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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 computationThu, 22 Dec 2011 14:36:57 -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/22/t1324582627g5uce594oooynwb.htm/, Retrieved Fri, 03 May 2024 12:25:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=159906, Retrieved Fri, 03 May 2024 12:25:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Central Tendency] [] [2011-12-22 19:32:39] [30b3e197115d238a51c18bcedc33a6a5]
- RM D    [Multiple Regression] [] [2011-12-22 19:36:57] [694c30abd2a3b2ee5cb46fc74cb5bfb9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1845	162687	95	595	115	0	48	21	82	73	20465	6200	23975	39	37
1797	201906	63	545	76	1	58	20	80	56	33629	10265	85634	46	43
192	7215	18	72	1	0	0	0	0	0	1423	603	1929	0	0
2444	146367	97	679	155	0	67	27	84	63	25629	8874	36294	54	54
3567	257045	139	1201	125	0	83	31	124	116	54002	20323	72255	93	86
6953	528532	268	1975	278	1	137	36	140	138	151036	26258	189748	198	181
1873	191582	60	602	93	1	65	26	100	76	33287	10165	61834	42	42
1740	195674	60	496	59	0	86	30	115	107	31172	8247	68167	59	59
2079	177020	45	670	87	0	62	30	109	50	28113	8683	38462	49	46
3120	330255	100	1047	130	1	72	27	108	81	57803	16957	101219	83	77
1946	121844	75	634	158	2	50	24	63	58	49830	8058	43270	49	49
2370	203938	72	743	120	0	88	30	118	91	52143	20488	76183	83	79
1962	116737	108	692	87	0	62	22	71	41	21055	7945	31476	39	37
3198	220751	120	1086	264	4	79	28	112	100	47007	13448	62157	93	92
1496	173259	65	420	51	4	56	18	63	61	28735	5389	46261	31	31
1574	156326	89	474	85	3	54	22	86	74	59147	6185	50063	29	28
1808	145178	59	442	100	0	81	37	148	147	78950	24369	64483	104	103
1309	89171	61	373	72	5	13	15	54	45	13497	70	2341	2	2
2820	172624	88	899	147	0	74	34	134	110	46154	17327	48149	46	48
799	43391	28	253	49	0	18	18	57	41	53249	3878	12743	27	25
1162	87927	62	399	40	0	31	15	59	37	10726	3149	18743	16	16
2818	241285	103	850	99	0	99	30	113	84	83700	20517	97057	108	106
1780	200429	75	648	127	1	38	25	96	67	40400	2570	17675	36	35
2316	146946	57	717	164	1	59	34	96	69	33797	5162	33106	33	33
1994	159763	89	619	41	1	54	21	78	58	36205	5299	53311	46	45
1806	207078	34	657	160	0	63	21	80	60	30165	7233	42754	65	64
2153	212394	167	691	92	0	66	25	93	88	58534	15657	59056	80	73
1458	201536	96	366	59	0	90	31	109	75	44663	15329	101621	81	78
3000	394662	121	994	89	0	72	31	115	98	92556	14881	118120	69	63
2236	217892	46	929	90	0	61	20	79	67	40078	16318	79572	69	69
1685	182286	45	490	76	0	61	28	103	84	34711	9556	42744	37	36
1667	188748	48	574	116	2	63	22	71	62	31076	10462	65931	45	41
2257	137978	107	738	92	4	53	17	66	35	74608	7192	38575	62	59
3402	259627	132	1039	361	0	120	25	100	74	58092	4362	28795	33	33
2571	236489	55	844	85	1	73	25	100	93	42009	14349	94440	77	76
1	0	1	0	0	0	0	0	0	0	0	0	0	0	0
2143	230761	65	1000	63	0	54	31	121	87	36022	10881	38229	34	27
1878	132807	54	629	138	3	54	14	51	39	23333	8022	31972	44	44
2224	161703	52	547	270	9	46	35	119	101	53349	13073	40071	43	43
2186	253254	68	811	64	0	83	34	136	135	92596	26641	132480	117	104
2533	269329	72	837	96	2	106	22	84	76	49598	14426	62797	125	120
1823	161273	61	682	62	0	44	34	136	118	44093	15604	40429	49	44
1095	107181	33	400	35	2	27	23	84	76	84205	9184	45545	76	71
2303	213097	81	831	66	1	73	24	92	65	63369	5989	57568	81	78
1365	139667	51	419	56	2	71	26	103	97	60132	11270	39019	111	106
1245	171101	99	334	41	2	44	23	85	70	37403	13958	53866	61	61
756	81407	33	216	49	1	23	35	106	63	24460	7162	38345	56	53
2419	247596	106	786	121	0	78	24	96	96	46456	13275	50210	54	51
2327	239807	90	752	113	1	60	31	124	112	66616	21224	80947	47	46
2787	172743	60	964	190	8	73	30	106	82	41554	10615	43461	55	55
658	48188	28	205	37	0	12	22	82	39	22346	2102	14812	14	14
2013	169355	71	506	52	0	104	23	87	69	30874	12396	37819	44	44
2616	325322	77	830	89	0	86	27	97	93	68701	18717	102738	115	113
2072	241518	80	694	73	0	57	30	107	76	35728	9724	54509	57	55
1912	195583	60	691	49	1	67	33	126	117	29010	9863	62956	48	46
1775	159913	57	547	77	8	44	12	43	31	23110	8374	55411	40	39
1943	223936	71	547	58	0	53	26	96	65	38844	8030	50611	51	51
1047	101694	26	329	75	1	26	26	100	78	27084	7509	26692	32	31
1190	157258	68	427	32	0	67	23	91	87	35139	14146	60056	36	36
2932	211586	101	993	59	10	36	38	136	85	57476	7768	25155	47	47
1868	181076	66	564	71	6	56	32	128	119	33277	13823	42840	51	53
2255	150518	84	836	91	0	52	21	83	65	31141	7230	39358	37	38
1392	141491	64	376	87	11	54	22	74	60	61281	10170	47241	52	52
1355	130108	40	471	48	3	61	26	96	67	25820	7573	49611	42	37
1321	166351	38	431	63	0	27	28	102	94	23284	5753	41833	11	11
1526	124197	43	483	41	0	58	33	122	100	35378	9791	48930	47	45
2335	195043	71	504	86	8	76	36	144	135	74990	19365	110600	59	59
2898	138708	66	887	152	2	93	25	90	71	29653	9422	52235	82	82
1118	116552	40	271	49	0	59	25	97	78	64622	12310	53986	49	49
340	31970	15	101	40	0	5	21	78	42	4157	1283	4105	6	6
2977	258158	115	1097	135	3	57	19	72	42	29245	6372	59331	83	81
1452	151194	79	470	83	1	42	12	45	8	50008	5413	47796	56	56
1550	135926	68	528	62	2	88	30	120	86	52338	10837	38302	114	105
1685	119629	73	475	91	1	53	21	59	41	13310	3394	14063	46	46
2728	171518	71	698	95	0	81	39	150	131	92901	12964	54414	46	46
1574	108949	45	425	82	2	35	32	117	91	10956	3495	9903	2	2
2413	183471	60	709	112	1	102	28	123	102	34241	11580	53987	51	51
2563	159966	98	824	70	0	71	29	114	91	75043	9970	88937	96	95
1080	93786	35	336	78	0	28	21	75	46	21152	4911	21928	20	18
1235	84971	72	395	105	0	34	31	114	60	42249	10138	29487	57	55
980	88882	76	234	49	0	54	26	94	69	42005	14697	35334	49	48
2246	304603	65	830	60	0	49	29	116	95	41152	8464	57596	51	48
1076	75101	30	334	49	1	30	23	86	17	14399	4204	29750	40	39
1638	145043	41	524	132	0	57	25	90	61	28263	10226	41029	40	40
1208	95827	48	393	49	0	54	22	87	55	17215	3456	12416	36	36
1866	173924	59	574	71	0	38	26	99	55	48140	8895	51158	64	60
2727	241957	238	672	102	0	63	33	132	124	62897	22557	79935	117	114
1208	115367	115	284	74	0	58	24	96	73	22883	6900	26552	40	39
1427	118689	65	452	49	7	46	24	91	73	41622	8620	25807	46	45
1610	164078	54	653	74	0	46	21	77	67	40715	7820	50620	61	59
1865	158931	42	684	59	5	51	28	104	66	65897	12112	61467	59	59
2413	184139	83	706	91	1	87	28	100	77	76542	13178	65292	94	93
1238	152856	58	417	68	0	39	25	94	83	37477	7028	55516	36	35
1468	146159	61	551	81	0	28	15	60	55	53216	6616	42006	51	47
974	62535	43	394	33	0	26	13	46	27	40911	9570	26273	39	36
2319	245196	117	730	166	0	52	36	135	115	57021	14612	90248	62	59
1890	199841	71	571	97	0	96	27	99	85	73116	11219	61476	79	79
223	19349	12	67	15	0	13	1	2	0	3895	786	9604	14	14
2527	247280	109	877	105	3	43	24	96	83	46609	11252	45108	45	42
2105	164457	86	865	61	0	42	31	109	90	29351	9289	47232	43	41
778	72128	30	306	11	0	30	4	15	4	2325	593	3439	8	8
1194	104253	26	382	45	0	59	21	68	60	31747	6562	30553	41	41
1424	151090	57	435	89	0	73	27	102	74	32665	8208	24751	25	24
1386	147990	68	348	72	1	40	26	93	55	19249	7488	34458	22	22
839	87448	42	227	27	1	36	12	46	24	15292	4574	24649	18	18
596	27676	22	194	59	0	2	16	59	17	5842	522	2342	3	1
1684	170326	52	413	127	0	103	29	116	105	33994	12840	52739	54	53
1168	132148	38	273	48	1	30	26	29	20	13018	1350	6245	6	6
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1106	95778	34	343	58	0	46	25	91	51	98177	10623	35381	50	49
1149	109001	68	376	57	0	25	21	76	76	37941	5322	19595	33	33
1485	158833	46	495	60	0	59	24	86	61	31032	7987	50848	54	50
1529	150013	66	448	77	1	60	21	84	70	32683	10566	39443	63	64
962	89887	63	313	71	0	36	21	65	38	34545	1900	27023	56	53
78	3616	5	14	5	0	0	0	0	0	0	0	0	0	0
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1184	199005	45	410	70	0	45	23	84	81	27525	10698	61022	49	48
1672	160930	93	606	76	0	79	33	114	78	66856	14884	63528	90	90
2142	177948	102	593	124	2	30	32	132	76	28549	6852	34835	51	46
1016	136061	40	312	56	0	43	23	92	89	38610	6873	37172	29	29
778	43410	19	292	63	0	7	1	3	3	2781	4	13	1	1
1857	184277	75	547	92	1	80	29	109	87	41211	9188	62548	68	64
1084	109873	45	315	58	0	32	20	81	55	22698	5141	31334	29	29
2297	151030	59	660	64	8	84	33	121	73	41194	4260	20839	27	27
731	60493	40	174	29	3	3	12	48	32	32689	443	5084	4	4
285	19764	12	75	19	1	10	2	8	4	5752	2416	9927	10	10
1872	177559	56	572	64	3	47	21	80	70	26757	9831	53229	47	47
1181	140281	35	389	79	0	35	28	107	102	22527	5953	29877	44	44
1725	164249	54	562	104	0	54	35	140	109	44810	9435	37310	53	51
256	11796	9	79	22	0	1	2	8	1	0	0	0	0	0
98	10674	9	33	7	0	0	0	0	0	0	0	0	0	0
1435	151322	59	487	37	0	46	18	56	39	100674	7642	50067	40	38
41	6836	3	11	5	0	0	1	4	0	0	0	0	0	0
1931	174712	68	664	48	6	51	21	70	45	57786	6837	47708	57	57
42	5118	3	6	1	0	5	0	0	0	0	0	0	0	0
528	40248	16	183	34	1	8	4	14	7	5444	775	6012	6	6
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1122	127628	51	342	53	0	38	29	104	86	28470	8191	27749	24	22
1305	88837	38	269	44	0	21	26	89	52	61849	1661	47555	34	34
81	7131	4	27	0	1	0	0	0	0	0	0	0	0	0
262	9056	15	99	18	0	0	4	12	1	2179	548	1336	10	10
1104	88589	29	306	52	1	18	19	60	49	8019	3080	11017	16	16
1290	144470	53	327	56	0	53	22	84	72	39644	13400	55184	93	93
1248	111408	20	459	50	1	17	22	88	56	23494	8181	43485	28	22

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159906&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159906&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159906&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 time6 seconds
R Server'AstonUniversity' @ aston.wessa.net






Multiple Linear Regression - Estimated Regression Equation
8[t] = + 1.20459940424296 + 0.00128909375053522`1`[t] + 6.648297013932e-06`2`[t] -0.00692747823873591`3`[t] -0.0042136213691442`4`[t] + 0.00474728575912808`5`[t] + 0.0447396863265375`6`[t] -0.00287173144943809`7`[t] + 0.27252073211154`9`[t] -0.0286784645483342`10`[t] + 1.3879979421038e-05`11`[t] -6.62549715279163e-05`12`[t] -9.18853259342673e-06`13`[t] -0.0711256107820883`14`[t] + 0.0760990447886487`15`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
8[t] =  +  1.20459940424296 +  0.00128909375053522`1`[t] +  6.648297013932e-06`2`[t] -0.00692747823873591`3`[t] -0.0042136213691442`4`[t] +  0.00474728575912808`5`[t] +  0.0447396863265375`6`[t] -0.00287173144943809`7`[t] +  0.27252073211154`9`[t] -0.0286784645483342`10`[t] +  1.3879979421038e-05`11`[t] -6.62549715279163e-05`12`[t] -9.18853259342673e-06`13`[t] -0.0711256107820883`14`[t] +  0.0760990447886487`15`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159906&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]8[t] =  +  1.20459940424296 +  0.00128909375053522`1`[t] +  6.648297013932e-06`2`[t] -0.00692747823873591`3`[t] -0.0042136213691442`4`[t] +  0.00474728575912808`5`[t] +  0.0447396863265375`6`[t] -0.00287173144943809`7`[t] +  0.27252073211154`9`[t] -0.0286784645483342`10`[t] +  1.3879979421038e-05`11`[t] -6.62549715279163e-05`12`[t] -9.18853259342673e-06`13`[t] -0.0711256107820883`14`[t] +  0.0760990447886487`15`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159906&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159906&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
8[t] = + 1.20459940424296 + 0.00128909375053522`1`[t] + 6.648297013932e-06`2`[t] -0.00692747823873591`3`[t] -0.0042136213691442`4`[t] + 0.00474728575912808`5`[t] + 0.0447396863265375`6`[t] -0.00287173144943809`7`[t] + 0.27252073211154`9`[t] -0.0286784645483342`10`[t] + 1.3879979421038e-05`11`[t] -6.62549715279163e-05`12`[t] -9.18853259342673e-06`13`[t] -0.0711256107820883`14`[t] + 0.0760990447886487`15`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.204599404242960.5160092.33450.0211170.010558
`1`0.001289093750535220.0012531.02840.305680.15284
`2`6.648297013932e-067e-060.90220.3686360.184318
`3`-0.006927478238735910.009807-0.70630.4812450.240622
`4`-0.00421362136914420.002992-1.40810.16150.08075
`5`0.004747285759128080.0059780.79420.4285620.214281
`6`0.04473968632653750.096850.46190.6448970.322449
`7`-0.002871731449438090.014589-0.19680.8442580.422129
`9`0.272520732111540.01467618.568600
`10`-0.02867846454833420.018028-1.59070.1141180.057059
`11`1.3879979421038e-051.4e-051.02690.3063910.153195
`12`-6.62549715279163e-058.3e-05-0.79370.428810.214405
`13`-9.18853259342673e-061.8e-05-0.49720.6198760.309938
`14`-0.07112561078208830.103196-0.68920.4919190.245959
`15`0.07609904478864870.1067280.7130.4771210.23856

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 1.20459940424296 & 0.516009 & 2.3345 & 0.021117 & 0.010558 \tabularnewline
`1` & 0.00128909375053522 & 0.001253 & 1.0284 & 0.30568 & 0.15284 \tabularnewline
`2` & 6.648297013932e-06 & 7e-06 & 0.9022 & 0.368636 & 0.184318 \tabularnewline
`3` & -0.00692747823873591 & 0.009807 & -0.7063 & 0.481245 & 0.240622 \tabularnewline
`4` & -0.0042136213691442 & 0.002992 & -1.4081 & 0.1615 & 0.08075 \tabularnewline
`5` & 0.00474728575912808 & 0.005978 & 0.7942 & 0.428562 & 0.214281 \tabularnewline
`6` & 0.0447396863265375 & 0.09685 & 0.4619 & 0.644897 & 0.322449 \tabularnewline
`7` & -0.00287173144943809 & 0.014589 & -0.1968 & 0.844258 & 0.422129 \tabularnewline
`9` & 0.27252073211154 & 0.014676 & 18.5686 & 0 & 0 \tabularnewline
`10` & -0.0286784645483342 & 0.018028 & -1.5907 & 0.114118 & 0.057059 \tabularnewline
`11` & 1.3879979421038e-05 & 1.4e-05 & 1.0269 & 0.306391 & 0.153195 \tabularnewline
`12` & -6.62549715279163e-05 & 8.3e-05 & -0.7937 & 0.42881 & 0.214405 \tabularnewline
`13` & -9.18853259342673e-06 & 1.8e-05 & -0.4972 & 0.619876 & 0.309938 \tabularnewline
`14` & -0.0711256107820883 & 0.103196 & -0.6892 & 0.491919 & 0.245959 \tabularnewline
`15` & 0.0760990447886487 & 0.106728 & 0.713 & 0.477121 & 0.23856 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159906&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]1.20459940424296[/C][C]0.516009[/C][C]2.3345[/C][C]0.021117[/C][C]0.010558[/C][/ROW]
[ROW][C]`1`[/C][C]0.00128909375053522[/C][C]0.001253[/C][C]1.0284[/C][C]0.30568[/C][C]0.15284[/C][/ROW]
[ROW][C]`2`[/C][C]6.648297013932e-06[/C][C]7e-06[/C][C]0.9022[/C][C]0.368636[/C][C]0.184318[/C][/ROW]
[ROW][C]`3`[/C][C]-0.00692747823873591[/C][C]0.009807[/C][C]-0.7063[/C][C]0.481245[/C][C]0.240622[/C][/ROW]
[ROW][C]`4`[/C][C]-0.0042136213691442[/C][C]0.002992[/C][C]-1.4081[/C][C]0.1615[/C][C]0.08075[/C][/ROW]
[ROW][C]`5`[/C][C]0.00474728575912808[/C][C]0.005978[/C][C]0.7942[/C][C]0.428562[/C][C]0.214281[/C][/ROW]
[ROW][C]`6`[/C][C]0.0447396863265375[/C][C]0.09685[/C][C]0.4619[/C][C]0.644897[/C][C]0.322449[/C][/ROW]
[ROW][C]`7`[/C][C]-0.00287173144943809[/C][C]0.014589[/C][C]-0.1968[/C][C]0.844258[/C][C]0.422129[/C][/ROW]
[ROW][C]`9`[/C][C]0.27252073211154[/C][C]0.014676[/C][C]18.5686[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`10`[/C][C]-0.0286784645483342[/C][C]0.018028[/C][C]-1.5907[/C][C]0.114118[/C][C]0.057059[/C][/ROW]
[ROW][C]`11`[/C][C]1.3879979421038e-05[/C][C]1.4e-05[/C][C]1.0269[/C][C]0.306391[/C][C]0.153195[/C][/ROW]
[ROW][C]`12`[/C][C]-6.62549715279163e-05[/C][C]8.3e-05[/C][C]-0.7937[/C][C]0.42881[/C][C]0.214405[/C][/ROW]
[ROW][C]`13`[/C][C]-9.18853259342673e-06[/C][C]1.8e-05[/C][C]-0.4972[/C][C]0.619876[/C][C]0.309938[/C][/ROW]
[ROW][C]`14`[/C][C]-0.0711256107820883[/C][C]0.103196[/C][C]-0.6892[/C][C]0.491919[/C][C]0.245959[/C][/ROW]
[ROW][C]`15`[/C][C]0.0760990447886487[/C][C]0.106728[/C][C]0.713[/C][C]0.477121[/C][C]0.23856[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159906&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159906&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.204599404242960.5160092.33450.0211170.010558
`1`0.001289093750535220.0012531.02840.305680.15284
`2`6.648297013932e-067e-060.90220.3686360.184318
`3`-0.006927478238735910.009807-0.70630.4812450.240622
`4`-0.00421362136914420.002992-1.40810.16150.08075
`5`0.004747285759128080.0059780.79420.4285620.214281
`6`0.04473968632653750.096850.46190.6448970.322449
`7`-0.002871731449438090.014589-0.19680.8442580.422129
`9`0.272520732111540.01467618.568600
`10`-0.02867846454833420.018028-1.59070.1141180.057059
`11`1.3879979421038e-051.4e-051.02690.3063910.153195
`12`-6.62549715279163e-058.3e-05-0.79370.428810.214405
`13`-9.18853259342673e-061.8e-05-0.49720.6198760.309938
`14`-0.07112561078208830.103196-0.68920.4919190.245959
`15`0.07609904478864870.1067280.7130.4771210.23856






Multiple Linear Regression - Regression Statistics
Multiple R0.973556726955744
R-squared0.94781270060078
Adjusted R-squared0.942148962681485
F-TEST (value)167.347556349982
F-TEST (DF numerator)14
F-TEST (DF denominator)129
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.33697641315457
Sum Squared Residuals704.528179477665

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.973556726955744 \tabularnewline
R-squared & 0.94781270060078 \tabularnewline
Adjusted R-squared & 0.942148962681485 \tabularnewline
F-TEST (value) & 167.347556349982 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 129 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.33697641315457 \tabularnewline
Sum Squared Residuals & 704.528179477665 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159906&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.973556726955744[/C][/ROW]
[ROW][C]R-squared[/C][C]0.94781270060078[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.942148962681485[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]167.347556349982[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]129[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.33697641315457[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]704.528179477665[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159906&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159906&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 R0.973556726955744
R-squared0.94781270060078
Adjusted R-squared0.942148962681485
F-TEST (value)167.347556349982
F-TEST (DF numerator)14
F-TEST (DF denominator)129
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.33697641315457
Sum Squared Residuals704.528179477665






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12121.8553643199395-0.855364319939483
22021.5655072648414-1.56550726484143
301.03881958969683-1.03881958969683
42723.12650324681123.87349675318879
53130.97812097151820.0218790284818334
63636.9735759225877-0.973575922587692
72626.7418634954626-0.741863495462559
83030.1006658213637-0.100665821363717
93029.90935482989620.0906451701038122
102728.5871670206802-1.58716702068022
112417.53752053253676.46247946746329
123030.6254616928958-0.625461692895804
132218.77242620138233.22757379861769
142829.8240582128567-1.82405821285674
151817.51552594525230.484474054747683
162223.3756711677079-1.37567116770788
173737.9186688190621-0.918668819062058
181515.6154973477152-0.61549734771525
193434.8694996671514-0.869499667151422
201816.14898989192491.85101010807511
211516.1424343701788-1.14243437017877
223030.0133455010123-0.0133455010122977
232526.6917757855589-1.69177578555889
243426.57510829003117.42489170996887
252121.1042667122143-0.104266712214321
262122.3589276165975-1.35892761659745
272523.48915468585681.51084531414317
283128.63735714768882.36264285231118
293130.51424313120970.485756868790257
302020.249416545234-0.249416545233974
312827.62228417050270.377715829497278
322218.93916038422923.06083961577082
331718.9112991897771-1.91129918977714
342528.9396743469468-3.9396743469468
352526.0488812235485-1.04888122354854
3601.19896101975477-1.19896101975477
373130.52557450225430.4744254977457
381414.6157939094128-0.615793909412814
393533.28727635934111.71272364065885
403432.97008941222761.02991058777238
412222.5846637509065-0.584663750906481
423434.2471656682827-0.247165668282727
432322.44422072280080.555779277199162
442425.0120456419251-1.01204564192508
452627.114176108424-1.11417610842404
462322.57120907963170.428790920368293
473528.43625147777446.56374852222563
482425.0262597120849-1.0262597120849
493131.9282756543179-0.928275654317925
503028.80166786119151.19833213880853
512222.7892797700079-0.789279770007886
522323.4587763654525-0.458776365452536
532725.84114723948251.15885276051746
543028.64792224961651.3520777503835
553331.96680094866011.03319905133995
561212.6622739021746-0.662273902174621
572626.6181614625583-0.618161462558251
582626.721458002766-0.721458002766041
592322.95540423841030.0445957615897356
603837.04207294186010.957927058139886
613233.4556205104707-1.45562051047069
622121.8971789155335-0.897178915533494
632221.1092241299860.890775870013988
642625.21121846777210.788781532227943
652826.86934986334611.13065013665394
663331.54645737573031.45354262426966
673637.8501592017084-1.85015920170837
682524.4177473242210.582252675778987
692526.0914631134217-1.09146311342175
702121.51841939361-0.518419393610029
711920.0671264908069-1.06712649080691
721214.0808392870156-2.08083928701563
733031.3163352934895-1.31633529348948
742116.95175529045814.04824470954193
753939.9275316220066-0.927531622006616
763231.34832502243710.651674977562895
772832.4758274602514-4.47582746025143
782929.9723548746096-0.972354874609618
792120.68582027533670.314179724663293
803130.72093321839060.279066781609435
812624.7142385669251.285761433075
822930.7160085695843-1.71600856958427
832324.3870393529168-1.38703935291683
842524.56559986238780.43440013761217
852223.6948959744727-1.69489597447266
862627.1918561777608-1.19185617776082
873333.5657827469506-0.56578274695056
882425.5280120265938-1.52801202659381
892424.5200719438693-0.520071943869273
902120.26020713818810.739792861811914
912828.1398259683297-0.139825968329747
922827.24108895472850.75891104527146
932524.75268392590790.247316074092135
941516.2660355170675-1.26603551706749
951312.41955036964840.58044963035157
963635.14282165274910.857178347250912
972726.89800475808920.101995241910776
9811.81754860199327-0.817548601993275
992425.4290285541552-1.42902855415523
1003127.48324046625313.51675953374689
10145.13018540890863-1.13018540890863
1022118.43111820953932.56888179046066
1032727.4351290378507-0.435129037850707
1042625.6403991892160.359600810784034
1051213.3095238767683-1.30952387676827
1061616.9402905887509-0.940290588750933
1072930.6446342800903-1.64463428009031
108269.7549502299001716.2450497700998
10901.20459940424297-1.20459940424297
1102525.5726605124717-0.57266051247171
1112120.24394869501960.756051304980422
1122422.97176343655761.02823656344244
1132122.7308560078014-1.73085600780144
1142118.30026223642442.69973776357565
11501.25929729722103-1.25929729722103
11601.20459940424297-1.20459940424297
1172322.06662476068560.93337523931436
1183330.00223559676632.99776440323366
1193235.8241211641039-3.82412116410392
1202324.3722401964845-1.37224019648448
12112.18781026054333-1.18781026054333
1222928.88297796581990.117022034180054
1232022.2044761043166-2.20447610431663
1243333.5143563194516-0.514356319451571
1251214.3629434780494-2.3629434780494
12623.3541949311618-1.3541949311618
1272121.5620466713726-0.562046671372563
1282828.1496921112359-0.149692111235947
1293536.909133289534-1.90913328953404
13023.47086327124067-1.47086327124067
13101.23372870510564-1.23372870510564
1321816.26370808656661.73629191343337
13312.34958709386716-1.34958709386716
1342119.9164359445821.08356405541797
13501.23709179146362-1.23709179146362
13645.06741789975541-1.06741789975541
13701.20459940424297-1.20459940424297
1382927.28806036179021.71193963820977
1392625.472961831770.52703816822999
14001.25968700044736-1.25968700044736
14144.43990493125143-0.439904931251428
1421916.79794954784942.20205045215057
1432222.6414482289212-0.641448228921245
1442223.1577379142531-1.15773791425305

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 21 & 21.8553643199395 & -0.855364319939483 \tabularnewline
2 & 20 & 21.5655072648414 & -1.56550726484143 \tabularnewline
3 & 0 & 1.03881958969683 & -1.03881958969683 \tabularnewline
4 & 27 & 23.1265032468112 & 3.87349675318879 \tabularnewline
5 & 31 & 30.9781209715182 & 0.0218790284818334 \tabularnewline
6 & 36 & 36.9735759225877 & -0.973575922587692 \tabularnewline
7 & 26 & 26.7418634954626 & -0.741863495462559 \tabularnewline
8 & 30 & 30.1006658213637 & -0.100665821363717 \tabularnewline
9 & 30 & 29.9093548298962 & 0.0906451701038122 \tabularnewline
10 & 27 & 28.5871670206802 & -1.58716702068022 \tabularnewline
11 & 24 & 17.5375205325367 & 6.46247946746329 \tabularnewline
12 & 30 & 30.6254616928958 & -0.625461692895804 \tabularnewline
13 & 22 & 18.7724262013823 & 3.22757379861769 \tabularnewline
14 & 28 & 29.8240582128567 & -1.82405821285674 \tabularnewline
15 & 18 & 17.5155259452523 & 0.484474054747683 \tabularnewline
16 & 22 & 23.3756711677079 & -1.37567116770788 \tabularnewline
17 & 37 & 37.9186688190621 & -0.918668819062058 \tabularnewline
18 & 15 & 15.6154973477152 & -0.61549734771525 \tabularnewline
19 & 34 & 34.8694996671514 & -0.869499667151422 \tabularnewline
20 & 18 & 16.1489898919249 & 1.85101010807511 \tabularnewline
21 & 15 & 16.1424343701788 & -1.14243437017877 \tabularnewline
22 & 30 & 30.0133455010123 & -0.0133455010122977 \tabularnewline
23 & 25 & 26.6917757855589 & -1.69177578555889 \tabularnewline
24 & 34 & 26.5751082900311 & 7.42489170996887 \tabularnewline
25 & 21 & 21.1042667122143 & -0.104266712214321 \tabularnewline
26 & 21 & 22.3589276165975 & -1.35892761659745 \tabularnewline
27 & 25 & 23.4891546858568 & 1.51084531414317 \tabularnewline
28 & 31 & 28.6373571476888 & 2.36264285231118 \tabularnewline
29 & 31 & 30.5142431312097 & 0.485756868790257 \tabularnewline
30 & 20 & 20.249416545234 & -0.249416545233974 \tabularnewline
31 & 28 & 27.6222841705027 & 0.377715829497278 \tabularnewline
32 & 22 & 18.9391603842292 & 3.06083961577082 \tabularnewline
33 & 17 & 18.9112991897771 & -1.91129918977714 \tabularnewline
34 & 25 & 28.9396743469468 & -3.9396743469468 \tabularnewline
35 & 25 & 26.0488812235485 & -1.04888122354854 \tabularnewline
36 & 0 & 1.19896101975477 & -1.19896101975477 \tabularnewline
37 & 31 & 30.5255745022543 & 0.4744254977457 \tabularnewline
38 & 14 & 14.6157939094128 & -0.615793909412814 \tabularnewline
39 & 35 & 33.2872763593411 & 1.71272364065885 \tabularnewline
40 & 34 & 32.9700894122276 & 1.02991058777238 \tabularnewline
41 & 22 & 22.5846637509065 & -0.584663750906481 \tabularnewline
42 & 34 & 34.2471656682827 & -0.247165668282727 \tabularnewline
43 & 23 & 22.4442207228008 & 0.555779277199162 \tabularnewline
44 & 24 & 25.0120456419251 & -1.01204564192508 \tabularnewline
45 & 26 & 27.114176108424 & -1.11417610842404 \tabularnewline
46 & 23 & 22.5712090796317 & 0.428790920368293 \tabularnewline
47 & 35 & 28.4362514777744 & 6.56374852222563 \tabularnewline
48 & 24 & 25.0262597120849 & -1.0262597120849 \tabularnewline
49 & 31 & 31.9282756543179 & -0.928275654317925 \tabularnewline
50 & 30 & 28.8016678611915 & 1.19833213880853 \tabularnewline
51 & 22 & 22.7892797700079 & -0.789279770007886 \tabularnewline
52 & 23 & 23.4587763654525 & -0.458776365452536 \tabularnewline
53 & 27 & 25.8411472394825 & 1.15885276051746 \tabularnewline
54 & 30 & 28.6479222496165 & 1.3520777503835 \tabularnewline
55 & 33 & 31.9668009486601 & 1.03319905133995 \tabularnewline
56 & 12 & 12.6622739021746 & -0.662273902174621 \tabularnewline
57 & 26 & 26.6181614625583 & -0.618161462558251 \tabularnewline
58 & 26 & 26.721458002766 & -0.721458002766041 \tabularnewline
59 & 23 & 22.9554042384103 & 0.0445957615897356 \tabularnewline
60 & 38 & 37.0420729418601 & 0.957927058139886 \tabularnewline
61 & 32 & 33.4556205104707 & -1.45562051047069 \tabularnewline
62 & 21 & 21.8971789155335 & -0.897178915533494 \tabularnewline
63 & 22 & 21.109224129986 & 0.890775870013988 \tabularnewline
64 & 26 & 25.2112184677721 & 0.788781532227943 \tabularnewline
65 & 28 & 26.8693498633461 & 1.13065013665394 \tabularnewline
66 & 33 & 31.5464573757303 & 1.45354262426966 \tabularnewline
67 & 36 & 37.8501592017084 & -1.85015920170837 \tabularnewline
68 & 25 & 24.417747324221 & 0.582252675778987 \tabularnewline
69 & 25 & 26.0914631134217 & -1.09146311342175 \tabularnewline
70 & 21 & 21.51841939361 & -0.518419393610029 \tabularnewline
71 & 19 & 20.0671264908069 & -1.06712649080691 \tabularnewline
72 & 12 & 14.0808392870156 & -2.08083928701563 \tabularnewline
73 & 30 & 31.3163352934895 & -1.31633529348948 \tabularnewline
74 & 21 & 16.9517552904581 & 4.04824470954193 \tabularnewline
75 & 39 & 39.9275316220066 & -0.927531622006616 \tabularnewline
76 & 32 & 31.3483250224371 & 0.651674977562895 \tabularnewline
77 & 28 & 32.4758274602514 & -4.47582746025143 \tabularnewline
78 & 29 & 29.9723548746096 & -0.972354874609618 \tabularnewline
79 & 21 & 20.6858202753367 & 0.314179724663293 \tabularnewline
80 & 31 & 30.7209332183906 & 0.279066781609435 \tabularnewline
81 & 26 & 24.714238566925 & 1.285761433075 \tabularnewline
82 & 29 & 30.7160085695843 & -1.71600856958427 \tabularnewline
83 & 23 & 24.3870393529168 & -1.38703935291683 \tabularnewline
84 & 25 & 24.5655998623878 & 0.43440013761217 \tabularnewline
85 & 22 & 23.6948959744727 & -1.69489597447266 \tabularnewline
86 & 26 & 27.1918561777608 & -1.19185617776082 \tabularnewline
87 & 33 & 33.5657827469506 & -0.56578274695056 \tabularnewline
88 & 24 & 25.5280120265938 & -1.52801202659381 \tabularnewline
89 & 24 & 24.5200719438693 & -0.520071943869273 \tabularnewline
90 & 21 & 20.2602071381881 & 0.739792861811914 \tabularnewline
91 & 28 & 28.1398259683297 & -0.139825968329747 \tabularnewline
92 & 28 & 27.2410889547285 & 0.75891104527146 \tabularnewline
93 & 25 & 24.7526839259079 & 0.247316074092135 \tabularnewline
94 & 15 & 16.2660355170675 & -1.26603551706749 \tabularnewline
95 & 13 & 12.4195503696484 & 0.58044963035157 \tabularnewline
96 & 36 & 35.1428216527491 & 0.857178347250912 \tabularnewline
97 & 27 & 26.8980047580892 & 0.101995241910776 \tabularnewline
98 & 1 & 1.81754860199327 & -0.817548601993275 \tabularnewline
99 & 24 & 25.4290285541552 & -1.42902855415523 \tabularnewline
100 & 31 & 27.4832404662531 & 3.51675953374689 \tabularnewline
101 & 4 & 5.13018540890863 & -1.13018540890863 \tabularnewline
102 & 21 & 18.4311182095393 & 2.56888179046066 \tabularnewline
103 & 27 & 27.4351290378507 & -0.435129037850707 \tabularnewline
104 & 26 & 25.640399189216 & 0.359600810784034 \tabularnewline
105 & 12 & 13.3095238767683 & -1.30952387676827 \tabularnewline
106 & 16 & 16.9402905887509 & -0.940290588750933 \tabularnewline
107 & 29 & 30.6446342800903 & -1.64463428009031 \tabularnewline
108 & 26 & 9.75495022990017 & 16.2450497700998 \tabularnewline
109 & 0 & 1.20459940424297 & -1.20459940424297 \tabularnewline
110 & 25 & 25.5726605124717 & -0.57266051247171 \tabularnewline
111 & 21 & 20.2439486950196 & 0.756051304980422 \tabularnewline
112 & 24 & 22.9717634365576 & 1.02823656344244 \tabularnewline
113 & 21 & 22.7308560078014 & -1.73085600780144 \tabularnewline
114 & 21 & 18.3002622364244 & 2.69973776357565 \tabularnewline
115 & 0 & 1.25929729722103 & -1.25929729722103 \tabularnewline
116 & 0 & 1.20459940424297 & -1.20459940424297 \tabularnewline
117 & 23 & 22.0666247606856 & 0.93337523931436 \tabularnewline
118 & 33 & 30.0022355967663 & 2.99776440323366 \tabularnewline
119 & 32 & 35.8241211641039 & -3.82412116410392 \tabularnewline
120 & 23 & 24.3722401964845 & -1.37224019648448 \tabularnewline
121 & 1 & 2.18781026054333 & -1.18781026054333 \tabularnewline
122 & 29 & 28.8829779658199 & 0.117022034180054 \tabularnewline
123 & 20 & 22.2044761043166 & -2.20447610431663 \tabularnewline
124 & 33 & 33.5143563194516 & -0.514356319451571 \tabularnewline
125 & 12 & 14.3629434780494 & -2.3629434780494 \tabularnewline
126 & 2 & 3.3541949311618 & -1.3541949311618 \tabularnewline
127 & 21 & 21.5620466713726 & -0.562046671372563 \tabularnewline
128 & 28 & 28.1496921112359 & -0.149692111235947 \tabularnewline
129 & 35 & 36.909133289534 & -1.90913328953404 \tabularnewline
130 & 2 & 3.47086327124067 & -1.47086327124067 \tabularnewline
131 & 0 & 1.23372870510564 & -1.23372870510564 \tabularnewline
132 & 18 & 16.2637080865666 & 1.73629191343337 \tabularnewline
133 & 1 & 2.34958709386716 & -1.34958709386716 \tabularnewline
134 & 21 & 19.916435944582 & 1.08356405541797 \tabularnewline
135 & 0 & 1.23709179146362 & -1.23709179146362 \tabularnewline
136 & 4 & 5.06741789975541 & -1.06741789975541 \tabularnewline
137 & 0 & 1.20459940424297 & -1.20459940424297 \tabularnewline
138 & 29 & 27.2880603617902 & 1.71193963820977 \tabularnewline
139 & 26 & 25.47296183177 & 0.52703816822999 \tabularnewline
140 & 0 & 1.25968700044736 & -1.25968700044736 \tabularnewline
141 & 4 & 4.43990493125143 & -0.439904931251428 \tabularnewline
142 & 19 & 16.7979495478494 & 2.20205045215057 \tabularnewline
143 & 22 & 22.6414482289212 & -0.641448228921245 \tabularnewline
144 & 22 & 23.1577379142531 & -1.15773791425305 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159906&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]21[/C][C]21.8553643199395[/C][C]-0.855364319939483[/C][/ROW]
[ROW][C]2[/C][C]20[/C][C]21.5655072648414[/C][C]-1.56550726484143[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]1.03881958969683[/C][C]-1.03881958969683[/C][/ROW]
[ROW][C]4[/C][C]27[/C][C]23.1265032468112[/C][C]3.87349675318879[/C][/ROW]
[ROW][C]5[/C][C]31[/C][C]30.9781209715182[/C][C]0.0218790284818334[/C][/ROW]
[ROW][C]6[/C][C]36[/C][C]36.9735759225877[/C][C]-0.973575922587692[/C][/ROW]
[ROW][C]7[/C][C]26[/C][C]26.7418634954626[/C][C]-0.741863495462559[/C][/ROW]
[ROW][C]8[/C][C]30[/C][C]30.1006658213637[/C][C]-0.100665821363717[/C][/ROW]
[ROW][C]9[/C][C]30[/C][C]29.9093548298962[/C][C]0.0906451701038122[/C][/ROW]
[ROW][C]10[/C][C]27[/C][C]28.5871670206802[/C][C]-1.58716702068022[/C][/ROW]
[ROW][C]11[/C][C]24[/C][C]17.5375205325367[/C][C]6.46247946746329[/C][/ROW]
[ROW][C]12[/C][C]30[/C][C]30.6254616928958[/C][C]-0.625461692895804[/C][/ROW]
[ROW][C]13[/C][C]22[/C][C]18.7724262013823[/C][C]3.22757379861769[/C][/ROW]
[ROW][C]14[/C][C]28[/C][C]29.8240582128567[/C][C]-1.82405821285674[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]17.5155259452523[/C][C]0.484474054747683[/C][/ROW]
[ROW][C]16[/C][C]22[/C][C]23.3756711677079[/C][C]-1.37567116770788[/C][/ROW]
[ROW][C]17[/C][C]37[/C][C]37.9186688190621[/C][C]-0.918668819062058[/C][/ROW]
[ROW][C]18[/C][C]15[/C][C]15.6154973477152[/C][C]-0.61549734771525[/C][/ROW]
[ROW][C]19[/C][C]34[/C][C]34.8694996671514[/C][C]-0.869499667151422[/C][/ROW]
[ROW][C]20[/C][C]18[/C][C]16.1489898919249[/C][C]1.85101010807511[/C][/ROW]
[ROW][C]21[/C][C]15[/C][C]16.1424343701788[/C][C]-1.14243437017877[/C][/ROW]
[ROW][C]22[/C][C]30[/C][C]30.0133455010123[/C][C]-0.0133455010122977[/C][/ROW]
[ROW][C]23[/C][C]25[/C][C]26.6917757855589[/C][C]-1.69177578555889[/C][/ROW]
[ROW][C]24[/C][C]34[/C][C]26.5751082900311[/C][C]7.42489170996887[/C][/ROW]
[ROW][C]25[/C][C]21[/C][C]21.1042667122143[/C][C]-0.104266712214321[/C][/ROW]
[ROW][C]26[/C][C]21[/C][C]22.3589276165975[/C][C]-1.35892761659745[/C][/ROW]
[ROW][C]27[/C][C]25[/C][C]23.4891546858568[/C][C]1.51084531414317[/C][/ROW]
[ROW][C]28[/C][C]31[/C][C]28.6373571476888[/C][C]2.36264285231118[/C][/ROW]
[ROW][C]29[/C][C]31[/C][C]30.5142431312097[/C][C]0.485756868790257[/C][/ROW]
[ROW][C]30[/C][C]20[/C][C]20.249416545234[/C][C]-0.249416545233974[/C][/ROW]
[ROW][C]31[/C][C]28[/C][C]27.6222841705027[/C][C]0.377715829497278[/C][/ROW]
[ROW][C]32[/C][C]22[/C][C]18.9391603842292[/C][C]3.06083961577082[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]18.9112991897771[/C][C]-1.91129918977714[/C][/ROW]
[ROW][C]34[/C][C]25[/C][C]28.9396743469468[/C][C]-3.9396743469468[/C][/ROW]
[ROW][C]35[/C][C]25[/C][C]26.0488812235485[/C][C]-1.04888122354854[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]1.19896101975477[/C][C]-1.19896101975477[/C][/ROW]
[ROW][C]37[/C][C]31[/C][C]30.5255745022543[/C][C]0.4744254977457[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]14.6157939094128[/C][C]-0.615793909412814[/C][/ROW]
[ROW][C]39[/C][C]35[/C][C]33.2872763593411[/C][C]1.71272364065885[/C][/ROW]
[ROW][C]40[/C][C]34[/C][C]32.9700894122276[/C][C]1.02991058777238[/C][/ROW]
[ROW][C]41[/C][C]22[/C][C]22.5846637509065[/C][C]-0.584663750906481[/C][/ROW]
[ROW][C]42[/C][C]34[/C][C]34.2471656682827[/C][C]-0.247165668282727[/C][/ROW]
[ROW][C]43[/C][C]23[/C][C]22.4442207228008[/C][C]0.555779277199162[/C][/ROW]
[ROW][C]44[/C][C]24[/C][C]25.0120456419251[/C][C]-1.01204564192508[/C][/ROW]
[ROW][C]45[/C][C]26[/C][C]27.114176108424[/C][C]-1.11417610842404[/C][/ROW]
[ROW][C]46[/C][C]23[/C][C]22.5712090796317[/C][C]0.428790920368293[/C][/ROW]
[ROW][C]47[/C][C]35[/C][C]28.4362514777744[/C][C]6.56374852222563[/C][/ROW]
[ROW][C]48[/C][C]24[/C][C]25.0262597120849[/C][C]-1.0262597120849[/C][/ROW]
[ROW][C]49[/C][C]31[/C][C]31.9282756543179[/C][C]-0.928275654317925[/C][/ROW]
[ROW][C]50[/C][C]30[/C][C]28.8016678611915[/C][C]1.19833213880853[/C][/ROW]
[ROW][C]51[/C][C]22[/C][C]22.7892797700079[/C][C]-0.789279770007886[/C][/ROW]
[ROW][C]52[/C][C]23[/C][C]23.4587763654525[/C][C]-0.458776365452536[/C][/ROW]
[ROW][C]53[/C][C]27[/C][C]25.8411472394825[/C][C]1.15885276051746[/C][/ROW]
[ROW][C]54[/C][C]30[/C][C]28.6479222496165[/C][C]1.3520777503835[/C][/ROW]
[ROW][C]55[/C][C]33[/C][C]31.9668009486601[/C][C]1.03319905133995[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.6622739021746[/C][C]-0.662273902174621[/C][/ROW]
[ROW][C]57[/C][C]26[/C][C]26.6181614625583[/C][C]-0.618161462558251[/C][/ROW]
[ROW][C]58[/C][C]26[/C][C]26.721458002766[/C][C]-0.721458002766041[/C][/ROW]
[ROW][C]59[/C][C]23[/C][C]22.9554042384103[/C][C]0.0445957615897356[/C][/ROW]
[ROW][C]60[/C][C]38[/C][C]37.0420729418601[/C][C]0.957927058139886[/C][/ROW]
[ROW][C]61[/C][C]32[/C][C]33.4556205104707[/C][C]-1.45562051047069[/C][/ROW]
[ROW][C]62[/C][C]21[/C][C]21.8971789155335[/C][C]-0.897178915533494[/C][/ROW]
[ROW][C]63[/C][C]22[/C][C]21.109224129986[/C][C]0.890775870013988[/C][/ROW]
[ROW][C]64[/C][C]26[/C][C]25.2112184677721[/C][C]0.788781532227943[/C][/ROW]
[ROW][C]65[/C][C]28[/C][C]26.8693498633461[/C][C]1.13065013665394[/C][/ROW]
[ROW][C]66[/C][C]33[/C][C]31.5464573757303[/C][C]1.45354262426966[/C][/ROW]
[ROW][C]67[/C][C]36[/C][C]37.8501592017084[/C][C]-1.85015920170837[/C][/ROW]
[ROW][C]68[/C][C]25[/C][C]24.417747324221[/C][C]0.582252675778987[/C][/ROW]
[ROW][C]69[/C][C]25[/C][C]26.0914631134217[/C][C]-1.09146311342175[/C][/ROW]
[ROW][C]70[/C][C]21[/C][C]21.51841939361[/C][C]-0.518419393610029[/C][/ROW]
[ROW][C]71[/C][C]19[/C][C]20.0671264908069[/C][C]-1.06712649080691[/C][/ROW]
[ROW][C]72[/C][C]12[/C][C]14.0808392870156[/C][C]-2.08083928701563[/C][/ROW]
[ROW][C]73[/C][C]30[/C][C]31.3163352934895[/C][C]-1.31633529348948[/C][/ROW]
[ROW][C]74[/C][C]21[/C][C]16.9517552904581[/C][C]4.04824470954193[/C][/ROW]
[ROW][C]75[/C][C]39[/C][C]39.9275316220066[/C][C]-0.927531622006616[/C][/ROW]
[ROW][C]76[/C][C]32[/C][C]31.3483250224371[/C][C]0.651674977562895[/C][/ROW]
[ROW][C]77[/C][C]28[/C][C]32.4758274602514[/C][C]-4.47582746025143[/C][/ROW]
[ROW][C]78[/C][C]29[/C][C]29.9723548746096[/C][C]-0.972354874609618[/C][/ROW]
[ROW][C]79[/C][C]21[/C][C]20.6858202753367[/C][C]0.314179724663293[/C][/ROW]
[ROW][C]80[/C][C]31[/C][C]30.7209332183906[/C][C]0.279066781609435[/C][/ROW]
[ROW][C]81[/C][C]26[/C][C]24.714238566925[/C][C]1.285761433075[/C][/ROW]
[ROW][C]82[/C][C]29[/C][C]30.7160085695843[/C][C]-1.71600856958427[/C][/ROW]
[ROW][C]83[/C][C]23[/C][C]24.3870393529168[/C][C]-1.38703935291683[/C][/ROW]
[ROW][C]84[/C][C]25[/C][C]24.5655998623878[/C][C]0.43440013761217[/C][/ROW]
[ROW][C]85[/C][C]22[/C][C]23.6948959744727[/C][C]-1.69489597447266[/C][/ROW]
[ROW][C]86[/C][C]26[/C][C]27.1918561777608[/C][C]-1.19185617776082[/C][/ROW]
[ROW][C]87[/C][C]33[/C][C]33.5657827469506[/C][C]-0.56578274695056[/C][/ROW]
[ROW][C]88[/C][C]24[/C][C]25.5280120265938[/C][C]-1.52801202659381[/C][/ROW]
[ROW][C]89[/C][C]24[/C][C]24.5200719438693[/C][C]-0.520071943869273[/C][/ROW]
[ROW][C]90[/C][C]21[/C][C]20.2602071381881[/C][C]0.739792861811914[/C][/ROW]
[ROW][C]91[/C][C]28[/C][C]28.1398259683297[/C][C]-0.139825968329747[/C][/ROW]
[ROW][C]92[/C][C]28[/C][C]27.2410889547285[/C][C]0.75891104527146[/C][/ROW]
[ROW][C]93[/C][C]25[/C][C]24.7526839259079[/C][C]0.247316074092135[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]16.2660355170675[/C][C]-1.26603551706749[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]12.4195503696484[/C][C]0.58044963035157[/C][/ROW]
[ROW][C]96[/C][C]36[/C][C]35.1428216527491[/C][C]0.857178347250912[/C][/ROW]
[ROW][C]97[/C][C]27[/C][C]26.8980047580892[/C][C]0.101995241910776[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]1.81754860199327[/C][C]-0.817548601993275[/C][/ROW]
[ROW][C]99[/C][C]24[/C][C]25.4290285541552[/C][C]-1.42902855415523[/C][/ROW]
[ROW][C]100[/C][C]31[/C][C]27.4832404662531[/C][C]3.51675953374689[/C][/ROW]
[ROW][C]101[/C][C]4[/C][C]5.13018540890863[/C][C]-1.13018540890863[/C][/ROW]
[ROW][C]102[/C][C]21[/C][C]18.4311182095393[/C][C]2.56888179046066[/C][/ROW]
[ROW][C]103[/C][C]27[/C][C]27.4351290378507[/C][C]-0.435129037850707[/C][/ROW]
[ROW][C]104[/C][C]26[/C][C]25.640399189216[/C][C]0.359600810784034[/C][/ROW]
[ROW][C]105[/C][C]12[/C][C]13.3095238767683[/C][C]-1.30952387676827[/C][/ROW]
[ROW][C]106[/C][C]16[/C][C]16.9402905887509[/C][C]-0.940290588750933[/C][/ROW]
[ROW][C]107[/C][C]29[/C][C]30.6446342800903[/C][C]-1.64463428009031[/C][/ROW]
[ROW][C]108[/C][C]26[/C][C]9.75495022990017[/C][C]16.2450497700998[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]1.20459940424297[/C][C]-1.20459940424297[/C][/ROW]
[ROW][C]110[/C][C]25[/C][C]25.5726605124717[/C][C]-0.57266051247171[/C][/ROW]
[ROW][C]111[/C][C]21[/C][C]20.2439486950196[/C][C]0.756051304980422[/C][/ROW]
[ROW][C]112[/C][C]24[/C][C]22.9717634365576[/C][C]1.02823656344244[/C][/ROW]
[ROW][C]113[/C][C]21[/C][C]22.7308560078014[/C][C]-1.73085600780144[/C][/ROW]
[ROW][C]114[/C][C]21[/C][C]18.3002622364244[/C][C]2.69973776357565[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]1.25929729722103[/C][C]-1.25929729722103[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]1.20459940424297[/C][C]-1.20459940424297[/C][/ROW]
[ROW][C]117[/C][C]23[/C][C]22.0666247606856[/C][C]0.93337523931436[/C][/ROW]
[ROW][C]118[/C][C]33[/C][C]30.0022355967663[/C][C]2.99776440323366[/C][/ROW]
[ROW][C]119[/C][C]32[/C][C]35.8241211641039[/C][C]-3.82412116410392[/C][/ROW]
[ROW][C]120[/C][C]23[/C][C]24.3722401964845[/C][C]-1.37224019648448[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]2.18781026054333[/C][C]-1.18781026054333[/C][/ROW]
[ROW][C]122[/C][C]29[/C][C]28.8829779658199[/C][C]0.117022034180054[/C][/ROW]
[ROW][C]123[/C][C]20[/C][C]22.2044761043166[/C][C]-2.20447610431663[/C][/ROW]
[ROW][C]124[/C][C]33[/C][C]33.5143563194516[/C][C]-0.514356319451571[/C][/ROW]
[ROW][C]125[/C][C]12[/C][C]14.3629434780494[/C][C]-2.3629434780494[/C][/ROW]
[ROW][C]126[/C][C]2[/C][C]3.3541949311618[/C][C]-1.3541949311618[/C][/ROW]
[ROW][C]127[/C][C]21[/C][C]21.5620466713726[/C][C]-0.562046671372563[/C][/ROW]
[ROW][C]128[/C][C]28[/C][C]28.1496921112359[/C][C]-0.149692111235947[/C][/ROW]
[ROW][C]129[/C][C]35[/C][C]36.909133289534[/C][C]-1.90913328953404[/C][/ROW]
[ROW][C]130[/C][C]2[/C][C]3.47086327124067[/C][C]-1.47086327124067[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]1.23372870510564[/C][C]-1.23372870510564[/C][/ROW]
[ROW][C]132[/C][C]18[/C][C]16.2637080865666[/C][C]1.73629191343337[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]2.34958709386716[/C][C]-1.34958709386716[/C][/ROW]
[ROW][C]134[/C][C]21[/C][C]19.916435944582[/C][C]1.08356405541797[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]1.23709179146362[/C][C]-1.23709179146362[/C][/ROW]
[ROW][C]136[/C][C]4[/C][C]5.06741789975541[/C][C]-1.06741789975541[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]1.20459940424297[/C][C]-1.20459940424297[/C][/ROW]
[ROW][C]138[/C][C]29[/C][C]27.2880603617902[/C][C]1.71193963820977[/C][/ROW]
[ROW][C]139[/C][C]26[/C][C]25.47296183177[/C][C]0.52703816822999[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]1.25968700044736[/C][C]-1.25968700044736[/C][/ROW]
[ROW][C]141[/C][C]4[/C][C]4.43990493125143[/C][C]-0.439904931251428[/C][/ROW]
[ROW][C]142[/C][C]19[/C][C]16.7979495478494[/C][C]2.20205045215057[/C][/ROW]
[ROW][C]143[/C][C]22[/C][C]22.6414482289212[/C][C]-0.641448228921245[/C][/ROW]
[ROW][C]144[/C][C]22[/C][C]23.1577379142531[/C][C]-1.15773791425305[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159906&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159906&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
12121.8553643199395-0.855364319939483
22021.5655072648414-1.56550726484143
301.03881958969683-1.03881958969683
42723.12650324681123.87349675318879
53130.97812097151820.0218790284818334
63636.9735759225877-0.973575922587692
72626.7418634954626-0.741863495462559
83030.1006658213637-0.100665821363717
93029.90935482989620.0906451701038122
102728.5871670206802-1.58716702068022
112417.53752053253676.46247946746329
123030.6254616928958-0.625461692895804
132218.77242620138233.22757379861769
142829.8240582128567-1.82405821285674
151817.51552594525230.484474054747683
162223.3756711677079-1.37567116770788
173737.9186688190621-0.918668819062058
181515.6154973477152-0.61549734771525
193434.8694996671514-0.869499667151422
201816.14898989192491.85101010807511
211516.1424343701788-1.14243437017877
223030.0133455010123-0.0133455010122977
232526.6917757855589-1.69177578555889
243426.57510829003117.42489170996887
252121.1042667122143-0.104266712214321
262122.3589276165975-1.35892761659745
272523.48915468585681.51084531414317
283128.63735714768882.36264285231118
293130.51424313120970.485756868790257
302020.249416545234-0.249416545233974
312827.62228417050270.377715829497278
322218.93916038422923.06083961577082
331718.9112991897771-1.91129918977714
342528.9396743469468-3.9396743469468
352526.0488812235485-1.04888122354854
3601.19896101975477-1.19896101975477
373130.52557450225430.4744254977457
381414.6157939094128-0.615793909412814
393533.28727635934111.71272364065885
403432.97008941222761.02991058777238
412222.5846637509065-0.584663750906481
423434.2471656682827-0.247165668282727
432322.44422072280080.555779277199162
442425.0120456419251-1.01204564192508
452627.114176108424-1.11417610842404
462322.57120907963170.428790920368293
473528.43625147777446.56374852222563
482425.0262597120849-1.0262597120849
493131.9282756543179-0.928275654317925
503028.80166786119151.19833213880853
512222.7892797700079-0.789279770007886
522323.4587763654525-0.458776365452536
532725.84114723948251.15885276051746
543028.64792224961651.3520777503835
553331.96680094866011.03319905133995
561212.6622739021746-0.662273902174621
572626.6181614625583-0.618161462558251
582626.721458002766-0.721458002766041
592322.95540423841030.0445957615897356
603837.04207294186010.957927058139886
613233.4556205104707-1.45562051047069
622121.8971789155335-0.897178915533494
632221.1092241299860.890775870013988
642625.21121846777210.788781532227943
652826.86934986334611.13065013665394
663331.54645737573031.45354262426966
673637.8501592017084-1.85015920170837
682524.4177473242210.582252675778987
692526.0914631134217-1.09146311342175
702121.51841939361-0.518419393610029
711920.0671264908069-1.06712649080691
721214.0808392870156-2.08083928701563
733031.3163352934895-1.31633529348948
742116.95175529045814.04824470954193
753939.9275316220066-0.927531622006616
763231.34832502243710.651674977562895
772832.4758274602514-4.47582746025143
782929.9723548746096-0.972354874609618
792120.68582027533670.314179724663293
803130.72093321839060.279066781609435
812624.7142385669251.285761433075
822930.7160085695843-1.71600856958427
832324.3870393529168-1.38703935291683
842524.56559986238780.43440013761217
852223.6948959744727-1.69489597447266
862627.1918561777608-1.19185617776082
873333.5657827469506-0.56578274695056
882425.5280120265938-1.52801202659381
892424.5200719438693-0.520071943869273
902120.26020713818810.739792861811914
912828.1398259683297-0.139825968329747
922827.24108895472850.75891104527146
932524.75268392590790.247316074092135
941516.2660355170675-1.26603551706749
951312.41955036964840.58044963035157
963635.14282165274910.857178347250912
972726.89800475808920.101995241910776
9811.81754860199327-0.817548601993275
992425.4290285541552-1.42902855415523
1003127.48324046625313.51675953374689
10145.13018540890863-1.13018540890863
1022118.43111820953932.56888179046066
1032727.4351290378507-0.435129037850707
1042625.6403991892160.359600810784034
1051213.3095238767683-1.30952387676827
1061616.9402905887509-0.940290588750933
1072930.6446342800903-1.64463428009031
108269.7549502299001716.2450497700998
10901.20459940424297-1.20459940424297
1102525.5726605124717-0.57266051247171
1112120.24394869501960.756051304980422
1122422.97176343655761.02823656344244
1132122.7308560078014-1.73085600780144
1142118.30026223642442.69973776357565
11501.25929729722103-1.25929729722103
11601.20459940424297-1.20459940424297
1172322.06662476068560.93337523931436
1183330.00223559676632.99776440323366
1193235.8241211641039-3.82412116410392
1202324.3722401964845-1.37224019648448
12112.18781026054333-1.18781026054333
1222928.88297796581990.117022034180054
1232022.2044761043166-2.20447610431663
1243333.5143563194516-0.514356319451571
1251214.3629434780494-2.3629434780494
12623.3541949311618-1.3541949311618
1272121.5620466713726-0.562046671372563
1282828.1496921112359-0.149692111235947
1293536.909133289534-1.90913328953404
13023.47086327124067-1.47086327124067
13101.23372870510564-1.23372870510564
1321816.26370808656661.73629191343337
13312.34958709386716-1.34958709386716
1342119.9164359445821.08356405541797
13501.23709179146362-1.23709179146362
13645.06741789975541-1.06741789975541
13701.20459940424297-1.20459940424297
1382927.28806036179021.71193963820977
1392625.472961831770.52703816822999
14001.25968700044736-1.25968700044736
14144.43990493125143-0.439904931251428
1421916.79794954784942.20205045215057
1432222.6414482289212-0.641448228921245
1442223.1577379142531-1.15773791425305






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.7460995951832450.5078008096335090.253900404816755
190.8400425531903490.3199148936193010.159957446809651
200.7474496669566340.5051006660867310.252550333043366
210.6520975168927030.6958049662145940.347902483107297
220.5533160115327710.8933679769344580.446683988467229
230.446057188848910.892114377697820.55394281115109
240.5450964716710340.9098070566579310.454903528328966
250.4748300271179390.9496600542358770.525169972882061
260.549045226955970.901909546088060.45095477304403
270.6580146076481740.6839707847036520.341985392351826
280.653709497112260.6925810057754790.34629050288774
290.6912123955959090.6175752088081820.308787604404091
300.6596853116647430.6806293766705150.340314688335257
310.5858882402244510.8282235195510980.414111759775549
320.541463328106430.917073343787140.45853667189357
330.5270750551265240.945849889746950.472924944873476
340.870050587006770.2598988259864610.129949412993231
350.8441164105870440.3117671788259120.155883589412956
360.8327897702079680.3344204595840640.167210229792032
370.7877471147754250.4245057704491510.212252885224575
380.7380351992338510.5239296015322970.261964800766149
390.6951026058766050.6097947882467910.304897394123395
400.6642412497780160.6715175004439670.335758750221984
410.655552527526780.688894944946440.34444747247322
420.6028827312628530.7942345374742950.397117268737147
430.5430314719980890.9139370560038210.456968528001911
440.4906662678730990.9813325357461990.509333732126901
450.4409032319889970.8818064639779940.559096768011003
460.3897894561154440.7795789122308870.610210543884556
470.5686524043799990.8626951912400030.431347595620002
480.5326015615961130.9347968768077730.467398438403887
490.4765191287446870.9530382574893750.523480871255313
500.4323605128777470.8647210257554940.567639487122253
510.4488893693265030.8977787386530060.551110630673497
520.4045885426046980.8091770852093970.595411457395302
530.4117281459438520.8234562918877040.588271854056148
540.3743736437496020.7487472874992030.625626356250398
550.3282740945811010.6565481891622020.671725905418899
560.2844709910426430.5689419820852860.715529008957357
570.2466103157359820.4932206314719640.753389684264018
580.2242065100901760.4484130201803520.775793489909824
590.1850501747385310.3701003494770620.814949825261469
600.1574981177125640.3149962354251280.842501882287436
610.135788183001810.271576366003620.86421181699819
620.1128715068052920.2257430136105840.887128493194708
630.09548147843237390.1909629568647480.904518521567626
640.08091214029494450.1618242805898890.919087859705056
650.06560390674992690.1312078134998540.934396093250073
660.05555293531022340.1111058706204470.944447064689777
670.055566483100460.111132966200920.94443351689954
680.04378579171697410.08757158343394820.956214208283026
690.03434806200681160.06869612401362310.965651937993188
700.03104304651024660.06208609302049310.968956953489753
710.02693569269214290.05387138538428580.973064307307857
720.0306323845496790.0612647690993580.969367615450321
730.02767080405564710.05534160811129420.972329195944353
740.04138848730491320.08277697460982640.958611512695087
750.03172301123063190.06344602246126390.968276988769368
760.02510214584106230.05020429168212450.974897854158938
770.0525592632901650.105118526580330.947440736709835
780.04272156404872360.08544312809744720.957278435951276
790.03274823724648290.06549647449296580.967251762753517
800.02856965422702820.05713930845405630.971430345772972
810.02489699816092330.04979399632184650.975103001839077
820.043615317962080.08723063592415990.95638468203792
830.03957962579872640.07915925159745280.960420374201274
840.03062937498308770.06125874996617550.969370625016912
850.02880139455083530.05760278910167060.971198605449165
860.02879312405586890.05758624811173780.971206875944131
870.02228592975233970.04457185950467930.97771407024766
880.01786893458025080.03573786916050160.98213106541975
890.01819866441037430.03639732882074870.981801335589626
900.01315337928724120.02630675857448250.986846620712759
910.01094978238226330.02189956476452660.989050217617737
920.008449982052132460.01689996410426490.991550017947868
930.005868981164781280.01173796232956260.994131018835219
940.004900955287890920.009801910575781830.99509904471211
950.00414028029747450.0082805605949490.995859719702526
960.006831315886143140.01366263177228630.993168684113857
970.006563603429680930.01312720685936190.993436396570319
980.004681502500938090.009363005001876180.995318497499062
990.006053539206853270.01210707841370650.993946460793147
1000.01189292811124070.02378585622248140.98810707188876
1010.06162362009107030.1232472401821410.93837637990893
1020.05374328515719250.1074865703143850.946256714842807
1030.0679110516451580.1358221032903160.932088948354842
1040.05124352758865070.1024870551773010.94875647241135
1050.06762287851651010.135245757033020.93237712148349
1060.05757054855040980.115141097100820.94242945144959
1070.05127666544787830.1025533308957570.948723334552122
1080.999387877161460.001224245677079170.000612122838539583
1090.9989391303352320.002121739329535440.00106086966476772
1100.9984995453363520.003000909327296030.00150045466364802
1110.9975359112187670.004928177562465210.0024640887812326
1120.9960727453060.007854509387999570.00392725469399978
1130.9942627443440990.01147451131180230.00573725565590116
1140.9973740186640340.005251962671932430.00262598133596622
1150.9951279616597580.009744076680483080.00487203834024154
1160.9910714131986020.01785717360279590.00892858680139794
1170.9990872518216320.001825496356735010.000912748178367504
1180.9981588580514260.003682283897148810.0018411419485744
1190.9971786595865320.005642680826935710.00282134041346785
1200.9932005201526540.01359895969469280.00679947984734641
1210.9857212682373350.028557463525330.014278731762665
1220.9686413433620880.06271731327582380.0313586566379119
1230.9425610948886790.1148778102226420.0574389051113212
1240.890433221091990.219133557816020.10956677890801
1250.9753694124659580.04926117506808470.0246305875340424
1260.9596039154588840.08079216908223160.0403960845411158

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.746099595183245 & 0.507800809633509 & 0.253900404816755 \tabularnewline
19 & 0.840042553190349 & 0.319914893619301 & 0.159957446809651 \tabularnewline
20 & 0.747449666956634 & 0.505100666086731 & 0.252550333043366 \tabularnewline
21 & 0.652097516892703 & 0.695804966214594 & 0.347902483107297 \tabularnewline
22 & 0.553316011532771 & 0.893367976934458 & 0.446683988467229 \tabularnewline
23 & 0.44605718884891 & 0.89211437769782 & 0.55394281115109 \tabularnewline
24 & 0.545096471671034 & 0.909807056657931 & 0.454903528328966 \tabularnewline
25 & 0.474830027117939 & 0.949660054235877 & 0.525169972882061 \tabularnewline
26 & 0.54904522695597 & 0.90190954608806 & 0.45095477304403 \tabularnewline
27 & 0.658014607648174 & 0.683970784703652 & 0.341985392351826 \tabularnewline
28 & 0.65370949711226 & 0.692581005775479 & 0.34629050288774 \tabularnewline
29 & 0.691212395595909 & 0.617575208808182 & 0.308787604404091 \tabularnewline
30 & 0.659685311664743 & 0.680629376670515 & 0.340314688335257 \tabularnewline
31 & 0.585888240224451 & 0.828223519551098 & 0.414111759775549 \tabularnewline
32 & 0.54146332810643 & 0.91707334378714 & 0.45853667189357 \tabularnewline
33 & 0.527075055126524 & 0.94584988974695 & 0.472924944873476 \tabularnewline
34 & 0.87005058700677 & 0.259898825986461 & 0.129949412993231 \tabularnewline
35 & 0.844116410587044 & 0.311767178825912 & 0.155883589412956 \tabularnewline
36 & 0.832789770207968 & 0.334420459584064 & 0.167210229792032 \tabularnewline
37 & 0.787747114775425 & 0.424505770449151 & 0.212252885224575 \tabularnewline
38 & 0.738035199233851 & 0.523929601532297 & 0.261964800766149 \tabularnewline
39 & 0.695102605876605 & 0.609794788246791 & 0.304897394123395 \tabularnewline
40 & 0.664241249778016 & 0.671517500443967 & 0.335758750221984 \tabularnewline
41 & 0.65555252752678 & 0.68889494494644 & 0.34444747247322 \tabularnewline
42 & 0.602882731262853 & 0.794234537474295 & 0.397117268737147 \tabularnewline
43 & 0.543031471998089 & 0.913937056003821 & 0.456968528001911 \tabularnewline
44 & 0.490666267873099 & 0.981332535746199 & 0.509333732126901 \tabularnewline
45 & 0.440903231988997 & 0.881806463977994 & 0.559096768011003 \tabularnewline
46 & 0.389789456115444 & 0.779578912230887 & 0.610210543884556 \tabularnewline
47 & 0.568652404379999 & 0.862695191240003 & 0.431347595620002 \tabularnewline
48 & 0.532601561596113 & 0.934796876807773 & 0.467398438403887 \tabularnewline
49 & 0.476519128744687 & 0.953038257489375 & 0.523480871255313 \tabularnewline
50 & 0.432360512877747 & 0.864721025755494 & 0.567639487122253 \tabularnewline
51 & 0.448889369326503 & 0.897778738653006 & 0.551110630673497 \tabularnewline
52 & 0.404588542604698 & 0.809177085209397 & 0.595411457395302 \tabularnewline
53 & 0.411728145943852 & 0.823456291887704 & 0.588271854056148 \tabularnewline
54 & 0.374373643749602 & 0.748747287499203 & 0.625626356250398 \tabularnewline
55 & 0.328274094581101 & 0.656548189162202 & 0.671725905418899 \tabularnewline
56 & 0.284470991042643 & 0.568941982085286 & 0.715529008957357 \tabularnewline
57 & 0.246610315735982 & 0.493220631471964 & 0.753389684264018 \tabularnewline
58 & 0.224206510090176 & 0.448413020180352 & 0.775793489909824 \tabularnewline
59 & 0.185050174738531 & 0.370100349477062 & 0.814949825261469 \tabularnewline
60 & 0.157498117712564 & 0.314996235425128 & 0.842501882287436 \tabularnewline
61 & 0.13578818300181 & 0.27157636600362 & 0.86421181699819 \tabularnewline
62 & 0.112871506805292 & 0.225743013610584 & 0.887128493194708 \tabularnewline
63 & 0.0954814784323739 & 0.190962956864748 & 0.904518521567626 \tabularnewline
64 & 0.0809121402949445 & 0.161824280589889 & 0.919087859705056 \tabularnewline
65 & 0.0656039067499269 & 0.131207813499854 & 0.934396093250073 \tabularnewline
66 & 0.0555529353102234 & 0.111105870620447 & 0.944447064689777 \tabularnewline
67 & 0.05556648310046 & 0.11113296620092 & 0.94443351689954 \tabularnewline
68 & 0.0437857917169741 & 0.0875715834339482 & 0.956214208283026 \tabularnewline
69 & 0.0343480620068116 & 0.0686961240136231 & 0.965651937993188 \tabularnewline
70 & 0.0310430465102466 & 0.0620860930204931 & 0.968956953489753 \tabularnewline
71 & 0.0269356926921429 & 0.0538713853842858 & 0.973064307307857 \tabularnewline
72 & 0.030632384549679 & 0.061264769099358 & 0.969367615450321 \tabularnewline
73 & 0.0276708040556471 & 0.0553416081112942 & 0.972329195944353 \tabularnewline
74 & 0.0413884873049132 & 0.0827769746098264 & 0.958611512695087 \tabularnewline
75 & 0.0317230112306319 & 0.0634460224612639 & 0.968276988769368 \tabularnewline
76 & 0.0251021458410623 & 0.0502042916821245 & 0.974897854158938 \tabularnewline
77 & 0.052559263290165 & 0.10511852658033 & 0.947440736709835 \tabularnewline
78 & 0.0427215640487236 & 0.0854431280974472 & 0.957278435951276 \tabularnewline
79 & 0.0327482372464829 & 0.0654964744929658 & 0.967251762753517 \tabularnewline
80 & 0.0285696542270282 & 0.0571393084540563 & 0.971430345772972 \tabularnewline
81 & 0.0248969981609233 & 0.0497939963218465 & 0.975103001839077 \tabularnewline
82 & 0.04361531796208 & 0.0872306359241599 & 0.95638468203792 \tabularnewline
83 & 0.0395796257987264 & 0.0791592515974528 & 0.960420374201274 \tabularnewline
84 & 0.0306293749830877 & 0.0612587499661755 & 0.969370625016912 \tabularnewline
85 & 0.0288013945508353 & 0.0576027891016706 & 0.971198605449165 \tabularnewline
86 & 0.0287931240558689 & 0.0575862481117378 & 0.971206875944131 \tabularnewline
87 & 0.0222859297523397 & 0.0445718595046793 & 0.97771407024766 \tabularnewline
88 & 0.0178689345802508 & 0.0357378691605016 & 0.98213106541975 \tabularnewline
89 & 0.0181986644103743 & 0.0363973288207487 & 0.981801335589626 \tabularnewline
90 & 0.0131533792872412 & 0.0263067585744825 & 0.986846620712759 \tabularnewline
91 & 0.0109497823822633 & 0.0218995647645266 & 0.989050217617737 \tabularnewline
92 & 0.00844998205213246 & 0.0168999641042649 & 0.991550017947868 \tabularnewline
93 & 0.00586898116478128 & 0.0117379623295626 & 0.994131018835219 \tabularnewline
94 & 0.00490095528789092 & 0.00980191057578183 & 0.99509904471211 \tabularnewline
95 & 0.0041402802974745 & 0.008280560594949 & 0.995859719702526 \tabularnewline
96 & 0.00683131588614314 & 0.0136626317722863 & 0.993168684113857 \tabularnewline
97 & 0.00656360342968093 & 0.0131272068593619 & 0.993436396570319 \tabularnewline
98 & 0.00468150250093809 & 0.00936300500187618 & 0.995318497499062 \tabularnewline
99 & 0.00605353920685327 & 0.0121070784137065 & 0.993946460793147 \tabularnewline
100 & 0.0118929281112407 & 0.0237858562224814 & 0.98810707188876 \tabularnewline
101 & 0.0616236200910703 & 0.123247240182141 & 0.93837637990893 \tabularnewline
102 & 0.0537432851571925 & 0.107486570314385 & 0.946256714842807 \tabularnewline
103 & 0.067911051645158 & 0.135822103290316 & 0.932088948354842 \tabularnewline
104 & 0.0512435275886507 & 0.102487055177301 & 0.94875647241135 \tabularnewline
105 & 0.0676228785165101 & 0.13524575703302 & 0.93237712148349 \tabularnewline
106 & 0.0575705485504098 & 0.11514109710082 & 0.94242945144959 \tabularnewline
107 & 0.0512766654478783 & 0.102553330895757 & 0.948723334552122 \tabularnewline
108 & 0.99938787716146 & 0.00122424567707917 & 0.000612122838539583 \tabularnewline
109 & 0.998939130335232 & 0.00212173932953544 & 0.00106086966476772 \tabularnewline
110 & 0.998499545336352 & 0.00300090932729603 & 0.00150045466364802 \tabularnewline
111 & 0.997535911218767 & 0.00492817756246521 & 0.0024640887812326 \tabularnewline
112 & 0.996072745306 & 0.00785450938799957 & 0.00392725469399978 \tabularnewline
113 & 0.994262744344099 & 0.0114745113118023 & 0.00573725565590116 \tabularnewline
114 & 0.997374018664034 & 0.00525196267193243 & 0.00262598133596622 \tabularnewline
115 & 0.995127961659758 & 0.00974407668048308 & 0.00487203834024154 \tabularnewline
116 & 0.991071413198602 & 0.0178571736027959 & 0.00892858680139794 \tabularnewline
117 & 0.999087251821632 & 0.00182549635673501 & 0.000912748178367504 \tabularnewline
118 & 0.998158858051426 & 0.00368228389714881 & 0.0018411419485744 \tabularnewline
119 & 0.997178659586532 & 0.00564268082693571 & 0.00282134041346785 \tabularnewline
120 & 0.993200520152654 & 0.0135989596946928 & 0.00679947984734641 \tabularnewline
121 & 0.985721268237335 & 0.02855746352533 & 0.014278731762665 \tabularnewline
122 & 0.968641343362088 & 0.0627173132758238 & 0.0313586566379119 \tabularnewline
123 & 0.942561094888679 & 0.114877810222642 & 0.0574389051113212 \tabularnewline
124 & 0.89043322109199 & 0.21913355781602 & 0.10956677890801 \tabularnewline
125 & 0.975369412465958 & 0.0492611750680847 & 0.0246305875340424 \tabularnewline
126 & 0.959603915458884 & 0.0807921690822316 & 0.0403960845411158 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159906&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]0.746099595183245[/C][C]0.507800809633509[/C][C]0.253900404816755[/C][/ROW]
[ROW][C]19[/C][C]0.840042553190349[/C][C]0.319914893619301[/C][C]0.159957446809651[/C][/ROW]
[ROW][C]20[/C][C]0.747449666956634[/C][C]0.505100666086731[/C][C]0.252550333043366[/C][/ROW]
[ROW][C]21[/C][C]0.652097516892703[/C][C]0.695804966214594[/C][C]0.347902483107297[/C][/ROW]
[ROW][C]22[/C][C]0.553316011532771[/C][C]0.893367976934458[/C][C]0.446683988467229[/C][/ROW]
[ROW][C]23[/C][C]0.44605718884891[/C][C]0.89211437769782[/C][C]0.55394281115109[/C][/ROW]
[ROW][C]24[/C][C]0.545096471671034[/C][C]0.909807056657931[/C][C]0.454903528328966[/C][/ROW]
[ROW][C]25[/C][C]0.474830027117939[/C][C]0.949660054235877[/C][C]0.525169972882061[/C][/ROW]
[ROW][C]26[/C][C]0.54904522695597[/C][C]0.90190954608806[/C][C]0.45095477304403[/C][/ROW]
[ROW][C]27[/C][C]0.658014607648174[/C][C]0.683970784703652[/C][C]0.341985392351826[/C][/ROW]
[ROW][C]28[/C][C]0.65370949711226[/C][C]0.692581005775479[/C][C]0.34629050288774[/C][/ROW]
[ROW][C]29[/C][C]0.691212395595909[/C][C]0.617575208808182[/C][C]0.308787604404091[/C][/ROW]
[ROW][C]30[/C][C]0.659685311664743[/C][C]0.680629376670515[/C][C]0.340314688335257[/C][/ROW]
[ROW][C]31[/C][C]0.585888240224451[/C][C]0.828223519551098[/C][C]0.414111759775549[/C][/ROW]
[ROW][C]32[/C][C]0.54146332810643[/C][C]0.91707334378714[/C][C]0.45853667189357[/C][/ROW]
[ROW][C]33[/C][C]0.527075055126524[/C][C]0.94584988974695[/C][C]0.472924944873476[/C][/ROW]
[ROW][C]34[/C][C]0.87005058700677[/C][C]0.259898825986461[/C][C]0.129949412993231[/C][/ROW]
[ROW][C]35[/C][C]0.844116410587044[/C][C]0.311767178825912[/C][C]0.155883589412956[/C][/ROW]
[ROW][C]36[/C][C]0.832789770207968[/C][C]0.334420459584064[/C][C]0.167210229792032[/C][/ROW]
[ROW][C]37[/C][C]0.787747114775425[/C][C]0.424505770449151[/C][C]0.212252885224575[/C][/ROW]
[ROW][C]38[/C][C]0.738035199233851[/C][C]0.523929601532297[/C][C]0.261964800766149[/C][/ROW]
[ROW][C]39[/C][C]0.695102605876605[/C][C]0.609794788246791[/C][C]0.304897394123395[/C][/ROW]
[ROW][C]40[/C][C]0.664241249778016[/C][C]0.671517500443967[/C][C]0.335758750221984[/C][/ROW]
[ROW][C]41[/C][C]0.65555252752678[/C][C]0.68889494494644[/C][C]0.34444747247322[/C][/ROW]
[ROW][C]42[/C][C]0.602882731262853[/C][C]0.794234537474295[/C][C]0.397117268737147[/C][/ROW]
[ROW][C]43[/C][C]0.543031471998089[/C][C]0.913937056003821[/C][C]0.456968528001911[/C][/ROW]
[ROW][C]44[/C][C]0.490666267873099[/C][C]0.981332535746199[/C][C]0.509333732126901[/C][/ROW]
[ROW][C]45[/C][C]0.440903231988997[/C][C]0.881806463977994[/C][C]0.559096768011003[/C][/ROW]
[ROW][C]46[/C][C]0.389789456115444[/C][C]0.779578912230887[/C][C]0.610210543884556[/C][/ROW]
[ROW][C]47[/C][C]0.568652404379999[/C][C]0.862695191240003[/C][C]0.431347595620002[/C][/ROW]
[ROW][C]48[/C][C]0.532601561596113[/C][C]0.934796876807773[/C][C]0.467398438403887[/C][/ROW]
[ROW][C]49[/C][C]0.476519128744687[/C][C]0.953038257489375[/C][C]0.523480871255313[/C][/ROW]
[ROW][C]50[/C][C]0.432360512877747[/C][C]0.864721025755494[/C][C]0.567639487122253[/C][/ROW]
[ROW][C]51[/C][C]0.448889369326503[/C][C]0.897778738653006[/C][C]0.551110630673497[/C][/ROW]
[ROW][C]52[/C][C]0.404588542604698[/C][C]0.809177085209397[/C][C]0.595411457395302[/C][/ROW]
[ROW][C]53[/C][C]0.411728145943852[/C][C]0.823456291887704[/C][C]0.588271854056148[/C][/ROW]
[ROW][C]54[/C][C]0.374373643749602[/C][C]0.748747287499203[/C][C]0.625626356250398[/C][/ROW]
[ROW][C]55[/C][C]0.328274094581101[/C][C]0.656548189162202[/C][C]0.671725905418899[/C][/ROW]
[ROW][C]56[/C][C]0.284470991042643[/C][C]0.568941982085286[/C][C]0.715529008957357[/C][/ROW]
[ROW][C]57[/C][C]0.246610315735982[/C][C]0.493220631471964[/C][C]0.753389684264018[/C][/ROW]
[ROW][C]58[/C][C]0.224206510090176[/C][C]0.448413020180352[/C][C]0.775793489909824[/C][/ROW]
[ROW][C]59[/C][C]0.185050174738531[/C][C]0.370100349477062[/C][C]0.814949825261469[/C][/ROW]
[ROW][C]60[/C][C]0.157498117712564[/C][C]0.314996235425128[/C][C]0.842501882287436[/C][/ROW]
[ROW][C]61[/C][C]0.13578818300181[/C][C]0.27157636600362[/C][C]0.86421181699819[/C][/ROW]
[ROW][C]62[/C][C]0.112871506805292[/C][C]0.225743013610584[/C][C]0.887128493194708[/C][/ROW]
[ROW][C]63[/C][C]0.0954814784323739[/C][C]0.190962956864748[/C][C]0.904518521567626[/C][/ROW]
[ROW][C]64[/C][C]0.0809121402949445[/C][C]0.161824280589889[/C][C]0.919087859705056[/C][/ROW]
[ROW][C]65[/C][C]0.0656039067499269[/C][C]0.131207813499854[/C][C]0.934396093250073[/C][/ROW]
[ROW][C]66[/C][C]0.0555529353102234[/C][C]0.111105870620447[/C][C]0.944447064689777[/C][/ROW]
[ROW][C]67[/C][C]0.05556648310046[/C][C]0.11113296620092[/C][C]0.94443351689954[/C][/ROW]
[ROW][C]68[/C][C]0.0437857917169741[/C][C]0.0875715834339482[/C][C]0.956214208283026[/C][/ROW]
[ROW][C]69[/C][C]0.0343480620068116[/C][C]0.0686961240136231[/C][C]0.965651937993188[/C][/ROW]
[ROW][C]70[/C][C]0.0310430465102466[/C][C]0.0620860930204931[/C][C]0.968956953489753[/C][/ROW]
[ROW][C]71[/C][C]0.0269356926921429[/C][C]0.0538713853842858[/C][C]0.973064307307857[/C][/ROW]
[ROW][C]72[/C][C]0.030632384549679[/C][C]0.061264769099358[/C][C]0.969367615450321[/C][/ROW]
[ROW][C]73[/C][C]0.0276708040556471[/C][C]0.0553416081112942[/C][C]0.972329195944353[/C][/ROW]
[ROW][C]74[/C][C]0.0413884873049132[/C][C]0.0827769746098264[/C][C]0.958611512695087[/C][/ROW]
[ROW][C]75[/C][C]0.0317230112306319[/C][C]0.0634460224612639[/C][C]0.968276988769368[/C][/ROW]
[ROW][C]76[/C][C]0.0251021458410623[/C][C]0.0502042916821245[/C][C]0.974897854158938[/C][/ROW]
[ROW][C]77[/C][C]0.052559263290165[/C][C]0.10511852658033[/C][C]0.947440736709835[/C][/ROW]
[ROW][C]78[/C][C]0.0427215640487236[/C][C]0.0854431280974472[/C][C]0.957278435951276[/C][/ROW]
[ROW][C]79[/C][C]0.0327482372464829[/C][C]0.0654964744929658[/C][C]0.967251762753517[/C][/ROW]
[ROW][C]80[/C][C]0.0285696542270282[/C][C]0.0571393084540563[/C][C]0.971430345772972[/C][/ROW]
[ROW][C]81[/C][C]0.0248969981609233[/C][C]0.0497939963218465[/C][C]0.975103001839077[/C][/ROW]
[ROW][C]82[/C][C]0.04361531796208[/C][C]0.0872306359241599[/C][C]0.95638468203792[/C][/ROW]
[ROW][C]83[/C][C]0.0395796257987264[/C][C]0.0791592515974528[/C][C]0.960420374201274[/C][/ROW]
[ROW][C]84[/C][C]0.0306293749830877[/C][C]0.0612587499661755[/C][C]0.969370625016912[/C][/ROW]
[ROW][C]85[/C][C]0.0288013945508353[/C][C]0.0576027891016706[/C][C]0.971198605449165[/C][/ROW]
[ROW][C]86[/C][C]0.0287931240558689[/C][C]0.0575862481117378[/C][C]0.971206875944131[/C][/ROW]
[ROW][C]87[/C][C]0.0222859297523397[/C][C]0.0445718595046793[/C][C]0.97771407024766[/C][/ROW]
[ROW][C]88[/C][C]0.0178689345802508[/C][C]0.0357378691605016[/C][C]0.98213106541975[/C][/ROW]
[ROW][C]89[/C][C]0.0181986644103743[/C][C]0.0363973288207487[/C][C]0.981801335589626[/C][/ROW]
[ROW][C]90[/C][C]0.0131533792872412[/C][C]0.0263067585744825[/C][C]0.986846620712759[/C][/ROW]
[ROW][C]91[/C][C]0.0109497823822633[/C][C]0.0218995647645266[/C][C]0.989050217617737[/C][/ROW]
[ROW][C]92[/C][C]0.00844998205213246[/C][C]0.0168999641042649[/C][C]0.991550017947868[/C][/ROW]
[ROW][C]93[/C][C]0.00586898116478128[/C][C]0.0117379623295626[/C][C]0.994131018835219[/C][/ROW]
[ROW][C]94[/C][C]0.00490095528789092[/C][C]0.00980191057578183[/C][C]0.99509904471211[/C][/ROW]
[ROW][C]95[/C][C]0.0041402802974745[/C][C]0.008280560594949[/C][C]0.995859719702526[/C][/ROW]
[ROW][C]96[/C][C]0.00683131588614314[/C][C]0.0136626317722863[/C][C]0.993168684113857[/C][/ROW]
[ROW][C]97[/C][C]0.00656360342968093[/C][C]0.0131272068593619[/C][C]0.993436396570319[/C][/ROW]
[ROW][C]98[/C][C]0.00468150250093809[/C][C]0.00936300500187618[/C][C]0.995318497499062[/C][/ROW]
[ROW][C]99[/C][C]0.00605353920685327[/C][C]0.0121070784137065[/C][C]0.993946460793147[/C][/ROW]
[ROW][C]100[/C][C]0.0118929281112407[/C][C]0.0237858562224814[/C][C]0.98810707188876[/C][/ROW]
[ROW][C]101[/C][C]0.0616236200910703[/C][C]0.123247240182141[/C][C]0.93837637990893[/C][/ROW]
[ROW][C]102[/C][C]0.0537432851571925[/C][C]0.107486570314385[/C][C]0.946256714842807[/C][/ROW]
[ROW][C]103[/C][C]0.067911051645158[/C][C]0.135822103290316[/C][C]0.932088948354842[/C][/ROW]
[ROW][C]104[/C][C]0.0512435275886507[/C][C]0.102487055177301[/C][C]0.94875647241135[/C][/ROW]
[ROW][C]105[/C][C]0.0676228785165101[/C][C]0.13524575703302[/C][C]0.93237712148349[/C][/ROW]
[ROW][C]106[/C][C]0.0575705485504098[/C][C]0.11514109710082[/C][C]0.94242945144959[/C][/ROW]
[ROW][C]107[/C][C]0.0512766654478783[/C][C]0.102553330895757[/C][C]0.948723334552122[/C][/ROW]
[ROW][C]108[/C][C]0.99938787716146[/C][C]0.00122424567707917[/C][C]0.000612122838539583[/C][/ROW]
[ROW][C]109[/C][C]0.998939130335232[/C][C]0.00212173932953544[/C][C]0.00106086966476772[/C][/ROW]
[ROW][C]110[/C][C]0.998499545336352[/C][C]0.00300090932729603[/C][C]0.00150045466364802[/C][/ROW]
[ROW][C]111[/C][C]0.997535911218767[/C][C]0.00492817756246521[/C][C]0.0024640887812326[/C][/ROW]
[ROW][C]112[/C][C]0.996072745306[/C][C]0.00785450938799957[/C][C]0.00392725469399978[/C][/ROW]
[ROW][C]113[/C][C]0.994262744344099[/C][C]0.0114745113118023[/C][C]0.00573725565590116[/C][/ROW]
[ROW][C]114[/C][C]0.997374018664034[/C][C]0.00525196267193243[/C][C]0.00262598133596622[/C][/ROW]
[ROW][C]115[/C][C]0.995127961659758[/C][C]0.00974407668048308[/C][C]0.00487203834024154[/C][/ROW]
[ROW][C]116[/C][C]0.991071413198602[/C][C]0.0178571736027959[/C][C]0.00892858680139794[/C][/ROW]
[ROW][C]117[/C][C]0.999087251821632[/C][C]0.00182549635673501[/C][C]0.000912748178367504[/C][/ROW]
[ROW][C]118[/C][C]0.998158858051426[/C][C]0.00368228389714881[/C][C]0.0018411419485744[/C][/ROW]
[ROW][C]119[/C][C]0.997178659586532[/C][C]0.00564268082693571[/C][C]0.00282134041346785[/C][/ROW]
[ROW][C]120[/C][C]0.993200520152654[/C][C]0.0135989596946928[/C][C]0.00679947984734641[/C][/ROW]
[ROW][C]121[/C][C]0.985721268237335[/C][C]0.02855746352533[/C][C]0.014278731762665[/C][/ROW]
[ROW][C]122[/C][C]0.968641343362088[/C][C]0.0627173132758238[/C][C]0.0313586566379119[/C][/ROW]
[ROW][C]123[/C][C]0.942561094888679[/C][C]0.114877810222642[/C][C]0.0574389051113212[/C][/ROW]
[ROW][C]124[/C][C]0.89043322109199[/C][C]0.21913355781602[/C][C]0.10956677890801[/C][/ROW]
[ROW][C]125[/C][C]0.975369412465958[/C][C]0.0492611750680847[/C][C]0.0246305875340424[/C][/ROW]
[ROW][C]126[/C][C]0.959603915458884[/C][C]0.0807921690822316[/C][C]0.0403960845411158[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159906&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159906&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
180.7460995951832450.5078008096335090.253900404816755
190.8400425531903490.3199148936193010.159957446809651
200.7474496669566340.5051006660867310.252550333043366
210.6520975168927030.6958049662145940.347902483107297
220.5533160115327710.8933679769344580.446683988467229
230.446057188848910.892114377697820.55394281115109
240.5450964716710340.9098070566579310.454903528328966
250.4748300271179390.9496600542358770.525169972882061
260.549045226955970.901909546088060.45095477304403
270.6580146076481740.6839707847036520.341985392351826
280.653709497112260.6925810057754790.34629050288774
290.6912123955959090.6175752088081820.308787604404091
300.6596853116647430.6806293766705150.340314688335257
310.5858882402244510.8282235195510980.414111759775549
320.541463328106430.917073343787140.45853667189357
330.5270750551265240.945849889746950.472924944873476
340.870050587006770.2598988259864610.129949412993231
350.8441164105870440.3117671788259120.155883589412956
360.8327897702079680.3344204595840640.167210229792032
370.7877471147754250.4245057704491510.212252885224575
380.7380351992338510.5239296015322970.261964800766149
390.6951026058766050.6097947882467910.304897394123395
400.6642412497780160.6715175004439670.335758750221984
410.655552527526780.688894944946440.34444747247322
420.6028827312628530.7942345374742950.397117268737147
430.5430314719980890.9139370560038210.456968528001911
440.4906662678730990.9813325357461990.509333732126901
450.4409032319889970.8818064639779940.559096768011003
460.3897894561154440.7795789122308870.610210543884556
470.5686524043799990.8626951912400030.431347595620002
480.5326015615961130.9347968768077730.467398438403887
490.4765191287446870.9530382574893750.523480871255313
500.4323605128777470.8647210257554940.567639487122253
510.4488893693265030.8977787386530060.551110630673497
520.4045885426046980.8091770852093970.595411457395302
530.4117281459438520.8234562918877040.588271854056148
540.3743736437496020.7487472874992030.625626356250398
550.3282740945811010.6565481891622020.671725905418899
560.2844709910426430.5689419820852860.715529008957357
570.2466103157359820.4932206314719640.753389684264018
580.2242065100901760.4484130201803520.775793489909824
590.1850501747385310.3701003494770620.814949825261469
600.1574981177125640.3149962354251280.842501882287436
610.135788183001810.271576366003620.86421181699819
620.1128715068052920.2257430136105840.887128493194708
630.09548147843237390.1909629568647480.904518521567626
640.08091214029494450.1618242805898890.919087859705056
650.06560390674992690.1312078134998540.934396093250073
660.05555293531022340.1111058706204470.944447064689777
670.055566483100460.111132966200920.94443351689954
680.04378579171697410.08757158343394820.956214208283026
690.03434806200681160.06869612401362310.965651937993188
700.03104304651024660.06208609302049310.968956953489753
710.02693569269214290.05387138538428580.973064307307857
720.0306323845496790.0612647690993580.969367615450321
730.02767080405564710.05534160811129420.972329195944353
740.04138848730491320.08277697460982640.958611512695087
750.03172301123063190.06344602246126390.968276988769368
760.02510214584106230.05020429168212450.974897854158938
770.0525592632901650.105118526580330.947440736709835
780.04272156404872360.08544312809744720.957278435951276
790.03274823724648290.06549647449296580.967251762753517
800.02856965422702820.05713930845405630.971430345772972
810.02489699816092330.04979399632184650.975103001839077
820.043615317962080.08723063592415990.95638468203792
830.03957962579872640.07915925159745280.960420374201274
840.03062937498308770.06125874996617550.969370625016912
850.02880139455083530.05760278910167060.971198605449165
860.02879312405586890.05758624811173780.971206875944131
870.02228592975233970.04457185950467930.97771407024766
880.01786893458025080.03573786916050160.98213106541975
890.01819866441037430.03639732882074870.981801335589626
900.01315337928724120.02630675857448250.986846620712759
910.01094978238226330.02189956476452660.989050217617737
920.008449982052132460.01689996410426490.991550017947868
930.005868981164781280.01173796232956260.994131018835219
940.004900955287890920.009801910575781830.99509904471211
950.00414028029747450.0082805605949490.995859719702526
960.006831315886143140.01366263177228630.993168684113857
970.006563603429680930.01312720685936190.993436396570319
980.004681502500938090.009363005001876180.995318497499062
990.006053539206853270.01210707841370650.993946460793147
1000.01189292811124070.02378585622248140.98810707188876
1010.06162362009107030.1232472401821410.93837637990893
1020.05374328515719250.1074865703143850.946256714842807
1030.0679110516451580.1358221032903160.932088948354842
1040.05124352758865070.1024870551773010.94875647241135
1050.06762287851651010.135245757033020.93237712148349
1060.05757054855040980.115141097100820.94242945144959
1070.05127666544787830.1025533308957570.948723334552122
1080.999387877161460.001224245677079170.000612122838539583
1090.9989391303352320.002121739329535440.00106086966476772
1100.9984995453363520.003000909327296030.00150045466364802
1110.9975359112187670.004928177562465210.0024640887812326
1120.9960727453060.007854509387999570.00392725469399978
1130.9942627443440990.01147451131180230.00573725565590116
1140.9973740186640340.005251962671932430.00262598133596622
1150.9951279616597580.009744076680483080.00487203834024154
1160.9910714131986020.01785717360279590.00892858680139794
1170.9990872518216320.001825496356735010.000912748178367504
1180.9981588580514260.003682283897148810.0018411419485744
1190.9971786595865320.005642680826935710.00282134041346785
1200.9932005201526540.01359895969469280.00679947984734641
1210.9857212682373350.028557463525330.014278731762665
1220.9686413433620880.06271731327582380.0313586566379119
1230.9425610948886790.1148778102226420.0574389051113212
1240.890433221091990.219133557816020.10956677890801
1250.9753694124659580.04926117506808470.0246305875340424
1260.9596039154588840.08079216908223160.0403960845411158






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level130.119266055045872NOK
5% type I error level300.275229357798165NOK
10% type I error level490.44954128440367NOK

\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 & 13 & 0.119266055045872 & NOK \tabularnewline
5% type I error level & 30 & 0.275229357798165 & NOK \tabularnewline
10% type I error level & 49 & 0.44954128440367 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159906&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]13[/C][C]0.119266055045872[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]30[/C][C]0.275229357798165[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]49[/C][C]0.44954128440367[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159906&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159906&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 level130.119266055045872NOK
5% type I error level300.275229357798165NOK
10% type I error level490.44954128440367NOK


Figures (Output of Computation)

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

Parameters (Session):
par1 = 3 ; par2 = none ; par3 = 0 ; par4 = no ;
Parameters (R input):
par1 = 8 ; 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')
}