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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Module--
Title produced by softwareMultiple Regression
Date of computationThu, 22 Dec 2011 14:23:54 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/22/t13245818475z3ei35p6ibqekz.htm/, Retrieved Fri, 03 May 2024 13:13:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=159884, Retrieved Fri, 03 May 2024 13:13:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact114

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [] [2011-12-22 19:20:56] [5a05da414fd67612c3b80d44effe0727]
- RM        [Multiple Regression] [] [2011-12-22 19:23:54] [95610e892c4b5c84ff80f4c898567a9d] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1845	162687	595	115	48	21	82	6200	37
1796	201906	545	76	58	20	80	10265	43
192	7215	72	1	0	0	0	603	0
2444	146367	679	155	67	27	84	8874	54
3567	257045	1201	125	83	31	124	20323	86
6917	524450	1967	278	136	36	140	26258	181
1840	188294	595	89	65	23	88	10165	42
1740	195674	496	59	86	30	115	8247	59
2078	177020	670	87	62	30	109	8683	46
3097	325899	1039	130	71	27	108	16957	77
1946	121844	634	158	50	24	63	8058	49
2370	203938	743	120	88	30	118	20488	79
1883	107394	681	87	50	22	71	7945	37
3198	220751	1086	264	79	28	112	13448	92
1490	172905	419	51	56	18	63	5389	31
1573	156326	474	85	54	22	86	6185	28
1807	145178	442	100	81	37	148	24369	103
1309	89171	373	72	13	15	54	70	2
2820	172624	899	147	74	34	134	17327	48
776	39790	242	49	18	18	57	3878	25
1162	87927	399	40	31	15	59	3149	16
2818	241285	850	99	99	30	113	20517	106
1760	195820	642	127	38	25	96	2570	35
2315	146946	717	164	59	34	96	5162	33
1994	159763	619	41	54	21	78	5299	45
1806	207078	657	160	63	21	80	7233	64
2152	212394	691	92	66	25	93	15657	73
1457	201536	366	59	90	31	109	15329	78
3000	394662	994	89	72	31	115	14881	63
2236	217892	929	90	61	20	79	16318	69
1685	182286	490	76	61	28	103	9556	36
1626	181740	553	116	61	22	71	10462	41
2257	137978	738	92	53	17	66	7192	59
3373	255929	1028	361	118	25	100	4362	33
2571	236489	844	85	73	25	100	14349	76
1	0	0	0	0	0	0	0	0
2142	230761	1000	63	54	31	121	10881	27
1878	132807	629	138	54	14	51	8022	44
2190	157118	532	270	46	35	119	13073	43
2186	253254	811	64	83	34	136	26641	104
2532	269329	837	96	106	22	84	14426	120
1823	161273	682	62	44	34	136	15604	44
1095	107181	400	35	27	23	84	9184	71
2162	195891	804	59	64	24	92	5989	78
1365	139667	419	56	71	26	103	11270	106
1244	171101	334	41	44	23	85	13958	61
756	81407	216	49	23	35	106	7162	53
2417	247563	786	121	78	24	96	13275	51
2327	239807	752	113	60	31	124	21224	46
2786	172743	964	190	73	30	106	10615	55
658	48188	205	37	12	22	82	2102	14
2012	169355	506	52	104	23	87	12396	44
2602	315622	830	89	83	27	97	18717	113
2071	241518	694	73	57	30	107	9724	55
1911	195583	691	49	67	33	126	9863	46
1775	159913	547	77	44	12	43	8374	39
1918	220241	538	58	53	26	96	8030	51
1046	101694	329	75	26	26	100	7509	31
1190	157258	427	32	67	23	91	14146	36
2890	202536	972	59	36	38	136	7768	47
1836	173505	541	71	56	32	128	13823	53
2254	150518	836	91	52	21	83	7230	38
1392	141491	376	87	54	22	74	10170	52
1325	125612	467	48	57	26	96	7573	37
1317	166049	430	63	27	28	102	5753	11
1525	124197	483	41	58	33	122	9791	45
2335	195043	504	86	76	36	144	19365	59
2897	138708	887	152	93	25	90	9422	82
1118	116552	271	49	59	25	97	12310	49
340	31970	101	40	5	21	78	1283	6
2977	258158	1097	135	57	19	72	6372	81
1449	151184	469	83	42	12	45	5413	56
1550	135926	528	62	88	30	120	10837	105
1684	119629	475	91	53	21	59	3394	46
2728	171518	698	95	81	39	150	12964	46
1574	108949	425	82	35	32	117	3495	2
2413	183471	709	112	102	28	123	11580	51
2563	159966	824	70	71	29	114	9970	95
1079	93786	336	78	28	21	75	4911	18
1235	84971	395	105	34	31	114	10138	55
980	88882	234	49	54	26	94	14697	48
2246	304603	830	60	49	29	116	8464	48
1076	75101	334	49	30	23	86	4204	39
1637	145043	524	132	57	25	90	10226	40
1208	95827	393	49	54	22	87	3456	36
1865	173924	574	71	38	26	99	8895	60
2726	241957	672	102	63	33	132	22557	114
1208	115367	284	74	58	24	96	6900	39
1419	118408	450	49	46	24	91	8620	45
1609	164078	653	74	46	21	77	7820	59
1864	158931	684	59	51	28	104	12112	59
2412	184139	706	91	87	28	100	13178	93
1238	152856	417	68	39	25	94	7028	35
1462	144014	549	81	28	15	60	6616	47
973	62535	394	33	26	13	46	9570	36
2319	245196	730	166	52	36	135	14612	59
1890	199841	571	97	96	27	99	11219	79
223	19349	67	15	13	1	2	786	14
2526	247280	877	105	43	24	96	11252	42
2072	159408	856	61	42	31	109	9289	41
778	72128	306	11	30	4	15	593	8
1194	104253	382	45	59	21	68	6562	41
1424	151090	435	89	73	27	102	8208	24
1328	137382	336	67	39	23	84	7488	22
839	87448	227	27	36	12	46	4574	18
596	27676	194	59	2	16	59	522	1
1671	165507	410	127	102	29	116	12840	53
1167	132148	273	48	30	26	29	1350	6
0	0	0	0	0	0	0	0	0
1106	95778	343	58	46	25	91	10623	49
1148	109001	376	57	25	21	76	5322	33
1485	158833	495	60	59	24	86	7987	50
1526	147690	448	77	60	21	84	10566	64
962	89887	313	71	36	21	65	1900	53
78	3616	14	5	0	0	0	0	0
0	0	0	0	0	0	0	0	0
1184	199005	410	70	45	23	84	10698	48
1671	160930	606	76	79	33	114	14884	90
2142	177948	593	124	30	32	132	6852	46
1015	136061	312	56	43	23	92	6873	29
778	43410	292	63	7	1	3	4	1
1856	184277	547	92	80	29	109	9188	64
1056	108858	302	58	32	20	81	5141	29
2234	141744	632	64	81	33	121	4260	27
731	60493	174	29	3	12	48	443	4
285	19764	75	19	10	2	8	2416	10
1872	177559	572	64	47	21	80	9831	47
1181	140281	389	79	35	28	107	5953	44
1725	164249	562	104	54	35	140	9435	51
256	11796	79	22	1	2	8	0	0
98	10674	33	7	0	0	0	0	0
1435	151322	487	37	46	18	56	7642	38
41	6836	11	5	0	1	4	0	0
1930	174712	664	48	51	21	70	6837	57
42	5118	6	1	5	0	0	0	0
528	40248	183	34	8	4	14	775	6
0	0	0	0	0	0	0	0	0
1121	127628	342	53	38	29	104	8191	22
1305	88837	269	44	21	26	89	1661	34
81	7131	27	0	0	0	0	0	0
262	9056	99	18	0	4	12	548	10
1099	87957	305	52	18	19	60	3080	16
1290	144470	327	56	53	22	84	13400	93
1248	111408	459	50	17	22	88	8181	22

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159884&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159884&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159884&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
NrSubmFeedbPR[t] = -0.97289291037149 -0.00246760324860148Pageview[t] + 3.15908764410989e-06TimeRfc[t] + 0.0104569408056439CRSCompeVi[t] -0.0141896128536233NrCompeVi[t] + 0.0441008938053942BloComp[t] + 3.40357681340039RevCompe[t] + 0.000821752183880505CompWrNrRev[t] -0.0481923334306038CompWRNrBl[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
NrSubmFeedbPR[t] =  -0.97289291037149 -0.00246760324860148Pageview[t] +  3.15908764410989e-06TimeRfc[t] +  0.0104569408056439CRSCompeVi[t] -0.0141896128536233NrCompeVi[t] +  0.0441008938053942BloComp[t] +  3.40357681340039RevCompe[t] +  0.000821752183880505CompWrNrRev[t] -0.0481923334306038CompWRNrBl[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159884&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]NrSubmFeedbPR[t] =  -0.97289291037149 -0.00246760324860148Pageview[t] +  3.15908764410989e-06TimeRfc[t] +  0.0104569408056439CRSCompeVi[t] -0.0141896128536233NrCompeVi[t] +  0.0441008938053942BloComp[t] +  3.40357681340039RevCompe[t] +  0.000821752183880505CompWrNrRev[t] -0.0481923334306038CompWRNrBl[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159884&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159884&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
NrSubmFeedbPR[t] = -0.97289291037149 -0.00246760324860148Pageview[t] + 3.15908764410989e-06TimeRfc[t] + 0.0104569408056439CRSCompeVi[t] -0.0141896128536233NrCompeVi[t] + 0.0441008938053942BloComp[t] + 3.40357681340039RevCompe[t] + 0.000821752183880505CompWrNrRev[t] -0.0481923334306038CompWRNrBl[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.972892910371491.850313-0.52580.599890.299945
Pageview-0.002467603248601480.003829-0.64450.5203590.26018
TimeRfc3.15908764410989e-062.2e-050.14350.8860950.443047
CRSCompeVi0.01045694080564390.009861.06050.2907910.145395
NrCompeVi-0.01418961285362330.021125-0.67170.5029150.251458
BloComp0.04410089380539420.0504330.87440.3834320.191716
RevCompe3.403576813400390.11172630.463500
CompWrNrRev0.0008217521838805050.0002383.45170.0007440.000372
CompWRNrBl-0.04819233343060380.045892-1.05010.2955410.14777

\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) & -0.97289291037149 & 1.850313 & -0.5258 & 0.59989 & 0.299945 \tabularnewline
Pageview & -0.00246760324860148 & 0.003829 & -0.6445 & 0.520359 & 0.26018 \tabularnewline
TimeRfc & 3.15908764410989e-06 & 2.2e-05 & 0.1435 & 0.886095 & 0.443047 \tabularnewline
CRSCompeVi & 0.0104569408056439 & 0.00986 & 1.0605 & 0.290791 & 0.145395 \tabularnewline
NrCompeVi & -0.0141896128536233 & 0.021125 & -0.6717 & 0.502915 & 0.251458 \tabularnewline
BloComp & 0.0441008938053942 & 0.050433 & 0.8744 & 0.383432 & 0.191716 \tabularnewline
RevCompe & 3.40357681340039 & 0.111726 & 30.4635 & 0 & 0 \tabularnewline
CompWrNrRev & 0.000821752183880505 & 0.000238 & 3.4517 & 0.000744 & 0.000372 \tabularnewline
CompWRNrBl & -0.0481923334306038 & 0.045892 & -1.0501 & 0.295541 & 0.14777 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159884&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]-0.97289291037149[/C][C]1.850313[/C][C]-0.5258[/C][C]0.59989[/C][C]0.299945[/C][/ROW]
[ROW][C]Pageview[/C][C]-0.00246760324860148[/C][C]0.003829[/C][C]-0.6445[/C][C]0.520359[/C][C]0.26018[/C][/ROW]
[ROW][C]TimeRfc[/C][C]3.15908764410989e-06[/C][C]2.2e-05[/C][C]0.1435[/C][C]0.886095[/C][C]0.443047[/C][/ROW]
[ROW][C]CRSCompeVi[/C][C]0.0104569408056439[/C][C]0.00986[/C][C]1.0605[/C][C]0.290791[/C][C]0.145395[/C][/ROW]
[ROW][C]NrCompeVi[/C][C]-0.0141896128536233[/C][C]0.021125[/C][C]-0.6717[/C][C]0.502915[/C][C]0.251458[/C][/ROW]
[ROW][C]BloComp[/C][C]0.0441008938053942[/C][C]0.050433[/C][C]0.8744[/C][C]0.383432[/C][C]0.191716[/C][/ROW]
[ROW][C]RevCompe[/C][C]3.40357681340039[/C][C]0.111726[/C][C]30.4635[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]CompWrNrRev[/C][C]0.000821752183880505[/C][C]0.000238[/C][C]3.4517[/C][C]0.000744[/C][C]0.000372[/C][/ROW]
[ROW][C]CompWRNrBl[/C][C]-0.0481923334306038[/C][C]0.045892[/C][C]-1.0501[/C][C]0.295541[/C][C]0.14777[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159884&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159884&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)-0.972892910371491.850313-0.52580.599890.299945
Pageview-0.002467603248601480.003829-0.64450.5203590.26018
TimeRfc3.15908764410989e-062.2e-050.14350.8860950.443047
CRSCompeVi0.01045694080564390.009861.06050.2907910.145395
NrCompeVi-0.01418961285362330.021125-0.67170.5029150.251458
BloComp0.04410089380539420.0504330.87440.3834320.191716
RevCompe3.403576813400390.11172630.463500
CompWrNrRev0.0008217521838805050.0002383.45170.0007440.000372
CompWRNrBl-0.04819233343060380.045892-1.05010.2955410.14777






Multiple Linear Regression - Regression Statistics
Multiple R0.975679623189994
R-squared0.951950727108168
Adjusted R-squared0.949103362788652
F-TEST (value)334.326984637494
F-TEST (DF numerator)8
F-TEST (DF denominator)135
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.48091755422979
Sum Squared Residuals9710.0049458218

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.975679623189994 \tabularnewline
R-squared & 0.951950727108168 \tabularnewline
Adjusted R-squared & 0.949103362788652 \tabularnewline
F-TEST (value) & 334.326984637494 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 135 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 8.48091755422979 \tabularnewline
Sum Squared Residuals & 9710.0049458218 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159884&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.975679623189994[/C][/ROW]
[ROW][C]R-squared[/C][C]0.951950727108168[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.949103362788652[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]334.326984637494[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]135[/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]8.48091755422979[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]9710.0049458218[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159884&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159884&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 R0.975679623189994
R-squared0.951950727108168
Adjusted R-squared0.949103362788652
F-TEST (value)334.326984637494
F-TEST (DF numerator)8
F-TEST (DF denominator)135
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.48091755422979
Sum Squared Residuals9710.0049458218






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
18276.48209907590135.51790092409866
28076.84615650595053.15384349404953
30-0.1896532247180590.189653224718059
48497.9007204672473-13.9007204672473
5124123.5494626454540.450537354546084
6140141.620812458742-1.62081245874226
78887.51841634706690.481583652933115
8115109.5347080913035.46529190869653
9109109.990289948084-0.990289948083814
10108106.6859746888291.3140253111709
116387.1489516541532-24.1489516541532
12118118.906946341778-0.906946341778454
137182.4359969504182-11.4359969504182
14112104.8446136577457.15538634225498
156366.2228811172395-3.22288111723947
168680.38317764549255.61682235450748
17148142.7957677158185.20423228418246
185450.54560204305273.45439795694727
19134130.8390628514813.16093714851878
205763.1133810742387-6.11338107423872
215954.27965628246734.72034371753272
22113118.544064711345-5.54406471134511
239687.40443882148918.59556117851088
2496119.924453071833-23.9244530718325
257876.54485492780311.45514507219689
268076.93755882414653.06244117585347
279397.6563108026364-4.65631080263644
28109117.376131097376-8.37613109737599
29115119.880915620059-4.88091562005941
307983.4810909223102-4.48109092231024
31103103.59858864692-0.59858864691959
327183.9157400643844-12.9157400643844
336663.57023631808122.42976368191878
3410089.42713020873210.572869791268
3510097.48701989132642.51298010867363
360-0.9753605136200980.975360513620098
37121119.5661123419131.43388765808706
385153.9349033356958-2.93490333569576
39119125.675628092198-6.67562809219833
40136138.267704836591-2.26770483659097
418486.6451278318141-2.64512783181411
42136129.6542289309566.34577106904367
438483.94812280171420.0518771982858189
449287.55195150004614.44804849995391
4510395.46380920432167.53619079567842
468588.1617664844331-3.1617664844331
47106122.452883561389-16.4528835613891
489693.92386004462812.0761399553719
49124123.6837147507960.316285249203616
50106111.481529953397-5.48152995339725
518275.63484249997766.36515750002236
528790.085385885084-3.085385885084
5397106.51181641758-9.51181641757999
54107110.862147718497-3.86214771849721
55126122.6207253661013.37927463389878
564347.5648498090154-4.56484980901542
579694.76404684025911.23595315974087
5810093.45956211126686.54043788873315
599191.7251061240664-0.725106124066353
60136136.904404360334-0.904404360333999
61128119.8834423380068.11655766199377
628379.26969584351723.73030415648281
637481.847855794058-7.847855794058
649695.80340308855750.196596911442513
65102100.592675446171.40732455382987
66122120.8782969366971.12170306330333
67144136.8817194762117.11828052378869
689092.4167177423485-2.41671774234851
699794.22078268428762.77921731571235
707871.23845612608146.76154387391855
717270.56659860444471.43340139555531
724544.10025405965090.899745940349121
73120110.1065494132519.89345058674927
745973.3100138551386-14.3100138551386
75150143.5362733781026.46372662189761
76117112.0015591322724.99844086772821
77123106.33363903923816.6663609607622
78114106.2807215478137.71927845218702
797574.94568476753720.0543152324628252
80114111.5792870887052.42071291129503
819499.2797803084001-5.27978030840009
82116107.7817717291678.21822827083346
838680.58698253416195.41301746583807
849093.1309679833667-3.13096798336666
858778.12844466806968.87155533193038
869994.55606648302914.44393351697086
87132126.7832368660265.21676313397382
889686.36472109449489.63527890550524
899188.53930511966922.46069488033084
907778.4399298420125-1.43992984201249
91104105.903943083339-1.90394308333898
92100105.232396539217-5.23239653921659
939490.74864828235613.25135171764386
946055.92607585417894.07392414582115
954651.9978263371978-5.99782633719777
96135133.3435288632761.65647113672386
9799101.131476113945-2.13147611394542
9822.97382057456219-0.973820574562186
999692.06040773739523.9395922626048
100109115.523881067908-6.52388106790845
1011515.4180030263077-0.418003026307748
1026877.2596699601981-9.25966996019813
10310299.98070545481212.01929454518792
1048483.84221038775560.157789612244372
1054644.54543630130471.45456369869528
1065953.76149892960355.23850107039646
107116109.1109812037986.88901879620194
1082989.3747600456982-60.3747600456982
1090-0.9728929103714980.972892910371498
1109192.8503527051993-1.85035270519933
1117675.02229762874030.977702371259708
1128688.6308068380595-2.63080683805947
1138479.03971042267524.96028957732476
1146571.2726743710525-6.27267437105253
1150-1.078493595830411.07849359583041
1160-0.9728929103714980.972892910371498
1178486.7728916983971-2.77289169839709
118114124.366284536089-10.3662845360889
119132112.39639163223119.6036083677686
1209283.84919585996538.15080414003473
12133.07130660766016-0.0713066076601599
122109106.141633711152.85836628885017
1238171.41002377728479.58997622271533
124121117.7525930589693.24740694103112
1254839.96889205714048.0311079428596
12687.652536794983690.347463205016313
1278079.40337430819590.596625691804044
12810799.11790832288657.88209167711354
129140126.492509052713.5074909473
13085.797846617308772.20215338269123
1310-0.9352461706103250.935246170610325
1325668.2731956602389-12.2731956602388
13342.395185977565331.60481402243467
1347077.6744859594088-7.6744859594088
1350-0.7913075352429970.791307535242997
1361413.59735121024520.40264878975483
1370-0.9728929103714980.972892910371498
138104105.538638515985-1.538638515985
1398987.42160978951051.57839021048954
1400-0.8679039177656930.867903917765693
1411212.7717319606555-0.771731960655522
1426066.2662770032282-6.26627700322816
1438482.67072313596681.3292768640332
1448881.68066942909166.31933057090844

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 82 & 76.4820990759013 & 5.51790092409866 \tabularnewline
2 & 80 & 76.8461565059505 & 3.15384349404953 \tabularnewline
3 & 0 & -0.189653224718059 & 0.189653224718059 \tabularnewline
4 & 84 & 97.9007204672473 & -13.9007204672473 \tabularnewline
5 & 124 & 123.549462645454 & 0.450537354546084 \tabularnewline
6 & 140 & 141.620812458742 & -1.62081245874226 \tabularnewline
7 & 88 & 87.5184163470669 & 0.481583652933115 \tabularnewline
8 & 115 & 109.534708091303 & 5.46529190869653 \tabularnewline
9 & 109 & 109.990289948084 & -0.990289948083814 \tabularnewline
10 & 108 & 106.685974688829 & 1.3140253111709 \tabularnewline
11 & 63 & 87.1489516541532 & -24.1489516541532 \tabularnewline
12 & 118 & 118.906946341778 & -0.906946341778454 \tabularnewline
13 & 71 & 82.4359969504182 & -11.4359969504182 \tabularnewline
14 & 112 & 104.844613657745 & 7.15538634225498 \tabularnewline
15 & 63 & 66.2228811172395 & -3.22288111723947 \tabularnewline
16 & 86 & 80.3831776454925 & 5.61682235450748 \tabularnewline
17 & 148 & 142.795767715818 & 5.20423228418246 \tabularnewline
18 & 54 & 50.5456020430527 & 3.45439795694727 \tabularnewline
19 & 134 & 130.839062851481 & 3.16093714851878 \tabularnewline
20 & 57 & 63.1133810742387 & -6.11338107423872 \tabularnewline
21 & 59 & 54.2796562824673 & 4.72034371753272 \tabularnewline
22 & 113 & 118.544064711345 & -5.54406471134511 \tabularnewline
23 & 96 & 87.4044388214891 & 8.59556117851088 \tabularnewline
24 & 96 & 119.924453071833 & -23.9244530718325 \tabularnewline
25 & 78 & 76.5448549278031 & 1.45514507219689 \tabularnewline
26 & 80 & 76.9375588241465 & 3.06244117585347 \tabularnewline
27 & 93 & 97.6563108026364 & -4.65631080263644 \tabularnewline
28 & 109 & 117.376131097376 & -8.37613109737599 \tabularnewline
29 & 115 & 119.880915620059 & -4.88091562005941 \tabularnewline
30 & 79 & 83.4810909223102 & -4.48109092231024 \tabularnewline
31 & 103 & 103.59858864692 & -0.59858864691959 \tabularnewline
32 & 71 & 83.9157400643844 & -12.9157400643844 \tabularnewline
33 & 66 & 63.5702363180812 & 2.42976368191878 \tabularnewline
34 & 100 & 89.427130208732 & 10.572869791268 \tabularnewline
35 & 100 & 97.4870198913264 & 2.51298010867363 \tabularnewline
36 & 0 & -0.975360513620098 & 0.975360513620098 \tabularnewline
37 & 121 & 119.566112341913 & 1.43388765808706 \tabularnewline
38 & 51 & 53.9349033356958 & -2.93490333569576 \tabularnewline
39 & 119 & 125.675628092198 & -6.67562809219833 \tabularnewline
40 & 136 & 138.267704836591 & -2.26770483659097 \tabularnewline
41 & 84 & 86.6451278318141 & -2.64512783181411 \tabularnewline
42 & 136 & 129.654228930956 & 6.34577106904367 \tabularnewline
43 & 84 & 83.9481228017142 & 0.0518771982858189 \tabularnewline
44 & 92 & 87.5519515000461 & 4.44804849995391 \tabularnewline
45 & 103 & 95.4638092043216 & 7.53619079567842 \tabularnewline
46 & 85 & 88.1617664844331 & -3.1617664844331 \tabularnewline
47 & 106 & 122.452883561389 & -16.4528835613891 \tabularnewline
48 & 96 & 93.9238600446281 & 2.0761399553719 \tabularnewline
49 & 124 & 123.683714750796 & 0.316285249203616 \tabularnewline
50 & 106 & 111.481529953397 & -5.48152995339725 \tabularnewline
51 & 82 & 75.6348424999776 & 6.36515750002236 \tabularnewline
52 & 87 & 90.085385885084 & -3.085385885084 \tabularnewline
53 & 97 & 106.51181641758 & -9.51181641757999 \tabularnewline
54 & 107 & 110.862147718497 & -3.86214771849721 \tabularnewline
55 & 126 & 122.620725366101 & 3.37927463389878 \tabularnewline
56 & 43 & 47.5648498090154 & -4.56484980901542 \tabularnewline
57 & 96 & 94.7640468402591 & 1.23595315974087 \tabularnewline
58 & 100 & 93.4595621112668 & 6.54043788873315 \tabularnewline
59 & 91 & 91.7251061240664 & -0.725106124066353 \tabularnewline
60 & 136 & 136.904404360334 & -0.904404360333999 \tabularnewline
61 & 128 & 119.883442338006 & 8.11655766199377 \tabularnewline
62 & 83 & 79.2696958435172 & 3.73030415648281 \tabularnewline
63 & 74 & 81.847855794058 & -7.847855794058 \tabularnewline
64 & 96 & 95.8034030885575 & 0.196596911442513 \tabularnewline
65 & 102 & 100.59267544617 & 1.40732455382987 \tabularnewline
66 & 122 & 120.878296936697 & 1.12170306330333 \tabularnewline
67 & 144 & 136.881719476211 & 7.11828052378869 \tabularnewline
68 & 90 & 92.4167177423485 & -2.41671774234851 \tabularnewline
69 & 97 & 94.2207826842876 & 2.77921731571235 \tabularnewline
70 & 78 & 71.2384561260814 & 6.76154387391855 \tabularnewline
71 & 72 & 70.5665986044447 & 1.43340139555531 \tabularnewline
72 & 45 & 44.1002540596509 & 0.899745940349121 \tabularnewline
73 & 120 & 110.106549413251 & 9.89345058674927 \tabularnewline
74 & 59 & 73.3100138551386 & -14.3100138551386 \tabularnewline
75 & 150 & 143.536273378102 & 6.46372662189761 \tabularnewline
76 & 117 & 112.001559132272 & 4.99844086772821 \tabularnewline
77 & 123 & 106.333639039238 & 16.6663609607622 \tabularnewline
78 & 114 & 106.280721547813 & 7.71927845218702 \tabularnewline
79 & 75 & 74.9456847675372 & 0.0543152324628252 \tabularnewline
80 & 114 & 111.579287088705 & 2.42071291129503 \tabularnewline
81 & 94 & 99.2797803084001 & -5.27978030840009 \tabularnewline
82 & 116 & 107.781771729167 & 8.21822827083346 \tabularnewline
83 & 86 & 80.5869825341619 & 5.41301746583807 \tabularnewline
84 & 90 & 93.1309679833667 & -3.13096798336666 \tabularnewline
85 & 87 & 78.1284446680696 & 8.87155533193038 \tabularnewline
86 & 99 & 94.5560664830291 & 4.44393351697086 \tabularnewline
87 & 132 & 126.783236866026 & 5.21676313397382 \tabularnewline
88 & 96 & 86.3647210944948 & 9.63527890550524 \tabularnewline
89 & 91 & 88.5393051196692 & 2.46069488033084 \tabularnewline
90 & 77 & 78.4399298420125 & -1.43992984201249 \tabularnewline
91 & 104 & 105.903943083339 & -1.90394308333898 \tabularnewline
92 & 100 & 105.232396539217 & -5.23239653921659 \tabularnewline
93 & 94 & 90.7486482823561 & 3.25135171764386 \tabularnewline
94 & 60 & 55.9260758541789 & 4.07392414582115 \tabularnewline
95 & 46 & 51.9978263371978 & -5.99782633719777 \tabularnewline
96 & 135 & 133.343528863276 & 1.65647113672386 \tabularnewline
97 & 99 & 101.131476113945 & -2.13147611394542 \tabularnewline
98 & 2 & 2.97382057456219 & -0.973820574562186 \tabularnewline
99 & 96 & 92.0604077373952 & 3.9395922626048 \tabularnewline
100 & 109 & 115.523881067908 & -6.52388106790845 \tabularnewline
101 & 15 & 15.4180030263077 & -0.418003026307748 \tabularnewline
102 & 68 & 77.2596699601981 & -9.25966996019813 \tabularnewline
103 & 102 & 99.9807054548121 & 2.01929454518792 \tabularnewline
104 & 84 & 83.8422103877556 & 0.157789612244372 \tabularnewline
105 & 46 & 44.5454363013047 & 1.45456369869528 \tabularnewline
106 & 59 & 53.7614989296035 & 5.23850107039646 \tabularnewline
107 & 116 & 109.110981203798 & 6.88901879620194 \tabularnewline
108 & 29 & 89.3747600456982 & -60.3747600456982 \tabularnewline
109 & 0 & -0.972892910371498 & 0.972892910371498 \tabularnewline
110 & 91 & 92.8503527051993 & -1.85035270519933 \tabularnewline
111 & 76 & 75.0222976287403 & 0.977702371259708 \tabularnewline
112 & 86 & 88.6308068380595 & -2.63080683805947 \tabularnewline
113 & 84 & 79.0397104226752 & 4.96028957732476 \tabularnewline
114 & 65 & 71.2726743710525 & -6.27267437105253 \tabularnewline
115 & 0 & -1.07849359583041 & 1.07849359583041 \tabularnewline
116 & 0 & -0.972892910371498 & 0.972892910371498 \tabularnewline
117 & 84 & 86.7728916983971 & -2.77289169839709 \tabularnewline
118 & 114 & 124.366284536089 & -10.3662845360889 \tabularnewline
119 & 132 & 112.396391632231 & 19.6036083677686 \tabularnewline
120 & 92 & 83.8491958599653 & 8.15080414003473 \tabularnewline
121 & 3 & 3.07130660766016 & -0.0713066076601599 \tabularnewline
122 & 109 & 106.14163371115 & 2.85836628885017 \tabularnewline
123 & 81 & 71.4100237772847 & 9.58997622271533 \tabularnewline
124 & 121 & 117.752593058969 & 3.24740694103112 \tabularnewline
125 & 48 & 39.9688920571404 & 8.0311079428596 \tabularnewline
126 & 8 & 7.65253679498369 & 0.347463205016313 \tabularnewline
127 & 80 & 79.4033743081959 & 0.596625691804044 \tabularnewline
128 & 107 & 99.1179083228865 & 7.88209167711354 \tabularnewline
129 & 140 & 126.4925090527 & 13.5074909473 \tabularnewline
130 & 8 & 5.79784661730877 & 2.20215338269123 \tabularnewline
131 & 0 & -0.935246170610325 & 0.935246170610325 \tabularnewline
132 & 56 & 68.2731956602389 & -12.2731956602388 \tabularnewline
133 & 4 & 2.39518597756533 & 1.60481402243467 \tabularnewline
134 & 70 & 77.6744859594088 & -7.6744859594088 \tabularnewline
135 & 0 & -0.791307535242997 & 0.791307535242997 \tabularnewline
136 & 14 & 13.5973512102452 & 0.40264878975483 \tabularnewline
137 & 0 & -0.972892910371498 & 0.972892910371498 \tabularnewline
138 & 104 & 105.538638515985 & -1.538638515985 \tabularnewline
139 & 89 & 87.4216097895105 & 1.57839021048954 \tabularnewline
140 & 0 & -0.867903917765693 & 0.867903917765693 \tabularnewline
141 & 12 & 12.7717319606555 & -0.771731960655522 \tabularnewline
142 & 60 & 66.2662770032282 & -6.26627700322816 \tabularnewline
143 & 84 & 82.6707231359668 & 1.3292768640332 \tabularnewline
144 & 88 & 81.6806694290916 & 6.31933057090844 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159884&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]82[/C][C]76.4820990759013[/C][C]5.51790092409866[/C][/ROW]
[ROW][C]2[/C][C]80[/C][C]76.8461565059505[/C][C]3.15384349404953[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]-0.189653224718059[/C][C]0.189653224718059[/C][/ROW]
[ROW][C]4[/C][C]84[/C][C]97.9007204672473[/C][C]-13.9007204672473[/C][/ROW]
[ROW][C]5[/C][C]124[/C][C]123.549462645454[/C][C]0.450537354546084[/C][/ROW]
[ROW][C]6[/C][C]140[/C][C]141.620812458742[/C][C]-1.62081245874226[/C][/ROW]
[ROW][C]7[/C][C]88[/C][C]87.5184163470669[/C][C]0.481583652933115[/C][/ROW]
[ROW][C]8[/C][C]115[/C][C]109.534708091303[/C][C]5.46529190869653[/C][/ROW]
[ROW][C]9[/C][C]109[/C][C]109.990289948084[/C][C]-0.990289948083814[/C][/ROW]
[ROW][C]10[/C][C]108[/C][C]106.685974688829[/C][C]1.3140253111709[/C][/ROW]
[ROW][C]11[/C][C]63[/C][C]87.1489516541532[/C][C]-24.1489516541532[/C][/ROW]
[ROW][C]12[/C][C]118[/C][C]118.906946341778[/C][C]-0.906946341778454[/C][/ROW]
[ROW][C]13[/C][C]71[/C][C]82.4359969504182[/C][C]-11.4359969504182[/C][/ROW]
[ROW][C]14[/C][C]112[/C][C]104.844613657745[/C][C]7.15538634225498[/C][/ROW]
[ROW][C]15[/C][C]63[/C][C]66.2228811172395[/C][C]-3.22288111723947[/C][/ROW]
[ROW][C]16[/C][C]86[/C][C]80.3831776454925[/C][C]5.61682235450748[/C][/ROW]
[ROW][C]17[/C][C]148[/C][C]142.795767715818[/C][C]5.20423228418246[/C][/ROW]
[ROW][C]18[/C][C]54[/C][C]50.5456020430527[/C][C]3.45439795694727[/C][/ROW]
[ROW][C]19[/C][C]134[/C][C]130.839062851481[/C][C]3.16093714851878[/C][/ROW]
[ROW][C]20[/C][C]57[/C][C]63.1133810742387[/C][C]-6.11338107423872[/C][/ROW]
[ROW][C]21[/C][C]59[/C][C]54.2796562824673[/C][C]4.72034371753272[/C][/ROW]
[ROW][C]22[/C][C]113[/C][C]118.544064711345[/C][C]-5.54406471134511[/C][/ROW]
[ROW][C]23[/C][C]96[/C][C]87.4044388214891[/C][C]8.59556117851088[/C][/ROW]
[ROW][C]24[/C][C]96[/C][C]119.924453071833[/C][C]-23.9244530718325[/C][/ROW]
[ROW][C]25[/C][C]78[/C][C]76.5448549278031[/C][C]1.45514507219689[/C][/ROW]
[ROW][C]26[/C][C]80[/C][C]76.9375588241465[/C][C]3.06244117585347[/C][/ROW]
[ROW][C]27[/C][C]93[/C][C]97.6563108026364[/C][C]-4.65631080263644[/C][/ROW]
[ROW][C]28[/C][C]109[/C][C]117.376131097376[/C][C]-8.37613109737599[/C][/ROW]
[ROW][C]29[/C][C]115[/C][C]119.880915620059[/C][C]-4.88091562005941[/C][/ROW]
[ROW][C]30[/C][C]79[/C][C]83.4810909223102[/C][C]-4.48109092231024[/C][/ROW]
[ROW][C]31[/C][C]103[/C][C]103.59858864692[/C][C]-0.59858864691959[/C][/ROW]
[ROW][C]32[/C][C]71[/C][C]83.9157400643844[/C][C]-12.9157400643844[/C][/ROW]
[ROW][C]33[/C][C]66[/C][C]63.5702363180812[/C][C]2.42976368191878[/C][/ROW]
[ROW][C]34[/C][C]100[/C][C]89.427130208732[/C][C]10.572869791268[/C][/ROW]
[ROW][C]35[/C][C]100[/C][C]97.4870198913264[/C][C]2.51298010867363[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]-0.975360513620098[/C][C]0.975360513620098[/C][/ROW]
[ROW][C]37[/C][C]121[/C][C]119.566112341913[/C][C]1.43388765808706[/C][/ROW]
[ROW][C]38[/C][C]51[/C][C]53.9349033356958[/C][C]-2.93490333569576[/C][/ROW]
[ROW][C]39[/C][C]119[/C][C]125.675628092198[/C][C]-6.67562809219833[/C][/ROW]
[ROW][C]40[/C][C]136[/C][C]138.267704836591[/C][C]-2.26770483659097[/C][/ROW]
[ROW][C]41[/C][C]84[/C][C]86.6451278318141[/C][C]-2.64512783181411[/C][/ROW]
[ROW][C]42[/C][C]136[/C][C]129.654228930956[/C][C]6.34577106904367[/C][/ROW]
[ROW][C]43[/C][C]84[/C][C]83.9481228017142[/C][C]0.0518771982858189[/C][/ROW]
[ROW][C]44[/C][C]92[/C][C]87.5519515000461[/C][C]4.44804849995391[/C][/ROW]
[ROW][C]45[/C][C]103[/C][C]95.4638092043216[/C][C]7.53619079567842[/C][/ROW]
[ROW][C]46[/C][C]85[/C][C]88.1617664844331[/C][C]-3.1617664844331[/C][/ROW]
[ROW][C]47[/C][C]106[/C][C]122.452883561389[/C][C]-16.4528835613891[/C][/ROW]
[ROW][C]48[/C][C]96[/C][C]93.9238600446281[/C][C]2.0761399553719[/C][/ROW]
[ROW][C]49[/C][C]124[/C][C]123.683714750796[/C][C]0.316285249203616[/C][/ROW]
[ROW][C]50[/C][C]106[/C][C]111.481529953397[/C][C]-5.48152995339725[/C][/ROW]
[ROW][C]51[/C][C]82[/C][C]75.6348424999776[/C][C]6.36515750002236[/C][/ROW]
[ROW][C]52[/C][C]87[/C][C]90.085385885084[/C][C]-3.085385885084[/C][/ROW]
[ROW][C]53[/C][C]97[/C][C]106.51181641758[/C][C]-9.51181641757999[/C][/ROW]
[ROW][C]54[/C][C]107[/C][C]110.862147718497[/C][C]-3.86214771849721[/C][/ROW]
[ROW][C]55[/C][C]126[/C][C]122.620725366101[/C][C]3.37927463389878[/C][/ROW]
[ROW][C]56[/C][C]43[/C][C]47.5648498090154[/C][C]-4.56484980901542[/C][/ROW]
[ROW][C]57[/C][C]96[/C][C]94.7640468402591[/C][C]1.23595315974087[/C][/ROW]
[ROW][C]58[/C][C]100[/C][C]93.4595621112668[/C][C]6.54043788873315[/C][/ROW]
[ROW][C]59[/C][C]91[/C][C]91.7251061240664[/C][C]-0.725106124066353[/C][/ROW]
[ROW][C]60[/C][C]136[/C][C]136.904404360334[/C][C]-0.904404360333999[/C][/ROW]
[ROW][C]61[/C][C]128[/C][C]119.883442338006[/C][C]8.11655766199377[/C][/ROW]
[ROW][C]62[/C][C]83[/C][C]79.2696958435172[/C][C]3.73030415648281[/C][/ROW]
[ROW][C]63[/C][C]74[/C][C]81.847855794058[/C][C]-7.847855794058[/C][/ROW]
[ROW][C]64[/C][C]96[/C][C]95.8034030885575[/C][C]0.196596911442513[/C][/ROW]
[ROW][C]65[/C][C]102[/C][C]100.59267544617[/C][C]1.40732455382987[/C][/ROW]
[ROW][C]66[/C][C]122[/C][C]120.878296936697[/C][C]1.12170306330333[/C][/ROW]
[ROW][C]67[/C][C]144[/C][C]136.881719476211[/C][C]7.11828052378869[/C][/ROW]
[ROW][C]68[/C][C]90[/C][C]92.4167177423485[/C][C]-2.41671774234851[/C][/ROW]
[ROW][C]69[/C][C]97[/C][C]94.2207826842876[/C][C]2.77921731571235[/C][/ROW]
[ROW][C]70[/C][C]78[/C][C]71.2384561260814[/C][C]6.76154387391855[/C][/ROW]
[ROW][C]71[/C][C]72[/C][C]70.5665986044447[/C][C]1.43340139555531[/C][/ROW]
[ROW][C]72[/C][C]45[/C][C]44.1002540596509[/C][C]0.899745940349121[/C][/ROW]
[ROW][C]73[/C][C]120[/C][C]110.106549413251[/C][C]9.89345058674927[/C][/ROW]
[ROW][C]74[/C][C]59[/C][C]73.3100138551386[/C][C]-14.3100138551386[/C][/ROW]
[ROW][C]75[/C][C]150[/C][C]143.536273378102[/C][C]6.46372662189761[/C][/ROW]
[ROW][C]76[/C][C]117[/C][C]112.001559132272[/C][C]4.99844086772821[/C][/ROW]
[ROW][C]77[/C][C]123[/C][C]106.333639039238[/C][C]16.6663609607622[/C][/ROW]
[ROW][C]78[/C][C]114[/C][C]106.280721547813[/C][C]7.71927845218702[/C][/ROW]
[ROW][C]79[/C][C]75[/C][C]74.9456847675372[/C][C]0.0543152324628252[/C][/ROW]
[ROW][C]80[/C][C]114[/C][C]111.579287088705[/C][C]2.42071291129503[/C][/ROW]
[ROW][C]81[/C][C]94[/C][C]99.2797803084001[/C][C]-5.27978030840009[/C][/ROW]
[ROW][C]82[/C][C]116[/C][C]107.781771729167[/C][C]8.21822827083346[/C][/ROW]
[ROW][C]83[/C][C]86[/C][C]80.5869825341619[/C][C]5.41301746583807[/C][/ROW]
[ROW][C]84[/C][C]90[/C][C]93.1309679833667[/C][C]-3.13096798336666[/C][/ROW]
[ROW][C]85[/C][C]87[/C][C]78.1284446680696[/C][C]8.87155533193038[/C][/ROW]
[ROW][C]86[/C][C]99[/C][C]94.5560664830291[/C][C]4.44393351697086[/C][/ROW]
[ROW][C]87[/C][C]132[/C][C]126.783236866026[/C][C]5.21676313397382[/C][/ROW]
[ROW][C]88[/C][C]96[/C][C]86.3647210944948[/C][C]9.63527890550524[/C][/ROW]
[ROW][C]89[/C][C]91[/C][C]88.5393051196692[/C][C]2.46069488033084[/C][/ROW]
[ROW][C]90[/C][C]77[/C][C]78.4399298420125[/C][C]-1.43992984201249[/C][/ROW]
[ROW][C]91[/C][C]104[/C][C]105.903943083339[/C][C]-1.90394308333898[/C][/ROW]
[ROW][C]92[/C][C]100[/C][C]105.232396539217[/C][C]-5.23239653921659[/C][/ROW]
[ROW][C]93[/C][C]94[/C][C]90.7486482823561[/C][C]3.25135171764386[/C][/ROW]
[ROW][C]94[/C][C]60[/C][C]55.9260758541789[/C][C]4.07392414582115[/C][/ROW]
[ROW][C]95[/C][C]46[/C][C]51.9978263371978[/C][C]-5.99782633719777[/C][/ROW]
[ROW][C]96[/C][C]135[/C][C]133.343528863276[/C][C]1.65647113672386[/C][/ROW]
[ROW][C]97[/C][C]99[/C][C]101.131476113945[/C][C]-2.13147611394542[/C][/ROW]
[ROW][C]98[/C][C]2[/C][C]2.97382057456219[/C][C]-0.973820574562186[/C][/ROW]
[ROW][C]99[/C][C]96[/C][C]92.0604077373952[/C][C]3.9395922626048[/C][/ROW]
[ROW][C]100[/C][C]109[/C][C]115.523881067908[/C][C]-6.52388106790845[/C][/ROW]
[ROW][C]101[/C][C]15[/C][C]15.4180030263077[/C][C]-0.418003026307748[/C][/ROW]
[ROW][C]102[/C][C]68[/C][C]77.2596699601981[/C][C]-9.25966996019813[/C][/ROW]
[ROW][C]103[/C][C]102[/C][C]99.9807054548121[/C][C]2.01929454518792[/C][/ROW]
[ROW][C]104[/C][C]84[/C][C]83.8422103877556[/C][C]0.157789612244372[/C][/ROW]
[ROW][C]105[/C][C]46[/C][C]44.5454363013047[/C][C]1.45456369869528[/C][/ROW]
[ROW][C]106[/C][C]59[/C][C]53.7614989296035[/C][C]5.23850107039646[/C][/ROW]
[ROW][C]107[/C][C]116[/C][C]109.110981203798[/C][C]6.88901879620194[/C][/ROW]
[ROW][C]108[/C][C]29[/C][C]89.3747600456982[/C][C]-60.3747600456982[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]-0.972892910371498[/C][C]0.972892910371498[/C][/ROW]
[ROW][C]110[/C][C]91[/C][C]92.8503527051993[/C][C]-1.85035270519933[/C][/ROW]
[ROW][C]111[/C][C]76[/C][C]75.0222976287403[/C][C]0.977702371259708[/C][/ROW]
[ROW][C]112[/C][C]86[/C][C]88.6308068380595[/C][C]-2.63080683805947[/C][/ROW]
[ROW][C]113[/C][C]84[/C][C]79.0397104226752[/C][C]4.96028957732476[/C][/ROW]
[ROW][C]114[/C][C]65[/C][C]71.2726743710525[/C][C]-6.27267437105253[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]-1.07849359583041[/C][C]1.07849359583041[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]-0.972892910371498[/C][C]0.972892910371498[/C][/ROW]
[ROW][C]117[/C][C]84[/C][C]86.7728916983971[/C][C]-2.77289169839709[/C][/ROW]
[ROW][C]118[/C][C]114[/C][C]124.366284536089[/C][C]-10.3662845360889[/C][/ROW]
[ROW][C]119[/C][C]132[/C][C]112.396391632231[/C][C]19.6036083677686[/C][/ROW]
[ROW][C]120[/C][C]92[/C][C]83.8491958599653[/C][C]8.15080414003473[/C][/ROW]
[ROW][C]121[/C][C]3[/C][C]3.07130660766016[/C][C]-0.0713066076601599[/C][/ROW]
[ROW][C]122[/C][C]109[/C][C]106.14163371115[/C][C]2.85836628885017[/C][/ROW]
[ROW][C]123[/C][C]81[/C][C]71.4100237772847[/C][C]9.58997622271533[/C][/ROW]
[ROW][C]124[/C][C]121[/C][C]117.752593058969[/C][C]3.24740694103112[/C][/ROW]
[ROW][C]125[/C][C]48[/C][C]39.9688920571404[/C][C]8.0311079428596[/C][/ROW]
[ROW][C]126[/C][C]8[/C][C]7.65253679498369[/C][C]0.347463205016313[/C][/ROW]
[ROW][C]127[/C][C]80[/C][C]79.4033743081959[/C][C]0.596625691804044[/C][/ROW]
[ROW][C]128[/C][C]107[/C][C]99.1179083228865[/C][C]7.88209167711354[/C][/ROW]
[ROW][C]129[/C][C]140[/C][C]126.4925090527[/C][C]13.5074909473[/C][/ROW]
[ROW][C]130[/C][C]8[/C][C]5.79784661730877[/C][C]2.20215338269123[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]-0.935246170610325[/C][C]0.935246170610325[/C][/ROW]
[ROW][C]132[/C][C]56[/C][C]68.2731956602389[/C][C]-12.2731956602388[/C][/ROW]
[ROW][C]133[/C][C]4[/C][C]2.39518597756533[/C][C]1.60481402243467[/C][/ROW]
[ROW][C]134[/C][C]70[/C][C]77.6744859594088[/C][C]-7.6744859594088[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]-0.791307535242997[/C][C]0.791307535242997[/C][/ROW]
[ROW][C]136[/C][C]14[/C][C]13.5973512102452[/C][C]0.40264878975483[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]-0.972892910371498[/C][C]0.972892910371498[/C][/ROW]
[ROW][C]138[/C][C]104[/C][C]105.538638515985[/C][C]-1.538638515985[/C][/ROW]
[ROW][C]139[/C][C]89[/C][C]87.4216097895105[/C][C]1.57839021048954[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]-0.867903917765693[/C][C]0.867903917765693[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]12.7717319606555[/C][C]-0.771731960655522[/C][/ROW]
[ROW][C]142[/C][C]60[/C][C]66.2662770032282[/C][C]-6.26627700322816[/C][/ROW]
[ROW][C]143[/C][C]84[/C][C]82.6707231359668[/C][C]1.3292768640332[/C][/ROW]
[ROW][C]144[/C][C]88[/C][C]81.6806694290916[/C][C]6.31933057090844[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159884&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159884&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
18276.48209907590135.51790092409866
28076.84615650595053.15384349404953
30-0.1896532247180590.189653224718059
48497.9007204672473-13.9007204672473
5124123.5494626454540.450537354546084
6140141.620812458742-1.62081245874226
78887.51841634706690.481583652933115
8115109.5347080913035.46529190869653
9109109.990289948084-0.990289948083814
10108106.6859746888291.3140253111709
116387.1489516541532-24.1489516541532
12118118.906946341778-0.906946341778454
137182.4359969504182-11.4359969504182
14112104.8446136577457.15538634225498
156366.2228811172395-3.22288111723947
168680.38317764549255.61682235450748
17148142.7957677158185.20423228418246
185450.54560204305273.45439795694727
19134130.8390628514813.16093714851878
205763.1133810742387-6.11338107423872
215954.27965628246734.72034371753272
22113118.544064711345-5.54406471134511
239687.40443882148918.59556117851088
2496119.924453071833-23.9244530718325
257876.54485492780311.45514507219689
268076.93755882414653.06244117585347
279397.6563108026364-4.65631080263644
28109117.376131097376-8.37613109737599
29115119.880915620059-4.88091562005941
307983.4810909223102-4.48109092231024
31103103.59858864692-0.59858864691959
327183.9157400643844-12.9157400643844
336663.57023631808122.42976368191878
3410089.42713020873210.572869791268
3510097.48701989132642.51298010867363
360-0.9753605136200980.975360513620098
37121119.5661123419131.43388765808706
385153.9349033356958-2.93490333569576
39119125.675628092198-6.67562809219833
40136138.267704836591-2.26770483659097
418486.6451278318141-2.64512783181411
42136129.6542289309566.34577106904367
438483.94812280171420.0518771982858189
449287.55195150004614.44804849995391
4510395.46380920432167.53619079567842
468588.1617664844331-3.1617664844331
47106122.452883561389-16.4528835613891
489693.92386004462812.0761399553719
49124123.6837147507960.316285249203616
50106111.481529953397-5.48152995339725
518275.63484249997766.36515750002236
528790.085385885084-3.085385885084
5397106.51181641758-9.51181641757999
54107110.862147718497-3.86214771849721
55126122.6207253661013.37927463389878
564347.5648498090154-4.56484980901542
579694.76404684025911.23595315974087
5810093.45956211126686.54043788873315
599191.7251061240664-0.725106124066353
60136136.904404360334-0.904404360333999
61128119.8834423380068.11655766199377
628379.26969584351723.73030415648281
637481.847855794058-7.847855794058
649695.80340308855750.196596911442513
65102100.592675446171.40732455382987
66122120.8782969366971.12170306330333
67144136.8817194762117.11828052378869
689092.4167177423485-2.41671774234851
699794.22078268428762.77921731571235
707871.23845612608146.76154387391855
717270.56659860444471.43340139555531
724544.10025405965090.899745940349121
73120110.1065494132519.89345058674927
745973.3100138551386-14.3100138551386
75150143.5362733781026.46372662189761
76117112.0015591322724.99844086772821
77123106.33363903923816.6663609607622
78114106.2807215478137.71927845218702
797574.94568476753720.0543152324628252
80114111.5792870887052.42071291129503
819499.2797803084001-5.27978030840009
82116107.7817717291678.21822827083346
838680.58698253416195.41301746583807
849093.1309679833667-3.13096798336666
858778.12844466806968.87155533193038
869994.55606648302914.44393351697086
87132126.7832368660265.21676313397382
889686.36472109449489.63527890550524
899188.53930511966922.46069488033084
907778.4399298420125-1.43992984201249
91104105.903943083339-1.90394308333898
92100105.232396539217-5.23239653921659
939490.74864828235613.25135171764386
946055.92607585417894.07392414582115
954651.9978263371978-5.99782633719777
96135133.3435288632761.65647113672386
9799101.131476113945-2.13147611394542
9822.97382057456219-0.973820574562186
999692.06040773739523.9395922626048
100109115.523881067908-6.52388106790845
1011515.4180030263077-0.418003026307748
1026877.2596699601981-9.25966996019813
10310299.98070545481212.01929454518792
1048483.84221038775560.157789612244372
1054644.54543630130471.45456369869528
1065953.76149892960355.23850107039646
107116109.1109812037986.88901879620194
1082989.3747600456982-60.3747600456982
1090-0.9728929103714980.972892910371498
1109192.8503527051993-1.85035270519933
1117675.02229762874030.977702371259708
1128688.6308068380595-2.63080683805947
1138479.03971042267524.96028957732476
1146571.2726743710525-6.27267437105253
1150-1.078493595830411.07849359583041
1160-0.9728929103714980.972892910371498
1178486.7728916983971-2.77289169839709
118114124.366284536089-10.3662845360889
119132112.39639163223119.6036083677686
1209283.84919585996538.15080414003473
12133.07130660766016-0.0713066076601599
122109106.141633711152.85836628885017
1238171.41002377728479.58997622271533
124121117.7525930589693.24740694103112
1254839.96889205714048.0311079428596
12687.652536794983690.347463205016313
1278079.40337430819590.596625691804044
12810799.11790832288657.88209167711354
129140126.492509052713.5074909473
13085.797846617308772.20215338269123
1310-0.9352461706103250.935246170610325
1325668.2731956602389-12.2731956602388
13342.395185977565331.60481402243467
1347077.6744859594088-7.6744859594088
1350-0.7913075352429970.791307535242997
1361413.59735121024520.40264878975483
1370-0.9728929103714980.972892910371498
138104105.538638515985-1.538638515985
1398987.42160978951051.57839021048954
1400-0.8679039177656930.867903917765693
1411212.7717319606555-0.771731960655522
1426066.2662770032282-6.26627700322816
1438482.67072313596681.3292768640332
1448881.68066942909166.31933057090844






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.5234385290841640.9531229418316710.476561470915836
130.4115985305376070.8231970610752150.588401469462393
140.6932731700698620.6134536598602760.306726829930138
150.6480693123946120.7038613752107760.351930687605388
160.6389180528615730.7221638942768530.361081947138427
170.638253208671170.7234935826576610.36174679132883
180.697345414720330.605309170559340.30265458527967
190.6971286971679490.6057426056641010.302871302832051
200.6199749775251570.7600500449496870.380025022474843
210.5974231868449610.8051536263100790.402576813155039
220.5412346572089920.9175306855820170.458765342791008
230.4852738516813860.9705477033627720.514726148318614
240.7292805611298630.5414388777402730.270719438870137
250.681138935788390.637722128423220.31886106421161
260.6138491708527240.7723016582945520.386150829147276
270.6146872939300130.7706254121399740.385312706069987
280.6286735110974330.7426529778051330.371326488902567
290.6831256727636670.6337486544726660.316874327236333
300.6651491261444750.669701747711050.334850873855525
310.6032320754844970.7935358490310060.396767924515503
320.6660844551354550.6678310897290910.333915544864545
330.6153359575904210.7693280848191570.384664042409579
340.6310850482148120.7378299035703750.368914951785188
350.5785530425742610.8428939148514780.421446957425739
360.5175777313147030.9648445373705940.482422268685297
370.4674731253367660.9349462506735310.532526874663234
380.4204412639205660.8408825278411310.579558736079434
390.3857665756507690.7715331513015380.614233424349231
400.33202868917930.66405737835860.6679713108207
410.2917413355881810.5834826711763620.708258664411819
420.3106267142288020.6212534284576040.689373285771198
430.273249571685690.546499143371380.72675042831431
440.2474318520537070.4948637041074140.752568147946293
450.2500884495965220.5001768991930440.749911550403478
460.209746008396420.4194920167928390.79025399160358
470.2663564243362240.5327128486724480.733643575663776
480.223860320071670.447720640143340.77613967992833
490.1880903258887120.3761806517774240.811909674111288
500.1740763280411070.3481526560822140.825923671958893
510.1865544749405640.3731089498811280.813445525059436
520.1550380641164490.3100761282328980.844961935883551
530.1672506009767830.3345012019535650.832749399023217
540.1384224126482630.2768448252965250.861577587351737
550.1203006321760040.2406012643520080.879699367823996
560.1052034711396170.2104069422792340.894796528860383
570.08557229296138940.1711445859227790.914427707038611
580.0861294614500850.172258922900170.913870538549915
590.06729743655781120.1345948731156220.932702563442189
600.05261550468884190.1052310093776840.947384495311158
610.0573057653325010.1146115306650020.942694234667499
620.04551529535723290.09103059071446570.954484704642767
630.04358114831216650.0871622966243330.956418851687833
640.03297792275463630.06595584550927260.967022077245364
650.02501648646195190.05003297292390380.974983513538048
660.01896518458376780.03793036916753560.981034815416232
670.01912284048194620.03824568096389240.980877159518054
680.01654065648351140.03308131296702280.983459343516489
690.01271597373428430.02543194746856860.987284026265716
700.01230329654550650.0246065930910130.987696703454493
710.009276805907457330.01855361181491470.990723194092543
720.00665776014254260.01331552028508520.993342239857457
730.008649771859534080.01729954371906820.991350228140466
740.02016371196212470.04032742392424950.979836288037875
750.01744859118719650.03489718237439310.982551408812803
760.01413837988630380.02827675977260770.985861620113696
770.02537049889232030.05074099778464060.97462950110768
780.02307649233875730.04615298467751460.976923507661243
790.01714932893205610.03429865786411220.982850671067944
800.01349444277589150.0269888855517830.986505557224108
810.01051443900810080.02102887801620160.989485560991899
820.01649188411176930.03298376822353860.983508115888231
830.01350568987626810.02701137975253620.986494310123732
840.01514349523746720.03028699047493430.984856504762533
850.01700131023404540.03400262046809070.982998689765955
860.01420628454630160.02841256909260330.985793715453698
870.01237956322801650.02475912645603310.987620436771984
880.01266841777515320.02533683555030650.987331582224847
890.009510029579498090.01902005915899620.990489970420502
900.006702711336973410.01340542267394680.993297288663027
910.004682428979942290.009364857959884570.995317571020058
920.00446778980616360.008935579612327210.995532210193836
930.003677987245560770.007355974491121550.996322012754439
940.002671736388059650.00534347277611930.99732826361194
950.003170894219157610.006341788438315230.996829105780842
960.003826055705024610.007652111410049210.996173944294975
970.002655237750497690.005310475500995370.997344762249502
980.00175897313221120.003517946264422390.998241026867789
990.001231437467801540.002462874935603070.998768562532198
1000.001095615673599340.002191231347198680.998904384326401
1010.0008853302213543720.001770660442708740.999114669778646
1020.000817118221995030.001634236443990060.999182881778005
1030.0005329107822293560.001065821564458710.999467089217771
1040.0003249560846608990.0006499121693217970.999675043915339
1050.0002360238604433150.0004720477208866310.999763976139557
1060.0001619934420169430.0003239868840338860.999838006557983
1070.0001122164379580090.0002244328759160190.999887783562042
1080.9999078174957960.0001843650084078339.21825042039167e-05
1090.9998285848462490.0003428303075017890.000171415153750895
1100.9997714291561550.0004571416876891870.000228570843844594
1110.9995561142237030.0008877715525938540.000443885776296927
1120.9991686198732740.001662760253452480.000831380126726242
1130.9985444541678180.002911091664364670.00145554583218234
1140.9985702605559730.002859478888054520.00142973944402726
1150.9973579165148120.005284166970376690.00264208348518835
1160.9954487856454530.009102428709093480.00455121435454674
1170.9946351274817750.01072974503644930.00536487251822464
1180.9948015080118180.01039698397636430.00519849198818216
1190.9962444097181280.007511180563743890.00375559028187194
1200.9947685090437870.01046298191242550.00523149095621274
1210.9939225591130380.01215488177392350.00607744088696177
1220.9889003147557780.0221993704884450.0110996852442225
1230.9869709662683010.02605806746339890.0130290337316994
1240.9884245666513660.02315086669726870.0115754333486344
1250.9902870613290310.01942587734193760.00971293867096881
1260.9800498670332880.03990026593342370.0199501329667119
1270.9891967520221940.02160649595561180.0108032479778059
1280.9760167409734650.04796651805306910.0239832590265346
1290.9854825204576730.02903495908465410.0145174795423271
1300.9680056405146390.06398871897072260.0319943594853613
1310.9197369138537060.1605261722925870.0802630861462937
1320.9019243016708120.1961513966583770.0980756983291883

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.523438529084164 & 0.953122941831671 & 0.476561470915836 \tabularnewline
13 & 0.411598530537607 & 0.823197061075215 & 0.588401469462393 \tabularnewline
14 & 0.693273170069862 & 0.613453659860276 & 0.306726829930138 \tabularnewline
15 & 0.648069312394612 & 0.703861375210776 & 0.351930687605388 \tabularnewline
16 & 0.638918052861573 & 0.722163894276853 & 0.361081947138427 \tabularnewline
17 & 0.63825320867117 & 0.723493582657661 & 0.36174679132883 \tabularnewline
18 & 0.69734541472033 & 0.60530917055934 & 0.30265458527967 \tabularnewline
19 & 0.697128697167949 & 0.605742605664101 & 0.302871302832051 \tabularnewline
20 & 0.619974977525157 & 0.760050044949687 & 0.380025022474843 \tabularnewline
21 & 0.597423186844961 & 0.805153626310079 & 0.402576813155039 \tabularnewline
22 & 0.541234657208992 & 0.917530685582017 & 0.458765342791008 \tabularnewline
23 & 0.485273851681386 & 0.970547703362772 & 0.514726148318614 \tabularnewline
24 & 0.729280561129863 & 0.541438877740273 & 0.270719438870137 \tabularnewline
25 & 0.68113893578839 & 0.63772212842322 & 0.31886106421161 \tabularnewline
26 & 0.613849170852724 & 0.772301658294552 & 0.386150829147276 \tabularnewline
27 & 0.614687293930013 & 0.770625412139974 & 0.385312706069987 \tabularnewline
28 & 0.628673511097433 & 0.742652977805133 & 0.371326488902567 \tabularnewline
29 & 0.683125672763667 & 0.633748654472666 & 0.316874327236333 \tabularnewline
30 & 0.665149126144475 & 0.66970174771105 & 0.334850873855525 \tabularnewline
31 & 0.603232075484497 & 0.793535849031006 & 0.396767924515503 \tabularnewline
32 & 0.666084455135455 & 0.667831089729091 & 0.333915544864545 \tabularnewline
33 & 0.615335957590421 & 0.769328084819157 & 0.384664042409579 \tabularnewline
34 & 0.631085048214812 & 0.737829903570375 & 0.368914951785188 \tabularnewline
35 & 0.578553042574261 & 0.842893914851478 & 0.421446957425739 \tabularnewline
36 & 0.517577731314703 & 0.964844537370594 & 0.482422268685297 \tabularnewline
37 & 0.467473125336766 & 0.934946250673531 & 0.532526874663234 \tabularnewline
38 & 0.420441263920566 & 0.840882527841131 & 0.579558736079434 \tabularnewline
39 & 0.385766575650769 & 0.771533151301538 & 0.614233424349231 \tabularnewline
40 & 0.3320286891793 & 0.6640573783586 & 0.6679713108207 \tabularnewline
41 & 0.291741335588181 & 0.583482671176362 & 0.708258664411819 \tabularnewline
42 & 0.310626714228802 & 0.621253428457604 & 0.689373285771198 \tabularnewline
43 & 0.27324957168569 & 0.54649914337138 & 0.72675042831431 \tabularnewline
44 & 0.247431852053707 & 0.494863704107414 & 0.752568147946293 \tabularnewline
45 & 0.250088449596522 & 0.500176899193044 & 0.749911550403478 \tabularnewline
46 & 0.20974600839642 & 0.419492016792839 & 0.79025399160358 \tabularnewline
47 & 0.266356424336224 & 0.532712848672448 & 0.733643575663776 \tabularnewline
48 & 0.22386032007167 & 0.44772064014334 & 0.77613967992833 \tabularnewline
49 & 0.188090325888712 & 0.376180651777424 & 0.811909674111288 \tabularnewline
50 & 0.174076328041107 & 0.348152656082214 & 0.825923671958893 \tabularnewline
51 & 0.186554474940564 & 0.373108949881128 & 0.813445525059436 \tabularnewline
52 & 0.155038064116449 & 0.310076128232898 & 0.844961935883551 \tabularnewline
53 & 0.167250600976783 & 0.334501201953565 & 0.832749399023217 \tabularnewline
54 & 0.138422412648263 & 0.276844825296525 & 0.861577587351737 \tabularnewline
55 & 0.120300632176004 & 0.240601264352008 & 0.879699367823996 \tabularnewline
56 & 0.105203471139617 & 0.210406942279234 & 0.894796528860383 \tabularnewline
57 & 0.0855722929613894 & 0.171144585922779 & 0.914427707038611 \tabularnewline
58 & 0.086129461450085 & 0.17225892290017 & 0.913870538549915 \tabularnewline
59 & 0.0672974365578112 & 0.134594873115622 & 0.932702563442189 \tabularnewline
60 & 0.0526155046888419 & 0.105231009377684 & 0.947384495311158 \tabularnewline
61 & 0.057305765332501 & 0.114611530665002 & 0.942694234667499 \tabularnewline
62 & 0.0455152953572329 & 0.0910305907144657 & 0.954484704642767 \tabularnewline
63 & 0.0435811483121665 & 0.087162296624333 & 0.956418851687833 \tabularnewline
64 & 0.0329779227546363 & 0.0659558455092726 & 0.967022077245364 \tabularnewline
65 & 0.0250164864619519 & 0.0500329729239038 & 0.974983513538048 \tabularnewline
66 & 0.0189651845837678 & 0.0379303691675356 & 0.981034815416232 \tabularnewline
67 & 0.0191228404819462 & 0.0382456809638924 & 0.980877159518054 \tabularnewline
68 & 0.0165406564835114 & 0.0330813129670228 & 0.983459343516489 \tabularnewline
69 & 0.0127159737342843 & 0.0254319474685686 & 0.987284026265716 \tabularnewline
70 & 0.0123032965455065 & 0.024606593091013 & 0.987696703454493 \tabularnewline
71 & 0.00927680590745733 & 0.0185536118149147 & 0.990723194092543 \tabularnewline
72 & 0.0066577601425426 & 0.0133155202850852 & 0.993342239857457 \tabularnewline
73 & 0.00864977185953408 & 0.0172995437190682 & 0.991350228140466 \tabularnewline
74 & 0.0201637119621247 & 0.0403274239242495 & 0.979836288037875 \tabularnewline
75 & 0.0174485911871965 & 0.0348971823743931 & 0.982551408812803 \tabularnewline
76 & 0.0141383798863038 & 0.0282767597726077 & 0.985861620113696 \tabularnewline
77 & 0.0253704988923203 & 0.0507409977846406 & 0.97462950110768 \tabularnewline
78 & 0.0230764923387573 & 0.0461529846775146 & 0.976923507661243 \tabularnewline
79 & 0.0171493289320561 & 0.0342986578641122 & 0.982850671067944 \tabularnewline
80 & 0.0134944427758915 & 0.026988885551783 & 0.986505557224108 \tabularnewline
81 & 0.0105144390081008 & 0.0210288780162016 & 0.989485560991899 \tabularnewline
82 & 0.0164918841117693 & 0.0329837682235386 & 0.983508115888231 \tabularnewline
83 & 0.0135056898762681 & 0.0270113797525362 & 0.986494310123732 \tabularnewline
84 & 0.0151434952374672 & 0.0302869904749343 & 0.984856504762533 \tabularnewline
85 & 0.0170013102340454 & 0.0340026204680907 & 0.982998689765955 \tabularnewline
86 & 0.0142062845463016 & 0.0284125690926033 & 0.985793715453698 \tabularnewline
87 & 0.0123795632280165 & 0.0247591264560331 & 0.987620436771984 \tabularnewline
88 & 0.0126684177751532 & 0.0253368355503065 & 0.987331582224847 \tabularnewline
89 & 0.00951002957949809 & 0.0190200591589962 & 0.990489970420502 \tabularnewline
90 & 0.00670271133697341 & 0.0134054226739468 & 0.993297288663027 \tabularnewline
91 & 0.00468242897994229 & 0.00936485795988457 & 0.995317571020058 \tabularnewline
92 & 0.0044677898061636 & 0.00893557961232721 & 0.995532210193836 \tabularnewline
93 & 0.00367798724556077 & 0.00735597449112155 & 0.996322012754439 \tabularnewline
94 & 0.00267173638805965 & 0.0053434727761193 & 0.99732826361194 \tabularnewline
95 & 0.00317089421915761 & 0.00634178843831523 & 0.996829105780842 \tabularnewline
96 & 0.00382605570502461 & 0.00765211141004921 & 0.996173944294975 \tabularnewline
97 & 0.00265523775049769 & 0.00531047550099537 & 0.997344762249502 \tabularnewline
98 & 0.0017589731322112 & 0.00351794626442239 & 0.998241026867789 \tabularnewline
99 & 0.00123143746780154 & 0.00246287493560307 & 0.998768562532198 \tabularnewline
100 & 0.00109561567359934 & 0.00219123134719868 & 0.998904384326401 \tabularnewline
101 & 0.000885330221354372 & 0.00177066044270874 & 0.999114669778646 \tabularnewline
102 & 0.00081711822199503 & 0.00163423644399006 & 0.999182881778005 \tabularnewline
103 & 0.000532910782229356 & 0.00106582156445871 & 0.999467089217771 \tabularnewline
104 & 0.000324956084660899 & 0.000649912169321797 & 0.999675043915339 \tabularnewline
105 & 0.000236023860443315 & 0.000472047720886631 & 0.999763976139557 \tabularnewline
106 & 0.000161993442016943 & 0.000323986884033886 & 0.999838006557983 \tabularnewline
107 & 0.000112216437958009 & 0.000224432875916019 & 0.999887783562042 \tabularnewline
108 & 0.999907817495796 & 0.000184365008407833 & 9.21825042039167e-05 \tabularnewline
109 & 0.999828584846249 & 0.000342830307501789 & 0.000171415153750895 \tabularnewline
110 & 0.999771429156155 & 0.000457141687689187 & 0.000228570843844594 \tabularnewline
111 & 0.999556114223703 & 0.000887771552593854 & 0.000443885776296927 \tabularnewline
112 & 0.999168619873274 & 0.00166276025345248 & 0.000831380126726242 \tabularnewline
113 & 0.998544454167818 & 0.00291109166436467 & 0.00145554583218234 \tabularnewline
114 & 0.998570260555973 & 0.00285947888805452 & 0.00142973944402726 \tabularnewline
115 & 0.997357916514812 & 0.00528416697037669 & 0.00264208348518835 \tabularnewline
116 & 0.995448785645453 & 0.00910242870909348 & 0.00455121435454674 \tabularnewline
117 & 0.994635127481775 & 0.0107297450364493 & 0.00536487251822464 \tabularnewline
118 & 0.994801508011818 & 0.0103969839763643 & 0.00519849198818216 \tabularnewline
119 & 0.996244409718128 & 0.00751118056374389 & 0.00375559028187194 \tabularnewline
120 & 0.994768509043787 & 0.0104629819124255 & 0.00523149095621274 \tabularnewline
121 & 0.993922559113038 & 0.0121548817739235 & 0.00607744088696177 \tabularnewline
122 & 0.988900314755778 & 0.022199370488445 & 0.0110996852442225 \tabularnewline
123 & 0.986970966268301 & 0.0260580674633989 & 0.0130290337316994 \tabularnewline
124 & 0.988424566651366 & 0.0231508666972687 & 0.0115754333486344 \tabularnewline
125 & 0.990287061329031 & 0.0194258773419376 & 0.00971293867096881 \tabularnewline
126 & 0.980049867033288 & 0.0399002659334237 & 0.0199501329667119 \tabularnewline
127 & 0.989196752022194 & 0.0216064959556118 & 0.0108032479778059 \tabularnewline
128 & 0.976016740973465 & 0.0479665180530691 & 0.0239832590265346 \tabularnewline
129 & 0.985482520457673 & 0.0290349590846541 & 0.0145174795423271 \tabularnewline
130 & 0.968005640514639 & 0.0639887189707226 & 0.0319943594853613 \tabularnewline
131 & 0.919736913853706 & 0.160526172292587 & 0.0802630861462937 \tabularnewline
132 & 0.901924301670812 & 0.196151396658377 & 0.0980756983291883 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159884&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]12[/C][C]0.523438529084164[/C][C]0.953122941831671[/C][C]0.476561470915836[/C][/ROW]
[ROW][C]13[/C][C]0.411598530537607[/C][C]0.823197061075215[/C][C]0.588401469462393[/C][/ROW]
[ROW][C]14[/C][C]0.693273170069862[/C][C]0.613453659860276[/C][C]0.306726829930138[/C][/ROW]
[ROW][C]15[/C][C]0.648069312394612[/C][C]0.703861375210776[/C][C]0.351930687605388[/C][/ROW]
[ROW][C]16[/C][C]0.638918052861573[/C][C]0.722163894276853[/C][C]0.361081947138427[/C][/ROW]
[ROW][C]17[/C][C]0.63825320867117[/C][C]0.723493582657661[/C][C]0.36174679132883[/C][/ROW]
[ROW][C]18[/C][C]0.69734541472033[/C][C]0.60530917055934[/C][C]0.30265458527967[/C][/ROW]
[ROW][C]19[/C][C]0.697128697167949[/C][C]0.605742605664101[/C][C]0.302871302832051[/C][/ROW]
[ROW][C]20[/C][C]0.619974977525157[/C][C]0.760050044949687[/C][C]0.380025022474843[/C][/ROW]
[ROW][C]21[/C][C]0.597423186844961[/C][C]0.805153626310079[/C][C]0.402576813155039[/C][/ROW]
[ROW][C]22[/C][C]0.541234657208992[/C][C]0.917530685582017[/C][C]0.458765342791008[/C][/ROW]
[ROW][C]23[/C][C]0.485273851681386[/C][C]0.970547703362772[/C][C]0.514726148318614[/C][/ROW]
[ROW][C]24[/C][C]0.729280561129863[/C][C]0.541438877740273[/C][C]0.270719438870137[/C][/ROW]
[ROW][C]25[/C][C]0.68113893578839[/C][C]0.63772212842322[/C][C]0.31886106421161[/C][/ROW]
[ROW][C]26[/C][C]0.613849170852724[/C][C]0.772301658294552[/C][C]0.386150829147276[/C][/ROW]
[ROW][C]27[/C][C]0.614687293930013[/C][C]0.770625412139974[/C][C]0.385312706069987[/C][/ROW]
[ROW][C]28[/C][C]0.628673511097433[/C][C]0.742652977805133[/C][C]0.371326488902567[/C][/ROW]
[ROW][C]29[/C][C]0.683125672763667[/C][C]0.633748654472666[/C][C]0.316874327236333[/C][/ROW]
[ROW][C]30[/C][C]0.665149126144475[/C][C]0.66970174771105[/C][C]0.334850873855525[/C][/ROW]
[ROW][C]31[/C][C]0.603232075484497[/C][C]0.793535849031006[/C][C]0.396767924515503[/C][/ROW]
[ROW][C]32[/C][C]0.666084455135455[/C][C]0.667831089729091[/C][C]0.333915544864545[/C][/ROW]
[ROW][C]33[/C][C]0.615335957590421[/C][C]0.769328084819157[/C][C]0.384664042409579[/C][/ROW]
[ROW][C]34[/C][C]0.631085048214812[/C][C]0.737829903570375[/C][C]0.368914951785188[/C][/ROW]
[ROW][C]35[/C][C]0.578553042574261[/C][C]0.842893914851478[/C][C]0.421446957425739[/C][/ROW]
[ROW][C]36[/C][C]0.517577731314703[/C][C]0.964844537370594[/C][C]0.482422268685297[/C][/ROW]
[ROW][C]37[/C][C]0.467473125336766[/C][C]0.934946250673531[/C][C]0.532526874663234[/C][/ROW]
[ROW][C]38[/C][C]0.420441263920566[/C][C]0.840882527841131[/C][C]0.579558736079434[/C][/ROW]
[ROW][C]39[/C][C]0.385766575650769[/C][C]0.771533151301538[/C][C]0.614233424349231[/C][/ROW]
[ROW][C]40[/C][C]0.3320286891793[/C][C]0.6640573783586[/C][C]0.6679713108207[/C][/ROW]
[ROW][C]41[/C][C]0.291741335588181[/C][C]0.583482671176362[/C][C]0.708258664411819[/C][/ROW]
[ROW][C]42[/C][C]0.310626714228802[/C][C]0.621253428457604[/C][C]0.689373285771198[/C][/ROW]
[ROW][C]43[/C][C]0.27324957168569[/C][C]0.54649914337138[/C][C]0.72675042831431[/C][/ROW]
[ROW][C]44[/C][C]0.247431852053707[/C][C]0.494863704107414[/C][C]0.752568147946293[/C][/ROW]
[ROW][C]45[/C][C]0.250088449596522[/C][C]0.500176899193044[/C][C]0.749911550403478[/C][/ROW]
[ROW][C]46[/C][C]0.20974600839642[/C][C]0.419492016792839[/C][C]0.79025399160358[/C][/ROW]
[ROW][C]47[/C][C]0.266356424336224[/C][C]0.532712848672448[/C][C]0.733643575663776[/C][/ROW]
[ROW][C]48[/C][C]0.22386032007167[/C][C]0.44772064014334[/C][C]0.77613967992833[/C][/ROW]
[ROW][C]49[/C][C]0.188090325888712[/C][C]0.376180651777424[/C][C]0.811909674111288[/C][/ROW]
[ROW][C]50[/C][C]0.174076328041107[/C][C]0.348152656082214[/C][C]0.825923671958893[/C][/ROW]
[ROW][C]51[/C][C]0.186554474940564[/C][C]0.373108949881128[/C][C]0.813445525059436[/C][/ROW]
[ROW][C]52[/C][C]0.155038064116449[/C][C]0.310076128232898[/C][C]0.844961935883551[/C][/ROW]
[ROW][C]53[/C][C]0.167250600976783[/C][C]0.334501201953565[/C][C]0.832749399023217[/C][/ROW]
[ROW][C]54[/C][C]0.138422412648263[/C][C]0.276844825296525[/C][C]0.861577587351737[/C][/ROW]
[ROW][C]55[/C][C]0.120300632176004[/C][C]0.240601264352008[/C][C]0.879699367823996[/C][/ROW]
[ROW][C]56[/C][C]0.105203471139617[/C][C]0.210406942279234[/C][C]0.894796528860383[/C][/ROW]
[ROW][C]57[/C][C]0.0855722929613894[/C][C]0.171144585922779[/C][C]0.914427707038611[/C][/ROW]
[ROW][C]58[/C][C]0.086129461450085[/C][C]0.17225892290017[/C][C]0.913870538549915[/C][/ROW]
[ROW][C]59[/C][C]0.0672974365578112[/C][C]0.134594873115622[/C][C]0.932702563442189[/C][/ROW]
[ROW][C]60[/C][C]0.0526155046888419[/C][C]0.105231009377684[/C][C]0.947384495311158[/C][/ROW]
[ROW][C]61[/C][C]0.057305765332501[/C][C]0.114611530665002[/C][C]0.942694234667499[/C][/ROW]
[ROW][C]62[/C][C]0.0455152953572329[/C][C]0.0910305907144657[/C][C]0.954484704642767[/C][/ROW]
[ROW][C]63[/C][C]0.0435811483121665[/C][C]0.087162296624333[/C][C]0.956418851687833[/C][/ROW]
[ROW][C]64[/C][C]0.0329779227546363[/C][C]0.0659558455092726[/C][C]0.967022077245364[/C][/ROW]
[ROW][C]65[/C][C]0.0250164864619519[/C][C]0.0500329729239038[/C][C]0.974983513538048[/C][/ROW]
[ROW][C]66[/C][C]0.0189651845837678[/C][C]0.0379303691675356[/C][C]0.981034815416232[/C][/ROW]
[ROW][C]67[/C][C]0.0191228404819462[/C][C]0.0382456809638924[/C][C]0.980877159518054[/C][/ROW]
[ROW][C]68[/C][C]0.0165406564835114[/C][C]0.0330813129670228[/C][C]0.983459343516489[/C][/ROW]
[ROW][C]69[/C][C]0.0127159737342843[/C][C]0.0254319474685686[/C][C]0.987284026265716[/C][/ROW]
[ROW][C]70[/C][C]0.0123032965455065[/C][C]0.024606593091013[/C][C]0.987696703454493[/C][/ROW]
[ROW][C]71[/C][C]0.00927680590745733[/C][C]0.0185536118149147[/C][C]0.990723194092543[/C][/ROW]
[ROW][C]72[/C][C]0.0066577601425426[/C][C]0.0133155202850852[/C][C]0.993342239857457[/C][/ROW]
[ROW][C]73[/C][C]0.00864977185953408[/C][C]0.0172995437190682[/C][C]0.991350228140466[/C][/ROW]
[ROW][C]74[/C][C]0.0201637119621247[/C][C]0.0403274239242495[/C][C]0.979836288037875[/C][/ROW]
[ROW][C]75[/C][C]0.0174485911871965[/C][C]0.0348971823743931[/C][C]0.982551408812803[/C][/ROW]
[ROW][C]76[/C][C]0.0141383798863038[/C][C]0.0282767597726077[/C][C]0.985861620113696[/C][/ROW]
[ROW][C]77[/C][C]0.0253704988923203[/C][C]0.0507409977846406[/C][C]0.97462950110768[/C][/ROW]
[ROW][C]78[/C][C]0.0230764923387573[/C][C]0.0461529846775146[/C][C]0.976923507661243[/C][/ROW]
[ROW][C]79[/C][C]0.0171493289320561[/C][C]0.0342986578641122[/C][C]0.982850671067944[/C][/ROW]
[ROW][C]80[/C][C]0.0134944427758915[/C][C]0.026988885551783[/C][C]0.986505557224108[/C][/ROW]
[ROW][C]81[/C][C]0.0105144390081008[/C][C]0.0210288780162016[/C][C]0.989485560991899[/C][/ROW]
[ROW][C]82[/C][C]0.0164918841117693[/C][C]0.0329837682235386[/C][C]0.983508115888231[/C][/ROW]
[ROW][C]83[/C][C]0.0135056898762681[/C][C]0.0270113797525362[/C][C]0.986494310123732[/C][/ROW]
[ROW][C]84[/C][C]0.0151434952374672[/C][C]0.0302869904749343[/C][C]0.984856504762533[/C][/ROW]
[ROW][C]85[/C][C]0.0170013102340454[/C][C]0.0340026204680907[/C][C]0.982998689765955[/C][/ROW]
[ROW][C]86[/C][C]0.0142062845463016[/C][C]0.0284125690926033[/C][C]0.985793715453698[/C][/ROW]
[ROW][C]87[/C][C]0.0123795632280165[/C][C]0.0247591264560331[/C][C]0.987620436771984[/C][/ROW]
[ROW][C]88[/C][C]0.0126684177751532[/C][C]0.0253368355503065[/C][C]0.987331582224847[/C][/ROW]
[ROW][C]89[/C][C]0.00951002957949809[/C][C]0.0190200591589962[/C][C]0.990489970420502[/C][/ROW]
[ROW][C]90[/C][C]0.00670271133697341[/C][C]0.0134054226739468[/C][C]0.993297288663027[/C][/ROW]
[ROW][C]91[/C][C]0.00468242897994229[/C][C]0.00936485795988457[/C][C]0.995317571020058[/C][/ROW]
[ROW][C]92[/C][C]0.0044677898061636[/C][C]0.00893557961232721[/C][C]0.995532210193836[/C][/ROW]
[ROW][C]93[/C][C]0.00367798724556077[/C][C]0.00735597449112155[/C][C]0.996322012754439[/C][/ROW]
[ROW][C]94[/C][C]0.00267173638805965[/C][C]0.0053434727761193[/C][C]0.99732826361194[/C][/ROW]
[ROW][C]95[/C][C]0.00317089421915761[/C][C]0.00634178843831523[/C][C]0.996829105780842[/C][/ROW]
[ROW][C]96[/C][C]0.00382605570502461[/C][C]0.00765211141004921[/C][C]0.996173944294975[/C][/ROW]
[ROW][C]97[/C][C]0.00265523775049769[/C][C]0.00531047550099537[/C][C]0.997344762249502[/C][/ROW]
[ROW][C]98[/C][C]0.0017589731322112[/C][C]0.00351794626442239[/C][C]0.998241026867789[/C][/ROW]
[ROW][C]99[/C][C]0.00123143746780154[/C][C]0.00246287493560307[/C][C]0.998768562532198[/C][/ROW]
[ROW][C]100[/C][C]0.00109561567359934[/C][C]0.00219123134719868[/C][C]0.998904384326401[/C][/ROW]
[ROW][C]101[/C][C]0.000885330221354372[/C][C]0.00177066044270874[/C][C]0.999114669778646[/C][/ROW]
[ROW][C]102[/C][C]0.00081711822199503[/C][C]0.00163423644399006[/C][C]0.999182881778005[/C][/ROW]
[ROW][C]103[/C][C]0.000532910782229356[/C][C]0.00106582156445871[/C][C]0.999467089217771[/C][/ROW]
[ROW][C]104[/C][C]0.000324956084660899[/C][C]0.000649912169321797[/C][C]0.999675043915339[/C][/ROW]
[ROW][C]105[/C][C]0.000236023860443315[/C][C]0.000472047720886631[/C][C]0.999763976139557[/C][/ROW]
[ROW][C]106[/C][C]0.000161993442016943[/C][C]0.000323986884033886[/C][C]0.999838006557983[/C][/ROW]
[ROW][C]107[/C][C]0.000112216437958009[/C][C]0.000224432875916019[/C][C]0.999887783562042[/C][/ROW]
[ROW][C]108[/C][C]0.999907817495796[/C][C]0.000184365008407833[/C][C]9.21825042039167e-05[/C][/ROW]
[ROW][C]109[/C][C]0.999828584846249[/C][C]0.000342830307501789[/C][C]0.000171415153750895[/C][/ROW]
[ROW][C]110[/C][C]0.999771429156155[/C][C]0.000457141687689187[/C][C]0.000228570843844594[/C][/ROW]
[ROW][C]111[/C][C]0.999556114223703[/C][C]0.000887771552593854[/C][C]0.000443885776296927[/C][/ROW]
[ROW][C]112[/C][C]0.999168619873274[/C][C]0.00166276025345248[/C][C]0.000831380126726242[/C][/ROW]
[ROW][C]113[/C][C]0.998544454167818[/C][C]0.00291109166436467[/C][C]0.00145554583218234[/C][/ROW]
[ROW][C]114[/C][C]0.998570260555973[/C][C]0.00285947888805452[/C][C]0.00142973944402726[/C][/ROW]
[ROW][C]115[/C][C]0.997357916514812[/C][C]0.00528416697037669[/C][C]0.00264208348518835[/C][/ROW]
[ROW][C]116[/C][C]0.995448785645453[/C][C]0.00910242870909348[/C][C]0.00455121435454674[/C][/ROW]
[ROW][C]117[/C][C]0.994635127481775[/C][C]0.0107297450364493[/C][C]0.00536487251822464[/C][/ROW]
[ROW][C]118[/C][C]0.994801508011818[/C][C]0.0103969839763643[/C][C]0.00519849198818216[/C][/ROW]
[ROW][C]119[/C][C]0.996244409718128[/C][C]0.00751118056374389[/C][C]0.00375559028187194[/C][/ROW]
[ROW][C]120[/C][C]0.994768509043787[/C][C]0.0104629819124255[/C][C]0.00523149095621274[/C][/ROW]
[ROW][C]121[/C][C]0.993922559113038[/C][C]0.0121548817739235[/C][C]0.00607744088696177[/C][/ROW]
[ROW][C]122[/C][C]0.988900314755778[/C][C]0.022199370488445[/C][C]0.0110996852442225[/C][/ROW]
[ROW][C]123[/C][C]0.986970966268301[/C][C]0.0260580674633989[/C][C]0.0130290337316994[/C][/ROW]
[ROW][C]124[/C][C]0.988424566651366[/C][C]0.0231508666972687[/C][C]0.0115754333486344[/C][/ROW]
[ROW][C]125[/C][C]0.990287061329031[/C][C]0.0194258773419376[/C][C]0.00971293867096881[/C][/ROW]
[ROW][C]126[/C][C]0.980049867033288[/C][C]0.0399002659334237[/C][C]0.0199501329667119[/C][/ROW]
[ROW][C]127[/C][C]0.989196752022194[/C][C]0.0216064959556118[/C][C]0.0108032479778059[/C][/ROW]
[ROW][C]128[/C][C]0.976016740973465[/C][C]0.0479665180530691[/C][C]0.0239832590265346[/C][/ROW]
[ROW][C]129[/C][C]0.985482520457673[/C][C]0.0290349590846541[/C][C]0.0145174795423271[/C][/ROW]
[ROW][C]130[/C][C]0.968005640514639[/C][C]0.0639887189707226[/C][C]0.0319943594853613[/C][/ROW]
[ROW][C]131[/C][C]0.919736913853706[/C][C]0.160526172292587[/C][C]0.0802630861462937[/C][/ROW]
[ROW][C]132[/C][C]0.901924301670812[/C][C]0.196151396658377[/C][C]0.0980756983291883[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159884&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159884&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
120.5234385290841640.9531229418316710.476561470915836
130.4115985305376070.8231970610752150.588401469462393
140.6932731700698620.6134536598602760.306726829930138
150.6480693123946120.7038613752107760.351930687605388
160.6389180528615730.7221638942768530.361081947138427
170.638253208671170.7234935826576610.36174679132883
180.697345414720330.605309170559340.30265458527967
190.6971286971679490.6057426056641010.302871302832051
200.6199749775251570.7600500449496870.380025022474843
210.5974231868449610.8051536263100790.402576813155039
220.5412346572089920.9175306855820170.458765342791008
230.4852738516813860.9705477033627720.514726148318614
240.7292805611298630.5414388777402730.270719438870137
250.681138935788390.637722128423220.31886106421161
260.6138491708527240.7723016582945520.386150829147276
270.6146872939300130.7706254121399740.385312706069987
280.6286735110974330.7426529778051330.371326488902567
290.6831256727636670.6337486544726660.316874327236333
300.6651491261444750.669701747711050.334850873855525
310.6032320754844970.7935358490310060.396767924515503
320.6660844551354550.6678310897290910.333915544864545
330.6153359575904210.7693280848191570.384664042409579
340.6310850482148120.7378299035703750.368914951785188
350.5785530425742610.8428939148514780.421446957425739
360.5175777313147030.9648445373705940.482422268685297
370.4674731253367660.9349462506735310.532526874663234
380.4204412639205660.8408825278411310.579558736079434
390.3857665756507690.7715331513015380.614233424349231
400.33202868917930.66405737835860.6679713108207
410.2917413355881810.5834826711763620.708258664411819
420.3106267142288020.6212534284576040.689373285771198
430.273249571685690.546499143371380.72675042831431
440.2474318520537070.4948637041074140.752568147946293
450.2500884495965220.5001768991930440.749911550403478
460.209746008396420.4194920167928390.79025399160358
470.2663564243362240.5327128486724480.733643575663776
480.223860320071670.447720640143340.77613967992833
490.1880903258887120.3761806517774240.811909674111288
500.1740763280411070.3481526560822140.825923671958893
510.1865544749405640.3731089498811280.813445525059436
520.1550380641164490.3100761282328980.844961935883551
530.1672506009767830.3345012019535650.832749399023217
540.1384224126482630.2768448252965250.861577587351737
550.1203006321760040.2406012643520080.879699367823996
560.1052034711396170.2104069422792340.894796528860383
570.08557229296138940.1711445859227790.914427707038611
580.0861294614500850.172258922900170.913870538549915
590.06729743655781120.1345948731156220.932702563442189
600.05261550468884190.1052310093776840.947384495311158
610.0573057653325010.1146115306650020.942694234667499
620.04551529535723290.09103059071446570.954484704642767
630.04358114831216650.0871622966243330.956418851687833
640.03297792275463630.06595584550927260.967022077245364
650.02501648646195190.05003297292390380.974983513538048
660.01896518458376780.03793036916753560.981034815416232
670.01912284048194620.03824568096389240.980877159518054
680.01654065648351140.03308131296702280.983459343516489
690.01271597373428430.02543194746856860.987284026265716
700.01230329654550650.0246065930910130.987696703454493
710.009276805907457330.01855361181491470.990723194092543
720.00665776014254260.01331552028508520.993342239857457
730.008649771859534080.01729954371906820.991350228140466
740.02016371196212470.04032742392424950.979836288037875
750.01744859118719650.03489718237439310.982551408812803
760.01413837988630380.02827675977260770.985861620113696
770.02537049889232030.05074099778464060.97462950110768
780.02307649233875730.04615298467751460.976923507661243
790.01714932893205610.03429865786411220.982850671067944
800.01349444277589150.0269888855517830.986505557224108
810.01051443900810080.02102887801620160.989485560991899
820.01649188411176930.03298376822353860.983508115888231
830.01350568987626810.02701137975253620.986494310123732
840.01514349523746720.03028699047493430.984856504762533
850.01700131023404540.03400262046809070.982998689765955
860.01420628454630160.02841256909260330.985793715453698
870.01237956322801650.02475912645603310.987620436771984
880.01266841777515320.02533683555030650.987331582224847
890.009510029579498090.01902005915899620.990489970420502
900.006702711336973410.01340542267394680.993297288663027
910.004682428979942290.009364857959884570.995317571020058
920.00446778980616360.008935579612327210.995532210193836
930.003677987245560770.007355974491121550.996322012754439
940.002671736388059650.00534347277611930.99732826361194
950.003170894219157610.006341788438315230.996829105780842
960.003826055705024610.007652111410049210.996173944294975
970.002655237750497690.005310475500995370.997344762249502
980.00175897313221120.003517946264422390.998241026867789
990.001231437467801540.002462874935603070.998768562532198
1000.001095615673599340.002191231347198680.998904384326401
1010.0008853302213543720.001770660442708740.999114669778646
1020.000817118221995030.001634236443990060.999182881778005
1030.0005329107822293560.001065821564458710.999467089217771
1040.0003249560846608990.0006499121693217970.999675043915339
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1080.9999078174957960.0001843650084078339.21825042039167e-05
1090.9998285848462490.0003428303075017890.000171415153750895
1100.9997714291561550.0004571416876891870.000228570843844594
1110.9995561142237030.0008877715525938540.000443885776296927
1120.9991686198732740.001662760253452480.000831380126726242
1130.9985444541678180.002911091664364670.00145554583218234
1140.9985702605559730.002859478888054520.00142973944402726
1150.9973579165148120.005284166970376690.00264208348518835
1160.9954487856454530.009102428709093480.00455121435454674
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1200.9947685090437870.01046298191242550.00523149095621274
1210.9939225591130380.01215488177392350.00607744088696177
1220.9889003147557780.0221993704884450.0110996852442225
1230.9869709662683010.02605806746339890.0130290337316994
1240.9884245666513660.02315086669726870.0115754333486344
1250.9902870613290310.01942587734193760.00971293867096881
1260.9800498670332880.03990026593342370.0199501329667119
1270.9891967520221940.02160649595561180.0108032479778059
1280.9760167409734650.04796651805306910.0239832590265346
1290.9854825204576730.02903495908465410.0145174795423271
1300.9680056405146390.06398871897072260.0319943594853613
1310.9197369138537060.1605261722925870.0802630861462937
1320.9019243016708120.1961513966583770.0980756983291883






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level270.223140495867769NOK
5% type I error level630.520661157024793NOK
10% type I error level690.570247933884298NOK

\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 & 27 & 0.223140495867769 & NOK \tabularnewline
5% type I error level & 63 & 0.520661157024793 & NOK \tabularnewline
10% type I error level & 69 & 0.570247933884298 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159884&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]27[/C][C]0.223140495867769[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]63[/C][C]0.520661157024793[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]69[/C][C]0.570247933884298[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159884&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159884&T=6

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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 level270.223140495867769NOK
5% type I error level630.520661157024793NOK
10% type I error level690.570247933884298NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')
}