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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 12:13:03 -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/t1324574000fnjervzmym1u4ci.htm/, Retrieved Fri, 03 May 2024 05:10:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=159743, Retrieved Fri, 03 May 2024 05:10:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact89

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-01 16:06:53] [84ec9e690346b814992f2f0baa963a63]
-   PD    [Multiple Regression] [] [2011-12-22 17:13:03] [5d1d3e74ee2cfe51b36819d331f689c0] [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
6918	524450	266	1967	278	1	136	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
3118	330194	99	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
1956	116374	107	689	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
776	39790	27	242	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
2152	212394	166	691	92	0	66	25	93	88	58534	15657	59056	80	73
1457	201536	95	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
2197	158599	51	533	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
2891	202536	100	972	59	10	36	38	136	85	57476	7768	25155	47	47
1837	173505	65	541	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
1326	125612	39	467	48	3	57	26	96	67	25820	7573	49611	42	37
1317	166049	36	430	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
1079	93786	34	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
2091	160833	85	860	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
1378	146461	67	346	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 time7 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159743&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159743&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159743&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
11[t] = + 1522.5881535717 + 9.94545293676321`1`[t] -0.0511824906315352`2`[t] + 34.2113064947836`3`[t] -15.3253925652345`4`[t] -17.0605097627345`5`[t] + 754.719080273837`6`[t] -58.2421469020124`7`[t] + 583.870549915536`8`[t] -49.5681229175314`9`[t] + 10.3246995473943`10`[t] + 0.254359925844351`12`[t] + 0.295200727842862`13`[t] + 761.483303129019`14`[t] -517.661994990815`15`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
11[t] =  +  1522.5881535717 +  9.94545293676321`1`[t] -0.0511824906315352`2`[t] +  34.2113064947836`3`[t] -15.3253925652345`4`[t] -17.0605097627345`5`[t] +  754.719080273837`6`[t] -58.2421469020124`7`[t] +  583.870549915536`8`[t] -49.5681229175314`9`[t] +  10.3246995473943`10`[t] +  0.254359925844351`12`[t] +  0.295200727842862`13`[t] +  761.483303129019`14`[t] -517.661994990815`15`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159743&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]11[t] =  +  1522.5881535717 +  9.94545293676321`1`[t] -0.0511824906315352`2`[t] +  34.2113064947836`3`[t] -15.3253925652345`4`[t] -17.0605097627345`5`[t] +  754.719080273837`6`[t] -58.2421469020124`7`[t] +  583.870549915536`8`[t] -49.5681229175314`9`[t] +  10.3246995473943`10`[t] +  0.254359925844351`12`[t] +  0.295200727842862`13`[t] +  761.483303129019`14`[t] -517.661994990815`15`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159743&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159743&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
11[t] = + 1522.5881535717 + 9.94545293676321`1`[t] -0.0511824906315352`2`[t] + 34.2113064947836`3`[t] -15.3253925652345`4`[t] -17.0605097627345`5`[t] + 754.719080273837`6`[t] -58.2421469020124`7`[t] + 583.870549915536`8`[t] -49.5681229175314`9`[t] + 10.3246995473943`10`[t] + 0.254359925844351`12`[t] + 0.295200727842862`13`[t] + 761.483303129019`14`[t] -517.661994990815`15`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1522.58815357173416.8126070.44560.6566210.328311
`1`9.945452936763218.1336061.22280.2236490.111825
`2`-0.05118249063153520.047714-1.07270.2854110.142705
`3`34.211306494783663.7874560.53630.5926520.296326
`4`-15.325392565234519.526439-0.78490.4339780.216989
`5`-17.060509762734538.867276-0.43890.6614370.330719
`6`754.719080273837625.3419861.20690.2296830.114841
`7`-58.242146902012494.905339-0.61370.5405030.270252
`8`583.870549915536568.8943521.02630.3066590.15333
`9`-49.5681229175314182.437575-0.27170.7862880.393144
`10`10.3246995473943118.083650.08740.9304610.46523
`12`0.2543599258443510.5425260.46880.6399730.319986
`13`0.2952007278428620.1173522.51550.0131140.006557
`14`761.483303129019668.4683041.13910.2567530.128376
`15`-517.661994990815693.625954-0.74630.4568360.228418

\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) & 1522.5881535717 & 3416.812607 & 0.4456 & 0.656621 & 0.328311 \tabularnewline
`1` & 9.94545293676321 & 8.133606 & 1.2228 & 0.223649 & 0.111825 \tabularnewline
`2` & -0.0511824906315352 & 0.047714 & -1.0727 & 0.285411 & 0.142705 \tabularnewline
`3` & 34.2113064947836 & 63.787456 & 0.5363 & 0.592652 & 0.296326 \tabularnewline
`4` & -15.3253925652345 & 19.526439 & -0.7849 & 0.433978 & 0.216989 \tabularnewline
`5` & -17.0605097627345 & 38.867276 & -0.4389 & 0.661437 & 0.330719 \tabularnewline
`6` & 754.719080273837 & 625.341986 & 1.2069 & 0.229683 & 0.114841 \tabularnewline
`7` & -58.2421469020124 & 94.905339 & -0.6137 & 0.540503 & 0.270252 \tabularnewline
`8` & 583.870549915536 & 568.894352 & 1.0263 & 0.306659 & 0.15333 \tabularnewline
`9` & -49.5681229175314 & 182.437575 & -0.2717 & 0.786288 & 0.393144 \tabularnewline
`10` & 10.3246995473943 & 118.08365 & 0.0874 & 0.930461 & 0.46523 \tabularnewline
`12` & 0.254359925844351 & 0.542526 & 0.4688 & 0.639973 & 0.319986 \tabularnewline
`13` & 0.295200727842862 & 0.117352 & 2.5155 & 0.013114 & 0.006557 \tabularnewline
`14` & 761.483303129019 & 668.468304 & 1.1391 & 0.256753 & 0.128376 \tabularnewline
`15` & -517.661994990815 & 693.625954 & -0.7463 & 0.456836 & 0.228418 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159743&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]1522.5881535717[/C][C]3416.812607[/C][C]0.4456[/C][C]0.656621[/C][C]0.328311[/C][/ROW]
[ROW][C]`1`[/C][C]9.94545293676321[/C][C]8.133606[/C][C]1.2228[/C][C]0.223649[/C][C]0.111825[/C][/ROW]
[ROW][C]`2`[/C][C]-0.0511824906315352[/C][C]0.047714[/C][C]-1.0727[/C][C]0.285411[/C][C]0.142705[/C][/ROW]
[ROW][C]`3`[/C][C]34.2113064947836[/C][C]63.787456[/C][C]0.5363[/C][C]0.592652[/C][C]0.296326[/C][/ROW]
[ROW][C]`4`[/C][C]-15.3253925652345[/C][C]19.526439[/C][C]-0.7849[/C][C]0.433978[/C][C]0.216989[/C][/ROW]
[ROW][C]`5`[/C][C]-17.0605097627345[/C][C]38.867276[/C][C]-0.4389[/C][C]0.661437[/C][C]0.330719[/C][/ROW]
[ROW][C]`6`[/C][C]754.719080273837[/C][C]625.341986[/C][C]1.2069[/C][C]0.229683[/C][C]0.114841[/C][/ROW]
[ROW][C]`7`[/C][C]-58.2421469020124[/C][C]94.905339[/C][C]-0.6137[/C][C]0.540503[/C][C]0.270252[/C][/ROW]
[ROW][C]`8`[/C][C]583.870549915536[/C][C]568.894352[/C][C]1.0263[/C][C]0.306659[/C][C]0.15333[/C][/ROW]
[ROW][C]`9`[/C][C]-49.5681229175314[/C][C]182.437575[/C][C]-0.2717[/C][C]0.786288[/C][C]0.393144[/C][/ROW]
[ROW][C]`10`[/C][C]10.3246995473943[/C][C]118.08365[/C][C]0.0874[/C][C]0.930461[/C][C]0.46523[/C][/ROW]
[ROW][C]`12`[/C][C]0.254359925844351[/C][C]0.542526[/C][C]0.4688[/C][C]0.639973[/C][C]0.319986[/C][/ROW]
[ROW][C]`13`[/C][C]0.295200727842862[/C][C]0.117352[/C][C]2.5155[/C][C]0.013114[/C][C]0.006557[/C][/ROW]
[ROW][C]`14`[/C][C]761.483303129019[/C][C]668.468304[/C][C]1.1391[/C][C]0.256753[/C][C]0.128376[/C][/ROW]
[ROW][C]`15`[/C][C]-517.661994990815[/C][C]693.625954[/C][C]-0.7463[/C][C]0.456836[/C][C]0.228418[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159743&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159743&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)1522.58815357173416.8126070.44560.6566210.328311
`1`9.945452936763218.1336061.22280.2236490.111825
`2`-0.05118249063153520.047714-1.07270.2854110.142705
`3`34.211306494783663.7874560.53630.5926520.296326
`4`-15.325392565234519.526439-0.78490.4339780.216989
`5`-17.060509762734538.867276-0.43890.6614370.330719
`6`754.719080273837625.3419861.20690.2296830.114841
`7`-58.242146902012494.905339-0.61370.5405030.270252
`8`583.870549915536568.8943521.02630.3066590.15333
`9`-49.5681229175314182.437575-0.27170.7862880.393144
`10`10.3246995473943118.083650.08740.9304610.46523
`12`0.2543599258443510.5425260.46880.6399730.319986
`13`0.2952007278428620.1173522.51550.0131140.006557
`14`761.483303129019668.4683041.13910.2567530.128376
`15`-517.661994990815693.625954-0.74630.4568360.228418






Multiple Linear Regression - Regression Statistics
Multiple R0.819145911762517
R-squared0.671000024757246
Adjusted R-squared0.635294601087489
F-TEST (value)18.7926638530719
F-TEST (DF numerator)14
F-TEST (DF denominator)129
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation15161.9532518892
Sum Squared Residuals29655142607.2092

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.819145911762517 \tabularnewline
R-squared & 0.671000024757246 \tabularnewline
Adjusted R-squared & 0.635294601087489 \tabularnewline
F-TEST (value) & 18.7926638530719 \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 & 15161.9532518892 \tabularnewline
Sum Squared Residuals & 29655142607.2092 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159743&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.819145911762517[/C][/ROW]
[ROW][C]R-squared[/C][C]0.671000024757246[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.635294601087489[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]18.7926638530719[/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]15161.9532518892[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]29655142607.2092[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159743&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159743&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 R0.819145911762517
R-squared0.671000024757246
Adjusted R-squared0.635294601087489
F-TEST (value)18.7926638530719
F-TEST (DF numerator)14
F-TEST (DF denominator)129
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation15161.9532518892
Sum Squared Residuals29655142607.2092






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12046529068.3293687938-8603.32936879378
23362947892.6985723723-14263.6985723723
314233280.96942926322-1857.96942926322
42562943092.5942791635-17463.5942791635
55400269174.0346314574-15172.0346314574
6151036145802.9147895495233.08521045108
73328740641.9263381836-7354.92633818365
83117246775.1192764389-15603.1192764389
92811339007.3524247515-10894.3524247515
105780366100.7031756169-8297.70317561688
114983041650.62545659298179.37454340711
125214361174.1795494327-9031.17954943267
132105534631.830573566-13576.830573566
144700760196.9964086805-13189.9964086805
152873532809.6306345503-4074.63063455032
165914735911.736630051123235.2633699489
177895067788.236728660111161.7632713399
181349715878.3050395503-2381.30503955034
194615446295.2500627692-141.25006276923
205324923008.59453289630240.405467104
211072618547.844495514-7821.84449551397
228370074261.74343551099438.25656448914
234040023673.451108678716726.5488913213
243379737454.9292672204-3657.92926722038
253620541457.8455389961-5252.84553899611
263016533322.9310244357-3157.93102443566
275853457170.75901529821363.24098470178
284466365762.2183184445-21099.2183184445
299255666348.826504715626207.1734952844
304007847773.8912423536-7695.89124235363
313471134829.4288545661-118.42885456612
323107642280.1429306929-11204.1429306929
337460844522.662931322130085.3370686779
345809225573.475824662132518.5241753379
354200960408.7597348744-18399.7597348744
3601566.74491300325-1566.74491300325
373602232670.19584003253351.80415996754
382333330630.8725897201-7297.87258972012
395334949556.80334700893792.19665299111
409259689918.14505982962677.85494017037
414959861002.5103928066-11404.5103928066
424409344181.2660528821-88.2660528820972
438420548215.332622282435989.6673777176
446336948876.614777930414492.3852220696
456013254806.08305906785325.91694093216
463740345926.0419688121-8523.04196881209
472446045452.491830329-20992.491830329
484645641045.00738917025410.99261082978
496661653659.730660200312956.2693397997
504155448271.1697537044-6717.16975370438
512234619590.28616946582755.71383053423
523087435479.1783001609-4605.17830016092
536870170356.6431050595-1655.64310505954
543572843798.571817062-8070.57181706203
552901046069.5217479927-17059.5217479927
562311040672.4900252989-17562.4900252989
573884439865.6715882754-1021.67158827542
582708429677.4289565059-2593.42895650588
593513936563.9769365195-1424.97693651947
605747650062.00752241687413.99247758315
613327746029.8790385209-12752.8790385209
623114132505.8136678761-1364.81366787614
636128147232.261499819614048.7385001804
642582041097.9820702254-15277.9820702254
652328426873.7341420229-3589.73414202293
663537844016.4559215699-8638.45592156994
677499076847.2869662447-1857.2869662447
682965354087.5567524957-24434.5567524957
696462241228.305786631623393.6942133684
70415713089.0877130265-8932.08771302649
712924550046.6641580365-20801.6641580365
725000834615.921823106615392.0781768934
735233858515.6779643785-6177.67796437846
741331029481.5695678113-16171.5695678113
759290152533.205068625140367.7949313749
761095622822.6063143846-11866.6063143846
773424142842.6733980924-8601.67339809241
787504369156.25294628495886.74705371509
792115223158.5652674214-2006.56526742139
804224941379.4189552218869.581044778225
814200539620.29787536672384.70212463335
824115239202.24494408221949.75505591777
831439931922.9361290537-17523.9361290537
842826333421.1132229066-5158.11322290656
851721522692.684406977-5477.68440697697
864814046857.00448854371282.99551145632
876289782113.6210481937-19216.6210481937
882288332445.1154194861-9562.11541948608
894162238504.70516549633117.29483450371
904071539012.0296127771702.970387223
916589750173.44417823815723.555821762
927654260500.861603088616041.1383969114
933747736439.06129505961037.93870494042
945321634212.059492178319003.9405078217
954091128205.351727070312705.6482729297
965702161533.7748321698-4512.77483216983
977311648521.389586805924594.6104131941
9838958054.04027707167-4159.04027707167
994660941069.61659722665539.38340277344
1002935141779.5538863483-12428.5538863483
10123254720.15926780509-2395.15926780509
1023174729087.89763417832659.10236582173
1033266524945.26549885277719.73450114729
1041924930496.8316742545-11247.8316742545
1051529219349.1298372657-4057.12983726566
106584211873.8730477035-6031.8730477035
1073399441622.0193468797-7628.0193468797
1081301819279.4561110712-6261.45611107117
10901522.5881535717-1522.5881535717
1109817736325.943046404761851.0569535953
1113794125969.535752756711971.4642472433
1123103240348.6300121278-9316.63001212775
1133268338384.385994592-5701.38599459201
1143454533639.088807669905.911192331044
11501984.45608426257-1984.45608426257
11601522.5881535717-1522.5881535717
1172752537854.7868103042-10329.7868103042
1186685656816.933915180710039.0660848193
1192854945741.1233094892-17192.1233094892
1203861027370.245156517811239.7548434822
12127812445.62830815326335.37169184674
1224121151147.5175821976-9936.5175821976
1232269826397.1906490771-3699.19064907707
1244119436436.57456947724757.42543052284
1253268913539.376079525419149.6239204746
12657529250.44985921261-3498.44985921261
1272675741329.1906641138-14572.1906641138
1282252731097.5596519083-8570.55965190835
1294481040579.99521349314230.00478650691
13002910.01864604907-2910.01864604907
13101633.66087183467-1633.66087183467
13210067434942.227562858165731.7724371419
13301814.81832872033-1814.81832872033
1345778643650.865295037914135.1347049621
13501380.75550968371-1380.75550968371
13654447313.95800164639-1869.95800164639
13701522.5881535717-1522.5881535717
1382847029362.2590685252-892.259068525178
1396184939214.89950876722634.100491233
14002440.96620774768-2440.96620774768
14121797076.63466292077-4897.63466292077
142801919652.0934965859-11633.0934965859
1433964451516.4313322597-11872.4313322597
1442349434705.8537970772-11211.8537970772

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 20465 & 29068.3293687938 & -8603.32936879378 \tabularnewline
2 & 33629 & 47892.6985723723 & -14263.6985723723 \tabularnewline
3 & 1423 & 3280.96942926322 & -1857.96942926322 \tabularnewline
4 & 25629 & 43092.5942791635 & -17463.5942791635 \tabularnewline
5 & 54002 & 69174.0346314574 & -15172.0346314574 \tabularnewline
6 & 151036 & 145802.914789549 & 5233.08521045108 \tabularnewline
7 & 33287 & 40641.9263381836 & -7354.92633818365 \tabularnewline
8 & 31172 & 46775.1192764389 & -15603.1192764389 \tabularnewline
9 & 28113 & 39007.3524247515 & -10894.3524247515 \tabularnewline
10 & 57803 & 66100.7031756169 & -8297.70317561688 \tabularnewline
11 & 49830 & 41650.6254565929 & 8179.37454340711 \tabularnewline
12 & 52143 & 61174.1795494327 & -9031.17954943267 \tabularnewline
13 & 21055 & 34631.830573566 & -13576.830573566 \tabularnewline
14 & 47007 & 60196.9964086805 & -13189.9964086805 \tabularnewline
15 & 28735 & 32809.6306345503 & -4074.63063455032 \tabularnewline
16 & 59147 & 35911.7366300511 & 23235.2633699489 \tabularnewline
17 & 78950 & 67788.2367286601 & 11161.7632713399 \tabularnewline
18 & 13497 & 15878.3050395503 & -2381.30503955034 \tabularnewline
19 & 46154 & 46295.2500627692 & -141.25006276923 \tabularnewline
20 & 53249 & 23008.594532896 & 30240.405467104 \tabularnewline
21 & 10726 & 18547.844495514 & -7821.84449551397 \tabularnewline
22 & 83700 & 74261.7434355109 & 9438.25656448914 \tabularnewline
23 & 40400 & 23673.4511086787 & 16726.5488913213 \tabularnewline
24 & 33797 & 37454.9292672204 & -3657.92926722038 \tabularnewline
25 & 36205 & 41457.8455389961 & -5252.84553899611 \tabularnewline
26 & 30165 & 33322.9310244357 & -3157.93102443566 \tabularnewline
27 & 58534 & 57170.7590152982 & 1363.24098470178 \tabularnewline
28 & 44663 & 65762.2183184445 & -21099.2183184445 \tabularnewline
29 & 92556 & 66348.8265047156 & 26207.1734952844 \tabularnewline
30 & 40078 & 47773.8912423536 & -7695.89124235363 \tabularnewline
31 & 34711 & 34829.4288545661 & -118.42885456612 \tabularnewline
32 & 31076 & 42280.1429306929 & -11204.1429306929 \tabularnewline
33 & 74608 & 44522.6629313221 & 30085.3370686779 \tabularnewline
34 & 58092 & 25573.4758246621 & 32518.5241753379 \tabularnewline
35 & 42009 & 60408.7597348744 & -18399.7597348744 \tabularnewline
36 & 0 & 1566.74491300325 & -1566.74491300325 \tabularnewline
37 & 36022 & 32670.1958400325 & 3351.80415996754 \tabularnewline
38 & 23333 & 30630.8725897201 & -7297.87258972012 \tabularnewline
39 & 53349 & 49556.8033470089 & 3792.19665299111 \tabularnewline
40 & 92596 & 89918.1450598296 & 2677.85494017037 \tabularnewline
41 & 49598 & 61002.5103928066 & -11404.5103928066 \tabularnewline
42 & 44093 & 44181.2660528821 & -88.2660528820972 \tabularnewline
43 & 84205 & 48215.3326222824 & 35989.6673777176 \tabularnewline
44 & 63369 & 48876.6147779304 & 14492.3852220696 \tabularnewline
45 & 60132 & 54806.0830590678 & 5325.91694093216 \tabularnewline
46 & 37403 & 45926.0419688121 & -8523.04196881209 \tabularnewline
47 & 24460 & 45452.491830329 & -20992.491830329 \tabularnewline
48 & 46456 & 41045.0073891702 & 5410.99261082978 \tabularnewline
49 & 66616 & 53659.7306602003 & 12956.2693397997 \tabularnewline
50 & 41554 & 48271.1697537044 & -6717.16975370438 \tabularnewline
51 & 22346 & 19590.2861694658 & 2755.71383053423 \tabularnewline
52 & 30874 & 35479.1783001609 & -4605.17830016092 \tabularnewline
53 & 68701 & 70356.6431050595 & -1655.64310505954 \tabularnewline
54 & 35728 & 43798.571817062 & -8070.57181706203 \tabularnewline
55 & 29010 & 46069.5217479927 & -17059.5217479927 \tabularnewline
56 & 23110 & 40672.4900252989 & -17562.4900252989 \tabularnewline
57 & 38844 & 39865.6715882754 & -1021.67158827542 \tabularnewline
58 & 27084 & 29677.4289565059 & -2593.42895650588 \tabularnewline
59 & 35139 & 36563.9769365195 & -1424.97693651947 \tabularnewline
60 & 57476 & 50062.0075224168 & 7413.99247758315 \tabularnewline
61 & 33277 & 46029.8790385209 & -12752.8790385209 \tabularnewline
62 & 31141 & 32505.8136678761 & -1364.81366787614 \tabularnewline
63 & 61281 & 47232.2614998196 & 14048.7385001804 \tabularnewline
64 & 25820 & 41097.9820702254 & -15277.9820702254 \tabularnewline
65 & 23284 & 26873.7341420229 & -3589.73414202293 \tabularnewline
66 & 35378 & 44016.4559215699 & -8638.45592156994 \tabularnewline
67 & 74990 & 76847.2869662447 & -1857.2869662447 \tabularnewline
68 & 29653 & 54087.5567524957 & -24434.5567524957 \tabularnewline
69 & 64622 & 41228.3057866316 & 23393.6942133684 \tabularnewline
70 & 4157 & 13089.0877130265 & -8932.08771302649 \tabularnewline
71 & 29245 & 50046.6641580365 & -20801.6641580365 \tabularnewline
72 & 50008 & 34615.9218231066 & 15392.0781768934 \tabularnewline
73 & 52338 & 58515.6779643785 & -6177.67796437846 \tabularnewline
74 & 13310 & 29481.5695678113 & -16171.5695678113 \tabularnewline
75 & 92901 & 52533.2050686251 & 40367.7949313749 \tabularnewline
76 & 10956 & 22822.6063143846 & -11866.6063143846 \tabularnewline
77 & 34241 & 42842.6733980924 & -8601.67339809241 \tabularnewline
78 & 75043 & 69156.2529462849 & 5886.74705371509 \tabularnewline
79 & 21152 & 23158.5652674214 & -2006.56526742139 \tabularnewline
80 & 42249 & 41379.4189552218 & 869.581044778225 \tabularnewline
81 & 42005 & 39620.2978753667 & 2384.70212463335 \tabularnewline
82 & 41152 & 39202.2449440822 & 1949.75505591777 \tabularnewline
83 & 14399 & 31922.9361290537 & -17523.9361290537 \tabularnewline
84 & 28263 & 33421.1132229066 & -5158.11322290656 \tabularnewline
85 & 17215 & 22692.684406977 & -5477.68440697697 \tabularnewline
86 & 48140 & 46857.0044885437 & 1282.99551145632 \tabularnewline
87 & 62897 & 82113.6210481937 & -19216.6210481937 \tabularnewline
88 & 22883 & 32445.1154194861 & -9562.11541948608 \tabularnewline
89 & 41622 & 38504.7051654963 & 3117.29483450371 \tabularnewline
90 & 40715 & 39012.029612777 & 1702.970387223 \tabularnewline
91 & 65897 & 50173.444178238 & 15723.555821762 \tabularnewline
92 & 76542 & 60500.8616030886 & 16041.1383969114 \tabularnewline
93 & 37477 & 36439.0612950596 & 1037.93870494042 \tabularnewline
94 & 53216 & 34212.0594921783 & 19003.9405078217 \tabularnewline
95 & 40911 & 28205.3517270703 & 12705.6482729297 \tabularnewline
96 & 57021 & 61533.7748321698 & -4512.77483216983 \tabularnewline
97 & 73116 & 48521.3895868059 & 24594.6104131941 \tabularnewline
98 & 3895 & 8054.04027707167 & -4159.04027707167 \tabularnewline
99 & 46609 & 41069.6165972266 & 5539.38340277344 \tabularnewline
100 & 29351 & 41779.5538863483 & -12428.5538863483 \tabularnewline
101 & 2325 & 4720.15926780509 & -2395.15926780509 \tabularnewline
102 & 31747 & 29087.8976341783 & 2659.10236582173 \tabularnewline
103 & 32665 & 24945.2654988527 & 7719.73450114729 \tabularnewline
104 & 19249 & 30496.8316742545 & -11247.8316742545 \tabularnewline
105 & 15292 & 19349.1298372657 & -4057.12983726566 \tabularnewline
106 & 5842 & 11873.8730477035 & -6031.8730477035 \tabularnewline
107 & 33994 & 41622.0193468797 & -7628.0193468797 \tabularnewline
108 & 13018 & 19279.4561110712 & -6261.45611107117 \tabularnewline
109 & 0 & 1522.5881535717 & -1522.5881535717 \tabularnewline
110 & 98177 & 36325.9430464047 & 61851.0569535953 \tabularnewline
111 & 37941 & 25969.5357527567 & 11971.4642472433 \tabularnewline
112 & 31032 & 40348.6300121278 & -9316.63001212775 \tabularnewline
113 & 32683 & 38384.385994592 & -5701.38599459201 \tabularnewline
114 & 34545 & 33639.088807669 & 905.911192331044 \tabularnewline
115 & 0 & 1984.45608426257 & -1984.45608426257 \tabularnewline
116 & 0 & 1522.5881535717 & -1522.5881535717 \tabularnewline
117 & 27525 & 37854.7868103042 & -10329.7868103042 \tabularnewline
118 & 66856 & 56816.9339151807 & 10039.0660848193 \tabularnewline
119 & 28549 & 45741.1233094892 & -17192.1233094892 \tabularnewline
120 & 38610 & 27370.2451565178 & 11239.7548434822 \tabularnewline
121 & 2781 & 2445.62830815326 & 335.37169184674 \tabularnewline
122 & 41211 & 51147.5175821976 & -9936.5175821976 \tabularnewline
123 & 22698 & 26397.1906490771 & -3699.19064907707 \tabularnewline
124 & 41194 & 36436.5745694772 & 4757.42543052284 \tabularnewline
125 & 32689 & 13539.3760795254 & 19149.6239204746 \tabularnewline
126 & 5752 & 9250.44985921261 & -3498.44985921261 \tabularnewline
127 & 26757 & 41329.1906641138 & -14572.1906641138 \tabularnewline
128 & 22527 & 31097.5596519083 & -8570.55965190835 \tabularnewline
129 & 44810 & 40579.9952134931 & 4230.00478650691 \tabularnewline
130 & 0 & 2910.01864604907 & -2910.01864604907 \tabularnewline
131 & 0 & 1633.66087183467 & -1633.66087183467 \tabularnewline
132 & 100674 & 34942.2275628581 & 65731.7724371419 \tabularnewline
133 & 0 & 1814.81832872033 & -1814.81832872033 \tabularnewline
134 & 57786 & 43650.8652950379 & 14135.1347049621 \tabularnewline
135 & 0 & 1380.75550968371 & -1380.75550968371 \tabularnewline
136 & 5444 & 7313.95800164639 & -1869.95800164639 \tabularnewline
137 & 0 & 1522.5881535717 & -1522.5881535717 \tabularnewline
138 & 28470 & 29362.2590685252 & -892.259068525178 \tabularnewline
139 & 61849 & 39214.899508767 & 22634.100491233 \tabularnewline
140 & 0 & 2440.96620774768 & -2440.96620774768 \tabularnewline
141 & 2179 & 7076.63466292077 & -4897.63466292077 \tabularnewline
142 & 8019 & 19652.0934965859 & -11633.0934965859 \tabularnewline
143 & 39644 & 51516.4313322597 & -11872.4313322597 \tabularnewline
144 & 23494 & 34705.8537970772 & -11211.8537970772 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159743&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]20465[/C][C]29068.3293687938[/C][C]-8603.32936879378[/C][/ROW]
[ROW][C]2[/C][C]33629[/C][C]47892.6985723723[/C][C]-14263.6985723723[/C][/ROW]
[ROW][C]3[/C][C]1423[/C][C]3280.96942926322[/C][C]-1857.96942926322[/C][/ROW]
[ROW][C]4[/C][C]25629[/C][C]43092.5942791635[/C][C]-17463.5942791635[/C][/ROW]
[ROW][C]5[/C][C]54002[/C][C]69174.0346314574[/C][C]-15172.0346314574[/C][/ROW]
[ROW][C]6[/C][C]151036[/C][C]145802.914789549[/C][C]5233.08521045108[/C][/ROW]
[ROW][C]7[/C][C]33287[/C][C]40641.9263381836[/C][C]-7354.92633818365[/C][/ROW]
[ROW][C]8[/C][C]31172[/C][C]46775.1192764389[/C][C]-15603.1192764389[/C][/ROW]
[ROW][C]9[/C][C]28113[/C][C]39007.3524247515[/C][C]-10894.3524247515[/C][/ROW]
[ROW][C]10[/C][C]57803[/C][C]66100.7031756169[/C][C]-8297.70317561688[/C][/ROW]
[ROW][C]11[/C][C]49830[/C][C]41650.6254565929[/C][C]8179.37454340711[/C][/ROW]
[ROW][C]12[/C][C]52143[/C][C]61174.1795494327[/C][C]-9031.17954943267[/C][/ROW]
[ROW][C]13[/C][C]21055[/C][C]34631.830573566[/C][C]-13576.830573566[/C][/ROW]
[ROW][C]14[/C][C]47007[/C][C]60196.9964086805[/C][C]-13189.9964086805[/C][/ROW]
[ROW][C]15[/C][C]28735[/C][C]32809.6306345503[/C][C]-4074.63063455032[/C][/ROW]
[ROW][C]16[/C][C]59147[/C][C]35911.7366300511[/C][C]23235.2633699489[/C][/ROW]
[ROW][C]17[/C][C]78950[/C][C]67788.2367286601[/C][C]11161.7632713399[/C][/ROW]
[ROW][C]18[/C][C]13497[/C][C]15878.3050395503[/C][C]-2381.30503955034[/C][/ROW]
[ROW][C]19[/C][C]46154[/C][C]46295.2500627692[/C][C]-141.25006276923[/C][/ROW]
[ROW][C]20[/C][C]53249[/C][C]23008.594532896[/C][C]30240.405467104[/C][/ROW]
[ROW][C]21[/C][C]10726[/C][C]18547.844495514[/C][C]-7821.84449551397[/C][/ROW]
[ROW][C]22[/C][C]83700[/C][C]74261.7434355109[/C][C]9438.25656448914[/C][/ROW]
[ROW][C]23[/C][C]40400[/C][C]23673.4511086787[/C][C]16726.5488913213[/C][/ROW]
[ROW][C]24[/C][C]33797[/C][C]37454.9292672204[/C][C]-3657.92926722038[/C][/ROW]
[ROW][C]25[/C][C]36205[/C][C]41457.8455389961[/C][C]-5252.84553899611[/C][/ROW]
[ROW][C]26[/C][C]30165[/C][C]33322.9310244357[/C][C]-3157.93102443566[/C][/ROW]
[ROW][C]27[/C][C]58534[/C][C]57170.7590152982[/C][C]1363.24098470178[/C][/ROW]
[ROW][C]28[/C][C]44663[/C][C]65762.2183184445[/C][C]-21099.2183184445[/C][/ROW]
[ROW][C]29[/C][C]92556[/C][C]66348.8265047156[/C][C]26207.1734952844[/C][/ROW]
[ROW][C]30[/C][C]40078[/C][C]47773.8912423536[/C][C]-7695.89124235363[/C][/ROW]
[ROW][C]31[/C][C]34711[/C][C]34829.4288545661[/C][C]-118.42885456612[/C][/ROW]
[ROW][C]32[/C][C]31076[/C][C]42280.1429306929[/C][C]-11204.1429306929[/C][/ROW]
[ROW][C]33[/C][C]74608[/C][C]44522.6629313221[/C][C]30085.3370686779[/C][/ROW]
[ROW][C]34[/C][C]58092[/C][C]25573.4758246621[/C][C]32518.5241753379[/C][/ROW]
[ROW][C]35[/C][C]42009[/C][C]60408.7597348744[/C][C]-18399.7597348744[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]1566.74491300325[/C][C]-1566.74491300325[/C][/ROW]
[ROW][C]37[/C][C]36022[/C][C]32670.1958400325[/C][C]3351.80415996754[/C][/ROW]
[ROW][C]38[/C][C]23333[/C][C]30630.8725897201[/C][C]-7297.87258972012[/C][/ROW]
[ROW][C]39[/C][C]53349[/C][C]49556.8033470089[/C][C]3792.19665299111[/C][/ROW]
[ROW][C]40[/C][C]92596[/C][C]89918.1450598296[/C][C]2677.85494017037[/C][/ROW]
[ROW][C]41[/C][C]49598[/C][C]61002.5103928066[/C][C]-11404.5103928066[/C][/ROW]
[ROW][C]42[/C][C]44093[/C][C]44181.2660528821[/C][C]-88.2660528820972[/C][/ROW]
[ROW][C]43[/C][C]84205[/C][C]48215.3326222824[/C][C]35989.6673777176[/C][/ROW]
[ROW][C]44[/C][C]63369[/C][C]48876.6147779304[/C][C]14492.3852220696[/C][/ROW]
[ROW][C]45[/C][C]60132[/C][C]54806.0830590678[/C][C]5325.91694093216[/C][/ROW]
[ROW][C]46[/C][C]37403[/C][C]45926.0419688121[/C][C]-8523.04196881209[/C][/ROW]
[ROW][C]47[/C][C]24460[/C][C]45452.491830329[/C][C]-20992.491830329[/C][/ROW]
[ROW][C]48[/C][C]46456[/C][C]41045.0073891702[/C][C]5410.99261082978[/C][/ROW]
[ROW][C]49[/C][C]66616[/C][C]53659.7306602003[/C][C]12956.2693397997[/C][/ROW]
[ROW][C]50[/C][C]41554[/C][C]48271.1697537044[/C][C]-6717.16975370438[/C][/ROW]
[ROW][C]51[/C][C]22346[/C][C]19590.2861694658[/C][C]2755.71383053423[/C][/ROW]
[ROW][C]52[/C][C]30874[/C][C]35479.1783001609[/C][C]-4605.17830016092[/C][/ROW]
[ROW][C]53[/C][C]68701[/C][C]70356.6431050595[/C][C]-1655.64310505954[/C][/ROW]
[ROW][C]54[/C][C]35728[/C][C]43798.571817062[/C][C]-8070.57181706203[/C][/ROW]
[ROW][C]55[/C][C]29010[/C][C]46069.5217479927[/C][C]-17059.5217479927[/C][/ROW]
[ROW][C]56[/C][C]23110[/C][C]40672.4900252989[/C][C]-17562.4900252989[/C][/ROW]
[ROW][C]57[/C][C]38844[/C][C]39865.6715882754[/C][C]-1021.67158827542[/C][/ROW]
[ROW][C]58[/C][C]27084[/C][C]29677.4289565059[/C][C]-2593.42895650588[/C][/ROW]
[ROW][C]59[/C][C]35139[/C][C]36563.9769365195[/C][C]-1424.97693651947[/C][/ROW]
[ROW][C]60[/C][C]57476[/C][C]50062.0075224168[/C][C]7413.99247758315[/C][/ROW]
[ROW][C]61[/C][C]33277[/C][C]46029.8790385209[/C][C]-12752.8790385209[/C][/ROW]
[ROW][C]62[/C][C]31141[/C][C]32505.8136678761[/C][C]-1364.81366787614[/C][/ROW]
[ROW][C]63[/C][C]61281[/C][C]47232.2614998196[/C][C]14048.7385001804[/C][/ROW]
[ROW][C]64[/C][C]25820[/C][C]41097.9820702254[/C][C]-15277.9820702254[/C][/ROW]
[ROW][C]65[/C][C]23284[/C][C]26873.7341420229[/C][C]-3589.73414202293[/C][/ROW]
[ROW][C]66[/C][C]35378[/C][C]44016.4559215699[/C][C]-8638.45592156994[/C][/ROW]
[ROW][C]67[/C][C]74990[/C][C]76847.2869662447[/C][C]-1857.2869662447[/C][/ROW]
[ROW][C]68[/C][C]29653[/C][C]54087.5567524957[/C][C]-24434.5567524957[/C][/ROW]
[ROW][C]69[/C][C]64622[/C][C]41228.3057866316[/C][C]23393.6942133684[/C][/ROW]
[ROW][C]70[/C][C]4157[/C][C]13089.0877130265[/C][C]-8932.08771302649[/C][/ROW]
[ROW][C]71[/C][C]29245[/C][C]50046.6641580365[/C][C]-20801.6641580365[/C][/ROW]
[ROW][C]72[/C][C]50008[/C][C]34615.9218231066[/C][C]15392.0781768934[/C][/ROW]
[ROW][C]73[/C][C]52338[/C][C]58515.6779643785[/C][C]-6177.67796437846[/C][/ROW]
[ROW][C]74[/C][C]13310[/C][C]29481.5695678113[/C][C]-16171.5695678113[/C][/ROW]
[ROW][C]75[/C][C]92901[/C][C]52533.2050686251[/C][C]40367.7949313749[/C][/ROW]
[ROW][C]76[/C][C]10956[/C][C]22822.6063143846[/C][C]-11866.6063143846[/C][/ROW]
[ROW][C]77[/C][C]34241[/C][C]42842.6733980924[/C][C]-8601.67339809241[/C][/ROW]
[ROW][C]78[/C][C]75043[/C][C]69156.2529462849[/C][C]5886.74705371509[/C][/ROW]
[ROW][C]79[/C][C]21152[/C][C]23158.5652674214[/C][C]-2006.56526742139[/C][/ROW]
[ROW][C]80[/C][C]42249[/C][C]41379.4189552218[/C][C]869.581044778225[/C][/ROW]
[ROW][C]81[/C][C]42005[/C][C]39620.2978753667[/C][C]2384.70212463335[/C][/ROW]
[ROW][C]82[/C][C]41152[/C][C]39202.2449440822[/C][C]1949.75505591777[/C][/ROW]
[ROW][C]83[/C][C]14399[/C][C]31922.9361290537[/C][C]-17523.9361290537[/C][/ROW]
[ROW][C]84[/C][C]28263[/C][C]33421.1132229066[/C][C]-5158.11322290656[/C][/ROW]
[ROW][C]85[/C][C]17215[/C][C]22692.684406977[/C][C]-5477.68440697697[/C][/ROW]
[ROW][C]86[/C][C]48140[/C][C]46857.0044885437[/C][C]1282.99551145632[/C][/ROW]
[ROW][C]87[/C][C]62897[/C][C]82113.6210481937[/C][C]-19216.6210481937[/C][/ROW]
[ROW][C]88[/C][C]22883[/C][C]32445.1154194861[/C][C]-9562.11541948608[/C][/ROW]
[ROW][C]89[/C][C]41622[/C][C]38504.7051654963[/C][C]3117.29483450371[/C][/ROW]
[ROW][C]90[/C][C]40715[/C][C]39012.029612777[/C][C]1702.970387223[/C][/ROW]
[ROW][C]91[/C][C]65897[/C][C]50173.444178238[/C][C]15723.555821762[/C][/ROW]
[ROW][C]92[/C][C]76542[/C][C]60500.8616030886[/C][C]16041.1383969114[/C][/ROW]
[ROW][C]93[/C][C]37477[/C][C]36439.0612950596[/C][C]1037.93870494042[/C][/ROW]
[ROW][C]94[/C][C]53216[/C][C]34212.0594921783[/C][C]19003.9405078217[/C][/ROW]
[ROW][C]95[/C][C]40911[/C][C]28205.3517270703[/C][C]12705.6482729297[/C][/ROW]
[ROW][C]96[/C][C]57021[/C][C]61533.7748321698[/C][C]-4512.77483216983[/C][/ROW]
[ROW][C]97[/C][C]73116[/C][C]48521.3895868059[/C][C]24594.6104131941[/C][/ROW]
[ROW][C]98[/C][C]3895[/C][C]8054.04027707167[/C][C]-4159.04027707167[/C][/ROW]
[ROW][C]99[/C][C]46609[/C][C]41069.6165972266[/C][C]5539.38340277344[/C][/ROW]
[ROW][C]100[/C][C]29351[/C][C]41779.5538863483[/C][C]-12428.5538863483[/C][/ROW]
[ROW][C]101[/C][C]2325[/C][C]4720.15926780509[/C][C]-2395.15926780509[/C][/ROW]
[ROW][C]102[/C][C]31747[/C][C]29087.8976341783[/C][C]2659.10236582173[/C][/ROW]
[ROW][C]103[/C][C]32665[/C][C]24945.2654988527[/C][C]7719.73450114729[/C][/ROW]
[ROW][C]104[/C][C]19249[/C][C]30496.8316742545[/C][C]-11247.8316742545[/C][/ROW]
[ROW][C]105[/C][C]15292[/C][C]19349.1298372657[/C][C]-4057.12983726566[/C][/ROW]
[ROW][C]106[/C][C]5842[/C][C]11873.8730477035[/C][C]-6031.8730477035[/C][/ROW]
[ROW][C]107[/C][C]33994[/C][C]41622.0193468797[/C][C]-7628.0193468797[/C][/ROW]
[ROW][C]108[/C][C]13018[/C][C]19279.4561110712[/C][C]-6261.45611107117[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]1522.5881535717[/C][C]-1522.5881535717[/C][/ROW]
[ROW][C]110[/C][C]98177[/C][C]36325.9430464047[/C][C]61851.0569535953[/C][/ROW]
[ROW][C]111[/C][C]37941[/C][C]25969.5357527567[/C][C]11971.4642472433[/C][/ROW]
[ROW][C]112[/C][C]31032[/C][C]40348.6300121278[/C][C]-9316.63001212775[/C][/ROW]
[ROW][C]113[/C][C]32683[/C][C]38384.385994592[/C][C]-5701.38599459201[/C][/ROW]
[ROW][C]114[/C][C]34545[/C][C]33639.088807669[/C][C]905.911192331044[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]1984.45608426257[/C][C]-1984.45608426257[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]1522.5881535717[/C][C]-1522.5881535717[/C][/ROW]
[ROW][C]117[/C][C]27525[/C][C]37854.7868103042[/C][C]-10329.7868103042[/C][/ROW]
[ROW][C]118[/C][C]66856[/C][C]56816.9339151807[/C][C]10039.0660848193[/C][/ROW]
[ROW][C]119[/C][C]28549[/C][C]45741.1233094892[/C][C]-17192.1233094892[/C][/ROW]
[ROW][C]120[/C][C]38610[/C][C]27370.2451565178[/C][C]11239.7548434822[/C][/ROW]
[ROW][C]121[/C][C]2781[/C][C]2445.62830815326[/C][C]335.37169184674[/C][/ROW]
[ROW][C]122[/C][C]41211[/C][C]51147.5175821976[/C][C]-9936.5175821976[/C][/ROW]
[ROW][C]123[/C][C]22698[/C][C]26397.1906490771[/C][C]-3699.19064907707[/C][/ROW]
[ROW][C]124[/C][C]41194[/C][C]36436.5745694772[/C][C]4757.42543052284[/C][/ROW]
[ROW][C]125[/C][C]32689[/C][C]13539.3760795254[/C][C]19149.6239204746[/C][/ROW]
[ROW][C]126[/C][C]5752[/C][C]9250.44985921261[/C][C]-3498.44985921261[/C][/ROW]
[ROW][C]127[/C][C]26757[/C][C]41329.1906641138[/C][C]-14572.1906641138[/C][/ROW]
[ROW][C]128[/C][C]22527[/C][C]31097.5596519083[/C][C]-8570.55965190835[/C][/ROW]
[ROW][C]129[/C][C]44810[/C][C]40579.9952134931[/C][C]4230.00478650691[/C][/ROW]
[ROW][C]130[/C][C]0[/C][C]2910.01864604907[/C][C]-2910.01864604907[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]1633.66087183467[/C][C]-1633.66087183467[/C][/ROW]
[ROW][C]132[/C][C]100674[/C][C]34942.2275628581[/C][C]65731.7724371419[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]1814.81832872033[/C][C]-1814.81832872033[/C][/ROW]
[ROW][C]134[/C][C]57786[/C][C]43650.8652950379[/C][C]14135.1347049621[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]1380.75550968371[/C][C]-1380.75550968371[/C][/ROW]
[ROW][C]136[/C][C]5444[/C][C]7313.95800164639[/C][C]-1869.95800164639[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]1522.5881535717[/C][C]-1522.5881535717[/C][/ROW]
[ROW][C]138[/C][C]28470[/C][C]29362.2590685252[/C][C]-892.259068525178[/C][/ROW]
[ROW][C]139[/C][C]61849[/C][C]39214.899508767[/C][C]22634.100491233[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]2440.96620774768[/C][C]-2440.96620774768[/C][/ROW]
[ROW][C]141[/C][C]2179[/C][C]7076.63466292077[/C][C]-4897.63466292077[/C][/ROW]
[ROW][C]142[/C][C]8019[/C][C]19652.0934965859[/C][C]-11633.0934965859[/C][/ROW]
[ROW][C]143[/C][C]39644[/C][C]51516.4313322597[/C][C]-11872.4313322597[/C][/ROW]
[ROW][C]144[/C][C]23494[/C][C]34705.8537970772[/C][C]-11211.8537970772[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159743&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159743&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
12046529068.3293687938-8603.32936879378
23362947892.6985723723-14263.6985723723
314233280.96942926322-1857.96942926322
42562943092.5942791635-17463.5942791635
55400269174.0346314574-15172.0346314574
6151036145802.9147895495233.08521045108
73328740641.9263381836-7354.92633818365
83117246775.1192764389-15603.1192764389
92811339007.3524247515-10894.3524247515
105780366100.7031756169-8297.70317561688
114983041650.62545659298179.37454340711
125214361174.1795494327-9031.17954943267
132105534631.830573566-13576.830573566
144700760196.9964086805-13189.9964086805
152873532809.6306345503-4074.63063455032
165914735911.736630051123235.2633699489
177895067788.236728660111161.7632713399
181349715878.3050395503-2381.30503955034
194615446295.2500627692-141.25006276923
205324923008.59453289630240.405467104
211072618547.844495514-7821.84449551397
228370074261.74343551099438.25656448914
234040023673.451108678716726.5488913213
243379737454.9292672204-3657.92926722038
253620541457.8455389961-5252.84553899611
263016533322.9310244357-3157.93102443566
275853457170.75901529821363.24098470178
284466365762.2183184445-21099.2183184445
299255666348.826504715626207.1734952844
304007847773.8912423536-7695.89124235363
313471134829.4288545661-118.42885456612
323107642280.1429306929-11204.1429306929
337460844522.662931322130085.3370686779
345809225573.475824662132518.5241753379
354200960408.7597348744-18399.7597348744
3601566.74491300325-1566.74491300325
373602232670.19584003253351.80415996754
382333330630.8725897201-7297.87258972012
395334949556.80334700893792.19665299111
409259689918.14505982962677.85494017037
414959861002.5103928066-11404.5103928066
424409344181.2660528821-88.2660528820972
438420548215.332622282435989.6673777176
446336948876.614777930414492.3852220696
456013254806.08305906785325.91694093216
463740345926.0419688121-8523.04196881209
472446045452.491830329-20992.491830329
484645641045.00738917025410.99261082978
496661653659.730660200312956.2693397997
504155448271.1697537044-6717.16975370438
512234619590.28616946582755.71383053423
523087435479.1783001609-4605.17830016092
536870170356.6431050595-1655.64310505954
543572843798.571817062-8070.57181706203
552901046069.5217479927-17059.5217479927
562311040672.4900252989-17562.4900252989
573884439865.6715882754-1021.67158827542
582708429677.4289565059-2593.42895650588
593513936563.9769365195-1424.97693651947
605747650062.00752241687413.99247758315
613327746029.8790385209-12752.8790385209
623114132505.8136678761-1364.81366787614
636128147232.261499819614048.7385001804
642582041097.9820702254-15277.9820702254
652328426873.7341420229-3589.73414202293
663537844016.4559215699-8638.45592156994
677499076847.2869662447-1857.2869662447
682965354087.5567524957-24434.5567524957
696462241228.305786631623393.6942133684
70415713089.0877130265-8932.08771302649
712924550046.6641580365-20801.6641580365
725000834615.921823106615392.0781768934
735233858515.6779643785-6177.67796437846
741331029481.5695678113-16171.5695678113
759290152533.205068625140367.7949313749
761095622822.6063143846-11866.6063143846
773424142842.6733980924-8601.67339809241
787504369156.25294628495886.74705371509
792115223158.5652674214-2006.56526742139
804224941379.4189552218869.581044778225
814200539620.29787536672384.70212463335
824115239202.24494408221949.75505591777
831439931922.9361290537-17523.9361290537
842826333421.1132229066-5158.11322290656
851721522692.684406977-5477.68440697697
864814046857.00448854371282.99551145632
876289782113.6210481937-19216.6210481937
882288332445.1154194861-9562.11541948608
894162238504.70516549633117.29483450371
904071539012.0296127771702.970387223
916589750173.44417823815723.555821762
927654260500.861603088616041.1383969114
933747736439.06129505961037.93870494042
945321634212.059492178319003.9405078217
954091128205.351727070312705.6482729297
965702161533.7748321698-4512.77483216983
977311648521.389586805924594.6104131941
9838958054.04027707167-4159.04027707167
994660941069.61659722665539.38340277344
1002935141779.5538863483-12428.5538863483
10123254720.15926780509-2395.15926780509
1023174729087.89763417832659.10236582173
1033266524945.26549885277719.73450114729
1041924930496.8316742545-11247.8316742545
1051529219349.1298372657-4057.12983726566
106584211873.8730477035-6031.8730477035
1073399441622.0193468797-7628.0193468797
1081301819279.4561110712-6261.45611107117
10901522.5881535717-1522.5881535717
1109817736325.943046404761851.0569535953
1113794125969.535752756711971.4642472433
1123103240348.6300121278-9316.63001212775
1133268338384.385994592-5701.38599459201
1143454533639.088807669905.911192331044
11501984.45608426257-1984.45608426257
11601522.5881535717-1522.5881535717
1172752537854.7868103042-10329.7868103042
1186685656816.933915180710039.0660848193
1192854945741.1233094892-17192.1233094892
1203861027370.245156517811239.7548434822
12127812445.62830815326335.37169184674
1224121151147.5175821976-9936.5175821976
1232269826397.1906490771-3699.19064907707
1244119436436.57456947724757.42543052284
1253268913539.376079525419149.6239204746
12657529250.44985921261-3498.44985921261
1272675741329.1906641138-14572.1906641138
1282252731097.5596519083-8570.55965190835
1294481040579.99521349314230.00478650691
13002910.01864604907-2910.01864604907
13101633.66087183467-1633.66087183467
13210067434942.227562858165731.7724371419
13301814.81832872033-1814.81832872033
1345778643650.865295037914135.1347049621
13501380.75550968371-1380.75550968371
13654447313.95800164639-1869.95800164639
13701522.5881535717-1522.5881535717
1382847029362.2590685252-892.259068525178
1396184939214.89950876722634.100491233
14002440.96620774768-2440.96620774768
14121797076.63466292077-4897.63466292077
142801919652.0934965859-11633.0934965859
1433964451516.4313322597-11872.4313322597
1442349434705.8537970772-11211.8537970772






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.5480332043016090.9039335913967810.451966795698391
190.5542904840557320.8914190318885350.445709515944268
200.5731837722016960.8536324555966070.426816227798304
210.444315447905470.8886308958109390.55568455209453
220.4769200172370820.9538400344741640.523079982762918
230.4822291635413870.9644583270827750.517770836458613
240.3788135098047960.7576270196095920.621186490195204
250.3311368688147110.6622737376294210.668863131185289
260.3302647312122070.6605294624244130.669735268787793
270.2875112706559020.5750225413118050.712488729344098
280.3736741543987480.7473483087974960.626325845601252
290.3640446398181160.7280892796362320.635955360181884
300.3168356192597750.6336712385195490.683164380740225
310.2488966298675520.4977932597351050.751103370132448
320.1949936446441230.3899872892882460.805006355355877
330.4102362436466220.8204724872932450.589763756353378
340.8490155346143850.3019689307712290.150984465385615
350.8326519533285720.3346960933428550.167348046671428
360.7872651760145770.4254696479708460.212734823985423
370.7521284104125880.4957431791748240.247871589587412
380.7059845623776210.5880308752447580.294015437622379
390.6775413595290090.6449172809419820.322458640470991
400.641601174137870.7167976517242590.35839882586213
410.5910451109032560.8179097781934880.408954889096744
420.532885932565390.934228134869220.46711406743461
430.7142577836622820.5714844326754360.285742216337718
440.6954448973494210.6091102053011590.304555102650579
450.6552905161411230.6894189677177540.344709483858877
460.6176823435113420.7646353129773170.382317656488658
470.7053019548514360.5893960902971270.294698045148564
480.6683993885734150.663201222853170.331600611426585
490.662970127820660.6740597443586810.33702987217934
500.6106539668651410.7786920662697180.389346033134859
510.5548510797640090.8902978404719830.445148920235991
520.5172941903623650.965411619275270.482705809637635
530.4723946391302730.9447892782605470.527605360869727
540.43630168394320.8726033678863990.5636983160568
550.4530328595512530.9060657191025060.546967140448747
560.4415107759029750.883021551805950.558489224097025
570.393779015125760.7875580302515210.606220984874239
580.3494815978078690.6989631956157390.650518402192131
590.3176601918877860.6353203837755730.682339808112214
600.2921500840990090.5843001681980190.70784991590099
610.2804325317844520.5608650635689040.719567468215548
620.236744316415260.473488632830520.76325568358474
630.2659374195397220.5318748390794450.734062580460278
640.2643461532871920.5286923065743830.735653846712808
650.2263330214345740.4526660428691480.773666978565426
660.2186701924732440.4373403849464880.781329807526756
670.1996597921636690.3993195843273390.800340207836331
680.2138527522307670.4277055044615350.786147247769233
690.2683393139762560.5366786279525120.731660686023744
700.2460953329802050.492190665960410.753904667019795
710.2659335992371980.5318671984743960.734066400762802
720.2731846260824830.5463692521649650.726815373917517
730.2379307421208210.4758614842416420.762069257879179
740.2224964749566970.4449929499133940.777503525043303
750.4811338672275450.9622677344550910.518866132772455
760.4622452457149930.9244904914299850.537754754285007
770.4297149651168070.8594299302336130.570285034883193
780.3999140595551240.7998281191102490.600085940444876
790.3500127776206950.7000255552413910.649987222379305
800.3045431998798270.6090863997596530.695456800120174
810.2595457413379510.5190914826759020.740454258662049
820.2178946860097230.4357893720194470.782105313990277
830.2703708447948340.5407416895896670.729629155205166
840.2360201365395970.4720402730791940.763979863460403
850.2093280909136380.4186561818272760.790671909086362
860.1761167775460420.3522335550920840.823883222453958
870.2070048112624710.4140096225249410.792995188737529
880.1860829330426150.3721658660852310.813917066957385
890.157392923882020.314785847764040.84260707611798
900.1286498185275220.2572996370550440.871350181472478
910.1239562447925730.2479124895851460.876043755207427
920.1180994005373650.236198801074730.881900599462635
930.09265364845107790.1853072969021560.907346351548922
940.1179592777883080.2359185555766160.882040722211692
950.1026447554601780.2052895109203560.897355244539822
960.08053464244346280.1610692848869260.919465357556537
970.1058465772538090.2116931545076190.894153422746191
980.08354310967663870.1670862193532770.916456890323361
990.08485583231826450.1697116646365290.915144167681735
1000.1481770439862660.2963540879725330.851822956013733
1010.1373802406523890.2747604813047790.862619759347611
1020.1204395444187880.2408790888375750.879560455581212
1030.09484470137244070.1896894027448810.905155298627559
1040.09738731968907680.1947746393781540.902612680310923
1050.110589771558150.2211795431162990.88941022844185
1060.09852588561311380.1970517712262280.901474114386886
1070.09258574512068560.1851714902413710.907414254879314
1080.08987008105540190.1797401621108040.910129918944598
1090.066175071271570.132350142543140.93382492872843
1100.8654649919993860.2690700160012280.134535008000614
1110.8528155516076090.2943688967847830.147184448392391
1120.9014362716345320.1971274567309360.0985637283654679
1130.8627361743700930.2745276512598140.137263825629907
1140.8120631701098860.3758736597802290.187936829890114
1150.7547537731378890.4904924537242220.245246226862111
1160.6877714430622140.6244571138755730.312228556937786
1170.6104039998142540.7791920003714930.389596000185747
1180.5572126892241320.8855746215517360.442787310775868
1190.5151994912658050.9696010174683910.484800508734196
1200.5038223774212820.9923552451574360.496177622578718
1210.4152183426265180.8304366852530360.584781657373482
1220.9758329532255320.04833409354893550.0241670467744678
1230.9543385887879670.09132282242406510.0456614112120326
1240.9046572802345190.1906854395309610.0953427197654806
1250.9536700158713460.09265996825730720.0463299841286536
1260.9094335685415370.1811328629169260.0905664314584632

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.548033204301609 & 0.903933591396781 & 0.451966795698391 \tabularnewline
19 & 0.554290484055732 & 0.891419031888535 & 0.445709515944268 \tabularnewline
20 & 0.573183772201696 & 0.853632455596607 & 0.426816227798304 \tabularnewline
21 & 0.44431544790547 & 0.888630895810939 & 0.55568455209453 \tabularnewline
22 & 0.476920017237082 & 0.953840034474164 & 0.523079982762918 \tabularnewline
23 & 0.482229163541387 & 0.964458327082775 & 0.517770836458613 \tabularnewline
24 & 0.378813509804796 & 0.757627019609592 & 0.621186490195204 \tabularnewline
25 & 0.331136868814711 & 0.662273737629421 & 0.668863131185289 \tabularnewline
26 & 0.330264731212207 & 0.660529462424413 & 0.669735268787793 \tabularnewline
27 & 0.287511270655902 & 0.575022541311805 & 0.712488729344098 \tabularnewline
28 & 0.373674154398748 & 0.747348308797496 & 0.626325845601252 \tabularnewline
29 & 0.364044639818116 & 0.728089279636232 & 0.635955360181884 \tabularnewline
30 & 0.316835619259775 & 0.633671238519549 & 0.683164380740225 \tabularnewline
31 & 0.248896629867552 & 0.497793259735105 & 0.751103370132448 \tabularnewline
32 & 0.194993644644123 & 0.389987289288246 & 0.805006355355877 \tabularnewline
33 & 0.410236243646622 & 0.820472487293245 & 0.589763756353378 \tabularnewline
34 & 0.849015534614385 & 0.301968930771229 & 0.150984465385615 \tabularnewline
35 & 0.832651953328572 & 0.334696093342855 & 0.167348046671428 \tabularnewline
36 & 0.787265176014577 & 0.425469647970846 & 0.212734823985423 \tabularnewline
37 & 0.752128410412588 & 0.495743179174824 & 0.247871589587412 \tabularnewline
38 & 0.705984562377621 & 0.588030875244758 & 0.294015437622379 \tabularnewline
39 & 0.677541359529009 & 0.644917280941982 & 0.322458640470991 \tabularnewline
40 & 0.64160117413787 & 0.716797651724259 & 0.35839882586213 \tabularnewline
41 & 0.591045110903256 & 0.817909778193488 & 0.408954889096744 \tabularnewline
42 & 0.53288593256539 & 0.93422813486922 & 0.46711406743461 \tabularnewline
43 & 0.714257783662282 & 0.571484432675436 & 0.285742216337718 \tabularnewline
44 & 0.695444897349421 & 0.609110205301159 & 0.304555102650579 \tabularnewline
45 & 0.655290516141123 & 0.689418967717754 & 0.344709483858877 \tabularnewline
46 & 0.617682343511342 & 0.764635312977317 & 0.382317656488658 \tabularnewline
47 & 0.705301954851436 & 0.589396090297127 & 0.294698045148564 \tabularnewline
48 & 0.668399388573415 & 0.66320122285317 & 0.331600611426585 \tabularnewline
49 & 0.66297012782066 & 0.674059744358681 & 0.33702987217934 \tabularnewline
50 & 0.610653966865141 & 0.778692066269718 & 0.389346033134859 \tabularnewline
51 & 0.554851079764009 & 0.890297840471983 & 0.445148920235991 \tabularnewline
52 & 0.517294190362365 & 0.96541161927527 & 0.482705809637635 \tabularnewline
53 & 0.472394639130273 & 0.944789278260547 & 0.527605360869727 \tabularnewline
54 & 0.4363016839432 & 0.872603367886399 & 0.5636983160568 \tabularnewline
55 & 0.453032859551253 & 0.906065719102506 & 0.546967140448747 \tabularnewline
56 & 0.441510775902975 & 0.88302155180595 & 0.558489224097025 \tabularnewline
57 & 0.39377901512576 & 0.787558030251521 & 0.606220984874239 \tabularnewline
58 & 0.349481597807869 & 0.698963195615739 & 0.650518402192131 \tabularnewline
59 & 0.317660191887786 & 0.635320383775573 & 0.682339808112214 \tabularnewline
60 & 0.292150084099009 & 0.584300168198019 & 0.70784991590099 \tabularnewline
61 & 0.280432531784452 & 0.560865063568904 & 0.719567468215548 \tabularnewline
62 & 0.23674431641526 & 0.47348863283052 & 0.76325568358474 \tabularnewline
63 & 0.265937419539722 & 0.531874839079445 & 0.734062580460278 \tabularnewline
64 & 0.264346153287192 & 0.528692306574383 & 0.735653846712808 \tabularnewline
65 & 0.226333021434574 & 0.452666042869148 & 0.773666978565426 \tabularnewline
66 & 0.218670192473244 & 0.437340384946488 & 0.781329807526756 \tabularnewline
67 & 0.199659792163669 & 0.399319584327339 & 0.800340207836331 \tabularnewline
68 & 0.213852752230767 & 0.427705504461535 & 0.786147247769233 \tabularnewline
69 & 0.268339313976256 & 0.536678627952512 & 0.731660686023744 \tabularnewline
70 & 0.246095332980205 & 0.49219066596041 & 0.753904667019795 \tabularnewline
71 & 0.265933599237198 & 0.531867198474396 & 0.734066400762802 \tabularnewline
72 & 0.273184626082483 & 0.546369252164965 & 0.726815373917517 \tabularnewline
73 & 0.237930742120821 & 0.475861484241642 & 0.762069257879179 \tabularnewline
74 & 0.222496474956697 & 0.444992949913394 & 0.777503525043303 \tabularnewline
75 & 0.481133867227545 & 0.962267734455091 & 0.518866132772455 \tabularnewline
76 & 0.462245245714993 & 0.924490491429985 & 0.537754754285007 \tabularnewline
77 & 0.429714965116807 & 0.859429930233613 & 0.570285034883193 \tabularnewline
78 & 0.399914059555124 & 0.799828119110249 & 0.600085940444876 \tabularnewline
79 & 0.350012777620695 & 0.700025555241391 & 0.649987222379305 \tabularnewline
80 & 0.304543199879827 & 0.609086399759653 & 0.695456800120174 \tabularnewline
81 & 0.259545741337951 & 0.519091482675902 & 0.740454258662049 \tabularnewline
82 & 0.217894686009723 & 0.435789372019447 & 0.782105313990277 \tabularnewline
83 & 0.270370844794834 & 0.540741689589667 & 0.729629155205166 \tabularnewline
84 & 0.236020136539597 & 0.472040273079194 & 0.763979863460403 \tabularnewline
85 & 0.209328090913638 & 0.418656181827276 & 0.790671909086362 \tabularnewline
86 & 0.176116777546042 & 0.352233555092084 & 0.823883222453958 \tabularnewline
87 & 0.207004811262471 & 0.414009622524941 & 0.792995188737529 \tabularnewline
88 & 0.186082933042615 & 0.372165866085231 & 0.813917066957385 \tabularnewline
89 & 0.15739292388202 & 0.31478584776404 & 0.84260707611798 \tabularnewline
90 & 0.128649818527522 & 0.257299637055044 & 0.871350181472478 \tabularnewline
91 & 0.123956244792573 & 0.247912489585146 & 0.876043755207427 \tabularnewline
92 & 0.118099400537365 & 0.23619880107473 & 0.881900599462635 \tabularnewline
93 & 0.0926536484510779 & 0.185307296902156 & 0.907346351548922 \tabularnewline
94 & 0.117959277788308 & 0.235918555576616 & 0.882040722211692 \tabularnewline
95 & 0.102644755460178 & 0.205289510920356 & 0.897355244539822 \tabularnewline
96 & 0.0805346424434628 & 0.161069284886926 & 0.919465357556537 \tabularnewline
97 & 0.105846577253809 & 0.211693154507619 & 0.894153422746191 \tabularnewline
98 & 0.0835431096766387 & 0.167086219353277 & 0.916456890323361 \tabularnewline
99 & 0.0848558323182645 & 0.169711664636529 & 0.915144167681735 \tabularnewline
100 & 0.148177043986266 & 0.296354087972533 & 0.851822956013733 \tabularnewline
101 & 0.137380240652389 & 0.274760481304779 & 0.862619759347611 \tabularnewline
102 & 0.120439544418788 & 0.240879088837575 & 0.879560455581212 \tabularnewline
103 & 0.0948447013724407 & 0.189689402744881 & 0.905155298627559 \tabularnewline
104 & 0.0973873196890768 & 0.194774639378154 & 0.902612680310923 \tabularnewline
105 & 0.11058977155815 & 0.221179543116299 & 0.88941022844185 \tabularnewline
106 & 0.0985258856131138 & 0.197051771226228 & 0.901474114386886 \tabularnewline
107 & 0.0925857451206856 & 0.185171490241371 & 0.907414254879314 \tabularnewline
108 & 0.0898700810554019 & 0.179740162110804 & 0.910129918944598 \tabularnewline
109 & 0.06617507127157 & 0.13235014254314 & 0.93382492872843 \tabularnewline
110 & 0.865464991999386 & 0.269070016001228 & 0.134535008000614 \tabularnewline
111 & 0.852815551607609 & 0.294368896784783 & 0.147184448392391 \tabularnewline
112 & 0.901436271634532 & 0.197127456730936 & 0.0985637283654679 \tabularnewline
113 & 0.862736174370093 & 0.274527651259814 & 0.137263825629907 \tabularnewline
114 & 0.812063170109886 & 0.375873659780229 & 0.187936829890114 \tabularnewline
115 & 0.754753773137889 & 0.490492453724222 & 0.245246226862111 \tabularnewline
116 & 0.687771443062214 & 0.624457113875573 & 0.312228556937786 \tabularnewline
117 & 0.610403999814254 & 0.779192000371493 & 0.389596000185747 \tabularnewline
118 & 0.557212689224132 & 0.885574621551736 & 0.442787310775868 \tabularnewline
119 & 0.515199491265805 & 0.969601017468391 & 0.484800508734196 \tabularnewline
120 & 0.503822377421282 & 0.992355245157436 & 0.496177622578718 \tabularnewline
121 & 0.415218342626518 & 0.830436685253036 & 0.584781657373482 \tabularnewline
122 & 0.975832953225532 & 0.0483340935489355 & 0.0241670467744678 \tabularnewline
123 & 0.954338588787967 & 0.0913228224240651 & 0.0456614112120326 \tabularnewline
124 & 0.904657280234519 & 0.190685439530961 & 0.0953427197654806 \tabularnewline
125 & 0.953670015871346 & 0.0926599682573072 & 0.0463299841286536 \tabularnewline
126 & 0.909433568541537 & 0.181132862916926 & 0.0905664314584632 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159743&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]0.548033204301609[/C][C]0.903933591396781[/C][C]0.451966795698391[/C][/ROW]
[ROW][C]19[/C][C]0.554290484055732[/C][C]0.891419031888535[/C][C]0.445709515944268[/C][/ROW]
[ROW][C]20[/C][C]0.573183772201696[/C][C]0.853632455596607[/C][C]0.426816227798304[/C][/ROW]
[ROW][C]21[/C][C]0.44431544790547[/C][C]0.888630895810939[/C][C]0.55568455209453[/C][/ROW]
[ROW][C]22[/C][C]0.476920017237082[/C][C]0.953840034474164[/C][C]0.523079982762918[/C][/ROW]
[ROW][C]23[/C][C]0.482229163541387[/C][C]0.964458327082775[/C][C]0.517770836458613[/C][/ROW]
[ROW][C]24[/C][C]0.378813509804796[/C][C]0.757627019609592[/C][C]0.621186490195204[/C][/ROW]
[ROW][C]25[/C][C]0.331136868814711[/C][C]0.662273737629421[/C][C]0.668863131185289[/C][/ROW]
[ROW][C]26[/C][C]0.330264731212207[/C][C]0.660529462424413[/C][C]0.669735268787793[/C][/ROW]
[ROW][C]27[/C][C]0.287511270655902[/C][C]0.575022541311805[/C][C]0.712488729344098[/C][/ROW]
[ROW][C]28[/C][C]0.373674154398748[/C][C]0.747348308797496[/C][C]0.626325845601252[/C][/ROW]
[ROW][C]29[/C][C]0.364044639818116[/C][C]0.728089279636232[/C][C]0.635955360181884[/C][/ROW]
[ROW][C]30[/C][C]0.316835619259775[/C][C]0.633671238519549[/C][C]0.683164380740225[/C][/ROW]
[ROW][C]31[/C][C]0.248896629867552[/C][C]0.497793259735105[/C][C]0.751103370132448[/C][/ROW]
[ROW][C]32[/C][C]0.194993644644123[/C][C]0.389987289288246[/C][C]0.805006355355877[/C][/ROW]
[ROW][C]33[/C][C]0.410236243646622[/C][C]0.820472487293245[/C][C]0.589763756353378[/C][/ROW]
[ROW][C]34[/C][C]0.849015534614385[/C][C]0.301968930771229[/C][C]0.150984465385615[/C][/ROW]
[ROW][C]35[/C][C]0.832651953328572[/C][C]0.334696093342855[/C][C]0.167348046671428[/C][/ROW]
[ROW][C]36[/C][C]0.787265176014577[/C][C]0.425469647970846[/C][C]0.212734823985423[/C][/ROW]
[ROW][C]37[/C][C]0.752128410412588[/C][C]0.495743179174824[/C][C]0.247871589587412[/C][/ROW]
[ROW][C]38[/C][C]0.705984562377621[/C][C]0.588030875244758[/C][C]0.294015437622379[/C][/ROW]
[ROW][C]39[/C][C]0.677541359529009[/C][C]0.644917280941982[/C][C]0.322458640470991[/C][/ROW]
[ROW][C]40[/C][C]0.64160117413787[/C][C]0.716797651724259[/C][C]0.35839882586213[/C][/ROW]
[ROW][C]41[/C][C]0.591045110903256[/C][C]0.817909778193488[/C][C]0.408954889096744[/C][/ROW]
[ROW][C]42[/C][C]0.53288593256539[/C][C]0.93422813486922[/C][C]0.46711406743461[/C][/ROW]
[ROW][C]43[/C][C]0.714257783662282[/C][C]0.571484432675436[/C][C]0.285742216337718[/C][/ROW]
[ROW][C]44[/C][C]0.695444897349421[/C][C]0.609110205301159[/C][C]0.304555102650579[/C][/ROW]
[ROW][C]45[/C][C]0.655290516141123[/C][C]0.689418967717754[/C][C]0.344709483858877[/C][/ROW]
[ROW][C]46[/C][C]0.617682343511342[/C][C]0.764635312977317[/C][C]0.382317656488658[/C][/ROW]
[ROW][C]47[/C][C]0.705301954851436[/C][C]0.589396090297127[/C][C]0.294698045148564[/C][/ROW]
[ROW][C]48[/C][C]0.668399388573415[/C][C]0.66320122285317[/C][C]0.331600611426585[/C][/ROW]
[ROW][C]49[/C][C]0.66297012782066[/C][C]0.674059744358681[/C][C]0.33702987217934[/C][/ROW]
[ROW][C]50[/C][C]0.610653966865141[/C][C]0.778692066269718[/C][C]0.389346033134859[/C][/ROW]
[ROW][C]51[/C][C]0.554851079764009[/C][C]0.890297840471983[/C][C]0.445148920235991[/C][/ROW]
[ROW][C]52[/C][C]0.517294190362365[/C][C]0.96541161927527[/C][C]0.482705809637635[/C][/ROW]
[ROW][C]53[/C][C]0.472394639130273[/C][C]0.944789278260547[/C][C]0.527605360869727[/C][/ROW]
[ROW][C]54[/C][C]0.4363016839432[/C][C]0.872603367886399[/C][C]0.5636983160568[/C][/ROW]
[ROW][C]55[/C][C]0.453032859551253[/C][C]0.906065719102506[/C][C]0.546967140448747[/C][/ROW]
[ROW][C]56[/C][C]0.441510775902975[/C][C]0.88302155180595[/C][C]0.558489224097025[/C][/ROW]
[ROW][C]57[/C][C]0.39377901512576[/C][C]0.787558030251521[/C][C]0.606220984874239[/C][/ROW]
[ROW][C]58[/C][C]0.349481597807869[/C][C]0.698963195615739[/C][C]0.650518402192131[/C][/ROW]
[ROW][C]59[/C][C]0.317660191887786[/C][C]0.635320383775573[/C][C]0.682339808112214[/C][/ROW]
[ROW][C]60[/C][C]0.292150084099009[/C][C]0.584300168198019[/C][C]0.70784991590099[/C][/ROW]
[ROW][C]61[/C][C]0.280432531784452[/C][C]0.560865063568904[/C][C]0.719567468215548[/C][/ROW]
[ROW][C]62[/C][C]0.23674431641526[/C][C]0.47348863283052[/C][C]0.76325568358474[/C][/ROW]
[ROW][C]63[/C][C]0.265937419539722[/C][C]0.531874839079445[/C][C]0.734062580460278[/C][/ROW]
[ROW][C]64[/C][C]0.264346153287192[/C][C]0.528692306574383[/C][C]0.735653846712808[/C][/ROW]
[ROW][C]65[/C][C]0.226333021434574[/C][C]0.452666042869148[/C][C]0.773666978565426[/C][/ROW]
[ROW][C]66[/C][C]0.218670192473244[/C][C]0.437340384946488[/C][C]0.781329807526756[/C][/ROW]
[ROW][C]67[/C][C]0.199659792163669[/C][C]0.399319584327339[/C][C]0.800340207836331[/C][/ROW]
[ROW][C]68[/C][C]0.213852752230767[/C][C]0.427705504461535[/C][C]0.786147247769233[/C][/ROW]
[ROW][C]69[/C][C]0.268339313976256[/C][C]0.536678627952512[/C][C]0.731660686023744[/C][/ROW]
[ROW][C]70[/C][C]0.246095332980205[/C][C]0.49219066596041[/C][C]0.753904667019795[/C][/ROW]
[ROW][C]71[/C][C]0.265933599237198[/C][C]0.531867198474396[/C][C]0.734066400762802[/C][/ROW]
[ROW][C]72[/C][C]0.273184626082483[/C][C]0.546369252164965[/C][C]0.726815373917517[/C][/ROW]
[ROW][C]73[/C][C]0.237930742120821[/C][C]0.475861484241642[/C][C]0.762069257879179[/C][/ROW]
[ROW][C]74[/C][C]0.222496474956697[/C][C]0.444992949913394[/C][C]0.777503525043303[/C][/ROW]
[ROW][C]75[/C][C]0.481133867227545[/C][C]0.962267734455091[/C][C]0.518866132772455[/C][/ROW]
[ROW][C]76[/C][C]0.462245245714993[/C][C]0.924490491429985[/C][C]0.537754754285007[/C][/ROW]
[ROW][C]77[/C][C]0.429714965116807[/C][C]0.859429930233613[/C][C]0.570285034883193[/C][/ROW]
[ROW][C]78[/C][C]0.399914059555124[/C][C]0.799828119110249[/C][C]0.600085940444876[/C][/ROW]
[ROW][C]79[/C][C]0.350012777620695[/C][C]0.700025555241391[/C][C]0.649987222379305[/C][/ROW]
[ROW][C]80[/C][C]0.304543199879827[/C][C]0.609086399759653[/C][C]0.695456800120174[/C][/ROW]
[ROW][C]81[/C][C]0.259545741337951[/C][C]0.519091482675902[/C][C]0.740454258662049[/C][/ROW]
[ROW][C]82[/C][C]0.217894686009723[/C][C]0.435789372019447[/C][C]0.782105313990277[/C][/ROW]
[ROW][C]83[/C][C]0.270370844794834[/C][C]0.540741689589667[/C][C]0.729629155205166[/C][/ROW]
[ROW][C]84[/C][C]0.236020136539597[/C][C]0.472040273079194[/C][C]0.763979863460403[/C][/ROW]
[ROW][C]85[/C][C]0.209328090913638[/C][C]0.418656181827276[/C][C]0.790671909086362[/C][/ROW]
[ROW][C]86[/C][C]0.176116777546042[/C][C]0.352233555092084[/C][C]0.823883222453958[/C][/ROW]
[ROW][C]87[/C][C]0.207004811262471[/C][C]0.414009622524941[/C][C]0.792995188737529[/C][/ROW]
[ROW][C]88[/C][C]0.186082933042615[/C][C]0.372165866085231[/C][C]0.813917066957385[/C][/ROW]
[ROW][C]89[/C][C]0.15739292388202[/C][C]0.31478584776404[/C][C]0.84260707611798[/C][/ROW]
[ROW][C]90[/C][C]0.128649818527522[/C][C]0.257299637055044[/C][C]0.871350181472478[/C][/ROW]
[ROW][C]91[/C][C]0.123956244792573[/C][C]0.247912489585146[/C][C]0.876043755207427[/C][/ROW]
[ROW][C]92[/C][C]0.118099400537365[/C][C]0.23619880107473[/C][C]0.881900599462635[/C][/ROW]
[ROW][C]93[/C][C]0.0926536484510779[/C][C]0.185307296902156[/C][C]0.907346351548922[/C][/ROW]
[ROW][C]94[/C][C]0.117959277788308[/C][C]0.235918555576616[/C][C]0.882040722211692[/C][/ROW]
[ROW][C]95[/C][C]0.102644755460178[/C][C]0.205289510920356[/C][C]0.897355244539822[/C][/ROW]
[ROW][C]96[/C][C]0.0805346424434628[/C][C]0.161069284886926[/C][C]0.919465357556537[/C][/ROW]
[ROW][C]97[/C][C]0.105846577253809[/C][C]0.211693154507619[/C][C]0.894153422746191[/C][/ROW]
[ROW][C]98[/C][C]0.0835431096766387[/C][C]0.167086219353277[/C][C]0.916456890323361[/C][/ROW]
[ROW][C]99[/C][C]0.0848558323182645[/C][C]0.169711664636529[/C][C]0.915144167681735[/C][/ROW]
[ROW][C]100[/C][C]0.148177043986266[/C][C]0.296354087972533[/C][C]0.851822956013733[/C][/ROW]
[ROW][C]101[/C][C]0.137380240652389[/C][C]0.274760481304779[/C][C]0.862619759347611[/C][/ROW]
[ROW][C]102[/C][C]0.120439544418788[/C][C]0.240879088837575[/C][C]0.879560455581212[/C][/ROW]
[ROW][C]103[/C][C]0.0948447013724407[/C][C]0.189689402744881[/C][C]0.905155298627559[/C][/ROW]
[ROW][C]104[/C][C]0.0973873196890768[/C][C]0.194774639378154[/C][C]0.902612680310923[/C][/ROW]
[ROW][C]105[/C][C]0.11058977155815[/C][C]0.221179543116299[/C][C]0.88941022844185[/C][/ROW]
[ROW][C]106[/C][C]0.0985258856131138[/C][C]0.197051771226228[/C][C]0.901474114386886[/C][/ROW]
[ROW][C]107[/C][C]0.0925857451206856[/C][C]0.185171490241371[/C][C]0.907414254879314[/C][/ROW]
[ROW][C]108[/C][C]0.0898700810554019[/C][C]0.179740162110804[/C][C]0.910129918944598[/C][/ROW]
[ROW][C]109[/C][C]0.06617507127157[/C][C]0.13235014254314[/C][C]0.93382492872843[/C][/ROW]
[ROW][C]110[/C][C]0.865464991999386[/C][C]0.269070016001228[/C][C]0.134535008000614[/C][/ROW]
[ROW][C]111[/C][C]0.852815551607609[/C][C]0.294368896784783[/C][C]0.147184448392391[/C][/ROW]
[ROW][C]112[/C][C]0.901436271634532[/C][C]0.197127456730936[/C][C]0.0985637283654679[/C][/ROW]
[ROW][C]113[/C][C]0.862736174370093[/C][C]0.274527651259814[/C][C]0.137263825629907[/C][/ROW]
[ROW][C]114[/C][C]0.812063170109886[/C][C]0.375873659780229[/C][C]0.187936829890114[/C][/ROW]
[ROW][C]115[/C][C]0.754753773137889[/C][C]0.490492453724222[/C][C]0.245246226862111[/C][/ROW]
[ROW][C]116[/C][C]0.687771443062214[/C][C]0.624457113875573[/C][C]0.312228556937786[/C][/ROW]
[ROW][C]117[/C][C]0.610403999814254[/C][C]0.779192000371493[/C][C]0.389596000185747[/C][/ROW]
[ROW][C]118[/C][C]0.557212689224132[/C][C]0.885574621551736[/C][C]0.442787310775868[/C][/ROW]
[ROW][C]119[/C][C]0.515199491265805[/C][C]0.969601017468391[/C][C]0.484800508734196[/C][/ROW]
[ROW][C]120[/C][C]0.503822377421282[/C][C]0.992355245157436[/C][C]0.496177622578718[/C][/ROW]
[ROW][C]121[/C][C]0.415218342626518[/C][C]0.830436685253036[/C][C]0.584781657373482[/C][/ROW]
[ROW][C]122[/C][C]0.975832953225532[/C][C]0.0483340935489355[/C][C]0.0241670467744678[/C][/ROW]
[ROW][C]123[/C][C]0.954338588787967[/C][C]0.0913228224240651[/C][C]0.0456614112120326[/C][/ROW]
[ROW][C]124[/C][C]0.904657280234519[/C][C]0.190685439530961[/C][C]0.0953427197654806[/C][/ROW]
[ROW][C]125[/C][C]0.953670015871346[/C][C]0.0926599682573072[/C][C]0.0463299841286536[/C][/ROW]
[ROW][C]126[/C][C]0.909433568541537[/C][C]0.181132862916926[/C][C]0.0905664314584632[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159743&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159743&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
180.5480332043016090.9039335913967810.451966795698391
190.5542904840557320.8914190318885350.445709515944268
200.5731837722016960.8536324555966070.426816227798304
210.444315447905470.8886308958109390.55568455209453
220.4769200172370820.9538400344741640.523079982762918
230.4822291635413870.9644583270827750.517770836458613
240.3788135098047960.7576270196095920.621186490195204
250.3311368688147110.6622737376294210.668863131185289
260.3302647312122070.6605294624244130.669735268787793
270.2875112706559020.5750225413118050.712488729344098
280.3736741543987480.7473483087974960.626325845601252
290.3640446398181160.7280892796362320.635955360181884
300.3168356192597750.6336712385195490.683164380740225
310.2488966298675520.4977932597351050.751103370132448
320.1949936446441230.3899872892882460.805006355355877
330.4102362436466220.8204724872932450.589763756353378
340.8490155346143850.3019689307712290.150984465385615
350.8326519533285720.3346960933428550.167348046671428
360.7872651760145770.4254696479708460.212734823985423
370.7521284104125880.4957431791748240.247871589587412
380.7059845623776210.5880308752447580.294015437622379
390.6775413595290090.6449172809419820.322458640470991
400.641601174137870.7167976517242590.35839882586213
410.5910451109032560.8179097781934880.408954889096744
420.532885932565390.934228134869220.46711406743461
430.7142577836622820.5714844326754360.285742216337718
440.6954448973494210.6091102053011590.304555102650579
450.6552905161411230.6894189677177540.344709483858877
460.6176823435113420.7646353129773170.382317656488658
470.7053019548514360.5893960902971270.294698045148564
480.6683993885734150.663201222853170.331600611426585
490.662970127820660.6740597443586810.33702987217934
500.6106539668651410.7786920662697180.389346033134859
510.5548510797640090.8902978404719830.445148920235991
520.5172941903623650.965411619275270.482705809637635
530.4723946391302730.9447892782605470.527605360869727
540.43630168394320.8726033678863990.5636983160568
550.4530328595512530.9060657191025060.546967140448747
560.4415107759029750.883021551805950.558489224097025
570.393779015125760.7875580302515210.606220984874239
580.3494815978078690.6989631956157390.650518402192131
590.3176601918877860.6353203837755730.682339808112214
600.2921500840990090.5843001681980190.70784991590099
610.2804325317844520.5608650635689040.719567468215548
620.236744316415260.473488632830520.76325568358474
630.2659374195397220.5318748390794450.734062580460278
640.2643461532871920.5286923065743830.735653846712808
650.2263330214345740.4526660428691480.773666978565426
660.2186701924732440.4373403849464880.781329807526756
670.1996597921636690.3993195843273390.800340207836331
680.2138527522307670.4277055044615350.786147247769233
690.2683393139762560.5366786279525120.731660686023744
700.2460953329802050.492190665960410.753904667019795
710.2659335992371980.5318671984743960.734066400762802
720.2731846260824830.5463692521649650.726815373917517
730.2379307421208210.4758614842416420.762069257879179
740.2224964749566970.4449929499133940.777503525043303
750.4811338672275450.9622677344550910.518866132772455
760.4622452457149930.9244904914299850.537754754285007
770.4297149651168070.8594299302336130.570285034883193
780.3999140595551240.7998281191102490.600085940444876
790.3500127776206950.7000255552413910.649987222379305
800.3045431998798270.6090863997596530.695456800120174
810.2595457413379510.5190914826759020.740454258662049
820.2178946860097230.4357893720194470.782105313990277
830.2703708447948340.5407416895896670.729629155205166
840.2360201365395970.4720402730791940.763979863460403
850.2093280909136380.4186561818272760.790671909086362
860.1761167775460420.3522335550920840.823883222453958
870.2070048112624710.4140096225249410.792995188737529
880.1860829330426150.3721658660852310.813917066957385
890.157392923882020.314785847764040.84260707611798
900.1286498185275220.2572996370550440.871350181472478
910.1239562447925730.2479124895851460.876043755207427
920.1180994005373650.236198801074730.881900599462635
930.09265364845107790.1853072969021560.907346351548922
940.1179592777883080.2359185555766160.882040722211692
950.1026447554601780.2052895109203560.897355244539822
960.08053464244346280.1610692848869260.919465357556537
970.1058465772538090.2116931545076190.894153422746191
980.08354310967663870.1670862193532770.916456890323361
990.08485583231826450.1697116646365290.915144167681735
1000.1481770439862660.2963540879725330.851822956013733
1010.1373802406523890.2747604813047790.862619759347611
1020.1204395444187880.2408790888375750.879560455581212
1030.09484470137244070.1896894027448810.905155298627559
1040.09738731968907680.1947746393781540.902612680310923
1050.110589771558150.2211795431162990.88941022844185
1060.09852588561311380.1970517712262280.901474114386886
1070.09258574512068560.1851714902413710.907414254879314
1080.08987008105540190.1797401621108040.910129918944598
1090.066175071271570.132350142543140.93382492872843
1100.8654649919993860.2690700160012280.134535008000614
1110.8528155516076090.2943688967847830.147184448392391
1120.9014362716345320.1971274567309360.0985637283654679
1130.8627361743700930.2745276512598140.137263825629907
1140.8120631701098860.3758736597802290.187936829890114
1150.7547537731378890.4904924537242220.245246226862111
1160.6877714430622140.6244571138755730.312228556937786
1170.6104039998142540.7791920003714930.389596000185747
1180.5572126892241320.8855746215517360.442787310775868
1190.5151994912658050.9696010174683910.484800508734196
1200.5038223774212820.9923552451574360.496177622578718
1210.4152183426265180.8304366852530360.584781657373482
1220.9758329532255320.04833409354893550.0241670467744678
1230.9543385887879670.09132282242406510.0456614112120326
1240.9046572802345190.1906854395309610.0953427197654806
1250.9536700158713460.09265996825730720.0463299841286536
1260.9094335685415370.1811328629169260.0905664314584632






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00917431192660551OK
10% type I error level30.0275229357798165OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.00917431192660551 & OK \tabularnewline
10% type I error level & 3 & 0.0275229357798165 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159743&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.00917431192660551[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.0275229357798165[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159743&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159743&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00917431192660551OK
10% type I error level30.0275229357798165OK


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 = 11 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 11 ; 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')
}