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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, 14 Dec 2011 13:06:16 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/14/t1323886184qno0usa8pbewzxf.htm/, Retrieved Wed, 01 May 2024 23:08:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=155170, Retrieved Wed, 01 May 2024 23:08:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact65

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [] [2010-12-05 18:04:16] [b98453cac15ba1066b407e146608df68]
- RMPD    [Multiple Regression] [Multiple regression] [2011-12-14 18:06:16] [8432dc408001a08517818ba7ac24bdb0] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1418	56	396	81	3	79	30	115	94	112285	24188	146283	144	145	11
869	56	297	55	4	58	28	109	103	84786	18273	98364	103	101	15
1530	54	559	50	12	60	38	146	93	83123	14130	86146	98	98	19
3201	92	1562	63	0	121	25	96	91	119182	33251	195663	150	144	23
1583	44	656	66	5	43	26	100	93	116174	27101	95757	84	84	16
1439	33	511	57	0	69	25	93	60	57635	16373	85584	80	79	21
1764	84	655	74	0	78	38	140	123	66198	19716	143983	130	127	24
1373	55	525	52	7	44	30	99	90	57793	9028	59238	60	60	15
4041	154	1436	108	0	158	47	181	168	97668	29498	151511	140	133	17
1706	53	612	43	4	102	30	116	115	133824	27563	136368	151	150	19
2152	119	865	75	3	77	31	116	71	101481	18293	112642	91	91	19
1036	41	385	32	0	82	23	88	66	99645	22530	94728	138	132	25
1929	58	639	85	0	101	36	135	117	99052	35082	121527	124	124	19
2242	75	963	86	1	80	30	108	108	67654	16116	127766	119	118	28
1220	33	398	56	5	50	25	89	84	65553	15849	98958	73	70	24
2515	92	966	135	0	123	34	129	120	69112	26569	85646	123	119	26
2147	100	801	63	0	73	31	118	114	82753	24785	98579	90	89	15
2352	112	892	81	5	81	31	118	94	85323	17569	130767	116	112	21
1638	73	513	52	0	105	33	125	120	72654	23825	131741	113	108	26
1222	40	469	44	0	47	25	95	81	30727	7869	53907	56	52	16
1677	60	643	39	3	94	35	135	133	117478	37791	146761	119	116	16
1579	62	535	73	4	44	42	154	122	74007	9605	82036	129	123	20
2452	77	992	59	2	107	33	127	124	101494	34461	171975	175	162	24
2662	99	937	64	0	84	36	136	126	79215	24919	159676	96	92	10
186	17	70	1	0	0	0	0	0	1423	603	1929	0	0	19
865	30	260	32	1	33	14	46	37	31081	12558	58391	41	41	25
1793	76	503	129	0	42	17	54	38	22996	7784	31580	47	47	22
2527	146	927	37	2	96	32	124	120	83122	28522	136815	126	120	15
1324	56	537	65	6	56	35	128	95	60578	14459	69107	80	79	21
2702	107	910	107	0	57	20	80	77	39992	14526	50495	70	65	22
1383	58	532	74	5	59	28	97	90	79892	22240	108016	73	70	27
1179	34	345	54	4	39	28	104	80	49810	11802	46341	57	55	26
4308	119	1635	715	2	76	34	125	110	100708	11912	79336	68	67	26
1831	66	557	66	0	91	39	149	138	82875	18220	93176	127	127	22
1438	66	452	32	0	76	28	118	100	72260	21884	127969	102	99	20
496	24	218	20	0	8	4	12	7	5950	2694	15049	7	7	22
2253	259	764	71	8	79	39	144	140	115762	15808	155135	148	141	21
2352	41	866	112	1	76	29	108	96	80670	25239	102996	112	109	22
2144	68	574	66	5	101	44	166	164	143558	29801	160604	137	133	20
4691	168	1276	190	1	94	21	80	78	117105	18450	158051	135	123	21
1112	43	379	66	1	27	16	60	49	23789	7132	44547	26	26	20
1973	105	798	56	0	123	35	127	124	105195	35940	174141	181	166	25
2474	94	921	127	8	105	23	84	62	149193	46230	184301	190	179	18
1226	57	503	50	2	41	29	111	99	95260	30546	129847	107	108	22
1389	53	382	52	0	72	25	98	70	55183	19746	117286	94	90	25
1496	103	464	42	5	67	27	105	104	106671	15977	71180	116	114	21
2269	121	717	76	8	75	36	135	116	73511	22583	109377	106	103	20
1833	62	690	67	2	114	28	107	91	92945	17274	85298	143	142	20
893	32	385	39	6	22	23	88	67	22618	3007	23824	26	25	18
1403	45	619	77	2	69	28	104	72	83737	21113	82981	113	113	8
1425	46	479	57	0	105	34	132	120	69094	17401	73815	120	118	22
1840	75	752	34	3	88	28	108	105	95536	23567	132190	134	129	26
1502	88	430	39	6	73	34	129	104	225920	13065	128754	54	51	18
1420	53	537	63	0	62	33	122	98	61370	14587	67808	78	76	20
2970	90	1000	106	0	118	38	147	111	106117	24021	131722	121	118	24
1644	78	465	47	2	100	35	87	71	84651	20537	106175	145	141	17
1654	45	711	162	0	24	24	90	69	15986	4527	25157	27	27	20
1054	46	299	57	5	67	29	109	107	95364	30495	76669	91	91	23
937	41	248	36	0	46	20	78	73	26706	7117	57283	48	48	20
3004	144	1162	263	1	57	29	111	107	89691	17719	105805	68	63	22
2547	91	905	63	1	135	37	141	129	126846	33473	72413	150	144	20
1626	63	512	63	2	124	33	124	118	102860	21115	96971	181	168	19
1468	53	472	77	6	33	25	93	73	51715	7236	71299	65	64	15
2445	62	905	79	1	98	32	124	119	55801	13790	77494	97	97	20
1964	63	786	110	4	58	29	112	104	111813	32902	120336	121	117	22
1381	32	489	56	2	68	28	108	107	120293	25131	93913	99	100	13
1659	62	617	43	0	131	31	117	90	161647	35947	181248	188	187	20
2888	117	925	111	10	110	52	199	197	115929	29848	146123	138	127	17
1290	34	351	71	0	37	21	78	36	24266	6943	32036	40	37	14
2845	92	1144	62	9	130	24	91	85	162901	42705	186646	254	245	22
1982	93	669	56	7	93	41	158	139	109825	31808	102255	87	87	24
1904	54	707	74	0	118	33	126	106	129838	26675	168237	178	177	22
1391	144	458	60	0	39	32	122	50	37510	8435	64219	51	49	23
1559	109	572	53	0	81	31	115	63	87771	36867	115338	176	177	17
2146	75	720	105	1	51	18	72	63	44418	12607	84845	66	60	23
874	50	273	32	0	28	23	91	69	192565	22609	153197	56	55	25
1590	61	508	133	1	40	17	45	41	35232	5892	29877	39	39	16
1590	55	506	79	0	56	20	78	56	40909	17014	63506	66	64	18
1210	77	451	51	0	27	12	39	25	13294	5394	22445	27	26	20
1281	72	407	67	0	83	30	119	93	140867	6440	68370	58	58	18
1105	53	370	66	3	28	13	50	44	44332	5818	42071	26	26	24
1272	42	316	76	0	59	22	88	87	61056	18647	50517	77	76	23
1944	71	603	65	0	133	42	155	110	101338	20556	103950	130	129	24
391	10	154	9	0	12	1	0	0	1168	238	5841	11	11	23
1605	65	577	45	0	106	32	123	83	65567	22392	84396	101	101	23
1988	66	617	115	0	44	25	99	80	32334	12237	35753	36	36	13
1386	41	411	97	0	71	36	136	98	40735	8388	55515	120	89	20
2395	86	975	53	1	116	31	117	82	91413	22120	209056	195	193	18
387	16	146	2	0	4	0	0	0	855	338	6622	4	4	21
1742	42	705	52	5	62	24	88	60	97068	11727	115814	89	84	17
620	19	184	44	0	12	13	39	28	44339	3704	11609	24	23	20
449	19	200	22	0	18	8	25	9	14116	3988	13155	39	39	19
800	45	274	35	0	14	13	52	33	10288	3030	18274	14	14	18
1684	65	502	74	0	60	19	75	59	65622	13520	72875	78	78	19
2699	95	964	144	2	98	33	124	115	76643	20923	142775	106	101	22
1204	64	369	89	8	32	38	145	120	92696	3769	20112	37	36	22
1138	38	417	42	2	25	24	87	66	94785	12252	61023	77	75	15
2158	65	822	99	0	100	43	162	152	93815	28864	132432	132	131	17
1111	52	389	52	0	46	43	165	139	86687	21721	112494	144	131	19
1421	62	466	29	1	45	14	54	38	34553	4821	45109	40	39	20
2833	65	1255	125	3	129	41	159	144	105547	33644	170875	153	144	22
2922	95	1024	95	3	136	45	170	160	213688	42935	214921	220	211	21
1002	29	400	40	0	59	31	119	114	71220	18864	100226	79	78	19
2186	247	719	128	4	63	31	120	119	91721	17939	78876	95	90	21
1035	29	356	73	11	14	30	112	101	111194	325	6940	12	12	18
1417	118	457	72	0	36	16	59	56	51009	13539	49025	63	57	16
3261	110	1402	128	0	113	37	136	133	135777	34538	122037	134	133	20
1587	67	600	61	4	47	30	107	83	51513	12198	53782	69	69	21
1424	42	480	73	0	92	35	130	116	74163	26924	127748	119	119	15
946	64	230	45	0	50	20	75	50	33416	10855	77395	63	61	20
1926	81	651	58	0	41	18	71	61	83305	11932	89324	55	49	23
3352	95	1367	97	9	91	31	120	97	98952	14300	103300	103	101	15
1641	67	564	50	1	111	31	116	98	102372	25515	112283	197	196	18
2035	63	716	37	3	41	21	79	78	37238	2805	10901	16	15	22
2312	83	747	50	10	120	39	150	117	103772	29402	120691	140	136	16
961	32	319	57	0	25	18	71	55	21399	5250	25899	21	21	17
1900	83	612	52	1	131	39	144	132	130115	28608	139296	167	163	24
1254	31	433	98	2	45	14	47	44	24874	8092	52678	32	29	13
1335	67	434	61	4	29	7	28	21	34988	4473	23853	36	35	23
1597	66	503	89	0	58	17	68	50	45549	1572	17306	13	13	5
1645	70	564	48	2	47	30	110	73	64466	14817	89455	96	96	19
2429	103	824	91	1	109	37	147	86	54990	16714	147866	151	151	24
872	34	239	70	0	37	32	111	48	34777	1669	14336	23	23	19
1018	40	459	37	0	15	17	68	48	27114	7768	30059	21	14	20
1314	31	288	247	6	7	24	80	68	37636	6387	22097	20	20	22
1335	42	498	46	0	54	22	88	87	65461	18715	96841	82	72	15
1403	46	454	72	2	54	12	48	43	30080	7936	41907	90	87	19
910	33	376	41	0	14	19	76	67	24094	8643	27080	25	21	25
616	18	225	24	2	16	13	51	46	69008	7294	35885	60	56	21
771	35	252	33	1	32	15	59	56	46090	7185	28313	85	82	19
1376	66	481	87	0	38	15	60	60	34029	8509	36134	41	38	17
1232	60	389	90	1	22	17	68	65	46300	13275	55764	26	25	15
1544	54	609	69	0	32	16	61	60	40662	10737	66956	49	47	21
1230	53	422	51	0	32	18	67	54	28987	8033	47487	46	45	24
1255	39	339	45	0	37	17	64	52	30594	5401	35619	41	41	22
721	45	245	25	0	32	16	64	61	27913	10856	45608	23	23	19
1109	36	384	38	7	0	23	91	61	42744	2154	7721	14	14	20
740	28	212	52	2	5	22	88	81	12934	6117	20634	16	16	21
728	30	229	74	7	10	13	49	40	41385	4820	31931	21	21	19
689	22	224	38	3	27	16	62	40	18653	5615	37754	32	27	22
995	31	333	26	0	29	20	76	68	30976	8702	40557	35	33	14
1613	55	384	67	6	25	22	88	79	63339	15340	94238	42	42	25
2048	54	636	132	2	55	17	66	47	25568	8030	44197	68	68	11
301	14	93	35	0	5	17	68	41	4154	1278	4103	6	6	16
1803	81	581	118	3	43	12	48	29	19474	4236	44144	68	67	19
861	43	304	43	1	34	17	68	60	39067	7196	27640	84	77	17
1451	30	407	64	0	35	23	90	79	65892	6371	28990	30	30	20
628	23	170	48	1	0	17	66	47	4143	1574	4694	0	0	22
1161	38	312	64	0	37	14	54	40	28579	9620	42648	36	36	20
979	53	340	75	0	26	21	77	42	38084	8645	25836	50	48	22
675	45	168	39	0	38	18	68	49	27717	8987	22779	30	29	15
1241	39	443	42	0	23	18	72	57	32928	5544	40820	30	28	23
1049	24	367	93	0	30	17	64	40	19499	6909	32378	33	33	20
1081	35	335	60	0	18	15	59	33	36874	6745	39613	37	33	17
1688	151	364	71	0	28	21	84	77	48259	16724	60865	83	80	20
617	30	206	27	2	21	14	56	45	28207	7025	20107	30	30	25
1656	57	490	79	1	50	15	58	45	45833	9078	48231	51	51	22
705	40	238	44	0	12	15	59	50	29156	4605	39725	19	18	16
1597	77	530	124	0	27	22	83	71	45588	9653	62991	41	39	25
982	35	291	81	0	41	21	81	67	45097	8914	49363	54	54	18
1212	63	397	92	1	12	18	72	62	28394	6700	24552	25	24	19
1143	44	467	42	0	21	17	61	54	18632	5788	31493	25	24	25
435	19	178	10	0	8	4	15	4	2325	593	3439	8	8	23
532	13	175	24	0	26	10	32	25	25139	4506	19555	26	26	24
882	42	299	64	0	27	16	62	40	27975	6382	21228	20	19	21
830	42	260	48	0	37	18	72	59	21792	6928	28893	46	47	21
652	49	227	49	0	29	12	41	24	26263	1514	21425	47	47	22
707	30	239	48	0	32	16	61	58	23686	9238	50276	37	37	21
954	49	333	62	0	35	21	67	42	49303	8204	37643	51	51	18
285	12	75	19	1	10	2	8	4	5752	2416	9927	10	10	13
733	20	261	45	0	17	17	66	63	20055	5432	27184	34	34	22
642	27	238	36	0	10	16	61	54	20154	5576	18475	12	11	23
894	14	329	44	0	17	16	64	39	19540	6095	35873	27	21	15

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'AstonUniversity' @ aston.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 & 9 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155170&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155170&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155170&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'AstonUniversity' @ aston.wessa.net






Multiple Linear Regression - Estimated Regression Equation
I1[t] = + 19.9178742685728 -0.00190990678327006pageviews[t] + 0.00798187645663885logins[t] + 0.00240263112631556compendium_views_info[t] + 0.00957174513234818compendium_views_pr[t] -0.0539187838154629shared_compendiums[t] + 0.00744555764255144blogged_computations[t] + 0.000129572863549165compendiums_reviewed[t] -0.0194555295390768feedback_messages_p1[t] + 0.0194498283309025feedback_messages_p120[t] -7.58925008073996e-06totsize[t] -1.5915369884817e-05totrevisions[t] + 1.8169927990216e-05totseconds[t] + 0.047285835542468tothyperlinks[t] -0.0519368652722589totblogs[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
I1[t] =  +  19.9178742685728 -0.00190990678327006pageviews[t] +  0.00798187645663885logins[t] +  0.00240263112631556compendium_views_info[t] +  0.00957174513234818compendium_views_pr[t] -0.0539187838154629shared_compendiums[t] +  0.00744555764255144blogged_computations[t] +  0.000129572863549165compendiums_reviewed[t] -0.0194555295390768feedback_messages_p1[t] +  0.0194498283309025feedback_messages_p120[t] -7.58925008073996e-06totsize[t] -1.5915369884817e-05totrevisions[t] +  1.8169927990216e-05totseconds[t] +  0.047285835542468tothyperlinks[t] -0.0519368652722589totblogs[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155170&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]I1[t] =  +  19.9178742685728 -0.00190990678327006pageviews[t] +  0.00798187645663885logins[t] +  0.00240263112631556compendium_views_info[t] +  0.00957174513234818compendium_views_pr[t] -0.0539187838154629shared_compendiums[t] +  0.00744555764255144blogged_computations[t] +  0.000129572863549165compendiums_reviewed[t] -0.0194555295390768feedback_messages_p1[t] +  0.0194498283309025feedback_messages_p120[t] -7.58925008073996e-06totsize[t] -1.5915369884817e-05totrevisions[t] +  1.8169927990216e-05totseconds[t] +  0.047285835542468tothyperlinks[t] -0.0519368652722589totblogs[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155170&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155170&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
I1[t] = + 19.9178742685728 -0.00190990678327006pageviews[t] + 0.00798187645663885logins[t] + 0.00240263112631556compendium_views_info[t] + 0.00957174513234818compendium_views_pr[t] -0.0539187838154629shared_compendiums[t] + 0.00744555764255144blogged_computations[t] + 0.000129572863549165compendiums_reviewed[t] -0.0194555295390768feedback_messages_p1[t] + 0.0194498283309025feedback_messages_p120[t] -7.58925008073996e-06totsize[t] -1.5915369884817e-05totrevisions[t] + 1.8169927990216e-05totseconds[t] + 0.047285835542468tothyperlinks[t] -0.0519368652722589totblogs[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)19.91787426857280.89289222.307200
pageviews-0.001909906783270060.001717-1.11250.2675960.133798
logins0.007981876456638850.0119180.66970.504010.252005
compendium_views_info0.002402631126315560.0036880.65140.5157150.257857
compendium_views_pr0.009571745132348180.0068291.40160.1629890.081494
shared_compendiums-0.05391878381546290.130537-0.41310.6801270.340064
blogged_computations0.007445557642551440.0240260.30990.7570470.378523
compendiums_reviewed0.0001295728635491650.2205126e-040.9995320.499766
feedback_messages_p1-0.01945552953907680.06188-0.31440.7536260.376813
feedback_messages_p1200.01944982833090250.0258070.75370.4521790.22609
totsize-7.58925008073996e-061.4e-05-0.5590.576950.288475
totrevisions-1.5915369884817e-057.1e-05-0.22530.8220450.411022
totseconds1.8169927990216e-051.6e-051.14790.2527460.126373
tothyperlinks0.0472858355424680.0863980.54730.5849440.292472
totblogs-0.05193686527225890.090682-0.57270.5676360.283818

\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) & 19.9178742685728 & 0.892892 & 22.3072 & 0 & 0 \tabularnewline
pageviews & -0.00190990678327006 & 0.001717 & -1.1125 & 0.267596 & 0.133798 \tabularnewline
logins & 0.00798187645663885 & 0.011918 & 0.6697 & 0.50401 & 0.252005 \tabularnewline
compendium_views_info & 0.00240263112631556 & 0.003688 & 0.6514 & 0.515715 & 0.257857 \tabularnewline
compendium_views_pr & 0.00957174513234818 & 0.006829 & 1.4016 & 0.162989 & 0.081494 \tabularnewline
shared_compendiums & -0.0539187838154629 & 0.130537 & -0.4131 & 0.680127 & 0.340064 \tabularnewline
blogged_computations & 0.00744555764255144 & 0.024026 & 0.3099 & 0.757047 & 0.378523 \tabularnewline
compendiums_reviewed & 0.000129572863549165 & 0.220512 & 6e-04 & 0.999532 & 0.499766 \tabularnewline
feedback_messages_p1 & -0.0194555295390768 & 0.06188 & -0.3144 & 0.753626 & 0.376813 \tabularnewline
feedback_messages_p120 & 0.0194498283309025 & 0.025807 & 0.7537 & 0.452179 & 0.22609 \tabularnewline
totsize & -7.58925008073996e-06 & 1.4e-05 & -0.559 & 0.57695 & 0.288475 \tabularnewline
totrevisions & -1.5915369884817e-05 & 7.1e-05 & -0.2253 & 0.822045 & 0.411022 \tabularnewline
totseconds & 1.8169927990216e-05 & 1.6e-05 & 1.1479 & 0.252746 & 0.126373 \tabularnewline
tothyperlinks & 0.047285835542468 & 0.086398 & 0.5473 & 0.584944 & 0.292472 \tabularnewline
totblogs & -0.0519368652722589 & 0.090682 & -0.5727 & 0.567636 & 0.283818 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155170&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]19.9178742685728[/C][C]0.892892[/C][C]22.3072[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]pageviews[/C][C]-0.00190990678327006[/C][C]0.001717[/C][C]-1.1125[/C][C]0.267596[/C][C]0.133798[/C][/ROW]
[ROW][C]logins[/C][C]0.00798187645663885[/C][C]0.011918[/C][C]0.6697[/C][C]0.50401[/C][C]0.252005[/C][/ROW]
[ROW][C]compendium_views_info[/C][C]0.00240263112631556[/C][C]0.003688[/C][C]0.6514[/C][C]0.515715[/C][C]0.257857[/C][/ROW]
[ROW][C]compendium_views_pr[/C][C]0.00957174513234818[/C][C]0.006829[/C][C]1.4016[/C][C]0.162989[/C][C]0.081494[/C][/ROW]
[ROW][C]shared_compendiums[/C][C]-0.0539187838154629[/C][C]0.130537[/C][C]-0.4131[/C][C]0.680127[/C][C]0.340064[/C][/ROW]
[ROW][C]blogged_computations[/C][C]0.00744555764255144[/C][C]0.024026[/C][C]0.3099[/C][C]0.757047[/C][C]0.378523[/C][/ROW]
[ROW][C]compendiums_reviewed[/C][C]0.000129572863549165[/C][C]0.220512[/C][C]6e-04[/C][C]0.999532[/C][C]0.499766[/C][/ROW]
[ROW][C]feedback_messages_p1[/C][C]-0.0194555295390768[/C][C]0.06188[/C][C]-0.3144[/C][C]0.753626[/C][C]0.376813[/C][/ROW]
[ROW][C]feedback_messages_p120[/C][C]0.0194498283309025[/C][C]0.025807[/C][C]0.7537[/C][C]0.452179[/C][C]0.22609[/C][/ROW]
[ROW][C]totsize[/C][C]-7.58925008073996e-06[/C][C]1.4e-05[/C][C]-0.559[/C][C]0.57695[/C][C]0.288475[/C][/ROW]
[ROW][C]totrevisions[/C][C]-1.5915369884817e-05[/C][C]7.1e-05[/C][C]-0.2253[/C][C]0.822045[/C][C]0.411022[/C][/ROW]
[ROW][C]totseconds[/C][C]1.8169927990216e-05[/C][C]1.6e-05[/C][C]1.1479[/C][C]0.252746[/C][C]0.126373[/C][/ROW]
[ROW][C]tothyperlinks[/C][C]0.047285835542468[/C][C]0.086398[/C][C]0.5473[/C][C]0.584944[/C][C]0.292472[/C][/ROW]
[ROW][C]totblogs[/C][C]-0.0519368652722589[/C][C]0.090682[/C][C]-0.5727[/C][C]0.567636[/C][C]0.283818[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155170&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155170&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)19.91787426857280.89289222.307200
pageviews-0.001909906783270060.001717-1.11250.2675960.133798
logins0.007981876456638850.0119180.66970.504010.252005
compendium_views_info0.002402631126315560.0036880.65140.5157150.257857
compendium_views_pr0.009571745132348180.0068291.40160.1629890.081494
shared_compendiums-0.05391878381546290.130537-0.41310.6801270.340064
blogged_computations0.007445557642551440.0240260.30990.7570470.378523
compendiums_reviewed0.0001295728635491650.2205126e-040.9995320.499766
feedback_messages_p1-0.01945552953907680.06188-0.31440.7536260.376813
feedback_messages_p1200.01944982833090250.0258070.75370.4521790.22609
totsize-7.58925008073996e-061.4e-05-0.5590.576950.288475
totrevisions-1.5915369884817e-057.1e-05-0.22530.8220450.411022
totseconds1.8169927990216e-051.6e-051.14790.2527460.126373
tothyperlinks0.0472858355424680.0863980.54730.5849440.292472
totblogs-0.05193686527225890.090682-0.57270.5676360.283818






Multiple Linear Regression - Regression Statistics
Multiple R0.187684450932498
R-squared0.0352254531218332
Adjusted R-squared-0.0502608991331943
F-TEST (value)0.412059377814447
F-TEST (DF numerator)14
F-TEST (DF denominator)158
p-value0.969454732372412
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.83746938599091
Sum Squared Residuals2326.73506356996

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.187684450932498 \tabularnewline
R-squared & 0.0352254531218332 \tabularnewline
Adjusted R-squared & -0.0502608991331943 \tabularnewline
F-TEST (value) & 0.412059377814447 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 158 \tabularnewline
p-value & 0.969454732372412 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.83746938599091 \tabularnewline
Sum Squared Residuals & 2326.73506356996 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155170&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.187684450932498[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0352254531218332[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0502608991331943[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.412059377814447[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]158[/C][/ROW]
[ROW][C]p-value[/C][C]0.969454732372412[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.83746938599091[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2326.73506356996[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155170&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155170&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 R0.187684450932498
R-squared0.0352254531218332
Adjusted R-squared-0.0502608991331943
F-TEST (value)0.412059377814447
F-TEST (DF numerator)14
F-TEST (DF denominator)158
p-value0.969454732372412
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.83746938599091
Sum Squared Residuals2326.73506356996






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11120.1037391852822-9.10373918528224
21520.5254533827416-5.52545338274164
31918.27509511089890.724904889101137
42321.43632577602891.56367422397108
51619.4069859463457-3.40698594634569
62119.61778338472821.38221661527177
72421.1066977302022.89330226979797
81519.4871338305997-4.48713383059967
91720.0963444452249-3.09634444522495
101919.8652847735212-0.865284773521232
111919.6555684837824-0.655568483782406
122519.95907049642485.04092950357518
131919.7726276630971-0.77262766309714
142820.96643325476447.03356674523564
152420.21622591894513.78377408105491
162620.45083250303415.5491674969659
171520.0141118032726-5.01411180327259
182120.22534665187650.774653348123453
192620.98777150526395.01222849473609
201620.0999185126619-4.09991851266191
211620.3910020476635-4.39100204766352
222019.36295362459990.637046375400121
232421.09886933210582.9011306678942
241020.5873555334737-10.5873555334737
251919.8907327508096-0.890732750809556
262519.88905040932715.1109495906729
272219.60352393120622.39647606879384
281520.4976017670557-5.49760176705569
292119.44958037923051.55041962076945
302219.50702744336172.49297255663828
312720.58120130093686.41879869906322
322619.00912472077286.9908752792272
332623.80522318779782.1947768122022
342219.56882430111922.43117569888084
352020.4191399551553-0.41913995515532
362219.99294921742212.00705078257793
372121.6445067113253-0.644506711325309
382219.67984434368812.3201556563119
392019.74983772315120.250162276848813
402119.47792555014331.52207444985671
412020.0089451640567-0.00894516405672422
422522.03330415339872.96669584660126
431820.4648497366594-2.46484973665936
442220.28617410033751.71382589966253
452520.26632095593144.73367904406862
462119.40618658532391.59381341467609
472019.49452024802850.505479751971496
482019.60018382088260.399816179117422
491819.3447595225216-1.34475952252163
50819.61892924892-11.61892924892
512219.89756013296912.10243986703086
522620.51082501470185.48917498529816
531819.2170743840842-1.2170743840842
542019.79573810851350.204261891486488
552419.36228748924284.63771251075724
561719.7904780586159-2.79047805861592
572020.2879624047917-0.287962404791652
582319.49021603732123.50978396267879
592020.1450075223051-0.14500752230508
602221.83812597515330.16187402484667
612018.71322198117291.28677801882708
621920.3292883566626-1.3292883566626
631519.4816160158661-4.48161601586612
642019.56946348529730.430536514702684
652220.13396825111421.86603174888576
661319.5097410473831-6.50974104738313
672020.2637131801119-0.263713180111902
681720.0970192898562-3.09701928985617
691418.9664649903372-4.96646499033725
702219.69057803629082.30942196370917
712419.08160274252564.91839725747436
722220.4835314204531.51646857954701
732319.59334070348633.40665929651373
741719.259318885605-2.25931888560498
752320.31399711287182.68600288712816
762520.14676758037294.85323241962707
771620.0299610155703-4.02996101557033
781819.6527791519478-1.65277915194785
792019.87074627523160.129253724768414
801819.581599445403-1.58159944540303
812419.89705311104264.10294688895737
822319.58413735985293.41586264014707
832419.20208522460694.79791477539307
842319.83886376533483.16113623466525
852319.41275751929683.5872424807032
861319.233735396462-6.23373539646199
872020.9253480308683-0.925348030868254
881820.9619796663117-2.96197966631168
892119.79600917242241.20399082757757
901719.7944465093235-2.79444650932348
912019.38128586527730.61871413472268
921919.61383812472-0.613838124720045
931819.6191654161523-1.61916541615229
941919.520569293207-0.520569293206989
952221.11235587554680.887644124453185
962218.6742059589543.32579404104598
971519.0642766282597-4.06427662825974
981720.4653025841061-3.46530258410606
991920.5306624876252-1.53066248762522
1002019.41398359674880.58601640325124
1012221.27321108254860.726788917451351
1022120.1704640134840.829535986515984
1031920.5897922101609-1.58979221016086
1042121.173702778741-0.173702778740952
1051818.2483363395415-0.248336339541471
1061620.4587820256295-4.4587820256295
1072020.0143359651774-0.0143359651774448
1082119.18905370015891.8109462998411
1091520.5782462058439-5.57824620584388
1102020.2842289889181-0.284228988918142
1112319.97459751654063.02540248345943
1121518.7584742416779-3.75847424167787
1131819.5708214560064-1.57082145600638
1142218.58318262356483.41681737643517
1151618.6487058572661-2.64870585726615
1161719.653725198154-2.65372519815404
1172420.1312134271563.86878657284404
1181320.5656396692852-7.56563966928519
1192319.37558236769873.62441763230125
120519.4218099189055-14.4218099189055
1211919.1288821530024-0.128882153002393
1222419.82787114036684.17212885963324
1231918.68474659848150.315253401518512
1242019.95697911097350.0430208890264686
1252220.13093547099711.86906452900295
1261520.8280794465755-5.82807944657546
1271919.7277322438962-0.727732243896201
1282519.93341125185035.06658874814967
1292119.51170311721891.48829688278114
1301919.5844070164052-0.584407016405203
1311720.3175785716296-3.31757857162958
1321520.2747904676679-5.27479046766785
1332120.35726726133080.642732738669235
1342419.83414523106174.16585476893835
1352219.25973849075922.74026150924079
1361920.2466752197217-1.24667521972174
1372018.13150588079451.86849411920549
1382119.63570279468791.36429720531215
1391919.6405299634299-0.64052996342988
1402219.85847438949362.14152561050636
1411419.6808027385779-5.68080273857789
1422519.32215892891125.67784107108885
1431119.3278420017823-8.32784200178232
1441619.5218713000008-3.52187130000079
1451919.7589256285396-0.758925628539616
1461719.8681688594697-2.86816885946974
1472018.81137948228951.1886205177105
1482219.37708454854932.62291545145066
1492019.60816557172420.391834428275824
1502219.43515612293762.56484387706241
1511519.6530568907396-4.65305689073963
1522319.57467990414183.42532009585815
1532019.81317358876570.186826411234337
1541719.5099781109431-2.50997811094315
1552019.77120507183250.228794928167474
1562519.46829939098075.53170060901932
1572219.35745041765752.64254958234252
1581620.1903587330668-4.19035873306676
1592520.47077168278374.52922831721632
1601819.9930625277743-1.99306252777432
1611919.842869071505-0.842869071504979
1622519.90652388391995.09347611608012
1632319.60635070890683.39364929109321
1642419.68618492884844.31381507115165
1652119.70515355656421.29484644343576
1662119.76003087411741.23996912588258
1672219.91201494926492.08798505073507
1682120.43697085895470.56302914104525
1691819.5991729572313-1.59917295723133
1701319.8261025379118-6.82610253791176
1712219.90255844645372.09744155354627
1722319.85378206062673.1462179393733
1731519.7683784509445-4.76837845094449

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 11 & 20.1037391852822 & -9.10373918528224 \tabularnewline
2 & 15 & 20.5254533827416 & -5.52545338274164 \tabularnewline
3 & 19 & 18.2750951108989 & 0.724904889101137 \tabularnewline
4 & 23 & 21.4363257760289 & 1.56367422397108 \tabularnewline
5 & 16 & 19.4069859463457 & -3.40698594634569 \tabularnewline
6 & 21 & 19.6177833847282 & 1.38221661527177 \tabularnewline
7 & 24 & 21.106697730202 & 2.89330226979797 \tabularnewline
8 & 15 & 19.4871338305997 & -4.48713383059967 \tabularnewline
9 & 17 & 20.0963444452249 & -3.09634444522495 \tabularnewline
10 & 19 & 19.8652847735212 & -0.865284773521232 \tabularnewline
11 & 19 & 19.6555684837824 & -0.655568483782406 \tabularnewline
12 & 25 & 19.9590704964248 & 5.04092950357518 \tabularnewline
13 & 19 & 19.7726276630971 & -0.77262766309714 \tabularnewline
14 & 28 & 20.9664332547644 & 7.03356674523564 \tabularnewline
15 & 24 & 20.2162259189451 & 3.78377408105491 \tabularnewline
16 & 26 & 20.4508325030341 & 5.5491674969659 \tabularnewline
17 & 15 & 20.0141118032726 & -5.01411180327259 \tabularnewline
18 & 21 & 20.2253466518765 & 0.774653348123453 \tabularnewline
19 & 26 & 20.9877715052639 & 5.01222849473609 \tabularnewline
20 & 16 & 20.0999185126619 & -4.09991851266191 \tabularnewline
21 & 16 & 20.3910020476635 & -4.39100204766352 \tabularnewline
22 & 20 & 19.3629536245999 & 0.637046375400121 \tabularnewline
23 & 24 & 21.0988693321058 & 2.9011306678942 \tabularnewline
24 & 10 & 20.5873555334737 & -10.5873555334737 \tabularnewline
25 & 19 & 19.8907327508096 & -0.890732750809556 \tabularnewline
26 & 25 & 19.8890504093271 & 5.1109495906729 \tabularnewline
27 & 22 & 19.6035239312062 & 2.39647606879384 \tabularnewline
28 & 15 & 20.4976017670557 & -5.49760176705569 \tabularnewline
29 & 21 & 19.4495803792305 & 1.55041962076945 \tabularnewline
30 & 22 & 19.5070274433617 & 2.49297255663828 \tabularnewline
31 & 27 & 20.5812013009368 & 6.41879869906322 \tabularnewline
32 & 26 & 19.0091247207728 & 6.9908752792272 \tabularnewline
33 & 26 & 23.8052231877978 & 2.1947768122022 \tabularnewline
34 & 22 & 19.5688243011192 & 2.43117569888084 \tabularnewline
35 & 20 & 20.4191399551553 & -0.41913995515532 \tabularnewline
36 & 22 & 19.9929492174221 & 2.00705078257793 \tabularnewline
37 & 21 & 21.6445067113253 & -0.644506711325309 \tabularnewline
38 & 22 & 19.6798443436881 & 2.3201556563119 \tabularnewline
39 & 20 & 19.7498377231512 & 0.250162276848813 \tabularnewline
40 & 21 & 19.4779255501433 & 1.52207444985671 \tabularnewline
41 & 20 & 20.0089451640567 & -0.00894516405672422 \tabularnewline
42 & 25 & 22.0333041533987 & 2.96669584660126 \tabularnewline
43 & 18 & 20.4648497366594 & -2.46484973665936 \tabularnewline
44 & 22 & 20.2861741003375 & 1.71382589966253 \tabularnewline
45 & 25 & 20.2663209559314 & 4.73367904406862 \tabularnewline
46 & 21 & 19.4061865853239 & 1.59381341467609 \tabularnewline
47 & 20 & 19.4945202480285 & 0.505479751971496 \tabularnewline
48 & 20 & 19.6001838208826 & 0.399816179117422 \tabularnewline
49 & 18 & 19.3447595225216 & -1.34475952252163 \tabularnewline
50 & 8 & 19.61892924892 & -11.61892924892 \tabularnewline
51 & 22 & 19.8975601329691 & 2.10243986703086 \tabularnewline
52 & 26 & 20.5108250147018 & 5.48917498529816 \tabularnewline
53 & 18 & 19.2170743840842 & -1.2170743840842 \tabularnewline
54 & 20 & 19.7957381085135 & 0.204261891486488 \tabularnewline
55 & 24 & 19.3622874892428 & 4.63771251075724 \tabularnewline
56 & 17 & 19.7904780586159 & -2.79047805861592 \tabularnewline
57 & 20 & 20.2879624047917 & -0.287962404791652 \tabularnewline
58 & 23 & 19.4902160373212 & 3.50978396267879 \tabularnewline
59 & 20 & 20.1450075223051 & -0.14500752230508 \tabularnewline
60 & 22 & 21.8381259751533 & 0.16187402484667 \tabularnewline
61 & 20 & 18.7132219811729 & 1.28677801882708 \tabularnewline
62 & 19 & 20.3292883566626 & -1.3292883566626 \tabularnewline
63 & 15 & 19.4816160158661 & -4.48161601586612 \tabularnewline
64 & 20 & 19.5694634852973 & 0.430536514702684 \tabularnewline
65 & 22 & 20.1339682511142 & 1.86603174888576 \tabularnewline
66 & 13 & 19.5097410473831 & -6.50974104738313 \tabularnewline
67 & 20 & 20.2637131801119 & -0.263713180111902 \tabularnewline
68 & 17 & 20.0970192898562 & -3.09701928985617 \tabularnewline
69 & 14 & 18.9664649903372 & -4.96646499033725 \tabularnewline
70 & 22 & 19.6905780362908 & 2.30942196370917 \tabularnewline
71 & 24 & 19.0816027425256 & 4.91839725747436 \tabularnewline
72 & 22 & 20.483531420453 & 1.51646857954701 \tabularnewline
73 & 23 & 19.5933407034863 & 3.40665929651373 \tabularnewline
74 & 17 & 19.259318885605 & -2.25931888560498 \tabularnewline
75 & 23 & 20.3139971128718 & 2.68600288712816 \tabularnewline
76 & 25 & 20.1467675803729 & 4.85323241962707 \tabularnewline
77 & 16 & 20.0299610155703 & -4.02996101557033 \tabularnewline
78 & 18 & 19.6527791519478 & -1.65277915194785 \tabularnewline
79 & 20 & 19.8707462752316 & 0.129253724768414 \tabularnewline
80 & 18 & 19.581599445403 & -1.58159944540303 \tabularnewline
81 & 24 & 19.8970531110426 & 4.10294688895737 \tabularnewline
82 & 23 & 19.5841373598529 & 3.41586264014707 \tabularnewline
83 & 24 & 19.2020852246069 & 4.79791477539307 \tabularnewline
84 & 23 & 19.8388637653348 & 3.16113623466525 \tabularnewline
85 & 23 & 19.4127575192968 & 3.5872424807032 \tabularnewline
86 & 13 & 19.233735396462 & -6.23373539646199 \tabularnewline
87 & 20 & 20.9253480308683 & -0.925348030868254 \tabularnewline
88 & 18 & 20.9619796663117 & -2.96197966631168 \tabularnewline
89 & 21 & 19.7960091724224 & 1.20399082757757 \tabularnewline
90 & 17 & 19.7944465093235 & -2.79444650932348 \tabularnewline
91 & 20 & 19.3812858652773 & 0.61871413472268 \tabularnewline
92 & 19 & 19.61383812472 & -0.613838124720045 \tabularnewline
93 & 18 & 19.6191654161523 & -1.61916541615229 \tabularnewline
94 & 19 & 19.520569293207 & -0.520569293206989 \tabularnewline
95 & 22 & 21.1123558755468 & 0.887644124453185 \tabularnewline
96 & 22 & 18.674205958954 & 3.32579404104598 \tabularnewline
97 & 15 & 19.0642766282597 & -4.06427662825974 \tabularnewline
98 & 17 & 20.4653025841061 & -3.46530258410606 \tabularnewline
99 & 19 & 20.5306624876252 & -1.53066248762522 \tabularnewline
100 & 20 & 19.4139835967488 & 0.58601640325124 \tabularnewline
101 & 22 & 21.2732110825486 & 0.726788917451351 \tabularnewline
102 & 21 & 20.170464013484 & 0.829535986515984 \tabularnewline
103 & 19 & 20.5897922101609 & -1.58979221016086 \tabularnewline
104 & 21 & 21.173702778741 & -0.173702778740952 \tabularnewline
105 & 18 & 18.2483363395415 & -0.248336339541471 \tabularnewline
106 & 16 & 20.4587820256295 & -4.4587820256295 \tabularnewline
107 & 20 & 20.0143359651774 & -0.0143359651774448 \tabularnewline
108 & 21 & 19.1890537001589 & 1.8109462998411 \tabularnewline
109 & 15 & 20.5782462058439 & -5.57824620584388 \tabularnewline
110 & 20 & 20.2842289889181 & -0.284228988918142 \tabularnewline
111 & 23 & 19.9745975165406 & 3.02540248345943 \tabularnewline
112 & 15 & 18.7584742416779 & -3.75847424167787 \tabularnewline
113 & 18 & 19.5708214560064 & -1.57082145600638 \tabularnewline
114 & 22 & 18.5831826235648 & 3.41681737643517 \tabularnewline
115 & 16 & 18.6487058572661 & -2.64870585726615 \tabularnewline
116 & 17 & 19.653725198154 & -2.65372519815404 \tabularnewline
117 & 24 & 20.131213427156 & 3.86878657284404 \tabularnewline
118 & 13 & 20.5656396692852 & -7.56563966928519 \tabularnewline
119 & 23 & 19.3755823676987 & 3.62441763230125 \tabularnewline
120 & 5 & 19.4218099189055 & -14.4218099189055 \tabularnewline
121 & 19 & 19.1288821530024 & -0.128882153002393 \tabularnewline
122 & 24 & 19.8278711403668 & 4.17212885963324 \tabularnewline
123 & 19 & 18.6847465984815 & 0.315253401518512 \tabularnewline
124 & 20 & 19.9569791109735 & 0.0430208890264686 \tabularnewline
125 & 22 & 20.1309354709971 & 1.86906452900295 \tabularnewline
126 & 15 & 20.8280794465755 & -5.82807944657546 \tabularnewline
127 & 19 & 19.7277322438962 & -0.727732243896201 \tabularnewline
128 & 25 & 19.9334112518503 & 5.06658874814967 \tabularnewline
129 & 21 & 19.5117031172189 & 1.48829688278114 \tabularnewline
130 & 19 & 19.5844070164052 & -0.584407016405203 \tabularnewline
131 & 17 & 20.3175785716296 & -3.31757857162958 \tabularnewline
132 & 15 & 20.2747904676679 & -5.27479046766785 \tabularnewline
133 & 21 & 20.3572672613308 & 0.642732738669235 \tabularnewline
134 & 24 & 19.8341452310617 & 4.16585476893835 \tabularnewline
135 & 22 & 19.2597384907592 & 2.74026150924079 \tabularnewline
136 & 19 & 20.2466752197217 & -1.24667521972174 \tabularnewline
137 & 20 & 18.1315058807945 & 1.86849411920549 \tabularnewline
138 & 21 & 19.6357027946879 & 1.36429720531215 \tabularnewline
139 & 19 & 19.6405299634299 & -0.64052996342988 \tabularnewline
140 & 22 & 19.8584743894936 & 2.14152561050636 \tabularnewline
141 & 14 & 19.6808027385779 & -5.68080273857789 \tabularnewline
142 & 25 & 19.3221589289112 & 5.67784107108885 \tabularnewline
143 & 11 & 19.3278420017823 & -8.32784200178232 \tabularnewline
144 & 16 & 19.5218713000008 & -3.52187130000079 \tabularnewline
145 & 19 & 19.7589256285396 & -0.758925628539616 \tabularnewline
146 & 17 & 19.8681688594697 & -2.86816885946974 \tabularnewline
147 & 20 & 18.8113794822895 & 1.1886205177105 \tabularnewline
148 & 22 & 19.3770845485493 & 2.62291545145066 \tabularnewline
149 & 20 & 19.6081655717242 & 0.391834428275824 \tabularnewline
150 & 22 & 19.4351561229376 & 2.56484387706241 \tabularnewline
151 & 15 & 19.6530568907396 & -4.65305689073963 \tabularnewline
152 & 23 & 19.5746799041418 & 3.42532009585815 \tabularnewline
153 & 20 & 19.8131735887657 & 0.186826411234337 \tabularnewline
154 & 17 & 19.5099781109431 & -2.50997811094315 \tabularnewline
155 & 20 & 19.7712050718325 & 0.228794928167474 \tabularnewline
156 & 25 & 19.4682993909807 & 5.53170060901932 \tabularnewline
157 & 22 & 19.3574504176575 & 2.64254958234252 \tabularnewline
158 & 16 & 20.1903587330668 & -4.19035873306676 \tabularnewline
159 & 25 & 20.4707716827837 & 4.52922831721632 \tabularnewline
160 & 18 & 19.9930625277743 & -1.99306252777432 \tabularnewline
161 & 19 & 19.842869071505 & -0.842869071504979 \tabularnewline
162 & 25 & 19.9065238839199 & 5.09347611608012 \tabularnewline
163 & 23 & 19.6063507089068 & 3.39364929109321 \tabularnewline
164 & 24 & 19.6861849288484 & 4.31381507115165 \tabularnewline
165 & 21 & 19.7051535565642 & 1.29484644343576 \tabularnewline
166 & 21 & 19.7600308741174 & 1.23996912588258 \tabularnewline
167 & 22 & 19.9120149492649 & 2.08798505073507 \tabularnewline
168 & 21 & 20.4369708589547 & 0.56302914104525 \tabularnewline
169 & 18 & 19.5991729572313 & -1.59917295723133 \tabularnewline
170 & 13 & 19.8261025379118 & -6.82610253791176 \tabularnewline
171 & 22 & 19.9025584464537 & 2.09744155354627 \tabularnewline
172 & 23 & 19.8537820606267 & 3.1462179393733 \tabularnewline
173 & 15 & 19.7683784509445 & -4.76837845094449 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155170&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]11[/C][C]20.1037391852822[/C][C]-9.10373918528224[/C][/ROW]
[ROW][C]2[/C][C]15[/C][C]20.5254533827416[/C][C]-5.52545338274164[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]18.2750951108989[/C][C]0.724904889101137[/C][/ROW]
[ROW][C]4[/C][C]23[/C][C]21.4363257760289[/C][C]1.56367422397108[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]19.4069859463457[/C][C]-3.40698594634569[/C][/ROW]
[ROW][C]6[/C][C]21[/C][C]19.6177833847282[/C][C]1.38221661527177[/C][/ROW]
[ROW][C]7[/C][C]24[/C][C]21.106697730202[/C][C]2.89330226979797[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]19.4871338305997[/C][C]-4.48713383059967[/C][/ROW]
[ROW][C]9[/C][C]17[/C][C]20.0963444452249[/C][C]-3.09634444522495[/C][/ROW]
[ROW][C]10[/C][C]19[/C][C]19.8652847735212[/C][C]-0.865284773521232[/C][/ROW]
[ROW][C]11[/C][C]19[/C][C]19.6555684837824[/C][C]-0.655568483782406[/C][/ROW]
[ROW][C]12[/C][C]25[/C][C]19.9590704964248[/C][C]5.04092950357518[/C][/ROW]
[ROW][C]13[/C][C]19[/C][C]19.7726276630971[/C][C]-0.77262766309714[/C][/ROW]
[ROW][C]14[/C][C]28[/C][C]20.9664332547644[/C][C]7.03356674523564[/C][/ROW]
[ROW][C]15[/C][C]24[/C][C]20.2162259189451[/C][C]3.78377408105491[/C][/ROW]
[ROW][C]16[/C][C]26[/C][C]20.4508325030341[/C][C]5.5491674969659[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]20.0141118032726[/C][C]-5.01411180327259[/C][/ROW]
[ROW][C]18[/C][C]21[/C][C]20.2253466518765[/C][C]0.774653348123453[/C][/ROW]
[ROW][C]19[/C][C]26[/C][C]20.9877715052639[/C][C]5.01222849473609[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]20.0999185126619[/C][C]-4.09991851266191[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]20.3910020476635[/C][C]-4.39100204766352[/C][/ROW]
[ROW][C]22[/C][C]20[/C][C]19.3629536245999[/C][C]0.637046375400121[/C][/ROW]
[ROW][C]23[/C][C]24[/C][C]21.0988693321058[/C][C]2.9011306678942[/C][/ROW]
[ROW][C]24[/C][C]10[/C][C]20.5873555334737[/C][C]-10.5873555334737[/C][/ROW]
[ROW][C]25[/C][C]19[/C][C]19.8907327508096[/C][C]-0.890732750809556[/C][/ROW]
[ROW][C]26[/C][C]25[/C][C]19.8890504093271[/C][C]5.1109495906729[/C][/ROW]
[ROW][C]27[/C][C]22[/C][C]19.6035239312062[/C][C]2.39647606879384[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]20.4976017670557[/C][C]-5.49760176705569[/C][/ROW]
[ROW][C]29[/C][C]21[/C][C]19.4495803792305[/C][C]1.55041962076945[/C][/ROW]
[ROW][C]30[/C][C]22[/C][C]19.5070274433617[/C][C]2.49297255663828[/C][/ROW]
[ROW][C]31[/C][C]27[/C][C]20.5812013009368[/C][C]6.41879869906322[/C][/ROW]
[ROW][C]32[/C][C]26[/C][C]19.0091247207728[/C][C]6.9908752792272[/C][/ROW]
[ROW][C]33[/C][C]26[/C][C]23.8052231877978[/C][C]2.1947768122022[/C][/ROW]
[ROW][C]34[/C][C]22[/C][C]19.5688243011192[/C][C]2.43117569888084[/C][/ROW]
[ROW][C]35[/C][C]20[/C][C]20.4191399551553[/C][C]-0.41913995515532[/C][/ROW]
[ROW][C]36[/C][C]22[/C][C]19.9929492174221[/C][C]2.00705078257793[/C][/ROW]
[ROW][C]37[/C][C]21[/C][C]21.6445067113253[/C][C]-0.644506711325309[/C][/ROW]
[ROW][C]38[/C][C]22[/C][C]19.6798443436881[/C][C]2.3201556563119[/C][/ROW]
[ROW][C]39[/C][C]20[/C][C]19.7498377231512[/C][C]0.250162276848813[/C][/ROW]
[ROW][C]40[/C][C]21[/C][C]19.4779255501433[/C][C]1.52207444985671[/C][/ROW]
[ROW][C]41[/C][C]20[/C][C]20.0089451640567[/C][C]-0.00894516405672422[/C][/ROW]
[ROW][C]42[/C][C]25[/C][C]22.0333041533987[/C][C]2.96669584660126[/C][/ROW]
[ROW][C]43[/C][C]18[/C][C]20.4648497366594[/C][C]-2.46484973665936[/C][/ROW]
[ROW][C]44[/C][C]22[/C][C]20.2861741003375[/C][C]1.71382589966253[/C][/ROW]
[ROW][C]45[/C][C]25[/C][C]20.2663209559314[/C][C]4.73367904406862[/C][/ROW]
[ROW][C]46[/C][C]21[/C][C]19.4061865853239[/C][C]1.59381341467609[/C][/ROW]
[ROW][C]47[/C][C]20[/C][C]19.4945202480285[/C][C]0.505479751971496[/C][/ROW]
[ROW][C]48[/C][C]20[/C][C]19.6001838208826[/C][C]0.399816179117422[/C][/ROW]
[ROW][C]49[/C][C]18[/C][C]19.3447595225216[/C][C]-1.34475952252163[/C][/ROW]
[ROW][C]50[/C][C]8[/C][C]19.61892924892[/C][C]-11.61892924892[/C][/ROW]
[ROW][C]51[/C][C]22[/C][C]19.8975601329691[/C][C]2.10243986703086[/C][/ROW]
[ROW][C]52[/C][C]26[/C][C]20.5108250147018[/C][C]5.48917498529816[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]19.2170743840842[/C][C]-1.2170743840842[/C][/ROW]
[ROW][C]54[/C][C]20[/C][C]19.7957381085135[/C][C]0.204261891486488[/C][/ROW]
[ROW][C]55[/C][C]24[/C][C]19.3622874892428[/C][C]4.63771251075724[/C][/ROW]
[ROW][C]56[/C][C]17[/C][C]19.7904780586159[/C][C]-2.79047805861592[/C][/ROW]
[ROW][C]57[/C][C]20[/C][C]20.2879624047917[/C][C]-0.287962404791652[/C][/ROW]
[ROW][C]58[/C][C]23[/C][C]19.4902160373212[/C][C]3.50978396267879[/C][/ROW]
[ROW][C]59[/C][C]20[/C][C]20.1450075223051[/C][C]-0.14500752230508[/C][/ROW]
[ROW][C]60[/C][C]22[/C][C]21.8381259751533[/C][C]0.16187402484667[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]18.7132219811729[/C][C]1.28677801882708[/C][/ROW]
[ROW][C]62[/C][C]19[/C][C]20.3292883566626[/C][C]-1.3292883566626[/C][/ROW]
[ROW][C]63[/C][C]15[/C][C]19.4816160158661[/C][C]-4.48161601586612[/C][/ROW]
[ROW][C]64[/C][C]20[/C][C]19.5694634852973[/C][C]0.430536514702684[/C][/ROW]
[ROW][C]65[/C][C]22[/C][C]20.1339682511142[/C][C]1.86603174888576[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]19.5097410473831[/C][C]-6.50974104738313[/C][/ROW]
[ROW][C]67[/C][C]20[/C][C]20.2637131801119[/C][C]-0.263713180111902[/C][/ROW]
[ROW][C]68[/C][C]17[/C][C]20.0970192898562[/C][C]-3.09701928985617[/C][/ROW]
[ROW][C]69[/C][C]14[/C][C]18.9664649903372[/C][C]-4.96646499033725[/C][/ROW]
[ROW][C]70[/C][C]22[/C][C]19.6905780362908[/C][C]2.30942196370917[/C][/ROW]
[ROW][C]71[/C][C]24[/C][C]19.0816027425256[/C][C]4.91839725747436[/C][/ROW]
[ROW][C]72[/C][C]22[/C][C]20.483531420453[/C][C]1.51646857954701[/C][/ROW]
[ROW][C]73[/C][C]23[/C][C]19.5933407034863[/C][C]3.40665929651373[/C][/ROW]
[ROW][C]74[/C][C]17[/C][C]19.259318885605[/C][C]-2.25931888560498[/C][/ROW]
[ROW][C]75[/C][C]23[/C][C]20.3139971128718[/C][C]2.68600288712816[/C][/ROW]
[ROW][C]76[/C][C]25[/C][C]20.1467675803729[/C][C]4.85323241962707[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]20.0299610155703[/C][C]-4.02996101557033[/C][/ROW]
[ROW][C]78[/C][C]18[/C][C]19.6527791519478[/C][C]-1.65277915194785[/C][/ROW]
[ROW][C]79[/C][C]20[/C][C]19.8707462752316[/C][C]0.129253724768414[/C][/ROW]
[ROW][C]80[/C][C]18[/C][C]19.581599445403[/C][C]-1.58159944540303[/C][/ROW]
[ROW][C]81[/C][C]24[/C][C]19.8970531110426[/C][C]4.10294688895737[/C][/ROW]
[ROW][C]82[/C][C]23[/C][C]19.5841373598529[/C][C]3.41586264014707[/C][/ROW]
[ROW][C]83[/C][C]24[/C][C]19.2020852246069[/C][C]4.79791477539307[/C][/ROW]
[ROW][C]84[/C][C]23[/C][C]19.8388637653348[/C][C]3.16113623466525[/C][/ROW]
[ROW][C]85[/C][C]23[/C][C]19.4127575192968[/C][C]3.5872424807032[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]19.233735396462[/C][C]-6.23373539646199[/C][/ROW]
[ROW][C]87[/C][C]20[/C][C]20.9253480308683[/C][C]-0.925348030868254[/C][/ROW]
[ROW][C]88[/C][C]18[/C][C]20.9619796663117[/C][C]-2.96197966631168[/C][/ROW]
[ROW][C]89[/C][C]21[/C][C]19.7960091724224[/C][C]1.20399082757757[/C][/ROW]
[ROW][C]90[/C][C]17[/C][C]19.7944465093235[/C][C]-2.79444650932348[/C][/ROW]
[ROW][C]91[/C][C]20[/C][C]19.3812858652773[/C][C]0.61871413472268[/C][/ROW]
[ROW][C]92[/C][C]19[/C][C]19.61383812472[/C][C]-0.613838124720045[/C][/ROW]
[ROW][C]93[/C][C]18[/C][C]19.6191654161523[/C][C]-1.61916541615229[/C][/ROW]
[ROW][C]94[/C][C]19[/C][C]19.520569293207[/C][C]-0.520569293206989[/C][/ROW]
[ROW][C]95[/C][C]22[/C][C]21.1123558755468[/C][C]0.887644124453185[/C][/ROW]
[ROW][C]96[/C][C]22[/C][C]18.674205958954[/C][C]3.32579404104598[/C][/ROW]
[ROW][C]97[/C][C]15[/C][C]19.0642766282597[/C][C]-4.06427662825974[/C][/ROW]
[ROW][C]98[/C][C]17[/C][C]20.4653025841061[/C][C]-3.46530258410606[/C][/ROW]
[ROW][C]99[/C][C]19[/C][C]20.5306624876252[/C][C]-1.53066248762522[/C][/ROW]
[ROW][C]100[/C][C]20[/C][C]19.4139835967488[/C][C]0.58601640325124[/C][/ROW]
[ROW][C]101[/C][C]22[/C][C]21.2732110825486[/C][C]0.726788917451351[/C][/ROW]
[ROW][C]102[/C][C]21[/C][C]20.170464013484[/C][C]0.829535986515984[/C][/ROW]
[ROW][C]103[/C][C]19[/C][C]20.5897922101609[/C][C]-1.58979221016086[/C][/ROW]
[ROW][C]104[/C][C]21[/C][C]21.173702778741[/C][C]-0.173702778740952[/C][/ROW]
[ROW][C]105[/C][C]18[/C][C]18.2483363395415[/C][C]-0.248336339541471[/C][/ROW]
[ROW][C]106[/C][C]16[/C][C]20.4587820256295[/C][C]-4.4587820256295[/C][/ROW]
[ROW][C]107[/C][C]20[/C][C]20.0143359651774[/C][C]-0.0143359651774448[/C][/ROW]
[ROW][C]108[/C][C]21[/C][C]19.1890537001589[/C][C]1.8109462998411[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]20.5782462058439[/C][C]-5.57824620584388[/C][/ROW]
[ROW][C]110[/C][C]20[/C][C]20.2842289889181[/C][C]-0.284228988918142[/C][/ROW]
[ROW][C]111[/C][C]23[/C][C]19.9745975165406[/C][C]3.02540248345943[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]18.7584742416779[/C][C]-3.75847424167787[/C][/ROW]
[ROW][C]113[/C][C]18[/C][C]19.5708214560064[/C][C]-1.57082145600638[/C][/ROW]
[ROW][C]114[/C][C]22[/C][C]18.5831826235648[/C][C]3.41681737643517[/C][/ROW]
[ROW][C]115[/C][C]16[/C][C]18.6487058572661[/C][C]-2.64870585726615[/C][/ROW]
[ROW][C]116[/C][C]17[/C][C]19.653725198154[/C][C]-2.65372519815404[/C][/ROW]
[ROW][C]117[/C][C]24[/C][C]20.131213427156[/C][C]3.86878657284404[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]20.5656396692852[/C][C]-7.56563966928519[/C][/ROW]
[ROW][C]119[/C][C]23[/C][C]19.3755823676987[/C][C]3.62441763230125[/C][/ROW]
[ROW][C]120[/C][C]5[/C][C]19.4218099189055[/C][C]-14.4218099189055[/C][/ROW]
[ROW][C]121[/C][C]19[/C][C]19.1288821530024[/C][C]-0.128882153002393[/C][/ROW]
[ROW][C]122[/C][C]24[/C][C]19.8278711403668[/C][C]4.17212885963324[/C][/ROW]
[ROW][C]123[/C][C]19[/C][C]18.6847465984815[/C][C]0.315253401518512[/C][/ROW]
[ROW][C]124[/C][C]20[/C][C]19.9569791109735[/C][C]0.0430208890264686[/C][/ROW]
[ROW][C]125[/C][C]22[/C][C]20.1309354709971[/C][C]1.86906452900295[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]20.8280794465755[/C][C]-5.82807944657546[/C][/ROW]
[ROW][C]127[/C][C]19[/C][C]19.7277322438962[/C][C]-0.727732243896201[/C][/ROW]
[ROW][C]128[/C][C]25[/C][C]19.9334112518503[/C][C]5.06658874814967[/C][/ROW]
[ROW][C]129[/C][C]21[/C][C]19.5117031172189[/C][C]1.48829688278114[/C][/ROW]
[ROW][C]130[/C][C]19[/C][C]19.5844070164052[/C][C]-0.584407016405203[/C][/ROW]
[ROW][C]131[/C][C]17[/C][C]20.3175785716296[/C][C]-3.31757857162958[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]20.2747904676679[/C][C]-5.27479046766785[/C][/ROW]
[ROW][C]133[/C][C]21[/C][C]20.3572672613308[/C][C]0.642732738669235[/C][/ROW]
[ROW][C]134[/C][C]24[/C][C]19.8341452310617[/C][C]4.16585476893835[/C][/ROW]
[ROW][C]135[/C][C]22[/C][C]19.2597384907592[/C][C]2.74026150924079[/C][/ROW]
[ROW][C]136[/C][C]19[/C][C]20.2466752197217[/C][C]-1.24667521972174[/C][/ROW]
[ROW][C]137[/C][C]20[/C][C]18.1315058807945[/C][C]1.86849411920549[/C][/ROW]
[ROW][C]138[/C][C]21[/C][C]19.6357027946879[/C][C]1.36429720531215[/C][/ROW]
[ROW][C]139[/C][C]19[/C][C]19.6405299634299[/C][C]-0.64052996342988[/C][/ROW]
[ROW][C]140[/C][C]22[/C][C]19.8584743894936[/C][C]2.14152561050636[/C][/ROW]
[ROW][C]141[/C][C]14[/C][C]19.6808027385779[/C][C]-5.68080273857789[/C][/ROW]
[ROW][C]142[/C][C]25[/C][C]19.3221589289112[/C][C]5.67784107108885[/C][/ROW]
[ROW][C]143[/C][C]11[/C][C]19.3278420017823[/C][C]-8.32784200178232[/C][/ROW]
[ROW][C]144[/C][C]16[/C][C]19.5218713000008[/C][C]-3.52187130000079[/C][/ROW]
[ROW][C]145[/C][C]19[/C][C]19.7589256285396[/C][C]-0.758925628539616[/C][/ROW]
[ROW][C]146[/C][C]17[/C][C]19.8681688594697[/C][C]-2.86816885946974[/C][/ROW]
[ROW][C]147[/C][C]20[/C][C]18.8113794822895[/C][C]1.1886205177105[/C][/ROW]
[ROW][C]148[/C][C]22[/C][C]19.3770845485493[/C][C]2.62291545145066[/C][/ROW]
[ROW][C]149[/C][C]20[/C][C]19.6081655717242[/C][C]0.391834428275824[/C][/ROW]
[ROW][C]150[/C][C]22[/C][C]19.4351561229376[/C][C]2.56484387706241[/C][/ROW]
[ROW][C]151[/C][C]15[/C][C]19.6530568907396[/C][C]-4.65305689073963[/C][/ROW]
[ROW][C]152[/C][C]23[/C][C]19.5746799041418[/C][C]3.42532009585815[/C][/ROW]
[ROW][C]153[/C][C]20[/C][C]19.8131735887657[/C][C]0.186826411234337[/C][/ROW]
[ROW][C]154[/C][C]17[/C][C]19.5099781109431[/C][C]-2.50997811094315[/C][/ROW]
[ROW][C]155[/C][C]20[/C][C]19.7712050718325[/C][C]0.228794928167474[/C][/ROW]
[ROW][C]156[/C][C]25[/C][C]19.4682993909807[/C][C]5.53170060901932[/C][/ROW]
[ROW][C]157[/C][C]22[/C][C]19.3574504176575[/C][C]2.64254958234252[/C][/ROW]
[ROW][C]158[/C][C]16[/C][C]20.1903587330668[/C][C]-4.19035873306676[/C][/ROW]
[ROW][C]159[/C][C]25[/C][C]20.4707716827837[/C][C]4.52922831721632[/C][/ROW]
[ROW][C]160[/C][C]18[/C][C]19.9930625277743[/C][C]-1.99306252777432[/C][/ROW]
[ROW][C]161[/C][C]19[/C][C]19.842869071505[/C][C]-0.842869071504979[/C][/ROW]
[ROW][C]162[/C][C]25[/C][C]19.9065238839199[/C][C]5.09347611608012[/C][/ROW]
[ROW][C]163[/C][C]23[/C][C]19.6063507089068[/C][C]3.39364929109321[/C][/ROW]
[ROW][C]164[/C][C]24[/C][C]19.6861849288484[/C][C]4.31381507115165[/C][/ROW]
[ROW][C]165[/C][C]21[/C][C]19.7051535565642[/C][C]1.29484644343576[/C][/ROW]
[ROW][C]166[/C][C]21[/C][C]19.7600308741174[/C][C]1.23996912588258[/C][/ROW]
[ROW][C]167[/C][C]22[/C][C]19.9120149492649[/C][C]2.08798505073507[/C][/ROW]
[ROW][C]168[/C][C]21[/C][C]20.4369708589547[/C][C]0.56302914104525[/C][/ROW]
[ROW][C]169[/C][C]18[/C][C]19.5991729572313[/C][C]-1.59917295723133[/C][/ROW]
[ROW][C]170[/C][C]13[/C][C]19.8261025379118[/C][C]-6.82610253791176[/C][/ROW]
[ROW][C]171[/C][C]22[/C][C]19.9025584464537[/C][C]2.09744155354627[/C][/ROW]
[ROW][C]172[/C][C]23[/C][C]19.8537820606267[/C][C]3.1462179393733[/C][/ROW]
[ROW][C]173[/C][C]15[/C][C]19.7683784509445[/C][C]-4.76837845094449[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155170&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155170&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
11120.1037391852822-9.10373918528224
21520.5254533827416-5.52545338274164
31918.27509511089890.724904889101137
42321.43632577602891.56367422397108
51619.4069859463457-3.40698594634569
62119.61778338472821.38221661527177
72421.1066977302022.89330226979797
81519.4871338305997-4.48713383059967
91720.0963444452249-3.09634444522495
101919.8652847735212-0.865284773521232
111919.6555684837824-0.655568483782406
122519.95907049642485.04092950357518
131919.7726276630971-0.77262766309714
142820.96643325476447.03356674523564
152420.21622591894513.78377408105491
162620.45083250303415.5491674969659
171520.0141118032726-5.01411180327259
182120.22534665187650.774653348123453
192620.98777150526395.01222849473609
201620.0999185126619-4.09991851266191
211620.3910020476635-4.39100204766352
222019.36295362459990.637046375400121
232421.09886933210582.9011306678942
241020.5873555334737-10.5873555334737
251919.8907327508096-0.890732750809556
262519.88905040932715.1109495906729
272219.60352393120622.39647606879384
281520.4976017670557-5.49760176705569
292119.44958037923051.55041962076945
302219.50702744336172.49297255663828
312720.58120130093686.41879869906322
322619.00912472077286.9908752792272
332623.80522318779782.1947768122022
342219.56882430111922.43117569888084
352020.4191399551553-0.41913995515532
362219.99294921742212.00705078257793
372121.6445067113253-0.644506711325309
382219.67984434368812.3201556563119
392019.74983772315120.250162276848813
402119.47792555014331.52207444985671
412020.0089451640567-0.00894516405672422
422522.03330415339872.96669584660126
431820.4648497366594-2.46484973665936
442220.28617410033751.71382589966253
452520.26632095593144.73367904406862
462119.40618658532391.59381341467609
472019.49452024802850.505479751971496
482019.60018382088260.399816179117422
491819.3447595225216-1.34475952252163
50819.61892924892-11.61892924892
512219.89756013296912.10243986703086
522620.51082501470185.48917498529816
531819.2170743840842-1.2170743840842
542019.79573810851350.204261891486488
552419.36228748924284.63771251075724
561719.7904780586159-2.79047805861592
572020.2879624047917-0.287962404791652
582319.49021603732123.50978396267879
592020.1450075223051-0.14500752230508
602221.83812597515330.16187402484667
612018.71322198117291.28677801882708
621920.3292883566626-1.3292883566626
631519.4816160158661-4.48161601586612
642019.56946348529730.430536514702684
652220.13396825111421.86603174888576
661319.5097410473831-6.50974104738313
672020.2637131801119-0.263713180111902
681720.0970192898562-3.09701928985617
691418.9664649903372-4.96646499033725
702219.69057803629082.30942196370917
712419.08160274252564.91839725747436
722220.4835314204531.51646857954701
732319.59334070348633.40665929651373
741719.259318885605-2.25931888560498
752320.31399711287182.68600288712816
762520.14676758037294.85323241962707
771620.0299610155703-4.02996101557033
781819.6527791519478-1.65277915194785
792019.87074627523160.129253724768414
801819.581599445403-1.58159944540303
812419.89705311104264.10294688895737
822319.58413735985293.41586264014707
832419.20208522460694.79791477539307
842319.83886376533483.16113623466525
852319.41275751929683.5872424807032
861319.233735396462-6.23373539646199
872020.9253480308683-0.925348030868254
881820.9619796663117-2.96197966631168
892119.79600917242241.20399082757757
901719.7944465093235-2.79444650932348
912019.38128586527730.61871413472268
921919.61383812472-0.613838124720045
931819.6191654161523-1.61916541615229
941919.520569293207-0.520569293206989
952221.11235587554680.887644124453185
962218.6742059589543.32579404104598
971519.0642766282597-4.06427662825974
981720.4653025841061-3.46530258410606
991920.5306624876252-1.53066248762522
1002019.41398359674880.58601640325124
1012221.27321108254860.726788917451351
1022120.1704640134840.829535986515984
1031920.5897922101609-1.58979221016086
1042121.173702778741-0.173702778740952
1051818.2483363395415-0.248336339541471
1061620.4587820256295-4.4587820256295
1072020.0143359651774-0.0143359651774448
1082119.18905370015891.8109462998411
1091520.5782462058439-5.57824620584388
1102020.2842289889181-0.284228988918142
1112319.97459751654063.02540248345943
1121518.7584742416779-3.75847424167787
1131819.5708214560064-1.57082145600638
1142218.58318262356483.41681737643517
1151618.6487058572661-2.64870585726615
1161719.653725198154-2.65372519815404
1172420.1312134271563.86878657284404
1181320.5656396692852-7.56563966928519
1192319.37558236769873.62441763230125
120519.4218099189055-14.4218099189055
1211919.1288821530024-0.128882153002393
1222419.82787114036684.17212885963324
1231918.68474659848150.315253401518512
1242019.95697911097350.0430208890264686
1252220.13093547099711.86906452900295
1261520.8280794465755-5.82807944657546
1271919.7277322438962-0.727732243896201
1282519.93341125185035.06658874814967
1292119.51170311721891.48829688278114
1301919.5844070164052-0.584407016405203
1311720.3175785716296-3.31757857162958
1321520.2747904676679-5.27479046766785
1332120.35726726133080.642732738669235
1342419.83414523106174.16585476893835
1352219.25973849075922.74026150924079
1361920.2466752197217-1.24667521972174
1372018.13150588079451.86849411920549
1382119.63570279468791.36429720531215
1391919.6405299634299-0.64052996342988
1402219.85847438949362.14152561050636
1411419.6808027385779-5.68080273857789
1422519.32215892891125.67784107108885
1431119.3278420017823-8.32784200178232
1441619.5218713000008-3.52187130000079
1451919.7589256285396-0.758925628539616
1461719.8681688594697-2.86816885946974
1472018.81137948228951.1886205177105
1482219.37708454854932.62291545145066
1492019.60816557172420.391834428275824
1502219.43515612293762.56484387706241
1511519.6530568907396-4.65305689073963
1522319.57467990414183.42532009585815
1532019.81317358876570.186826411234337
1541719.5099781109431-2.50997811094315
1552019.77120507183250.228794928167474
1562519.46829939098075.53170060901932
1572219.35745041765752.64254958234252
1581620.1903587330668-4.19035873306676
1592520.47077168278374.52922831721632
1601819.9930625277743-1.99306252777432
1611919.842869071505-0.842869071504979
1622519.90652388391995.09347611608012
1632319.60635070890683.39364929109321
1642419.68618492884844.31381507115165
1652119.70515355656421.29484644343576
1662119.76003087411741.23996912588258
1672219.91201494926492.08798505073507
1682120.43697085895470.56302914104525
1691819.5991729572313-1.59917295723133
1701319.8261025379118-6.82610253791176
1712219.90255844645372.09744155354627
1722319.85378206062673.1462179393733
1731519.7683784509445-4.76837845094449






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.963090161687730.07381967662454090.0369098383122704
190.9600145803211830.07997083935763450.0399854196788172
200.9693232479138140.06135350417237230.0306767520861861
210.9547921310142170.09041573797156660.0452078689857833
220.9235252821396940.1529494357206120.0764747178603061
230.8983254234211020.2033491531577960.101674576578898
240.9157402113621310.1685195772757390.0842597886378694
250.878618817291960.2427623654160820.121381182708041
260.8819158571560180.2361682856879640.118084142843982
270.838271546741430.3234569065171380.161728453258569
280.8124605952988740.3750788094022520.187539404701126
290.7586516930613810.4826966138772380.241348306938619
300.7796273434012920.4407453131974160.220372656598708
310.8060907042693720.3878185914612560.193909295730628
320.8971685044248850.2056629911502290.102831495575115
330.90429323053310.1914135389338020.0957067694669011
340.9146808089463730.1706383821072540.0853191910536268
350.9121105937661040.1757788124677910.0878894062338957
360.8860007660450430.2279984679099140.113999233954957
370.8882660818382270.2234678363235450.111733918161773
380.8631321043261420.2737357913477150.136867895673858
390.8677471975368450.264505604926310.132252802463155
400.8344873336970630.3310253326058740.165512666302937
410.7944180332702160.4111639334595680.205581966729784
420.7884363387613840.4231273224772330.211563661238616
430.7982910503442130.4034178993115750.201708949655787
440.8452284167995940.3095431664008110.154771583200406
450.8467778741802870.3064442516394270.153222125819713
460.8216895201604150.356620959679170.178310479839585
470.78664792204870.42670415590260.2133520779513
480.7637274558269480.4725450883461050.236272544173052
490.7326880694524720.5346238610950560.267311930547528
500.9525433763618310.0949132472763380.047456623638169
510.9403019739858740.1193960520282520.0596980260141258
520.9485019142285350.102996171542930.0514980857714649
530.9409633767020160.1180732465959670.0590366232979836
540.924026924994410.1519461500111780.0759730750055892
550.9348797313464620.1302405373070760.065120268653538
560.9388812656867890.1222374686264220.061118734313211
570.9241180750015840.1517638499968330.0758819249984164
580.9242519624560560.1514960750878880.0757480375439442
590.9081479630272570.1837040739454870.0918520369727433
600.89101416171330.2179716765734020.108985838286701
610.8685243508739530.2629512982520950.131475649126047
620.8808097510092010.2383804979815970.119190248990799
630.8960050125385360.2079899749229280.103994987461464
640.8741359990714620.2517280018570760.125864000928538
650.8590725547179280.2818548905641440.140927445282072
660.8911060611096380.2177878777807240.108893938890362
670.8680216083955470.2639567832089050.131978391604453
680.868002001031780.2639959979364390.131997998968219
690.8912079713933240.2175840572133520.108792028606676
700.874943208277090.2501135834458210.125056791722911
710.8906800626107350.218639874778530.109319937389265
720.8729678470522720.2540643058954560.127032152947728
730.8655075605083630.2689848789832750.134492439491637
740.8447794859677360.3104410280645280.155220514032264
750.8318922216584460.3362155566831080.168107778341554
760.8548538327266660.2902923345466690.145146167273334
770.8574664887725940.2850670224548120.142533511227406
780.8355083375270820.3289833249458350.164491662472918
790.8045481876735460.3909036246529080.195451812326454
800.7766226501889510.4467546996220980.223377349811049
810.7815954175166240.4368091649667510.218404582483376
820.7809696325145080.4380607349709840.219030367485492
830.8039427202959330.3921145594081340.196057279704067
840.784763888816020.4304722223679590.215236111183979
850.8040084629005880.3919830741988230.195991537099412
860.8429956979490420.3140086041019160.157004302050958
870.8387663102202530.3224673795594950.161233689779747
880.84149574489620.3170085102075990.1585042551038
890.8125963083727130.3748073832545730.187403691627287
900.8035445151742090.3929109696515820.196455484825791
910.7693758380316210.4612483239367580.230624161968379
920.7346007854496030.5307984291007950.265399214550397
930.7045621313390010.5908757373219980.295437868660999
940.6636051523648160.6727896952703680.336394847635184
950.6265108994335320.7469782011329350.373489100566468
960.6270980970964750.745803805807050.372901902903525
970.6808325966996560.6383348066006880.319167403300344
980.6675785977537340.6648428044925330.332421402246266
990.64659192288180.70681615423640.3534080771182
1000.6022696142720370.7954607714559260.397730385727963
1010.6227979671308530.7544040657382950.377202032869147
1020.5852185008082260.8295629983835470.414781499191774
1030.5472034463075750.9055931073848510.452796553692425
1040.506990333960580.986019332078840.49300966603942
1050.4630635610509550.926127122101910.536936438949045
1060.4689892881885160.9379785763770320.531010711811484
1070.4384564159872540.8769128319745080.561543584012746
1080.4032886508062240.8065773016124490.596711349193776
1090.4514406443996050.902881288799210.548559355600395
1100.404519037155280.809038074310560.59548096284472
1110.396651027977470.793302055954940.60334897202253
1120.4101437187208640.8202874374417280.589856281279136
1130.4037300851018790.8074601702037590.596269914898121
1140.3836478725381950.767295745076390.616352127461805
1150.3653633284390680.7307266568781350.634636671560932
1160.3358098415314840.6716196830629680.664190158468516
1170.3750006108583820.7500012217167640.624999389141618
1180.460510678837130.921021357674260.53948932116287
1190.4820967713860520.9641935427721050.517903228613948
1200.8183967620744840.3632064758510320.181603237925516
1210.8373083317339960.3253833365320080.162691668266004
1220.8311650796312580.3376698407374850.168834920368742
1230.7963850718411370.4072298563177250.203614928158863
1240.7576707960937040.4846584078125910.242329203906296
1250.7505512696123130.4988974607753740.249448730387687
1260.7440823303540520.5118353392918960.255917669645948
1270.7019084564753320.5961830870493370.298091543524668
1280.7492999929319310.5014000141361380.250700007068069
1290.7074367405391790.5851265189216420.292563259460821
1300.6538175109504130.6923649780991750.346182489049587
1310.604518380059130.7909632398817410.395481619940871
1320.6247818973152430.7504362053695130.375218102684757
1330.5854307702984330.8291384594031340.414569229701567
1340.5482088407252750.903582318549450.451791159274725
1350.5120447076711690.9759105846576630.487955292328831
1360.4987943670143890.9975887340287780.501205632985611
1370.4890302092584850.978060418516970.510969790741515
1380.4263004158072080.8526008316144160.573699584192792
1390.4550888333870530.9101776667741060.544911166612947
1400.5150235468524630.9699529062950740.484976453147537
1410.651627847312510.6967443053749790.348372152687489
1420.621320387427770.757359225144460.37867961257223
1430.8338963218063680.3322073563872640.166103678193632
1440.7970668393438830.4058663213122340.202933160656117
1450.7631004625332110.4737990749335770.236899537466789
1460.6916963742624790.6166072514750420.308303625737521
1470.6216876256078590.7566247487842830.378312374392141
1480.8059158824082960.3881682351834080.194084117591704
1490.7962722724246580.4074554551506850.203727727575342
1500.7306246064274510.5387507871450970.269375393572549
1510.6404913615074210.7190172769851570.359508638492579
1520.5285362294480110.9429275411039780.471463770551989
1530.6570189302370870.6859621395258260.342981069762913
1540.5149462139538950.970107572092210.485053786046105
1550.7604902694878380.4790194610243240.239509730512162

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.96309016168773 & 0.0738196766245409 & 0.0369098383122704 \tabularnewline
19 & 0.960014580321183 & 0.0799708393576345 & 0.0399854196788172 \tabularnewline
20 & 0.969323247913814 & 0.0613535041723723 & 0.0306767520861861 \tabularnewline
21 & 0.954792131014217 & 0.0904157379715666 & 0.0452078689857833 \tabularnewline
22 & 0.923525282139694 & 0.152949435720612 & 0.0764747178603061 \tabularnewline
23 & 0.898325423421102 & 0.203349153157796 & 0.101674576578898 \tabularnewline
24 & 0.915740211362131 & 0.168519577275739 & 0.0842597886378694 \tabularnewline
25 & 0.87861881729196 & 0.242762365416082 & 0.121381182708041 \tabularnewline
26 & 0.881915857156018 & 0.236168285687964 & 0.118084142843982 \tabularnewline
27 & 0.83827154674143 & 0.323456906517138 & 0.161728453258569 \tabularnewline
28 & 0.812460595298874 & 0.375078809402252 & 0.187539404701126 \tabularnewline
29 & 0.758651693061381 & 0.482696613877238 & 0.241348306938619 \tabularnewline
30 & 0.779627343401292 & 0.440745313197416 & 0.220372656598708 \tabularnewline
31 & 0.806090704269372 & 0.387818591461256 & 0.193909295730628 \tabularnewline
32 & 0.897168504424885 & 0.205662991150229 & 0.102831495575115 \tabularnewline
33 & 0.9042932305331 & 0.191413538933802 & 0.0957067694669011 \tabularnewline
34 & 0.914680808946373 & 0.170638382107254 & 0.0853191910536268 \tabularnewline
35 & 0.912110593766104 & 0.175778812467791 & 0.0878894062338957 \tabularnewline
36 & 0.886000766045043 & 0.227998467909914 & 0.113999233954957 \tabularnewline
37 & 0.888266081838227 & 0.223467836323545 & 0.111733918161773 \tabularnewline
38 & 0.863132104326142 & 0.273735791347715 & 0.136867895673858 \tabularnewline
39 & 0.867747197536845 & 0.26450560492631 & 0.132252802463155 \tabularnewline
40 & 0.834487333697063 & 0.331025332605874 & 0.165512666302937 \tabularnewline
41 & 0.794418033270216 & 0.411163933459568 & 0.205581966729784 \tabularnewline
42 & 0.788436338761384 & 0.423127322477233 & 0.211563661238616 \tabularnewline
43 & 0.798291050344213 & 0.403417899311575 & 0.201708949655787 \tabularnewline
44 & 0.845228416799594 & 0.309543166400811 & 0.154771583200406 \tabularnewline
45 & 0.846777874180287 & 0.306444251639427 & 0.153222125819713 \tabularnewline
46 & 0.821689520160415 & 0.35662095967917 & 0.178310479839585 \tabularnewline
47 & 0.7866479220487 & 0.4267041559026 & 0.2133520779513 \tabularnewline
48 & 0.763727455826948 & 0.472545088346105 & 0.236272544173052 \tabularnewline
49 & 0.732688069452472 & 0.534623861095056 & 0.267311930547528 \tabularnewline
50 & 0.952543376361831 & 0.094913247276338 & 0.047456623638169 \tabularnewline
51 & 0.940301973985874 & 0.119396052028252 & 0.0596980260141258 \tabularnewline
52 & 0.948501914228535 & 0.10299617154293 & 0.0514980857714649 \tabularnewline
53 & 0.940963376702016 & 0.118073246595967 & 0.0590366232979836 \tabularnewline
54 & 0.92402692499441 & 0.151946150011178 & 0.0759730750055892 \tabularnewline
55 & 0.934879731346462 & 0.130240537307076 & 0.065120268653538 \tabularnewline
56 & 0.938881265686789 & 0.122237468626422 & 0.061118734313211 \tabularnewline
57 & 0.924118075001584 & 0.151763849996833 & 0.0758819249984164 \tabularnewline
58 & 0.924251962456056 & 0.151496075087888 & 0.0757480375439442 \tabularnewline
59 & 0.908147963027257 & 0.183704073945487 & 0.0918520369727433 \tabularnewline
60 & 0.8910141617133 & 0.217971676573402 & 0.108985838286701 \tabularnewline
61 & 0.868524350873953 & 0.262951298252095 & 0.131475649126047 \tabularnewline
62 & 0.880809751009201 & 0.238380497981597 & 0.119190248990799 \tabularnewline
63 & 0.896005012538536 & 0.207989974922928 & 0.103994987461464 \tabularnewline
64 & 0.874135999071462 & 0.251728001857076 & 0.125864000928538 \tabularnewline
65 & 0.859072554717928 & 0.281854890564144 & 0.140927445282072 \tabularnewline
66 & 0.891106061109638 & 0.217787877780724 & 0.108893938890362 \tabularnewline
67 & 0.868021608395547 & 0.263956783208905 & 0.131978391604453 \tabularnewline
68 & 0.86800200103178 & 0.263995997936439 & 0.131997998968219 \tabularnewline
69 & 0.891207971393324 & 0.217584057213352 & 0.108792028606676 \tabularnewline
70 & 0.87494320827709 & 0.250113583445821 & 0.125056791722911 \tabularnewline
71 & 0.890680062610735 & 0.21863987477853 & 0.109319937389265 \tabularnewline
72 & 0.872967847052272 & 0.254064305895456 & 0.127032152947728 \tabularnewline
73 & 0.865507560508363 & 0.268984878983275 & 0.134492439491637 \tabularnewline
74 & 0.844779485967736 & 0.310441028064528 & 0.155220514032264 \tabularnewline
75 & 0.831892221658446 & 0.336215556683108 & 0.168107778341554 \tabularnewline
76 & 0.854853832726666 & 0.290292334546669 & 0.145146167273334 \tabularnewline
77 & 0.857466488772594 & 0.285067022454812 & 0.142533511227406 \tabularnewline
78 & 0.835508337527082 & 0.328983324945835 & 0.164491662472918 \tabularnewline
79 & 0.804548187673546 & 0.390903624652908 & 0.195451812326454 \tabularnewline
80 & 0.776622650188951 & 0.446754699622098 & 0.223377349811049 \tabularnewline
81 & 0.781595417516624 & 0.436809164966751 & 0.218404582483376 \tabularnewline
82 & 0.780969632514508 & 0.438060734970984 & 0.219030367485492 \tabularnewline
83 & 0.803942720295933 & 0.392114559408134 & 0.196057279704067 \tabularnewline
84 & 0.78476388881602 & 0.430472222367959 & 0.215236111183979 \tabularnewline
85 & 0.804008462900588 & 0.391983074198823 & 0.195991537099412 \tabularnewline
86 & 0.842995697949042 & 0.314008604101916 & 0.157004302050958 \tabularnewline
87 & 0.838766310220253 & 0.322467379559495 & 0.161233689779747 \tabularnewline
88 & 0.8414957448962 & 0.317008510207599 & 0.1585042551038 \tabularnewline
89 & 0.812596308372713 & 0.374807383254573 & 0.187403691627287 \tabularnewline
90 & 0.803544515174209 & 0.392910969651582 & 0.196455484825791 \tabularnewline
91 & 0.769375838031621 & 0.461248323936758 & 0.230624161968379 \tabularnewline
92 & 0.734600785449603 & 0.530798429100795 & 0.265399214550397 \tabularnewline
93 & 0.704562131339001 & 0.590875737321998 & 0.295437868660999 \tabularnewline
94 & 0.663605152364816 & 0.672789695270368 & 0.336394847635184 \tabularnewline
95 & 0.626510899433532 & 0.746978201132935 & 0.373489100566468 \tabularnewline
96 & 0.627098097096475 & 0.74580380580705 & 0.372901902903525 \tabularnewline
97 & 0.680832596699656 & 0.638334806600688 & 0.319167403300344 \tabularnewline
98 & 0.667578597753734 & 0.664842804492533 & 0.332421402246266 \tabularnewline
99 & 0.6465919228818 & 0.7068161542364 & 0.3534080771182 \tabularnewline
100 & 0.602269614272037 & 0.795460771455926 & 0.397730385727963 \tabularnewline
101 & 0.622797967130853 & 0.754404065738295 & 0.377202032869147 \tabularnewline
102 & 0.585218500808226 & 0.829562998383547 & 0.414781499191774 \tabularnewline
103 & 0.547203446307575 & 0.905593107384851 & 0.452796553692425 \tabularnewline
104 & 0.50699033396058 & 0.98601933207884 & 0.49300966603942 \tabularnewline
105 & 0.463063561050955 & 0.92612712210191 & 0.536936438949045 \tabularnewline
106 & 0.468989288188516 & 0.937978576377032 & 0.531010711811484 \tabularnewline
107 & 0.438456415987254 & 0.876912831974508 & 0.561543584012746 \tabularnewline
108 & 0.403288650806224 & 0.806577301612449 & 0.596711349193776 \tabularnewline
109 & 0.451440644399605 & 0.90288128879921 & 0.548559355600395 \tabularnewline
110 & 0.40451903715528 & 0.80903807431056 & 0.59548096284472 \tabularnewline
111 & 0.39665102797747 & 0.79330205595494 & 0.60334897202253 \tabularnewline
112 & 0.410143718720864 & 0.820287437441728 & 0.589856281279136 \tabularnewline
113 & 0.403730085101879 & 0.807460170203759 & 0.596269914898121 \tabularnewline
114 & 0.383647872538195 & 0.76729574507639 & 0.616352127461805 \tabularnewline
115 & 0.365363328439068 & 0.730726656878135 & 0.634636671560932 \tabularnewline
116 & 0.335809841531484 & 0.671619683062968 & 0.664190158468516 \tabularnewline
117 & 0.375000610858382 & 0.750001221716764 & 0.624999389141618 \tabularnewline
118 & 0.46051067883713 & 0.92102135767426 & 0.53948932116287 \tabularnewline
119 & 0.482096771386052 & 0.964193542772105 & 0.517903228613948 \tabularnewline
120 & 0.818396762074484 & 0.363206475851032 & 0.181603237925516 \tabularnewline
121 & 0.837308331733996 & 0.325383336532008 & 0.162691668266004 \tabularnewline
122 & 0.831165079631258 & 0.337669840737485 & 0.168834920368742 \tabularnewline
123 & 0.796385071841137 & 0.407229856317725 & 0.203614928158863 \tabularnewline
124 & 0.757670796093704 & 0.484658407812591 & 0.242329203906296 \tabularnewline
125 & 0.750551269612313 & 0.498897460775374 & 0.249448730387687 \tabularnewline
126 & 0.744082330354052 & 0.511835339291896 & 0.255917669645948 \tabularnewline
127 & 0.701908456475332 & 0.596183087049337 & 0.298091543524668 \tabularnewline
128 & 0.749299992931931 & 0.501400014136138 & 0.250700007068069 \tabularnewline
129 & 0.707436740539179 & 0.585126518921642 & 0.292563259460821 \tabularnewline
130 & 0.653817510950413 & 0.692364978099175 & 0.346182489049587 \tabularnewline
131 & 0.60451838005913 & 0.790963239881741 & 0.395481619940871 \tabularnewline
132 & 0.624781897315243 & 0.750436205369513 & 0.375218102684757 \tabularnewline
133 & 0.585430770298433 & 0.829138459403134 & 0.414569229701567 \tabularnewline
134 & 0.548208840725275 & 0.90358231854945 & 0.451791159274725 \tabularnewline
135 & 0.512044707671169 & 0.975910584657663 & 0.487955292328831 \tabularnewline
136 & 0.498794367014389 & 0.997588734028778 & 0.501205632985611 \tabularnewline
137 & 0.489030209258485 & 0.97806041851697 & 0.510969790741515 \tabularnewline
138 & 0.426300415807208 & 0.852600831614416 & 0.573699584192792 \tabularnewline
139 & 0.455088833387053 & 0.910177666774106 & 0.544911166612947 \tabularnewline
140 & 0.515023546852463 & 0.969952906295074 & 0.484976453147537 \tabularnewline
141 & 0.65162784731251 & 0.696744305374979 & 0.348372152687489 \tabularnewline
142 & 0.62132038742777 & 0.75735922514446 & 0.37867961257223 \tabularnewline
143 & 0.833896321806368 & 0.332207356387264 & 0.166103678193632 \tabularnewline
144 & 0.797066839343883 & 0.405866321312234 & 0.202933160656117 \tabularnewline
145 & 0.763100462533211 & 0.473799074933577 & 0.236899537466789 \tabularnewline
146 & 0.691696374262479 & 0.616607251475042 & 0.308303625737521 \tabularnewline
147 & 0.621687625607859 & 0.756624748784283 & 0.378312374392141 \tabularnewline
148 & 0.805915882408296 & 0.388168235183408 & 0.194084117591704 \tabularnewline
149 & 0.796272272424658 & 0.407455455150685 & 0.203727727575342 \tabularnewline
150 & 0.730624606427451 & 0.538750787145097 & 0.269375393572549 \tabularnewline
151 & 0.640491361507421 & 0.719017276985157 & 0.359508638492579 \tabularnewline
152 & 0.528536229448011 & 0.942927541103978 & 0.471463770551989 \tabularnewline
153 & 0.657018930237087 & 0.685962139525826 & 0.342981069762913 \tabularnewline
154 & 0.514946213953895 & 0.97010757209221 & 0.485053786046105 \tabularnewline
155 & 0.760490269487838 & 0.479019461024324 & 0.239509730512162 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155170&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]18[/C][C]0.96309016168773[/C][C]0.0738196766245409[/C][C]0.0369098383122704[/C][/ROW]
[ROW][C]19[/C][C]0.960014580321183[/C][C]0.0799708393576345[/C][C]0.0399854196788172[/C][/ROW]
[ROW][C]20[/C][C]0.969323247913814[/C][C]0.0613535041723723[/C][C]0.0306767520861861[/C][/ROW]
[ROW][C]21[/C][C]0.954792131014217[/C][C]0.0904157379715666[/C][C]0.0452078689857833[/C][/ROW]
[ROW][C]22[/C][C]0.923525282139694[/C][C]0.152949435720612[/C][C]0.0764747178603061[/C][/ROW]
[ROW][C]23[/C][C]0.898325423421102[/C][C]0.203349153157796[/C][C]0.101674576578898[/C][/ROW]
[ROW][C]24[/C][C]0.915740211362131[/C][C]0.168519577275739[/C][C]0.0842597886378694[/C][/ROW]
[ROW][C]25[/C][C]0.87861881729196[/C][C]0.242762365416082[/C][C]0.121381182708041[/C][/ROW]
[ROW][C]26[/C][C]0.881915857156018[/C][C]0.236168285687964[/C][C]0.118084142843982[/C][/ROW]
[ROW][C]27[/C][C]0.83827154674143[/C][C]0.323456906517138[/C][C]0.161728453258569[/C][/ROW]
[ROW][C]28[/C][C]0.812460595298874[/C][C]0.375078809402252[/C][C]0.187539404701126[/C][/ROW]
[ROW][C]29[/C][C]0.758651693061381[/C][C]0.482696613877238[/C][C]0.241348306938619[/C][/ROW]
[ROW][C]30[/C][C]0.779627343401292[/C][C]0.440745313197416[/C][C]0.220372656598708[/C][/ROW]
[ROW][C]31[/C][C]0.806090704269372[/C][C]0.387818591461256[/C][C]0.193909295730628[/C][/ROW]
[ROW][C]32[/C][C]0.897168504424885[/C][C]0.205662991150229[/C][C]0.102831495575115[/C][/ROW]
[ROW][C]33[/C][C]0.9042932305331[/C][C]0.191413538933802[/C][C]0.0957067694669011[/C][/ROW]
[ROW][C]34[/C][C]0.914680808946373[/C][C]0.170638382107254[/C][C]0.0853191910536268[/C][/ROW]
[ROW][C]35[/C][C]0.912110593766104[/C][C]0.175778812467791[/C][C]0.0878894062338957[/C][/ROW]
[ROW][C]36[/C][C]0.886000766045043[/C][C]0.227998467909914[/C][C]0.113999233954957[/C][/ROW]
[ROW][C]37[/C][C]0.888266081838227[/C][C]0.223467836323545[/C][C]0.111733918161773[/C][/ROW]
[ROW][C]38[/C][C]0.863132104326142[/C][C]0.273735791347715[/C][C]0.136867895673858[/C][/ROW]
[ROW][C]39[/C][C]0.867747197536845[/C][C]0.26450560492631[/C][C]0.132252802463155[/C][/ROW]
[ROW][C]40[/C][C]0.834487333697063[/C][C]0.331025332605874[/C][C]0.165512666302937[/C][/ROW]
[ROW][C]41[/C][C]0.794418033270216[/C][C]0.411163933459568[/C][C]0.205581966729784[/C][/ROW]
[ROW][C]42[/C][C]0.788436338761384[/C][C]0.423127322477233[/C][C]0.211563661238616[/C][/ROW]
[ROW][C]43[/C][C]0.798291050344213[/C][C]0.403417899311575[/C][C]0.201708949655787[/C][/ROW]
[ROW][C]44[/C][C]0.845228416799594[/C][C]0.309543166400811[/C][C]0.154771583200406[/C][/ROW]
[ROW][C]45[/C][C]0.846777874180287[/C][C]0.306444251639427[/C][C]0.153222125819713[/C][/ROW]
[ROW][C]46[/C][C]0.821689520160415[/C][C]0.35662095967917[/C][C]0.178310479839585[/C][/ROW]
[ROW][C]47[/C][C]0.7866479220487[/C][C]0.4267041559026[/C][C]0.2133520779513[/C][/ROW]
[ROW][C]48[/C][C]0.763727455826948[/C][C]0.472545088346105[/C][C]0.236272544173052[/C][/ROW]
[ROW][C]49[/C][C]0.732688069452472[/C][C]0.534623861095056[/C][C]0.267311930547528[/C][/ROW]
[ROW][C]50[/C][C]0.952543376361831[/C][C]0.094913247276338[/C][C]0.047456623638169[/C][/ROW]
[ROW][C]51[/C][C]0.940301973985874[/C][C]0.119396052028252[/C][C]0.0596980260141258[/C][/ROW]
[ROW][C]52[/C][C]0.948501914228535[/C][C]0.10299617154293[/C][C]0.0514980857714649[/C][/ROW]
[ROW][C]53[/C][C]0.940963376702016[/C][C]0.118073246595967[/C][C]0.0590366232979836[/C][/ROW]
[ROW][C]54[/C][C]0.92402692499441[/C][C]0.151946150011178[/C][C]0.0759730750055892[/C][/ROW]
[ROW][C]55[/C][C]0.934879731346462[/C][C]0.130240537307076[/C][C]0.065120268653538[/C][/ROW]
[ROW][C]56[/C][C]0.938881265686789[/C][C]0.122237468626422[/C][C]0.061118734313211[/C][/ROW]
[ROW][C]57[/C][C]0.924118075001584[/C][C]0.151763849996833[/C][C]0.0758819249984164[/C][/ROW]
[ROW][C]58[/C][C]0.924251962456056[/C][C]0.151496075087888[/C][C]0.0757480375439442[/C][/ROW]
[ROW][C]59[/C][C]0.908147963027257[/C][C]0.183704073945487[/C][C]0.0918520369727433[/C][/ROW]
[ROW][C]60[/C][C]0.8910141617133[/C][C]0.217971676573402[/C][C]0.108985838286701[/C][/ROW]
[ROW][C]61[/C][C]0.868524350873953[/C][C]0.262951298252095[/C][C]0.131475649126047[/C][/ROW]
[ROW][C]62[/C][C]0.880809751009201[/C][C]0.238380497981597[/C][C]0.119190248990799[/C][/ROW]
[ROW][C]63[/C][C]0.896005012538536[/C][C]0.207989974922928[/C][C]0.103994987461464[/C][/ROW]
[ROW][C]64[/C][C]0.874135999071462[/C][C]0.251728001857076[/C][C]0.125864000928538[/C][/ROW]
[ROW][C]65[/C][C]0.859072554717928[/C][C]0.281854890564144[/C][C]0.140927445282072[/C][/ROW]
[ROW][C]66[/C][C]0.891106061109638[/C][C]0.217787877780724[/C][C]0.108893938890362[/C][/ROW]
[ROW][C]67[/C][C]0.868021608395547[/C][C]0.263956783208905[/C][C]0.131978391604453[/C][/ROW]
[ROW][C]68[/C][C]0.86800200103178[/C][C]0.263995997936439[/C][C]0.131997998968219[/C][/ROW]
[ROW][C]69[/C][C]0.891207971393324[/C][C]0.217584057213352[/C][C]0.108792028606676[/C][/ROW]
[ROW][C]70[/C][C]0.87494320827709[/C][C]0.250113583445821[/C][C]0.125056791722911[/C][/ROW]
[ROW][C]71[/C][C]0.890680062610735[/C][C]0.21863987477853[/C][C]0.109319937389265[/C][/ROW]
[ROW][C]72[/C][C]0.872967847052272[/C][C]0.254064305895456[/C][C]0.127032152947728[/C][/ROW]
[ROW][C]73[/C][C]0.865507560508363[/C][C]0.268984878983275[/C][C]0.134492439491637[/C][/ROW]
[ROW][C]74[/C][C]0.844779485967736[/C][C]0.310441028064528[/C][C]0.155220514032264[/C][/ROW]
[ROW][C]75[/C][C]0.831892221658446[/C][C]0.336215556683108[/C][C]0.168107778341554[/C][/ROW]
[ROW][C]76[/C][C]0.854853832726666[/C][C]0.290292334546669[/C][C]0.145146167273334[/C][/ROW]
[ROW][C]77[/C][C]0.857466488772594[/C][C]0.285067022454812[/C][C]0.142533511227406[/C][/ROW]
[ROW][C]78[/C][C]0.835508337527082[/C][C]0.328983324945835[/C][C]0.164491662472918[/C][/ROW]
[ROW][C]79[/C][C]0.804548187673546[/C][C]0.390903624652908[/C][C]0.195451812326454[/C][/ROW]
[ROW][C]80[/C][C]0.776622650188951[/C][C]0.446754699622098[/C][C]0.223377349811049[/C][/ROW]
[ROW][C]81[/C][C]0.781595417516624[/C][C]0.436809164966751[/C][C]0.218404582483376[/C][/ROW]
[ROW][C]82[/C][C]0.780969632514508[/C][C]0.438060734970984[/C][C]0.219030367485492[/C][/ROW]
[ROW][C]83[/C][C]0.803942720295933[/C][C]0.392114559408134[/C][C]0.196057279704067[/C][/ROW]
[ROW][C]84[/C][C]0.78476388881602[/C][C]0.430472222367959[/C][C]0.215236111183979[/C][/ROW]
[ROW][C]85[/C][C]0.804008462900588[/C][C]0.391983074198823[/C][C]0.195991537099412[/C][/ROW]
[ROW][C]86[/C][C]0.842995697949042[/C][C]0.314008604101916[/C][C]0.157004302050958[/C][/ROW]
[ROW][C]87[/C][C]0.838766310220253[/C][C]0.322467379559495[/C][C]0.161233689779747[/C][/ROW]
[ROW][C]88[/C][C]0.8414957448962[/C][C]0.317008510207599[/C][C]0.1585042551038[/C][/ROW]
[ROW][C]89[/C][C]0.812596308372713[/C][C]0.374807383254573[/C][C]0.187403691627287[/C][/ROW]
[ROW][C]90[/C][C]0.803544515174209[/C][C]0.392910969651582[/C][C]0.196455484825791[/C][/ROW]
[ROW][C]91[/C][C]0.769375838031621[/C][C]0.461248323936758[/C][C]0.230624161968379[/C][/ROW]
[ROW][C]92[/C][C]0.734600785449603[/C][C]0.530798429100795[/C][C]0.265399214550397[/C][/ROW]
[ROW][C]93[/C][C]0.704562131339001[/C][C]0.590875737321998[/C][C]0.295437868660999[/C][/ROW]
[ROW][C]94[/C][C]0.663605152364816[/C][C]0.672789695270368[/C][C]0.336394847635184[/C][/ROW]
[ROW][C]95[/C][C]0.626510899433532[/C][C]0.746978201132935[/C][C]0.373489100566468[/C][/ROW]
[ROW][C]96[/C][C]0.627098097096475[/C][C]0.74580380580705[/C][C]0.372901902903525[/C][/ROW]
[ROW][C]97[/C][C]0.680832596699656[/C][C]0.638334806600688[/C][C]0.319167403300344[/C][/ROW]
[ROW][C]98[/C][C]0.667578597753734[/C][C]0.664842804492533[/C][C]0.332421402246266[/C][/ROW]
[ROW][C]99[/C][C]0.6465919228818[/C][C]0.7068161542364[/C][C]0.3534080771182[/C][/ROW]
[ROW][C]100[/C][C]0.602269614272037[/C][C]0.795460771455926[/C][C]0.397730385727963[/C][/ROW]
[ROW][C]101[/C][C]0.622797967130853[/C][C]0.754404065738295[/C][C]0.377202032869147[/C][/ROW]
[ROW][C]102[/C][C]0.585218500808226[/C][C]0.829562998383547[/C][C]0.414781499191774[/C][/ROW]
[ROW][C]103[/C][C]0.547203446307575[/C][C]0.905593107384851[/C][C]0.452796553692425[/C][/ROW]
[ROW][C]104[/C][C]0.50699033396058[/C][C]0.98601933207884[/C][C]0.49300966603942[/C][/ROW]
[ROW][C]105[/C][C]0.463063561050955[/C][C]0.92612712210191[/C][C]0.536936438949045[/C][/ROW]
[ROW][C]106[/C][C]0.468989288188516[/C][C]0.937978576377032[/C][C]0.531010711811484[/C][/ROW]
[ROW][C]107[/C][C]0.438456415987254[/C][C]0.876912831974508[/C][C]0.561543584012746[/C][/ROW]
[ROW][C]108[/C][C]0.403288650806224[/C][C]0.806577301612449[/C][C]0.596711349193776[/C][/ROW]
[ROW][C]109[/C][C]0.451440644399605[/C][C]0.90288128879921[/C][C]0.548559355600395[/C][/ROW]
[ROW][C]110[/C][C]0.40451903715528[/C][C]0.80903807431056[/C][C]0.59548096284472[/C][/ROW]
[ROW][C]111[/C][C]0.39665102797747[/C][C]0.79330205595494[/C][C]0.60334897202253[/C][/ROW]
[ROW][C]112[/C][C]0.410143718720864[/C][C]0.820287437441728[/C][C]0.589856281279136[/C][/ROW]
[ROW][C]113[/C][C]0.403730085101879[/C][C]0.807460170203759[/C][C]0.596269914898121[/C][/ROW]
[ROW][C]114[/C][C]0.383647872538195[/C][C]0.76729574507639[/C][C]0.616352127461805[/C][/ROW]
[ROW][C]115[/C][C]0.365363328439068[/C][C]0.730726656878135[/C][C]0.634636671560932[/C][/ROW]
[ROW][C]116[/C][C]0.335809841531484[/C][C]0.671619683062968[/C][C]0.664190158468516[/C][/ROW]
[ROW][C]117[/C][C]0.375000610858382[/C][C]0.750001221716764[/C][C]0.624999389141618[/C][/ROW]
[ROW][C]118[/C][C]0.46051067883713[/C][C]0.92102135767426[/C][C]0.53948932116287[/C][/ROW]
[ROW][C]119[/C][C]0.482096771386052[/C][C]0.964193542772105[/C][C]0.517903228613948[/C][/ROW]
[ROW][C]120[/C][C]0.818396762074484[/C][C]0.363206475851032[/C][C]0.181603237925516[/C][/ROW]
[ROW][C]121[/C][C]0.837308331733996[/C][C]0.325383336532008[/C][C]0.162691668266004[/C][/ROW]
[ROW][C]122[/C][C]0.831165079631258[/C][C]0.337669840737485[/C][C]0.168834920368742[/C][/ROW]
[ROW][C]123[/C][C]0.796385071841137[/C][C]0.407229856317725[/C][C]0.203614928158863[/C][/ROW]
[ROW][C]124[/C][C]0.757670796093704[/C][C]0.484658407812591[/C][C]0.242329203906296[/C][/ROW]
[ROW][C]125[/C][C]0.750551269612313[/C][C]0.498897460775374[/C][C]0.249448730387687[/C][/ROW]
[ROW][C]126[/C][C]0.744082330354052[/C][C]0.511835339291896[/C][C]0.255917669645948[/C][/ROW]
[ROW][C]127[/C][C]0.701908456475332[/C][C]0.596183087049337[/C][C]0.298091543524668[/C][/ROW]
[ROW][C]128[/C][C]0.749299992931931[/C][C]0.501400014136138[/C][C]0.250700007068069[/C][/ROW]
[ROW][C]129[/C][C]0.707436740539179[/C][C]0.585126518921642[/C][C]0.292563259460821[/C][/ROW]
[ROW][C]130[/C][C]0.653817510950413[/C][C]0.692364978099175[/C][C]0.346182489049587[/C][/ROW]
[ROW][C]131[/C][C]0.60451838005913[/C][C]0.790963239881741[/C][C]0.395481619940871[/C][/ROW]
[ROW][C]132[/C][C]0.624781897315243[/C][C]0.750436205369513[/C][C]0.375218102684757[/C][/ROW]
[ROW][C]133[/C][C]0.585430770298433[/C][C]0.829138459403134[/C][C]0.414569229701567[/C][/ROW]
[ROW][C]134[/C][C]0.548208840725275[/C][C]0.90358231854945[/C][C]0.451791159274725[/C][/ROW]
[ROW][C]135[/C][C]0.512044707671169[/C][C]0.975910584657663[/C][C]0.487955292328831[/C][/ROW]
[ROW][C]136[/C][C]0.498794367014389[/C][C]0.997588734028778[/C][C]0.501205632985611[/C][/ROW]
[ROW][C]137[/C][C]0.489030209258485[/C][C]0.97806041851697[/C][C]0.510969790741515[/C][/ROW]
[ROW][C]138[/C][C]0.426300415807208[/C][C]0.852600831614416[/C][C]0.573699584192792[/C][/ROW]
[ROW][C]139[/C][C]0.455088833387053[/C][C]0.910177666774106[/C][C]0.544911166612947[/C][/ROW]
[ROW][C]140[/C][C]0.515023546852463[/C][C]0.969952906295074[/C][C]0.484976453147537[/C][/ROW]
[ROW][C]141[/C][C]0.65162784731251[/C][C]0.696744305374979[/C][C]0.348372152687489[/C][/ROW]
[ROW][C]142[/C][C]0.62132038742777[/C][C]0.75735922514446[/C][C]0.37867961257223[/C][/ROW]
[ROW][C]143[/C][C]0.833896321806368[/C][C]0.332207356387264[/C][C]0.166103678193632[/C][/ROW]
[ROW][C]144[/C][C]0.797066839343883[/C][C]0.405866321312234[/C][C]0.202933160656117[/C][/ROW]
[ROW][C]145[/C][C]0.763100462533211[/C][C]0.473799074933577[/C][C]0.236899537466789[/C][/ROW]
[ROW][C]146[/C][C]0.691696374262479[/C][C]0.616607251475042[/C][C]0.308303625737521[/C][/ROW]
[ROW][C]147[/C][C]0.621687625607859[/C][C]0.756624748784283[/C][C]0.378312374392141[/C][/ROW]
[ROW][C]148[/C][C]0.805915882408296[/C][C]0.388168235183408[/C][C]0.194084117591704[/C][/ROW]
[ROW][C]149[/C][C]0.796272272424658[/C][C]0.407455455150685[/C][C]0.203727727575342[/C][/ROW]
[ROW][C]150[/C][C]0.730624606427451[/C][C]0.538750787145097[/C][C]0.269375393572549[/C][/ROW]
[ROW][C]151[/C][C]0.640491361507421[/C][C]0.719017276985157[/C][C]0.359508638492579[/C][/ROW]
[ROW][C]152[/C][C]0.528536229448011[/C][C]0.942927541103978[/C][C]0.471463770551989[/C][/ROW]
[ROW][C]153[/C][C]0.657018930237087[/C][C]0.685962139525826[/C][C]0.342981069762913[/C][/ROW]
[ROW][C]154[/C][C]0.514946213953895[/C][C]0.97010757209221[/C][C]0.485053786046105[/C][/ROW]
[ROW][C]155[/C][C]0.760490269487838[/C][C]0.479019461024324[/C][C]0.239509730512162[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155170&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155170&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
180.963090161687730.07381967662454090.0369098383122704
190.9600145803211830.07997083935763450.0399854196788172
200.9693232479138140.06135350417237230.0306767520861861
210.9547921310142170.09041573797156660.0452078689857833
220.9235252821396940.1529494357206120.0764747178603061
230.8983254234211020.2033491531577960.101674576578898
240.9157402113621310.1685195772757390.0842597886378694
250.878618817291960.2427623654160820.121381182708041
260.8819158571560180.2361682856879640.118084142843982
270.838271546741430.3234569065171380.161728453258569
280.8124605952988740.3750788094022520.187539404701126
290.7586516930613810.4826966138772380.241348306938619
300.7796273434012920.4407453131974160.220372656598708
310.8060907042693720.3878185914612560.193909295730628
320.8971685044248850.2056629911502290.102831495575115
330.90429323053310.1914135389338020.0957067694669011
340.9146808089463730.1706383821072540.0853191910536268
350.9121105937661040.1757788124677910.0878894062338957
360.8860007660450430.2279984679099140.113999233954957
370.8882660818382270.2234678363235450.111733918161773
380.8631321043261420.2737357913477150.136867895673858
390.8677471975368450.264505604926310.132252802463155
400.8344873336970630.3310253326058740.165512666302937
410.7944180332702160.4111639334595680.205581966729784
420.7884363387613840.4231273224772330.211563661238616
430.7982910503442130.4034178993115750.201708949655787
440.8452284167995940.3095431664008110.154771583200406
450.8467778741802870.3064442516394270.153222125819713
460.8216895201604150.356620959679170.178310479839585
470.78664792204870.42670415590260.2133520779513
480.7637274558269480.4725450883461050.236272544173052
490.7326880694524720.5346238610950560.267311930547528
500.9525433763618310.0949132472763380.047456623638169
510.9403019739858740.1193960520282520.0596980260141258
520.9485019142285350.102996171542930.0514980857714649
530.9409633767020160.1180732465959670.0590366232979836
540.924026924994410.1519461500111780.0759730750055892
550.9348797313464620.1302405373070760.065120268653538
560.9388812656867890.1222374686264220.061118734313211
570.9241180750015840.1517638499968330.0758819249984164
580.9242519624560560.1514960750878880.0757480375439442
590.9081479630272570.1837040739454870.0918520369727433
600.89101416171330.2179716765734020.108985838286701
610.8685243508739530.2629512982520950.131475649126047
620.8808097510092010.2383804979815970.119190248990799
630.8960050125385360.2079899749229280.103994987461464
640.8741359990714620.2517280018570760.125864000928538
650.8590725547179280.2818548905641440.140927445282072
660.8911060611096380.2177878777807240.108893938890362
670.8680216083955470.2639567832089050.131978391604453
680.868002001031780.2639959979364390.131997998968219
690.8912079713933240.2175840572133520.108792028606676
700.874943208277090.2501135834458210.125056791722911
710.8906800626107350.218639874778530.109319937389265
720.8729678470522720.2540643058954560.127032152947728
730.8655075605083630.2689848789832750.134492439491637
740.8447794859677360.3104410280645280.155220514032264
750.8318922216584460.3362155566831080.168107778341554
760.8548538327266660.2902923345466690.145146167273334
770.8574664887725940.2850670224548120.142533511227406
780.8355083375270820.3289833249458350.164491662472918
790.8045481876735460.3909036246529080.195451812326454
800.7766226501889510.4467546996220980.223377349811049
810.7815954175166240.4368091649667510.218404582483376
820.7809696325145080.4380607349709840.219030367485492
830.8039427202959330.3921145594081340.196057279704067
840.784763888816020.4304722223679590.215236111183979
850.8040084629005880.3919830741988230.195991537099412
860.8429956979490420.3140086041019160.157004302050958
870.8387663102202530.3224673795594950.161233689779747
880.84149574489620.3170085102075990.1585042551038
890.8125963083727130.3748073832545730.187403691627287
900.8035445151742090.3929109696515820.196455484825791
910.7693758380316210.4612483239367580.230624161968379
920.7346007854496030.5307984291007950.265399214550397
930.7045621313390010.5908757373219980.295437868660999
940.6636051523648160.6727896952703680.336394847635184
950.6265108994335320.7469782011329350.373489100566468
960.6270980970964750.745803805807050.372901902903525
970.6808325966996560.6383348066006880.319167403300344
980.6675785977537340.6648428044925330.332421402246266
990.64659192288180.70681615423640.3534080771182
1000.6022696142720370.7954607714559260.397730385727963
1010.6227979671308530.7544040657382950.377202032869147
1020.5852185008082260.8295629983835470.414781499191774
1030.5472034463075750.9055931073848510.452796553692425
1040.506990333960580.986019332078840.49300966603942
1050.4630635610509550.926127122101910.536936438949045
1060.4689892881885160.9379785763770320.531010711811484
1070.4384564159872540.8769128319745080.561543584012746
1080.4032886508062240.8065773016124490.596711349193776
1090.4514406443996050.902881288799210.548559355600395
1100.404519037155280.809038074310560.59548096284472
1110.396651027977470.793302055954940.60334897202253
1120.4101437187208640.8202874374417280.589856281279136
1130.4037300851018790.8074601702037590.596269914898121
1140.3836478725381950.767295745076390.616352127461805
1150.3653633284390680.7307266568781350.634636671560932
1160.3358098415314840.6716196830629680.664190158468516
1170.3750006108583820.7500012217167640.624999389141618
1180.460510678837130.921021357674260.53948932116287
1190.4820967713860520.9641935427721050.517903228613948
1200.8183967620744840.3632064758510320.181603237925516
1210.8373083317339960.3253833365320080.162691668266004
1220.8311650796312580.3376698407374850.168834920368742
1230.7963850718411370.4072298563177250.203614928158863
1240.7576707960937040.4846584078125910.242329203906296
1250.7505512696123130.4988974607753740.249448730387687
1260.7440823303540520.5118353392918960.255917669645948
1270.7019084564753320.5961830870493370.298091543524668
1280.7492999929319310.5014000141361380.250700007068069
1290.7074367405391790.5851265189216420.292563259460821
1300.6538175109504130.6923649780991750.346182489049587
1310.604518380059130.7909632398817410.395481619940871
1320.6247818973152430.7504362053695130.375218102684757
1330.5854307702984330.8291384594031340.414569229701567
1340.5482088407252750.903582318549450.451791159274725
1350.5120447076711690.9759105846576630.487955292328831
1360.4987943670143890.9975887340287780.501205632985611
1370.4890302092584850.978060418516970.510969790741515
1380.4263004158072080.8526008316144160.573699584192792
1390.4550888333870530.9101776667741060.544911166612947
1400.5150235468524630.9699529062950740.484976453147537
1410.651627847312510.6967443053749790.348372152687489
1420.621320387427770.757359225144460.37867961257223
1430.8338963218063680.3322073563872640.166103678193632
1440.7970668393438830.4058663213122340.202933160656117
1450.7631004625332110.4737990749335770.236899537466789
1460.6916963742624790.6166072514750420.308303625737521
1470.6216876256078590.7566247487842830.378312374392141
1480.8059158824082960.3881682351834080.194084117591704
1490.7962722724246580.4074554551506850.203727727575342
1500.7306246064274510.5387507871450970.269375393572549
1510.6404913615074210.7190172769851570.359508638492579
1520.5285362294480110.9429275411039780.471463770551989
1530.6570189302370870.6859621395258260.342981069762913
1540.5149462139538950.970107572092210.485053786046105
1550.7604902694878380.4790194610243240.239509730512162






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level50.036231884057971OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 5 & 0.036231884057971 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155170&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]5[/C][C]0.036231884057971[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155170&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155170&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level50.036231884057971OK


Figures (Output of Computation)

Figure 1
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Figure 2
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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 = kendall ;
Parameters (R input):
par1 = 15 ; 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')
}