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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 computationFri, 23 Dec 2011 11:32:50 -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/23/t1324657983o4gghnj2tpcvzhc.htm/, Retrieved Mon, 29 Apr 2024 23:28:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=160565, Retrieved Mon, 29 Apr 2024 23:28:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact73

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-12-23 16:32:50] [1118fb1265e4c78f2f623b6bb1012fba] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1844	162687	94	595	115	0	48	21	82	73	20465	6200	23975	39	37
1796	201906	62	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
2443	146367	96	679	155	0	67	27	84	63	25629	8874	36294	54	54
3566	257045	138	1201	125	0	83	31	124	116	54002	20323	72255	93	86
6917	524450	265	1967	278	1	136	36	140	138	151036	26258	189748	198	181
1840	188294	58	595	89	1	65	23	88	71	33287	10165	61834	42	42
1739	195674	59	496	59	0	86	30	115	107	31172	8247	68167	59	59
2078	177020	44	670	87	0	62	30	109	50	28113	8683	38462	49	46
3097	325899	98	1039	130	1	71	27	108	81	57803	16957	101219	83	77
1946	121844	75	634	158	2	50	24	63	58	49830	8058	43270	49	49
2369	203938	71	743	120	0	88	30	118	91	52143	20488	76183	83	79
1880	107220	104	681	87	0	50	22	71	41	21055	7945	31476	39	37
3198	220751	120	1086	264	4	79	28	112	100	47007	13448	62157	93	92
1490	172905	62	419	51	4	56	18	63	61	28735	5389	46261	31	31
1573	156326	88	474	85	3	54	22	86	74	59147	6185	50063	29	28
1807	145178	58	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
756	32443	25	235	49	0	14	18	57	41	53249	3878	12743	27	25
1162	87927	62	399	40	0	31	15	59	37	10726	3149	18743	16	16
2817	241285	102	850	99	0	99	30	113	84	83700	20517	97057	108	106
1760	195820	72	642	127	1	38	25	96	67	40400	2570	17675	36	35
2315	146946	56	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
1805	207078	33	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
2234	217808	44	929	90	0	61	20	79	67	40078	16318	79572	69	69
1684	182286	44	490	76	0	61	28	103	84	34711	9556	42744	37	36
1625	181740	46	553	116	2	61	22	71	62	31076	10462	65931	45	41
2256	137978	106	738	92	4	53	17	66	35	74608	7192	38575	62	59
3373	255929	130	1028	361	0	118	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
2142	230761	64	1000	63	0	54	31	121	87	36022	10881	38229	34	27
1848	130843	53	619	138	3	53	14	51	39	23333	8022	31972	44	44
2190	157118	49	532	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
2532	269329	71	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
2162	195891	79	804	59	1	64	24	92	65	63369	5989	57568	81	78
1365	139667	51	419	56	2	71	26	103	97	60132	11270	39019	111	106
1244	171101	98	334	41	2	44	23	85	70	37403	13958	53866	61	61
755	81407	32	216	49	1	23	35	106	63	24460	7162	38345	56	53
2417	247563	104	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
2786	172743	59	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
2012	169355	70	506	52	0	104	23	87	69	30874	12396	37819	44	44
2602	315622	76	830	89	0	83	27	97	93	68701	18717	102738	115	113
2071	241518	79	694	73	0	57	30	107	76	35728	9724	54509	57	55
1911	195583	59	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
1918	220241	69	538	58	0	53	26	96	65	38844	8030	50611	51	51
1046	101694	25	329	75	1	26	26	100	78	27084	7509	26692	32	31
1178	151985	67	421	32	0	67	23	91	87	35139	14146	60056	36	36
2890	202536	99	972	59	10	36	38	136	85	57476	7768	25155	47	47
1836	173505	64	541	71	6	56	32	128	119	33277	13823	42840	51	53
2254	150518	83	836	91	0	52	21	83	65	31141	7230	39358	37	38
1389	141273	61	376	87	11	54	22	74	60	61281	10170	47241	52	52
1325	125612	38	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
1525	124197	42	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
2897	138708	65	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
2969	255587	114	1093	135	3	56	19	72	42	29245	6372	59331	83	81
1449	151184	78	469	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
1684	119629	72	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
1234	84971	71	395	105	0	34	31	114	60	42249	10138	29487	57	55
966	88797	75	228	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
1075	75101	29	334	49	1	30	23	86	17	14399	4204	29750	40	39
1637	145043	40	524	132	0	57	25	90	61	28263	10226	41029	40	40
1207	95827	47	393	49	0	54	22	87	55	17215	3456	12416	36	36
1865	173924	58	574	71	0	38	26	99	55	48140	8895	51158	64	60
2726	241957	237	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
1419	118408	64	450	49	7	46	24	91	73	41622	8620	25807	46	45
1609	164078	53	653	74	0	46	21	77	67	40715	7820	50620	61	59
1864	158931	41	684	59	5	51	28	104	66	65897	12112	61467	59	59
2412	184139	82	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
1462	144014	59	549	81	0	28	15	60	55	53216	6616	42006	51	47
973	62535	42	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
2526	247280	108	877	105	3	43	24	96	83	46609	11252	45108	45	42
2072	159408	83	856	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
1193	104253	25	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
1327	137382	65	336	67	1	39	23	84	52	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
1671	165507	50	410	127	0	102	29	116	105	33994	12840	52739	54	53
1167	132148	37	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
1105	95778	33	343	58	0	46	25	91	51	98177	10623	35381	50	49
1148	109001	67	376	57	0	25	21	76	76	37941	5322	19595	33	33
1484	158833	45	495	60	0	59	24	86	61	31032	7987	50848	54	50
1526	147690	63	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
1671	160930	92	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
1015	136061	39	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
1856	184277	74	547	92	1	80	29	109	87	41211	9188	62548	68	64
1056	108858	43	302	58	0	32	20	81	55	22698	5141	31334	29	29
2234	141744	58	632	64	8	81	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
1930	174712	67	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
1121	127628	50	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
1099	87957	26	305	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
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160565&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]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160565&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160565&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
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
Variabele2[t] = + 6866.7591886368 -2.75598687938126Variabele1[t] + 281.304762761804Variabele3[t] + 127.954357326127Variabele4[t] -14.4071224715022Variabele5[t] -183.353662352009Variabele6[t] + 334.564372717018Variabele7[t] + 898.85963757976Variabele8[t] -327.407344766138Variabele9[t] + 469.472044956653Variabele10[t] -0.174782718407281Variabele11[t] -1.86420748251385Variabele12[t] + 1.3035410595653Variabele13[t] + 953.180875157842Variabele14[t] -1214.57710429252`Variabele15 `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Variabele2[t] =  +  6866.7591886368 -2.75598687938126Variabele1[t] +  281.304762761804Variabele3[t] +  127.954357326127Variabele4[t] -14.4071224715022Variabele5[t] -183.353662352009Variabele6[t] +  334.564372717018Variabele7[t] +  898.85963757976Variabele8[t] -327.407344766138Variabele9[t] +  469.472044956653Variabele10[t] -0.174782718407281Variabele11[t] -1.86420748251385Variabele12[t] +  1.3035410595653Variabele13[t] +  953.180875157842Variabele14[t] -1214.57710429252`Variabele15
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160565&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Variabele2[t] =  +  6866.7591886368 -2.75598687938126Variabele1[t] +  281.304762761804Variabele3[t] +  127.954357326127Variabele4[t] -14.4071224715022Variabele5[t] -183.353662352009Variabele6[t] +  334.564372717018Variabele7[t] +  898.85963757976Variabele8[t] -327.407344766138Variabele9[t] +  469.472044956653Variabele10[t] -0.174782718407281Variabele11[t] -1.86420748251385Variabele12[t] +  1.3035410595653Variabele13[t] +  953.180875157842Variabele14[t] -1214.57710429252`Variabele15
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160565&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160565&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
Variabele2[t] = + 6866.7591886368 -2.75598687938126Variabele1[t] + 281.304762761804Variabele3[t] + 127.954357326127Variabele4[t] -14.4071224715022Variabele5[t] -183.353662352009Variabele6[t] + 334.564372717018Variabele7[t] + 898.85963757976Variabele8[t] -327.407344766138Variabele9[t] + 469.472044956653Variabele10[t] -0.174782718407281Variabele11[t] -1.86420748251385Variabele12[t] + 1.3035410595653Variabele13[t] + 953.180875157842Variabele14[t] -1214.57710429252`Variabele15 `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6866.75918863686200.1512471.10750.2701320.135066
Variabele1-2.7559868793812614.893448-0.1850.8534830.426741
Variabele3281.304762761804113.8289892.47130.0147660.007383
Variabele4127.95435732612733.7115743.79560.0002260.000113
Variabele5-14.407122471502270.876659-0.20330.8392440.419622
Variabele6-183.3536623520091146.41988-0.15990.8731820.436591
Variabele7334.564372717018173.034541.93350.0553640.027682
Variabele8898.859637579761038.6736680.86540.388430.194215
Variabele9-327.407344766138331.795239-0.98680.32560.1628
Variabele10469.472044956653212.1925252.21250.0286930.014346
Variabele11-0.1747827184072810.159952-1.09270.2765520.138276
Variabele12-1.864207482513850.974651-1.91270.0580040.029002
Variabele131.30354105956530.186616.985400
Variabele14953.1808751578421222.2447350.77990.4369010.218451
`Variabele15 `-1214.577104292521263.239981-0.96150.3381110.169055

\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) & 6866.7591886368 & 6200.151247 & 1.1075 & 0.270132 & 0.135066 \tabularnewline
Variabele1 & -2.75598687938126 & 14.893448 & -0.185 & 0.853483 & 0.426741 \tabularnewline
Variabele3 & 281.304762761804 & 113.828989 & 2.4713 & 0.014766 & 0.007383 \tabularnewline
Variabele4 & 127.954357326127 & 33.711574 & 3.7956 & 0.000226 & 0.000113 \tabularnewline
Variabele5 & -14.4071224715022 & 70.876659 & -0.2033 & 0.839244 & 0.419622 \tabularnewline
Variabele6 & -183.353662352009 & 1146.41988 & -0.1599 & 0.873182 & 0.436591 \tabularnewline
Variabele7 & 334.564372717018 & 173.03454 & 1.9335 & 0.055364 & 0.027682 \tabularnewline
Variabele8 & 898.85963757976 & 1038.673668 & 0.8654 & 0.38843 & 0.194215 \tabularnewline
Variabele9 & -327.407344766138 & 331.795239 & -0.9868 & 0.3256 & 0.1628 \tabularnewline
Variabele10 & 469.472044956653 & 212.192525 & 2.2125 & 0.028693 & 0.014346 \tabularnewline
Variabele11 & -0.174782718407281 & 0.159952 & -1.0927 & 0.276552 & 0.138276 \tabularnewline
Variabele12 & -1.86420748251385 & 0.974651 & -1.9127 & 0.058004 & 0.029002 \tabularnewline
Variabele13 & 1.3035410595653 & 0.18661 & 6.9854 & 0 & 0 \tabularnewline
Variabele14 & 953.180875157842 & 1222.244735 & 0.7799 & 0.436901 & 0.218451 \tabularnewline
`Variabele15
` & -1214.57710429252 & 1263.239981 & -0.9615 & 0.338111 & 0.169055 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160565&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]6866.7591886368[/C][C]6200.151247[/C][C]1.1075[/C][C]0.270132[/C][C]0.135066[/C][/ROW]
[ROW][C]Variabele1[/C][C]-2.75598687938126[/C][C]14.893448[/C][C]-0.185[/C][C]0.853483[/C][C]0.426741[/C][/ROW]
[ROW][C]Variabele3[/C][C]281.304762761804[/C][C]113.828989[/C][C]2.4713[/C][C]0.014766[/C][C]0.007383[/C][/ROW]
[ROW][C]Variabele4[/C][C]127.954357326127[/C][C]33.711574[/C][C]3.7956[/C][C]0.000226[/C][C]0.000113[/C][/ROW]
[ROW][C]Variabele5[/C][C]-14.4071224715022[/C][C]70.876659[/C][C]-0.2033[/C][C]0.839244[/C][C]0.419622[/C][/ROW]
[ROW][C]Variabele6[/C][C]-183.353662352009[/C][C]1146.41988[/C][C]-0.1599[/C][C]0.873182[/C][C]0.436591[/C][/ROW]
[ROW][C]Variabele7[/C][C]334.564372717018[/C][C]173.03454[/C][C]1.9335[/C][C]0.055364[/C][C]0.027682[/C][/ROW]
[ROW][C]Variabele8[/C][C]898.85963757976[/C][C]1038.673668[/C][C]0.8654[/C][C]0.38843[/C][C]0.194215[/C][/ROW]
[ROW][C]Variabele9[/C][C]-327.407344766138[/C][C]331.795239[/C][C]-0.9868[/C][C]0.3256[/C][C]0.1628[/C][/ROW]
[ROW][C]Variabele10[/C][C]469.472044956653[/C][C]212.192525[/C][C]2.2125[/C][C]0.028693[/C][C]0.014346[/C][/ROW]
[ROW][C]Variabele11[/C][C]-0.174782718407281[/C][C]0.159952[/C][C]-1.0927[/C][C]0.276552[/C][C]0.138276[/C][/ROW]
[ROW][C]Variabele12[/C][C]-1.86420748251385[/C][C]0.974651[/C][C]-1.9127[/C][C]0.058004[/C][C]0.029002[/C][/ROW]
[ROW][C]Variabele13[/C][C]1.3035410595653[/C][C]0.18661[/C][C]6.9854[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Variabele14[/C][C]953.180875157842[/C][C]1222.244735[/C][C]0.7799[/C][C]0.436901[/C][C]0.218451[/C][/ROW]
[ROW][C]`Variabele15
`[/C][C]-1214.57710429252[/C][C]1263.239981[/C][C]-0.9615[/C][C]0.338111[/C][C]0.169055[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160565&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160565&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)6866.75918863686200.1512471.10750.2701320.135066
Variabele1-2.7559868793812614.893448-0.1850.8534830.426741
Variabele3281.304762761804113.8289892.47130.0147660.007383
Variabele4127.95435732612733.7115743.79560.0002260.000113
Variabele5-14.407122471502270.876659-0.20330.8392440.419622
Variabele6-183.3536623520091146.41988-0.15990.8731820.436591
Variabele7334.564372717018173.034541.93350.0553640.027682
Variabele8898.859637579761038.6736680.86540.388430.194215
Variabele9-327.407344766138331.795239-0.98680.32560.1628
Variabele10469.472044956653212.1925252.21250.0286930.014346
Variabele11-0.1747827184072810.159952-1.09270.2765520.138276
Variabele12-1.864207482513850.974651-1.91270.0580040.029002
Variabele131.30354105956530.186616.985400
Variabele14953.1808751578421222.2447350.77990.4369010.218451
`Variabele15 `-1214.577104292521263.239981-0.96150.3381110.169055






Multiple Linear Regression - Regression Statistics
Multiple R0.945919567517234
R-squared0.894763828211991
Adjusted R-squared0.883342848328021
F-TEST (value)78.343875683368
F-TEST (DF numerator)14
F-TEST (DF denominator)129
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation27638.0709213051
Sum Squared Residuals98538322388.3909

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.945919567517234 \tabularnewline
R-squared & 0.894763828211991 \tabularnewline
Adjusted R-squared & 0.883342848328021 \tabularnewline
F-TEST (value) & 78.343875683368 \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 & 27638.0709213051 \tabularnewline
Sum Squared Residuals & 98538322388.3909 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160565&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.945919567517234[/C][/ROW]
[ROW][C]R-squared[/C][C]0.894763828211991[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.883342848328021[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]78.343875683368[/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]27638.0709213051[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]98538322388.3909[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160565&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160565&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 R0.945919567517234
R-squared0.894763828211991
Adjusted R-squared0.883342848328021
F-TEST (value)78.343875683368
F-TEST (DF numerator)14
F-TEST (DF denominator)129
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation27638.0709213051
Sum Squared Residuals98538322388.3909






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1162687153414.6733497149272.32665028621
2201906203527.58714469-1621.58714469024
3721521741.0998261697-14526.0998261697
4146367172719.429520108-26352.429520108
5257045288279.642894942-31234.6428949422
6524450547543.686447532-23093.6864475322
7188294184576.654207063717.34579294004
8195674202220.06924155-6546.06924154976
9177020153359.74185748523660.2581425154
10325899283298.52169898942600.478301011
11121844165849.661290503-44005.6612905031
12203938209309.552311544-5371.55231154416
13107220164104.040972704-56884.0409727037
14220751252750.514897967-31999.5148979666
15172905152403.37654113620501.6234588644
16156326167612.663555658-11286.6635556579
17145178153088.744851147-7910.74485114656
188917187509.39414768331661.60585231674
19172624207793.92064338-35169.9206433799
203244358074.7651877639-25631.7651877639
2187927105995.345453615-18068.3454536148
22241285245496.300028658-4211.30002865821
23195820140604.42943374955215.5705662508
24146946175698.644602937-28752.6446029372
25159763185948.265190567-26185.2651905672
26207078156064.75944618851013.240553812
27212394215294.502139385-2900.50213938522
28201536211609.984140412-10073.9841404119
29394662318157.70342424476504.2965757564
30217808222899.267151508-5091.26715150795
31182286150875.19851549531410.801484505
32181740184198.167003069-2458.16700306902
33137978161947.858053263-23969.8580532634
34255929235051.06502514220877.9349748582
35236489249754.777978765-13265.7779787654
3607145.30796451924-7145.30796451924
37230761216039.72721228714721.2727877129
38130843136418.436090192-5575.43609019249
39157118139750.83262270817367.1673772919
40253254292051.952468566-38797.9524685662
41269329208320.53294408161008.4670559195
42161273170695.722064281-9422.72206428129
43107181115065.554572378-7884.55457237807
44195891203624.62334963-7733.62334963044
45139667125232.12236503114434.8776349687
46171101134926.92384777436174.0761522257
478140795926.3933252715-14519.3933252715
48247563211709.03210349335853.9678965069
49239807223339.62129393316467.3787060666
50172743205334.817996361-32591.817996361
514818861703.3690942123-13515.3690942123
52169355153678.60760183515676.3923981647
53315622249326.83711613166295.1628838693
54241518192026.13680305849491.8631969424
55195583219968.033440635-24385.0334406347
56159913154745.514812165167.48518783961
57220241160064.93915975860176.0608402421
5810169496708.68759451194985.31240548815
59151985166377.395073513-14392.3950735134
60202536185995.89300795516540.1069920453
61173505156866.85077927116638.149220729
62150518190773.530436128-40255.5304361282
63141273125138.50910959416134.4908904058
64125612156015.467650055-30403.4676500551
65166049149274.19999983516774.8000001652
66124197161250.428903358-37053.4289033585
67195043235747.159661294-40704.1596612943
68138708209466.813159808-70758.8131598081
69116552119399.883671771-2847.8836717711
703197037889.6362455568-5919.63624555677
71255587241153.91330360914433.086696391
72151184126141.38997103225042.6100289682
73135926147226.882641229-11300.8826412286
74119629115956.8159732763672.1840267238
75171518200311.087695633-28793.0876956328
76108949116864.468902423-7915.46890242336
77183471202397.023416563-18926.0234165633
78159966247366.644355421-87400.6443554211
799378693543.4887979472242.511202052773
8084971102234.809494887-17263.8094948869
818879796549.5611554975-7752.56115549747
82304603215800.45143425488802.5485657463
837510183629.3098992234-8528.30989922345
84145043138490.4034289676552.59657103289
859582798842.5479242932-3015.5479242932
86173924149776.18504628924147.814953711
87241957240490.3472255981466.6527744017
88115367123485.969855292-8118.9698552919
89118408117476.098021235931.901978764618
90164078171114.723769773-7036.72376977258
91158931168788.17373072-9857.17373071967
92184139193620.856500559-9481.85650055859
93152856160376.913341337-7520.91334133707
94144014142190.4840445281823.515955472
956253586643.6333038619-24108.6333038619
96245196251822.022866411-6626.02286641093
97199841182966.44707193916874.5529280611
981934929291.6635235505-9942.66352355046
99247280205491.7080018441788.2919981596
100159408211947.477778007-52539.477778007
1017212863636.31002744468491.68997255541
102104253114689.791667568-10436.7916675685
103151090129325.23209834321764.7679016572
104137382115813.64156439221568.3584356079
1058744880105.99276316527342.00723683477
1062767641804.5569903817-14128.5569903817
107165507164434.7783020571072.2216979434
10813214883197.587403690948950.4125963091
10906866.75918863682-6866.75918863682
1109577885469.87316534410308.126834656
111109001108241.084811184759.91518881628
112158833156413.3462569662419.65374303425
113147690129048.30481965818641.695180342
11489887103094.607427621-13207.6074276208
11536169777.64141606236-6161.64141606236
11606866.75918863682-6866.75918863682
117199005157166.01853756441838.9814624359
118160930179827.836425501-18897.8364255007
119177948155029.91911712422918.0808828761
120136061122190.53369117913870.4663088211
1214341049451.4420944661-6041.44209446615
122184277193325.113223516-9048.11322351633
123108858101556.9209825117301.07901748947
124141744151886.353004813-10142.3530048133
1256049347540.377218766112952.6227812339
1261976427815.4546482567-8051.45464825673
127177559164546.30683621213012.6931637882
128140281124234.57801203716046.4219879633
129164249154366.5949535959882.40504640528
1301179618466.9038814665-6670.90388146648
1311067413250.0592737753-2576.05927377535
132151322138228.98687202213093.0131279784
13368368522.37059161269-1686.37059161269
134174712162155.88740067212556.1125993275
135511810021.0629130586-4903.06291305857
1364024841501.7144621773-1253.71446217728
13706866.75918863682-6866.75918863682
138127628118025.7745020449602.22549795626
13988837112610.919403487-23773.9194034867
140713111040.1572879076-3909.15728790756
141905620633.5690169529-11577.5690169529
1428795778741.0202554579215.97974454299
143144470118776.85275243225693.1472475676
144111408127117.242170741-15709.2421707411

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 162687 & 153414.673349714 & 9272.32665028621 \tabularnewline
2 & 201906 & 203527.58714469 & -1621.58714469024 \tabularnewline
3 & 7215 & 21741.0998261697 & -14526.0998261697 \tabularnewline
4 & 146367 & 172719.429520108 & -26352.429520108 \tabularnewline
5 & 257045 & 288279.642894942 & -31234.6428949422 \tabularnewline
6 & 524450 & 547543.686447532 & -23093.6864475322 \tabularnewline
7 & 188294 & 184576.65420706 & 3717.34579294004 \tabularnewline
8 & 195674 & 202220.06924155 & -6546.06924154976 \tabularnewline
9 & 177020 & 153359.741857485 & 23660.2581425154 \tabularnewline
10 & 325899 & 283298.521698989 & 42600.478301011 \tabularnewline
11 & 121844 & 165849.661290503 & -44005.6612905031 \tabularnewline
12 & 203938 & 209309.552311544 & -5371.55231154416 \tabularnewline
13 & 107220 & 164104.040972704 & -56884.0409727037 \tabularnewline
14 & 220751 & 252750.514897967 & -31999.5148979666 \tabularnewline
15 & 172905 & 152403.376541136 & 20501.6234588644 \tabularnewline
16 & 156326 & 167612.663555658 & -11286.6635556579 \tabularnewline
17 & 145178 & 153088.744851147 & -7910.74485114656 \tabularnewline
18 & 89171 & 87509.3941476833 & 1661.60585231674 \tabularnewline
19 & 172624 & 207793.92064338 & -35169.9206433799 \tabularnewline
20 & 32443 & 58074.7651877639 & -25631.7651877639 \tabularnewline
21 & 87927 & 105995.345453615 & -18068.3454536148 \tabularnewline
22 & 241285 & 245496.300028658 & -4211.30002865821 \tabularnewline
23 & 195820 & 140604.429433749 & 55215.5705662508 \tabularnewline
24 & 146946 & 175698.644602937 & -28752.6446029372 \tabularnewline
25 & 159763 & 185948.265190567 & -26185.2651905672 \tabularnewline
26 & 207078 & 156064.759446188 & 51013.240553812 \tabularnewline
27 & 212394 & 215294.502139385 & -2900.50213938522 \tabularnewline
28 & 201536 & 211609.984140412 & -10073.9841404119 \tabularnewline
29 & 394662 & 318157.703424244 & 76504.2965757564 \tabularnewline
30 & 217808 & 222899.267151508 & -5091.26715150795 \tabularnewline
31 & 182286 & 150875.198515495 & 31410.801484505 \tabularnewline
32 & 181740 & 184198.167003069 & -2458.16700306902 \tabularnewline
33 & 137978 & 161947.858053263 & -23969.8580532634 \tabularnewline
34 & 255929 & 235051.065025142 & 20877.9349748582 \tabularnewline
35 & 236489 & 249754.777978765 & -13265.7779787654 \tabularnewline
36 & 0 & 7145.30796451924 & -7145.30796451924 \tabularnewline
37 & 230761 & 216039.727212287 & 14721.2727877129 \tabularnewline
38 & 130843 & 136418.436090192 & -5575.43609019249 \tabularnewline
39 & 157118 & 139750.832622708 & 17367.1673772919 \tabularnewline
40 & 253254 & 292051.952468566 & -38797.9524685662 \tabularnewline
41 & 269329 & 208320.532944081 & 61008.4670559195 \tabularnewline
42 & 161273 & 170695.722064281 & -9422.72206428129 \tabularnewline
43 & 107181 & 115065.554572378 & -7884.55457237807 \tabularnewline
44 & 195891 & 203624.62334963 & -7733.62334963044 \tabularnewline
45 & 139667 & 125232.122365031 & 14434.8776349687 \tabularnewline
46 & 171101 & 134926.923847774 & 36174.0761522257 \tabularnewline
47 & 81407 & 95926.3933252715 & -14519.3933252715 \tabularnewline
48 & 247563 & 211709.032103493 & 35853.9678965069 \tabularnewline
49 & 239807 & 223339.621293933 & 16467.3787060666 \tabularnewline
50 & 172743 & 205334.817996361 & -32591.817996361 \tabularnewline
51 & 48188 & 61703.3690942123 & -13515.3690942123 \tabularnewline
52 & 169355 & 153678.607601835 & 15676.3923981647 \tabularnewline
53 & 315622 & 249326.837116131 & 66295.1628838693 \tabularnewline
54 & 241518 & 192026.136803058 & 49491.8631969424 \tabularnewline
55 & 195583 & 219968.033440635 & -24385.0334406347 \tabularnewline
56 & 159913 & 154745.51481216 & 5167.48518783961 \tabularnewline
57 & 220241 & 160064.939159758 & 60176.0608402421 \tabularnewline
58 & 101694 & 96708.6875945119 & 4985.31240548815 \tabularnewline
59 & 151985 & 166377.395073513 & -14392.3950735134 \tabularnewline
60 & 202536 & 185995.893007955 & 16540.1069920453 \tabularnewline
61 & 173505 & 156866.850779271 & 16638.149220729 \tabularnewline
62 & 150518 & 190773.530436128 & -40255.5304361282 \tabularnewline
63 & 141273 & 125138.509109594 & 16134.4908904058 \tabularnewline
64 & 125612 & 156015.467650055 & -30403.4676500551 \tabularnewline
65 & 166049 & 149274.199999835 & 16774.8000001652 \tabularnewline
66 & 124197 & 161250.428903358 & -37053.4289033585 \tabularnewline
67 & 195043 & 235747.159661294 & -40704.1596612943 \tabularnewline
68 & 138708 & 209466.813159808 & -70758.8131598081 \tabularnewline
69 & 116552 & 119399.883671771 & -2847.8836717711 \tabularnewline
70 & 31970 & 37889.6362455568 & -5919.63624555677 \tabularnewline
71 & 255587 & 241153.913303609 & 14433.086696391 \tabularnewline
72 & 151184 & 126141.389971032 & 25042.6100289682 \tabularnewline
73 & 135926 & 147226.882641229 & -11300.8826412286 \tabularnewline
74 & 119629 & 115956.815973276 & 3672.1840267238 \tabularnewline
75 & 171518 & 200311.087695633 & -28793.0876956328 \tabularnewline
76 & 108949 & 116864.468902423 & -7915.46890242336 \tabularnewline
77 & 183471 & 202397.023416563 & -18926.0234165633 \tabularnewline
78 & 159966 & 247366.644355421 & -87400.6443554211 \tabularnewline
79 & 93786 & 93543.4887979472 & 242.511202052773 \tabularnewline
80 & 84971 & 102234.809494887 & -17263.8094948869 \tabularnewline
81 & 88797 & 96549.5611554975 & -7752.56115549747 \tabularnewline
82 & 304603 & 215800.451434254 & 88802.5485657463 \tabularnewline
83 & 75101 & 83629.3098992234 & -8528.30989922345 \tabularnewline
84 & 145043 & 138490.403428967 & 6552.59657103289 \tabularnewline
85 & 95827 & 98842.5479242932 & -3015.5479242932 \tabularnewline
86 & 173924 & 149776.185046289 & 24147.814953711 \tabularnewline
87 & 241957 & 240490.347225598 & 1466.6527744017 \tabularnewline
88 & 115367 & 123485.969855292 & -8118.9698552919 \tabularnewline
89 & 118408 & 117476.098021235 & 931.901978764618 \tabularnewline
90 & 164078 & 171114.723769773 & -7036.72376977258 \tabularnewline
91 & 158931 & 168788.17373072 & -9857.17373071967 \tabularnewline
92 & 184139 & 193620.856500559 & -9481.85650055859 \tabularnewline
93 & 152856 & 160376.913341337 & -7520.91334133707 \tabularnewline
94 & 144014 & 142190.484044528 & 1823.515955472 \tabularnewline
95 & 62535 & 86643.6333038619 & -24108.6333038619 \tabularnewline
96 & 245196 & 251822.022866411 & -6626.02286641093 \tabularnewline
97 & 199841 & 182966.447071939 & 16874.5529280611 \tabularnewline
98 & 19349 & 29291.6635235505 & -9942.66352355046 \tabularnewline
99 & 247280 & 205491.70800184 & 41788.2919981596 \tabularnewline
100 & 159408 & 211947.477778007 & -52539.477778007 \tabularnewline
101 & 72128 & 63636.3100274446 & 8491.68997255541 \tabularnewline
102 & 104253 & 114689.791667568 & -10436.7916675685 \tabularnewline
103 & 151090 & 129325.232098343 & 21764.7679016572 \tabularnewline
104 & 137382 & 115813.641564392 & 21568.3584356079 \tabularnewline
105 & 87448 & 80105.9927631652 & 7342.00723683477 \tabularnewline
106 & 27676 & 41804.5569903817 & -14128.5569903817 \tabularnewline
107 & 165507 & 164434.778302057 & 1072.2216979434 \tabularnewline
108 & 132148 & 83197.5874036909 & 48950.4125963091 \tabularnewline
109 & 0 & 6866.75918863682 & -6866.75918863682 \tabularnewline
110 & 95778 & 85469.873165344 & 10308.126834656 \tabularnewline
111 & 109001 & 108241.084811184 & 759.91518881628 \tabularnewline
112 & 158833 & 156413.346256966 & 2419.65374303425 \tabularnewline
113 & 147690 & 129048.304819658 & 18641.695180342 \tabularnewline
114 & 89887 & 103094.607427621 & -13207.6074276208 \tabularnewline
115 & 3616 & 9777.64141606236 & -6161.64141606236 \tabularnewline
116 & 0 & 6866.75918863682 & -6866.75918863682 \tabularnewline
117 & 199005 & 157166.018537564 & 41838.9814624359 \tabularnewline
118 & 160930 & 179827.836425501 & -18897.8364255007 \tabularnewline
119 & 177948 & 155029.919117124 & 22918.0808828761 \tabularnewline
120 & 136061 & 122190.533691179 & 13870.4663088211 \tabularnewline
121 & 43410 & 49451.4420944661 & -6041.44209446615 \tabularnewline
122 & 184277 & 193325.113223516 & -9048.11322351633 \tabularnewline
123 & 108858 & 101556.920982511 & 7301.07901748947 \tabularnewline
124 & 141744 & 151886.353004813 & -10142.3530048133 \tabularnewline
125 & 60493 & 47540.3772187661 & 12952.6227812339 \tabularnewline
126 & 19764 & 27815.4546482567 & -8051.45464825673 \tabularnewline
127 & 177559 & 164546.306836212 & 13012.6931637882 \tabularnewline
128 & 140281 & 124234.578012037 & 16046.4219879633 \tabularnewline
129 & 164249 & 154366.594953595 & 9882.40504640528 \tabularnewline
130 & 11796 & 18466.9038814665 & -6670.90388146648 \tabularnewline
131 & 10674 & 13250.0592737753 & -2576.05927377535 \tabularnewline
132 & 151322 & 138228.986872022 & 13093.0131279784 \tabularnewline
133 & 6836 & 8522.37059161269 & -1686.37059161269 \tabularnewline
134 & 174712 & 162155.887400672 & 12556.1125993275 \tabularnewline
135 & 5118 & 10021.0629130586 & -4903.06291305857 \tabularnewline
136 & 40248 & 41501.7144621773 & -1253.71446217728 \tabularnewline
137 & 0 & 6866.75918863682 & -6866.75918863682 \tabularnewline
138 & 127628 & 118025.774502044 & 9602.22549795626 \tabularnewline
139 & 88837 & 112610.919403487 & -23773.9194034867 \tabularnewline
140 & 7131 & 11040.1572879076 & -3909.15728790756 \tabularnewline
141 & 9056 & 20633.5690169529 & -11577.5690169529 \tabularnewline
142 & 87957 & 78741.020255457 & 9215.97974454299 \tabularnewline
143 & 144470 & 118776.852752432 & 25693.1472475676 \tabularnewline
144 & 111408 & 127117.242170741 & -15709.2421707411 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160565&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]162687[/C][C]153414.673349714[/C][C]9272.32665028621[/C][/ROW]
[ROW][C]2[/C][C]201906[/C][C]203527.58714469[/C][C]-1621.58714469024[/C][/ROW]
[ROW][C]3[/C][C]7215[/C][C]21741.0998261697[/C][C]-14526.0998261697[/C][/ROW]
[ROW][C]4[/C][C]146367[/C][C]172719.429520108[/C][C]-26352.429520108[/C][/ROW]
[ROW][C]5[/C][C]257045[/C][C]288279.642894942[/C][C]-31234.6428949422[/C][/ROW]
[ROW][C]6[/C][C]524450[/C][C]547543.686447532[/C][C]-23093.6864475322[/C][/ROW]
[ROW][C]7[/C][C]188294[/C][C]184576.65420706[/C][C]3717.34579294004[/C][/ROW]
[ROW][C]8[/C][C]195674[/C][C]202220.06924155[/C][C]-6546.06924154976[/C][/ROW]
[ROW][C]9[/C][C]177020[/C][C]153359.741857485[/C][C]23660.2581425154[/C][/ROW]
[ROW][C]10[/C][C]325899[/C][C]283298.521698989[/C][C]42600.478301011[/C][/ROW]
[ROW][C]11[/C][C]121844[/C][C]165849.661290503[/C][C]-44005.6612905031[/C][/ROW]
[ROW][C]12[/C][C]203938[/C][C]209309.552311544[/C][C]-5371.55231154416[/C][/ROW]
[ROW][C]13[/C][C]107220[/C][C]164104.040972704[/C][C]-56884.0409727037[/C][/ROW]
[ROW][C]14[/C][C]220751[/C][C]252750.514897967[/C][C]-31999.5148979666[/C][/ROW]
[ROW][C]15[/C][C]172905[/C][C]152403.376541136[/C][C]20501.6234588644[/C][/ROW]
[ROW][C]16[/C][C]156326[/C][C]167612.663555658[/C][C]-11286.6635556579[/C][/ROW]
[ROW][C]17[/C][C]145178[/C][C]153088.744851147[/C][C]-7910.74485114656[/C][/ROW]
[ROW][C]18[/C][C]89171[/C][C]87509.3941476833[/C][C]1661.60585231674[/C][/ROW]
[ROW][C]19[/C][C]172624[/C][C]207793.92064338[/C][C]-35169.9206433799[/C][/ROW]
[ROW][C]20[/C][C]32443[/C][C]58074.7651877639[/C][C]-25631.7651877639[/C][/ROW]
[ROW][C]21[/C][C]87927[/C][C]105995.345453615[/C][C]-18068.3454536148[/C][/ROW]
[ROW][C]22[/C][C]241285[/C][C]245496.300028658[/C][C]-4211.30002865821[/C][/ROW]
[ROW][C]23[/C][C]195820[/C][C]140604.429433749[/C][C]55215.5705662508[/C][/ROW]
[ROW][C]24[/C][C]146946[/C][C]175698.644602937[/C][C]-28752.6446029372[/C][/ROW]
[ROW][C]25[/C][C]159763[/C][C]185948.265190567[/C][C]-26185.2651905672[/C][/ROW]
[ROW][C]26[/C][C]207078[/C][C]156064.759446188[/C][C]51013.240553812[/C][/ROW]
[ROW][C]27[/C][C]212394[/C][C]215294.502139385[/C][C]-2900.50213938522[/C][/ROW]
[ROW][C]28[/C][C]201536[/C][C]211609.984140412[/C][C]-10073.9841404119[/C][/ROW]
[ROW][C]29[/C][C]394662[/C][C]318157.703424244[/C][C]76504.2965757564[/C][/ROW]
[ROW][C]30[/C][C]217808[/C][C]222899.267151508[/C][C]-5091.26715150795[/C][/ROW]
[ROW][C]31[/C][C]182286[/C][C]150875.198515495[/C][C]31410.801484505[/C][/ROW]
[ROW][C]32[/C][C]181740[/C][C]184198.167003069[/C][C]-2458.16700306902[/C][/ROW]
[ROW][C]33[/C][C]137978[/C][C]161947.858053263[/C][C]-23969.8580532634[/C][/ROW]
[ROW][C]34[/C][C]255929[/C][C]235051.065025142[/C][C]20877.9349748582[/C][/ROW]
[ROW][C]35[/C][C]236489[/C][C]249754.777978765[/C][C]-13265.7779787654[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]7145.30796451924[/C][C]-7145.30796451924[/C][/ROW]
[ROW][C]37[/C][C]230761[/C][C]216039.727212287[/C][C]14721.2727877129[/C][/ROW]
[ROW][C]38[/C][C]130843[/C][C]136418.436090192[/C][C]-5575.43609019249[/C][/ROW]
[ROW][C]39[/C][C]157118[/C][C]139750.832622708[/C][C]17367.1673772919[/C][/ROW]
[ROW][C]40[/C][C]253254[/C][C]292051.952468566[/C][C]-38797.9524685662[/C][/ROW]
[ROW][C]41[/C][C]269329[/C][C]208320.532944081[/C][C]61008.4670559195[/C][/ROW]
[ROW][C]42[/C][C]161273[/C][C]170695.722064281[/C][C]-9422.72206428129[/C][/ROW]
[ROW][C]43[/C][C]107181[/C][C]115065.554572378[/C][C]-7884.55457237807[/C][/ROW]
[ROW][C]44[/C][C]195891[/C][C]203624.62334963[/C][C]-7733.62334963044[/C][/ROW]
[ROW][C]45[/C][C]139667[/C][C]125232.122365031[/C][C]14434.8776349687[/C][/ROW]
[ROW][C]46[/C][C]171101[/C][C]134926.923847774[/C][C]36174.0761522257[/C][/ROW]
[ROW][C]47[/C][C]81407[/C][C]95926.3933252715[/C][C]-14519.3933252715[/C][/ROW]
[ROW][C]48[/C][C]247563[/C][C]211709.032103493[/C][C]35853.9678965069[/C][/ROW]
[ROW][C]49[/C][C]239807[/C][C]223339.621293933[/C][C]16467.3787060666[/C][/ROW]
[ROW][C]50[/C][C]172743[/C][C]205334.817996361[/C][C]-32591.817996361[/C][/ROW]
[ROW][C]51[/C][C]48188[/C][C]61703.3690942123[/C][C]-13515.3690942123[/C][/ROW]
[ROW][C]52[/C][C]169355[/C][C]153678.607601835[/C][C]15676.3923981647[/C][/ROW]
[ROW][C]53[/C][C]315622[/C][C]249326.837116131[/C][C]66295.1628838693[/C][/ROW]
[ROW][C]54[/C][C]241518[/C][C]192026.136803058[/C][C]49491.8631969424[/C][/ROW]
[ROW][C]55[/C][C]195583[/C][C]219968.033440635[/C][C]-24385.0334406347[/C][/ROW]
[ROW][C]56[/C][C]159913[/C][C]154745.51481216[/C][C]5167.48518783961[/C][/ROW]
[ROW][C]57[/C][C]220241[/C][C]160064.939159758[/C][C]60176.0608402421[/C][/ROW]
[ROW][C]58[/C][C]101694[/C][C]96708.6875945119[/C][C]4985.31240548815[/C][/ROW]
[ROW][C]59[/C][C]151985[/C][C]166377.395073513[/C][C]-14392.3950735134[/C][/ROW]
[ROW][C]60[/C][C]202536[/C][C]185995.893007955[/C][C]16540.1069920453[/C][/ROW]
[ROW][C]61[/C][C]173505[/C][C]156866.850779271[/C][C]16638.149220729[/C][/ROW]
[ROW][C]62[/C][C]150518[/C][C]190773.530436128[/C][C]-40255.5304361282[/C][/ROW]
[ROW][C]63[/C][C]141273[/C][C]125138.509109594[/C][C]16134.4908904058[/C][/ROW]
[ROW][C]64[/C][C]125612[/C][C]156015.467650055[/C][C]-30403.4676500551[/C][/ROW]
[ROW][C]65[/C][C]166049[/C][C]149274.199999835[/C][C]16774.8000001652[/C][/ROW]
[ROW][C]66[/C][C]124197[/C][C]161250.428903358[/C][C]-37053.4289033585[/C][/ROW]
[ROW][C]67[/C][C]195043[/C][C]235747.159661294[/C][C]-40704.1596612943[/C][/ROW]
[ROW][C]68[/C][C]138708[/C][C]209466.813159808[/C][C]-70758.8131598081[/C][/ROW]
[ROW][C]69[/C][C]116552[/C][C]119399.883671771[/C][C]-2847.8836717711[/C][/ROW]
[ROW][C]70[/C][C]31970[/C][C]37889.6362455568[/C][C]-5919.63624555677[/C][/ROW]
[ROW][C]71[/C][C]255587[/C][C]241153.913303609[/C][C]14433.086696391[/C][/ROW]
[ROW][C]72[/C][C]151184[/C][C]126141.389971032[/C][C]25042.6100289682[/C][/ROW]
[ROW][C]73[/C][C]135926[/C][C]147226.882641229[/C][C]-11300.8826412286[/C][/ROW]
[ROW][C]74[/C][C]119629[/C][C]115956.815973276[/C][C]3672.1840267238[/C][/ROW]
[ROW][C]75[/C][C]171518[/C][C]200311.087695633[/C][C]-28793.0876956328[/C][/ROW]
[ROW][C]76[/C][C]108949[/C][C]116864.468902423[/C][C]-7915.46890242336[/C][/ROW]
[ROW][C]77[/C][C]183471[/C][C]202397.023416563[/C][C]-18926.0234165633[/C][/ROW]
[ROW][C]78[/C][C]159966[/C][C]247366.644355421[/C][C]-87400.6443554211[/C][/ROW]
[ROW][C]79[/C][C]93786[/C][C]93543.4887979472[/C][C]242.511202052773[/C][/ROW]
[ROW][C]80[/C][C]84971[/C][C]102234.809494887[/C][C]-17263.8094948869[/C][/ROW]
[ROW][C]81[/C][C]88797[/C][C]96549.5611554975[/C][C]-7752.56115549747[/C][/ROW]
[ROW][C]82[/C][C]304603[/C][C]215800.451434254[/C][C]88802.5485657463[/C][/ROW]
[ROW][C]83[/C][C]75101[/C][C]83629.3098992234[/C][C]-8528.30989922345[/C][/ROW]
[ROW][C]84[/C][C]145043[/C][C]138490.403428967[/C][C]6552.59657103289[/C][/ROW]
[ROW][C]85[/C][C]95827[/C][C]98842.5479242932[/C][C]-3015.5479242932[/C][/ROW]
[ROW][C]86[/C][C]173924[/C][C]149776.185046289[/C][C]24147.814953711[/C][/ROW]
[ROW][C]87[/C][C]241957[/C][C]240490.347225598[/C][C]1466.6527744017[/C][/ROW]
[ROW][C]88[/C][C]115367[/C][C]123485.969855292[/C][C]-8118.9698552919[/C][/ROW]
[ROW][C]89[/C][C]118408[/C][C]117476.098021235[/C][C]931.901978764618[/C][/ROW]
[ROW][C]90[/C][C]164078[/C][C]171114.723769773[/C][C]-7036.72376977258[/C][/ROW]
[ROW][C]91[/C][C]158931[/C][C]168788.17373072[/C][C]-9857.17373071967[/C][/ROW]
[ROW][C]92[/C][C]184139[/C][C]193620.856500559[/C][C]-9481.85650055859[/C][/ROW]
[ROW][C]93[/C][C]152856[/C][C]160376.913341337[/C][C]-7520.91334133707[/C][/ROW]
[ROW][C]94[/C][C]144014[/C][C]142190.484044528[/C][C]1823.515955472[/C][/ROW]
[ROW][C]95[/C][C]62535[/C][C]86643.6333038619[/C][C]-24108.6333038619[/C][/ROW]
[ROW][C]96[/C][C]245196[/C][C]251822.022866411[/C][C]-6626.02286641093[/C][/ROW]
[ROW][C]97[/C][C]199841[/C][C]182966.447071939[/C][C]16874.5529280611[/C][/ROW]
[ROW][C]98[/C][C]19349[/C][C]29291.6635235505[/C][C]-9942.66352355046[/C][/ROW]
[ROW][C]99[/C][C]247280[/C][C]205491.70800184[/C][C]41788.2919981596[/C][/ROW]
[ROW][C]100[/C][C]159408[/C][C]211947.477778007[/C][C]-52539.477778007[/C][/ROW]
[ROW][C]101[/C][C]72128[/C][C]63636.3100274446[/C][C]8491.68997255541[/C][/ROW]
[ROW][C]102[/C][C]104253[/C][C]114689.791667568[/C][C]-10436.7916675685[/C][/ROW]
[ROW][C]103[/C][C]151090[/C][C]129325.232098343[/C][C]21764.7679016572[/C][/ROW]
[ROW][C]104[/C][C]137382[/C][C]115813.641564392[/C][C]21568.3584356079[/C][/ROW]
[ROW][C]105[/C][C]87448[/C][C]80105.9927631652[/C][C]7342.00723683477[/C][/ROW]
[ROW][C]106[/C][C]27676[/C][C]41804.5569903817[/C][C]-14128.5569903817[/C][/ROW]
[ROW][C]107[/C][C]165507[/C][C]164434.778302057[/C][C]1072.2216979434[/C][/ROW]
[ROW][C]108[/C][C]132148[/C][C]83197.5874036909[/C][C]48950.4125963091[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]6866.75918863682[/C][C]-6866.75918863682[/C][/ROW]
[ROW][C]110[/C][C]95778[/C][C]85469.873165344[/C][C]10308.126834656[/C][/ROW]
[ROW][C]111[/C][C]109001[/C][C]108241.084811184[/C][C]759.91518881628[/C][/ROW]
[ROW][C]112[/C][C]158833[/C][C]156413.346256966[/C][C]2419.65374303425[/C][/ROW]
[ROW][C]113[/C][C]147690[/C][C]129048.304819658[/C][C]18641.695180342[/C][/ROW]
[ROW][C]114[/C][C]89887[/C][C]103094.607427621[/C][C]-13207.6074276208[/C][/ROW]
[ROW][C]115[/C][C]3616[/C][C]9777.64141606236[/C][C]-6161.64141606236[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]6866.75918863682[/C][C]-6866.75918863682[/C][/ROW]
[ROW][C]117[/C][C]199005[/C][C]157166.018537564[/C][C]41838.9814624359[/C][/ROW]
[ROW][C]118[/C][C]160930[/C][C]179827.836425501[/C][C]-18897.8364255007[/C][/ROW]
[ROW][C]119[/C][C]177948[/C][C]155029.919117124[/C][C]22918.0808828761[/C][/ROW]
[ROW][C]120[/C][C]136061[/C][C]122190.533691179[/C][C]13870.4663088211[/C][/ROW]
[ROW][C]121[/C][C]43410[/C][C]49451.4420944661[/C][C]-6041.44209446615[/C][/ROW]
[ROW][C]122[/C][C]184277[/C][C]193325.113223516[/C][C]-9048.11322351633[/C][/ROW]
[ROW][C]123[/C][C]108858[/C][C]101556.920982511[/C][C]7301.07901748947[/C][/ROW]
[ROW][C]124[/C][C]141744[/C][C]151886.353004813[/C][C]-10142.3530048133[/C][/ROW]
[ROW][C]125[/C][C]60493[/C][C]47540.3772187661[/C][C]12952.6227812339[/C][/ROW]
[ROW][C]126[/C][C]19764[/C][C]27815.4546482567[/C][C]-8051.45464825673[/C][/ROW]
[ROW][C]127[/C][C]177559[/C][C]164546.306836212[/C][C]13012.6931637882[/C][/ROW]
[ROW][C]128[/C][C]140281[/C][C]124234.578012037[/C][C]16046.4219879633[/C][/ROW]
[ROW][C]129[/C][C]164249[/C][C]154366.594953595[/C][C]9882.40504640528[/C][/ROW]
[ROW][C]130[/C][C]11796[/C][C]18466.9038814665[/C][C]-6670.90388146648[/C][/ROW]
[ROW][C]131[/C][C]10674[/C][C]13250.0592737753[/C][C]-2576.05927377535[/C][/ROW]
[ROW][C]132[/C][C]151322[/C][C]138228.986872022[/C][C]13093.0131279784[/C][/ROW]
[ROW][C]133[/C][C]6836[/C][C]8522.37059161269[/C][C]-1686.37059161269[/C][/ROW]
[ROW][C]134[/C][C]174712[/C][C]162155.887400672[/C][C]12556.1125993275[/C][/ROW]
[ROW][C]135[/C][C]5118[/C][C]10021.0629130586[/C][C]-4903.06291305857[/C][/ROW]
[ROW][C]136[/C][C]40248[/C][C]41501.7144621773[/C][C]-1253.71446217728[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]6866.75918863682[/C][C]-6866.75918863682[/C][/ROW]
[ROW][C]138[/C][C]127628[/C][C]118025.774502044[/C][C]9602.22549795626[/C][/ROW]
[ROW][C]139[/C][C]88837[/C][C]112610.919403487[/C][C]-23773.9194034867[/C][/ROW]
[ROW][C]140[/C][C]7131[/C][C]11040.1572879076[/C][C]-3909.15728790756[/C][/ROW]
[ROW][C]141[/C][C]9056[/C][C]20633.5690169529[/C][C]-11577.5690169529[/C][/ROW]
[ROW][C]142[/C][C]87957[/C][C]78741.020255457[/C][C]9215.97974454299[/C][/ROW]
[ROW][C]143[/C][C]144470[/C][C]118776.852752432[/C][C]25693.1472475676[/C][/ROW]
[ROW][C]144[/C][C]111408[/C][C]127117.242170741[/C][C]-15709.2421707411[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160565&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160565&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
1162687153414.6733497149272.32665028621
2201906203527.58714469-1621.58714469024
3721521741.0998261697-14526.0998261697
4146367172719.429520108-26352.429520108
5257045288279.642894942-31234.6428949422
6524450547543.686447532-23093.6864475322
7188294184576.654207063717.34579294004
8195674202220.06924155-6546.06924154976
9177020153359.74185748523660.2581425154
10325899283298.52169898942600.478301011
11121844165849.661290503-44005.6612905031
12203938209309.552311544-5371.55231154416
13107220164104.040972704-56884.0409727037
14220751252750.514897967-31999.5148979666
15172905152403.37654113620501.6234588644
16156326167612.663555658-11286.6635556579
17145178153088.744851147-7910.74485114656
188917187509.39414768331661.60585231674
19172624207793.92064338-35169.9206433799
203244358074.7651877639-25631.7651877639
2187927105995.345453615-18068.3454536148
22241285245496.300028658-4211.30002865821
23195820140604.42943374955215.5705662508
24146946175698.644602937-28752.6446029372
25159763185948.265190567-26185.2651905672
26207078156064.75944618851013.240553812
27212394215294.502139385-2900.50213938522
28201536211609.984140412-10073.9841404119
29394662318157.70342424476504.2965757564
30217808222899.267151508-5091.26715150795
31182286150875.19851549531410.801484505
32181740184198.167003069-2458.16700306902
33137978161947.858053263-23969.8580532634
34255929235051.06502514220877.9349748582
35236489249754.777978765-13265.7779787654
3607145.30796451924-7145.30796451924
37230761216039.72721228714721.2727877129
38130843136418.436090192-5575.43609019249
39157118139750.83262270817367.1673772919
40253254292051.952468566-38797.9524685662
41269329208320.53294408161008.4670559195
42161273170695.722064281-9422.72206428129
43107181115065.554572378-7884.55457237807
44195891203624.62334963-7733.62334963044
45139667125232.12236503114434.8776349687
46171101134926.92384777436174.0761522257
478140795926.3933252715-14519.3933252715
48247563211709.03210349335853.9678965069
49239807223339.62129393316467.3787060666
50172743205334.817996361-32591.817996361
514818861703.3690942123-13515.3690942123
52169355153678.60760183515676.3923981647
53315622249326.83711613166295.1628838693
54241518192026.13680305849491.8631969424
55195583219968.033440635-24385.0334406347
56159913154745.514812165167.48518783961
57220241160064.93915975860176.0608402421
5810169496708.68759451194985.31240548815
59151985166377.395073513-14392.3950735134
60202536185995.89300795516540.1069920453
61173505156866.85077927116638.149220729
62150518190773.530436128-40255.5304361282
63141273125138.50910959416134.4908904058
64125612156015.467650055-30403.4676500551
65166049149274.19999983516774.8000001652
66124197161250.428903358-37053.4289033585
67195043235747.159661294-40704.1596612943
68138708209466.813159808-70758.8131598081
69116552119399.883671771-2847.8836717711
703197037889.6362455568-5919.63624555677
71255587241153.91330360914433.086696391
72151184126141.38997103225042.6100289682
73135926147226.882641229-11300.8826412286
74119629115956.8159732763672.1840267238
75171518200311.087695633-28793.0876956328
76108949116864.468902423-7915.46890242336
77183471202397.023416563-18926.0234165633
78159966247366.644355421-87400.6443554211
799378693543.4887979472242.511202052773
8084971102234.809494887-17263.8094948869
818879796549.5611554975-7752.56115549747
82304603215800.45143425488802.5485657463
837510183629.3098992234-8528.30989922345
84145043138490.4034289676552.59657103289
859582798842.5479242932-3015.5479242932
86173924149776.18504628924147.814953711
87241957240490.3472255981466.6527744017
88115367123485.969855292-8118.9698552919
89118408117476.098021235931.901978764618
90164078171114.723769773-7036.72376977258
91158931168788.17373072-9857.17373071967
92184139193620.856500559-9481.85650055859
93152856160376.913341337-7520.91334133707
94144014142190.4840445281823.515955472
956253586643.6333038619-24108.6333038619
96245196251822.022866411-6626.02286641093
97199841182966.44707193916874.5529280611
981934929291.6635235505-9942.66352355046
99247280205491.7080018441788.2919981596
100159408211947.477778007-52539.477778007
1017212863636.31002744468491.68997255541
102104253114689.791667568-10436.7916675685
103151090129325.23209834321764.7679016572
104137382115813.64156439221568.3584356079
1058744880105.99276316527342.00723683477
1062767641804.5569903817-14128.5569903817
107165507164434.7783020571072.2216979434
10813214883197.587403690948950.4125963091
10906866.75918863682-6866.75918863682
1109577885469.87316534410308.126834656
111109001108241.084811184759.91518881628
112158833156413.3462569662419.65374303425
113147690129048.30481965818641.695180342
11489887103094.607427621-13207.6074276208
11536169777.64141606236-6161.64141606236
11606866.75918863682-6866.75918863682
117199005157166.01853756441838.9814624359
118160930179827.836425501-18897.8364255007
119177948155029.91911712422918.0808828761
120136061122190.53369117913870.4663088211
1214341049451.4420944661-6041.44209446615
122184277193325.113223516-9048.11322351633
123108858101556.9209825117301.07901748947
124141744151886.353004813-10142.3530048133
1256049347540.377218766112952.6227812339
1261976427815.4546482567-8051.45464825673
127177559164546.30683621213012.6931637882
128140281124234.57801203716046.4219879633
129164249154366.5949535959882.40504640528
1301179618466.9038814665-6670.90388146648
1311067413250.0592737753-2576.05927377535
132151322138228.98687202213093.0131279784
13368368522.37059161269-1686.37059161269
134174712162155.88740067212556.1125993275
135511810021.0629130586-4903.06291305857
1364024841501.7144621773-1253.71446217728
13706866.75918863682-6866.75918863682
138127628118025.7745020449602.22549795626
13988837112610.919403487-23773.9194034867
140713111040.1572879076-3909.15728790756
141905620633.5690169529-11577.5690169529
1428795778741.0202554579215.97974454299
143144470118776.85275243225693.1472475676
144111408127117.242170741-15709.2421707411






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.7671284625520330.4657430748959340.232871537447967
190.6756416562557190.6487166874885610.324358343744281
200.5877557545119010.8244884909761970.412244245488099
210.4601742164889880.9203484329779760.539825783511012
220.426077385838740.852154771677480.57392261416126
230.6909345848316770.6181308303366470.309065415168323
240.6193604239423850.761279152115230.380639576057615
250.5968377789984380.8063244420031240.403162221001562
260.5638543422850410.8722913154299170.436145657714959
270.645987555209310.708024889581380.35401244479069
280.5661747205867440.8676505588265130.433825279413256
290.8329836996516840.3340326006966310.167016300348316
300.7870005416791440.4259989166417120.212999458320856
310.7697256107419450.460548778516110.230274389258055
320.7269390137232420.5461219725535170.273060986276758
330.6896015937282990.6207968125434030.310398406271701
340.6290611840471890.7418776319056220.370938815952811
350.5876116623196860.8247766753606280.412388337680314
360.5206490278014650.958701944397070.479350972198535
370.5465670436428090.9068659127143820.453432956357191
380.5106433917606220.9787132164787560.489356608239378
390.4529321150737240.9058642301474490.547067884926276
400.6996078658412750.600784268317450.300392134158725
410.8807969260234710.2384061479530580.119203073976529
420.8552835694578460.2894328610843080.144716430542154
430.8342212644949270.3315574710101460.165778735505073
440.8215154637524880.3569690724950240.178484536247512
450.7837712271163050.4324575457673910.216228772883695
460.8742773245577460.2514453508845080.125722675442254
470.8492421227438730.3015157545122540.150757877256127
480.8743841562527530.2512316874944950.125615843747247
490.8591362736510130.2817274526979750.140863726348987
500.8736607410568420.2526785178863160.126339258943158
510.8557482894810250.2885034210379510.144251710518975
520.8280742438363210.3438515123273570.171925756163679
530.9451856945404090.1096286109191810.0548143054595905
540.9712153537463710.0575692925072590.0287846462536295
550.9698733762827750.06025324743445020.0301266237172251
560.9601879060573270.07962418788534680.0398120939426734
570.9877243133481090.02455137330378130.0122756866518907
580.982696307887360.03460738422527920.0173036921126396
590.9773629163290820.04527416734183610.0226370836709181
600.9710956854325010.05780862913499780.0289043145674989
610.9632319389695810.07353612206083830.0367680610304191
620.9735502596938540.05289948061229120.0264497403061456
630.9654398096956460.06912038060870820.0345601903043541
640.9683102567225350.06337948655492910.0316897432774645
650.9617602514919860.07647949701602810.0382397485080141
660.9692828673620550.06143426527588950.0307171326379448
670.9815424363242290.03691512735154190.018457563675771
680.9980116623710910.003976675257818960.00198833762890948
690.9971021803340350.005795639331930670.00289781966596533
700.9957414787390740.008517042521851650.00425852126092582
710.9938978787058350.01220424258833080.00610212129416541
720.9943501124917090.01129977501658270.00564988750829134
730.9927915831074890.01441683378502240.00720841689251121
740.9901149952217470.01977000955650550.00988500477825275
750.9932886373681960.01342272526360760.00671136263180379
760.9929050908384320.01418981832313540.0070949091615677
770.9935464687985610.01290706240287780.00645353120143888
780.9999477942855890.0001044114288214095.22057144107044e-05
790.9999085160540420.0001829678919155819.14839459577907e-05
800.9998562248285760.0002875503428484590.000143775171424229
810.9997602439163220.0004795121673563960.000239756083678198
820.9999998537464482.92507105042746e-071.46253552521373e-07
830.9999997193512785.61297444440568e-072.80648722220284e-07
840.9999994965691381.00686172497346e-065.03430862486728e-07
850.999999024433831.95113234003717e-069.75566170018583e-07
860.999999327223321.34555336073274e-066.72776680366372e-07
870.9999993371891081.32562178421614e-066.62810892108072e-07
880.9999990394055011.92118899840454e-069.6059449920227e-07
890.9999987513358422.49732831636916e-061.24866415818458e-06
900.9999978326204624.33475907679781e-062.16737953839891e-06
910.9999955765358458.84692831025149e-064.42346415512574e-06
920.9999963023057657.3953884706478e-063.6976942353239e-06
930.9999923644959351.52710081295193e-057.63550406475966e-06
940.9999845447574093.09104851824942e-051.54552425912471e-05
950.9999918303710941.63392578128138e-058.1696289064069e-06
960.9999967301332866.53973342784585e-063.26986671392293e-06
970.9999941848625761.16302748479226e-055.8151374239613e-06
980.9999881211688142.37576623718278e-051.18788311859139e-05
990.9999841393688813.1721262238902e-051.5860631119451e-05
1000.9999995815547798.36890442696768e-074.18445221348384e-07
1010.9999997863641564.27271687975552e-072.13635843987776e-07
1020.9999994820479681.0359040636136e-065.17952031806802e-07
1030.9999996116036017.76792798981277e-073.88396399490639e-07
1040.9999991002042571.79959148558328e-068.99795742791639e-07
1050.9999989532080862.09358382747622e-061.04679191373811e-06
1060.9999973307382285.33852354310099e-062.66926177155049e-06
1070.9999987722029142.45559417187512e-061.22779708593756e-06
1080.999999040840991.91831801932807e-069.59159009664033e-07
1090.9999972533398215.49332035780253e-062.74666017890127e-06
1100.9999923300504461.53398991071728e-057.66994955358642e-06
1110.9999949573876611.00852246774e-055.04261233870002e-06
1120.9999972887631755.42247364946689e-062.71123682473344e-06
1130.9999918208439161.63583121688525e-058.17915608442624e-06
1140.9999758425462524.83149074960414e-052.41574537480207e-05
1150.9999297708037760.0001404583924478687.02291962239339e-05
1160.9998026655094880.0003946689810238740.000197334490511937
1170.9999973708411625.25831767539121e-062.6291588376956e-06
1180.9999995118150769.76369847858089e-074.88184923929045e-07
1190.999998689779142.62044172004744e-061.31022086002372e-06
1200.9999929305003611.41389992782865e-057.06949963914326e-06
1210.9999745115650935.09768698136447e-052.54884349068223e-05
1220.999867435165560.0002651296688793530.000132564834439676
1230.9999576346639918.47306720176855e-054.23653360088428e-05
1240.9997876255855210.0004247488289570950.000212374414478547
1250.9986231235407830.002753752918434630.00137687645921732
1260.9966510347839250.006697930432150440.00334896521607522

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.767128462552033 & 0.465743074895934 & 0.232871537447967 \tabularnewline
19 & 0.675641656255719 & 0.648716687488561 & 0.324358343744281 \tabularnewline
20 & 0.587755754511901 & 0.824488490976197 & 0.412244245488099 \tabularnewline
21 & 0.460174216488988 & 0.920348432977976 & 0.539825783511012 \tabularnewline
22 & 0.42607738583874 & 0.85215477167748 & 0.57392261416126 \tabularnewline
23 & 0.690934584831677 & 0.618130830336647 & 0.309065415168323 \tabularnewline
24 & 0.619360423942385 & 0.76127915211523 & 0.380639576057615 \tabularnewline
25 & 0.596837778998438 & 0.806324442003124 & 0.403162221001562 \tabularnewline
26 & 0.563854342285041 & 0.872291315429917 & 0.436145657714959 \tabularnewline
27 & 0.64598755520931 & 0.70802488958138 & 0.35401244479069 \tabularnewline
28 & 0.566174720586744 & 0.867650558826513 & 0.433825279413256 \tabularnewline
29 & 0.832983699651684 & 0.334032600696631 & 0.167016300348316 \tabularnewline
30 & 0.787000541679144 & 0.425998916641712 & 0.212999458320856 \tabularnewline
31 & 0.769725610741945 & 0.46054877851611 & 0.230274389258055 \tabularnewline
32 & 0.726939013723242 & 0.546121972553517 & 0.273060986276758 \tabularnewline
33 & 0.689601593728299 & 0.620796812543403 & 0.310398406271701 \tabularnewline
34 & 0.629061184047189 & 0.741877631905622 & 0.370938815952811 \tabularnewline
35 & 0.587611662319686 & 0.824776675360628 & 0.412388337680314 \tabularnewline
36 & 0.520649027801465 & 0.95870194439707 & 0.479350972198535 \tabularnewline
37 & 0.546567043642809 & 0.906865912714382 & 0.453432956357191 \tabularnewline
38 & 0.510643391760622 & 0.978713216478756 & 0.489356608239378 \tabularnewline
39 & 0.452932115073724 & 0.905864230147449 & 0.547067884926276 \tabularnewline
40 & 0.699607865841275 & 0.60078426831745 & 0.300392134158725 \tabularnewline
41 & 0.880796926023471 & 0.238406147953058 & 0.119203073976529 \tabularnewline
42 & 0.855283569457846 & 0.289432861084308 & 0.144716430542154 \tabularnewline
43 & 0.834221264494927 & 0.331557471010146 & 0.165778735505073 \tabularnewline
44 & 0.821515463752488 & 0.356969072495024 & 0.178484536247512 \tabularnewline
45 & 0.783771227116305 & 0.432457545767391 & 0.216228772883695 \tabularnewline
46 & 0.874277324557746 & 0.251445350884508 & 0.125722675442254 \tabularnewline
47 & 0.849242122743873 & 0.301515754512254 & 0.150757877256127 \tabularnewline
48 & 0.874384156252753 & 0.251231687494495 & 0.125615843747247 \tabularnewline
49 & 0.859136273651013 & 0.281727452697975 & 0.140863726348987 \tabularnewline
50 & 0.873660741056842 & 0.252678517886316 & 0.126339258943158 \tabularnewline
51 & 0.855748289481025 & 0.288503421037951 & 0.144251710518975 \tabularnewline
52 & 0.828074243836321 & 0.343851512327357 & 0.171925756163679 \tabularnewline
53 & 0.945185694540409 & 0.109628610919181 & 0.0548143054595905 \tabularnewline
54 & 0.971215353746371 & 0.057569292507259 & 0.0287846462536295 \tabularnewline
55 & 0.969873376282775 & 0.0602532474344502 & 0.0301266237172251 \tabularnewline
56 & 0.960187906057327 & 0.0796241878853468 & 0.0398120939426734 \tabularnewline
57 & 0.987724313348109 & 0.0245513733037813 & 0.0122756866518907 \tabularnewline
58 & 0.98269630788736 & 0.0346073842252792 & 0.0173036921126396 \tabularnewline
59 & 0.977362916329082 & 0.0452741673418361 & 0.0226370836709181 \tabularnewline
60 & 0.971095685432501 & 0.0578086291349978 & 0.0289043145674989 \tabularnewline
61 & 0.963231938969581 & 0.0735361220608383 & 0.0367680610304191 \tabularnewline
62 & 0.973550259693854 & 0.0528994806122912 & 0.0264497403061456 \tabularnewline
63 & 0.965439809695646 & 0.0691203806087082 & 0.0345601903043541 \tabularnewline
64 & 0.968310256722535 & 0.0633794865549291 & 0.0316897432774645 \tabularnewline
65 & 0.961760251491986 & 0.0764794970160281 & 0.0382397485080141 \tabularnewline
66 & 0.969282867362055 & 0.0614342652758895 & 0.0307171326379448 \tabularnewline
67 & 0.981542436324229 & 0.0369151273515419 & 0.018457563675771 \tabularnewline
68 & 0.998011662371091 & 0.00397667525781896 & 0.00198833762890948 \tabularnewline
69 & 0.997102180334035 & 0.00579563933193067 & 0.00289781966596533 \tabularnewline
70 & 0.995741478739074 & 0.00851704252185165 & 0.00425852126092582 \tabularnewline
71 & 0.993897878705835 & 0.0122042425883308 & 0.00610212129416541 \tabularnewline
72 & 0.994350112491709 & 0.0112997750165827 & 0.00564988750829134 \tabularnewline
73 & 0.992791583107489 & 0.0144168337850224 & 0.00720841689251121 \tabularnewline
74 & 0.990114995221747 & 0.0197700095565055 & 0.00988500477825275 \tabularnewline
75 & 0.993288637368196 & 0.0134227252636076 & 0.00671136263180379 \tabularnewline
76 & 0.992905090838432 & 0.0141898183231354 & 0.0070949091615677 \tabularnewline
77 & 0.993546468798561 & 0.0129070624028778 & 0.00645353120143888 \tabularnewline
78 & 0.999947794285589 & 0.000104411428821409 & 5.22057144107044e-05 \tabularnewline
79 & 0.999908516054042 & 0.000182967891915581 & 9.14839459577907e-05 \tabularnewline
80 & 0.999856224828576 & 0.000287550342848459 & 0.000143775171424229 \tabularnewline
81 & 0.999760243916322 & 0.000479512167356396 & 0.000239756083678198 \tabularnewline
82 & 0.999999853746448 & 2.92507105042746e-07 & 1.46253552521373e-07 \tabularnewline
83 & 0.999999719351278 & 5.61297444440568e-07 & 2.80648722220284e-07 \tabularnewline
84 & 0.999999496569138 & 1.00686172497346e-06 & 5.03430862486728e-07 \tabularnewline
85 & 0.99999902443383 & 1.95113234003717e-06 & 9.75566170018583e-07 \tabularnewline
86 & 0.99999932722332 & 1.34555336073274e-06 & 6.72776680366372e-07 \tabularnewline
87 & 0.999999337189108 & 1.32562178421614e-06 & 6.62810892108072e-07 \tabularnewline
88 & 0.999999039405501 & 1.92118899840454e-06 & 9.6059449920227e-07 \tabularnewline
89 & 0.999998751335842 & 2.49732831636916e-06 & 1.24866415818458e-06 \tabularnewline
90 & 0.999997832620462 & 4.33475907679781e-06 & 2.16737953839891e-06 \tabularnewline
91 & 0.999995576535845 & 8.84692831025149e-06 & 4.42346415512574e-06 \tabularnewline
92 & 0.999996302305765 & 7.3953884706478e-06 & 3.6976942353239e-06 \tabularnewline
93 & 0.999992364495935 & 1.52710081295193e-05 & 7.63550406475966e-06 \tabularnewline
94 & 0.999984544757409 & 3.09104851824942e-05 & 1.54552425912471e-05 \tabularnewline
95 & 0.999991830371094 & 1.63392578128138e-05 & 8.1696289064069e-06 \tabularnewline
96 & 0.999996730133286 & 6.53973342784585e-06 & 3.26986671392293e-06 \tabularnewline
97 & 0.999994184862576 & 1.16302748479226e-05 & 5.8151374239613e-06 \tabularnewline
98 & 0.999988121168814 & 2.37576623718278e-05 & 1.18788311859139e-05 \tabularnewline
99 & 0.999984139368881 & 3.1721262238902e-05 & 1.5860631119451e-05 \tabularnewline
100 & 0.999999581554779 & 8.36890442696768e-07 & 4.18445221348384e-07 \tabularnewline
101 & 0.999999786364156 & 4.27271687975552e-07 & 2.13635843987776e-07 \tabularnewline
102 & 0.999999482047968 & 1.0359040636136e-06 & 5.17952031806802e-07 \tabularnewline
103 & 0.999999611603601 & 7.76792798981277e-07 & 3.88396399490639e-07 \tabularnewline
104 & 0.999999100204257 & 1.79959148558328e-06 & 8.99795742791639e-07 \tabularnewline
105 & 0.999998953208086 & 2.09358382747622e-06 & 1.04679191373811e-06 \tabularnewline
106 & 0.999997330738228 & 5.33852354310099e-06 & 2.66926177155049e-06 \tabularnewline
107 & 0.999998772202914 & 2.45559417187512e-06 & 1.22779708593756e-06 \tabularnewline
108 & 0.99999904084099 & 1.91831801932807e-06 & 9.59159009664033e-07 \tabularnewline
109 & 0.999997253339821 & 5.49332035780253e-06 & 2.74666017890127e-06 \tabularnewline
110 & 0.999992330050446 & 1.53398991071728e-05 & 7.66994955358642e-06 \tabularnewline
111 & 0.999994957387661 & 1.00852246774e-05 & 5.04261233870002e-06 \tabularnewline
112 & 0.999997288763175 & 5.42247364946689e-06 & 2.71123682473344e-06 \tabularnewline
113 & 0.999991820843916 & 1.63583121688525e-05 & 8.17915608442624e-06 \tabularnewline
114 & 0.999975842546252 & 4.83149074960414e-05 & 2.41574537480207e-05 \tabularnewline
115 & 0.999929770803776 & 0.000140458392447868 & 7.02291962239339e-05 \tabularnewline
116 & 0.999802665509488 & 0.000394668981023874 & 0.000197334490511937 \tabularnewline
117 & 0.999997370841162 & 5.25831767539121e-06 & 2.6291588376956e-06 \tabularnewline
118 & 0.999999511815076 & 9.76369847858089e-07 & 4.88184923929045e-07 \tabularnewline
119 & 0.99999868977914 & 2.62044172004744e-06 & 1.31022086002372e-06 \tabularnewline
120 & 0.999992930500361 & 1.41389992782865e-05 & 7.06949963914326e-06 \tabularnewline
121 & 0.999974511565093 & 5.09768698136447e-05 & 2.54884349068223e-05 \tabularnewline
122 & 0.99986743516556 & 0.000265129668879353 & 0.000132564834439676 \tabularnewline
123 & 0.999957634663991 & 8.47306720176855e-05 & 4.23653360088428e-05 \tabularnewline
124 & 0.999787625585521 & 0.000424748828957095 & 0.000212374414478547 \tabularnewline
125 & 0.998623123540783 & 0.00275375291843463 & 0.00137687645921732 \tabularnewline
126 & 0.996651034783925 & 0.00669793043215044 & 0.00334896521607522 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160565&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]0.767128462552033[/C][C]0.465743074895934[/C][C]0.232871537447967[/C][/ROW]
[ROW][C]19[/C][C]0.675641656255719[/C][C]0.648716687488561[/C][C]0.324358343744281[/C][/ROW]
[ROW][C]20[/C][C]0.587755754511901[/C][C]0.824488490976197[/C][C]0.412244245488099[/C][/ROW]
[ROW][C]21[/C][C]0.460174216488988[/C][C]0.920348432977976[/C][C]0.539825783511012[/C][/ROW]
[ROW][C]22[/C][C]0.42607738583874[/C][C]0.85215477167748[/C][C]0.57392261416126[/C][/ROW]
[ROW][C]23[/C][C]0.690934584831677[/C][C]0.618130830336647[/C][C]0.309065415168323[/C][/ROW]
[ROW][C]24[/C][C]0.619360423942385[/C][C]0.76127915211523[/C][C]0.380639576057615[/C][/ROW]
[ROW][C]25[/C][C]0.596837778998438[/C][C]0.806324442003124[/C][C]0.403162221001562[/C][/ROW]
[ROW][C]26[/C][C]0.563854342285041[/C][C]0.872291315429917[/C][C]0.436145657714959[/C][/ROW]
[ROW][C]27[/C][C]0.64598755520931[/C][C]0.70802488958138[/C][C]0.35401244479069[/C][/ROW]
[ROW][C]28[/C][C]0.566174720586744[/C][C]0.867650558826513[/C][C]0.433825279413256[/C][/ROW]
[ROW][C]29[/C][C]0.832983699651684[/C][C]0.334032600696631[/C][C]0.167016300348316[/C][/ROW]
[ROW][C]30[/C][C]0.787000541679144[/C][C]0.425998916641712[/C][C]0.212999458320856[/C][/ROW]
[ROW][C]31[/C][C]0.769725610741945[/C][C]0.46054877851611[/C][C]0.230274389258055[/C][/ROW]
[ROW][C]32[/C][C]0.726939013723242[/C][C]0.546121972553517[/C][C]0.273060986276758[/C][/ROW]
[ROW][C]33[/C][C]0.689601593728299[/C][C]0.620796812543403[/C][C]0.310398406271701[/C][/ROW]
[ROW][C]34[/C][C]0.629061184047189[/C][C]0.741877631905622[/C][C]0.370938815952811[/C][/ROW]
[ROW][C]35[/C][C]0.587611662319686[/C][C]0.824776675360628[/C][C]0.412388337680314[/C][/ROW]
[ROW][C]36[/C][C]0.520649027801465[/C][C]0.95870194439707[/C][C]0.479350972198535[/C][/ROW]
[ROW][C]37[/C][C]0.546567043642809[/C][C]0.906865912714382[/C][C]0.453432956357191[/C][/ROW]
[ROW][C]38[/C][C]0.510643391760622[/C][C]0.978713216478756[/C][C]0.489356608239378[/C][/ROW]
[ROW][C]39[/C][C]0.452932115073724[/C][C]0.905864230147449[/C][C]0.547067884926276[/C][/ROW]
[ROW][C]40[/C][C]0.699607865841275[/C][C]0.60078426831745[/C][C]0.300392134158725[/C][/ROW]
[ROW][C]41[/C][C]0.880796926023471[/C][C]0.238406147953058[/C][C]0.119203073976529[/C][/ROW]
[ROW][C]42[/C][C]0.855283569457846[/C][C]0.289432861084308[/C][C]0.144716430542154[/C][/ROW]
[ROW][C]43[/C][C]0.834221264494927[/C][C]0.331557471010146[/C][C]0.165778735505073[/C][/ROW]
[ROW][C]44[/C][C]0.821515463752488[/C][C]0.356969072495024[/C][C]0.178484536247512[/C][/ROW]
[ROW][C]45[/C][C]0.783771227116305[/C][C]0.432457545767391[/C][C]0.216228772883695[/C][/ROW]
[ROW][C]46[/C][C]0.874277324557746[/C][C]0.251445350884508[/C][C]0.125722675442254[/C][/ROW]
[ROW][C]47[/C][C]0.849242122743873[/C][C]0.301515754512254[/C][C]0.150757877256127[/C][/ROW]
[ROW][C]48[/C][C]0.874384156252753[/C][C]0.251231687494495[/C][C]0.125615843747247[/C][/ROW]
[ROW][C]49[/C][C]0.859136273651013[/C][C]0.281727452697975[/C][C]0.140863726348987[/C][/ROW]
[ROW][C]50[/C][C]0.873660741056842[/C][C]0.252678517886316[/C][C]0.126339258943158[/C][/ROW]
[ROW][C]51[/C][C]0.855748289481025[/C][C]0.288503421037951[/C][C]0.144251710518975[/C][/ROW]
[ROW][C]52[/C][C]0.828074243836321[/C][C]0.343851512327357[/C][C]0.171925756163679[/C][/ROW]
[ROW][C]53[/C][C]0.945185694540409[/C][C]0.109628610919181[/C][C]0.0548143054595905[/C][/ROW]
[ROW][C]54[/C][C]0.971215353746371[/C][C]0.057569292507259[/C][C]0.0287846462536295[/C][/ROW]
[ROW][C]55[/C][C]0.969873376282775[/C][C]0.0602532474344502[/C][C]0.0301266237172251[/C][/ROW]
[ROW][C]56[/C][C]0.960187906057327[/C][C]0.0796241878853468[/C][C]0.0398120939426734[/C][/ROW]
[ROW][C]57[/C][C]0.987724313348109[/C][C]0.0245513733037813[/C][C]0.0122756866518907[/C][/ROW]
[ROW][C]58[/C][C]0.98269630788736[/C][C]0.0346073842252792[/C][C]0.0173036921126396[/C][/ROW]
[ROW][C]59[/C][C]0.977362916329082[/C][C]0.0452741673418361[/C][C]0.0226370836709181[/C][/ROW]
[ROW][C]60[/C][C]0.971095685432501[/C][C]0.0578086291349978[/C][C]0.0289043145674989[/C][/ROW]
[ROW][C]61[/C][C]0.963231938969581[/C][C]0.0735361220608383[/C][C]0.0367680610304191[/C][/ROW]
[ROW][C]62[/C][C]0.973550259693854[/C][C]0.0528994806122912[/C][C]0.0264497403061456[/C][/ROW]
[ROW][C]63[/C][C]0.965439809695646[/C][C]0.0691203806087082[/C][C]0.0345601903043541[/C][/ROW]
[ROW][C]64[/C][C]0.968310256722535[/C][C]0.0633794865549291[/C][C]0.0316897432774645[/C][/ROW]
[ROW][C]65[/C][C]0.961760251491986[/C][C]0.0764794970160281[/C][C]0.0382397485080141[/C][/ROW]
[ROW][C]66[/C][C]0.969282867362055[/C][C]0.0614342652758895[/C][C]0.0307171326379448[/C][/ROW]
[ROW][C]67[/C][C]0.981542436324229[/C][C]0.0369151273515419[/C][C]0.018457563675771[/C][/ROW]
[ROW][C]68[/C][C]0.998011662371091[/C][C]0.00397667525781896[/C][C]0.00198833762890948[/C][/ROW]
[ROW][C]69[/C][C]0.997102180334035[/C][C]0.00579563933193067[/C][C]0.00289781966596533[/C][/ROW]
[ROW][C]70[/C][C]0.995741478739074[/C][C]0.00851704252185165[/C][C]0.00425852126092582[/C][/ROW]
[ROW][C]71[/C][C]0.993897878705835[/C][C]0.0122042425883308[/C][C]0.00610212129416541[/C][/ROW]
[ROW][C]72[/C][C]0.994350112491709[/C][C]0.0112997750165827[/C][C]0.00564988750829134[/C][/ROW]
[ROW][C]73[/C][C]0.992791583107489[/C][C]0.0144168337850224[/C][C]0.00720841689251121[/C][/ROW]
[ROW][C]74[/C][C]0.990114995221747[/C][C]0.0197700095565055[/C][C]0.00988500477825275[/C][/ROW]
[ROW][C]75[/C][C]0.993288637368196[/C][C]0.0134227252636076[/C][C]0.00671136263180379[/C][/ROW]
[ROW][C]76[/C][C]0.992905090838432[/C][C]0.0141898183231354[/C][C]0.0070949091615677[/C][/ROW]
[ROW][C]77[/C][C]0.993546468798561[/C][C]0.0129070624028778[/C][C]0.00645353120143888[/C][/ROW]
[ROW][C]78[/C][C]0.999947794285589[/C][C]0.000104411428821409[/C][C]5.22057144107044e-05[/C][/ROW]
[ROW][C]79[/C][C]0.999908516054042[/C][C]0.000182967891915581[/C][C]9.14839459577907e-05[/C][/ROW]
[ROW][C]80[/C][C]0.999856224828576[/C][C]0.000287550342848459[/C][C]0.000143775171424229[/C][/ROW]
[ROW][C]81[/C][C]0.999760243916322[/C][C]0.000479512167356396[/C][C]0.000239756083678198[/C][/ROW]
[ROW][C]82[/C][C]0.999999853746448[/C][C]2.92507105042746e-07[/C][C]1.46253552521373e-07[/C][/ROW]
[ROW][C]83[/C][C]0.999999719351278[/C][C]5.61297444440568e-07[/C][C]2.80648722220284e-07[/C][/ROW]
[ROW][C]84[/C][C]0.999999496569138[/C][C]1.00686172497346e-06[/C][C]5.03430862486728e-07[/C][/ROW]
[ROW][C]85[/C][C]0.99999902443383[/C][C]1.95113234003717e-06[/C][C]9.75566170018583e-07[/C][/ROW]
[ROW][C]86[/C][C]0.99999932722332[/C][C]1.34555336073274e-06[/C][C]6.72776680366372e-07[/C][/ROW]
[ROW][C]87[/C][C]0.999999337189108[/C][C]1.32562178421614e-06[/C][C]6.62810892108072e-07[/C][/ROW]
[ROW][C]88[/C][C]0.999999039405501[/C][C]1.92118899840454e-06[/C][C]9.6059449920227e-07[/C][/ROW]
[ROW][C]89[/C][C]0.999998751335842[/C][C]2.49732831636916e-06[/C][C]1.24866415818458e-06[/C][/ROW]
[ROW][C]90[/C][C]0.999997832620462[/C][C]4.33475907679781e-06[/C][C]2.16737953839891e-06[/C][/ROW]
[ROW][C]91[/C][C]0.999995576535845[/C][C]8.84692831025149e-06[/C][C]4.42346415512574e-06[/C][/ROW]
[ROW][C]92[/C][C]0.999996302305765[/C][C]7.3953884706478e-06[/C][C]3.6976942353239e-06[/C][/ROW]
[ROW][C]93[/C][C]0.999992364495935[/C][C]1.52710081295193e-05[/C][C]7.63550406475966e-06[/C][/ROW]
[ROW][C]94[/C][C]0.999984544757409[/C][C]3.09104851824942e-05[/C][C]1.54552425912471e-05[/C][/ROW]
[ROW][C]95[/C][C]0.999991830371094[/C][C]1.63392578128138e-05[/C][C]8.1696289064069e-06[/C][/ROW]
[ROW][C]96[/C][C]0.999996730133286[/C][C]6.53973342784585e-06[/C][C]3.26986671392293e-06[/C][/ROW]
[ROW][C]97[/C][C]0.999994184862576[/C][C]1.16302748479226e-05[/C][C]5.8151374239613e-06[/C][/ROW]
[ROW][C]98[/C][C]0.999988121168814[/C][C]2.37576623718278e-05[/C][C]1.18788311859139e-05[/C][/ROW]
[ROW][C]99[/C][C]0.999984139368881[/C][C]3.1721262238902e-05[/C][C]1.5860631119451e-05[/C][/ROW]
[ROW][C]100[/C][C]0.999999581554779[/C][C]8.36890442696768e-07[/C][C]4.18445221348384e-07[/C][/ROW]
[ROW][C]101[/C][C]0.999999786364156[/C][C]4.27271687975552e-07[/C][C]2.13635843987776e-07[/C][/ROW]
[ROW][C]102[/C][C]0.999999482047968[/C][C]1.0359040636136e-06[/C][C]5.17952031806802e-07[/C][/ROW]
[ROW][C]103[/C][C]0.999999611603601[/C][C]7.76792798981277e-07[/C][C]3.88396399490639e-07[/C][/ROW]
[ROW][C]104[/C][C]0.999999100204257[/C][C]1.79959148558328e-06[/C][C]8.99795742791639e-07[/C][/ROW]
[ROW][C]105[/C][C]0.999998953208086[/C][C]2.09358382747622e-06[/C][C]1.04679191373811e-06[/C][/ROW]
[ROW][C]106[/C][C]0.999997330738228[/C][C]5.33852354310099e-06[/C][C]2.66926177155049e-06[/C][/ROW]
[ROW][C]107[/C][C]0.999998772202914[/C][C]2.45559417187512e-06[/C][C]1.22779708593756e-06[/C][/ROW]
[ROW][C]108[/C][C]0.99999904084099[/C][C]1.91831801932807e-06[/C][C]9.59159009664033e-07[/C][/ROW]
[ROW][C]109[/C][C]0.999997253339821[/C][C]5.49332035780253e-06[/C][C]2.74666017890127e-06[/C][/ROW]
[ROW][C]110[/C][C]0.999992330050446[/C][C]1.53398991071728e-05[/C][C]7.66994955358642e-06[/C][/ROW]
[ROW][C]111[/C][C]0.999994957387661[/C][C]1.00852246774e-05[/C][C]5.04261233870002e-06[/C][/ROW]
[ROW][C]112[/C][C]0.999997288763175[/C][C]5.42247364946689e-06[/C][C]2.71123682473344e-06[/C][/ROW]
[ROW][C]113[/C][C]0.999991820843916[/C][C]1.63583121688525e-05[/C][C]8.17915608442624e-06[/C][/ROW]
[ROW][C]114[/C][C]0.999975842546252[/C][C]4.83149074960414e-05[/C][C]2.41574537480207e-05[/C][/ROW]
[ROW][C]115[/C][C]0.999929770803776[/C][C]0.000140458392447868[/C][C]7.02291962239339e-05[/C][/ROW]
[ROW][C]116[/C][C]0.999802665509488[/C][C]0.000394668981023874[/C][C]0.000197334490511937[/C][/ROW]
[ROW][C]117[/C][C]0.999997370841162[/C][C]5.25831767539121e-06[/C][C]2.6291588376956e-06[/C][/ROW]
[ROW][C]118[/C][C]0.999999511815076[/C][C]9.76369847858089e-07[/C][C]4.88184923929045e-07[/C][/ROW]
[ROW][C]119[/C][C]0.99999868977914[/C][C]2.62044172004744e-06[/C][C]1.31022086002372e-06[/C][/ROW]
[ROW][C]120[/C][C]0.999992930500361[/C][C]1.41389992782865e-05[/C][C]7.06949963914326e-06[/C][/ROW]
[ROW][C]121[/C][C]0.999974511565093[/C][C]5.09768698136447e-05[/C][C]2.54884349068223e-05[/C][/ROW]
[ROW][C]122[/C][C]0.99986743516556[/C][C]0.000265129668879353[/C][C]0.000132564834439676[/C][/ROW]
[ROW][C]123[/C][C]0.999957634663991[/C][C]8.47306720176855e-05[/C][C]4.23653360088428e-05[/C][/ROW]
[ROW][C]124[/C][C]0.999787625585521[/C][C]0.000424748828957095[/C][C]0.000212374414478547[/C][/ROW]
[ROW][C]125[/C][C]0.998623123540783[/C][C]0.00275375291843463[/C][C]0.00137687645921732[/C][/ROW]
[ROW][C]126[/C][C]0.996651034783925[/C][C]0.00669793043215044[/C][C]0.00334896521607522[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160565&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160565&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
180.7671284625520330.4657430748959340.232871537447967
190.6756416562557190.6487166874885610.324358343744281
200.5877557545119010.8244884909761970.412244245488099
210.4601742164889880.9203484329779760.539825783511012
220.426077385838740.852154771677480.57392261416126
230.6909345848316770.6181308303366470.309065415168323
240.6193604239423850.761279152115230.380639576057615
250.5968377789984380.8063244420031240.403162221001562
260.5638543422850410.8722913154299170.436145657714959
270.645987555209310.708024889581380.35401244479069
280.5661747205867440.8676505588265130.433825279413256
290.8329836996516840.3340326006966310.167016300348316
300.7870005416791440.4259989166417120.212999458320856
310.7697256107419450.460548778516110.230274389258055
320.7269390137232420.5461219725535170.273060986276758
330.6896015937282990.6207968125434030.310398406271701
340.6290611840471890.7418776319056220.370938815952811
350.5876116623196860.8247766753606280.412388337680314
360.5206490278014650.958701944397070.479350972198535
370.5465670436428090.9068659127143820.453432956357191
380.5106433917606220.9787132164787560.489356608239378
390.4529321150737240.9058642301474490.547067884926276
400.6996078658412750.600784268317450.300392134158725
410.8807969260234710.2384061479530580.119203073976529
420.8552835694578460.2894328610843080.144716430542154
430.8342212644949270.3315574710101460.165778735505073
440.8215154637524880.3569690724950240.178484536247512
450.7837712271163050.4324575457673910.216228772883695
460.8742773245577460.2514453508845080.125722675442254
470.8492421227438730.3015157545122540.150757877256127
480.8743841562527530.2512316874944950.125615843747247
490.8591362736510130.2817274526979750.140863726348987
500.8736607410568420.2526785178863160.126339258943158
510.8557482894810250.2885034210379510.144251710518975
520.8280742438363210.3438515123273570.171925756163679
530.9451856945404090.1096286109191810.0548143054595905
540.9712153537463710.0575692925072590.0287846462536295
550.9698733762827750.06025324743445020.0301266237172251
560.9601879060573270.07962418788534680.0398120939426734
570.9877243133481090.02455137330378130.0122756866518907
580.982696307887360.03460738422527920.0173036921126396
590.9773629163290820.04527416734183610.0226370836709181
600.9710956854325010.05780862913499780.0289043145674989
610.9632319389695810.07353612206083830.0367680610304191
620.9735502596938540.05289948061229120.0264497403061456
630.9654398096956460.06912038060870820.0345601903043541
640.9683102567225350.06337948655492910.0316897432774645
650.9617602514919860.07647949701602810.0382397485080141
660.9692828673620550.06143426527588950.0307171326379448
670.9815424363242290.03691512735154190.018457563675771
680.9980116623710910.003976675257818960.00198833762890948
690.9971021803340350.005795639331930670.00289781966596533
700.9957414787390740.008517042521851650.00425852126092582
710.9938978787058350.01220424258833080.00610212129416541
720.9943501124917090.01129977501658270.00564988750829134
730.9927915831074890.01441683378502240.00720841689251121
740.9901149952217470.01977000955650550.00988500477825275
750.9932886373681960.01342272526360760.00671136263180379
760.9929050908384320.01418981832313540.0070949091615677
770.9935464687985610.01290706240287780.00645353120143888
780.9999477942855890.0001044114288214095.22057144107044e-05
790.9999085160540420.0001829678919155819.14839459577907e-05
800.9998562248285760.0002875503428484590.000143775171424229
810.9997602439163220.0004795121673563960.000239756083678198
820.9999998537464482.92507105042746e-071.46253552521373e-07
830.9999997193512785.61297444440568e-072.80648722220284e-07
840.9999994965691381.00686172497346e-065.03430862486728e-07
850.999999024433831.95113234003717e-069.75566170018583e-07
860.999999327223321.34555336073274e-066.72776680366372e-07
870.9999993371891081.32562178421614e-066.62810892108072e-07
880.9999990394055011.92118899840454e-069.6059449920227e-07
890.9999987513358422.49732831636916e-061.24866415818458e-06
900.9999978326204624.33475907679781e-062.16737953839891e-06
910.9999955765358458.84692831025149e-064.42346415512574e-06
920.9999963023057657.3953884706478e-063.6976942353239e-06
930.9999923644959351.52710081295193e-057.63550406475966e-06
940.9999845447574093.09104851824942e-051.54552425912471e-05
950.9999918303710941.63392578128138e-058.1696289064069e-06
960.9999967301332866.53973342784585e-063.26986671392293e-06
970.9999941848625761.16302748479226e-055.8151374239613e-06
980.9999881211688142.37576623718278e-051.18788311859139e-05
990.9999841393688813.1721262238902e-051.5860631119451e-05
1000.9999995815547798.36890442696768e-074.18445221348384e-07
1010.9999997863641564.27271687975552e-072.13635843987776e-07
1020.9999994820479681.0359040636136e-065.17952031806802e-07
1030.9999996116036017.76792798981277e-073.88396399490639e-07
1040.9999991002042571.79959148558328e-068.99795742791639e-07
1050.9999989532080862.09358382747622e-061.04679191373811e-06
1060.9999973307382285.33852354310099e-062.66926177155049e-06
1070.9999987722029142.45559417187512e-061.22779708593756e-06
1080.999999040840991.91831801932807e-069.59159009664033e-07
1090.9999972533398215.49332035780253e-062.74666017890127e-06
1100.9999923300504461.53398991071728e-057.66994955358642e-06
1110.9999949573876611.00852246774e-055.04261233870002e-06
1120.9999972887631755.42247364946689e-062.71123682473344e-06
1130.9999918208439161.63583121688525e-058.17915608442624e-06
1140.9999758425462524.83149074960414e-052.41574537480207e-05
1150.9999297708037760.0001404583924478687.02291962239339e-05
1160.9998026655094880.0003946689810238740.000197334490511937
1170.9999973708411625.25831767539121e-062.6291588376956e-06
1180.9999995118150769.76369847858089e-074.88184923929045e-07
1190.999998689779142.62044172004744e-061.31022086002372e-06
1200.9999929305003611.41389992782865e-057.06949963914326e-06
1210.9999745115650935.09768698136447e-052.54884349068223e-05
1220.999867435165560.0002651296688793530.000132564834439676
1230.9999576346639918.47306720176855e-054.23653360088428e-05
1240.9997876255855210.0004247488289570950.000212374414478547
1250.9986231235407830.002753752918434630.00137687645921732
1260.9966510347839250.006697930432150440.00334896521607522






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level520.477064220183486NOK
5% type I error level630.577981651376147NOK
10% type I error level730.669724770642202NOK

\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 & 52 & 0.477064220183486 & NOK \tabularnewline
5% type I error level & 63 & 0.577981651376147 & NOK \tabularnewline
10% type I error level & 73 & 0.669724770642202 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160565&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]52[/C][C]0.477064220183486[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]63[/C][C]0.577981651376147[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]73[/C][C]0.669724770642202[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160565&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160565&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 level520.477064220183486NOK
5% type I error level630.577981651376147NOK
10% type I error level730.669724770642202NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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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 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}