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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 11 May 2014 06:15:54 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/May/11/t1399803519pcnasv5ljhrp84f.htm/, Retrieved Mon, 13 May 2024 21:26:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=234785, Retrieved Mon, 13 May 2024 21:26:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact156

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [] [2014-05-11 10:15:54] [05fc9f73518f9509c56332c989d681e3] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
426483000	1,23	2,45	8890176	484574	2254011	6304844	10064618	2,504817258	2,255626206	2,962674171	1,827409639	1,208305917	1,936778645	1,892836705	0,398673148	0,972620687
392467400	1,22	2,46	8194413	478106	2013875	5471891	11338363	2,588856875	2,254290073	2,938077034	1,831345609	1,185836751	1,906554431	1,885169505	0,399505617	0,984474191
373475300	1,21	2,45	7722000	506039	2308944	5581708	9435079	2,556707487	2,287742329	2,939493469	1,907240597	1,170278385	1,894044662	1,924780838	0,404486894	0,983756493
376229000	1,22	2,43	7769178	508171	2278649	5421028	8143581	2,594016199	2,239527102	2,956607191	1,888363743	1,200370656	1,905171842	1,882148595	0,402920396	0,966264668
360973900	1,21	2,44	7449343	468388	2109718	5136152	7775342	2,575034646	2,233533534	2,9327336	1,881638905	1,155476057	1,954426073	1,886685234	0,4016707	0,973505243
387759400	1,22	2,41	7929370	466709	2070365	4948893	7656876	2,615118718	2,251360414	2,952193338	1,792641818	1,159318026	1,980369923	1,925798586	0,407072689	0,972388494
363641500	1,21	2,41	7473017	499053	2041975	4866528	8203164	2,594231025	2,24638899	2,935100796	1,777924095	1,15677345	1,952753935	1,916127136	0,405994214	0,956496302
357819500	1,20	2,41	7472424	499697	2130112	5110882	8447687	2,575670686	2,199553867	2,90232174	1,824053622	1,152792372	2,013870964	1,877602867	0,401523799	0,967290254
360434200	1,18	2,41	7292436	456662	2012391	4775552	8482877	2,574788367	2,235532502	2,903412333	1,787895792	1,156076314	2,008427246	1,916247394	0,403091706	0,972827816
345951300	1,19	2,38	7215340	467478	1995215	4690143	8131924	2,570518804	2,296327563	2,928809441	1,781207298	1,15670592	2,024102879	1,937465256	0,403156126	0,978399381
336657100	1,20	2,41	7216230	453126	1959695	4521167	8184292	2,545130261	2,343530681	2,893994751	1,833008936	1,149696334	1,971698305	1,921862741	0,399150576	0,971701193
337127700	1,19	2,39	7378041	449584	2079820	4618744	8006102	2,51291806	2,235155581	2,838043094	1,72924162	1,140621377	2,018155007	1,914078703	0,409922903	0,961555321
372484800	1,19	2,37	7877412	423896	2201750	4921010	8052832	2,498666613	2,285846046	2,907516473	1,744426032	1,148618686	2,01322723	1,932872355	0,410151329	0,97313966
335083000	1,20	2,40	7158125	460454	1980527	4739711	7854934	2,585554611	2,251969718	2,923322842	1,718287938	1,145658372	1,983054803	1,918439731	0,399816562	0,97514637
330515900	1,21	2,35	7137912	454105	2023721	4767867	7609626	2,625265124	2,279027295	2,928429104	1,783161608	1,122518618	2,022636838	1,942432221	0,398001307	0,973541278
339073600	1,20	2,35	7290803	453042	2136317	4856393	7640934	2,633979914	2,295531465	2,963569115	1,799557886	1,125493546	2,00384571	1,942732501	0,397523163	0,970674019
334975800	1,20	2,33	7425266	433082	2205673	4684931	8422297	2,610121782	2,377658441	2,978760345	1,811652783	1,135483871	1,997123073	1,952412882	0,393298052	0,951093747
325365500	1,20	2,35	7450430	460163	2163485	4583205	7980377	2,637666548	2,407760615	2,995807623	1,840063532	1,150970178	1,989838078	1,935577624	0,39223767	0,95789287
373425000	1,21	2,36	9214042	421051	2844091	5216686	9541323	2,670848065	2,438597099	3,021960269	1,848191879	1,166471293	2,01284155	1,866730632	0,395571396	0,938691723
345543300	1,21	2,41	8158864	435182	2458147	4583585	8839590	2,628940335	2,40412153	2,995748946	1,828287663	1,181252071	2,012007505	1,869074727	0,395336283	0,943717873
296672600	1,21	2,37	6515759	495363	1972304	4307098	7677033	2,579245412	2,382700906	2,96870356	1,834118593	1,163746833	1,989895531	1,960129117	0,402538467	0,985855372
299371600	1,20	2,34	6308487	472805	2153601	4748004	8354688	2,557071998	2,323591198	2,869143428	1,782762493	1,132997307	2,039054085	1,93933447	0,396531041	0,951508052
300932000	1,21	2,37	6366367	452921	2066530	4710073	8150927	2,574247298	2,343267282	2,904963658	1,815650297	1,14631303	2,146552028	1,954225439	0,399164344	0,952297846
316971300	1,21	2,34	6770097	450870	2152437	4867230	7846633	2,549499278	2,198970416	2,886225112	1,81255746	1,153624363	2,125245631	1,969225094	0,402279337	0,968402152
317006100	1,21	2,34	6700697	472551	2189294	4794611	8461058	2,577396677	2,249484806	2,901268394	1,747183549	1,159082223	2,159487812	1,949595432	0,396583809	0,969768321
336893400	1,20	2,33	7140792	462772	2253024	4883881	8425126	2,498397416	2,194952752	2,930932704	1,823166618	1,169421635	2,18612421	1,950339118	0,397019953	0,978901366
329263800	1,19	2,33	6891715	507189	2151817	4711492	8351766	2,563524072	2,222290866	2,919415504	1,820784942	1,152115929	2,337412218	1,952322235	0,39532274	0,962546842
333734400	1,20	2,34	7057521	513235	2141496	4810043	7956264	2,62486165	2,265730474	2,894430908	1,756938248	1,166865759	2,311676624	1,942532361	0,395444504	0,969432165
320830600	1,20	2,37	6806593	602342	2240864	5020983	8502847	2,709464912	2,244913761	2,933674032	1,806360947	1,156561837	2,520299106	1,906299774	0,39452353	0,976874839
335913000	1,20	2,38	7068776	638260	2198530	5071676	8671279	2,744712827	2,294221112	2,945368644	1,822648204	1,178924931	2,638087419	1,958035545	0,398311341	0,987343594
322307800	1,22	2,41	6868085	618068	2213237	5096684	8230049	2,75516125	2,304893775	2,981639492	1,828013387	1,173543701	2,629311874	1,990794085	0,400319144	0,98433502
343715900	1,22	2,39	7245015	607338	2252202	5263979	8404517	2,656234673	2,297782228	2,921159041	1,812348955	1,187552023	2,654889698	1,968342191	0,410197676	0,974971595
340015600	1,21	2,38	7160726	1002379	2419597	5523848	8872254	2,611062604	2,273074388	2,925545029	1,86111541	1,186151444	3,752995083	1,963642851	0,407457534	0,98772325
365757600	1,25	2,45	7927365	755302	2334515	5259355	9651748	2,675909804	2,287184647	2,965121278	1,89589154	1,14579455	2,947602196	1,935722676	0,403261436	0,983455696
376561300	1,25	2,41	8275238	724580	2155819	5044615	9070647	2,696625095	2,282507167	2,935906941	1,821685628	1,139173716	2,783817399	1,935064457	0,410767255	0,989948908
348192100	1,27	2,46	7510220	706447	2532345	5875038	8649186	2,698152801	2,286902153	2,942463266	1,854124155	1,152087401	2,756392325	1,907059876	0,40217671	0,998569999
360480000	1,28	2,40	7751398	991278	2221561	5321561	9030492	2,71573763	2,274888121	2,897591647	1,825784706	1,156027033	3,279355921	1,961858198	0,415018013	0,986262844
398134000	1,27	2,31	8701633	852996	2302538	5261199	9069668	2,765954843	2,249845265	2,907356363	1,867838496	1,123516926	3,006372274	1,927310963	0,408027864	0,993788566
373407800	1,28	2,42	8164755	673183	2350319	5621057	9116009	2,764382511	2,285656732	2,938901764	1,953847153	1,158491576	2,66346789	1,945845414	0,410298407	0,991172516
401817300	1,29	2,46	8534307	686730	2287028	5303894	10336764	2,720861639	2,29151196	2,955250365	1,960182497	1,177596115	2,650499653	1,959756822	0,418520535	0,995712653
388741700	1,26	2,45	8333017	768403	2262802	5325086	8941018	2,694286406	2,281690207	2,950776683	1,929866507	1,178394459	2,601234867	1,968206769	0,424902575	1,00336602
391988000	1,27	2,48	8568251	720603	2641195	6602036	10163717	2,698283644	2,268140989	2,958699829	1,807259986	1,1730764	2,669183171	1,945666457	0,418035824	1,013489324
401446600	1,25	2,45	8613013	688646	2886395	7354948	10028886	2,691642322	2,268783924	2,947819538	1,907538978	1,172440663	2,66925549	1,959442342	0,416733719	1,006608061
419775800	1,27	2,45	9139357	717093	2430852	6231237	10190148	2,662469007	2,273535118	2,958428899	2,025662225	1,187007656	2,661865624	1,957943591	0,423096985	1,012965111
389653100	1,27	2,43	8385716	806356	2412703	6066821	11198930	2,739786747	2,291996385	2,915142787	2,001330113	1,181594436	2,544646498	1,941833787	0,416923555	1,018626679
396474200	1,27	2,44	8451237	649995	2365468	6209715	10355548	2,761340381	2,28117324	2,92991582	2,02259022	1,180676788	2,524337358	1,917998528	0,413175803	1,015116518
420184700	1,29	2,46	9033401	540044	2057798	5353594	9396952	2,794038938	2,283323474	2,932973707	1,979553848	1,174044002	2,504137507	1,919696975	0,412036948	0,996406315
405051200	1,26	2,48	8565930	591115	2390239	6427650	9238064	2,780640374	2,250931582	2,949276213	2,010072351	1,174055851	2,464021217	1,922802194	0,413498196	1,012106599
399740200	1,27	2,52	8562307	493197	2456918	6941697	9286880	2,803382786	2,214853189	2,961242859	2,001885306	1,191268135	2,469470619	1,896220676	0,409531286	1,012543996
431447900	1,27	2,51	9255216	574142	2048758	5514399	10943146	2,777275832	2,235229392	2,962110342	1,96553825	1,212968646	2,484135163	1,903100084	0,411398648	1,013673658
492574400	1,28	2,50	10502760	545220	2513095	7322716	11297607	2,755296999	2,24412698	2,962678898	1,984683424	1,203682165	2,480213688	1,909857581	0,415350446	1,008737153
513063100	1,28	2,50	10855161	484423	2887292	9651951	9982802	2,730573932	2,241384165	2,964295671	2,025362397	1,20218925	2,493923076	1,915355654	0,41008891	1,015082619
444485500	1,28	2,53	9473338	561620	2295291	6686974	11849225	2,722271995	2,277890645	3,010426578	1,954345214	1,196644279	2,486597184	1,915204855	0,408247349	1,021114344
396731900	1,27	2,54	8521439	554667	2160295	5573380	9895998	2,731978098	2,27674121	2,957666063	1,896173299	1,191584797	2,548860369	1,901719809	0,414627982	1,01521733
393125000	1,24	2,54	8169912	695658	2430452	5428766	10512292	2,7351187	2,273255047	2,960841697	1,877050344	1,180898969	2,668822957	1,964233666	0,413328622	1,003216345
423595200	1,25	2,53	8705590	694559	2381670	5352882	10001971	2,722926531	2,280801636	2,947315508	1,891071584	1,177675362	2,673613322	1,943162729	0,416390027	1,019242297
416921900	1,25	2,48	8600302	613095	2215665	5114736	9450060	2,709466819	2,258515479	2,986857661	1,929851709	1,183238327	2,695063926	1,939779251	0,414407207	1,033885576
377906400	1,24	2,47	7884570	602933	2350453	5800681	9047810	2,658228748	2,238687969	2,965530095	1,81232073	1,177929738	2,647119156	1,938010941	0,41758102	1,036163098
355881000	1,24	2,44	7509946	614260	2263940	5430653	9034858	2,67726119	2,265618371	2,963806079	1,805078309	1,167292131	2,602528322	1,939639827	0,414830334	1,035035341
369946600	1,23	2,44	7796000	580581	2223827	5325139	9626461	2,668783361	2,258536562	2,970767404	1,873292595	1,170423612	2,60812682	1,947520604	0,412373468	1,027131077
365069300	1,24	2,43	7651158	617713	2071658	4874369	8887882	2,684284116	2,267801742	2,945489967	1,892642333	1,16855169	2,607034457	1,944771383	0,409242041	1,032301302
352563300	1,23	2,41	7430052	605519	2118606	4747271	8699165	2,674773506	2,274064148	2,880278609	1,903231388	1,152289294	2,607220734	1,958178043	0,407616437	1,036826092
347027600	1,24	2,42	7581024	609843	1980701	4500918	8756626	2,708286803	2,328436933	2,933969622	1,847039411	1,163890354	2,716974942	1,911131983	0,401857632	1,031351228
385909400	1,24	2,43	8431470	592140	2141976	4660010	9120578	2,68768583	2,328076502	2,945146151	1,872792415	1,172496773	2,701632288	1,907530971	0,400149721	1,025343618
366115500	1,24	2,42	7903994	582844	2262595	4916788	9410935	2,637087651	2,332851979	2,950611993	1,828067929	1,176204009	2,695714201	1,908274981	0,399914174	1,029248728
335636500	1,25	2,46	7462642	614646	2044949	4649568	8540660	2,6649113	2,301822389	3,006646486	1,830310984	1,187869082	2,673619258	1,911805775	0,403370723	1,042490002
334444000	1,26	2,47	7424743	607572	2055490	4677774	8577630	2,70379257	2,284728433	2,987296246	1,89216733	1,155841078	2,65986327	1,904032008	0,399623765	1,031810593
333868400	1,26	2,46	7480504	620835	2111968	4862450	8963865	2,692752449	2,272585886	2,995862498	1,905202054	1,169219266	2,644923015	1,914369166	0,402592682	1,029324628
340429400	1,27	2,43	7863944	581938	2153156	4836102	8831677	2,710268737	2,319870318	3,016846843	1,921286624	1,177710996	2,593916953	1,910905689	0,400425223	1,018960815
328931900	1,26	2,46	7703698	609333	2149987	4707458	8680975	2,744609363	2,375733423	3,065345042	1,838642525	1,177652684	2,485946164	1,89212828	0,400727453	1,022330177
346925200	1,28	2,46	8508132	619133	2805043	5364205	10889743	2,778108105	2,386711356	3,075626298	1,868692424	1,190557137	2,41013849	1,848373431	0,410194723	0,999775908
357185000	1,29	2,47	8933008	572585	2449477	4351596	9842291	2,787451782	2,42990257	3,085168296	1,953690304	1,209076997	2,448618227	1,837561521	0,404787014	1,01165073
363991400	1,28	2,48	8491850	599516	2168905	4208876	8005657	2,749925351	2,38590371	3,009987028	1,962835448	1,217624949	2,582711351	1,881404332	0,404537256	1,033397689
309173000	1,27	2,43	6940275	655034	2218929	5062032	8714475	2,686136188	2,365394566	2,976340006	1,951968418	1,169010641	2,747864608	1,88312674	0,405386659	1,015357088
307814900	1,30	2,42	6917191	668502	2144176	4893322	8555468	2,689714987	2,348605104	2,960358941	1,932534542	1,173842416	2,852020886	1,897918395	0,404574893	1,031243111
318811500	1,30	2,45	7096722	666124	2170967	4848894	8571300	2,693922608	2,309905393	2,979496794	1,961825112	1,179643264	2,880487578	1,900152515	0,404366554	1,037790197
324608200	1,28	2,43	7105114	732417	2240876	4922093	8764326	2,718535107	2,244987788	2,926040868	1,979972106	1,188395124	2,919031351	1,89094543	0,403596798	1,02987826
348699200	1,29	2,44	7647797	702229	2330906	5351141	9089938	2,691121945	2,260824708	2,958450414	1,937882018	1,196103186	2,83546709	1,894071133	0,403689631	1,029983117
337818700	1,27	2,42	7440408	684271	2188360	5017799	8778446	2,70214501	2,273761827	2,955947772	1,96485623	1,17969509	2,822411776	1,878417831	0,404427262	1,037387901
328230600	1,26	2,43	7255613	633638	2067367	4923300	8809264	2,681099229	2,225945145	2,947879911	1,985381608	1,141434593	2,790286668	1,859589037	0,403092562	1,047718712
328834500	1,27	2,43	7231703	693374	2189597	4915221	9521789	2,70062141	2,265040438	2,95919207	2,010110894	1,170486834	2,790061494	1,861561742	0,405904602	1,041517789
332574900	1,27	2,40	7278022	707616	2356724	5348984	9438993	2,717976305	2,292874112	2,912328255	1,999617946	1,187415012	2,844154174	1,863975294	0,415474043	1,0517865
335226200	1,27	2,39	7382680	722553	2250295	5135063	9045288	2,701417472	2,276312065	2,823566879	1,974706909	1,178149226	2,885755626	1,884344699	0,416076665	1,055177719
353195400	1,28	2,42	7622740	712532	2243913	5339400	9272049	2,661857362	2,272199591	2,869075689	1,981144183	1,191701973	2,864556636	1,907276318	0,41665766	1,05160996
372262200	1,29	2,41	8295038	687023	2172504	5122639	9978418	2,679762688	2,288585651	2,885144962	1,935805708	1,184893098	2,823581058	1,91487434	0,414164218	1,041479865
380936500	1,28	2,37	8136158	646716	2301051	5710269	9776284	2,654425119	2,289200403	2,92504442	1,894851936	1,19802165	2,754786164	1,815560768	0,413657307	1,046861888
375061700	1,30	2,38	8240817	657284	2245784	5187058	9601480	2,689196718	2,319057389	2,964360505	1,873604656	1,185171314	2,718410962	1,837459283	0,403048808	1,049759538
361528600	1,30	2,37	7993962	701042	2159896	5277273	11193789	2,662270307	2,31843994	3,01307436	1,810098093	1,169358764	2,743356237	1,881032692	0,408213966	1,03692188
369655600	1,30	2,38	7997958	744939	2374240	5431043	9607554	2,693765842	2,294204142	2,966660575	1,871589773	1,171628562	2,914863728	1,896798215	0,411488445	1,053931064
412395900	1,29	2,37	8914911	823561	2533022	6064885	9870457	2,694993497	2,301021098	2,995957274	1,910747051	1,179453245	2,90717997	1,849252226	0,408853501	1,040343352
413616300	1,30	2,40	9082346	810516	2419167	5849883	10260040	2,653484053	2,240610247	3,001361089	1,862265345	1,185852377	2,893968321	1,878185192	0,417707958	1,049031508
393339200	1,29	2,66	8690947	755964	2379061	5763961	9578120	2,656735463	2,211580027	2,986246323	1,886935264	1,205149637	2,926765265	1,903390041	0,425875668	1,051250829
403557600	1,28	2,50	8678669	707347	2264684	5612253	9693065	2,63103904	2,210327986	2,925391328	1,807661506	1,191240495	2,907747558	1,859971746	0,419421901	1,05740214
455120200	1,30	2,60	9768461	727181	2378165	5996108	12413462	2,65041403	2,19422179	2,982452731	1,825419803	1,235218975	2,87729367	1,878526576	0,42352897	1,050239756
403219500	1,30	2,64	8751448	1110335	2536093	6163859	13143933	2,595484702	2,205946109	2,959796783	1,778533183	1,219947849	2,508280858	1,887459095	0,424724243	1,047993825
397089300	1,31	2,67	8737854	939274	2559486	6806073	11118547	2,568428812	2,22152511	2,986794483	1,809918547	1,208439579	2,680640535	1,873310519	0,425045105	1,065463071
448901600	1,32	2,72	9684075	842499	2340159	5770678	11289800	2,649222884	2,187731643	2,963375318	1,805011343	1,226141192	2,707410494	1,847873522	0,415243593	1,065186397
542612700	1,33	2,73	11529582	785788	2235562	5305632	11573959	2,692905606	2,186877403	2,938724582	1,834128195	1,256185609	2,739563902	1,860066814	0,412959676	1,060809169
457822400	1,32	2,48	9854882	812169	2300728	5714880	10511958	2,710379864	2,244484545	2,950912537	1,821728674	1,231558638	2,605514541	1,857239576	0,40765032	1,054436875
412639000	1,30	2,41	9030507	730023	2090042	5307840	12515693	2,718514748	2,279783617	2,959052129	1,882904984	1,219801553	2,580887633	1,866741476	0,410622985	1,086226987
489210000	1,31	2,47	10656814	823033	1976051	4951640	12966759	2,729006188	2,265457024	2,959047815	1,859726604	1,258144881	2,552854675	1,863426872	0,41149217	1,084338072
412869700	1,30	2,54	9111428	976731	2104956	5576975	10668160	2,724737624	2,289462193	2,944276603	1,896960151	1,23265398	2,41323202	1,870199237	0,415926778	1,074832422
440872100	1,30	2,56	9642906	738606	2489023	6787849	13948692	2,730724307	2,291888017	2,981037274	1,892069149	1,205648088	2,578434775	1,863886118	0,416384429	1,062520399
419946500	1,30	2,52	9217060	685173	2598916	7685812	16087616	2,716968793	2,29668492	2,959620516	1,821482717	1,201144817	2,642835828	1,864637511	0,414113278	1,086878411
407476700	1,29	2,52	8816389	642519	2302455	6451885	12159456	2,702114378	2,318185457	2,9760826	1,903057618	1,203997361	2,635682576	1,868263639	0,409851983	1,084575348
416175800	1,29	2,51	9074790	677849	2427969	5521297	10633146	2,718917977	2,294193807	2,940903824	1,904193237	1,212756284	2,668008478	1,883177489	0,409013109	1,067397552
389131900	1,30	2,51	8601172	826348	2132820	5268035	10770809	2,704542918	2,23409979	2,973255083	1,857929898	1,206461622	2,841809714	1,894815555	0,410822064	1,04315078
447030200	1,30	2,51	9735782	757562	2560376	6159480	10548925	2,717311394	2,186379194	2,973362563	1,864550789	1,213635416	2,732193863	1,898971273	0,417123046	1,067512352
428311100	1,29	2,46	9222117	825217	2454605	6391178	10123204	2,696537479	2,2046823	2,973023198	1,762010504	1,209635337	2,763599207	1,888831294	0,413401841	1,100863179
384596200	1,27	2,45	8197462	831800	2169005	5446149	11471988	2,65323339	2,264959519	2,944	1,707751875	1,200428001	2,752180531	1,875125278	0,412651694	1,113737077
391147100	1,26	2,45	8161117	890944	2072759	5055640	10599314	2,656847623	2,285277046	2,970712287	1,725128302	1,221405286	2,668272337	1,875858627	0,410987479	1,102970452
379847800	1,25	2,43	8085780	818812	2201360	5234681	10501150	2,68840173	2,288625225	3,002692573	1,835910698	1,218576951	2,75776767	1,883603496	0,410007021	1,070603697
364431300	1,26	2,42	7777563	813389	2215184	5456357	9476948	2,702713317	2,299230359	2,999452007	1,879256466	1,227618962	2,837470317	1,882887244	0,411940992	1,086029757
378402900	1,27	2,39	8192525	791213	2140796	5055154	9854999	2,711022051	2,321891253	2,973988498	1,860613281	1,219326172	2,795636668	1,879571383	0,407448988	1,072047077
364713400	1,26	2,39	8222640	753162	2064345	4986559	9020688	2,687873468	2,337535129	3,009436817	1,857021191	1,225537073	2,76981684	1,878656159	0,41077387	1,078852318
399466200	1,25	2,39	8852425	744738	2246763	5314687	9639666	2,719101948	2,33880085	3,034231024	1,876345796	1,230269355	2,795837284	1,841411286	0,406615344	1,072090819
360783600	1,25	2,41	8047626	740853	2196948	5029952	10016963	2,714630813	2,290887791	3,007439311	1,880816873	1,218710873	2,791096748	1,855609066	0,40071109	1,083545324
356600800	1,25	2,37	8079925	828505	1987852	4569712	9221363	2,709502388	2,193859495	2,958101457	1,89514652	1,211570297	2,786898772	1,862204445	0,401803464	1,095450614
351141200	1,26	2,38	8099820	764325	2013311	4661941	9163961	2,735607888	2,017806522	3,081504548	1,891959507	1,185688863	2,776968326	1,860246189	0,40525702	1,086912011
325866500	1,26	2,41	7444464	779152	2024477	4649692	9600997	2,820171462	2,130092524	3,092087101	1,906442444	1,188298584	2,802245654	1,875842876	0,406466124	1,069476232

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=234785&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=234785&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=234785&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
QBEPIL[t] = + 238412423.389073 -118206580.907723PBEPIL[t] + 20649129.8051515PBELUX[t] + 45.6916866799959BUDBEER[t] -2.14111752506672BUDCHIL[t] -33.9544395422275BUDAMB[t] + 10.7384089292779BUDWATER[t] -0.2758337566658BUDSISSS[t] -14407855.5728688Gpspontanegisting[t] -35528113.7966534GPHogegisting[t] -93569909.8508445GPAngelsa[t] -12199480.1802457GPZuur[t] + 79117731.8661403GPlaagalc[t] + 4348121.90042767GPChill[t] + 83029242.5839783GPAmbient[t] + 117468813.231695GPWaters[t] -5813131.98119861`Gpsiss\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
QBEPIL[t] =  +  238412423.389073 -118206580.907723PBEPIL[t] +  20649129.8051515PBELUX[t] +  45.6916866799959BUDBEER[t] -2.14111752506672BUDCHIL[t] -33.9544395422275BUDAMB[t] +  10.7384089292779BUDWATER[t] -0.2758337566658BUDSISSS[t] -14407855.5728688Gpspontanegisting[t] -35528113.7966534GPHogegisting[t] -93569909.8508445GPAngelsa[t] -12199480.1802457GPZuur[t] +  79117731.8661403GPlaagalc[t] +  4348121.90042767GPChill[t] +  83029242.5839783GPAmbient[t] +  117468813.231695GPWaters[t] -5813131.98119861`Gpsiss\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=234785&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]QBEPIL[t] =  +  238412423.389073 -118206580.907723PBEPIL[t] +  20649129.8051515PBELUX[t] +  45.6916866799959BUDBEER[t] -2.14111752506672BUDCHIL[t] -33.9544395422275BUDAMB[t] +  10.7384089292779BUDWATER[t] -0.2758337566658BUDSISSS[t] -14407855.5728688Gpspontanegisting[t] -35528113.7966534GPHogegisting[t] -93569909.8508445GPAngelsa[t] -12199480.1802457GPZuur[t] +  79117731.8661403GPlaagalc[t] +  4348121.90042767GPChill[t] +  83029242.5839783GPAmbient[t] +  117468813.231695GPWaters[t] -5813131.98119861`Gpsiss\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=234785&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=234785&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
QBEPIL[t] = + 238412423.389073 -118206580.907723PBEPIL[t] + 20649129.8051515PBELUX[t] + 45.6916866799959BUDBEER[t] -2.14111752506672BUDCHIL[t] -33.9544395422275BUDAMB[t] + 10.7384089292779BUDWATER[t] -0.2758337566658BUDSISSS[t] -14407855.5728688Gpspontanegisting[t] -35528113.7966534GPHogegisting[t] -93569909.8508445GPAngelsa[t] -12199480.1802457GPZuur[t] + 79117731.8661403GPlaagalc[t] + 4348121.90042767GPChill[t] + 83029242.5839783GPAmbient[t] + 117468813.231695GPWaters[t] -5813131.98119861`Gpsiss\r`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)238412423.389073123149840.8135941.9360.0556140.027807
PBEPIL-118206580.90772349108047.972864-2.40710.0178610.008931
PBELUX20649129.805151515498594.0427221.33230.1856940.092847
BUDBEER45.69168667999591.5677629.144600
BUDCHIL-2.1411175250667211.107635-0.19280.8475260.423763
BUDAMB-33.95443954222757.141965-4.75426e-063e-06
BUDWATER10.73840892927791.9571775.486700
BUDSISSS-0.27583375666580.959221-0.28760.7742610.387131
Gpspontanegisting-14407855.572868818493089.649102-0.77910.4377110.218855
GPHogegisting-35528113.796653415896883.61605-2.23490.0275830.013791
GPAngelsa-93569909.850844521533327.305487-4.34543.3e-051.6e-05
GPZuur-12199480.180245714799888.258041-0.82430.4116760.205838
GPlaagalc79117731.866140351749824.6634531.52890.1293670.064683
GPChill4348121.900427674673935.2889760.93030.3543950.177198
GPAmbient83029242.583978332757327.5045862.53470.0127590.00638
GPWaters117468813.231695172672194.0607330.68030.4978410.248921
`Gpsiss\r`-5813131.9811986142295525.093115-0.13740.8909510.445475

\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) & 238412423.389073 & 123149840.813594 & 1.936 & 0.055614 & 0.027807 \tabularnewline
PBEPIL & -118206580.907723 & 49108047.972864 & -2.4071 & 0.017861 & 0.008931 \tabularnewline
PBELUX & 20649129.8051515 & 15498594.042722 & 1.3323 & 0.185694 & 0.092847 \tabularnewline
BUDBEER & 45.6916866799959 & 1.56776 & 29.1446 & 0 & 0 \tabularnewline
BUDCHIL & -2.14111752506672 & 11.107635 & -0.1928 & 0.847526 & 0.423763 \tabularnewline
BUDAMB & -33.9544395422275 & 7.141965 & -4.7542 & 6e-06 & 3e-06 \tabularnewline
BUDWATER & 10.7384089292779 & 1.957177 & 5.4867 & 0 & 0 \tabularnewline
BUDSISSS & -0.2758337566658 & 0.959221 & -0.2876 & 0.774261 & 0.387131 \tabularnewline
Gpspontanegisting & -14407855.5728688 & 18493089.649102 & -0.7791 & 0.437711 & 0.218855 \tabularnewline
GPHogegisting & -35528113.7966534 & 15896883.61605 & -2.2349 & 0.027583 & 0.013791 \tabularnewline
GPAngelsa & -93569909.8508445 & 21533327.305487 & -4.3454 & 3.3e-05 & 1.6e-05 \tabularnewline
GPZuur & -12199480.1802457 & 14799888.258041 & -0.8243 & 0.411676 & 0.205838 \tabularnewline
GPlaagalc & 79117731.8661403 & 51749824.663453 & 1.5289 & 0.129367 & 0.064683 \tabularnewline
GPChill & 4348121.90042767 & 4673935.288976 & 0.9303 & 0.354395 & 0.177198 \tabularnewline
GPAmbient & 83029242.5839783 & 32757327.504586 & 2.5347 & 0.012759 & 0.00638 \tabularnewline
GPWaters & 117468813.231695 & 172672194.060733 & 0.6803 & 0.497841 & 0.248921 \tabularnewline
`Gpsiss\r` & -5813131.98119861 & 42295525.093115 & -0.1374 & 0.890951 & 0.445475 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=234785&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]238412423.389073[/C][C]123149840.813594[/C][C]1.936[/C][C]0.055614[/C][C]0.027807[/C][/ROW]
[ROW][C]PBEPIL[/C][C]-118206580.907723[/C][C]49108047.972864[/C][C]-2.4071[/C][C]0.017861[/C][C]0.008931[/C][/ROW]
[ROW][C]PBELUX[/C][C]20649129.8051515[/C][C]15498594.042722[/C][C]1.3323[/C][C]0.185694[/C][C]0.092847[/C][/ROW]
[ROW][C]BUDBEER[/C][C]45.6916866799959[/C][C]1.56776[/C][C]29.1446[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]BUDCHIL[/C][C]-2.14111752506672[/C][C]11.107635[/C][C]-0.1928[/C][C]0.847526[/C][C]0.423763[/C][/ROW]
[ROW][C]BUDAMB[/C][C]-33.9544395422275[/C][C]7.141965[/C][C]-4.7542[/C][C]6e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]BUDWATER[/C][C]10.7384089292779[/C][C]1.957177[/C][C]5.4867[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]BUDSISSS[/C][C]-0.2758337566658[/C][C]0.959221[/C][C]-0.2876[/C][C]0.774261[/C][C]0.387131[/C][/ROW]
[ROW][C]Gpspontanegisting[/C][C]-14407855.5728688[/C][C]18493089.649102[/C][C]-0.7791[/C][C]0.437711[/C][C]0.218855[/C][/ROW]
[ROW][C]GPHogegisting[/C][C]-35528113.7966534[/C][C]15896883.61605[/C][C]-2.2349[/C][C]0.027583[/C][C]0.013791[/C][/ROW]
[ROW][C]GPAngelsa[/C][C]-93569909.8508445[/C][C]21533327.305487[/C][C]-4.3454[/C][C]3.3e-05[/C][C]1.6e-05[/C][/ROW]
[ROW][C]GPZuur[/C][C]-12199480.1802457[/C][C]14799888.258041[/C][C]-0.8243[/C][C]0.411676[/C][C]0.205838[/C][/ROW]
[ROW][C]GPlaagalc[/C][C]79117731.8661403[/C][C]51749824.663453[/C][C]1.5289[/C][C]0.129367[/C][C]0.064683[/C][/ROW]
[ROW][C]GPChill[/C][C]4348121.90042767[/C][C]4673935.288976[/C][C]0.9303[/C][C]0.354395[/C][C]0.177198[/C][/ROW]
[ROW][C]GPAmbient[/C][C]83029242.5839783[/C][C]32757327.504586[/C][C]2.5347[/C][C]0.012759[/C][C]0.00638[/C][/ROW]
[ROW][C]GPWaters[/C][C]117468813.231695[/C][C]172672194.060733[/C][C]0.6803[/C][C]0.497841[/C][C]0.248921[/C][/ROW]
[ROW][C]`Gpsiss\r`[/C][C]-5813131.98119861[/C][C]42295525.093115[/C][C]-0.1374[/C][C]0.890951[/C][C]0.445475[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=234785&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=234785&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)238412423.389073123149840.8135941.9360.0556140.027807
PBEPIL-118206580.90772349108047.972864-2.40710.0178610.008931
PBELUX20649129.805151515498594.0427221.33230.1856940.092847
BUDBEER45.69168667999591.5677629.144600
BUDCHIL-2.1411175250667211.107635-0.19280.8475260.423763
BUDAMB-33.95443954222757.141965-4.75426e-063e-06
BUDWATER10.73840892927791.9571775.486700
BUDSISSS-0.27583375666580.959221-0.28760.7742610.387131
Gpspontanegisting-14407855.572868818493089.649102-0.77910.4377110.218855
GPHogegisting-35528113.796653415896883.61605-2.23490.0275830.013791
GPAngelsa-93569909.850844521533327.305487-4.34543.3e-051.6e-05
GPZuur-12199480.180245714799888.258041-0.82430.4116760.205838
GPlaagalc79117731.866140351749824.6634531.52890.1293670.064683
GPChill4348121.900427674673935.2889760.93030.3543950.177198
GPAmbient83029242.583978332757327.5045862.53470.0127590.00638
GPWaters117468813.231695172672194.0607330.68030.4978410.248921
`Gpsiss\r`-5813131.9811986142295525.093115-0.13740.8909510.445475






Multiple Linear Regression - Regression Statistics
Multiple R0.985836467656327
R-squared0.971873540961104
Adjusted R-squared0.967504382275451
F-TEST (value)222.439515450033
F-TEST (DF numerator)16
F-TEST (DF denominator)103
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8066695.04596108
Sum Squared Residuals6702371603346897

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.985836467656327 \tabularnewline
R-squared & 0.971873540961104 \tabularnewline
Adjusted R-squared & 0.967504382275451 \tabularnewline
F-TEST (value) & 222.439515450033 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 103 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 8066695.04596108 \tabularnewline
Sum Squared Residuals & 6702371603346897 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=234785&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.985836467656327[/C][/ROW]
[ROW][C]R-squared[/C][C]0.971873540961104[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.967504382275451[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]222.439515450033[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]103[/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]8066695.04596108[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]6702371603346897[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=234785&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=234785&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 R0.985836467656327
R-squared0.971873540961104
Adjusted R-squared0.967504382275451
F-TEST (value)222.439515450033
F-TEST (DF numerator)16
F-TEST (DF denominator)103
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8066695.04596108
Sum Squared Residuals6702371603346897






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1426483000423792922.5214372690077.47856258
2392467400390835822.9305431631577.06945664
3373475300362660762.33547410814537.6645255
4376229000361487807.66877914741192.3312205
5360973900350778459.89222210195440.107778
6387759400372631155.9311115128244.0688902
7363641500353918996.6241819722503.37581906
8357819500355236475.2932282583024.70677159
9360434200352525610.7468377908589.25316345
10345951300344402742.9266621548557.07333766
11336657100342093541.657981-5436441.65798117
12337127700358259690.530213-21131990.5302132
13372484800373661466.761178-1176666.76117825
14335083000341776539.065505-6693539.06550516
15330515900334882281.62304-4366381.62303963
16339073600336110428.632052963171.3679504
17334975800334511698.670815464101.329185514
18325365500332702091.824146-7336591.82414646
19373425000387648538.82479-14223538.8247902
20345543300352765105.142282-7221805.14228208
21296672600301198884.849798-4526284.84979846
22299371600298641248.811984730351.188015746
23300932000301733824.724162-801824.724161536
24316971300327698251.354948-10726951.3549475
25317006100317747260.854656-741160.854656309
26336893400338023797.540334-1130397.5403339
27329263800327881591.7413091382208.2586907
28333734400336892981.87196-3158581.87196003
29320830600316774550.8174774056049.18252316
30335913000334227597.7229771685402.27702352
31322307800321767468.478841540331.521159478
32343715900347123787.37865-3407887.37864956
33340015600344781012.842736-4765412.84273602
34365757600361845702.5573313911897.44266935
35376561300383962161.012908-7400861.01290837
36348192100340293947.2851657898152.71483518
37360480000366220062.999801-5740062.99980068
38398134000397096188.2101191037811.78988087
39373407800374115139.562485-707339.562485365
40401817300391383563.82389210433736.1761079
41388741700389562290.347491-820590.347491354
42391988000398693176.270743-6705176.27074252
43401446600403196572.129641-1749972.12964088
44419775800427701665.755347-7925865.75534697
45389653100390778442.286826-1125342.28682618
46396474200393556950.6123562917249.38764417
47420184700419153861.4612051030838.53879517
48405051200401544633.0711493506566.92885064
49399740200403119384.081466-3379184.08146581
50431447900435053943.775784-3606043.77578361
51492574400494300069.651055-1725669.65105505
52513063100522750486.613113-9687386.6131132
53444485500442451998.6133992033501.38660055
54396731900398607866.172863-1875966.17286309
55393125000379692909.76508613432090.2349139
56423595200403049871.58688220545328.413118
57416921900397359672.29894919562227.7010505
58377906400373010653.4802934895746.51970724
59355881000352019123.9264723861876.07352827
60369946600365983346.2866723963253.71332802
61365069300359224821.3053545844478.69464586
62352563300352507664.28882255635.7111778952
63347027600350533449.000897-3505849.00089706
64385909400384867336.4194741042063.58052622
65366115500360033731.6723676081768.32763297
66335636500341084105.797552-5447605.79755226
67334444000335812574.169281-1368574.1692808
68333868400339929298.905894-6060898.90589409
69340429400349962181.304775-9532781.30477499
70328931900335124409.258363-6192509.25836251
71346925200349796705.049458-2871505.04945839
72357185000366253947.270501-9068947.27050112
73363991400369699110.638032-5707710.63803154
74309173000308251406.705715921593.294285236
75307814900308338476.801476-523576.801475501
76318811500315646929.550163164570.44984015
77324608200322982061.7599991626138.24000071
78348699200346157329.6648322541870.33516826
79337818700336693037.3678771125662.63212287
80328230600330391013.443146-2160413.44314587
81328834500323352329.8123125482170.18768823
82332574900329938274.3567962636625.64320376
83335226200346533388.115857-11307188.1158573
84353195400358661770.368377-5466370.36837748
85372262200385837741.109874-13575541.1098744
86380936500370534128.31493610402371.6850636
87375061700363841238.4044911220461.59551
88361528600355490802.4693076037797.53069333
89369655600357116865.95034412538734.0496561
90412395900394092361.7800318303538.2199695
91413616300409328632.6297084287667.37029221
92393339200405546321.979712-12207121.9797117
93403557600406684411.965501-3126811.96550125
94455120200455804532.531786-684332.531785861
95403219500406727786.410288-3508286.41028834
96397089300408098648.305499-11009348.3054992
97448901600448214947.784803686652.215197334
98542612700534803164.6713197809535.32868105
99457822400450081253.5558697741146.4441314
100412639000412775611.872217-136611.872217143
101489210000490253743.958039-1043743.95803898
102412869700423564272.589562-10694572.5895625
103440872100442458803.262214-1586703.26221391
104419946500430081554.946261-10135054.9462611
105407476700407869984.34495-393284.344950021
106416175800411522079.8990254653720.10097496
107389131900397102530.883938-7970630.88393845
108447030200446663711.385062366488.614937735
109428311100428674023.555984-362923.555984447
110384596200382957531.8521081638668.1478922
111391147100379404995.25267511742104.7473254
112379847800370433297.6258889414502.37411205
113364431300357498389.9224236932910.07757749
114378402900372943427.4372945459472.56270587
115364713400374831145.002818-10117745.0028178
116399466200395861419.4461063604780.55389445
117360783600361742483.612561-958883.612561455
118356600800372567955.421331-15967155.421331
119351141200365353253.069964-14212053.0699642
120325866500330845611.537997-4979111.53799745

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 426483000 & 423792922.521437 & 2690077.47856258 \tabularnewline
2 & 392467400 & 390835822.930543 & 1631577.06945664 \tabularnewline
3 & 373475300 & 362660762.335474 & 10814537.6645255 \tabularnewline
4 & 376229000 & 361487807.668779 & 14741192.3312205 \tabularnewline
5 & 360973900 & 350778459.892222 & 10195440.107778 \tabularnewline
6 & 387759400 & 372631155.93111 & 15128244.0688902 \tabularnewline
7 & 363641500 & 353918996.624181 & 9722503.37581906 \tabularnewline
8 & 357819500 & 355236475.293228 & 2583024.70677159 \tabularnewline
9 & 360434200 & 352525610.746837 & 7908589.25316345 \tabularnewline
10 & 345951300 & 344402742.926662 & 1548557.07333766 \tabularnewline
11 & 336657100 & 342093541.657981 & -5436441.65798117 \tabularnewline
12 & 337127700 & 358259690.530213 & -21131990.5302132 \tabularnewline
13 & 372484800 & 373661466.761178 & -1176666.76117825 \tabularnewline
14 & 335083000 & 341776539.065505 & -6693539.06550516 \tabularnewline
15 & 330515900 & 334882281.62304 & -4366381.62303963 \tabularnewline
16 & 339073600 & 336110428.63205 & 2963171.3679504 \tabularnewline
17 & 334975800 & 334511698.670815 & 464101.329185514 \tabularnewline
18 & 325365500 & 332702091.824146 & -7336591.82414646 \tabularnewline
19 & 373425000 & 387648538.82479 & -14223538.8247902 \tabularnewline
20 & 345543300 & 352765105.142282 & -7221805.14228208 \tabularnewline
21 & 296672600 & 301198884.849798 & -4526284.84979846 \tabularnewline
22 & 299371600 & 298641248.811984 & 730351.188015746 \tabularnewline
23 & 300932000 & 301733824.724162 & -801824.724161536 \tabularnewline
24 & 316971300 & 327698251.354948 & -10726951.3549475 \tabularnewline
25 & 317006100 & 317747260.854656 & -741160.854656309 \tabularnewline
26 & 336893400 & 338023797.540334 & -1130397.5403339 \tabularnewline
27 & 329263800 & 327881591.741309 & 1382208.2586907 \tabularnewline
28 & 333734400 & 336892981.87196 & -3158581.87196003 \tabularnewline
29 & 320830600 & 316774550.817477 & 4056049.18252316 \tabularnewline
30 & 335913000 & 334227597.722977 & 1685402.27702352 \tabularnewline
31 & 322307800 & 321767468.478841 & 540331.521159478 \tabularnewline
32 & 343715900 & 347123787.37865 & -3407887.37864956 \tabularnewline
33 & 340015600 & 344781012.842736 & -4765412.84273602 \tabularnewline
34 & 365757600 & 361845702.557331 & 3911897.44266935 \tabularnewline
35 & 376561300 & 383962161.012908 & -7400861.01290837 \tabularnewline
36 & 348192100 & 340293947.285165 & 7898152.71483518 \tabularnewline
37 & 360480000 & 366220062.999801 & -5740062.99980068 \tabularnewline
38 & 398134000 & 397096188.210119 & 1037811.78988087 \tabularnewline
39 & 373407800 & 374115139.562485 & -707339.562485365 \tabularnewline
40 & 401817300 & 391383563.823892 & 10433736.1761079 \tabularnewline
41 & 388741700 & 389562290.347491 & -820590.347491354 \tabularnewline
42 & 391988000 & 398693176.270743 & -6705176.27074252 \tabularnewline
43 & 401446600 & 403196572.129641 & -1749972.12964088 \tabularnewline
44 & 419775800 & 427701665.755347 & -7925865.75534697 \tabularnewline
45 & 389653100 & 390778442.286826 & -1125342.28682618 \tabularnewline
46 & 396474200 & 393556950.612356 & 2917249.38764417 \tabularnewline
47 & 420184700 & 419153861.461205 & 1030838.53879517 \tabularnewline
48 & 405051200 & 401544633.071149 & 3506566.92885064 \tabularnewline
49 & 399740200 & 403119384.081466 & -3379184.08146581 \tabularnewline
50 & 431447900 & 435053943.775784 & -3606043.77578361 \tabularnewline
51 & 492574400 & 494300069.651055 & -1725669.65105505 \tabularnewline
52 & 513063100 & 522750486.613113 & -9687386.6131132 \tabularnewline
53 & 444485500 & 442451998.613399 & 2033501.38660055 \tabularnewline
54 & 396731900 & 398607866.172863 & -1875966.17286309 \tabularnewline
55 & 393125000 & 379692909.765086 & 13432090.2349139 \tabularnewline
56 & 423595200 & 403049871.586882 & 20545328.413118 \tabularnewline
57 & 416921900 & 397359672.298949 & 19562227.7010505 \tabularnewline
58 & 377906400 & 373010653.480293 & 4895746.51970724 \tabularnewline
59 & 355881000 & 352019123.926472 & 3861876.07352827 \tabularnewline
60 & 369946600 & 365983346.286672 & 3963253.71332802 \tabularnewline
61 & 365069300 & 359224821.305354 & 5844478.69464586 \tabularnewline
62 & 352563300 & 352507664.288822 & 55635.7111778952 \tabularnewline
63 & 347027600 & 350533449.000897 & -3505849.00089706 \tabularnewline
64 & 385909400 & 384867336.419474 & 1042063.58052622 \tabularnewline
65 & 366115500 & 360033731.672367 & 6081768.32763297 \tabularnewline
66 & 335636500 & 341084105.797552 & -5447605.79755226 \tabularnewline
67 & 334444000 & 335812574.169281 & -1368574.1692808 \tabularnewline
68 & 333868400 & 339929298.905894 & -6060898.90589409 \tabularnewline
69 & 340429400 & 349962181.304775 & -9532781.30477499 \tabularnewline
70 & 328931900 & 335124409.258363 & -6192509.25836251 \tabularnewline
71 & 346925200 & 349796705.049458 & -2871505.04945839 \tabularnewline
72 & 357185000 & 366253947.270501 & -9068947.27050112 \tabularnewline
73 & 363991400 & 369699110.638032 & -5707710.63803154 \tabularnewline
74 & 309173000 & 308251406.705715 & 921593.294285236 \tabularnewline
75 & 307814900 & 308338476.801476 & -523576.801475501 \tabularnewline
76 & 318811500 & 315646929.55016 & 3164570.44984015 \tabularnewline
77 & 324608200 & 322982061.759999 & 1626138.24000071 \tabularnewline
78 & 348699200 & 346157329.664832 & 2541870.33516826 \tabularnewline
79 & 337818700 & 336693037.367877 & 1125662.63212287 \tabularnewline
80 & 328230600 & 330391013.443146 & -2160413.44314587 \tabularnewline
81 & 328834500 & 323352329.812312 & 5482170.18768823 \tabularnewline
82 & 332574900 & 329938274.356796 & 2636625.64320376 \tabularnewline
83 & 335226200 & 346533388.115857 & -11307188.1158573 \tabularnewline
84 & 353195400 & 358661770.368377 & -5466370.36837748 \tabularnewline
85 & 372262200 & 385837741.109874 & -13575541.1098744 \tabularnewline
86 & 380936500 & 370534128.314936 & 10402371.6850636 \tabularnewline
87 & 375061700 & 363841238.40449 & 11220461.59551 \tabularnewline
88 & 361528600 & 355490802.469307 & 6037797.53069333 \tabularnewline
89 & 369655600 & 357116865.950344 & 12538734.0496561 \tabularnewline
90 & 412395900 & 394092361.78003 & 18303538.2199695 \tabularnewline
91 & 413616300 & 409328632.629708 & 4287667.37029221 \tabularnewline
92 & 393339200 & 405546321.979712 & -12207121.9797117 \tabularnewline
93 & 403557600 & 406684411.965501 & -3126811.96550125 \tabularnewline
94 & 455120200 & 455804532.531786 & -684332.531785861 \tabularnewline
95 & 403219500 & 406727786.410288 & -3508286.41028834 \tabularnewline
96 & 397089300 & 408098648.305499 & -11009348.3054992 \tabularnewline
97 & 448901600 & 448214947.784803 & 686652.215197334 \tabularnewline
98 & 542612700 & 534803164.671319 & 7809535.32868105 \tabularnewline
99 & 457822400 & 450081253.555869 & 7741146.4441314 \tabularnewline
100 & 412639000 & 412775611.872217 & -136611.872217143 \tabularnewline
101 & 489210000 & 490253743.958039 & -1043743.95803898 \tabularnewline
102 & 412869700 & 423564272.589562 & -10694572.5895625 \tabularnewline
103 & 440872100 & 442458803.262214 & -1586703.26221391 \tabularnewline
104 & 419946500 & 430081554.946261 & -10135054.9462611 \tabularnewline
105 & 407476700 & 407869984.34495 & -393284.344950021 \tabularnewline
106 & 416175800 & 411522079.899025 & 4653720.10097496 \tabularnewline
107 & 389131900 & 397102530.883938 & -7970630.88393845 \tabularnewline
108 & 447030200 & 446663711.385062 & 366488.614937735 \tabularnewline
109 & 428311100 & 428674023.555984 & -362923.555984447 \tabularnewline
110 & 384596200 & 382957531.852108 & 1638668.1478922 \tabularnewline
111 & 391147100 & 379404995.252675 & 11742104.7473254 \tabularnewline
112 & 379847800 & 370433297.625888 & 9414502.37411205 \tabularnewline
113 & 364431300 & 357498389.922423 & 6932910.07757749 \tabularnewline
114 & 378402900 & 372943427.437294 & 5459472.56270587 \tabularnewline
115 & 364713400 & 374831145.002818 & -10117745.0028178 \tabularnewline
116 & 399466200 & 395861419.446106 & 3604780.55389445 \tabularnewline
117 & 360783600 & 361742483.612561 & -958883.612561455 \tabularnewline
118 & 356600800 & 372567955.421331 & -15967155.421331 \tabularnewline
119 & 351141200 & 365353253.069964 & -14212053.0699642 \tabularnewline
120 & 325866500 & 330845611.537997 & -4979111.53799745 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=234785&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]426483000[/C][C]423792922.521437[/C][C]2690077.47856258[/C][/ROW]
[ROW][C]2[/C][C]392467400[/C][C]390835822.930543[/C][C]1631577.06945664[/C][/ROW]
[ROW][C]3[/C][C]373475300[/C][C]362660762.335474[/C][C]10814537.6645255[/C][/ROW]
[ROW][C]4[/C][C]376229000[/C][C]361487807.668779[/C][C]14741192.3312205[/C][/ROW]
[ROW][C]5[/C][C]360973900[/C][C]350778459.892222[/C][C]10195440.107778[/C][/ROW]
[ROW][C]6[/C][C]387759400[/C][C]372631155.93111[/C][C]15128244.0688902[/C][/ROW]
[ROW][C]7[/C][C]363641500[/C][C]353918996.624181[/C][C]9722503.37581906[/C][/ROW]
[ROW][C]8[/C][C]357819500[/C][C]355236475.293228[/C][C]2583024.70677159[/C][/ROW]
[ROW][C]9[/C][C]360434200[/C][C]352525610.746837[/C][C]7908589.25316345[/C][/ROW]
[ROW][C]10[/C][C]345951300[/C][C]344402742.926662[/C][C]1548557.07333766[/C][/ROW]
[ROW][C]11[/C][C]336657100[/C][C]342093541.657981[/C][C]-5436441.65798117[/C][/ROW]
[ROW][C]12[/C][C]337127700[/C][C]358259690.530213[/C][C]-21131990.5302132[/C][/ROW]
[ROW][C]13[/C][C]372484800[/C][C]373661466.761178[/C][C]-1176666.76117825[/C][/ROW]
[ROW][C]14[/C][C]335083000[/C][C]341776539.065505[/C][C]-6693539.06550516[/C][/ROW]
[ROW][C]15[/C][C]330515900[/C][C]334882281.62304[/C][C]-4366381.62303963[/C][/ROW]
[ROW][C]16[/C][C]339073600[/C][C]336110428.63205[/C][C]2963171.3679504[/C][/ROW]
[ROW][C]17[/C][C]334975800[/C][C]334511698.670815[/C][C]464101.329185514[/C][/ROW]
[ROW][C]18[/C][C]325365500[/C][C]332702091.824146[/C][C]-7336591.82414646[/C][/ROW]
[ROW][C]19[/C][C]373425000[/C][C]387648538.82479[/C][C]-14223538.8247902[/C][/ROW]
[ROW][C]20[/C][C]345543300[/C][C]352765105.142282[/C][C]-7221805.14228208[/C][/ROW]
[ROW][C]21[/C][C]296672600[/C][C]301198884.849798[/C][C]-4526284.84979846[/C][/ROW]
[ROW][C]22[/C][C]299371600[/C][C]298641248.811984[/C][C]730351.188015746[/C][/ROW]
[ROW][C]23[/C][C]300932000[/C][C]301733824.724162[/C][C]-801824.724161536[/C][/ROW]
[ROW][C]24[/C][C]316971300[/C][C]327698251.354948[/C][C]-10726951.3549475[/C][/ROW]
[ROW][C]25[/C][C]317006100[/C][C]317747260.854656[/C][C]-741160.854656309[/C][/ROW]
[ROW][C]26[/C][C]336893400[/C][C]338023797.540334[/C][C]-1130397.5403339[/C][/ROW]
[ROW][C]27[/C][C]329263800[/C][C]327881591.741309[/C][C]1382208.2586907[/C][/ROW]
[ROW][C]28[/C][C]333734400[/C][C]336892981.87196[/C][C]-3158581.87196003[/C][/ROW]
[ROW][C]29[/C][C]320830600[/C][C]316774550.817477[/C][C]4056049.18252316[/C][/ROW]
[ROW][C]30[/C][C]335913000[/C][C]334227597.722977[/C][C]1685402.27702352[/C][/ROW]
[ROW][C]31[/C][C]322307800[/C][C]321767468.478841[/C][C]540331.521159478[/C][/ROW]
[ROW][C]32[/C][C]343715900[/C][C]347123787.37865[/C][C]-3407887.37864956[/C][/ROW]
[ROW][C]33[/C][C]340015600[/C][C]344781012.842736[/C][C]-4765412.84273602[/C][/ROW]
[ROW][C]34[/C][C]365757600[/C][C]361845702.557331[/C][C]3911897.44266935[/C][/ROW]
[ROW][C]35[/C][C]376561300[/C][C]383962161.012908[/C][C]-7400861.01290837[/C][/ROW]
[ROW][C]36[/C][C]348192100[/C][C]340293947.285165[/C][C]7898152.71483518[/C][/ROW]
[ROW][C]37[/C][C]360480000[/C][C]366220062.999801[/C][C]-5740062.99980068[/C][/ROW]
[ROW][C]38[/C][C]398134000[/C][C]397096188.210119[/C][C]1037811.78988087[/C][/ROW]
[ROW][C]39[/C][C]373407800[/C][C]374115139.562485[/C][C]-707339.562485365[/C][/ROW]
[ROW][C]40[/C][C]401817300[/C][C]391383563.823892[/C][C]10433736.1761079[/C][/ROW]
[ROW][C]41[/C][C]388741700[/C][C]389562290.347491[/C][C]-820590.347491354[/C][/ROW]
[ROW][C]42[/C][C]391988000[/C][C]398693176.270743[/C][C]-6705176.27074252[/C][/ROW]
[ROW][C]43[/C][C]401446600[/C][C]403196572.129641[/C][C]-1749972.12964088[/C][/ROW]
[ROW][C]44[/C][C]419775800[/C][C]427701665.755347[/C][C]-7925865.75534697[/C][/ROW]
[ROW][C]45[/C][C]389653100[/C][C]390778442.286826[/C][C]-1125342.28682618[/C][/ROW]
[ROW][C]46[/C][C]396474200[/C][C]393556950.612356[/C][C]2917249.38764417[/C][/ROW]
[ROW][C]47[/C][C]420184700[/C][C]419153861.461205[/C][C]1030838.53879517[/C][/ROW]
[ROW][C]48[/C][C]405051200[/C][C]401544633.071149[/C][C]3506566.92885064[/C][/ROW]
[ROW][C]49[/C][C]399740200[/C][C]403119384.081466[/C][C]-3379184.08146581[/C][/ROW]
[ROW][C]50[/C][C]431447900[/C][C]435053943.775784[/C][C]-3606043.77578361[/C][/ROW]
[ROW][C]51[/C][C]492574400[/C][C]494300069.651055[/C][C]-1725669.65105505[/C][/ROW]
[ROW][C]52[/C][C]513063100[/C][C]522750486.613113[/C][C]-9687386.6131132[/C][/ROW]
[ROW][C]53[/C][C]444485500[/C][C]442451998.613399[/C][C]2033501.38660055[/C][/ROW]
[ROW][C]54[/C][C]396731900[/C][C]398607866.172863[/C][C]-1875966.17286309[/C][/ROW]
[ROW][C]55[/C][C]393125000[/C][C]379692909.765086[/C][C]13432090.2349139[/C][/ROW]
[ROW][C]56[/C][C]423595200[/C][C]403049871.586882[/C][C]20545328.413118[/C][/ROW]
[ROW][C]57[/C][C]416921900[/C][C]397359672.298949[/C][C]19562227.7010505[/C][/ROW]
[ROW][C]58[/C][C]377906400[/C][C]373010653.480293[/C][C]4895746.51970724[/C][/ROW]
[ROW][C]59[/C][C]355881000[/C][C]352019123.926472[/C][C]3861876.07352827[/C][/ROW]
[ROW][C]60[/C][C]369946600[/C][C]365983346.286672[/C][C]3963253.71332802[/C][/ROW]
[ROW][C]61[/C][C]365069300[/C][C]359224821.305354[/C][C]5844478.69464586[/C][/ROW]
[ROW][C]62[/C][C]352563300[/C][C]352507664.288822[/C][C]55635.7111778952[/C][/ROW]
[ROW][C]63[/C][C]347027600[/C][C]350533449.000897[/C][C]-3505849.00089706[/C][/ROW]
[ROW][C]64[/C][C]385909400[/C][C]384867336.419474[/C][C]1042063.58052622[/C][/ROW]
[ROW][C]65[/C][C]366115500[/C][C]360033731.672367[/C][C]6081768.32763297[/C][/ROW]
[ROW][C]66[/C][C]335636500[/C][C]341084105.797552[/C][C]-5447605.79755226[/C][/ROW]
[ROW][C]67[/C][C]334444000[/C][C]335812574.169281[/C][C]-1368574.1692808[/C][/ROW]
[ROW][C]68[/C][C]333868400[/C][C]339929298.905894[/C][C]-6060898.90589409[/C][/ROW]
[ROW][C]69[/C][C]340429400[/C][C]349962181.304775[/C][C]-9532781.30477499[/C][/ROW]
[ROW][C]70[/C][C]328931900[/C][C]335124409.258363[/C][C]-6192509.25836251[/C][/ROW]
[ROW][C]71[/C][C]346925200[/C][C]349796705.049458[/C][C]-2871505.04945839[/C][/ROW]
[ROW][C]72[/C][C]357185000[/C][C]366253947.270501[/C][C]-9068947.27050112[/C][/ROW]
[ROW][C]73[/C][C]363991400[/C][C]369699110.638032[/C][C]-5707710.63803154[/C][/ROW]
[ROW][C]74[/C][C]309173000[/C][C]308251406.705715[/C][C]921593.294285236[/C][/ROW]
[ROW][C]75[/C][C]307814900[/C][C]308338476.801476[/C][C]-523576.801475501[/C][/ROW]
[ROW][C]76[/C][C]318811500[/C][C]315646929.55016[/C][C]3164570.44984015[/C][/ROW]
[ROW][C]77[/C][C]324608200[/C][C]322982061.759999[/C][C]1626138.24000071[/C][/ROW]
[ROW][C]78[/C][C]348699200[/C][C]346157329.664832[/C][C]2541870.33516826[/C][/ROW]
[ROW][C]79[/C][C]337818700[/C][C]336693037.367877[/C][C]1125662.63212287[/C][/ROW]
[ROW][C]80[/C][C]328230600[/C][C]330391013.443146[/C][C]-2160413.44314587[/C][/ROW]
[ROW][C]81[/C][C]328834500[/C][C]323352329.812312[/C][C]5482170.18768823[/C][/ROW]
[ROW][C]82[/C][C]332574900[/C][C]329938274.356796[/C][C]2636625.64320376[/C][/ROW]
[ROW][C]83[/C][C]335226200[/C][C]346533388.115857[/C][C]-11307188.1158573[/C][/ROW]
[ROW][C]84[/C][C]353195400[/C][C]358661770.368377[/C][C]-5466370.36837748[/C][/ROW]
[ROW][C]85[/C][C]372262200[/C][C]385837741.109874[/C][C]-13575541.1098744[/C][/ROW]
[ROW][C]86[/C][C]380936500[/C][C]370534128.314936[/C][C]10402371.6850636[/C][/ROW]
[ROW][C]87[/C][C]375061700[/C][C]363841238.40449[/C][C]11220461.59551[/C][/ROW]
[ROW][C]88[/C][C]361528600[/C][C]355490802.469307[/C][C]6037797.53069333[/C][/ROW]
[ROW][C]89[/C][C]369655600[/C][C]357116865.950344[/C][C]12538734.0496561[/C][/ROW]
[ROW][C]90[/C][C]412395900[/C][C]394092361.78003[/C][C]18303538.2199695[/C][/ROW]
[ROW][C]91[/C][C]413616300[/C][C]409328632.629708[/C][C]4287667.37029221[/C][/ROW]
[ROW][C]92[/C][C]393339200[/C][C]405546321.979712[/C][C]-12207121.9797117[/C][/ROW]
[ROW][C]93[/C][C]403557600[/C][C]406684411.965501[/C][C]-3126811.96550125[/C][/ROW]
[ROW][C]94[/C][C]455120200[/C][C]455804532.531786[/C][C]-684332.531785861[/C][/ROW]
[ROW][C]95[/C][C]403219500[/C][C]406727786.410288[/C][C]-3508286.41028834[/C][/ROW]
[ROW][C]96[/C][C]397089300[/C][C]408098648.305499[/C][C]-11009348.3054992[/C][/ROW]
[ROW][C]97[/C][C]448901600[/C][C]448214947.784803[/C][C]686652.215197334[/C][/ROW]
[ROW][C]98[/C][C]542612700[/C][C]534803164.671319[/C][C]7809535.32868105[/C][/ROW]
[ROW][C]99[/C][C]457822400[/C][C]450081253.555869[/C][C]7741146.4441314[/C][/ROW]
[ROW][C]100[/C][C]412639000[/C][C]412775611.872217[/C][C]-136611.872217143[/C][/ROW]
[ROW][C]101[/C][C]489210000[/C][C]490253743.958039[/C][C]-1043743.95803898[/C][/ROW]
[ROW][C]102[/C][C]412869700[/C][C]423564272.589562[/C][C]-10694572.5895625[/C][/ROW]
[ROW][C]103[/C][C]440872100[/C][C]442458803.262214[/C][C]-1586703.26221391[/C][/ROW]
[ROW][C]104[/C][C]419946500[/C][C]430081554.946261[/C][C]-10135054.9462611[/C][/ROW]
[ROW][C]105[/C][C]407476700[/C][C]407869984.34495[/C][C]-393284.344950021[/C][/ROW]
[ROW][C]106[/C][C]416175800[/C][C]411522079.899025[/C][C]4653720.10097496[/C][/ROW]
[ROW][C]107[/C][C]389131900[/C][C]397102530.883938[/C][C]-7970630.88393845[/C][/ROW]
[ROW][C]108[/C][C]447030200[/C][C]446663711.385062[/C][C]366488.614937735[/C][/ROW]
[ROW][C]109[/C][C]428311100[/C][C]428674023.555984[/C][C]-362923.555984447[/C][/ROW]
[ROW][C]110[/C][C]384596200[/C][C]382957531.852108[/C][C]1638668.1478922[/C][/ROW]
[ROW][C]111[/C][C]391147100[/C][C]379404995.252675[/C][C]11742104.7473254[/C][/ROW]
[ROW][C]112[/C][C]379847800[/C][C]370433297.625888[/C][C]9414502.37411205[/C][/ROW]
[ROW][C]113[/C][C]364431300[/C][C]357498389.922423[/C][C]6932910.07757749[/C][/ROW]
[ROW][C]114[/C][C]378402900[/C][C]372943427.437294[/C][C]5459472.56270587[/C][/ROW]
[ROW][C]115[/C][C]364713400[/C][C]374831145.002818[/C][C]-10117745.0028178[/C][/ROW]
[ROW][C]116[/C][C]399466200[/C][C]395861419.446106[/C][C]3604780.55389445[/C][/ROW]
[ROW][C]117[/C][C]360783600[/C][C]361742483.612561[/C][C]-958883.612561455[/C][/ROW]
[ROW][C]118[/C][C]356600800[/C][C]372567955.421331[/C][C]-15967155.421331[/C][/ROW]
[ROW][C]119[/C][C]351141200[/C][C]365353253.069964[/C][C]-14212053.0699642[/C][/ROW]
[ROW][C]120[/C][C]325866500[/C][C]330845611.537997[/C][C]-4979111.53799745[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=234785&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=234785&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
1426483000423792922.5214372690077.47856258
2392467400390835822.9305431631577.06945664
3373475300362660762.33547410814537.6645255
4376229000361487807.66877914741192.3312205
5360973900350778459.89222210195440.107778
6387759400372631155.9311115128244.0688902
7363641500353918996.6241819722503.37581906
8357819500355236475.2932282583024.70677159
9360434200352525610.7468377908589.25316345
10345951300344402742.9266621548557.07333766
11336657100342093541.657981-5436441.65798117
12337127700358259690.530213-21131990.5302132
13372484800373661466.761178-1176666.76117825
14335083000341776539.065505-6693539.06550516
15330515900334882281.62304-4366381.62303963
16339073600336110428.632052963171.3679504
17334975800334511698.670815464101.329185514
18325365500332702091.824146-7336591.82414646
19373425000387648538.82479-14223538.8247902
20345543300352765105.142282-7221805.14228208
21296672600301198884.849798-4526284.84979846
22299371600298641248.811984730351.188015746
23300932000301733824.724162-801824.724161536
24316971300327698251.354948-10726951.3549475
25317006100317747260.854656-741160.854656309
26336893400338023797.540334-1130397.5403339
27329263800327881591.7413091382208.2586907
28333734400336892981.87196-3158581.87196003
29320830600316774550.8174774056049.18252316
30335913000334227597.7229771685402.27702352
31322307800321767468.478841540331.521159478
32343715900347123787.37865-3407887.37864956
33340015600344781012.842736-4765412.84273602
34365757600361845702.5573313911897.44266935
35376561300383962161.012908-7400861.01290837
36348192100340293947.2851657898152.71483518
37360480000366220062.999801-5740062.99980068
38398134000397096188.2101191037811.78988087
39373407800374115139.562485-707339.562485365
40401817300391383563.82389210433736.1761079
41388741700389562290.347491-820590.347491354
42391988000398693176.270743-6705176.27074252
43401446600403196572.129641-1749972.12964088
44419775800427701665.755347-7925865.75534697
45389653100390778442.286826-1125342.28682618
46396474200393556950.6123562917249.38764417
47420184700419153861.4612051030838.53879517
48405051200401544633.0711493506566.92885064
49399740200403119384.081466-3379184.08146581
50431447900435053943.775784-3606043.77578361
51492574400494300069.651055-1725669.65105505
52513063100522750486.613113-9687386.6131132
53444485500442451998.6133992033501.38660055
54396731900398607866.172863-1875966.17286309
55393125000379692909.76508613432090.2349139
56423595200403049871.58688220545328.413118
57416921900397359672.29894919562227.7010505
58377906400373010653.4802934895746.51970724
59355881000352019123.9264723861876.07352827
60369946600365983346.2866723963253.71332802
61365069300359224821.3053545844478.69464586
62352563300352507664.28882255635.7111778952
63347027600350533449.000897-3505849.00089706
64385909400384867336.4194741042063.58052622
65366115500360033731.6723676081768.32763297
66335636500341084105.797552-5447605.79755226
67334444000335812574.169281-1368574.1692808
68333868400339929298.905894-6060898.90589409
69340429400349962181.304775-9532781.30477499
70328931900335124409.258363-6192509.25836251
71346925200349796705.049458-2871505.04945839
72357185000366253947.270501-9068947.27050112
73363991400369699110.638032-5707710.63803154
74309173000308251406.705715921593.294285236
75307814900308338476.801476-523576.801475501
76318811500315646929.550163164570.44984015
77324608200322982061.7599991626138.24000071
78348699200346157329.6648322541870.33516826
79337818700336693037.3678771125662.63212287
80328230600330391013.443146-2160413.44314587
81328834500323352329.8123125482170.18768823
82332574900329938274.3567962636625.64320376
83335226200346533388.115857-11307188.1158573
84353195400358661770.368377-5466370.36837748
85372262200385837741.109874-13575541.1098744
86380936500370534128.31493610402371.6850636
87375061700363841238.4044911220461.59551
88361528600355490802.4693076037797.53069333
89369655600357116865.95034412538734.0496561
90412395900394092361.7800318303538.2199695
91413616300409328632.6297084287667.37029221
92393339200405546321.979712-12207121.9797117
93403557600406684411.965501-3126811.96550125
94455120200455804532.531786-684332.531785861
95403219500406727786.410288-3508286.41028834
96397089300408098648.305499-11009348.3054992
97448901600448214947.784803686652.215197334
98542612700534803164.6713197809535.32868105
99457822400450081253.5558697741146.4441314
100412639000412775611.872217-136611.872217143
101489210000490253743.958039-1043743.95803898
102412869700423564272.589562-10694572.5895625
103440872100442458803.262214-1586703.26221391
104419946500430081554.946261-10135054.9462611
105407476700407869984.34495-393284.344950021
106416175800411522079.8990254653720.10097496
107389131900397102530.883938-7970630.88393845
108447030200446663711.385062366488.614937735
109428311100428674023.555984-362923.555984447
110384596200382957531.8521081638668.1478922
111391147100379404995.25267511742104.7473254
112379847800370433297.6258889414502.37411205
113364431300357498389.9224236932910.07757749
114378402900372943427.4372945459472.56270587
115364713400374831145.002818-10117745.0028178
116399466200395861419.4461063604780.55389445
117360783600361742483.612561-958883.612561455
118356600800372567955.421331-15967155.421331
119351141200365353253.069964-14212053.0699642
120325866500330845611.537997-4979111.53799745






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.4583814878482570.9167629756965130.541618512151744
210.3257445331200990.6514890662401970.674255466879901
220.5105102770895620.9789794458208770.489489722910438
230.6394985158917170.7210029682165660.360501484108283
240.5745006371815480.8509987256369050.425499362818452
250.7496098399685150.5007803200629710.250390160031485
260.7021949309998240.5956101380003520.297805069000176
270.6120114395985140.7759771208029710.387988560401486
280.5335908982169450.9328182035661110.466409101783055
290.4610747820175960.9221495640351920.538925217982404
300.4056748941685790.8113497883371580.594325105831421
310.3747951526262970.7495903052525940.625204847373703
320.303135417323070.606270834646140.69686458267693
330.2527757523133370.5055515046266740.747224247686663
340.2499092326904490.4998184653808980.750090767309551
350.2105251137725790.4210502275451580.789474886227421
360.1884594729140220.3769189458280450.811540527085978
370.1629630820035890.3259261640071780.837036917996411
380.1871868537792020.3743737075584040.812813146220798
390.1627647671492120.3255295342984230.837235232850788
400.1865370107408610.3730740214817220.813462989259139
410.1786513503423110.3573027006846210.821348649657689
420.2024729767766980.4049459535533970.797527023223302
430.1784509703728310.3569019407456620.821549029627169
440.2227438864248630.4454877728497260.777256113575137
450.1815904520656420.3631809041312840.818409547934358
460.1432210008137750.286442001627550.856778999186225
470.1091028190212130.2182056380424250.890897180978787
480.09347397266198370.1869479453239670.906526027338016
490.1189385126327020.2378770252654030.881061487367298
500.0974438850763840.1948877701527680.902556114923616
510.07618249415743470.1523649883148690.923817505842565
520.07820523739222580.1564104747844520.921794762607774
530.05782432674543510.115648653490870.942175673254565
540.0419622549937460.08392450998749190.958037745006254
550.04355694339393310.08711388678786620.956443056606067
560.1410069383712420.2820138767424840.858993061628758
570.415931091958510.8318621839170210.58406890804149
580.3868842142187450.7737684284374890.613115785781255
590.36353145864130.7270629172826010.6364685413587
600.3716729711939440.7433459423878880.628327028806056
610.4736645457575660.9473290915151310.526335454242434
620.5332968196945680.9334063606108640.466703180305432
630.4747271046799320.9494542093598630.525272895320068
640.4151345866548480.8302691733096970.584865413345152
650.4076648431114980.8153296862229970.592335156888502
660.4577618421622320.9155236843244640.542238157837768
670.4786496012482530.9572992024965070.521350398751747
680.5179447419562590.9641105160874810.482055258043741
690.4939387295991870.9878774591983740.506061270400813
700.4493549991724770.8987099983449530.550645000827523
710.3950232601339640.7900465202679280.604976739866036
720.6045121234609590.7909757530780810.395487876539041
730.7041213910006690.5917572179986630.295878608999331
740.6987904161545890.6024191676908230.301209583845411
750.6999960590241460.6000078819517080.300003940975854
760.6496679898821580.7006640202356850.350332010117842
770.587861756456290.824276487087420.41213824354371
780.5243075720292870.9513848559414260.475692427970713
790.4615304505170150.923060901034030.538469549482985
800.479266681777540.958533363555080.52073331822246
810.5371487533448530.9257024933102940.462851246655147
820.4853755560581840.9707511121163690.514624443941816
830.4796297889048430.9592595778096850.520370211095157
840.481503783123160.9630075662463210.51849621687684
850.4395799258604090.8791598517208190.560420074139591
860.5802529581121410.8394940837757190.419747041887859
870.5787679719593430.8424640560813140.421232028040657
880.5185613283768760.9628773432462480.481438671623124
890.4746093222149060.9492186444298120.525390677785094
900.5761541183617540.8476917632764920.423845881638246
910.6646413695056080.6707172609887830.335358630494392
920.6711811494868030.6576377010263950.328818850513197
930.6199784594803670.7600430810392670.380021540519633
940.5314395865343560.9371208269312880.468560413465644
950.4687626797267280.9375253594534570.531237320273272
960.382736857789460.765473715578920.61726314221054
970.3403380515373860.6806761030747720.659661948462614
980.2493070777749740.4986141555499490.750692922225026
990.2308265701144380.4616531402288770.769173429885562
1000.165513476531450.3310269530629010.83448652346855

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 & 0.458381487848257 & 0.916762975696513 & 0.541618512151744 \tabularnewline
21 & 0.325744533120099 & 0.651489066240197 & 0.674255466879901 \tabularnewline
22 & 0.510510277089562 & 0.978979445820877 & 0.489489722910438 \tabularnewline
23 & 0.639498515891717 & 0.721002968216566 & 0.360501484108283 \tabularnewline
24 & 0.574500637181548 & 0.850998725636905 & 0.425499362818452 \tabularnewline
25 & 0.749609839968515 & 0.500780320062971 & 0.250390160031485 \tabularnewline
26 & 0.702194930999824 & 0.595610138000352 & 0.297805069000176 \tabularnewline
27 & 0.612011439598514 & 0.775977120802971 & 0.387988560401486 \tabularnewline
28 & 0.533590898216945 & 0.932818203566111 & 0.466409101783055 \tabularnewline
29 & 0.461074782017596 & 0.922149564035192 & 0.538925217982404 \tabularnewline
30 & 0.405674894168579 & 0.811349788337158 & 0.594325105831421 \tabularnewline
31 & 0.374795152626297 & 0.749590305252594 & 0.625204847373703 \tabularnewline
32 & 0.30313541732307 & 0.60627083464614 & 0.69686458267693 \tabularnewline
33 & 0.252775752313337 & 0.505551504626674 & 0.747224247686663 \tabularnewline
34 & 0.249909232690449 & 0.499818465380898 & 0.750090767309551 \tabularnewline
35 & 0.210525113772579 & 0.421050227545158 & 0.789474886227421 \tabularnewline
36 & 0.188459472914022 & 0.376918945828045 & 0.811540527085978 \tabularnewline
37 & 0.162963082003589 & 0.325926164007178 & 0.837036917996411 \tabularnewline
38 & 0.187186853779202 & 0.374373707558404 & 0.812813146220798 \tabularnewline
39 & 0.162764767149212 & 0.325529534298423 & 0.837235232850788 \tabularnewline
40 & 0.186537010740861 & 0.373074021481722 & 0.813462989259139 \tabularnewline
41 & 0.178651350342311 & 0.357302700684621 & 0.821348649657689 \tabularnewline
42 & 0.202472976776698 & 0.404945953553397 & 0.797527023223302 \tabularnewline
43 & 0.178450970372831 & 0.356901940745662 & 0.821549029627169 \tabularnewline
44 & 0.222743886424863 & 0.445487772849726 & 0.777256113575137 \tabularnewline
45 & 0.181590452065642 & 0.363180904131284 & 0.818409547934358 \tabularnewline
46 & 0.143221000813775 & 0.28644200162755 & 0.856778999186225 \tabularnewline
47 & 0.109102819021213 & 0.218205638042425 & 0.890897180978787 \tabularnewline
48 & 0.0934739726619837 & 0.186947945323967 & 0.906526027338016 \tabularnewline
49 & 0.118938512632702 & 0.237877025265403 & 0.881061487367298 \tabularnewline
50 & 0.097443885076384 & 0.194887770152768 & 0.902556114923616 \tabularnewline
51 & 0.0761824941574347 & 0.152364988314869 & 0.923817505842565 \tabularnewline
52 & 0.0782052373922258 & 0.156410474784452 & 0.921794762607774 \tabularnewline
53 & 0.0578243267454351 & 0.11564865349087 & 0.942175673254565 \tabularnewline
54 & 0.041962254993746 & 0.0839245099874919 & 0.958037745006254 \tabularnewline
55 & 0.0435569433939331 & 0.0871138867878662 & 0.956443056606067 \tabularnewline
56 & 0.141006938371242 & 0.282013876742484 & 0.858993061628758 \tabularnewline
57 & 0.41593109195851 & 0.831862183917021 & 0.58406890804149 \tabularnewline
58 & 0.386884214218745 & 0.773768428437489 & 0.613115785781255 \tabularnewline
59 & 0.3635314586413 & 0.727062917282601 & 0.6364685413587 \tabularnewline
60 & 0.371672971193944 & 0.743345942387888 & 0.628327028806056 \tabularnewline
61 & 0.473664545757566 & 0.947329091515131 & 0.526335454242434 \tabularnewline
62 & 0.533296819694568 & 0.933406360610864 & 0.466703180305432 \tabularnewline
63 & 0.474727104679932 & 0.949454209359863 & 0.525272895320068 \tabularnewline
64 & 0.415134586654848 & 0.830269173309697 & 0.584865413345152 \tabularnewline
65 & 0.407664843111498 & 0.815329686222997 & 0.592335156888502 \tabularnewline
66 & 0.457761842162232 & 0.915523684324464 & 0.542238157837768 \tabularnewline
67 & 0.478649601248253 & 0.957299202496507 & 0.521350398751747 \tabularnewline
68 & 0.517944741956259 & 0.964110516087481 & 0.482055258043741 \tabularnewline
69 & 0.493938729599187 & 0.987877459198374 & 0.506061270400813 \tabularnewline
70 & 0.449354999172477 & 0.898709998344953 & 0.550645000827523 \tabularnewline
71 & 0.395023260133964 & 0.790046520267928 & 0.604976739866036 \tabularnewline
72 & 0.604512123460959 & 0.790975753078081 & 0.395487876539041 \tabularnewline
73 & 0.704121391000669 & 0.591757217998663 & 0.295878608999331 \tabularnewline
74 & 0.698790416154589 & 0.602419167690823 & 0.301209583845411 \tabularnewline
75 & 0.699996059024146 & 0.600007881951708 & 0.300003940975854 \tabularnewline
76 & 0.649667989882158 & 0.700664020235685 & 0.350332010117842 \tabularnewline
77 & 0.58786175645629 & 0.82427648708742 & 0.41213824354371 \tabularnewline
78 & 0.524307572029287 & 0.951384855941426 & 0.475692427970713 \tabularnewline
79 & 0.461530450517015 & 0.92306090103403 & 0.538469549482985 \tabularnewline
80 & 0.47926668177754 & 0.95853336355508 & 0.52073331822246 \tabularnewline
81 & 0.537148753344853 & 0.925702493310294 & 0.462851246655147 \tabularnewline
82 & 0.485375556058184 & 0.970751112116369 & 0.514624443941816 \tabularnewline
83 & 0.479629788904843 & 0.959259577809685 & 0.520370211095157 \tabularnewline
84 & 0.48150378312316 & 0.963007566246321 & 0.51849621687684 \tabularnewline
85 & 0.439579925860409 & 0.879159851720819 & 0.560420074139591 \tabularnewline
86 & 0.580252958112141 & 0.839494083775719 & 0.419747041887859 \tabularnewline
87 & 0.578767971959343 & 0.842464056081314 & 0.421232028040657 \tabularnewline
88 & 0.518561328376876 & 0.962877343246248 & 0.481438671623124 \tabularnewline
89 & 0.474609322214906 & 0.949218644429812 & 0.525390677785094 \tabularnewline
90 & 0.576154118361754 & 0.847691763276492 & 0.423845881638246 \tabularnewline
91 & 0.664641369505608 & 0.670717260988783 & 0.335358630494392 \tabularnewline
92 & 0.671181149486803 & 0.657637701026395 & 0.328818850513197 \tabularnewline
93 & 0.619978459480367 & 0.760043081039267 & 0.380021540519633 \tabularnewline
94 & 0.531439586534356 & 0.937120826931288 & 0.468560413465644 \tabularnewline
95 & 0.468762679726728 & 0.937525359453457 & 0.531237320273272 \tabularnewline
96 & 0.38273685778946 & 0.76547371557892 & 0.61726314221054 \tabularnewline
97 & 0.340338051537386 & 0.680676103074772 & 0.659661948462614 \tabularnewline
98 & 0.249307077774974 & 0.498614155549949 & 0.750692922225026 \tabularnewline
99 & 0.230826570114438 & 0.461653140228877 & 0.769173429885562 \tabularnewline
100 & 0.16551347653145 & 0.331026953062901 & 0.83448652346855 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=234785&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]20[/C][C]0.458381487848257[/C][C]0.916762975696513[/C][C]0.541618512151744[/C][/ROW]
[ROW][C]21[/C][C]0.325744533120099[/C][C]0.651489066240197[/C][C]0.674255466879901[/C][/ROW]
[ROW][C]22[/C][C]0.510510277089562[/C][C]0.978979445820877[/C][C]0.489489722910438[/C][/ROW]
[ROW][C]23[/C][C]0.639498515891717[/C][C]0.721002968216566[/C][C]0.360501484108283[/C][/ROW]
[ROW][C]24[/C][C]0.574500637181548[/C][C]0.850998725636905[/C][C]0.425499362818452[/C][/ROW]
[ROW][C]25[/C][C]0.749609839968515[/C][C]0.500780320062971[/C][C]0.250390160031485[/C][/ROW]
[ROW][C]26[/C][C]0.702194930999824[/C][C]0.595610138000352[/C][C]0.297805069000176[/C][/ROW]
[ROW][C]27[/C][C]0.612011439598514[/C][C]0.775977120802971[/C][C]0.387988560401486[/C][/ROW]
[ROW][C]28[/C][C]0.533590898216945[/C][C]0.932818203566111[/C][C]0.466409101783055[/C][/ROW]
[ROW][C]29[/C][C]0.461074782017596[/C][C]0.922149564035192[/C][C]0.538925217982404[/C][/ROW]
[ROW][C]30[/C][C]0.405674894168579[/C][C]0.811349788337158[/C][C]0.594325105831421[/C][/ROW]
[ROW][C]31[/C][C]0.374795152626297[/C][C]0.749590305252594[/C][C]0.625204847373703[/C][/ROW]
[ROW][C]32[/C][C]0.30313541732307[/C][C]0.60627083464614[/C][C]0.69686458267693[/C][/ROW]
[ROW][C]33[/C][C]0.252775752313337[/C][C]0.505551504626674[/C][C]0.747224247686663[/C][/ROW]
[ROW][C]34[/C][C]0.249909232690449[/C][C]0.499818465380898[/C][C]0.750090767309551[/C][/ROW]
[ROW][C]35[/C][C]0.210525113772579[/C][C]0.421050227545158[/C][C]0.789474886227421[/C][/ROW]
[ROW][C]36[/C][C]0.188459472914022[/C][C]0.376918945828045[/C][C]0.811540527085978[/C][/ROW]
[ROW][C]37[/C][C]0.162963082003589[/C][C]0.325926164007178[/C][C]0.837036917996411[/C][/ROW]
[ROW][C]38[/C][C]0.187186853779202[/C][C]0.374373707558404[/C][C]0.812813146220798[/C][/ROW]
[ROW][C]39[/C][C]0.162764767149212[/C][C]0.325529534298423[/C][C]0.837235232850788[/C][/ROW]
[ROW][C]40[/C][C]0.186537010740861[/C][C]0.373074021481722[/C][C]0.813462989259139[/C][/ROW]
[ROW][C]41[/C][C]0.178651350342311[/C][C]0.357302700684621[/C][C]0.821348649657689[/C][/ROW]
[ROW][C]42[/C][C]0.202472976776698[/C][C]0.404945953553397[/C][C]0.797527023223302[/C][/ROW]
[ROW][C]43[/C][C]0.178450970372831[/C][C]0.356901940745662[/C][C]0.821549029627169[/C][/ROW]
[ROW][C]44[/C][C]0.222743886424863[/C][C]0.445487772849726[/C][C]0.777256113575137[/C][/ROW]
[ROW][C]45[/C][C]0.181590452065642[/C][C]0.363180904131284[/C][C]0.818409547934358[/C][/ROW]
[ROW][C]46[/C][C]0.143221000813775[/C][C]0.28644200162755[/C][C]0.856778999186225[/C][/ROW]
[ROW][C]47[/C][C]0.109102819021213[/C][C]0.218205638042425[/C][C]0.890897180978787[/C][/ROW]
[ROW][C]48[/C][C]0.0934739726619837[/C][C]0.186947945323967[/C][C]0.906526027338016[/C][/ROW]
[ROW][C]49[/C][C]0.118938512632702[/C][C]0.237877025265403[/C][C]0.881061487367298[/C][/ROW]
[ROW][C]50[/C][C]0.097443885076384[/C][C]0.194887770152768[/C][C]0.902556114923616[/C][/ROW]
[ROW][C]51[/C][C]0.0761824941574347[/C][C]0.152364988314869[/C][C]0.923817505842565[/C][/ROW]
[ROW][C]52[/C][C]0.0782052373922258[/C][C]0.156410474784452[/C][C]0.921794762607774[/C][/ROW]
[ROW][C]53[/C][C]0.0578243267454351[/C][C]0.11564865349087[/C][C]0.942175673254565[/C][/ROW]
[ROW][C]54[/C][C]0.041962254993746[/C][C]0.0839245099874919[/C][C]0.958037745006254[/C][/ROW]
[ROW][C]55[/C][C]0.0435569433939331[/C][C]0.0871138867878662[/C][C]0.956443056606067[/C][/ROW]
[ROW][C]56[/C][C]0.141006938371242[/C][C]0.282013876742484[/C][C]0.858993061628758[/C][/ROW]
[ROW][C]57[/C][C]0.41593109195851[/C][C]0.831862183917021[/C][C]0.58406890804149[/C][/ROW]
[ROW][C]58[/C][C]0.386884214218745[/C][C]0.773768428437489[/C][C]0.613115785781255[/C][/ROW]
[ROW][C]59[/C][C]0.3635314586413[/C][C]0.727062917282601[/C][C]0.6364685413587[/C][/ROW]
[ROW][C]60[/C][C]0.371672971193944[/C][C]0.743345942387888[/C][C]0.628327028806056[/C][/ROW]
[ROW][C]61[/C][C]0.473664545757566[/C][C]0.947329091515131[/C][C]0.526335454242434[/C][/ROW]
[ROW][C]62[/C][C]0.533296819694568[/C][C]0.933406360610864[/C][C]0.466703180305432[/C][/ROW]
[ROW][C]63[/C][C]0.474727104679932[/C][C]0.949454209359863[/C][C]0.525272895320068[/C][/ROW]
[ROW][C]64[/C][C]0.415134586654848[/C][C]0.830269173309697[/C][C]0.584865413345152[/C][/ROW]
[ROW][C]65[/C][C]0.407664843111498[/C][C]0.815329686222997[/C][C]0.592335156888502[/C][/ROW]
[ROW][C]66[/C][C]0.457761842162232[/C][C]0.915523684324464[/C][C]0.542238157837768[/C][/ROW]
[ROW][C]67[/C][C]0.478649601248253[/C][C]0.957299202496507[/C][C]0.521350398751747[/C][/ROW]
[ROW][C]68[/C][C]0.517944741956259[/C][C]0.964110516087481[/C][C]0.482055258043741[/C][/ROW]
[ROW][C]69[/C][C]0.493938729599187[/C][C]0.987877459198374[/C][C]0.506061270400813[/C][/ROW]
[ROW][C]70[/C][C]0.449354999172477[/C][C]0.898709998344953[/C][C]0.550645000827523[/C][/ROW]
[ROW][C]71[/C][C]0.395023260133964[/C][C]0.790046520267928[/C][C]0.604976739866036[/C][/ROW]
[ROW][C]72[/C][C]0.604512123460959[/C][C]0.790975753078081[/C][C]0.395487876539041[/C][/ROW]
[ROW][C]73[/C][C]0.704121391000669[/C][C]0.591757217998663[/C][C]0.295878608999331[/C][/ROW]
[ROW][C]74[/C][C]0.698790416154589[/C][C]0.602419167690823[/C][C]0.301209583845411[/C][/ROW]
[ROW][C]75[/C][C]0.699996059024146[/C][C]0.600007881951708[/C][C]0.300003940975854[/C][/ROW]
[ROW][C]76[/C][C]0.649667989882158[/C][C]0.700664020235685[/C][C]0.350332010117842[/C][/ROW]
[ROW][C]77[/C][C]0.58786175645629[/C][C]0.82427648708742[/C][C]0.41213824354371[/C][/ROW]
[ROW][C]78[/C][C]0.524307572029287[/C][C]0.951384855941426[/C][C]0.475692427970713[/C][/ROW]
[ROW][C]79[/C][C]0.461530450517015[/C][C]0.92306090103403[/C][C]0.538469549482985[/C][/ROW]
[ROW][C]80[/C][C]0.47926668177754[/C][C]0.95853336355508[/C][C]0.52073331822246[/C][/ROW]
[ROW][C]81[/C][C]0.537148753344853[/C][C]0.925702493310294[/C][C]0.462851246655147[/C][/ROW]
[ROW][C]82[/C][C]0.485375556058184[/C][C]0.970751112116369[/C][C]0.514624443941816[/C][/ROW]
[ROW][C]83[/C][C]0.479629788904843[/C][C]0.959259577809685[/C][C]0.520370211095157[/C][/ROW]
[ROW][C]84[/C][C]0.48150378312316[/C][C]0.963007566246321[/C][C]0.51849621687684[/C][/ROW]
[ROW][C]85[/C][C]0.439579925860409[/C][C]0.879159851720819[/C][C]0.560420074139591[/C][/ROW]
[ROW][C]86[/C][C]0.580252958112141[/C][C]0.839494083775719[/C][C]0.419747041887859[/C][/ROW]
[ROW][C]87[/C][C]0.578767971959343[/C][C]0.842464056081314[/C][C]0.421232028040657[/C][/ROW]
[ROW][C]88[/C][C]0.518561328376876[/C][C]0.962877343246248[/C][C]0.481438671623124[/C][/ROW]
[ROW][C]89[/C][C]0.474609322214906[/C][C]0.949218644429812[/C][C]0.525390677785094[/C][/ROW]
[ROW][C]90[/C][C]0.576154118361754[/C][C]0.847691763276492[/C][C]0.423845881638246[/C][/ROW]
[ROW][C]91[/C][C]0.664641369505608[/C][C]0.670717260988783[/C][C]0.335358630494392[/C][/ROW]
[ROW][C]92[/C][C]0.671181149486803[/C][C]0.657637701026395[/C][C]0.328818850513197[/C][/ROW]
[ROW][C]93[/C][C]0.619978459480367[/C][C]0.760043081039267[/C][C]0.380021540519633[/C][/ROW]
[ROW][C]94[/C][C]0.531439586534356[/C][C]0.937120826931288[/C][C]0.468560413465644[/C][/ROW]
[ROW][C]95[/C][C]0.468762679726728[/C][C]0.937525359453457[/C][C]0.531237320273272[/C][/ROW]
[ROW][C]96[/C][C]0.38273685778946[/C][C]0.76547371557892[/C][C]0.61726314221054[/C][/ROW]
[ROW][C]97[/C][C]0.340338051537386[/C][C]0.680676103074772[/C][C]0.659661948462614[/C][/ROW]
[ROW][C]98[/C][C]0.249307077774974[/C][C]0.498614155549949[/C][C]0.750692922225026[/C][/ROW]
[ROW][C]99[/C][C]0.230826570114438[/C][C]0.461653140228877[/C][C]0.769173429885562[/C][/ROW]
[ROW][C]100[/C][C]0.16551347653145[/C][C]0.331026953062901[/C][C]0.83448652346855[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=234785&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=234785&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
200.4583814878482570.9167629756965130.541618512151744
210.3257445331200990.6514890662401970.674255466879901
220.5105102770895620.9789794458208770.489489722910438
230.6394985158917170.7210029682165660.360501484108283
240.5745006371815480.8509987256369050.425499362818452
250.7496098399685150.5007803200629710.250390160031485
260.7021949309998240.5956101380003520.297805069000176
270.6120114395985140.7759771208029710.387988560401486
280.5335908982169450.9328182035661110.466409101783055
290.4610747820175960.9221495640351920.538925217982404
300.4056748941685790.8113497883371580.594325105831421
310.3747951526262970.7495903052525940.625204847373703
320.303135417323070.606270834646140.69686458267693
330.2527757523133370.5055515046266740.747224247686663
340.2499092326904490.4998184653808980.750090767309551
350.2105251137725790.4210502275451580.789474886227421
360.1884594729140220.3769189458280450.811540527085978
370.1629630820035890.3259261640071780.837036917996411
380.1871868537792020.3743737075584040.812813146220798
390.1627647671492120.3255295342984230.837235232850788
400.1865370107408610.3730740214817220.813462989259139
410.1786513503423110.3573027006846210.821348649657689
420.2024729767766980.4049459535533970.797527023223302
430.1784509703728310.3569019407456620.821549029627169
440.2227438864248630.4454877728497260.777256113575137
450.1815904520656420.3631809041312840.818409547934358
460.1432210008137750.286442001627550.856778999186225
470.1091028190212130.2182056380424250.890897180978787
480.09347397266198370.1869479453239670.906526027338016
490.1189385126327020.2378770252654030.881061487367298
500.0974438850763840.1948877701527680.902556114923616
510.07618249415743470.1523649883148690.923817505842565
520.07820523739222580.1564104747844520.921794762607774
530.05782432674543510.115648653490870.942175673254565
540.0419622549937460.08392450998749190.958037745006254
550.04355694339393310.08711388678786620.956443056606067
560.1410069383712420.2820138767424840.858993061628758
570.415931091958510.8318621839170210.58406890804149
580.3868842142187450.7737684284374890.613115785781255
590.36353145864130.7270629172826010.6364685413587
600.3716729711939440.7433459423878880.628327028806056
610.4736645457575660.9473290915151310.526335454242434
620.5332968196945680.9334063606108640.466703180305432
630.4747271046799320.9494542093598630.525272895320068
640.4151345866548480.8302691733096970.584865413345152
650.4076648431114980.8153296862229970.592335156888502
660.4577618421622320.9155236843244640.542238157837768
670.4786496012482530.9572992024965070.521350398751747
680.5179447419562590.9641105160874810.482055258043741
690.4939387295991870.9878774591983740.506061270400813
700.4493549991724770.8987099983449530.550645000827523
710.3950232601339640.7900465202679280.604976739866036
720.6045121234609590.7909757530780810.395487876539041
730.7041213910006690.5917572179986630.295878608999331
740.6987904161545890.6024191676908230.301209583845411
750.6999960590241460.6000078819517080.300003940975854
760.6496679898821580.7006640202356850.350332010117842
770.587861756456290.824276487087420.41213824354371
780.5243075720292870.9513848559414260.475692427970713
790.4615304505170150.923060901034030.538469549482985
800.479266681777540.958533363555080.52073331822246
810.5371487533448530.9257024933102940.462851246655147
820.4853755560581840.9707511121163690.514624443941816
830.4796297889048430.9592595778096850.520370211095157
840.481503783123160.9630075662463210.51849621687684
850.4395799258604090.8791598517208190.560420074139591
860.5802529581121410.8394940837757190.419747041887859
870.5787679719593430.8424640560813140.421232028040657
880.5185613283768760.9628773432462480.481438671623124
890.4746093222149060.9492186444298120.525390677785094
900.5761541183617540.8476917632764920.423845881638246
910.6646413695056080.6707172609887830.335358630494392
920.6711811494868030.6576377010263950.328818850513197
930.6199784594803670.7600430810392670.380021540519633
940.5314395865343560.9371208269312880.468560413465644
950.4687626797267280.9375253594534570.531237320273272
960.382736857789460.765473715578920.61726314221054
970.3403380515373860.6806761030747720.659661948462614
980.2493070777749740.4986141555499490.750692922225026
990.2308265701144380.4616531402288770.769173429885562
1000.165513476531450.3310269530629010.83448652346855






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level20.0246913580246914OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 2 & 0.0246913580246914 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=234785&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0246913580246914[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=234785&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=234785&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level20.0246913580246914OK


Figures (Output of Computation)

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

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