1	0	0
9,417354541	1	0	0
12,72121096	0	1	0
12,96195986	0	1	0
9,568014816	0	1	0
9,928180165	0	1	0
9,563669726	1	0	0
10,59663473	0	1	0
10,57041909	1	0	0
11,6022633	1	0	0
10,31015194	1	0	0
10,20359214	1	0	0
9,398975291	1	0	0
11,78504964	1	0	0
10,73203937	0	0	0
10,98359637	0	1	0
13,12498993	0	1	0
11,75978554	0	1	0
NA	0	1	0
9,313708905	1	0	0
11,93034549	0	1	0
11,17557684	0	1	0
10,96888771	0	0	1
12,14718745	0	0	0
10,80413659	0	1	0
9,562475024	0	1	0
10,81977828	1	0	0
10,67398922	1	0	0
11,8874639	1	0	0
10,00595388	1	0	0
11,8257342	0	1	0
NA	0	1	0
11,20854474	1	0	0
9,618867454	0	1	0
11,05830688	0	1	0
11,87059991	0	1	0
NA	1	0	0
NA	1	0	0
10,04324949	1	0	0
NA	0	1	0
11,16574813	1	0	0
NA	1	0	0
11,39928633	1	0	0
11,34651663	1	0	0
10,74926998	1	0	0
12,05314871	0	1	0
10,5118935	0	0	1
NA	1	0	0
9,742085646	1	0	0
11,34030866	0	1	0
10,54534144	0	1	0
11,88325378	0	1	0
11,47553508	1	0	0
11,7905572	1	0	0
NA	1	0	0
14,25974756	0	1	0
11,22524339	0	1	0
10,70539945	1	0	0
10,39830574	1	0	0
12,61271706	1	0	0
10,00333289	0	1	0
9,433483923	1	0	0
NA	0	1	0
12,54650133	1	0	0
11,14759892	1	0	0
NA	1	0	0
10,1266311	0	0	1
10,20905866	1	0	0
9,775654181	0	1	0
12,10625231	1	0	0
12,86490526	0	1	0
9,384629757	0	0	1
9,733232897	1	0	0
9,392078425	1	0	0
11,46163217	1	0	0
9,928180165	0	1	0
NA	0	1	0
10,59663473	1	0	0
NA	1	0	0
10,30985226	1	0	0
11,34918226	1	0	0
10,51737505	1	0	0
10,06598898	0	0	1
9,279213236	1	0	0
11,00209984	1	0	0
9,287301413	1	0	0
11,75665565	1	0	0
10,4487146	1	0	0
10,61189274	0	1	0
10,30895266	0	1	0
10,81977828	0	1	0
9,29679326	1	0	0
10,96950778	1	0	0
9,82978719	0	1	0
10,46310334	0	1	0
NA	0	1	0
10,98796711	0	1	0
9,604407435	1	0	0
11,23321156	1	0	0
11,47221805	0	1	0
9,546669741	0	1	0
NA	0	1	0
9,46109909	1	0	0
13,03276466	0	1	0
10,77726736	0	1	0
10,81500692	1	0	0
11,66650455	1	0	0
NA	0	1	0
10,9511749	0	1	0
11,91664238	0	0	0
9,588708312	1	0	0
11,25508694	1	0	0
13,9887981	0	1	0
10,45036541	1	0	0
12,29733373	0	1	0
10,30895266	0	1	0
9,431241411	0	1	0
9,519000851	1	0	0
12,00941278	1	0	0
9,957028319	0	1	0
11,62973024	1	0	0
9,69879768	0	0	1
9,693877016	1	0	0
9,585827257	1	0	0
10,00278975	0	1	0
11,00209984	0	1	0
12,46843691	0	1	0
10,42942836	1	0	0
NA	1	0	0
NA	1	0	0
9,215327913	0	1	0
9,878169745	0	1	0
NA	0	1	0
10,36407196	1	0	0
12,5021469	1	0	0
10,30895266	1	0	0
10,08647555	0	1	0
9,223552703	0	0	0
NA	0	0	0
NA	1	0	0
11,65054599	1	0	0
11,44862158	1	0	0
9,305650552	0	0	1
NA	0	0	1
10,08580911	1	0	0
9,903487553	1	0	0
NA	1	0	0
11,09843979	0	1	0
10,70398577	0	0	1
9,651301316	1	0	0
10,0647557	1	0	0
11,19862559	1	0	0
10,2065138	1	0	0
NA	0	1	0
10,0895521	0	0	1
12,06870533	0	0	1
11,05515076	0	0	1
9,590487672	1	0	0
9,416541203	1	0	0
10,59663473	0	1	0
11,65572626	1	0	0
9,449357272	1	0	0
NA	1	0	0
10,98063784	1	0	0
10,5971596	1	0	0
NA	1	0	0
11,18163976	1	0	0
11,51292546	0	1	0
9,463508636	0	1	0
10,46310334	0	1	0
9,25598273	0	1	0
11,31491345	1	0	0
9,971379819	0	1	0
NA	0	1	0
9,853456619	1	0	0
NA	1	0	0
NA	1	0	0
12,41963848	1	0	0
12,91770786	0	0	1
9,409191231	0	0	1
10,58405595	0	1	0
13,16443579	1	0	0
9,904487053	0	1	0
11,46163217	0	1	0
9,492507264	0	1	0
10,86666187	1	0	0
10,04450957	1	0	0
11,21187442	0	1	0
11,33881014	0	1	0
10,33624352	0	0	1
NA	0	0	1
11,80312935	1	0	0
NA	1	0	0
11,16083997	0	1	0
10,00784757	0	0	0
10,41631118	0	1	0
NA	1	0	0
NA	1	0	0
9,702961291	0	1	0
NA	1	0	0
9,453286551	0	0	1
12,60480178	0	1	0
9,986126886	0	1	0
12,20607265	0	1	0
10,66082945	0	1	0
9,807196892	1	0	0
9,451716692	1	0	0
11,93979323	1	0	0
9,798127037	1	0	0
9,774687805	0	1	0
10,28236909	1	0	0
NA	1	0	0
9,945684601	1	0	0
11,13657246	0	0	1
9,305650552	0	1	0
9,84686429	0	0	1
9,717157974	1	0	0
9,305650552	0	0	1
NA	0	0	1
NA	0	0	1
13,06727341	1	0	0
10,98529272	0	1	0
10,73072847	0	0	1
9,667765219	0	0	1
12,57374935	1	0	0
10,4115995	0	1	0
10,66807128	0	0	1
10,00802773	1	0	0
10,55059079	0	0	1
10,57681461	1	0	0
10,21603295	1	0	0
12,20753657	0	1	0
9,216819338	0	1	0
12,74256602	0	1	0
9,338293736	1	0	0
11,23425599	1	0	0
10,77895629	0	1	0
10,39249692	1	0	0
11,83353687	1	0	0
10,23995979	1	0	0
NA	0	0	1
11,49740565	1	0	0
10,40426284	1	0	0
NA	1	0	0
9,594036922	1	0	0
NA	1	0	0
14,86654195	0	1	0
9,509333236	0	1	0
9,314880551	1	0	0
9,541512876	1	0	0
10,30169304	0	1	0
9,694308542	1	0	0
NA	1	0	0
9,68059397	0	0	1
NA	0	1	0
NA	0	1	0
12,22283143	0	1	0
11,7486456	1	0	0
9,275191344	1	0	0
10,33064892	1	0	0
13,61598343	0	0	0
13,54002053	0	1	0
9,546812609	0	1	0
11,46163217	0	1	0
11,00209984	0	1	0
9,58342002	1	0	0
11,22524339	0	1	0
11,54659232	1	0	0
15,03100918	0	1	0
12,85723048	0	1	0
NA	0	1	0
NA	0	1	0
9,755045547	0	1	0
NA	0	1	0
9,61580548	1	0	0
10,19046938	1	0	0
14,72750648	0	1	0
10,16819522	0	1	0
10,08776553	0	1	0
NA	0	1	0
9,652651581	1	0	0
9,305650552	1	0	0
10,30895266	0	0	1
9,900783901	0	1	0
11,60423675	0	0	1
9,61580548	1	0	0
NA	1	0	0
11,01287491	1	0	0
11,56264853	0	1	0
9,37160852	0	0	1
NA	1	0	0
NA	1	0	0
9,496946866	1	0	0
10,29214554	1	0	0
14,55329022	0	1	0
11,22984612	0	1	0
12,45980748	1	0	0
9,70454869	1	0	0
11,23630202	0	1	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
11,72310535	1	0	0
NA	1	0	0
10,43429226	1	0	0
12,28303369	0	1	0
9,433483923	1	0	0
NA	1	0	0
10,31201463	0	1	0
NA	1	0	0
NA	1	0	0
9,521861034	1	0	0
9,49912184	1	0	0
10,75930615	0	1	0
9,502114062	1	0	0
11,51782345	1	0	0
10,07314594	1	0	0
9,850877602	0	0	1
9,217812387	1	0	0
9,296518068	1	0	0
9,477156252	0	1	0
NA	1	0	0
NA	1	0	0
10,05212305	1	0	0
13,62714034	0	1	0
11,8244681	1	0	0
10,64823048	1	0	0
NA	1	0	0
9,375346207	1	0	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
9,573315428	1	0	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
10,75864728	1	0	0
11,51292546	0	1	0
12,0404076	1	0	0
13,43188424	0	1	0
10,47956707	1	0	0
10,01023211	1	0	0
NA	1	0	0
10,30895266	1	0	0
10,29741974	1	0	0
10,21533771	1	0	0
NA	1	0	0
9,461799234	0	1	0
9,668714136	1	0	0
NA	0	0	1
11,78725631	0	1	0
10,30895266	0	1	0
10,68230771	1	0	0
9,392661929	1	0	0
10,66592756	1	0	0
9,510444964	1	0	0
10,02428825	1	0	0
11,73039696	1	0	0
10,71014197	1	0	0
NA	0	1	0
NA	1	0	0
10,97671365	1	0	0
9,449357272	0	1	0
13,1263554	0	1	0
10,47294051	0	0	0
NA	1	0	0
NA	1	0	0
10,34174248	0	0	1
9,643420647	0	1	0
10,6038834	0	1	0
NA	0	1	0
10,40976284	1	0	0
9,940012327	0	1	0
10,3609124	1	0	0
12,10735667	1	0	0
9,52434787	1	0	0
11,23733005	1	0	0
9,521494801	1	0	0
9,263312257	1	0	0
NA	1	0	0
NA	1	0	0
13,20633681	0	1	0
10,66024313	1	0	0
9,969696553	1	0	0
9,903487553	0	1	0
9,679093219	1	0	0
11,5972758	1	0	0
9,622450023	0	1	0
12,42592278	0	1	0
12,24899324	1	0	0
9,76995616	0	0	1
11,04404121	1	0	0
10,0961719	0	1	0
12,34962739	1	0	0
10,95778216	1	0	0
9,903487553	1	0	0
NA	1	0	0
9,903487553	0	1	0
11,14764215	0	1	0
9,82119231	0	1	0
9,350015354	0	1	0
9,98994044	1	0	0
11,53092254	1	0	0
NA	1	0	0
14,02449839	0	1	0
13,02747011	0	1	0
9,226705726	1	0	0
NA	1	0	0
NA	1	0	0
11,62029895	0	1	0
9,588845299	0	1	0
9,45273738	1	0	0
11,27754485	0	1	0
10,35271433	1	0	0
10,66634735	0	1	0
10,88177608	1	0	0
9,964441532	1	0	0
10,95168336	1	0	0
NA	1	0	0
11,27357631	1	0	0
9,659056522	0	1	0
10,13277221	0	0	1
11,50720916	0	1	0
9,789870826	1	0	0
9,61580548	0	1	0
13,20492905	0	1	0
9,627799925	1	0	0
11,57105285	0	1	0
11,76056649	1	0	0
11,58907136	1	0	0
9,778151061	1	0	0
10,37648669	0	1	0
12,20607265	1	0	0
9,825526011	0	1	0
11,04368954	0	1	0
11,44398259	0	1	0
10,49562585	0	1	0
9,903487553	0	1	0
NA	1	0	0
9,512369038	0	1	0
10,1266311	0	1	0
NA	0	1	0
9,546812609	0	0	1
9,908873025	1	0	0
9,820920829	0	1	0
10,46310334	0	1	0
10,40728856	0	1	0
11,08214255	0	1	0
NA	0	0	1
NA	0	0	1
11,16261593	0	0	1
9,61580548	0	1	0
10,7831143	1	0	0
10,67148707	1	0	0
11,78291443	1	0	0
10,98569942	1	0	0
11,04775812	1	0	0
10,28636616	1	0	0
11,68435458	0	1	0
9,903487553	0	1	0
11,15988676	0	1	0
NA	0	1	0
10,54402478	0	1	0
11,34864027	1	0	0
11,62789566	0	1	0
14,21563141	0	1	0
9,468851067	1	0	0
9,46498259	1	0	0
10,30895266	0	0	1
NA	0	0	1
9,715711145	0	0	1
9,752664663	0	1	0
10,35894845	0	0	1
13,23730683	1	0	0
10,55968544	0	1	0
11,06352414	1	0	0
9,322418275	1	0	0
9,61580548	0	1	0
NA	0	1	0
10,80012645	0	1	0
NA	0	0	0
10,79133766	1	0	0
12,5287719	1	0	0
10,17473532	0	1	0
NA	0	1	0
9,504873919	1	0	0
9,71111566	0	1	0
10,45042329	1	0	0
NA	0	1	0
10,98529272	0	1	0
10,56134429	1	0	0
NA	0	1	0
NA	0	1	0
9,897268253	1	0	0
9,488199355	1	0	0
9,29118276	1	0	0
11,78699784	1	0	0
9,703388916	0	0	1
11,65268741	1	0	0
9,798127037	0	1	0
9,546812609	0	1	0
NA	0	1	0
9,392661929	1	0	0
NA	1	0	0
NA	0	1	0
9,328123408	0	1	0
9,579003174	0	1	0
9,396819939	1	0	0
14,48285109	0	1	0
NA	0	0	1
9,779736745	0	0	1
9,221577004	0	0	1
10,89094479	1	0	0
NA	0	1	0
10,56359488	0	1	0
11,23056255	0	1	0
9,772125234	0	0	1
NA	0	0	1
9,392661929	1	0	0
NA	1	0	0
11,66514857	1	0	0
9,280052984	1	0	0
11,15806316	0	1	0
12,20899338	0	1	0
9,37585481	1	0	0
NA	1	0	0
9,680344001	0	1	0
9,951467865	0	1	0
NA	0	1	0
10,40728856	1	0	0
11,23848862	1	0	0
10,23458825	1	0	0
10,14435314	0	1	0
9,823578169	1	0	0
9,736369829	1	0	0
9,297251744	1	0	0
9,86526634	1	0	0
9,809176873	0	1	0
9,380336279	0	1	0
10,59663473	1	0	0
NA	1	0	0
10,56553099	1	0	0
NA	0	1	0
NA	0	1	0
9,656947423	1	0	0
13,17718295	0	1	0
10,81977828	0	1	0
10,59663473	0	1	0
9,740968623	0	0	1
12,06976025	1	0	0
10,25878154	1	0	0
9,392661929	0	1	0
12,86416502	0	1	0
NA	0	1	0
9,37160852	0	1	0
NA	0	1	0
9,433483923	1	0	0
NA	1	0	0
11,08572075	1	0	0
9,574636203	0	1	0
9,585277542	1	0	0
9,520395295	1	0	0
NA	1	0	0
10,49819466	0	1	0
10,01873404	0	1	0
9,60238246	0	0	1
12,37123957	0	0	0
11,96146283	1	0	0
10,3205518	1	0	0
NA	1	0	0
15,00189316	0	1	0
NA	1	0	0
NA	1	0	0
11,2185544	1	0	0
NA	1	0	0
NA	1	0	0
11,08214255	1	0	0
NA	1	0	0
12,03486905	0	1	0
11,41254143	0	0	0
9,369734424	0	1	0
NA	0	1	0
11,28978191	0	1	0
11,76756768	1	0	0
9,729134165	0	1	0
9,875448096	1	0	0
12,21602298	0	1	0
11,81303006	0	1	0
9,903487553	1	0	0
NA	1	0	0
9,739673667	1	0	0
9,758692968	0	0	1
9,414504957	1	0	0
10,22918769	0	1	0
9,860057995	1	0	0
12,07537153	1	0	0
NA	1	0	0
10,02127059	1	0	0
10,62132735	0	1	0
12,2932086	0	1	0
10,18338944	0	1	0
10,858999	0	1	0
9,553930076	1	0	0
9,909071931	1	0	0
9,5965547	1	0	0
NA	1	0	0
9,903487553	0	0	1
9,825526011	1	0	0
9,215327913	1	0	0
9,238247325	1	0	0
NA	1	0	0
11,49786259	1	0	0
10,76983145	0	0	1
9,480367509	1	0	0
10,71007502	1	0	0
NA	1	0	0
11,17234882	1	0	0
10,69686461	1	0	0
10,81452451	1	0	0
9,76995616	0	1	0
10,30209587	0	0	1
10,74656222	0	1	0
11,09331072	1	0	0
9,487972109	0	0	1
9,472704636	0	1	0
11,81303006	0	1	0
9,80642584	1	0	0
10,08580911	1	0	0
10,89942091	0	0	1
10,14643373	1	0	0
11,52936952	1	0	0
NA	1	0	0
12,53148393	0	1	0
9,339436945	0	0	1
11,00209984	0	1	0
11,95413088	1	0	0
14,33705829	0	1	0
10,45558947	0	0	0
9,771212513	0	0	1
9,251866119	0	0	1
9,409191231	0	1	0
NA	1	0	0
NA	1	0	0
10,03889219	1	0	0
10,6454249	0	1	0
NA	1	0	0
NA	1	0	0
9,991544216	0	0	1
10,00056889	1	0	0
9,277064004	1	0	0
9,277064004	1	0	0
NA	0	0	1
11,53607542	1	0	0
10,65242415	0	1	0
11,22524339	1	0	0
10,46310334	0	1	0
NA	0	1	0
NA	1	0	0
NA	1	0	0
9,409191231	0	1	0
11,52832626	0	0	1
NA	1	0	0
NA	0	1	0
11,19747472	1	0	0
12,80856181	1	0	0
10,83958091	1	0	0
13,25685406	0	1	0
9,494616651	1	0	0
9,848450417	0	1	0
9,704426672	0	1	0
9,794174793	0	1	0
11,51292546	1	0	0
9,586376669	1	0	0
11,03438954	1	0	0
9,653614941	1	0	0
9,937599082	1	0	0
NA	1	0	0
10,50386133	1	0	0
NA	1	0	0
10,09481014	1	0	0
NA	1	0	0
10,69507637	1	0	0
NA	1	0	0
13,37721333	0	0	1
10,50916871	0	1	0
9,396902923	0	1	0
10,05190756	0	1	0
12,76568843	0	1	0
12,62303474	0	1	0
9,798127037	0	0	0
NA	0	0	0
11,97736364	1	0	0
11,31447453	1	0	0
NA	0	1	0
NA	1	0	0
10,13197679	1	0	0
11,00209984	1	0	0
9,309914177	0	0	1
10,73639668	1	0	0
9,994241916	0	1	0
NA	0	1	0
10,46310334	0	1	0
10,38899537	0	1	0
NA	0	1	0
NA	1	0	0
10,21950195	1	0	0
NA	1	0	0
10,46027076	0	1	0
9,660013735	0	0	0
10,3829773	1	0	0
10,24288408	1	0	0
9,59096619	1	0	0
NA	1	0	0
9,722025626	1	0	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
9,358760377	0	1	0
10,81404186	0	1	0
10,04706828	0	0	1
10,60164715	0	0	1
10,59663473	0	1	0
NA	0	1	0
10,36407196	1	0	0
9,798127037	0	0	1
NA	1	0	0
9,225031921	1	0	0
9,323669057	1	0	0
NA	1	0	0
10,1266311	1	0	0
NA	1	0	0
NA	0	0	1
NA	0	0	1
11,4870947	1	0	0
NA	0	0	1
NA	0	0	1
9,328834266	1	0	0
9,70320567	1	0	0
NA	1	0	0
10,85185818	0	0	1
11,01862914	0	1	0
9,466763949	0	0	0
11,61728548	1	0	0
12,13951609	0	1	0
11,30116686	1	0	0
11,21188794	1	0	0
9,732817848	0	1	0
9,410092464	1	0	0
12,02694598	1	0	0
11,50967017	0	1	0
9,220290703	1	0	0
10,66352206	1	0	0
9,667765219	1	0	0
NA	1	0	0
13,30867364	0	1	0
10,29194213	1	0	0
NA	1	0	0
10,9252188	0	1	0
10,57339169	1	0	0
9,975808214	0	0	1
10,30895266	0	1	0
9,710751957	0	0	1
9,358760377	1	0	0
9,83397669	1	0	0
9,643939319	1	0	0
11,26735735	0	1	0
NA	0	1	0
10,50089446	1	0	0
10,6454249	0	1	0
NA	0	1	0
9,502636782	0	1	0
11,6324847	1	0	0
11,29266525	1	0	0
10,48570317	1	0	0
NA	1	0	0
12,8346813	0	1	0
11,07168809	1	0	0
9,628392596	1	0	0
10,92649588	0	1	0
10,1266311	1	0	0
10,54428825	1	0	0
14,67026625	0	1	0
9,463586267	1	0	0
NA	1	0	0
10,86856845	0	1	0
NA	0	1	0
11,35040654	0	0	1
10,85166447	1	0	0
13,56061831	0	1	0
9,917143879	0	0	1
11,33377488	0	0	1
13,65308457	0	1	0
12,92070241	1	0	0
11,58603651	0	0	0
10,858999	1	0	0
NA	1	0	0
13,05978134	0	1	0
10,23995979	1	0	0
9,648595303	1	0	0
10,81142348	1	0	0
9,726034127	1	0	0
11,55469101	0	1	0
10,05625156	1	0	0
NA	1	0	0
12,21602298	0	1	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
14,57783019	0	1	0
10,63103616	1	0	0
9,28126471	0	0	1
9,893437217	0	1	0
10,1064284	1	0	0
10,1064284	1	0	0
9,27799902	0	1	0
11,31630212	0	1	0
10,94876939	1	0	0
9,517236663	0	0	1
12,02972322	0	1	0
10,1266311	1	0	0
12,56374709	0	1	0
9,827685839	0	0	1
NA	1	0	0
NA	1	0	0
10,32744724	0	1	0
NA	0	1	0
10,60539624	0	1	0
NA	0	1	0
NA	0	1	0
NA	0	1	0
10,30895266	0	1	0
NA	0	1	0
11,48408351	0	1	0
13,38472764	0	1	0
9,392661929	0	1	0
NA	0	1	0
9,635608107	0	1	0
10,46021345	1	0	0
11,02100337	1	0	0
12,64863763	0	1	0
9,581903928	1	0	0
10,80567936	1	0	0
NA	1	0	0
9,46498259	1	0	0
9,753594463	0	0	1
9,717398909	0	1	0
9,61580548	0	1	0
9,694431801	0	1	0
NA	0	1	0
NA	0	1	0
9,230142999	0	1	0
NA	0	1	0
9,436997743	0	1	0
NA	0	0	1
9,61580548	1	0	0
9,392661929	1	0	0
10,49548755	1	0	0
11,47525475	1	0	0
10,20894815	0	1	0
10,1266311	1	0	0
NA	1	0	0
9,667765219	0	1	0
10,27832162	1	0	0
NA	1	0	0
11,81175517	0	1	0
10,49703537	0	0	1
9,66516691	0	1	0
NA	0	1	0
12,05054101	1	0	0
10,73761333	1	0	0
11,38625051	0	1	0
9,540938245	0	0	0
NA	0	0	0
9,29468152	1	0	0
10,04324949	0	1	0
10,76553327	0	1	0
10,25590344	1	0	0
10,19854238	1	0	0
9,715711145	1	0	0
9,784140795	0	1	0
10,6454249	1	0	0
9,903487553	1	0	0
NA	1	0	0
9,61580548	1	0	0
12,84345867	0	1	0
12,87798342	0	1	0
9,798127037	0	1	0
9,852194258	0	1	0
NA	0	1	0
10,63648025	0	1	0
9,392661929	0	1	0
9,687195476	1	0	0
NA	1	0	0
11,00589264	0	0	1
NA	0	0	1
9,372033961	0	1	0
9,546812609	0	1	0
NA	0	1	0
9,305650552	1	0	0
NA	1	0	0
9,851615143	0	0	1
10,46626975	0	1	0
NA	1	0	0
9,935325309	1	0	0
NA	1	0	0
12,77379548	0	1	0
10,29458325	0	1	0
10,54534144	0	1	0
10,66888562	1	0	0
9,61580548	1	0	0
10,77128108	0	1	0
9,621456152	1	0	0
NA	1	0	0
11,1861976	0	1	0
9,239899174	1	0	0
11,46268425	0	0	0
14,06544949	0	1	0
NA	0	1	0
12,82233878	0	1	0
9,38890488	1	0	0
9,665674427	1	0	0
13,00792848	0	1	0
11,51292546	0	1	0
9,514879561	0	1	0
NA	0	1	0
10,82637647	1	0	0
9,998706819	1	0	0
9,510444964	1	0	0
11,58742979	0	1	0
10,37349118	0	1	0
10,80035079	1	0	0
NA	1	0	0
12,09514108	0	1	0
10,13400386	1	0	0
NA	1	0	0
13,13429195	0	1	0
NA	0	1	0
9,323669057	1	0	0
13,48882705	0	1	0
9,354094336	1	0	0
NA	1	0	0
9,435721418	1	0	0
9,732402628	0	1	0
NA	0	1	0
NA	0	1	0
10,91412436	1	0	0
9,862665558	1	0	0
9,639326675	0	1	0
NA	0	1	0
11,06866755	0	1	0
9,724001971	0	1	0
11,32653564	1	0	0
NA	1	0	0
13,29719193	0	1	0
10,01663765	1	0	0
NA	1	0	0
11,30936393	1	0	0
NA	1	0	0
9,86526634	1	0	0
NA	1	0	0
10,13559085	0	0	0
9,648595303	0	1	0
11,67717639	1	0	0
11,00209984	1	0	0
10,06734808	1	0	0
13,26057978	0	1	0
11,20638672	0	0	0
10,30895266	0	1	0
9,259130536	0	0	0
11,66540618	1	0	0
10,29231502	1	0	0
NA	1	0	0
10,37349118	1	0	0
NA	1	0	0
10,6454249	0	0	0
12,04920244	1	0	0
NA	1	0	0
9,629182277	1	0	0
NA	1	0	0
9,312535884	0	1	0
9,910860307	1	0	0
9,699472382	1	0	0
11,62204889	1	0	0
10,49182962	0	1	0
9,798127037	0	0	1
11,47626148	0	1	0
9,259130536	0	0	1
10,46310334	0	1	0
9,220290703	1	0	0
10,16761948	0	0	1
13,27206876	0	1	0
9,778547718	1	0	0
10,43373338	0	1	0
10,90595598	1	0	0
13,80984956	0	0	1
11,77608172	1	0	0
9,236008119	0	1	0
9,343033914	1	0	0
NA	0	1	0
NA	0	1	0
NA	0	0	1
NA	0	1	0
12,70015895	0	1	0
15,06938691	0	1	0
11,53105021	0	1	0
9,942323576	0	1	0
11,75665565	0	0	0
9,72853875	0	0	1
11,01039864	0	0	1
10,04102964	0	1	0
9,358070484	1	0	0
9,258368341	0	1	0
12,27839331	1	0	0
10,24835304	1	0	0
NA	1	0	0
NA	1	0	0
11,30913101	1	0	0
9,835529859	1	0	0
11,60823564	0	1	0
9,385889044	1	0	0
9,61580548	1	0	0
NA	1	0	0
11,28978191	0	1	0
11,08214255	1	0	0
12,0179342	1	0	0
11,13377117	0	0	1
10,85849887	1	0	0
9,96697865	1	0	0
9,754813515	0	0	1
NA	0	0	1
10,22201401	1	0	0
10,04324949	0	1	0
11,39196614	1	0	0
10,22012145	1	0	0
NA	1	0	0
10,8406	1	0	0
9,211939093	0	1	0
13,35679814	0	1	0
11,68813848	0	1	0

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	24 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time24 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309585&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

24 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309585&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309585&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	24 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 10.95 -0.528639Yard[t] + 0.138373Main[t] -0.718706Industry[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  10.95 -0.528639Yard[t] +  0.138373Main[t] -0.718706Industry[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309585&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  10.95 -0.528639Yard[t] +  0.138373Main[t] -0.718706Industry[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309585&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309585&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 10.95 -0.528639Yard[t] + 0.138373Main[t] -0.718706Industry[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+10.95	0.1612	+6.7940e+01	0	0
Yard	-0.5286	0.1651	-3.2010e+00	0.001388	0.000694
Main	+0.1384	0.1662	+8.3250e-01	0.4052	0.2026
Industry	-0.7187	0.1799	-3.9950e+00	6.707e-05	3.354e-05

\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) & +10.95 &  0.1612 & +6.7940e+01 &  0 &  0 \tabularnewline
Yard & -0.5286 &  0.1651 & -3.2010e+00 &  0.001388 &  0.000694 \tabularnewline
Main & +0.1384 &  0.1662 & +8.3250e-01 &  0.4052 &  0.2026 \tabularnewline
Industry & -0.7187 &  0.1799 & -3.9950e+00 &  6.707e-05 &  3.354e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309585&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]+10.95[/C][C] 0.1612[/C][C]+6.7940e+01[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]Yard[/C][C]-0.5286[/C][C] 0.1651[/C][C]-3.2010e+00[/C][C] 0.001388[/C][C] 0.000694[/C][/ROW]
[ROW][C]Main[/C][C]+0.1384[/C][C] 0.1662[/C][C]+8.3250e-01[/C][C] 0.4052[/C][C] 0.2026[/C][/ROW]
[ROW][C]Industry[/C][C]-0.7187[/C][C] 0.1799[/C][C]-3.9950e+00[/C][C] 6.707e-05[/C][C] 3.354e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309585&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309585&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+10.95	0.1612	+6.7940e+01	0	0
Yard	-0.5286	0.1651	-3.2010e+00	0.001388	0.000694
Main	+0.1384	0.1662	+8.3250e-01	0.4052	0.2026
Industry	-0.7187	0.1799	-3.9950e+00	6.707e-05	3.354e-05

Multiple Linear Regression - Regression Statistics
Multiple R	0.2871
R-squared	0.08243
Adjusted R-squared	0.08111
F-TEST (value)	62.29
F-TEST (DF numerator)	3
F-TEST (DF denominator)	2080
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.151
Sum Squared Residuals	2755

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.2871 \tabularnewline
R-squared &  0.08243 \tabularnewline
Adjusted R-squared &  0.08111 \tabularnewline
F-TEST (value) &  62.29 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 2080 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.151 \tabularnewline
Sum Squared Residuals &  2755 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309585&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.2871[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.08243[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.08111[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 62.29[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]2080[/C][/ROW]
[ROW][C]p-value[/C][C] 0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.151[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 2755[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309585&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309585&T=3

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.2871
R-squared	0.08243
Adjusted R-squared	0.08111
F-TEST (value)	62.29
F-TEST (DF numerator)	3
F-TEST (DF denominator)	2080
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.151
Sum Squared Residuals	2755

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309585&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309585&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309585&T=4

As an alternative you can also use a QR Code:

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

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0, df1 = 2, df2 = 2078, p-value = 1
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0, df1 = 6, df2 = 2074, p-value = 1
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0, df1 = 2, df2 = 2078, p-value = 1

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0, df1 = 2, df2 = 2078, p-value = 1
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0, df1 = 6, df2 = 2074, p-value = 1
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0, df1 = 2, df2 = 2078, p-value = 1
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309585&T=5

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 0, df1 = 2, df2 = 2078, p-value = 1
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 0, df1 = 6, df2 = 2074, p-value = 1
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0, df1 = 2, df2 = 2078, p-value = 1
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309585&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309585&T=5

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0, df1 = 2, df2 = 2078, p-value = 1
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0, df1 = 6, df2 = 2074, p-value = 1
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0, df1 = 2, df2 = 2078, p-value = 1

Variance Inflation Factors (Multicollinearity)
> vif Yard Main Industry 10.72128 10.28891 4.55634

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
    Yard     Main Industry 
10.72128 10.28891  4.55634 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309585&T=6

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
    Yard     Main Industry 
10.72128 10.28891  4.55634 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309585&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309585&T=6

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)
> vif Yard Main Industry 10.72128 10.28891 4.55634

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

PNG link

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PDF link

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$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, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$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.row.start(a)
a<-table.element(a, mywarning)
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,'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$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
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,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code