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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 computationWed, 04 Apr 2012 08:00:43 -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/2012/Apr/04/t1333540944w017ozyh1rhutvr.htm/, Retrieved Sun, 28 Apr 2024 20:13:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=164290, Retrieved Sun, 28 Apr 2024 20:13:00 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact178

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [OMEC] [2012-04-04 11:30:04] [91ce4971c808115c699d50336245df56]
- RMPD    [Multiple Regression] [Multipele regressie] [2012-04-04 12:00:43] [7a9891c1925ad1e8ddfe52b8c5887b5b] [Current]
- R  D      [Multiple Regression] [double log] [2012-04-04 12:07:18] [91ce4971c808115c699d50336245df56]
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Dataset

Dataseries X:
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Histogram
Boxplots 
4264830	1,2299	2,4507	2,3512	2,5048	2,6602	3,2726	1,7959	1,8637	2,6811	,4830	,9489	1,1893	1,7715	8890176	484573,7000	2254011	6304844	10064618
3924674	1,2209	2,4601	2,3169	2,5889	2,6139	3,2211	1,7877	1,8556	2,6060	,4849	,9677	1,1862	1,8036	8194413	478105,6000	2013875	5471891	11338363
3734753	1,2130	2,4453	2,3552	2,5567	2,6274	3,2333	1,7796	1,8391	2,5823	,4860	,9693	1,2060	1,7653	7722000	506038,6000	2308944	5581708	9435079
3762290	1,2207	2,4276	2,2755	2,5940	2,6533	3,2340	1,7974	1,8207	2,5901	,4877	,9467	1,1882	1,7768	7769178	508171,2000	2278649	5421028	8143581
3609739	1,2137	2,4355	2,2578	2,5750	2,6374	3,2505	1,7887	1,8006	2,5352	,4858	,9574	1,1892	1,8121	7449343	468388,0000	2109718	5136152	7775342
3877594	1,2196	2,4066	2,3053	2,6151	2,6382	3,2437	1,7605	1,8392	2,5723	,4910	,9608	1,1870	1,8162	7929370	466709,4000	2070365	4948893	7656876
3636415	1,2114	2,4084	2,2789	2,5942	2,6430	3,2608	1,7584	1,8342	2,6363	,4883	,9394	1,1736	1,8184	7473017	499052,6000	2041975	4866528	8203164
3578195	1,2032	2,4098	2,1956	2,5757	2,5574	3,2483	1,7660	1,7863	2,6663	,4866	,9593	1,1817	1,8251	7472424	499696,8000	2130112	5110882	8447687
3604342	1,1824	2,4088	2,2423	2,5748	2,5670	3,2275	1,7650	1,8080	2,6378	,4884	,9750	1,1724	1,7774	7292436	456661,5000	2012391	4775552	8482877
3459513	1,1912	2,3769	2,3296	2,5705	2,6246	3,2059	1,7373	1,7833	2,6311	,4876	,9736	1,1860	1,7920	7215340	467478,2000	1995215	4690143	8131924
3366571	1,1973	2,4100	2,3557	2,5451	2,4956	3,2362	1,7508	1,7409	2,6072	,4922	,9579	1,2090	1,7957	7216230	453125,9000	1959695	4521167	8184292
3371277	1,1887	2,3860	2,2274	2,5129	2,4101	3,2215	1,6954	1,7380	2,6166	,4883	,9517	1,1703	1,7744	7378041	449583,6000	2079820	4618744	8006102
3724848	1,1851	2,3731	2,3054	2,4987	2,5017	3,2090	1,7146	1,7689	2,6291	,4878	,9633	1,2087	1,8084	7877412	423895,6000	2201750	4921010	8052832
3350830	1,1963	2,4014	2,2742	2,5856	2,4824	3,2480	1,7681	1,7627	2,4861	,4922	,9649	1,2071	1,8223	7158125	460454,3000	1980527	4739711	7854934
3305159	1,2080	2,3548	2,2849	2,6253	2,5210	3,2653	1,7747	1,7404	2,5230	,4958	,9688	1,2153	1,7983	7137912	454104,7000	2023721	4767867	7609626
3390736	1,2037	2,3503	2,3097	2,6340	2,5352	3,2335	1,7722	1,7671	2,5187	,4945	,9648	1,1871	1,8126	7290803	453042,3000	2136317	4856393	7640934
3349758	1,1998	2,3330	2,4261	2,6101	2,5073	3,2509	1,7721	1,7462	2,5590	,4955	,9458	1,2188	1,7889	7425266	433081,7000	2205673	4684931	8422297
3253655	1,2025	2,3530	2,4569	2,6377	2,5171	3,2511	1,7673	1,7820	2,6114	,4983	,9534	1,2459	1,8015	7450430	460163,3000	2163485	4583205	7980377
3734250	1,2096	2,3584	2,4756	2,6708	2,4904	3,2301	1,7677	1,7846	2,6088	,5086	,9374	1,2340	1,8192	9214042	421050,9000	2844091	5216686	9541323
3455433	1,2129	2,4147	2,4517	2,6289	2,4905	3,2194	1,7942	1,7887	2,5935	,5075	,9370	1,2241	1,8048	8158864	435182,1000	2458147	4583585	8839590
2966726	1,2098	2,3653	2,4406	2,5792	2,5133	3,2309	1,7928	1,7778	2,4121	,5062	,9786	1,2325	1,8148	6515759	495363,4000	1972304	4307098	7677033
2993716	1,1991	2,3449	2,3625	2,5571	2,4577	3,2406	1,7610	1,7439	2,2854	,4998	,9312	1,2118	1,7409	6308487	472804,8000	2153601	4748004	8354688
3009320	1,2067	2,3720	2,3382	2,5742	2,4979	3,2489	1,7550	1,7631	2,5536	,4971	,9316	1,2162	1,7753	6366367	452920,8000	2066530	4710073	8150927
3169713	1,2141	2,3396	2,1605	2,5495	2,4990	3,2397	1,7832	1,7628	2,5642	,4993	,9616	1,2292	1,7880	6770097	450870,4000	2152437	4867230	7846633
3170061	1,2075	2,3357	2,2267	2,5774	2,5186	3,2401	1,8055	1,7743	2,7059	,4916	,9670	1,1842	1,7505	6700697	472550,5000	2189294	4794611	8461058
3368934	1,1996	2,3283	2,1558	2,4984	2,5816	3,2904	1,7660	1,8370	2,6862	,4938	,9729	1,2116	1,7549	7140792	462772,3000	2253024	4883881	8425126
3292638	1,1920	2,3327	2,2009	2,5635	2,5377	3,2526	1,7456	1,8516	2,7393	,4961	,9453	1,2185	1,7171	6891715	507189,1000	2151817	4711492	8351766
3337344	1,1987	2,3373	2,2594	2,6249	2,5318	3,2815	1,7672	1,8406	2,7516	,4982	,9538	1,2234	1,7579	7057521	513234,9000	2141496	4810043	7956264
3208306	1,2005	2,3722	2,2282	2,7095	2,5655	3,3185	1,7230	1,8294	2,8767	,5010	,9632	1,2250	1,7682	6806593	602342,3000	2240864	5020983	8502847
3359130	1,1991	2,3756	2,3156	2,7447	2,5948	3,2888	1,7993	1,8273	2,9164	,4994	,9764	1,2261	1,7634	7068776	638260,4000	2198530	5071676	8671279
3223078	1,2161	2,4096	2,3361	2,7552	2,5921	3,3127	1,7701	1,8204	2,9822	,4994	,9783	1,2229	1,7767	6868085	618068,3000	2213237	5096684	8230049
3437159	1,2192	2,3883	2,3333	2,6562	2,5815	3,3319	1,7959	1,8379	2,9503	,5016	,9710	1,2031	1,7400	7245015	607338,2000	2252202	5263979	8404517
3400156	1,2104	2,3770	2,3043	2,6111	2,5861	3,3254	1,7927	1,8380	2,9161	,4921	,9787	1,2141	1,7786	7160726	1002378,8000	2419597	5523848	8872254
3657576	1,2529	2,4496	2,3170	2,6759	2,5917	3,3463	1,8160	1,6534	2,9694	,4974	,9821	1,1945	1,7110	7927365	755301,7000	2334515	5259355	9651748
3765613	1,2532	2,4084	2,3103	2,6966	2,5327	3,3515	1,8063	1,6417	2,9796	,4984	,9892	1,2390	1,7631	8275238	724579,8000	2155819	5044615	9070647
3481921	1,2705	2,4551	2,3220	2,6982	2,5768	3,3587	1,8008	1,6593	2,9542	,4868	,9870	1,2403	1,6563	7510220	706446,6000	2532345	5875038	8649186
3604800	1,2802	2,3990	2,3220	2,7157	2,5420	3,3755	1,7556	1,6523	2,9450	,5028	,9699	1,2282	1,6909	7751398	991277,6000	2221561	5321561	9030492
3981340	1,2748	2,3142	2,2987	2,7660	2,5773	3,3235	1,7315	1,6354	2,8637	,4989	,9769	1,2344	1,7361	8701633	852995,5000	2302538	5261199	9069668
3734078	1,2799	2,4180	2,3263	2,7644	2,6154	3,3498	1,7731	1,6506	2,9262	,5015	,9686	1,2281	1,7815	8164755	673183,0000	2350319	5621057	9116009
4018173	1,2903	2,4613	2,3407	2,7209	2,6333	3,3459	1,7818	1,6491	2,9362	,5096	,9713	1,2368	1,7949	8534307	686730,2000	2287028	5303894	10336764
3887417	1,2631	2,4524	2,3224	2,6943	2,6373	3,3431	1,7717	1,6786	2,9432	,5069	,9750	1,2363	1,7912	8333017	768402,5000	2262802	5325086	8941018
3919880	1,2666	2,4768	2,3247	2,6983	2,6347	3,3052	1,7450	1,6621	2,9038	,5027	,9821	1,2299	1,7979	8568251	720602,9000	2641195	6602036	10163717
4014466	1,2546	2,4489	2,3244	2,6916	2,6286	3,3063	1,7503	1,6614	2,9245	,5066	,9721	1,2181	1,7230	8613013	688646,1000	2886395	7354948	10028886
4197758	1,2654	2,4547	2,3308	2,6625	2,6616	3,3537	1,7840	1,6638	2,8455	,5123	,9723	1,2131	1,8039	9139357	717092,7000	2430852	6231237	10190148
3896531	1,2664	2,4281	2,3628	2,7398	2,5881	3,3691	1,7585	1,6321	2,8777	,5083	,9803	1,2677	1,8070	8385716	806355,7000	2412703	6066821	11198930
3964742	1,2746	2,4398	2,3711	2,7613	2,6002	3,3620	1,7305	1,6436	2,8599	,5083	,9783	1,2275	1,7787	8451237	649994,8000	2365468	6209715	10355548
4201847	1,2869	2,4573	2,3455	2,7940	2,6299	3,3723	1,7690	1,6567	2,8622	,5109	,9456	1,2166	1,7704	9033401	540044,0000	2057798	5353594	9396952
4050512	1,2640	2,4781	2,2756	2,7806	2,6236	3,3797	1,7805	1,6795	2,8419	,5083	,9664	1,1885	1,7398	8565930	591115,2000	2390239	6427650	9238064
3997402	1,2687	2,5170	2,2058	2,8034	2,6486	3,3841	1,8024	1,6771	2,8561	,5069	,9719	1,1829	1,7573	8562307	493197,1000	2456918	6941697	9286880
4314479	1,2695	2,5142	2,2569	2,7773	2,6384	3,3972	1,8131	1,6942	2,8896	,5086	,9708	1,2104	1,7510	9255216	574142,3000	2048758	5514399	10943146
4925744	1,2798	2,5001	2,3198	2,7553	2,6399	3,3655	1,7896	1,7070	2,9099	,5101	,9690	1,2517	1,7580	10502760	545219,9000	2513095	7322716	11297607
5130631	1,2790	2,4973	2,3273	2,7306	2,6294	3,3907	1,7880	1,7134	2,9106	,5109	,9765	1,2428	1,7853	10855161	484422,8000	2887292	9651951	9982802
4444855	1,2752	2,5323	2,3559	2,7223	2,6462	3,4323	1,7946	1,6913	2,8710	,5077	,9803	1,2158	1,7696	9473338	561619,5000	2295291	6686974	11849225
3967319	1,2689	2,5427	2,3555	2,7320	2,6138	3,3459	1,7622	1,6844	2,8542	,5220	,9776	1,2207	1,7613	8521439	554666,8000	2160295	5573380	9895998
3931250	1,2431	2,5384	2,3526	2,7351	2,6020	3,3846	1,7782	1,6642	2,9097	,5062	,9617	1,1762	1,7560	8169912	695658,1000	2430452	5428766	10512292
4235952	1,2487	2,5344	2,3427	2,7229	2,6324	3,4067	1,8108	1,6549	2,9152	,5158	,9819	1,1878	1,7765	8705590	694558,9000	2381670	5352882	10001971
4169219	1,2476	2,4775	2,3204	2,7095	2,6391	3,3927	1,8177	1,6651	2,9210	,5128	1,0029	1,1969	1,6700	8600302	613095,3000	2215665	5114736	9450060
3779064	1,2441	2,4689	2,2714	2,6582	2,6173	3,3851	1,7963	1,6442	2,9124	,5155	1,0117	1,2031	1,7499	7884570	602932,9000	2350453	5800681	9047810
3558810	1,2378	2,4438	2,3035	2,6773	2,6178	3,3995	1,7779	1,6246	2,8250	,5105	1,0155	1,1965	1,8116	7509946	614260,3000	2263940	5430653	9034858
3699466	1,2340	2,4396	2,2903	2,6688	2,6119	3,3647	1,7589	1,6181	2,8404	,5106	1,0054	1,1876	1,8100	7796000	580581,2000	2223827	5325139	9626461
3650693	1,2351	2,4303	2,3019	2,6843	2,5892	3,3928	1,7638	1,6444	2,8615	,5077	1,0110	1,1711	1,8314	7651158	617712,8000	2071658	4874369	8887882
3525633	1,2339	2,4120	2,3036	2,6748	2,4352	3,3794	1,7634	1,5997	2,6592	,5086	1,0194	1,2049	1,8433	7430052	605519,3000	2118606	4747271	8699165
3470276	1,2389	2,4174	2,3582	2,7083	2,4701	3,3686	1,7728	1,6014	2,7440	,5052	1,0131	1,2209	1,8520	7581024	609842,5000	1980701	4500918	8756626
3859094	1,2381	2,4344	2,3478	2,6877	2,5432	3,3390	1,7831	1,5984	2,8676	,5006	1,0120	1,2089	1,8345	8431470	592139,8000	2141976	4660010	9120578
3661155	1,2407	2,4171	2,3481	2,6371	2,5442	3,3462	1,7786	1,5905	2,8917	,4961	1,0109	1,2103	1,8250	7903994	582844,1000	2262595	4916788	9410935
3356365	1,2473	2,4580	2,3022	2,6649	2,5919	3,3577	1,7872	1,6349	2,9004	,5017	1,0229	1,2381	1,8398	7462642	614645,5000	2044949	4649568	8540660
3344440	1,2551	2,4651	2,2399	2,7038	2,5110	3,3539	1,8445	1,6544	2,9008	,5005	1,0168	1,2195	1,8318	7424743	607572,3000	2055490	4677774	8577630
3338684	1,2610	2,4588	2,2202	2,6928	2,5273	3,3417	1,9138	1,6508	2,8825	,5069	1,0167	1,2116	1,8221	7480504	620834,6000	2111968	4862450	8963865
3404294	1,2656	2,4345	2,2728	2,7103	2,5734	3,3592	1,9236	1,6402	2,8648	,5081	1,0055	1,2127	1,8254	7863944	581937,9000	2153156	4836102	8831677
3289319	1,2599	2,4610	2,3694	2,7446	2,5698	3,3419	1,8583	1,6354	2,8186	,5095	1,0092	1,2137	1,8471	7703698	609332,7000	2149987	4707458	8680975
3469252	1,2787	2,4573	2,3844	2,7781	2,6008	3,3488	1,7614	1,6739	2,8019	,5323	,9907	1,2169	1,8627	8508132	619133,2000	2805043	5364205	10889743
3571850	1,2859	2,4721	2,4129	2,7875	2,6202	3,3350	1,7984	1,6679	2,8320	,5155	,9962	1,2294	1,8552	8933008	572585,1000	2449477	4351596	9842291
3639914	1,2781	2,4808	2,3703	2,7499	2,6286	3,3421	1,8078	1,6975	2,9729	,5046	1,0118	1,2364	1,8544	8491850	599515,9000	2168905	4208876	8005657
3091730	1,2745	2,4331	2,3678	2,6861	2,6275	3,3648	1,7988	1,6422	2,9822	,5019	,9881	1,2121	1,8444	6940275	655034,4000	2218929	5062032	8714475
3078149	1,3028	2,4226	2,3573	2,6897	2,6157	3,3664	1,8101	1,6592	2,9523	,5033	1,0049	1,2040	1,8438	6917191	668501,5000	2144176	4893322	8555468
3188115	1,2951	2,4477	2,2981	2,6939	2,6520	3,3774	1,8001	1,6638	2,9461	,5005	1,0150	1,2180	1,8304	7096722	666124,0000	2170967	4848894	8571300
3246082	1,2793	2,4289	2,1834	2,7185	2,6326	3,3735	1,7969	1,6544	2,9571	,4996	1,0119	1,2086	1,8324	7105114	732417,3000	2240876	4922093	8764326
3486992	1,2857	2,4430	2,2247	2,6911	2,6407	3,3637	1,7648	1,6601	2,9561	,5061	1,0088	1,2060	1,8568	7647797	702229,1000	2330906	5351141	9089938
3378187	1,2678	2,4194	2,2438	2,7021	2,6535	3,3894	1,7629	1,6685	2,9755	,5049	1,0101	1,2006	1,8333	7440408	684271,4000	2188360	5017799	8778446
3282306	1,2646	2,4325	2,1688	2,6811	2,6626	3,3806	1,7622	1,6664	2,9839	,5037	1,0265	1,2127	1,8348	7255613	633638,0000	2067367	4923300	8809264
3288345	1,2720	2,4281	2,2284	2,7006	2,6419	3,3882	1,7818	1,6510	2,9808	,5072	1,0214	1,2103	1,8029	7231703	693374,4000	2189597	4915221	9521789
3325749	1,2692	2,3966	2,2726	2,7180	2,6455	3,3876	1,7726	1,6346	3,0123	,5168	1,0194	1,2157	1,8206	7278022	707615,8000	2356724	5348984	9438993
3352262	1,2736	2,3909	2,2531	2,7014	2,6774	3,3845	1,8017	1,6741	2,9964	,5188	1,0331	1,2224	1,8076	7382680	722553,2000	2250295	5135063	9045288
3531954	1,2771	2,4206	2,2456	2,6619	2,6770	3,3887	1,8011	1,6621	2,9805	,5174	1,0282	1,2044	1,8068	7622740	712532,2000	2243913	5339400	9272049
3722622	1,2907	2,4086	2,2671	2,6798	2,6540	3,3944	1,8091	1,6791	2,9153	,5163	1,0159	1,2253	1,7630	8295038	687023,1000	2172504	5122639	9978418
3809365	1,2823	2,3697	2,2782	2,6544	2,6518	3,3718	1,7947	1,6592	2,9141	,4970	1,0193	1,2116	1,7315	8136158	646716,0000	2301051	5710269	9776284
3750617	1,2975	2,3821	2,3244	2,6892	2,6660	3,4668	1,8145	1,6596	3,0144	,4922	1,0226	1,2373	1,7623	8240817	657284,1000	2245784	5187058	9601480
3615286	1,2964	2,3712	2,3281	2,6623	2,6677	3,5198	1,8089	1,6624	3,0176	,4936	1,0151	1,2217	1,6130	7993962	701042,4000	2159896	5277273	11193789
3696556	1,3026	2,3817	2,2963	2,6938	2,5743	3,5015	1,7856	1,6588	2,9250	,4879	1,0284	1,2446	1,7259	7997958	744939,0000	2374240	5431043	9607554
4123959	1,2948	2,3711	2,2998	2,6950	2,6609	3,4849	1,7905	1,6646	2,9262	,4854	1,0194	1,2346	1,7100	8914911	823561,4000	2533022	6064885	9870457
4136163	1,2970	2,3975	2,2011	2,6535	2,6602	3,4844	1,7874	1,6601	2,9760	,4957	1,0169	1,2533	1,7804	9082346	810516,3000	2419167	5849883	10260040
3933392	1,2896	2,6581	2,1464	2,6567	2,6288	3,4891	1,7898	1,6737	2,9952	,5104	1,0219	1,2294	1,7537	8690947	755964,4000	2379061	5763961	9578120
4035576	1,2823	2,4968	2,1477	2,6310	2,5838	3,4709	1,7997	1,6529	2,9656	,5005	1,0309	1,2384	1,7660	8678669	707346,5000	2264684	5612253	9693065
4551202	1,3029	2,6003	2,1254	2,6504	2,6408	3,4544	1,8594	1,7204	3,0132	,5033	1,0229	1,2127	1,7014	9768461	727180,9000	2378165	5996108	12413462
4032195	1,2990	2,6425	2,1157	2,5955	2,6396	3,4419	1,9336	1,7326	3,0184	,5006	1,0194	1,1914	1,6946	8751448	1110334,5000	2536093	6163859	13143933
3970893	1,3086	2,6739	2,1699	2,5684	2,6780	3,4456	1,8056	1,7151	3,0246	,5018	1,0247	1,1991	1,7259	8737854	939273,6000	2559486	6806073	11118547
4489016	1,3161	2,7162	2,1976	2,6492	2,6473	3,4710	1,6959	1,7377	3,0339	,4893	1,0230	1,2380	1,7388	9684075	842498,6000	2340159	5770678	11289800
5426127	1,3310	2,7279	2,2091	2,6929	2,6357	3,4572	1,7363	1,7592	3,0140	,4930	1,0218	1,2282	1,7355	11529582	785787,6000	2235562	5305632	11573959
4578224	1,3155	2,4848	2,2816	2,7104	2,6568	3,5125	1,7383	1,7369	2,8995	,4860	1,0024	1,2275	1,7866	9854882	812169,3000	2300728	5714880	10511958
4126390	1,3019	2,4149	2,3217	2,7185	2,6242	3,4518	1,7288	1,7133	2,9048	,4917	1,0354	1,2394	1,8376	9030507	730023,4000	2090042	5307840	12515693
4892100	1,3125	2,4700	2,3240	2,7290	2,5959	3,4650	1,7622	1,7467	2,9634	,4811	1,0394	1,2411	1,8184	10656814	823032,5000	1976051	4951640	12966759
4128697	1,3047	2,5361	2,3311	2,7247	2,5745	3,4695	1,7535	1,7140	2,9492	,4866	1,0317	1,2149	1,8172	9111428	976730,7000	2104956	5576975	10668160
4408721	1,3048	2,5626	2,3180	2,7307	2,6402	3,4608	1,7931	1,7197	2,9427	,4915	1,0160	1,2037	1,8294	9642906	738605,6000	2489023	6787849	13948692
4199465	1,2965	2,5174	2,3101	2,7170	2,6114	3,4689	1,7925	1,7207	2,9386	,4921	1,0406	1,1912	1,8169	9217060	685173,0000	2598916	7685812	16087616
4074767	1,2917	2,5181	2,3337	2,7021	2,6044	3,4658	1,8276	1,7093	2,9521	,4871	1,0456	1,3122	1,8227	8816389	642518,6000	2302455	6451885	12159456
4161758	1,2946	2,5059	2,3117	2,7189	2,5808	3,4367	1,8196	1,6986	2,9405	,4832	1,0318	1,2216	1,8303	9074790	677848,7000	2427969	5521297	10633146
3891319	1,3014	2,5068	2,1893	2,7045	2,6334	3,4753	1,8494	1,6655	2,9891	,4808	,9944	1,2304	1,8165	8601172	826347,5000	2132820	5268035	10770809
4470302	1,2996	2,5076	2,1303	2,7173	2,6266	3,4940	1,8534	1,6939	2,9779	,4822	1,0263	1,2393	1,8657	9735782	757562,4000	2560376	6159480	10548925
4283111	1,2939	2,4635	2,1530	2,6965	2,6261	3,4986	1,8624	1,6935	2,9656	,4823	1,0767	1,2306	1,8873	9222117	825217,4000	2454605	6391178	10123204
3845962	1,2659	2,4482	2,2520	2,6532	2,6219	3,4817	1,8317	1,6880	2,9391	,4874	1,0925	1,2280	1,9577	8197462	831800,1000	2169005	5446149	11471988
3911471	1,2591	2,4496	2,2873	2,6568	2,5764	3,5217	1,8734	1,7178	2,9923	,4809	1,0840	1,2478	1,9650	8161117	890943,7000	2072759	5055640	10599314
3798478	1,2506	2,4275	2,2874	2,6884	2,6527	3,5067	1,8806	1,6938	2,9913	,4790	1,0473	1,2443	1,9478	8085780	818812,4000	2201360	5234681	10501150
3644313	1,2638	2,4233	2,2936	2,7027	2,6711	3,5260	1,9179	1,7262	2,9611	,4959	1,0635	1,2874	1,9661	7777563	813389,4000	2215184	5456357	9476948
3784029	1,2661	2,3870	2,3180	2,7110	2,6323	3,5443	1,9145	1,7150	2,9894	,4800	1,0403	1,2843	1,9592	8192525	791213,0000	2140796	5055154	9854999
3647134	1,2596	2,3949	2,3151	2,6879	2,6333	3,5167	1,9304	1,7398	2,9760	,4827	1,0607	1,2593	1,9530	8222640	753161,9000	2064345	4986559	9020688
3994662	1,2542	2,3853	2,3403	2,7191	2,6491	3,4653	1,9046	1,7543	3,0096	,4829	1,0644	1,2245	1,9561	8852425	744738,3000	2246763	5314687	9639666
3607836	1,2539	2,4133	2,3204	2,7146	2,5823	3,4967	1,9119	1,7148	2,9680	,4740	1,0712	1,2311	1,9576	8047626	740853,2000	2196948	5029952	10016963
3566008	1,2548	2,3681	2,1607	2,7095	2,3672	3,5128	1,8923	1,7246	2,9793	,4772	1,0848	1,2505	1,9549	8079925	828505,4000	1987852	4569712	9221363
3511412	1,2606	2,3832	1,8827	2,7356	2,6429	3,5276	1,9149	1,7249	2,9455	,4789	1,0773	1,2476	1,9183	8099820	764325,4000	2013311	4661941	9163961
3258665	1,2614	2,4082	2,0185	2,8202	2,6340	3,5227	1,9172	1,7247	2,9485	,4806	1,0542	1,2487	1,9012	7444464	779151,6000	2024477	4649692	9600997
3486573	1,2626	2,4641	2,0727	2,7569	2,6312	3,5438	1,9138	1,7178	2,9581	,4816	1,0385	1,2677	1,9330	8060967	780635,1000	2175719	4883549	9629093
3369443	1,2719	2,4734	2,2192	2,7722	2,7039	3,5446	1,9508	1,7363	3,0063	,4832	1,0442	1,2609	1,9171	7904184	772651,9000	2459717	4927239	9266651
3465544	1,2798	2,4978	2,3992	2,7747	2,6937	3,5515	1,9510	1,7367	3,0513	,4853	1,0433	1,2563	1,9332	8532755	796750,8000	2436148	5077345	11454028
3905224	1,2909	2,5224	2,4687	2,8096	2,6996	3,5635	1,9719	1,7840	3,0844	,4955	1,0402	1,2499	1,8988	10077590	774563,7000	2533141	4551562	10051577
3733881	1,3027	2,5336	2,4277	2,7732	2,6797	3,5866	1,9727	1,7663	3,0785	,4900	1,0589	1,2522	1,9184	9163186	781544,8000	2438635	4807379	8887058
3220642	1,2565	2,5136	2,3863	2,7560	2,6850	3,5616	1,9428	1,7169	3,0362	,4892	1,0823	1,2450	1,9011	7027349	846743,7000	2294455	5560041	9590767
3225812	1,2509	2,4282	2,3860	2,7331	2,6891	3,5248	1,9311	1,7318	3,0287	,5220	1,0825	1,2487	1,8955	7000371	852582,7000	2233829	5637553	9269821
3354461	1,2624	2,5319	2,3886	2,7156	2,6870	3,5410	1,9221	1,7135	3,0139	,5214	1,0805	1,2335	1,8871	7234027	837685,9000	2231864	5502635	9242497
3352261	1,2486	2,5218	2,3603	2,7327	2,6654	3,5510	1,9338	1,7101	3,0868	,5145	1,0685	1,2267	1,8848	7166769	872753,1000	2248620	5354221	9621983
3450652	1,2383	2,4867	2,3185	2,7075	2,6610	3,5552	1,9111	1,7224	3,1237	,5383	1,0613	1,2389	1,8759	7538708	863745,7000	2348107	5707447	10101244

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=164290&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=164290&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=164290&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 time7 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
QBEPIL[t] = + 6455284.6223278 -2898205.63993182PBEPIL[t] + 169135.598876252PBELUX[t] -21761.5219670579PBABD[t] -275414.999387818PBFRU[t] + 548342.066610686PBEPAL[t] + 176513.952533997PBESTO[t] -754144.194774959PBEWIT[t] -419533.991684182PBENA[t] -14517.3414029572PCHSAN[t] -1366691.61342445PWABR[t] + 213640.490332651PSOCOLA[t] -535113.97930309PSOBIT[t] -519760.444190232PSPORT[t] + 0.45699191232378BUDBEER[t] + 0.289832437066179BUDCHIL[t] -0.540369202887511BUDAMB[t] + 0.160047386397583BUDWATER[t] -0.00959676513018011BUDSISSS[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
QBEPIL[t] =  +  6455284.6223278 -2898205.63993182PBEPIL[t] +  169135.598876252PBELUX[t] -21761.5219670579PBABD[t] -275414.999387818PBFRU[t] +  548342.066610686PBEPAL[t] +  176513.952533997PBESTO[t] -754144.194774959PBEWIT[t] -419533.991684182PBENA[t] -14517.3414029572PCHSAN[t] -1366691.61342445PWABR[t] +  213640.490332651PSOCOLA[t] -535113.97930309PSOBIT[t] -519760.444190232PSPORT[t] +  0.45699191232378BUDBEER[t] +  0.289832437066179BUDCHIL[t] -0.540369202887511BUDAMB[t] +  0.160047386397583BUDWATER[t] -0.00959676513018011BUDSISSS[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=164290&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]QBEPIL[t] =  +  6455284.6223278 -2898205.63993182PBEPIL[t] +  169135.598876252PBELUX[t] -21761.5219670579PBABD[t] -275414.999387818PBFRU[t] +  548342.066610686PBEPAL[t] +  176513.952533997PBESTO[t] -754144.194774959PBEWIT[t] -419533.991684182PBENA[t] -14517.3414029572PCHSAN[t] -1366691.61342445PWABR[t] +  213640.490332651PSOCOLA[t] -535113.97930309PSOBIT[t] -519760.444190232PSPORT[t] +  0.45699191232378BUDBEER[t] +  0.289832437066179BUDCHIL[t] -0.540369202887511BUDAMB[t] +  0.160047386397583BUDWATER[t] -0.00959676513018011BUDSISSS[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=164290&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=164290&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] = + 6455284.6223278 -2898205.63993182PBEPIL[t] + 169135.598876252PBELUX[t] -21761.5219670579PBABD[t] -275414.999387818PBFRU[t] + 548342.066610686PBEPAL[t] + 176513.952533997PBESTO[t] -754144.194774959PBEWIT[t] -419533.991684182PBENA[t] -14517.3414029572PCHSAN[t] -1366691.61342445PWABR[t] + 213640.490332651PSOCOLA[t] -535113.97930309PSOBIT[t] -519760.444190232PSPORT[t] + 0.45699191232378BUDBEER[t] + 0.289832437066179BUDCHIL[t] -0.540369202887511BUDAMB[t] + 0.160047386397583BUDWATER[t] -0.00959676513018011BUDSISSS[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6455284.6223278958996.0214966.731300
PBEPIL-2898205.63993182542775.96243-5.339600
PBELUX169135.598876252157926.3016661.0710.2865020.143251
PBABD-21761.5219670579100653.776817-0.21620.8292270.414614
PBFRU-275414.999387818189470.556553-1.45360.1488780.074439
PBEPAL548342.066610686184253.766152.9760.0035850.001792
PBESTO176513.952533997235881.8705360.74830.4558530.227926
PBEWIT-754144.194774959205182.511123-3.67550.0003670.000184
PBENA-419533.991684182187119.143376-2.24210.0269450.013472
PCHSAN-14517.3414029572112073.516234-0.12950.8971690.448585
PWABR-1366691.61342445881628.287839-1.55020.1239420.061971
PSOCOLA213640.490332651534434.300570.39980.6901080.345054
PSOBIT-535113.97930309435812.114985-1.22790.2220990.111049
PSPORT-519760.444190232187742.643614-2.76850.0066020.003301
BUDBEER0.456991912323780.01496730.53300
BUDCHIL0.2898324370661790.099542.91170.0043460.002173
BUDAMB-0.5403692028875110.0621-8.701700
BUDWATER0.1600473863975830.0168099.521700
BUDSISSS-0.009596765130180110.009509-1.00920.315070.157535

\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) & 6455284.6223278 & 958996.021496 & 6.7313 & 0 & 0 \tabularnewline
PBEPIL & -2898205.63993182 & 542775.96243 & -5.3396 & 0 & 0 \tabularnewline
PBELUX & 169135.598876252 & 157926.301666 & 1.071 & 0.286502 & 0.143251 \tabularnewline
PBABD & -21761.5219670579 & 100653.776817 & -0.2162 & 0.829227 & 0.414614 \tabularnewline
PBFRU & -275414.999387818 & 189470.556553 & -1.4536 & 0.148878 & 0.074439 \tabularnewline
PBEPAL & 548342.066610686 & 184253.76615 & 2.976 & 0.003585 & 0.001792 \tabularnewline
PBESTO & 176513.952533997 & 235881.870536 & 0.7483 & 0.455853 & 0.227926 \tabularnewline
PBEWIT & -754144.194774959 & 205182.511123 & -3.6755 & 0.000367 & 0.000184 \tabularnewline
PBENA & -419533.991684182 & 187119.143376 & -2.2421 & 0.026945 & 0.013472 \tabularnewline
PCHSAN & -14517.3414029572 & 112073.516234 & -0.1295 & 0.897169 & 0.448585 \tabularnewline
PWABR & -1366691.61342445 & 881628.287839 & -1.5502 & 0.123942 & 0.061971 \tabularnewline
PSOCOLA & 213640.490332651 & 534434.30057 & 0.3998 & 0.690108 & 0.345054 \tabularnewline
PSOBIT & -535113.97930309 & 435812.114985 & -1.2279 & 0.222099 & 0.111049 \tabularnewline
PSPORT & -519760.444190232 & 187742.643614 & -2.7685 & 0.006602 & 0.003301 \tabularnewline
BUDBEER & 0.45699191232378 & 0.014967 & 30.533 & 0 & 0 \tabularnewline
BUDCHIL & 0.289832437066179 & 0.09954 & 2.9117 & 0.004346 & 0.002173 \tabularnewline
BUDAMB & -0.540369202887511 & 0.0621 & -8.7017 & 0 & 0 \tabularnewline
BUDWATER & 0.160047386397583 & 0.016809 & 9.5217 & 0 & 0 \tabularnewline
BUDSISSS & -0.00959676513018011 & 0.009509 & -1.0092 & 0.31507 & 0.157535 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=164290&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]6455284.6223278[/C][C]958996.021496[/C][C]6.7313[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]PBEPIL[/C][C]-2898205.63993182[/C][C]542775.96243[/C][C]-5.3396[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]PBELUX[/C][C]169135.598876252[/C][C]157926.301666[/C][C]1.071[/C][C]0.286502[/C][C]0.143251[/C][/ROW]
[ROW][C]PBABD[/C][C]-21761.5219670579[/C][C]100653.776817[/C][C]-0.2162[/C][C]0.829227[/C][C]0.414614[/C][/ROW]
[ROW][C]PBFRU[/C][C]-275414.999387818[/C][C]189470.556553[/C][C]-1.4536[/C][C]0.148878[/C][C]0.074439[/C][/ROW]
[ROW][C]PBEPAL[/C][C]548342.066610686[/C][C]184253.76615[/C][C]2.976[/C][C]0.003585[/C][C]0.001792[/C][/ROW]
[ROW][C]PBESTO[/C][C]176513.952533997[/C][C]235881.870536[/C][C]0.7483[/C][C]0.455853[/C][C]0.227926[/C][/ROW]
[ROW][C]PBEWIT[/C][C]-754144.194774959[/C][C]205182.511123[/C][C]-3.6755[/C][C]0.000367[/C][C]0.000184[/C][/ROW]
[ROW][C]PBENA[/C][C]-419533.991684182[/C][C]187119.143376[/C][C]-2.2421[/C][C]0.026945[/C][C]0.013472[/C][/ROW]
[ROW][C]PCHSAN[/C][C]-14517.3414029572[/C][C]112073.516234[/C][C]-0.1295[/C][C]0.897169[/C][C]0.448585[/C][/ROW]
[ROW][C]PWABR[/C][C]-1366691.61342445[/C][C]881628.287839[/C][C]-1.5502[/C][C]0.123942[/C][C]0.061971[/C][/ROW]
[ROW][C]PSOCOLA[/C][C]213640.490332651[/C][C]534434.30057[/C][C]0.3998[/C][C]0.690108[/C][C]0.345054[/C][/ROW]
[ROW][C]PSOBIT[/C][C]-535113.97930309[/C][C]435812.114985[/C][C]-1.2279[/C][C]0.222099[/C][C]0.111049[/C][/ROW]
[ROW][C]PSPORT[/C][C]-519760.444190232[/C][C]187742.643614[/C][C]-2.7685[/C][C]0.006602[/C][C]0.003301[/C][/ROW]
[ROW][C]BUDBEER[/C][C]0.45699191232378[/C][C]0.014967[/C][C]30.533[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]BUDCHIL[/C][C]0.289832437066179[/C][C]0.09954[/C][C]2.9117[/C][C]0.004346[/C][C]0.002173[/C][/ROW]
[ROW][C]BUDAMB[/C][C]-0.540369202887511[/C][C]0.0621[/C][C]-8.7017[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]BUDWATER[/C][C]0.160047386397583[/C][C]0.016809[/C][C]9.5217[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]BUDSISSS[/C][C]-0.00959676513018011[/C][C]0.009509[/C][C]-1.0092[/C][C]0.31507[/C][C]0.157535[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=164290&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=164290&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)6455284.6223278958996.0214966.731300
PBEPIL-2898205.63993182542775.96243-5.339600
PBELUX169135.598876252157926.3016661.0710.2865020.143251
PBABD-21761.5219670579100653.776817-0.21620.8292270.414614
PBFRU-275414.999387818189470.556553-1.45360.1488780.074439
PBEPAL548342.066610686184253.766152.9760.0035850.001792
PBESTO176513.952533997235881.8705360.74830.4558530.227926
PBEWIT-754144.194774959205182.511123-3.67550.0003670.000184
PBENA-419533.991684182187119.143376-2.24210.0269450.013472
PCHSAN-14517.3414029572112073.516234-0.12950.8971690.448585
PWABR-1366691.61342445881628.287839-1.55020.1239420.061971
PSOCOLA213640.490332651534434.300570.39980.6901080.345054
PSOBIT-535113.97930309435812.114985-1.22790.2220990.111049
PSPORT-519760.444190232187742.643614-2.76850.0066020.003301
BUDBEER0.456991912323780.01496730.53300
BUDCHIL0.2898324370661790.099542.91170.0043460.002173
BUDAMB-0.5403692028875110.0621-8.701700
BUDWATER0.1600473863975830.0168099.521700
BUDSISSS-0.009596765130180110.009509-1.00920.315070.157535






Multiple Linear Regression - Regression Statistics
Multiple R0.983454175201326
R-squared0.967182114720921
Adjusted R-squared0.961860295486475
F-TEST (value)181.739001667111
F-TEST (DF numerator)18
F-TEST (DF denominator)111
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation85978.6565460835
Sum Squared Residuals820548561343.103

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.983454175201326 \tabularnewline
R-squared & 0.967182114720921 \tabularnewline
Adjusted R-squared & 0.961860295486475 \tabularnewline
F-TEST (value) & 181.739001667111 \tabularnewline
F-TEST (DF numerator) & 18 \tabularnewline
F-TEST (DF denominator) & 111 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 85978.6565460835 \tabularnewline
Sum Squared Residuals & 820548561343.103 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=164290&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.983454175201326[/C][/ROW]
[ROW][C]R-squared[/C][C]0.967182114720921[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.961860295486475[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]181.739001667111[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]18[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]111[/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]85978.6565460835[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]820548561343.103[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=164290&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=164290&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 R0.983454175201326
R-squared0.967182114720921
Adjusted R-squared0.961860295486475
F-TEST (value)181.739001667111
F-TEST (DF numerator)18
F-TEST (DF denominator)111
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation85978.6565460835
Sum Squared Residuals820548561343.103






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
142648304308556.89842545-43726.8984254533
239246743940796.2542104-16122.254210404
337347533668906.8909925765846.1090074346
437622903665188.9320188197101.0679811904
536097393579948.031109629790.9688903978
638775943753634.90914976123959.090850236
736364153595323.8851503441091.1148496619
835781953578826.81624041-631.816240413998
936043423577372.1232904126969.8767095918
1034595133557689.23709058-98176.2370905763
1133665713458625.11347171-92054.1134717084
1233712773545652.6865105-174375.686510498
1337248483744260.79085802-19412.7908580234
1433508303416236.53463446-65406.5346344632
1533051593367782.92425066-62623.9242506625
1633907363400634.14132398-9898.14132398053
1733497583382265.87391311-32507.8739131132
1832536553369652.992721-115997.992720999
1937342503813685.38865331-79435.3886533136
2034554333452435.382309582997.61769041555
2129667262986757.215026-20031.2150260029
2229937162946314.4201242447401.5798757576
2330093202987939.6213132421380.378686763
2431697133104770.4712704864942.5287295249
2531700613093614.5808049776446.4191950339
2633689343345538.6231598823395.3768401214
2732926383260907.0525407331730.9474592696
2833373443291883.7969911445460.20300886
2932083063209120.16590773-814.165907725628
3033591303322255.1853349936874.8146650111
3132230783200025.6591550623052.3408449381
3234371593384300.1422501852858.8577498242
3334001563436491.52727069-36335.5272706878
3436575763687341.7418581-29765.7418580974
3537656133821688.90348735-56075.9034873535
3634819213450163.2165330931757.7834669084
3736048003660219.85664837-55419.8566483681
3839813404005195.69972738-23855.6997273794
3937340783703001.9812377831076.0187622243
4040181733817320.53007629200852.469923708
4138874173867440.7454970819976.254502919
4239198803968798.67765042-48918.6776504217
4340144664031824.21587291-17358.2158729076
4441977584276993.30275568-79235.3027556767
4538965313873710.4075364922820.592463514
4639647423944658.9772817520083.0227182488
4742018474159281.0897737442565.910226257
4840505124048181.932975342330.06702466075
4939974024047875.24731591-50473.2473159098
5043144794334490.77265-20011.7726500012
5149257444882892.5090624142851.4909375848
5251306315206285.79802924-75654.7980292363
5344448554492443.05539082-47588.0553908171
5439673193962364.503525144954.49647485798
5539312503781522.42652021149727.573479787
5642359524005926.21408119230025.785918808
5741692194039532.74384327129686.256156732
5837790643739276.4270804939787.5729195068
5935588103572356.73317604-13546.73317604
6036994663716115.94126037-16649.9412603746
6136506933648918.367750241774.63224975604
6235256333414877.1479799110755.852020097
6334702763494085.50896928-23809.5089692759
6438590943871641.3462539-12547.3462538957
6536611553622775.3562578838379.643742117
6633563653469911.26889763-113546.268897626
6733444403336259.338297958180.66170204631
6833386843303009.3300431335674.6699568722
6934042943437697.87524738-33403.8752473775
7032893193398394.70802604-109075.708026036
7134692523464581.632565264670.36743474396
7235718503667529.60456483-95679.6045648337
7336399143654692.17272236-14778.1727223614
7430917303134513.12917884-42783.129178839
7530781493043141.78483435007.2151660018
7631881153162456.1600159925658.8399840114
7732460823195353.37225950728.6277409976
7834869923445689.5571745741302.4428254263
7933781873443021.1639049-64834.163904905
8032823063415375.50600249-133069.506002486
8132883453312430.66347193-24085.6634719332
8233257493304258.1552054121490.8447945891
8333522623356770.83937857-4508.83937856802
8435319543520623.8171844311330.1828155706
8537226223757371.28090424-34749.2809042356
8638093653788964.5768777920400.4231222108
8737506173720617.5948507629999.4051492424
8836152863770452.73294468-155166.732944676
8936965563590510.66885286106045.331147143
9041239594119384.995641014574.00435898745
9141361634168983.64473975-32820.6447397481
9239333924038065.72205085-104673.722050851
9340355764033757.152722261818.84727774007
9445512024460587.1382879290614.8617120806
9540321954029000.067143773194.93285622798
9639708934171157.16276288-200264.162762878
9744890164539209.46250283-50193.4625028313
9854261275245679.89846552180447.101534484
9945782244535964.6445453242259.3554546838
10041263904144168.51062067-17778.5106206727
10148921004861422.3016122730677.6983877324
10241286974302069.43982018-173372.439820176
10344087214425466.37907936-16745.3790793632
10441994654302885.52343391-103420.523433905
10540747674039317.526493935449.4735061013
10641617583990439.74000173171318.259998275
10738913193945865.67600021-54546.6760002137
10844703024320682.90105457149619.098945432
10942831114216326.2462245766784.7537754254
11038459623810208.004302835753.9956971974
11139114713756875.34526086154595.654739142
11237984783723250.7692372475227.2307627549
11336443133495624.93467084148688.065329161
11437840293646392.47877127137636.521228729
11536471343704810.36867151-57676.3686715103
11639946623973068.3538553421593.6461446559
11736078363579048.407911428787.5920886007
11835660083545757.3566620120250.643337987
11935114123676168.98055588-164756.980555881
12032586653338041.35708402-79376.3570840216
12134865733573963.58844791-87390.5884479143
12233694433338801.1793146930641.8206853076
12334655443611115.43628158-145571.436281584
12439052244124394.2464775-219170.246477503
12537338813790509.14108255-56628.1410825452
12632206423222435.35872203-1793.35872203038
12732258123222724.105208663087.89479133894
12833544613323169.4734673531291.5265326542
12933522613293506.6846699958754.3153300119
13034506523464591.717204-13939.7172039986

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4264830 & 4308556.89842545 & -43726.8984254533 \tabularnewline
2 & 3924674 & 3940796.2542104 & -16122.254210404 \tabularnewline
3 & 3734753 & 3668906.89099257 & 65846.1090074346 \tabularnewline
4 & 3762290 & 3665188.93201881 & 97101.0679811904 \tabularnewline
5 & 3609739 & 3579948.0311096 & 29790.9688903978 \tabularnewline
6 & 3877594 & 3753634.90914976 & 123959.090850236 \tabularnewline
7 & 3636415 & 3595323.88515034 & 41091.1148496619 \tabularnewline
8 & 3578195 & 3578826.81624041 & -631.816240413998 \tabularnewline
9 & 3604342 & 3577372.12329041 & 26969.8767095918 \tabularnewline
10 & 3459513 & 3557689.23709058 & -98176.2370905763 \tabularnewline
11 & 3366571 & 3458625.11347171 & -92054.1134717084 \tabularnewline
12 & 3371277 & 3545652.6865105 & -174375.686510498 \tabularnewline
13 & 3724848 & 3744260.79085802 & -19412.7908580234 \tabularnewline
14 & 3350830 & 3416236.53463446 & -65406.5346344632 \tabularnewline
15 & 3305159 & 3367782.92425066 & -62623.9242506625 \tabularnewline
16 & 3390736 & 3400634.14132398 & -9898.14132398053 \tabularnewline
17 & 3349758 & 3382265.87391311 & -32507.8739131132 \tabularnewline
18 & 3253655 & 3369652.992721 & -115997.992720999 \tabularnewline
19 & 3734250 & 3813685.38865331 & -79435.3886533136 \tabularnewline
20 & 3455433 & 3452435.38230958 & 2997.61769041555 \tabularnewline
21 & 2966726 & 2986757.215026 & -20031.2150260029 \tabularnewline
22 & 2993716 & 2946314.42012424 & 47401.5798757576 \tabularnewline
23 & 3009320 & 2987939.62131324 & 21380.378686763 \tabularnewline
24 & 3169713 & 3104770.47127048 & 64942.5287295249 \tabularnewline
25 & 3170061 & 3093614.58080497 & 76446.4191950339 \tabularnewline
26 & 3368934 & 3345538.62315988 & 23395.3768401214 \tabularnewline
27 & 3292638 & 3260907.05254073 & 31730.9474592696 \tabularnewline
28 & 3337344 & 3291883.79699114 & 45460.20300886 \tabularnewline
29 & 3208306 & 3209120.16590773 & -814.165907725628 \tabularnewline
30 & 3359130 & 3322255.18533499 & 36874.8146650111 \tabularnewline
31 & 3223078 & 3200025.65915506 & 23052.3408449381 \tabularnewline
32 & 3437159 & 3384300.14225018 & 52858.8577498242 \tabularnewline
33 & 3400156 & 3436491.52727069 & -36335.5272706878 \tabularnewline
34 & 3657576 & 3687341.7418581 & -29765.7418580974 \tabularnewline
35 & 3765613 & 3821688.90348735 & -56075.9034873535 \tabularnewline
36 & 3481921 & 3450163.21653309 & 31757.7834669084 \tabularnewline
37 & 3604800 & 3660219.85664837 & -55419.8566483681 \tabularnewline
38 & 3981340 & 4005195.69972738 & -23855.6997273794 \tabularnewline
39 & 3734078 & 3703001.98123778 & 31076.0187622243 \tabularnewline
40 & 4018173 & 3817320.53007629 & 200852.469923708 \tabularnewline
41 & 3887417 & 3867440.74549708 & 19976.254502919 \tabularnewline
42 & 3919880 & 3968798.67765042 & -48918.6776504217 \tabularnewline
43 & 4014466 & 4031824.21587291 & -17358.2158729076 \tabularnewline
44 & 4197758 & 4276993.30275568 & -79235.3027556767 \tabularnewline
45 & 3896531 & 3873710.40753649 & 22820.592463514 \tabularnewline
46 & 3964742 & 3944658.97728175 & 20083.0227182488 \tabularnewline
47 & 4201847 & 4159281.08977374 & 42565.910226257 \tabularnewline
48 & 4050512 & 4048181.93297534 & 2330.06702466075 \tabularnewline
49 & 3997402 & 4047875.24731591 & -50473.2473159098 \tabularnewline
50 & 4314479 & 4334490.77265 & -20011.7726500012 \tabularnewline
51 & 4925744 & 4882892.50906241 & 42851.4909375848 \tabularnewline
52 & 5130631 & 5206285.79802924 & -75654.7980292363 \tabularnewline
53 & 4444855 & 4492443.05539082 & -47588.0553908171 \tabularnewline
54 & 3967319 & 3962364.50352514 & 4954.49647485798 \tabularnewline
55 & 3931250 & 3781522.42652021 & 149727.573479787 \tabularnewline
56 & 4235952 & 4005926.21408119 & 230025.785918808 \tabularnewline
57 & 4169219 & 4039532.74384327 & 129686.256156732 \tabularnewline
58 & 3779064 & 3739276.42708049 & 39787.5729195068 \tabularnewline
59 & 3558810 & 3572356.73317604 & -13546.73317604 \tabularnewline
60 & 3699466 & 3716115.94126037 & -16649.9412603746 \tabularnewline
61 & 3650693 & 3648918.36775024 & 1774.63224975604 \tabularnewline
62 & 3525633 & 3414877.1479799 & 110755.852020097 \tabularnewline
63 & 3470276 & 3494085.50896928 & -23809.5089692759 \tabularnewline
64 & 3859094 & 3871641.3462539 & -12547.3462538957 \tabularnewline
65 & 3661155 & 3622775.35625788 & 38379.643742117 \tabularnewline
66 & 3356365 & 3469911.26889763 & -113546.268897626 \tabularnewline
67 & 3344440 & 3336259.33829795 & 8180.66170204631 \tabularnewline
68 & 3338684 & 3303009.33004313 & 35674.6699568722 \tabularnewline
69 & 3404294 & 3437697.87524738 & -33403.8752473775 \tabularnewline
70 & 3289319 & 3398394.70802604 & -109075.708026036 \tabularnewline
71 & 3469252 & 3464581.63256526 & 4670.36743474396 \tabularnewline
72 & 3571850 & 3667529.60456483 & -95679.6045648337 \tabularnewline
73 & 3639914 & 3654692.17272236 & -14778.1727223614 \tabularnewline
74 & 3091730 & 3134513.12917884 & -42783.129178839 \tabularnewline
75 & 3078149 & 3043141.784834 & 35007.2151660018 \tabularnewline
76 & 3188115 & 3162456.16001599 & 25658.8399840114 \tabularnewline
77 & 3246082 & 3195353.372259 & 50728.6277409976 \tabularnewline
78 & 3486992 & 3445689.55717457 & 41302.4428254263 \tabularnewline
79 & 3378187 & 3443021.1639049 & -64834.163904905 \tabularnewline
80 & 3282306 & 3415375.50600249 & -133069.506002486 \tabularnewline
81 & 3288345 & 3312430.66347193 & -24085.6634719332 \tabularnewline
82 & 3325749 & 3304258.15520541 & 21490.8447945891 \tabularnewline
83 & 3352262 & 3356770.83937857 & -4508.83937856802 \tabularnewline
84 & 3531954 & 3520623.81718443 & 11330.1828155706 \tabularnewline
85 & 3722622 & 3757371.28090424 & -34749.2809042356 \tabularnewline
86 & 3809365 & 3788964.57687779 & 20400.4231222108 \tabularnewline
87 & 3750617 & 3720617.59485076 & 29999.4051492424 \tabularnewline
88 & 3615286 & 3770452.73294468 & -155166.732944676 \tabularnewline
89 & 3696556 & 3590510.66885286 & 106045.331147143 \tabularnewline
90 & 4123959 & 4119384.99564101 & 4574.00435898745 \tabularnewline
91 & 4136163 & 4168983.64473975 & -32820.6447397481 \tabularnewline
92 & 3933392 & 4038065.72205085 & -104673.722050851 \tabularnewline
93 & 4035576 & 4033757.15272226 & 1818.84727774007 \tabularnewline
94 & 4551202 & 4460587.13828792 & 90614.8617120806 \tabularnewline
95 & 4032195 & 4029000.06714377 & 3194.93285622798 \tabularnewline
96 & 3970893 & 4171157.16276288 & -200264.162762878 \tabularnewline
97 & 4489016 & 4539209.46250283 & -50193.4625028313 \tabularnewline
98 & 5426127 & 5245679.89846552 & 180447.101534484 \tabularnewline
99 & 4578224 & 4535964.64454532 & 42259.3554546838 \tabularnewline
100 & 4126390 & 4144168.51062067 & -17778.5106206727 \tabularnewline
101 & 4892100 & 4861422.30161227 & 30677.6983877324 \tabularnewline
102 & 4128697 & 4302069.43982018 & -173372.439820176 \tabularnewline
103 & 4408721 & 4425466.37907936 & -16745.3790793632 \tabularnewline
104 & 4199465 & 4302885.52343391 & -103420.523433905 \tabularnewline
105 & 4074767 & 4039317.5264939 & 35449.4735061013 \tabularnewline
106 & 4161758 & 3990439.74000173 & 171318.259998275 \tabularnewline
107 & 3891319 & 3945865.67600021 & -54546.6760002137 \tabularnewline
108 & 4470302 & 4320682.90105457 & 149619.098945432 \tabularnewline
109 & 4283111 & 4216326.24622457 & 66784.7537754254 \tabularnewline
110 & 3845962 & 3810208.0043028 & 35753.9956971974 \tabularnewline
111 & 3911471 & 3756875.34526086 & 154595.654739142 \tabularnewline
112 & 3798478 & 3723250.76923724 & 75227.2307627549 \tabularnewline
113 & 3644313 & 3495624.93467084 & 148688.065329161 \tabularnewline
114 & 3784029 & 3646392.47877127 & 137636.521228729 \tabularnewline
115 & 3647134 & 3704810.36867151 & -57676.3686715103 \tabularnewline
116 & 3994662 & 3973068.35385534 & 21593.6461446559 \tabularnewline
117 & 3607836 & 3579048.4079114 & 28787.5920886007 \tabularnewline
118 & 3566008 & 3545757.35666201 & 20250.643337987 \tabularnewline
119 & 3511412 & 3676168.98055588 & -164756.980555881 \tabularnewline
120 & 3258665 & 3338041.35708402 & -79376.3570840216 \tabularnewline
121 & 3486573 & 3573963.58844791 & -87390.5884479143 \tabularnewline
122 & 3369443 & 3338801.17931469 & 30641.8206853076 \tabularnewline
123 & 3465544 & 3611115.43628158 & -145571.436281584 \tabularnewline
124 & 3905224 & 4124394.2464775 & -219170.246477503 \tabularnewline
125 & 3733881 & 3790509.14108255 & -56628.1410825452 \tabularnewline
126 & 3220642 & 3222435.35872203 & -1793.35872203038 \tabularnewline
127 & 3225812 & 3222724.10520866 & 3087.89479133894 \tabularnewline
128 & 3354461 & 3323169.47346735 & 31291.5265326542 \tabularnewline
129 & 3352261 & 3293506.68466999 & 58754.3153300119 \tabularnewline
130 & 3450652 & 3464591.717204 & -13939.7172039986 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=164290&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]4264830[/C][C]4308556.89842545[/C][C]-43726.8984254533[/C][/ROW]
[ROW][C]2[/C][C]3924674[/C][C]3940796.2542104[/C][C]-16122.254210404[/C][/ROW]
[ROW][C]3[/C][C]3734753[/C][C]3668906.89099257[/C][C]65846.1090074346[/C][/ROW]
[ROW][C]4[/C][C]3762290[/C][C]3665188.93201881[/C][C]97101.0679811904[/C][/ROW]
[ROW][C]5[/C][C]3609739[/C][C]3579948.0311096[/C][C]29790.9688903978[/C][/ROW]
[ROW][C]6[/C][C]3877594[/C][C]3753634.90914976[/C][C]123959.090850236[/C][/ROW]
[ROW][C]7[/C][C]3636415[/C][C]3595323.88515034[/C][C]41091.1148496619[/C][/ROW]
[ROW][C]8[/C][C]3578195[/C][C]3578826.81624041[/C][C]-631.816240413998[/C][/ROW]
[ROW][C]9[/C][C]3604342[/C][C]3577372.12329041[/C][C]26969.8767095918[/C][/ROW]
[ROW][C]10[/C][C]3459513[/C][C]3557689.23709058[/C][C]-98176.2370905763[/C][/ROW]
[ROW][C]11[/C][C]3366571[/C][C]3458625.11347171[/C][C]-92054.1134717084[/C][/ROW]
[ROW][C]12[/C][C]3371277[/C][C]3545652.6865105[/C][C]-174375.686510498[/C][/ROW]
[ROW][C]13[/C][C]3724848[/C][C]3744260.79085802[/C][C]-19412.7908580234[/C][/ROW]
[ROW][C]14[/C][C]3350830[/C][C]3416236.53463446[/C][C]-65406.5346344632[/C][/ROW]
[ROW][C]15[/C][C]3305159[/C][C]3367782.92425066[/C][C]-62623.9242506625[/C][/ROW]
[ROW][C]16[/C][C]3390736[/C][C]3400634.14132398[/C][C]-9898.14132398053[/C][/ROW]
[ROW][C]17[/C][C]3349758[/C][C]3382265.87391311[/C][C]-32507.8739131132[/C][/ROW]
[ROW][C]18[/C][C]3253655[/C][C]3369652.992721[/C][C]-115997.992720999[/C][/ROW]
[ROW][C]19[/C][C]3734250[/C][C]3813685.38865331[/C][C]-79435.3886533136[/C][/ROW]
[ROW][C]20[/C][C]3455433[/C][C]3452435.38230958[/C][C]2997.61769041555[/C][/ROW]
[ROW][C]21[/C][C]2966726[/C][C]2986757.215026[/C][C]-20031.2150260029[/C][/ROW]
[ROW][C]22[/C][C]2993716[/C][C]2946314.42012424[/C][C]47401.5798757576[/C][/ROW]
[ROW][C]23[/C][C]3009320[/C][C]2987939.62131324[/C][C]21380.378686763[/C][/ROW]
[ROW][C]24[/C][C]3169713[/C][C]3104770.47127048[/C][C]64942.5287295249[/C][/ROW]
[ROW][C]25[/C][C]3170061[/C][C]3093614.58080497[/C][C]76446.4191950339[/C][/ROW]
[ROW][C]26[/C][C]3368934[/C][C]3345538.62315988[/C][C]23395.3768401214[/C][/ROW]
[ROW][C]27[/C][C]3292638[/C][C]3260907.05254073[/C][C]31730.9474592696[/C][/ROW]
[ROW][C]28[/C][C]3337344[/C][C]3291883.79699114[/C][C]45460.20300886[/C][/ROW]
[ROW][C]29[/C][C]3208306[/C][C]3209120.16590773[/C][C]-814.165907725628[/C][/ROW]
[ROW][C]30[/C][C]3359130[/C][C]3322255.18533499[/C][C]36874.8146650111[/C][/ROW]
[ROW][C]31[/C][C]3223078[/C][C]3200025.65915506[/C][C]23052.3408449381[/C][/ROW]
[ROW][C]32[/C][C]3437159[/C][C]3384300.14225018[/C][C]52858.8577498242[/C][/ROW]
[ROW][C]33[/C][C]3400156[/C][C]3436491.52727069[/C][C]-36335.5272706878[/C][/ROW]
[ROW][C]34[/C][C]3657576[/C][C]3687341.7418581[/C][C]-29765.7418580974[/C][/ROW]
[ROW][C]35[/C][C]3765613[/C][C]3821688.90348735[/C][C]-56075.9034873535[/C][/ROW]
[ROW][C]36[/C][C]3481921[/C][C]3450163.21653309[/C][C]31757.7834669084[/C][/ROW]
[ROW][C]37[/C][C]3604800[/C][C]3660219.85664837[/C][C]-55419.8566483681[/C][/ROW]
[ROW][C]38[/C][C]3981340[/C][C]4005195.69972738[/C][C]-23855.6997273794[/C][/ROW]
[ROW][C]39[/C][C]3734078[/C][C]3703001.98123778[/C][C]31076.0187622243[/C][/ROW]
[ROW][C]40[/C][C]4018173[/C][C]3817320.53007629[/C][C]200852.469923708[/C][/ROW]
[ROW][C]41[/C][C]3887417[/C][C]3867440.74549708[/C][C]19976.254502919[/C][/ROW]
[ROW][C]42[/C][C]3919880[/C][C]3968798.67765042[/C][C]-48918.6776504217[/C][/ROW]
[ROW][C]43[/C][C]4014466[/C][C]4031824.21587291[/C][C]-17358.2158729076[/C][/ROW]
[ROW][C]44[/C][C]4197758[/C][C]4276993.30275568[/C][C]-79235.3027556767[/C][/ROW]
[ROW][C]45[/C][C]3896531[/C][C]3873710.40753649[/C][C]22820.592463514[/C][/ROW]
[ROW][C]46[/C][C]3964742[/C][C]3944658.97728175[/C][C]20083.0227182488[/C][/ROW]
[ROW][C]47[/C][C]4201847[/C][C]4159281.08977374[/C][C]42565.910226257[/C][/ROW]
[ROW][C]48[/C][C]4050512[/C][C]4048181.93297534[/C][C]2330.06702466075[/C][/ROW]
[ROW][C]49[/C][C]3997402[/C][C]4047875.24731591[/C][C]-50473.2473159098[/C][/ROW]
[ROW][C]50[/C][C]4314479[/C][C]4334490.77265[/C][C]-20011.7726500012[/C][/ROW]
[ROW][C]51[/C][C]4925744[/C][C]4882892.50906241[/C][C]42851.4909375848[/C][/ROW]
[ROW][C]52[/C][C]5130631[/C][C]5206285.79802924[/C][C]-75654.7980292363[/C][/ROW]
[ROW][C]53[/C][C]4444855[/C][C]4492443.05539082[/C][C]-47588.0553908171[/C][/ROW]
[ROW][C]54[/C][C]3967319[/C][C]3962364.50352514[/C][C]4954.49647485798[/C][/ROW]
[ROW][C]55[/C][C]3931250[/C][C]3781522.42652021[/C][C]149727.573479787[/C][/ROW]
[ROW][C]56[/C][C]4235952[/C][C]4005926.21408119[/C][C]230025.785918808[/C][/ROW]
[ROW][C]57[/C][C]4169219[/C][C]4039532.74384327[/C][C]129686.256156732[/C][/ROW]
[ROW][C]58[/C][C]3779064[/C][C]3739276.42708049[/C][C]39787.5729195068[/C][/ROW]
[ROW][C]59[/C][C]3558810[/C][C]3572356.73317604[/C][C]-13546.73317604[/C][/ROW]
[ROW][C]60[/C][C]3699466[/C][C]3716115.94126037[/C][C]-16649.9412603746[/C][/ROW]
[ROW][C]61[/C][C]3650693[/C][C]3648918.36775024[/C][C]1774.63224975604[/C][/ROW]
[ROW][C]62[/C][C]3525633[/C][C]3414877.1479799[/C][C]110755.852020097[/C][/ROW]
[ROW][C]63[/C][C]3470276[/C][C]3494085.50896928[/C][C]-23809.5089692759[/C][/ROW]
[ROW][C]64[/C][C]3859094[/C][C]3871641.3462539[/C][C]-12547.3462538957[/C][/ROW]
[ROW][C]65[/C][C]3661155[/C][C]3622775.35625788[/C][C]38379.643742117[/C][/ROW]
[ROW][C]66[/C][C]3356365[/C][C]3469911.26889763[/C][C]-113546.268897626[/C][/ROW]
[ROW][C]67[/C][C]3344440[/C][C]3336259.33829795[/C][C]8180.66170204631[/C][/ROW]
[ROW][C]68[/C][C]3338684[/C][C]3303009.33004313[/C][C]35674.6699568722[/C][/ROW]
[ROW][C]69[/C][C]3404294[/C][C]3437697.87524738[/C][C]-33403.8752473775[/C][/ROW]
[ROW][C]70[/C][C]3289319[/C][C]3398394.70802604[/C][C]-109075.708026036[/C][/ROW]
[ROW][C]71[/C][C]3469252[/C][C]3464581.63256526[/C][C]4670.36743474396[/C][/ROW]
[ROW][C]72[/C][C]3571850[/C][C]3667529.60456483[/C][C]-95679.6045648337[/C][/ROW]
[ROW][C]73[/C][C]3639914[/C][C]3654692.17272236[/C][C]-14778.1727223614[/C][/ROW]
[ROW][C]74[/C][C]3091730[/C][C]3134513.12917884[/C][C]-42783.129178839[/C][/ROW]
[ROW][C]75[/C][C]3078149[/C][C]3043141.784834[/C][C]35007.2151660018[/C][/ROW]
[ROW][C]76[/C][C]3188115[/C][C]3162456.16001599[/C][C]25658.8399840114[/C][/ROW]
[ROW][C]77[/C][C]3246082[/C][C]3195353.372259[/C][C]50728.6277409976[/C][/ROW]
[ROW][C]78[/C][C]3486992[/C][C]3445689.55717457[/C][C]41302.4428254263[/C][/ROW]
[ROW][C]79[/C][C]3378187[/C][C]3443021.1639049[/C][C]-64834.163904905[/C][/ROW]
[ROW][C]80[/C][C]3282306[/C][C]3415375.50600249[/C][C]-133069.506002486[/C][/ROW]
[ROW][C]81[/C][C]3288345[/C][C]3312430.66347193[/C][C]-24085.6634719332[/C][/ROW]
[ROW][C]82[/C][C]3325749[/C][C]3304258.15520541[/C][C]21490.8447945891[/C][/ROW]
[ROW][C]83[/C][C]3352262[/C][C]3356770.83937857[/C][C]-4508.83937856802[/C][/ROW]
[ROW][C]84[/C][C]3531954[/C][C]3520623.81718443[/C][C]11330.1828155706[/C][/ROW]
[ROW][C]85[/C][C]3722622[/C][C]3757371.28090424[/C][C]-34749.2809042356[/C][/ROW]
[ROW][C]86[/C][C]3809365[/C][C]3788964.57687779[/C][C]20400.4231222108[/C][/ROW]
[ROW][C]87[/C][C]3750617[/C][C]3720617.59485076[/C][C]29999.4051492424[/C][/ROW]
[ROW][C]88[/C][C]3615286[/C][C]3770452.73294468[/C][C]-155166.732944676[/C][/ROW]
[ROW][C]89[/C][C]3696556[/C][C]3590510.66885286[/C][C]106045.331147143[/C][/ROW]
[ROW][C]90[/C][C]4123959[/C][C]4119384.99564101[/C][C]4574.00435898745[/C][/ROW]
[ROW][C]91[/C][C]4136163[/C][C]4168983.64473975[/C][C]-32820.6447397481[/C][/ROW]
[ROW][C]92[/C][C]3933392[/C][C]4038065.72205085[/C][C]-104673.722050851[/C][/ROW]
[ROW][C]93[/C][C]4035576[/C][C]4033757.15272226[/C][C]1818.84727774007[/C][/ROW]
[ROW][C]94[/C][C]4551202[/C][C]4460587.13828792[/C][C]90614.8617120806[/C][/ROW]
[ROW][C]95[/C][C]4032195[/C][C]4029000.06714377[/C][C]3194.93285622798[/C][/ROW]
[ROW][C]96[/C][C]3970893[/C][C]4171157.16276288[/C][C]-200264.162762878[/C][/ROW]
[ROW][C]97[/C][C]4489016[/C][C]4539209.46250283[/C][C]-50193.4625028313[/C][/ROW]
[ROW][C]98[/C][C]5426127[/C][C]5245679.89846552[/C][C]180447.101534484[/C][/ROW]
[ROW][C]99[/C][C]4578224[/C][C]4535964.64454532[/C][C]42259.3554546838[/C][/ROW]
[ROW][C]100[/C][C]4126390[/C][C]4144168.51062067[/C][C]-17778.5106206727[/C][/ROW]
[ROW][C]101[/C][C]4892100[/C][C]4861422.30161227[/C][C]30677.6983877324[/C][/ROW]
[ROW][C]102[/C][C]4128697[/C][C]4302069.43982018[/C][C]-173372.439820176[/C][/ROW]
[ROW][C]103[/C][C]4408721[/C][C]4425466.37907936[/C][C]-16745.3790793632[/C][/ROW]
[ROW][C]104[/C][C]4199465[/C][C]4302885.52343391[/C][C]-103420.523433905[/C][/ROW]
[ROW][C]105[/C][C]4074767[/C][C]4039317.5264939[/C][C]35449.4735061013[/C][/ROW]
[ROW][C]106[/C][C]4161758[/C][C]3990439.74000173[/C][C]171318.259998275[/C][/ROW]
[ROW][C]107[/C][C]3891319[/C][C]3945865.67600021[/C][C]-54546.6760002137[/C][/ROW]
[ROW][C]108[/C][C]4470302[/C][C]4320682.90105457[/C][C]149619.098945432[/C][/ROW]
[ROW][C]109[/C][C]4283111[/C][C]4216326.24622457[/C][C]66784.7537754254[/C][/ROW]
[ROW][C]110[/C][C]3845962[/C][C]3810208.0043028[/C][C]35753.9956971974[/C][/ROW]
[ROW][C]111[/C][C]3911471[/C][C]3756875.34526086[/C][C]154595.654739142[/C][/ROW]
[ROW][C]112[/C][C]3798478[/C][C]3723250.76923724[/C][C]75227.2307627549[/C][/ROW]
[ROW][C]113[/C][C]3644313[/C][C]3495624.93467084[/C][C]148688.065329161[/C][/ROW]
[ROW][C]114[/C][C]3784029[/C][C]3646392.47877127[/C][C]137636.521228729[/C][/ROW]
[ROW][C]115[/C][C]3647134[/C][C]3704810.36867151[/C][C]-57676.3686715103[/C][/ROW]
[ROW][C]116[/C][C]3994662[/C][C]3973068.35385534[/C][C]21593.6461446559[/C][/ROW]
[ROW][C]117[/C][C]3607836[/C][C]3579048.4079114[/C][C]28787.5920886007[/C][/ROW]
[ROW][C]118[/C][C]3566008[/C][C]3545757.35666201[/C][C]20250.643337987[/C][/ROW]
[ROW][C]119[/C][C]3511412[/C][C]3676168.98055588[/C][C]-164756.980555881[/C][/ROW]
[ROW][C]120[/C][C]3258665[/C][C]3338041.35708402[/C][C]-79376.3570840216[/C][/ROW]
[ROW][C]121[/C][C]3486573[/C][C]3573963.58844791[/C][C]-87390.5884479143[/C][/ROW]
[ROW][C]122[/C][C]3369443[/C][C]3338801.17931469[/C][C]30641.8206853076[/C][/ROW]
[ROW][C]123[/C][C]3465544[/C][C]3611115.43628158[/C][C]-145571.436281584[/C][/ROW]
[ROW][C]124[/C][C]3905224[/C][C]4124394.2464775[/C][C]-219170.246477503[/C][/ROW]
[ROW][C]125[/C][C]3733881[/C][C]3790509.14108255[/C][C]-56628.1410825452[/C][/ROW]
[ROW][C]126[/C][C]3220642[/C][C]3222435.35872203[/C][C]-1793.35872203038[/C][/ROW]
[ROW][C]127[/C][C]3225812[/C][C]3222724.10520866[/C][C]3087.89479133894[/C][/ROW]
[ROW][C]128[/C][C]3354461[/C][C]3323169.47346735[/C][C]31291.5265326542[/C][/ROW]
[ROW][C]129[/C][C]3352261[/C][C]3293506.68466999[/C][C]58754.3153300119[/C][/ROW]
[ROW][C]130[/C][C]3450652[/C][C]3464591.717204[/C][C]-13939.7172039986[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=164290&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=164290&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
142648304308556.89842545-43726.8984254533
239246743940796.2542104-16122.254210404
337347533668906.8909925765846.1090074346
437622903665188.9320188197101.0679811904
536097393579948.031109629790.9688903978
638775943753634.90914976123959.090850236
736364153595323.8851503441091.1148496619
835781953578826.81624041-631.816240413998
936043423577372.1232904126969.8767095918
1034595133557689.23709058-98176.2370905763
1133665713458625.11347171-92054.1134717084
1233712773545652.6865105-174375.686510498
1337248483744260.79085802-19412.7908580234
1433508303416236.53463446-65406.5346344632
1533051593367782.92425066-62623.9242506625
1633907363400634.14132398-9898.14132398053
1733497583382265.87391311-32507.8739131132
1832536553369652.992721-115997.992720999
1937342503813685.38865331-79435.3886533136
2034554333452435.382309582997.61769041555
2129667262986757.215026-20031.2150260029
2229937162946314.4201242447401.5798757576
2330093202987939.6213132421380.378686763
2431697133104770.4712704864942.5287295249
2531700613093614.5808049776446.4191950339
2633689343345538.6231598823395.3768401214
2732926383260907.0525407331730.9474592696
2833373443291883.7969911445460.20300886
2932083063209120.16590773-814.165907725628
3033591303322255.1853349936874.8146650111
3132230783200025.6591550623052.3408449381
3234371593384300.1422501852858.8577498242
3334001563436491.52727069-36335.5272706878
3436575763687341.7418581-29765.7418580974
3537656133821688.90348735-56075.9034873535
3634819213450163.2165330931757.7834669084
3736048003660219.85664837-55419.8566483681
3839813404005195.69972738-23855.6997273794
3937340783703001.9812377831076.0187622243
4040181733817320.53007629200852.469923708
4138874173867440.7454970819976.254502919
4239198803968798.67765042-48918.6776504217
4340144664031824.21587291-17358.2158729076
4441977584276993.30275568-79235.3027556767
4538965313873710.4075364922820.592463514
4639647423944658.9772817520083.0227182488
4742018474159281.0897737442565.910226257
4840505124048181.932975342330.06702466075
4939974024047875.24731591-50473.2473159098
5043144794334490.77265-20011.7726500012
5149257444882892.5090624142851.4909375848
5251306315206285.79802924-75654.7980292363
5344448554492443.05539082-47588.0553908171
5439673193962364.503525144954.49647485798
5539312503781522.42652021149727.573479787
5642359524005926.21408119230025.785918808
5741692194039532.74384327129686.256156732
5837790643739276.4270804939787.5729195068
5935588103572356.73317604-13546.73317604
6036994663716115.94126037-16649.9412603746
6136506933648918.367750241774.63224975604
6235256333414877.1479799110755.852020097
6334702763494085.50896928-23809.5089692759
6438590943871641.3462539-12547.3462538957
6536611553622775.3562578838379.643742117
6633563653469911.26889763-113546.268897626
6733444403336259.338297958180.66170204631
6833386843303009.3300431335674.6699568722
6934042943437697.87524738-33403.8752473775
7032893193398394.70802604-109075.708026036
7134692523464581.632565264670.36743474396
7235718503667529.60456483-95679.6045648337
7336399143654692.17272236-14778.1727223614
7430917303134513.12917884-42783.129178839
7530781493043141.78483435007.2151660018
7631881153162456.1600159925658.8399840114
7732460823195353.37225950728.6277409976
7834869923445689.5571745741302.4428254263
7933781873443021.1639049-64834.163904905
8032823063415375.50600249-133069.506002486
8132883453312430.66347193-24085.6634719332
8233257493304258.1552054121490.8447945891
8333522623356770.83937857-4508.83937856802
8435319543520623.8171844311330.1828155706
8537226223757371.28090424-34749.2809042356
8638093653788964.5768777920400.4231222108
8737506173720617.5948507629999.4051492424
8836152863770452.73294468-155166.732944676
8936965563590510.66885286106045.331147143
9041239594119384.995641014574.00435898745
9141361634168983.64473975-32820.6447397481
9239333924038065.72205085-104673.722050851
9340355764033757.152722261818.84727774007
9445512024460587.1382879290614.8617120806
9540321954029000.067143773194.93285622798
9639708934171157.16276288-200264.162762878
9744890164539209.46250283-50193.4625028313
9854261275245679.89846552180447.101534484
9945782244535964.6445453242259.3554546838
10041263904144168.51062067-17778.5106206727
10148921004861422.3016122730677.6983877324
10241286974302069.43982018-173372.439820176
10344087214425466.37907936-16745.3790793632
10441994654302885.52343391-103420.523433905
10540747674039317.526493935449.4735061013
10641617583990439.74000173171318.259998275
10738913193945865.67600021-54546.6760002137
10844703024320682.90105457149619.098945432
10942831114216326.2462245766784.7537754254
11038459623810208.004302835753.9956971974
11139114713756875.34526086154595.654739142
11237984783723250.7692372475227.2307627549
11336443133495624.93467084148688.065329161
11437840293646392.47877127137636.521228729
11536471343704810.36867151-57676.3686715103
11639946623973068.3538553421593.6461446559
11736078363579048.407911428787.5920886007
11835660083545757.3566620120250.643337987
11935114123676168.98055588-164756.980555881
12032586653338041.35708402-79376.3570840216
12134865733573963.58844791-87390.5884479143
12233694433338801.1793146930641.8206853076
12334655443611115.43628158-145571.436281584
12439052244124394.2464775-219170.246477503
12537338813790509.14108255-56628.1410825452
12632206423222435.35872203-1793.35872203038
12732258123222724.105208663087.89479133894
12833544613323169.4734673531291.5265326542
12933522613293506.6846699958754.3153300119
13034506523464591.717204-13939.7172039986






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
220.5579379527171740.8841240945656520.442062047282826
230.3886303095670750.7772606191341510.611369690432925
240.2956902999144490.5913805998288980.704309700085551
250.1871071052184760.3742142104369520.812892894781524
260.1480023239231370.2960046478462740.851997676076863
270.0873832003159190.1747664006318380.912616799684081
280.06491372413634690.1298274482726940.935086275863653
290.05476459715511540.1095291943102310.945235402844885
300.03866738335915870.07733476671831750.961332616640841
310.02172226948638050.04344453897276090.97827773051362
320.0143266246146230.02865324922924610.985673375385377
330.0076087847844920.0152175695689840.992391215215508
340.006757347178772280.01351469435754460.993242652821228
350.006057469640534680.01211493928106940.993942530359465
360.003377856403736530.006755712807473060.996622143596263
370.001805710727593210.003611421455186430.998194289272407
380.001114762366748250.00222952473349650.998885237633252
390.0005959340841906660.001191868168381330.999404065915809
400.004795300567191610.009590601134383210.995204699432808
410.002974346536577320.005948693073154650.997025653463423
420.002464434535236020.004928869070472030.997535565464764
430.001430952260642390.002861904521284780.998569047739358
440.001276913354378180.002553826708756360.998723086645622
450.001366436649324930.002732873298649850.998633563350675
460.0009061809115058390.001812361823011680.999093819088494
470.0005330457982232160.001066091596446430.999466954201777
480.000283449970277820.0005668999405556410.999716550029722
490.0003034775785738490.0006069551571476980.999696522421426
500.0001712720545033430.0003425441090066870.999828727945497
510.0002219744912226070.0004439489824452140.999778025508777
520.0001929866243134410.0003859732486268830.999807013375687
530.0001141387478587490.0002282774957174980.999885861252141
546.00197057381973e-050.0001200394114763950.999939980294262
550.001104345997901990.002208691995803980.998895654002098
560.01788836541458570.03577673082917140.982111634585414
570.02692432458550290.05384864917100580.973075675414497
580.02019935624144340.04039871248288680.979800643758557
590.0150489807575670.0300979615151340.984951019242433
600.01036585576730950.02073171153461890.989634144232691
610.007885683392861930.01577136678572390.992114316607138
620.01950763234106030.03901526468212060.98049236765894
630.01339183782826010.02678367565652020.98660816217174
640.009068019347757730.01813603869551550.990931980652242
650.006798572540360590.01359714508072120.993201427459639
660.01537431965383630.03074863930767260.984625680346164
670.01100815749946130.02201631499892260.988991842500539
680.008093174145924980.016186348291850.991906825854075
690.007451204386740410.01490240877348080.99254879561326
700.0115110172161090.0230220344322180.988488982783891
710.01190368399985590.02380736799971180.988096316000144
720.02477381427380360.04954762854760720.975226185726196
730.02016621809711310.04033243619422630.979833781902887
740.01557886203806250.03115772407612510.984421137961937
750.01187146253423940.02374292506847880.988128537465761
760.008162467819553890.01632493563910780.991837532180446
770.00639217535891520.01278435071783040.993607824641085
780.004664568567086160.009329137134172330.995335431432914
790.003990024797810620.007980049595621250.996009975202189
800.006215919867977770.01243183973595550.993784080132022
810.004263742076016670.008527484152033340.995736257923983
820.002776196023453570.005552392046907140.997223803976546
830.001732473245247070.003464946490494150.998267526754753
840.001074289567122080.002148579134244150.998925710432878
850.0008239748952714840.001647949790542970.999176025104728
860.0005281933120885090.001056386624177020.999471806687911
870.0003534893032391710.0007069786064783430.999646510696761
880.0007714363220231890.001542872644046380.999228563677977
890.001377258050428710.002754516100857430.998622741949571
900.0007991447709367470.001598289541873490.999200855229063
910.0005167850630804010.00103357012616080.99948321493692
920.001752928724865950.00350585744973190.998247071275134
930.00384127714568960.007682554291379210.99615872285431
940.00261446823666250.0052289364733250.997385531763338
950.01069082356476960.02138164712953920.98930917643523
960.01223138809103120.02446277618206230.987768611908969
970.02357807403624140.04715614807248280.976421925963759
980.04742531855201930.09485063710403870.952574681447981
990.03293912095058120.06587824190116230.967060879049419
1000.03634775026150450.0726955005230090.963652249738496
1010.2463897621845030.4927795243690050.753610237815497
1020.6013367394812190.7973265210375610.398663260518781
1030.508431187570560.9831376248588790.49156881242944
1040.4122023306816670.8244046613633340.587797669318333
1050.3239273804216150.647854760843230.676072619578385
1060.2720201635617990.5440403271235980.727979836438201
1070.3500014834025370.7000029668050730.649998516597463
1080.2872297180331780.5744594360663560.712770281966822

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
22 & 0.557937952717174 & 0.884124094565652 & 0.442062047282826 \tabularnewline
23 & 0.388630309567075 & 0.777260619134151 & 0.611369690432925 \tabularnewline
24 & 0.295690299914449 & 0.591380599828898 & 0.704309700085551 \tabularnewline
25 & 0.187107105218476 & 0.374214210436952 & 0.812892894781524 \tabularnewline
26 & 0.148002323923137 & 0.296004647846274 & 0.851997676076863 \tabularnewline
27 & 0.087383200315919 & 0.174766400631838 & 0.912616799684081 \tabularnewline
28 & 0.0649137241363469 & 0.129827448272694 & 0.935086275863653 \tabularnewline
29 & 0.0547645971551154 & 0.109529194310231 & 0.945235402844885 \tabularnewline
30 & 0.0386673833591587 & 0.0773347667183175 & 0.961332616640841 \tabularnewline
31 & 0.0217222694863805 & 0.0434445389727609 & 0.97827773051362 \tabularnewline
32 & 0.014326624614623 & 0.0286532492292461 & 0.985673375385377 \tabularnewline
33 & 0.007608784784492 & 0.015217569568984 & 0.992391215215508 \tabularnewline
34 & 0.00675734717877228 & 0.0135146943575446 & 0.993242652821228 \tabularnewline
35 & 0.00605746964053468 & 0.0121149392810694 & 0.993942530359465 \tabularnewline
36 & 0.00337785640373653 & 0.00675571280747306 & 0.996622143596263 \tabularnewline
37 & 0.00180571072759321 & 0.00361142145518643 & 0.998194289272407 \tabularnewline
38 & 0.00111476236674825 & 0.0022295247334965 & 0.998885237633252 \tabularnewline
39 & 0.000595934084190666 & 0.00119186816838133 & 0.999404065915809 \tabularnewline
40 & 0.00479530056719161 & 0.00959060113438321 & 0.995204699432808 \tabularnewline
41 & 0.00297434653657732 & 0.00594869307315465 & 0.997025653463423 \tabularnewline
42 & 0.00246443453523602 & 0.00492886907047203 & 0.997535565464764 \tabularnewline
43 & 0.00143095226064239 & 0.00286190452128478 & 0.998569047739358 \tabularnewline
44 & 0.00127691335437818 & 0.00255382670875636 & 0.998723086645622 \tabularnewline
45 & 0.00136643664932493 & 0.00273287329864985 & 0.998633563350675 \tabularnewline
46 & 0.000906180911505839 & 0.00181236182301168 & 0.999093819088494 \tabularnewline
47 & 0.000533045798223216 & 0.00106609159644643 & 0.999466954201777 \tabularnewline
48 & 0.00028344997027782 & 0.000566899940555641 & 0.999716550029722 \tabularnewline
49 & 0.000303477578573849 & 0.000606955157147698 & 0.999696522421426 \tabularnewline
50 & 0.000171272054503343 & 0.000342544109006687 & 0.999828727945497 \tabularnewline
51 & 0.000221974491222607 & 0.000443948982445214 & 0.999778025508777 \tabularnewline
52 & 0.000192986624313441 & 0.000385973248626883 & 0.999807013375687 \tabularnewline
53 & 0.000114138747858749 & 0.000228277495717498 & 0.999885861252141 \tabularnewline
54 & 6.00197057381973e-05 & 0.000120039411476395 & 0.999939980294262 \tabularnewline
55 & 0.00110434599790199 & 0.00220869199580398 & 0.998895654002098 \tabularnewline
56 & 0.0178883654145857 & 0.0357767308291714 & 0.982111634585414 \tabularnewline
57 & 0.0269243245855029 & 0.0538486491710058 & 0.973075675414497 \tabularnewline
58 & 0.0201993562414434 & 0.0403987124828868 & 0.979800643758557 \tabularnewline
59 & 0.015048980757567 & 0.030097961515134 & 0.984951019242433 \tabularnewline
60 & 0.0103658557673095 & 0.0207317115346189 & 0.989634144232691 \tabularnewline
61 & 0.00788568339286193 & 0.0157713667857239 & 0.992114316607138 \tabularnewline
62 & 0.0195076323410603 & 0.0390152646821206 & 0.98049236765894 \tabularnewline
63 & 0.0133918378282601 & 0.0267836756565202 & 0.98660816217174 \tabularnewline
64 & 0.00906801934775773 & 0.0181360386955155 & 0.990931980652242 \tabularnewline
65 & 0.00679857254036059 & 0.0135971450807212 & 0.993201427459639 \tabularnewline
66 & 0.0153743196538363 & 0.0307486393076726 & 0.984625680346164 \tabularnewline
67 & 0.0110081574994613 & 0.0220163149989226 & 0.988991842500539 \tabularnewline
68 & 0.00809317414592498 & 0.01618634829185 & 0.991906825854075 \tabularnewline
69 & 0.00745120438674041 & 0.0149024087734808 & 0.99254879561326 \tabularnewline
70 & 0.011511017216109 & 0.023022034432218 & 0.988488982783891 \tabularnewline
71 & 0.0119036839998559 & 0.0238073679997118 & 0.988096316000144 \tabularnewline
72 & 0.0247738142738036 & 0.0495476285476072 & 0.975226185726196 \tabularnewline
73 & 0.0201662180971131 & 0.0403324361942263 & 0.979833781902887 \tabularnewline
74 & 0.0155788620380625 & 0.0311577240761251 & 0.984421137961937 \tabularnewline
75 & 0.0118714625342394 & 0.0237429250684788 & 0.988128537465761 \tabularnewline
76 & 0.00816246781955389 & 0.0163249356391078 & 0.991837532180446 \tabularnewline
77 & 0.0063921753589152 & 0.0127843507178304 & 0.993607824641085 \tabularnewline
78 & 0.00466456856708616 & 0.00932913713417233 & 0.995335431432914 \tabularnewline
79 & 0.00399002479781062 & 0.00798004959562125 & 0.996009975202189 \tabularnewline
80 & 0.00621591986797777 & 0.0124318397359555 & 0.993784080132022 \tabularnewline
81 & 0.00426374207601667 & 0.00852748415203334 & 0.995736257923983 \tabularnewline
82 & 0.00277619602345357 & 0.00555239204690714 & 0.997223803976546 \tabularnewline
83 & 0.00173247324524707 & 0.00346494649049415 & 0.998267526754753 \tabularnewline
84 & 0.00107428956712208 & 0.00214857913424415 & 0.998925710432878 \tabularnewline
85 & 0.000823974895271484 & 0.00164794979054297 & 0.999176025104728 \tabularnewline
86 & 0.000528193312088509 & 0.00105638662417702 & 0.999471806687911 \tabularnewline
87 & 0.000353489303239171 & 0.000706978606478343 & 0.999646510696761 \tabularnewline
88 & 0.000771436322023189 & 0.00154287264404638 & 0.999228563677977 \tabularnewline
89 & 0.00137725805042871 & 0.00275451610085743 & 0.998622741949571 \tabularnewline
90 & 0.000799144770936747 & 0.00159828954187349 & 0.999200855229063 \tabularnewline
91 & 0.000516785063080401 & 0.0010335701261608 & 0.99948321493692 \tabularnewline
92 & 0.00175292872486595 & 0.0035058574497319 & 0.998247071275134 \tabularnewline
93 & 0.0038412771456896 & 0.00768255429137921 & 0.99615872285431 \tabularnewline
94 & 0.0026144682366625 & 0.005228936473325 & 0.997385531763338 \tabularnewline
95 & 0.0106908235647696 & 0.0213816471295392 & 0.98930917643523 \tabularnewline
96 & 0.0122313880910312 & 0.0244627761820623 & 0.987768611908969 \tabularnewline
97 & 0.0235780740362414 & 0.0471561480724828 & 0.976421925963759 \tabularnewline
98 & 0.0474253185520193 & 0.0948506371040387 & 0.952574681447981 \tabularnewline
99 & 0.0329391209505812 & 0.0658782419011623 & 0.967060879049419 \tabularnewline
100 & 0.0363477502615045 & 0.072695500523009 & 0.963652249738496 \tabularnewline
101 & 0.246389762184503 & 0.492779524369005 & 0.753610237815497 \tabularnewline
102 & 0.601336739481219 & 0.797326521037561 & 0.398663260518781 \tabularnewline
103 & 0.50843118757056 & 0.983137624858879 & 0.49156881242944 \tabularnewline
104 & 0.412202330681667 & 0.824404661363334 & 0.587797669318333 \tabularnewline
105 & 0.323927380421615 & 0.64785476084323 & 0.676072619578385 \tabularnewline
106 & 0.272020163561799 & 0.544040327123598 & 0.727979836438201 \tabularnewline
107 & 0.350001483402537 & 0.700002966805073 & 0.649998516597463 \tabularnewline
108 & 0.287229718033178 & 0.574459436066356 & 0.712770281966822 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=164290&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]22[/C][C]0.557937952717174[/C][C]0.884124094565652[/C][C]0.442062047282826[/C][/ROW]
[ROW][C]23[/C][C]0.388630309567075[/C][C]0.777260619134151[/C][C]0.611369690432925[/C][/ROW]
[ROW][C]24[/C][C]0.295690299914449[/C][C]0.591380599828898[/C][C]0.704309700085551[/C][/ROW]
[ROW][C]25[/C][C]0.187107105218476[/C][C]0.374214210436952[/C][C]0.812892894781524[/C][/ROW]
[ROW][C]26[/C][C]0.148002323923137[/C][C]0.296004647846274[/C][C]0.851997676076863[/C][/ROW]
[ROW][C]27[/C][C]0.087383200315919[/C][C]0.174766400631838[/C][C]0.912616799684081[/C][/ROW]
[ROW][C]28[/C][C]0.0649137241363469[/C][C]0.129827448272694[/C][C]0.935086275863653[/C][/ROW]
[ROW][C]29[/C][C]0.0547645971551154[/C][C]0.109529194310231[/C][C]0.945235402844885[/C][/ROW]
[ROW][C]30[/C][C]0.0386673833591587[/C][C]0.0773347667183175[/C][C]0.961332616640841[/C][/ROW]
[ROW][C]31[/C][C]0.0217222694863805[/C][C]0.0434445389727609[/C][C]0.97827773051362[/C][/ROW]
[ROW][C]32[/C][C]0.014326624614623[/C][C]0.0286532492292461[/C][C]0.985673375385377[/C][/ROW]
[ROW][C]33[/C][C]0.007608784784492[/C][C]0.015217569568984[/C][C]0.992391215215508[/C][/ROW]
[ROW][C]34[/C][C]0.00675734717877228[/C][C]0.0135146943575446[/C][C]0.993242652821228[/C][/ROW]
[ROW][C]35[/C][C]0.00605746964053468[/C][C]0.0121149392810694[/C][C]0.993942530359465[/C][/ROW]
[ROW][C]36[/C][C]0.00337785640373653[/C][C]0.00675571280747306[/C][C]0.996622143596263[/C][/ROW]
[ROW][C]37[/C][C]0.00180571072759321[/C][C]0.00361142145518643[/C][C]0.998194289272407[/C][/ROW]
[ROW][C]38[/C][C]0.00111476236674825[/C][C]0.0022295247334965[/C][C]0.998885237633252[/C][/ROW]
[ROW][C]39[/C][C]0.000595934084190666[/C][C]0.00119186816838133[/C][C]0.999404065915809[/C][/ROW]
[ROW][C]40[/C][C]0.00479530056719161[/C][C]0.00959060113438321[/C][C]0.995204699432808[/C][/ROW]
[ROW][C]41[/C][C]0.00297434653657732[/C][C]0.00594869307315465[/C][C]0.997025653463423[/C][/ROW]
[ROW][C]42[/C][C]0.00246443453523602[/C][C]0.00492886907047203[/C][C]0.997535565464764[/C][/ROW]
[ROW][C]43[/C][C]0.00143095226064239[/C][C]0.00286190452128478[/C][C]0.998569047739358[/C][/ROW]
[ROW][C]44[/C][C]0.00127691335437818[/C][C]0.00255382670875636[/C][C]0.998723086645622[/C][/ROW]
[ROW][C]45[/C][C]0.00136643664932493[/C][C]0.00273287329864985[/C][C]0.998633563350675[/C][/ROW]
[ROW][C]46[/C][C]0.000906180911505839[/C][C]0.00181236182301168[/C][C]0.999093819088494[/C][/ROW]
[ROW][C]47[/C][C]0.000533045798223216[/C][C]0.00106609159644643[/C][C]0.999466954201777[/C][/ROW]
[ROW][C]48[/C][C]0.00028344997027782[/C][C]0.000566899940555641[/C][C]0.999716550029722[/C][/ROW]
[ROW][C]49[/C][C]0.000303477578573849[/C][C]0.000606955157147698[/C][C]0.999696522421426[/C][/ROW]
[ROW][C]50[/C][C]0.000171272054503343[/C][C]0.000342544109006687[/C][C]0.999828727945497[/C][/ROW]
[ROW][C]51[/C][C]0.000221974491222607[/C][C]0.000443948982445214[/C][C]0.999778025508777[/C][/ROW]
[ROW][C]52[/C][C]0.000192986624313441[/C][C]0.000385973248626883[/C][C]0.999807013375687[/C][/ROW]
[ROW][C]53[/C][C]0.000114138747858749[/C][C]0.000228277495717498[/C][C]0.999885861252141[/C][/ROW]
[ROW][C]54[/C][C]6.00197057381973e-05[/C][C]0.000120039411476395[/C][C]0.999939980294262[/C][/ROW]
[ROW][C]55[/C][C]0.00110434599790199[/C][C]0.00220869199580398[/C][C]0.998895654002098[/C][/ROW]
[ROW][C]56[/C][C]0.0178883654145857[/C][C]0.0357767308291714[/C][C]0.982111634585414[/C][/ROW]
[ROW][C]57[/C][C]0.0269243245855029[/C][C]0.0538486491710058[/C][C]0.973075675414497[/C][/ROW]
[ROW][C]58[/C][C]0.0201993562414434[/C][C]0.0403987124828868[/C][C]0.979800643758557[/C][/ROW]
[ROW][C]59[/C][C]0.015048980757567[/C][C]0.030097961515134[/C][C]0.984951019242433[/C][/ROW]
[ROW][C]60[/C][C]0.0103658557673095[/C][C]0.0207317115346189[/C][C]0.989634144232691[/C][/ROW]
[ROW][C]61[/C][C]0.00788568339286193[/C][C]0.0157713667857239[/C][C]0.992114316607138[/C][/ROW]
[ROW][C]62[/C][C]0.0195076323410603[/C][C]0.0390152646821206[/C][C]0.98049236765894[/C][/ROW]
[ROW][C]63[/C][C]0.0133918378282601[/C][C]0.0267836756565202[/C][C]0.98660816217174[/C][/ROW]
[ROW][C]64[/C][C]0.00906801934775773[/C][C]0.0181360386955155[/C][C]0.990931980652242[/C][/ROW]
[ROW][C]65[/C][C]0.00679857254036059[/C][C]0.0135971450807212[/C][C]0.993201427459639[/C][/ROW]
[ROW][C]66[/C][C]0.0153743196538363[/C][C]0.0307486393076726[/C][C]0.984625680346164[/C][/ROW]
[ROW][C]67[/C][C]0.0110081574994613[/C][C]0.0220163149989226[/C][C]0.988991842500539[/C][/ROW]
[ROW][C]68[/C][C]0.00809317414592498[/C][C]0.01618634829185[/C][C]0.991906825854075[/C][/ROW]
[ROW][C]69[/C][C]0.00745120438674041[/C][C]0.0149024087734808[/C][C]0.99254879561326[/C][/ROW]
[ROW][C]70[/C][C]0.011511017216109[/C][C]0.023022034432218[/C][C]0.988488982783891[/C][/ROW]
[ROW][C]71[/C][C]0.0119036839998559[/C][C]0.0238073679997118[/C][C]0.988096316000144[/C][/ROW]
[ROW][C]72[/C][C]0.0247738142738036[/C][C]0.0495476285476072[/C][C]0.975226185726196[/C][/ROW]
[ROW][C]73[/C][C]0.0201662180971131[/C][C]0.0403324361942263[/C][C]0.979833781902887[/C][/ROW]
[ROW][C]74[/C][C]0.0155788620380625[/C][C]0.0311577240761251[/C][C]0.984421137961937[/C][/ROW]
[ROW][C]75[/C][C]0.0118714625342394[/C][C]0.0237429250684788[/C][C]0.988128537465761[/C][/ROW]
[ROW][C]76[/C][C]0.00816246781955389[/C][C]0.0163249356391078[/C][C]0.991837532180446[/C][/ROW]
[ROW][C]77[/C][C]0.0063921753589152[/C][C]0.0127843507178304[/C][C]0.993607824641085[/C][/ROW]
[ROW][C]78[/C][C]0.00466456856708616[/C][C]0.00932913713417233[/C][C]0.995335431432914[/C][/ROW]
[ROW][C]79[/C][C]0.00399002479781062[/C][C]0.00798004959562125[/C][C]0.996009975202189[/C][/ROW]
[ROW][C]80[/C][C]0.00621591986797777[/C][C]0.0124318397359555[/C][C]0.993784080132022[/C][/ROW]
[ROW][C]81[/C][C]0.00426374207601667[/C][C]0.00852748415203334[/C][C]0.995736257923983[/C][/ROW]
[ROW][C]82[/C][C]0.00277619602345357[/C][C]0.00555239204690714[/C][C]0.997223803976546[/C][/ROW]
[ROW][C]83[/C][C]0.00173247324524707[/C][C]0.00346494649049415[/C][C]0.998267526754753[/C][/ROW]
[ROW][C]84[/C][C]0.00107428956712208[/C][C]0.00214857913424415[/C][C]0.998925710432878[/C][/ROW]
[ROW][C]85[/C][C]0.000823974895271484[/C][C]0.00164794979054297[/C][C]0.999176025104728[/C][/ROW]
[ROW][C]86[/C][C]0.000528193312088509[/C][C]0.00105638662417702[/C][C]0.999471806687911[/C][/ROW]
[ROW][C]87[/C][C]0.000353489303239171[/C][C]0.000706978606478343[/C][C]0.999646510696761[/C][/ROW]
[ROW][C]88[/C][C]0.000771436322023189[/C][C]0.00154287264404638[/C][C]0.999228563677977[/C][/ROW]
[ROW][C]89[/C][C]0.00137725805042871[/C][C]0.00275451610085743[/C][C]0.998622741949571[/C][/ROW]
[ROW][C]90[/C][C]0.000799144770936747[/C][C]0.00159828954187349[/C][C]0.999200855229063[/C][/ROW]
[ROW][C]91[/C][C]0.000516785063080401[/C][C]0.0010335701261608[/C][C]0.99948321493692[/C][/ROW]
[ROW][C]92[/C][C]0.00175292872486595[/C][C]0.0035058574497319[/C][C]0.998247071275134[/C][/ROW]
[ROW][C]93[/C][C]0.0038412771456896[/C][C]0.00768255429137921[/C][C]0.99615872285431[/C][/ROW]
[ROW][C]94[/C][C]0.0026144682366625[/C][C]0.005228936473325[/C][C]0.997385531763338[/C][/ROW]
[ROW][C]95[/C][C]0.0106908235647696[/C][C]0.0213816471295392[/C][C]0.98930917643523[/C][/ROW]
[ROW][C]96[/C][C]0.0122313880910312[/C][C]0.0244627761820623[/C][C]0.987768611908969[/C][/ROW]
[ROW][C]97[/C][C]0.0235780740362414[/C][C]0.0471561480724828[/C][C]0.976421925963759[/C][/ROW]
[ROW][C]98[/C][C]0.0474253185520193[/C][C]0.0948506371040387[/C][C]0.952574681447981[/C][/ROW]
[ROW][C]99[/C][C]0.0329391209505812[/C][C]0.0658782419011623[/C][C]0.967060879049419[/C][/ROW]
[ROW][C]100[/C][C]0.0363477502615045[/C][C]0.072695500523009[/C][C]0.963652249738496[/C][/ROW]
[ROW][C]101[/C][C]0.246389762184503[/C][C]0.492779524369005[/C][C]0.753610237815497[/C][/ROW]
[ROW][C]102[/C][C]0.601336739481219[/C][C]0.797326521037561[/C][C]0.398663260518781[/C][/ROW]
[ROW][C]103[/C][C]0.50843118757056[/C][C]0.983137624858879[/C][C]0.49156881242944[/C][/ROW]
[ROW][C]104[/C][C]0.412202330681667[/C][C]0.824404661363334[/C][C]0.587797669318333[/C][/ROW]
[ROW][C]105[/C][C]0.323927380421615[/C][C]0.64785476084323[/C][C]0.676072619578385[/C][/ROW]
[ROW][C]106[/C][C]0.272020163561799[/C][C]0.544040327123598[/C][C]0.727979836438201[/C][/ROW]
[ROW][C]107[/C][C]0.350001483402537[/C][C]0.700002966805073[/C][C]0.649998516597463[/C][/ROW]
[ROW][C]108[/C][C]0.287229718033178[/C][C]0.574459436066356[/C][C]0.712770281966822[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=164290&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=164290&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
220.5579379527171740.8841240945656520.442062047282826
230.3886303095670750.7772606191341510.611369690432925
240.2956902999144490.5913805998288980.704309700085551
250.1871071052184760.3742142104369520.812892894781524
260.1480023239231370.2960046478462740.851997676076863
270.0873832003159190.1747664006318380.912616799684081
280.06491372413634690.1298274482726940.935086275863653
290.05476459715511540.1095291943102310.945235402844885
300.03866738335915870.07733476671831750.961332616640841
310.02172226948638050.04344453897276090.97827773051362
320.0143266246146230.02865324922924610.985673375385377
330.0076087847844920.0152175695689840.992391215215508
340.006757347178772280.01351469435754460.993242652821228
350.006057469640534680.01211493928106940.993942530359465
360.003377856403736530.006755712807473060.996622143596263
370.001805710727593210.003611421455186430.998194289272407
380.001114762366748250.00222952473349650.998885237633252
390.0005959340841906660.001191868168381330.999404065915809
400.004795300567191610.009590601134383210.995204699432808
410.002974346536577320.005948693073154650.997025653463423
420.002464434535236020.004928869070472030.997535565464764
430.001430952260642390.002861904521284780.998569047739358
440.001276913354378180.002553826708756360.998723086645622
450.001366436649324930.002732873298649850.998633563350675
460.0009061809115058390.001812361823011680.999093819088494
470.0005330457982232160.001066091596446430.999466954201777
480.000283449970277820.0005668999405556410.999716550029722
490.0003034775785738490.0006069551571476980.999696522421426
500.0001712720545033430.0003425441090066870.999828727945497
510.0002219744912226070.0004439489824452140.999778025508777
520.0001929866243134410.0003859732486268830.999807013375687
530.0001141387478587490.0002282774957174980.999885861252141
546.00197057381973e-050.0001200394114763950.999939980294262
550.001104345997901990.002208691995803980.998895654002098
560.01788836541458570.03577673082917140.982111634585414
570.02692432458550290.05384864917100580.973075675414497
580.02019935624144340.04039871248288680.979800643758557
590.0150489807575670.0300979615151340.984951019242433
600.01036585576730950.02073171153461890.989634144232691
610.007885683392861930.01577136678572390.992114316607138
620.01950763234106030.03901526468212060.98049236765894
630.01339183782826010.02678367565652020.98660816217174
640.009068019347757730.01813603869551550.990931980652242
650.006798572540360590.01359714508072120.993201427459639
660.01537431965383630.03074863930767260.984625680346164
670.01100815749946130.02201631499892260.988991842500539
680.008093174145924980.016186348291850.991906825854075
690.007451204386740410.01490240877348080.99254879561326
700.0115110172161090.0230220344322180.988488982783891
710.01190368399985590.02380736799971180.988096316000144
720.02477381427380360.04954762854760720.975226185726196
730.02016621809711310.04033243619422630.979833781902887
740.01557886203806250.03115772407612510.984421137961937
750.01187146253423940.02374292506847880.988128537465761
760.008162467819553890.01632493563910780.991837532180446
770.00639217535891520.01278435071783040.993607824641085
780.004664568567086160.009329137134172330.995335431432914
790.003990024797810620.007980049595621250.996009975202189
800.006215919867977770.01243183973595550.993784080132022
810.004263742076016670.008527484152033340.995736257923983
820.002776196023453570.005552392046907140.997223803976546
830.001732473245247070.003464946490494150.998267526754753
840.001074289567122080.002148579134244150.998925710432878
850.0008239748952714840.001647949790542970.999176025104728
860.0005281933120885090.001056386624177020.999471806687911
870.0003534893032391710.0007069786064783430.999646510696761
880.0007714363220231890.001542872644046380.999228563677977
890.001377258050428710.002754516100857430.998622741949571
900.0007991447709367470.001598289541873490.999200855229063
910.0005167850630804010.00103357012616080.99948321493692
920.001752928724865950.00350585744973190.998247071275134
930.00384127714568960.007682554291379210.99615872285431
940.00261446823666250.0052289364733250.997385531763338
950.01069082356476960.02138164712953920.98930917643523
960.01223138809103120.02446277618206230.987768611908969
970.02357807403624140.04715614807248280.976421925963759
980.04742531855201930.09485063710403870.952574681447981
990.03293912095058120.06587824190116230.967060879049419
1000.03634775026150450.0726955005230090.963652249738496
1010.2463897621845030.4927795243690050.753610237815497
1020.6013367394812190.7973265210375610.398663260518781
1030.508431187570560.9831376248588790.49156881242944
1040.4122023306816670.8244046613633340.587797669318333
1050.3239273804216150.647854760843230.676072619578385
1060.2720201635617990.5440403271235980.727979836438201
1070.3500014834025370.7000029668050730.649998516597463
1080.2872297180331780.5744594360663560.712770281966822






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level360.413793103448276NOK
5% type I error level660.758620689655172NOK
10% type I error level710.816091954022989NOK

\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 & 36 & 0.413793103448276 & NOK \tabularnewline
5% type I error level & 66 & 0.758620689655172 & NOK \tabularnewline
10% type I error level & 71 & 0.816091954022989 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=164290&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]36[/C][C]0.413793103448276[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]66[/C][C]0.758620689655172[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]71[/C][C]0.816091954022989[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=164290&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=164290&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 level360.413793103448276NOK
5% type I error level660.758620689655172NOK
10% type I error level710.816091954022989NOK


Figures (Output of Computation)

Figure 1
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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')
}