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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 21 Nov 2011 10:46:36 -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/Nov/21/t13218904169ita01ngabguadi.htm/, Retrieved Fri, 29 Mar 2024 10:04:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145775, Retrieved Fri, 29 Mar 2024 10:04:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact176
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Concern, tut] [2010-12-02 14:07:40] [e73e9643c012a54583c6a406017b2645]
-    D    [Multiple Regression] [WS7 - Eerste Regr...] [2011-11-21 15:46:36] [1eaf8805ffdd770d1a6587425a1bf41e] [Current]
- RM        [Multiple Regression] [] [2011-11-23 15:05:33] [d0c153a232569da05656a074c1bdec10]
-    D      [Multiple Regression] [multiple regression] [2012-11-04 15:20:41] [456f9f31a5baae2eb9a0b13ee35c0d42]
-    D        [Multiple Regression] [multiple regression] [2012-11-04 17:07:32] [456f9f31a5baae2eb9a0b13ee35c0d42]
-               [Multiple Regression] [] [2012-11-05 14:05:58] [754b40392725f52c97d5cd2ee03f2d3f]
- R             [Multiple Regression] [] [2012-12-16 13:14:37] [c5439e3d68865f12e8f66507a7e773a8]
- R  D      [Multiple Regression] [Workshop 7 ] [2012-11-05 14:37:18] [8cc09667e588c76c8c3bfb3e2ed3290a]
-    D      [Multiple Regression] [ws7] [2012-11-05 15:55:01] [8cc09667e588c76c8c3bfb3e2ed3290a]
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Dataseries X:
1	812	58085	13	20	10345	10823	13
2	537	65968	26	28	17607	44480	27
3	186	7176	0	0	1423	1929	0
4	1405	78306	37	40	20050	30032	37
5	1859	123860	45	48	21212	27669	39
6	3347	226694	80	40	93979	114967	99
7	735	58032	21	35	15524	29951	21
8	609	72513	36	40	16182	38824	33
9	1100	65784	35	40	19238	26517	36
10	1743	164794	36	52	28909	63570	44
11	833	66288	35	24	22357	27131	33
12	1143	85319	46	44	25560	41061	47
13	888	45400	20	24	9954	18810	19
14	1460	78191	24	32	18490	27582	41
15	638	61175	18	28	17777	37026	22
16	854	72377	15	40	25268	24252	17
17	724	49850	48	40	37525	32579	46
18	323	15580	0	20	6023	0	0
19	1467	65240	37	67	25042	29666	31
20	412	13397	8	16	35713	7533	20
21	580	35385	10	44	7039	11892	10
22	1177	90047	51	36	40841	51557	55
23	595	47802	4	40	9214	5737	6
24	1103	61598	24	29	17446	11203	17
25	1037	73756	38	32	10295	28714	33
26	611	65152	19	28	13206	24268	33
27	1111	78226	20	41	26093	30749	32
28	542	66026	31	40	20744	46643	37
29	1341	170050	36	44	68013	64530	44
30	1169	91493	19	28	12840	35346	22
31	752	56374	20	56	12672	5766	15
32	889	85227	34	26	10872	29217	18
33	1009	50281	26	12	21325	15912	25
34	577	29008	0	32	24542	3728	7
35	1015	84775	29	36	16401	37494	35
36	0	0	0	0	0	0	0
37	562	55273	8	32	12821	13214	14
38	1115	62498	35	31	14662	19576	31
39	1015	35361	3	48	22190	13632	9
40	976	89502	41	72	37929	67378	59
41	940	73972	42	24	18009	29387	62
42	680	53655	10	56	11076	15936	12
43	404	40064	10	28	24981	18156	23
44	938	58480	26	36	30691	23750	31
45	580	48473	27	44	29164	15559	57
46	396	30737	0	32	13985	21713	23
47	256	27044	13	32	7588	12023	14
48	932	92011	30	32	20023	23588	31
49	734	56303	11	32	25524	28661	17
50	998	52792	24	36	14717	16874	24
51	425	33820	10	42	6832	11804	11
52	631	44121	14	28	9624	12949	16
53	903	103438	23	36	24300	38340	32
54	804	82720	27	32	21790	36573	36
55	915	89612	40	48	16493	40068	37
56	753	60722	22	20	9269	25561	25
57	674	64096	26	32	20105	31287	30
58	382	25090	8	32	11216	8383	10
59	550	57096	27	52	15569	29178	16
60	509	19608	0	40	21799	1237	3
61	423	28665	0	56	3772	10241	0
62	696	28400	16	24	6057	8219	17
63	460	27697	7	22	20828	9348	9
64	475	42406	18	36	9976	25242	22
65	373	47859	7	26	14055	24267	5
66	754	54987	24	44	17455	25902	23
67	936	58198	14	44	39553	51849	16
68	1501	61854	39	36	14818	29065	53
69	499	35185	16	36	17065	22417	23
70	80	12207	0	16	1536	1714	0
71	1517	108584	39	32	11938	29085	51
72	552	43273	17	10	24589	22118	25
73	517	39695	24	40	21332	14803	51
74	917	40699	27	25	13229	13243	46
75	691	38999	22	48	11331	13985	16
76	459	17667	0	36	853	657	0
77	683	59058	26	32	19821	26171	25
78	887	54106	19	24	34666	34867	34
79	410	23795	12	35	15051	12297	14
80	590	33465	23	17	27969	17487	32
81	439	36937	32	36	17897	13461	24
82	621	77075	19	40	6031	15192	16
83	537	32346	17	40	7153	16584	19
84	699	48592	25	36	13365	22892	27
85	477	26642	14	32	11197	7081	24
86	813	51086	11	40	25291	21623	12
87	1171	95985	20	60	28994	41992	43
88	400	24612	14	44	10461	11301	13
89	352	30113	14	28	16415	15230	19
90	639	53398	22	40	8495	14667	24
91	773	54198	25	28	18318	23795	27
92	1050	65255	35	36	25143	28055	26
93	489	59960	9	36	20471	29162	14
94	573	48096	16	20	14561	14962	26
95	334	17371	12	22	16902	8749	15
96	1229	115424	20	52	12994	37310	30
97	701	69334	33	48	29697	31551	33
98	222	19349	13	2	3895	9604	14
99	810	63594	11	44	9807	13937	11
100	739	49547	11	22	10711	16850	12
101	231	13066	8	3	2325	3439	8
102	425	25497	22	20	19000	16638	22
103	578	55049	13	32	22418	12847	12
104	305	24912	6	28	7872	13462	6
105	323	22431	12	24	5650	8086	10
106	463	24716	2	45	3979	2255	1
107	519	52452	33	40	14956	25918	31
108	294	17850	5	0	3738	3255	5
109	0	0	0	0	0	0	0
110	565	35269	34	28	10586	16138	35
111	462	27554	12	28	18122	5941	15
112	630	55167	34	32	17899	27123	36
113	498	36708	30	32	10913	19148	27
114	403	40920	21	13	18060	15214	36
115	38	3058	0	0	0	0	0
116	0	0	0	0	0	0	0
117	559	86153	28	40	15452	34998	29
118	592	37545	11	43	33996	18998	19
119	799	54332	9	32	8877	10651	16
120	406	33277	14	32	18708	13465	15
121	778	43410	7	3	2781	13	1
122	706	69693	41	28	20854	32505	36
123	367	31897	21	16	8179	15769	22
124	639	34563	28	37	7139	5936	16
125	481	39830	1	32	13798	4174	1
126	214	16145	10	4	5619	9876	10
127	538	38139	26	28	13050	17678	31
128	451	49667	7	36	11297	14633	22
129	629	48133	24	40	16170	13380	22
130	256	11796	1	8	0	0	0
131	80	7627	0	0	0	0	0
132	555	60315	11	21	20539	5652	10
133	41	6836	0	4	0	0	0
134	497	28834	17	12	10056	3636	9
135	42	5118	5	0	0	0	0
136	339	20825	4	6	2418	1695	0
137	0	0	0	0	0	0	0
138	375	32626	6	32	11806	8778	7
139	203	11747	0	36	15924	4148	2
140	81	7131	0	0	0	0	0
141	61	4194	0	0	0	0	0
142	313	21416	15	12	7084	10404	16
143	227	18648	0	24	14831	20794	25
144	462	38232	12	36	6585	11200	6




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145775&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145775&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145775&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
PlaatsHoF[t] = + 111.289470306828 -0.0486478183017716Pageviews[t] + 0.000218804779702596TimeRFC[t] + 0.108209569313708BloggedComp[t] -0.35904311766664PeerReviews[t] + 0.000143242716991712CharComp[t] -0.000685656508686347SecComp[t] + 0.105313709370402HypComp[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PlaatsHoF[t] =  +  111.289470306828 -0.0486478183017716Pageviews[t] +  0.000218804779702596TimeRFC[t] +  0.108209569313708BloggedComp[t] -0.35904311766664PeerReviews[t] +  0.000143242716991712CharComp[t] -0.000685656508686347SecComp[t] +  0.105313709370402HypComp[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145775&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PlaatsHoF[t] =  +  111.289470306828 -0.0486478183017716Pageviews[t] +  0.000218804779702596TimeRFC[t] +  0.108209569313708BloggedComp[t] -0.35904311766664PeerReviews[t] +  0.000143242716991712CharComp[t] -0.000685656508686347SecComp[t] +  0.105313709370402HypComp[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145775&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145775&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
PlaatsHoF[t] = + 111.289470306828 -0.0486478183017716Pageviews[t] + 0.000218804779702596TimeRFC[t] + 0.108209569313708BloggedComp[t] -0.35904311766664PeerReviews[t] + 0.000143242716991712CharComp[t] -0.000685656508686347SecComp[t] + 0.105313709370402HypComp[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)111.2894703068287.15072815.563400
Pageviews-0.04864781830177160.016268-2.99040.0033080.001654
TimeRFC0.0002188047797025960.0002580.84790.3979850.198993
BloggedComp0.1082095693137080.5026450.21530.8298710.414936
PeerReviews-0.359043117666640.240831-1.49090.1383160.069158
CharComp0.0001432427169917120.000420.34110.7335580.366779
SecComp-0.0006856565086863470.000478-1.43430.153790.076895
HypComp0.1053137093704020.4522740.23290.8162250.408113

\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) & 111.289470306828 & 7.150728 & 15.5634 & 0 & 0 \tabularnewline
Pageviews & -0.0486478183017716 & 0.016268 & -2.9904 & 0.003308 & 0.001654 \tabularnewline
TimeRFC & 0.000218804779702596 & 0.000258 & 0.8479 & 0.397985 & 0.198993 \tabularnewline
BloggedComp & 0.108209569313708 & 0.502645 & 0.2153 & 0.829871 & 0.414936 \tabularnewline
PeerReviews & -0.35904311766664 & 0.240831 & -1.4909 & 0.138316 & 0.069158 \tabularnewline
CharComp & 0.000143242716991712 & 0.00042 & 0.3411 & 0.733558 & 0.366779 \tabularnewline
SecComp & -0.000685656508686347 & 0.000478 & -1.4343 & 0.15379 & 0.076895 \tabularnewline
HypComp & 0.105313709370402 & 0.452274 & 0.2329 & 0.816225 & 0.408113 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145775&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]111.289470306828[/C][C]7.150728[/C][C]15.5634[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Pageviews[/C][C]-0.0486478183017716[/C][C]0.016268[/C][C]-2.9904[/C][C]0.003308[/C][C]0.001654[/C][/ROW]
[ROW][C]TimeRFC[/C][C]0.000218804779702596[/C][C]0.000258[/C][C]0.8479[/C][C]0.397985[/C][C]0.198993[/C][/ROW]
[ROW][C]BloggedComp[/C][C]0.108209569313708[/C][C]0.502645[/C][C]0.2153[/C][C]0.829871[/C][C]0.414936[/C][/ROW]
[ROW][C]PeerReviews[/C][C]-0.35904311766664[/C][C]0.240831[/C][C]-1.4909[/C][C]0.138316[/C][C]0.069158[/C][/ROW]
[ROW][C]CharComp[/C][C]0.000143242716991712[/C][C]0.00042[/C][C]0.3411[/C][C]0.733558[/C][C]0.366779[/C][/ROW]
[ROW][C]SecComp[/C][C]-0.000685656508686347[/C][C]0.000478[/C][C]-1.4343[/C][C]0.15379[/C][C]0.076895[/C][/ROW]
[ROW][C]HypComp[/C][C]0.105313709370402[/C][C]0.452274[/C][C]0.2329[/C][C]0.816225[/C][C]0.408113[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145775&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145775&T=2

As an alternative you can also use a 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)111.2894703068287.15072815.563400
Pageviews-0.04864781830177160.016268-2.99040.0033080.001654
TimeRFC0.0002188047797025960.0002580.84790.3979850.198993
BloggedComp0.1082095693137080.5026450.21530.8298710.414936
PeerReviews-0.359043117666640.240831-1.49090.1383160.069158
CharComp0.0001432427169917120.000420.34110.7335580.366779
SecComp-0.0006856565086863470.000478-1.43430.153790.076895
HypComp0.1053137093704020.4522740.23290.8162250.408113







Multiple Linear Regression - Regression Statistics
Multiple R0.550852069179421
R-squared0.30343800211925
Adjusted R-squared0.267585546345976
F-TEST (value)8.46352071495884
F-TEST (DF numerator)7
F-TEST (DF denominator)136
p-value1.41265560360537e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation35.6987555715602
Sum Squared Residuals173318.556312688

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.550852069179421 \tabularnewline
R-squared & 0.30343800211925 \tabularnewline
Adjusted R-squared & 0.267585546345976 \tabularnewline
F-TEST (value) & 8.46352071495884 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 136 \tabularnewline
p-value & 1.41265560360537e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 35.6987555715602 \tabularnewline
Sum Squared Residuals & 173318.556312688 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145775&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.550852069179421[/C][/ROW]
[ROW][C]R-squared[/C][C]0.30343800211925[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.267585546345976[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.46352071495884[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]136[/C][/ROW]
[ROW][C]p-value[/C][C]1.41265560360537e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]35.6987555715602[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]173318.556312688[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145775&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145775&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.550852069179421
R-squared0.30343800211925
Adjusted R-squared0.267585546345976
F-TEST (value)8.46352071495884
F-TEST (DF numerator)7
F-TEST (DF denominator)136
p-value1.41265560360537e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation35.6987555715602
Sum Squared Residuals173318.556312688







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1174.1526432581426-73.1526432581426
2267.2274902583935-65.2274902583935
33102.692322182868-99.6923221828679
4435.8920294836925-31.8920294836925
5523.7639663083478-18.7639663083478
66-62.578013287179468.5780132871794
7761.8360844116757-54.8360844116757
8866.236337502596-58.236337502596
9949.9617773081344-40.9617773081344
101012.9669629103981-2.96696291039814
111168.5155520961963-57.5155520961963
121243.9902482819945-31.9902482819945
131362.1007007702107-49.1007007702107
141436.5345121129403-22.5345121129403
151565.008319077075-50.008319077075
161661.6233342342723-45.6233342342723
171765.6898129345724-48.6898129345724
181892.6670919942308-74.6670919942308
191920.6569329895687-1.65693298956872
202091.3557343314163-71.3557343314163
212170.0079367146927-49.0079367146927
222242.6188751642114-20.6188751642114
232376.8925473274574-53.8925473274574
242459.9015784244122-35.9015784244122
252554.8645271184574-29.8645271184574
262676.5515004309394-50.5515004309394
272747.8258093612383-20.8258093612383
282863.2488867472947-35.2488867472947
292941.4889016880653-12.4889016880653
303046.3629740799762-16.3629740799762
313168.540370312982-37.540370312982
323264.453794467112-32.453794467112
333366.4878034666148-33.4878034666148
343479.7739196926072-45.7739196926072
353551.0009336965754-16.0009336965754
3636111.289470306828-75.2894703068283
373779.6703314988664-42.6703314988664
383855.3215501934907-17.3215501934907
393947.5191593535034-8.51915935350343
404027.426550530468512.5734494695315
414166.6334356050027-25.6334356050027
424262.848304143791-20.848304143791
434384.9826168623684-41.9826168623684
444459.718061959306-15.718061959306
454580.315903386271-35.3159033862709
464676.7987619472546-30.7987619472546
474788.9879993833803-41.9879993833803
484867.7986657347854-19.7986657347854
494963.3971216543754-14.3971216543754
505057.0274311625329-7.02743116253286
515178.0600055462835-27.0600055462835
525275.8933304466698-23.8933304466698
535360.1192532211388-7.1192532211388
545463.5444712237384-9.54447122373839
555552.26478799197052.7352120080295
565669.5781685897886-13.5781685897886
575768.4365918870364-11.4365918870364
585884.4840013226323-26.4840013226323
595965.1865437958783-6.18654379587832
606079.046662219538-19.046662219538
616170.3955708090578-9.39557080905784
626273.7815061485915-11.7815061485915
636385.351993918603-22.3519939186031
646472.9211614718002-8.92116147180016
656580.9389763957963-15.9389763957963
666660.60220826761185.39779173238821
676736.006243954782730.9937560452173
686830.873257598874837.1267424011252
696973.0149463468614-4.01494634686136
7070103.368710463261-33.3687104632608
717141.118932758364729.8810672416353
727283.143032581232-11.1430325812321
737381.336188450644-8.336188450644
747467.18937647261236.81062352738767
757565.07273264300959.92726735699054
767679.5719032247089-3.57190322470886
777769.83699226214057.1630077378595
787858.055986534168119.9440134658319
797980.4811502268692-1.48115022686918
808081.6809654394087-1.68096543940873
818184.4137458697284-3.41374586972836
828277.7702331434414.22976685655899
838371.375537347329511.6244626526705
848466.758354434789417.2416455652106
858583.2156961118171.78430388818295
868659.805800939717326.1941990602827
878735.83603690811151.163963091889
888878.05153909682089.94846090317918
898986.12577432167852.87422567832151
909073.59398974962316.406010250377
919167.347713766720323.6522862332797
929252.452764405199339.5472355948007
939373.080154120465719.9198458795343
949483.053516822957410.9464831770426
959590.24350831400194.7564916859981
969639.68943658056756.310563419433
979764.79088928270332.209110717297
9898100.85922369649-2.85922369649014
999964.199054094969334.8009459050307
10010070.715934758306429.2840652416936
101101101.51685099082-0.516850990819533
10210285.023321405689216.9766785943108
10310380.799710856869822.2002891431302
10410485.027981522400918.9720184775991
10510589.483954932099515.5160450679005
10610677.362109265058728.6378907349413
10710774.363409667533932.6365903324661
108108100.263922777567.73607722243969
109109111.289470306828-2.28947030682833
11011079.283619296516430.7163807034836
11111186.19049752820824.809502471792
11211272.660027093568239.3399729064318
11311378.129377055152934.8706229448471
11411494.189410425616819.8105895743832
115115110.1099582276924.89004177230846
116116111.2894703068284.71052969317167
11711772.885048839666744.1149511603333
11811870.300976061763447.6990239382366
11911969.446128607648749.5538713923513
12012083.87230243610736.127697563893
12112183.114878957847137.8851210421529
12212271.067869487416650.9321305125834
12312389.619014424122533.3809855758775
12412476.148908670943347.8510913290567
12512584.44354033437740.556459665623
12612699.143837821712226.8561621782878
12712779.2351877477147.76481225229
12812881.95049888381246.0495011161881
12912974.916080266983954.0839197330161
13013098.652514630927231.3474853690728
131131109.06646889747821.9335311025217
13213291.2574098994640.74259010054
133133109.35448675983623.6455132401639
13413490.846791975944543.1532080240555
135135110.9071526472424.0928473527597
13613696.81722211855239.1827778814481
137137111.28947030682825.7105296931717
13813885.754767485938952.2452325140612
13913990.706231948821948.2937680511781
140140108.90929390844431.090706091556
141141109.23962063649331.760379363507
14214293.629432908913548.3705670910865
14314386.20938628856156.790613711439
14414479.448267835090664.5517321649094

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 74.1526432581426 & -73.1526432581426 \tabularnewline
2 & 2 & 67.2274902583935 & -65.2274902583935 \tabularnewline
3 & 3 & 102.692322182868 & -99.6923221828679 \tabularnewline
4 & 4 & 35.8920294836925 & -31.8920294836925 \tabularnewline
5 & 5 & 23.7639663083478 & -18.7639663083478 \tabularnewline
6 & 6 & -62.5780132871794 & 68.5780132871794 \tabularnewline
7 & 7 & 61.8360844116757 & -54.8360844116757 \tabularnewline
8 & 8 & 66.236337502596 & -58.236337502596 \tabularnewline
9 & 9 & 49.9617773081344 & -40.9617773081344 \tabularnewline
10 & 10 & 12.9669629103981 & -2.96696291039814 \tabularnewline
11 & 11 & 68.5155520961963 & -57.5155520961963 \tabularnewline
12 & 12 & 43.9902482819945 & -31.9902482819945 \tabularnewline
13 & 13 & 62.1007007702107 & -49.1007007702107 \tabularnewline
14 & 14 & 36.5345121129403 & -22.5345121129403 \tabularnewline
15 & 15 & 65.008319077075 & -50.008319077075 \tabularnewline
16 & 16 & 61.6233342342723 & -45.6233342342723 \tabularnewline
17 & 17 & 65.6898129345724 & -48.6898129345724 \tabularnewline
18 & 18 & 92.6670919942308 & -74.6670919942308 \tabularnewline
19 & 19 & 20.6569329895687 & -1.65693298956872 \tabularnewline
20 & 20 & 91.3557343314163 & -71.3557343314163 \tabularnewline
21 & 21 & 70.0079367146927 & -49.0079367146927 \tabularnewline
22 & 22 & 42.6188751642114 & -20.6188751642114 \tabularnewline
23 & 23 & 76.8925473274574 & -53.8925473274574 \tabularnewline
24 & 24 & 59.9015784244122 & -35.9015784244122 \tabularnewline
25 & 25 & 54.8645271184574 & -29.8645271184574 \tabularnewline
26 & 26 & 76.5515004309394 & -50.5515004309394 \tabularnewline
27 & 27 & 47.8258093612383 & -20.8258093612383 \tabularnewline
28 & 28 & 63.2488867472947 & -35.2488867472947 \tabularnewline
29 & 29 & 41.4889016880653 & -12.4889016880653 \tabularnewline
30 & 30 & 46.3629740799762 & -16.3629740799762 \tabularnewline
31 & 31 & 68.540370312982 & -37.540370312982 \tabularnewline
32 & 32 & 64.453794467112 & -32.453794467112 \tabularnewline
33 & 33 & 66.4878034666148 & -33.4878034666148 \tabularnewline
34 & 34 & 79.7739196926072 & -45.7739196926072 \tabularnewline
35 & 35 & 51.0009336965754 & -16.0009336965754 \tabularnewline
36 & 36 & 111.289470306828 & -75.2894703068283 \tabularnewline
37 & 37 & 79.6703314988664 & -42.6703314988664 \tabularnewline
38 & 38 & 55.3215501934907 & -17.3215501934907 \tabularnewline
39 & 39 & 47.5191593535034 & -8.51915935350343 \tabularnewline
40 & 40 & 27.4265505304685 & 12.5734494695315 \tabularnewline
41 & 41 & 66.6334356050027 & -25.6334356050027 \tabularnewline
42 & 42 & 62.848304143791 & -20.848304143791 \tabularnewline
43 & 43 & 84.9826168623684 & -41.9826168623684 \tabularnewline
44 & 44 & 59.718061959306 & -15.718061959306 \tabularnewline
45 & 45 & 80.315903386271 & -35.3159033862709 \tabularnewline
46 & 46 & 76.7987619472546 & -30.7987619472546 \tabularnewline
47 & 47 & 88.9879993833803 & -41.9879993833803 \tabularnewline
48 & 48 & 67.7986657347854 & -19.7986657347854 \tabularnewline
49 & 49 & 63.3971216543754 & -14.3971216543754 \tabularnewline
50 & 50 & 57.0274311625329 & -7.02743116253286 \tabularnewline
51 & 51 & 78.0600055462835 & -27.0600055462835 \tabularnewline
52 & 52 & 75.8933304466698 & -23.8933304466698 \tabularnewline
53 & 53 & 60.1192532211388 & -7.1192532211388 \tabularnewline
54 & 54 & 63.5444712237384 & -9.54447122373839 \tabularnewline
55 & 55 & 52.2647879919705 & 2.7352120080295 \tabularnewline
56 & 56 & 69.5781685897886 & -13.5781685897886 \tabularnewline
57 & 57 & 68.4365918870364 & -11.4365918870364 \tabularnewline
58 & 58 & 84.4840013226323 & -26.4840013226323 \tabularnewline
59 & 59 & 65.1865437958783 & -6.18654379587832 \tabularnewline
60 & 60 & 79.046662219538 & -19.046662219538 \tabularnewline
61 & 61 & 70.3955708090578 & -9.39557080905784 \tabularnewline
62 & 62 & 73.7815061485915 & -11.7815061485915 \tabularnewline
63 & 63 & 85.351993918603 & -22.3519939186031 \tabularnewline
64 & 64 & 72.9211614718002 & -8.92116147180016 \tabularnewline
65 & 65 & 80.9389763957963 & -15.9389763957963 \tabularnewline
66 & 66 & 60.6022082676118 & 5.39779173238821 \tabularnewline
67 & 67 & 36.0062439547827 & 30.9937560452173 \tabularnewline
68 & 68 & 30.8732575988748 & 37.1267424011252 \tabularnewline
69 & 69 & 73.0149463468614 & -4.01494634686136 \tabularnewline
70 & 70 & 103.368710463261 & -33.3687104632608 \tabularnewline
71 & 71 & 41.1189327583647 & 29.8810672416353 \tabularnewline
72 & 72 & 83.143032581232 & -11.1430325812321 \tabularnewline
73 & 73 & 81.336188450644 & -8.336188450644 \tabularnewline
74 & 74 & 67.1893764726123 & 6.81062352738767 \tabularnewline
75 & 75 & 65.0727326430095 & 9.92726735699054 \tabularnewline
76 & 76 & 79.5719032247089 & -3.57190322470886 \tabularnewline
77 & 77 & 69.8369922621405 & 7.1630077378595 \tabularnewline
78 & 78 & 58.0559865341681 & 19.9440134658319 \tabularnewline
79 & 79 & 80.4811502268692 & -1.48115022686918 \tabularnewline
80 & 80 & 81.6809654394087 & -1.68096543940873 \tabularnewline
81 & 81 & 84.4137458697284 & -3.41374586972836 \tabularnewline
82 & 82 & 77.770233143441 & 4.22976685655899 \tabularnewline
83 & 83 & 71.3755373473295 & 11.6244626526705 \tabularnewline
84 & 84 & 66.7583544347894 & 17.2416455652106 \tabularnewline
85 & 85 & 83.215696111817 & 1.78430388818295 \tabularnewline
86 & 86 & 59.8058009397173 & 26.1941990602827 \tabularnewline
87 & 87 & 35.836036908111 & 51.163963091889 \tabularnewline
88 & 88 & 78.0515390968208 & 9.94846090317918 \tabularnewline
89 & 89 & 86.1257743216785 & 2.87422567832151 \tabularnewline
90 & 90 & 73.593989749623 & 16.406010250377 \tabularnewline
91 & 91 & 67.3477137667203 & 23.6522862332797 \tabularnewline
92 & 92 & 52.4527644051993 & 39.5472355948007 \tabularnewline
93 & 93 & 73.0801541204657 & 19.9198458795343 \tabularnewline
94 & 94 & 83.0535168229574 & 10.9464831770426 \tabularnewline
95 & 95 & 90.2435083140019 & 4.7564916859981 \tabularnewline
96 & 96 & 39.689436580567 & 56.310563419433 \tabularnewline
97 & 97 & 64.790889282703 & 32.209110717297 \tabularnewline
98 & 98 & 100.85922369649 & -2.85922369649014 \tabularnewline
99 & 99 & 64.1990540949693 & 34.8009459050307 \tabularnewline
100 & 100 & 70.7159347583064 & 29.2840652416936 \tabularnewline
101 & 101 & 101.51685099082 & -0.516850990819533 \tabularnewline
102 & 102 & 85.0233214056892 & 16.9766785943108 \tabularnewline
103 & 103 & 80.7997108568698 & 22.2002891431302 \tabularnewline
104 & 104 & 85.0279815224009 & 18.9720184775991 \tabularnewline
105 & 105 & 89.4839549320995 & 15.5160450679005 \tabularnewline
106 & 106 & 77.3621092650587 & 28.6378907349413 \tabularnewline
107 & 107 & 74.3634096675339 & 32.6365903324661 \tabularnewline
108 & 108 & 100.26392277756 & 7.73607722243969 \tabularnewline
109 & 109 & 111.289470306828 & -2.28947030682833 \tabularnewline
110 & 110 & 79.2836192965164 & 30.7163807034836 \tabularnewline
111 & 111 & 86.190497528208 & 24.809502471792 \tabularnewline
112 & 112 & 72.6600270935682 & 39.3399729064318 \tabularnewline
113 & 113 & 78.1293770551529 & 34.8706229448471 \tabularnewline
114 & 114 & 94.1894104256168 & 19.8105895743832 \tabularnewline
115 & 115 & 110.109958227692 & 4.89004177230846 \tabularnewline
116 & 116 & 111.289470306828 & 4.71052969317167 \tabularnewline
117 & 117 & 72.8850488396667 & 44.1149511603333 \tabularnewline
118 & 118 & 70.3009760617634 & 47.6990239382366 \tabularnewline
119 & 119 & 69.4461286076487 & 49.5538713923513 \tabularnewline
120 & 120 & 83.872302436107 & 36.127697563893 \tabularnewline
121 & 121 & 83.1148789578471 & 37.8851210421529 \tabularnewline
122 & 122 & 71.0678694874166 & 50.9321305125834 \tabularnewline
123 & 123 & 89.6190144241225 & 33.3809855758775 \tabularnewline
124 & 124 & 76.1489086709433 & 47.8510913290567 \tabularnewline
125 & 125 & 84.443540334377 & 40.556459665623 \tabularnewline
126 & 126 & 99.1438378217122 & 26.8561621782878 \tabularnewline
127 & 127 & 79.23518774771 & 47.76481225229 \tabularnewline
128 & 128 & 81.950498883812 & 46.0495011161881 \tabularnewline
129 & 129 & 74.9160802669839 & 54.0839197330161 \tabularnewline
130 & 130 & 98.6525146309272 & 31.3474853690728 \tabularnewline
131 & 131 & 109.066468897478 & 21.9335311025217 \tabularnewline
132 & 132 & 91.25740989946 & 40.74259010054 \tabularnewline
133 & 133 & 109.354486759836 & 23.6455132401639 \tabularnewline
134 & 134 & 90.8467919759445 & 43.1532080240555 \tabularnewline
135 & 135 & 110.90715264724 & 24.0928473527597 \tabularnewline
136 & 136 & 96.817222118552 & 39.1827778814481 \tabularnewline
137 & 137 & 111.289470306828 & 25.7105296931717 \tabularnewline
138 & 138 & 85.7547674859389 & 52.2452325140612 \tabularnewline
139 & 139 & 90.7062319488219 & 48.2937680511781 \tabularnewline
140 & 140 & 108.909293908444 & 31.090706091556 \tabularnewline
141 & 141 & 109.239620636493 & 31.760379363507 \tabularnewline
142 & 142 & 93.6294329089135 & 48.3705670910865 \tabularnewline
143 & 143 & 86.209386288561 & 56.790613711439 \tabularnewline
144 & 144 & 79.4482678350906 & 64.5517321649094 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145775&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]1[/C][C]74.1526432581426[/C][C]-73.1526432581426[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]67.2274902583935[/C][C]-65.2274902583935[/C][/ROW]
[ROW][C]3[/C][C]3[/C][C]102.692322182868[/C][C]-99.6923221828679[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]35.8920294836925[/C][C]-31.8920294836925[/C][/ROW]
[ROW][C]5[/C][C]5[/C][C]23.7639663083478[/C][C]-18.7639663083478[/C][/ROW]
[ROW][C]6[/C][C]6[/C][C]-62.5780132871794[/C][C]68.5780132871794[/C][/ROW]
[ROW][C]7[/C][C]7[/C][C]61.8360844116757[/C][C]-54.8360844116757[/C][/ROW]
[ROW][C]8[/C][C]8[/C][C]66.236337502596[/C][C]-58.236337502596[/C][/ROW]
[ROW][C]9[/C][C]9[/C][C]49.9617773081344[/C][C]-40.9617773081344[/C][/ROW]
[ROW][C]10[/C][C]10[/C][C]12.9669629103981[/C][C]-2.96696291039814[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]68.5155520961963[/C][C]-57.5155520961963[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]43.9902482819945[/C][C]-31.9902482819945[/C][/ROW]
[ROW][C]13[/C][C]13[/C][C]62.1007007702107[/C][C]-49.1007007702107[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]36.5345121129403[/C][C]-22.5345121129403[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]65.008319077075[/C][C]-50.008319077075[/C][/ROW]
[ROW][C]16[/C][C]16[/C][C]61.6233342342723[/C][C]-45.6233342342723[/C][/ROW]
[ROW][C]17[/C][C]17[/C][C]65.6898129345724[/C][C]-48.6898129345724[/C][/ROW]
[ROW][C]18[/C][C]18[/C][C]92.6670919942308[/C][C]-74.6670919942308[/C][/ROW]
[ROW][C]19[/C][C]19[/C][C]20.6569329895687[/C][C]-1.65693298956872[/C][/ROW]
[ROW][C]20[/C][C]20[/C][C]91.3557343314163[/C][C]-71.3557343314163[/C][/ROW]
[ROW][C]21[/C][C]21[/C][C]70.0079367146927[/C][C]-49.0079367146927[/C][/ROW]
[ROW][C]22[/C][C]22[/C][C]42.6188751642114[/C][C]-20.6188751642114[/C][/ROW]
[ROW][C]23[/C][C]23[/C][C]76.8925473274574[/C][C]-53.8925473274574[/C][/ROW]
[ROW][C]24[/C][C]24[/C][C]59.9015784244122[/C][C]-35.9015784244122[/C][/ROW]
[ROW][C]25[/C][C]25[/C][C]54.8645271184574[/C][C]-29.8645271184574[/C][/ROW]
[ROW][C]26[/C][C]26[/C][C]76.5515004309394[/C][C]-50.5515004309394[/C][/ROW]
[ROW][C]27[/C][C]27[/C][C]47.8258093612383[/C][C]-20.8258093612383[/C][/ROW]
[ROW][C]28[/C][C]28[/C][C]63.2488867472947[/C][C]-35.2488867472947[/C][/ROW]
[ROW][C]29[/C][C]29[/C][C]41.4889016880653[/C][C]-12.4889016880653[/C][/ROW]
[ROW][C]30[/C][C]30[/C][C]46.3629740799762[/C][C]-16.3629740799762[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]68.540370312982[/C][C]-37.540370312982[/C][/ROW]
[ROW][C]32[/C][C]32[/C][C]64.453794467112[/C][C]-32.453794467112[/C][/ROW]
[ROW][C]33[/C][C]33[/C][C]66.4878034666148[/C][C]-33.4878034666148[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]79.7739196926072[/C][C]-45.7739196926072[/C][/ROW]
[ROW][C]35[/C][C]35[/C][C]51.0009336965754[/C][C]-16.0009336965754[/C][/ROW]
[ROW][C]36[/C][C]36[/C][C]111.289470306828[/C][C]-75.2894703068283[/C][/ROW]
[ROW][C]37[/C][C]37[/C][C]79.6703314988664[/C][C]-42.6703314988664[/C][/ROW]
[ROW][C]38[/C][C]38[/C][C]55.3215501934907[/C][C]-17.3215501934907[/C][/ROW]
[ROW][C]39[/C][C]39[/C][C]47.5191593535034[/C][C]-8.51915935350343[/C][/ROW]
[ROW][C]40[/C][C]40[/C][C]27.4265505304685[/C][C]12.5734494695315[/C][/ROW]
[ROW][C]41[/C][C]41[/C][C]66.6334356050027[/C][C]-25.6334356050027[/C][/ROW]
[ROW][C]42[/C][C]42[/C][C]62.848304143791[/C][C]-20.848304143791[/C][/ROW]
[ROW][C]43[/C][C]43[/C][C]84.9826168623684[/C][C]-41.9826168623684[/C][/ROW]
[ROW][C]44[/C][C]44[/C][C]59.718061959306[/C][C]-15.718061959306[/C][/ROW]
[ROW][C]45[/C][C]45[/C][C]80.315903386271[/C][C]-35.3159033862709[/C][/ROW]
[ROW][C]46[/C][C]46[/C][C]76.7987619472546[/C][C]-30.7987619472546[/C][/ROW]
[ROW][C]47[/C][C]47[/C][C]88.9879993833803[/C][C]-41.9879993833803[/C][/ROW]
[ROW][C]48[/C][C]48[/C][C]67.7986657347854[/C][C]-19.7986657347854[/C][/ROW]
[ROW][C]49[/C][C]49[/C][C]63.3971216543754[/C][C]-14.3971216543754[/C][/ROW]
[ROW][C]50[/C][C]50[/C][C]57.0274311625329[/C][C]-7.02743116253286[/C][/ROW]
[ROW][C]51[/C][C]51[/C][C]78.0600055462835[/C][C]-27.0600055462835[/C][/ROW]
[ROW][C]52[/C][C]52[/C][C]75.8933304466698[/C][C]-23.8933304466698[/C][/ROW]
[ROW][C]53[/C][C]53[/C][C]60.1192532211388[/C][C]-7.1192532211388[/C][/ROW]
[ROW][C]54[/C][C]54[/C][C]63.5444712237384[/C][C]-9.54447122373839[/C][/ROW]
[ROW][C]55[/C][C]55[/C][C]52.2647879919705[/C][C]2.7352120080295[/C][/ROW]
[ROW][C]56[/C][C]56[/C][C]69.5781685897886[/C][C]-13.5781685897886[/C][/ROW]
[ROW][C]57[/C][C]57[/C][C]68.4365918870364[/C][C]-11.4365918870364[/C][/ROW]
[ROW][C]58[/C][C]58[/C][C]84.4840013226323[/C][C]-26.4840013226323[/C][/ROW]
[ROW][C]59[/C][C]59[/C][C]65.1865437958783[/C][C]-6.18654379587832[/C][/ROW]
[ROW][C]60[/C][C]60[/C][C]79.046662219538[/C][C]-19.046662219538[/C][/ROW]
[ROW][C]61[/C][C]61[/C][C]70.3955708090578[/C][C]-9.39557080905784[/C][/ROW]
[ROW][C]62[/C][C]62[/C][C]73.7815061485915[/C][C]-11.7815061485915[/C][/ROW]
[ROW][C]63[/C][C]63[/C][C]85.351993918603[/C][C]-22.3519939186031[/C][/ROW]
[ROW][C]64[/C][C]64[/C][C]72.9211614718002[/C][C]-8.92116147180016[/C][/ROW]
[ROW][C]65[/C][C]65[/C][C]80.9389763957963[/C][C]-15.9389763957963[/C][/ROW]
[ROW][C]66[/C][C]66[/C][C]60.6022082676118[/C][C]5.39779173238821[/C][/ROW]
[ROW][C]67[/C][C]67[/C][C]36.0062439547827[/C][C]30.9937560452173[/C][/ROW]
[ROW][C]68[/C][C]68[/C][C]30.8732575988748[/C][C]37.1267424011252[/C][/ROW]
[ROW][C]69[/C][C]69[/C][C]73.0149463468614[/C][C]-4.01494634686136[/C][/ROW]
[ROW][C]70[/C][C]70[/C][C]103.368710463261[/C][C]-33.3687104632608[/C][/ROW]
[ROW][C]71[/C][C]71[/C][C]41.1189327583647[/C][C]29.8810672416353[/C][/ROW]
[ROW][C]72[/C][C]72[/C][C]83.143032581232[/C][C]-11.1430325812321[/C][/ROW]
[ROW][C]73[/C][C]73[/C][C]81.336188450644[/C][C]-8.336188450644[/C][/ROW]
[ROW][C]74[/C][C]74[/C][C]67.1893764726123[/C][C]6.81062352738767[/C][/ROW]
[ROW][C]75[/C][C]75[/C][C]65.0727326430095[/C][C]9.92726735699054[/C][/ROW]
[ROW][C]76[/C][C]76[/C][C]79.5719032247089[/C][C]-3.57190322470886[/C][/ROW]
[ROW][C]77[/C][C]77[/C][C]69.8369922621405[/C][C]7.1630077378595[/C][/ROW]
[ROW][C]78[/C][C]78[/C][C]58.0559865341681[/C][C]19.9440134658319[/C][/ROW]
[ROW][C]79[/C][C]79[/C][C]80.4811502268692[/C][C]-1.48115022686918[/C][/ROW]
[ROW][C]80[/C][C]80[/C][C]81.6809654394087[/C][C]-1.68096543940873[/C][/ROW]
[ROW][C]81[/C][C]81[/C][C]84.4137458697284[/C][C]-3.41374586972836[/C][/ROW]
[ROW][C]82[/C][C]82[/C][C]77.770233143441[/C][C]4.22976685655899[/C][/ROW]
[ROW][C]83[/C][C]83[/C][C]71.3755373473295[/C][C]11.6244626526705[/C][/ROW]
[ROW][C]84[/C][C]84[/C][C]66.7583544347894[/C][C]17.2416455652106[/C][/ROW]
[ROW][C]85[/C][C]85[/C][C]83.215696111817[/C][C]1.78430388818295[/C][/ROW]
[ROW][C]86[/C][C]86[/C][C]59.8058009397173[/C][C]26.1941990602827[/C][/ROW]
[ROW][C]87[/C][C]87[/C][C]35.836036908111[/C][C]51.163963091889[/C][/ROW]
[ROW][C]88[/C][C]88[/C][C]78.0515390968208[/C][C]9.94846090317918[/C][/ROW]
[ROW][C]89[/C][C]89[/C][C]86.1257743216785[/C][C]2.87422567832151[/C][/ROW]
[ROW][C]90[/C][C]90[/C][C]73.593989749623[/C][C]16.406010250377[/C][/ROW]
[ROW][C]91[/C][C]91[/C][C]67.3477137667203[/C][C]23.6522862332797[/C][/ROW]
[ROW][C]92[/C][C]92[/C][C]52.4527644051993[/C][C]39.5472355948007[/C][/ROW]
[ROW][C]93[/C][C]93[/C][C]73.0801541204657[/C][C]19.9198458795343[/C][/ROW]
[ROW][C]94[/C][C]94[/C][C]83.0535168229574[/C][C]10.9464831770426[/C][/ROW]
[ROW][C]95[/C][C]95[/C][C]90.2435083140019[/C][C]4.7564916859981[/C][/ROW]
[ROW][C]96[/C][C]96[/C][C]39.689436580567[/C][C]56.310563419433[/C][/ROW]
[ROW][C]97[/C][C]97[/C][C]64.790889282703[/C][C]32.209110717297[/C][/ROW]
[ROW][C]98[/C][C]98[/C][C]100.85922369649[/C][C]-2.85922369649014[/C][/ROW]
[ROW][C]99[/C][C]99[/C][C]64.1990540949693[/C][C]34.8009459050307[/C][/ROW]
[ROW][C]100[/C][C]100[/C][C]70.7159347583064[/C][C]29.2840652416936[/C][/ROW]
[ROW][C]101[/C][C]101[/C][C]101.51685099082[/C][C]-0.516850990819533[/C][/ROW]
[ROW][C]102[/C][C]102[/C][C]85.0233214056892[/C][C]16.9766785943108[/C][/ROW]
[ROW][C]103[/C][C]103[/C][C]80.7997108568698[/C][C]22.2002891431302[/C][/ROW]
[ROW][C]104[/C][C]104[/C][C]85.0279815224009[/C][C]18.9720184775991[/C][/ROW]
[ROW][C]105[/C][C]105[/C][C]89.4839549320995[/C][C]15.5160450679005[/C][/ROW]
[ROW][C]106[/C][C]106[/C][C]77.3621092650587[/C][C]28.6378907349413[/C][/ROW]
[ROW][C]107[/C][C]107[/C][C]74.3634096675339[/C][C]32.6365903324661[/C][/ROW]
[ROW][C]108[/C][C]108[/C][C]100.26392277756[/C][C]7.73607722243969[/C][/ROW]
[ROW][C]109[/C][C]109[/C][C]111.289470306828[/C][C]-2.28947030682833[/C][/ROW]
[ROW][C]110[/C][C]110[/C][C]79.2836192965164[/C][C]30.7163807034836[/C][/ROW]
[ROW][C]111[/C][C]111[/C][C]86.190497528208[/C][C]24.809502471792[/C][/ROW]
[ROW][C]112[/C][C]112[/C][C]72.6600270935682[/C][C]39.3399729064318[/C][/ROW]
[ROW][C]113[/C][C]113[/C][C]78.1293770551529[/C][C]34.8706229448471[/C][/ROW]
[ROW][C]114[/C][C]114[/C][C]94.1894104256168[/C][C]19.8105895743832[/C][/ROW]
[ROW][C]115[/C][C]115[/C][C]110.109958227692[/C][C]4.89004177230846[/C][/ROW]
[ROW][C]116[/C][C]116[/C][C]111.289470306828[/C][C]4.71052969317167[/C][/ROW]
[ROW][C]117[/C][C]117[/C][C]72.8850488396667[/C][C]44.1149511603333[/C][/ROW]
[ROW][C]118[/C][C]118[/C][C]70.3009760617634[/C][C]47.6990239382366[/C][/ROW]
[ROW][C]119[/C][C]119[/C][C]69.4461286076487[/C][C]49.5538713923513[/C][/ROW]
[ROW][C]120[/C][C]120[/C][C]83.872302436107[/C][C]36.127697563893[/C][/ROW]
[ROW][C]121[/C][C]121[/C][C]83.1148789578471[/C][C]37.8851210421529[/C][/ROW]
[ROW][C]122[/C][C]122[/C][C]71.0678694874166[/C][C]50.9321305125834[/C][/ROW]
[ROW][C]123[/C][C]123[/C][C]89.6190144241225[/C][C]33.3809855758775[/C][/ROW]
[ROW][C]124[/C][C]124[/C][C]76.1489086709433[/C][C]47.8510913290567[/C][/ROW]
[ROW][C]125[/C][C]125[/C][C]84.443540334377[/C][C]40.556459665623[/C][/ROW]
[ROW][C]126[/C][C]126[/C][C]99.1438378217122[/C][C]26.8561621782878[/C][/ROW]
[ROW][C]127[/C][C]127[/C][C]79.23518774771[/C][C]47.76481225229[/C][/ROW]
[ROW][C]128[/C][C]128[/C][C]81.950498883812[/C][C]46.0495011161881[/C][/ROW]
[ROW][C]129[/C][C]129[/C][C]74.9160802669839[/C][C]54.0839197330161[/C][/ROW]
[ROW][C]130[/C][C]130[/C][C]98.6525146309272[/C][C]31.3474853690728[/C][/ROW]
[ROW][C]131[/C][C]131[/C][C]109.066468897478[/C][C]21.9335311025217[/C][/ROW]
[ROW][C]132[/C][C]132[/C][C]91.25740989946[/C][C]40.74259010054[/C][/ROW]
[ROW][C]133[/C][C]133[/C][C]109.354486759836[/C][C]23.6455132401639[/C][/ROW]
[ROW][C]134[/C][C]134[/C][C]90.8467919759445[/C][C]43.1532080240555[/C][/ROW]
[ROW][C]135[/C][C]135[/C][C]110.90715264724[/C][C]24.0928473527597[/C][/ROW]
[ROW][C]136[/C][C]136[/C][C]96.817222118552[/C][C]39.1827778814481[/C][/ROW]
[ROW][C]137[/C][C]137[/C][C]111.289470306828[/C][C]25.7105296931717[/C][/ROW]
[ROW][C]138[/C][C]138[/C][C]85.7547674859389[/C][C]52.2452325140612[/C][/ROW]
[ROW][C]139[/C][C]139[/C][C]90.7062319488219[/C][C]48.2937680511781[/C][/ROW]
[ROW][C]140[/C][C]140[/C][C]108.909293908444[/C][C]31.090706091556[/C][/ROW]
[ROW][C]141[/C][C]141[/C][C]109.239620636493[/C][C]31.760379363507[/C][/ROW]
[ROW][C]142[/C][C]142[/C][C]93.6294329089135[/C][C]48.3705670910865[/C][/ROW]
[ROW][C]143[/C][C]143[/C][C]86.209386288561[/C][C]56.790613711439[/C][/ROW]
[ROW][C]144[/C][C]144[/C][C]79.4482678350906[/C][C]64.5517321649094[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145775&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145775&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1174.1526432581426-73.1526432581426
2267.2274902583935-65.2274902583935
33102.692322182868-99.6923221828679
4435.8920294836925-31.8920294836925
5523.7639663083478-18.7639663083478
66-62.578013287179468.5780132871794
7761.8360844116757-54.8360844116757
8866.236337502596-58.236337502596
9949.9617773081344-40.9617773081344
101012.9669629103981-2.96696291039814
111168.5155520961963-57.5155520961963
121243.9902482819945-31.9902482819945
131362.1007007702107-49.1007007702107
141436.5345121129403-22.5345121129403
151565.008319077075-50.008319077075
161661.6233342342723-45.6233342342723
171765.6898129345724-48.6898129345724
181892.6670919942308-74.6670919942308
191920.6569329895687-1.65693298956872
202091.3557343314163-71.3557343314163
212170.0079367146927-49.0079367146927
222242.6188751642114-20.6188751642114
232376.8925473274574-53.8925473274574
242459.9015784244122-35.9015784244122
252554.8645271184574-29.8645271184574
262676.5515004309394-50.5515004309394
272747.8258093612383-20.8258093612383
282863.2488867472947-35.2488867472947
292941.4889016880653-12.4889016880653
303046.3629740799762-16.3629740799762
313168.540370312982-37.540370312982
323264.453794467112-32.453794467112
333366.4878034666148-33.4878034666148
343479.7739196926072-45.7739196926072
353551.0009336965754-16.0009336965754
3636111.289470306828-75.2894703068283
373779.6703314988664-42.6703314988664
383855.3215501934907-17.3215501934907
393947.5191593535034-8.51915935350343
404027.426550530468512.5734494695315
414166.6334356050027-25.6334356050027
424262.848304143791-20.848304143791
434384.9826168623684-41.9826168623684
444459.718061959306-15.718061959306
454580.315903386271-35.3159033862709
464676.7987619472546-30.7987619472546
474788.9879993833803-41.9879993833803
484867.7986657347854-19.7986657347854
494963.3971216543754-14.3971216543754
505057.0274311625329-7.02743116253286
515178.0600055462835-27.0600055462835
525275.8933304466698-23.8933304466698
535360.1192532211388-7.1192532211388
545463.5444712237384-9.54447122373839
555552.26478799197052.7352120080295
565669.5781685897886-13.5781685897886
575768.4365918870364-11.4365918870364
585884.4840013226323-26.4840013226323
595965.1865437958783-6.18654379587832
606079.046662219538-19.046662219538
616170.3955708090578-9.39557080905784
626273.7815061485915-11.7815061485915
636385.351993918603-22.3519939186031
646472.9211614718002-8.92116147180016
656580.9389763957963-15.9389763957963
666660.60220826761185.39779173238821
676736.006243954782730.9937560452173
686830.873257598874837.1267424011252
696973.0149463468614-4.01494634686136
7070103.368710463261-33.3687104632608
717141.118932758364729.8810672416353
727283.143032581232-11.1430325812321
737381.336188450644-8.336188450644
747467.18937647261236.81062352738767
757565.07273264300959.92726735699054
767679.5719032247089-3.57190322470886
777769.83699226214057.1630077378595
787858.055986534168119.9440134658319
797980.4811502268692-1.48115022686918
808081.6809654394087-1.68096543940873
818184.4137458697284-3.41374586972836
828277.7702331434414.22976685655899
838371.375537347329511.6244626526705
848466.758354434789417.2416455652106
858583.2156961118171.78430388818295
868659.805800939717326.1941990602827
878735.83603690811151.163963091889
888878.05153909682089.94846090317918
898986.12577432167852.87422567832151
909073.59398974962316.406010250377
919167.347713766720323.6522862332797
929252.452764405199339.5472355948007
939373.080154120465719.9198458795343
949483.053516822957410.9464831770426
959590.24350831400194.7564916859981
969639.68943658056756.310563419433
979764.79088928270332.209110717297
9898100.85922369649-2.85922369649014
999964.199054094969334.8009459050307
10010070.715934758306429.2840652416936
101101101.51685099082-0.516850990819533
10210285.023321405689216.9766785943108
10310380.799710856869822.2002891431302
10410485.027981522400918.9720184775991
10510589.483954932099515.5160450679005
10610677.362109265058728.6378907349413
10710774.363409667533932.6365903324661
108108100.263922777567.73607722243969
109109111.289470306828-2.28947030682833
11011079.283619296516430.7163807034836
11111186.19049752820824.809502471792
11211272.660027093568239.3399729064318
11311378.129377055152934.8706229448471
11411494.189410425616819.8105895743832
115115110.1099582276924.89004177230846
116116111.2894703068284.71052969317167
11711772.885048839666744.1149511603333
11811870.300976061763447.6990239382366
11911969.446128607648749.5538713923513
12012083.87230243610736.127697563893
12112183.114878957847137.8851210421529
12212271.067869487416650.9321305125834
12312389.619014424122533.3809855758775
12412476.148908670943347.8510913290567
12512584.44354033437740.556459665623
12612699.143837821712226.8561621782878
12712779.2351877477147.76481225229
12812881.95049888381246.0495011161881
12912974.916080266983954.0839197330161
13013098.652514630927231.3474853690728
131131109.06646889747821.9335311025217
13213291.2574098994640.74259010054
133133109.35448675983623.6455132401639
13413490.846791975944543.1532080240555
135135110.9071526472424.0928473527597
13613696.81722211855239.1827778814481
137137111.28947030682825.7105296931717
13813885.754767485938952.2452325140612
13913990.706231948821948.2937680511781
140140108.90929390844431.090706091556
141141109.23962063649331.760379363507
14214293.629432908913548.3705670910865
14314386.20938628856156.790613711439
14414479.448267835090664.5517321649094







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.002234229337283880.004468458674567770.997765770662716
120.0002196111008550670.0004392222017101340.999780388899145
130.0002911316852839520.0005822633705679050.999708868314716
144.10818633613399e-058.21637267226798e-050.999958918136639
152.08441506650273e-054.16883013300546e-050.999979155849335
163.82063530819471e-067.64127061638943e-060.999996179364692
176.07312542381477e-071.21462508476295e-060.999999392687458
182.17507677126454e-074.35015354252908e-070.999999782492323
193.18372707795643e-086.36745415591286e-080.999999968162729
207.36495777972794e-091.47299155594559e-080.999999992635042
211.65153501421082e-093.30307002842164e-090.999999998348465
222.60845752376821e-095.21691504753641e-090.999999997391542
238.3451220388292e-101.66902440776584e-090.999999999165488
244.63368836333475e-099.2673767266695e-090.999999995366312
253.84629111608542e-087.69258223217085e-080.999999961537089
262.85289248641133e-085.70578497282266e-080.999999971471075
271.21863677116986e-082.43727354233972e-080.999999987813632
281.00884000737771e-082.01768001475542e-080.9999999899116
293.42441948055646e-096.84883896111291e-090.99999999657558
302.13301534456684e-084.26603068913368e-080.999999978669847
311.08122226767131e-082.16244453534263e-080.999999989187777
326.41639559224469e-081.28327911844894e-070.999999935836044
331.7959255490078e-073.5918510980156e-070.999999820407445
341.43326044127729e-072.86652088255458e-070.999999856673956
352.64027998208148e-075.28055996416296e-070.999999735972002
368.78684527904906e-071.75736905580981e-060.999999121315472
371.03296750538479e-062.06593501076958e-060.999998967032495
382.247447933637e-064.49489586727401e-060.999997752552066
392.4763178496178e-064.95263569923559e-060.99999752368215
402.45301047306088e-064.90602094612175e-060.999997546989527
412.58251205908944e-065.16502411817887e-060.99999741748794
422.46389006600359e-064.92778013200717e-060.999997536109934
432.6206888068709e-065.24137761374181e-060.999997379311193
443.64013071788317e-067.28026143576635e-060.999996359869282
452.57684500224907e-065.15369000449814e-060.999997423154998
462.21888387740262e-064.43776775480524e-060.999997781116123
475.47821502813591e-061.09564300562718e-050.999994521784972
481.49569963687323e-052.99139927374645e-050.999985043003631
493.1274211201965e-056.25484224039301e-050.999968725788798
507.80269363135263e-050.0001560538726270530.999921973063686
510.0001328570113008240.0002657140226016480.9998671429887
520.0003294976051775570.0006589952103551140.999670502394822
530.0005660338958918850.001132067791783770.999433966104108
540.001034119252051280.002068238504102560.998965880747949
550.001978106756487730.003956213512975470.998021893243512
560.005167711256692810.01033542251338560.994832288743307
570.009523360317966060.01904672063593210.990476639682034
580.0167869159557040.03357383191140790.983213084044296
590.02666540141016180.05333080282032370.973334598589838
600.03700590219813740.07401180439627480.962994097801863
610.04244519485903050.0848903897180610.95755480514097
620.0774738328591870.1549476657183740.922526167140813
630.1366140285140590.2732280570281170.863385971485941
640.17605322296690.3521064459338010.8239467770331
650.2800216344899370.5600432689798740.719978365510063
660.3410315052494340.6820630104988680.658968494750566
670.3569498094752110.7138996189504230.643050190524789
680.4601143297300640.9202286594601290.539885670269936
690.5060041000166130.9879917999667730.493995899983386
700.7159624016194820.5680751967610360.284037598380518
710.7863447846785380.4273104306429250.213655215321462
720.86803406444410.2639318711118010.131965935555901
730.8719589935671370.2560820128657260.128041006432863
740.8758563366620060.2482873266759880.124143663337994
750.9005305818794240.1989388362411510.0994694181205755
760.9336106250439880.1327787499120230.0663893749560116
770.9576304563812620.08473908723747610.042369543618738
780.9624364528550170.0751270942899650.0375635471449825
790.9748045245195910.05039095096081720.0251954754804086
800.9834549473628880.03309010527422480.0165450526371124
810.9914565291301660.01708694173966860.00854347086983428
820.9975102567917510.004979486416497510.00248974320824876
830.9980793723554980.003841255289004390.00192062764450219
840.9985420100820030.002915979835994610.0014579899179973
850.999254701478130.001490597043740920.00074529852187046
860.9994037260712470.001192547857505750.000596273928752877
870.9993193713696210.001361257260757720.000680628630378859
880.9996377433212360.000724513357527490.000362256678763745
890.9998321981094640.0003356037810721690.000167801890536084
900.9999334615027670.0001330769944652956.65384972326475e-05
910.999947502940380.0001049941192389925.24970596194959e-05
920.9999575585591268.48828817483165e-054.24414408741582e-05
930.9999688165191926.23669616161675e-053.11834808080837e-05
940.9999829805698653.40388602704418e-051.70194301352209e-05
950.999992107349771.57853004596289e-057.89265022981447e-06
960.9999923348726351.5330254730351e-057.6651273651755e-06
970.9999931713498281.3657300343419e-056.8286501717095e-06
980.999997086908795.8261824195093e-062.91309120975465e-06
990.9999979305896384.13882072370627e-062.06941036185313e-06
1000.9999984866168953.02676621027229e-061.51338310513614e-06
1010.9999993326712921.33465741558478e-066.67328707792392e-07
1020.9999994438418541.11231629133423e-065.56158145667117e-07
1030.9999997107342715.78531458041552e-072.89265729020776e-07
1040.999999859611542.80776917707143e-071.40388458853572e-07
1050.9999999136756211.72648758096308e-078.63243790481542e-08
1060.9999999771756724.56486554949688e-082.28243277474844e-08
1070.999999979174664.16506814978661e-082.08253407489331e-08
1080.9999999881504142.36991711814635e-081.18495855907318e-08
1090.9999999974433145.11337175489061e-092.55668587744531e-09
1100.9999999952513139.4973749661805e-094.74868748309025e-09
1110.9999999958575638.28487391371724e-094.14243695685862e-09
1120.999999992316651.53667018369899e-087.68335091849496e-09
1130.9999999917849431.64301143441858e-088.21505717209292e-09
1140.9999999833059873.3388025144915e-081.66940125724575e-08
1150.9999999943224421.13551161707791e-085.67755808538955e-09
1160.9999999994192051.16158912352791e-095.80794561763956e-10
1170.9999999990923641.8152718439787e-099.0763592198935e-10
1180.9999999983377933.32441348244025e-091.66220674122012e-09
1190.9999999959218028.15639655561647e-094.07819827780824e-09
1200.9999999977463264.50734862614396e-092.25367431307198e-09
1210.9999999922780751.5443849316176e-087.721924658088e-09
1220.9999999751520434.9695913814052e-082.4847956907026e-08
1230.9999999686998546.26002914492694e-083.13001457246347e-08
1240.9999998580021572.83995685262422e-071.41997842631211e-07
1250.9999998248778723.50244256111349e-071.75122128055674e-07
1260.9999999959489348.1021326556373e-094.05106632781865e-09
1270.999999981500523.6998960234361e-081.84994801171805e-08
1280.999999878991572.42016859131107e-071.21008429565553e-07
1290.9999989515290472.09694190637656e-061.04847095318828e-06
1300.9999916515729661.66968540675641e-058.34842703378204e-06
1310.9999678714677136.42570645739648e-053.21285322869824e-05
1320.9997956327176450.000408734564710480.00020436728235524
1330.9998436409013230.0003127181973530750.000156359098676538

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.00223422933728388 & 0.00446845867456777 & 0.997765770662716 \tabularnewline
12 & 0.000219611100855067 & 0.000439222201710134 & 0.999780388899145 \tabularnewline
13 & 0.000291131685283952 & 0.000582263370567905 & 0.999708868314716 \tabularnewline
14 & 4.10818633613399e-05 & 8.21637267226798e-05 & 0.999958918136639 \tabularnewline
15 & 2.08441506650273e-05 & 4.16883013300546e-05 & 0.999979155849335 \tabularnewline
16 & 3.82063530819471e-06 & 7.64127061638943e-06 & 0.999996179364692 \tabularnewline
17 & 6.07312542381477e-07 & 1.21462508476295e-06 & 0.999999392687458 \tabularnewline
18 & 2.17507677126454e-07 & 4.35015354252908e-07 & 0.999999782492323 \tabularnewline
19 & 3.18372707795643e-08 & 6.36745415591286e-08 & 0.999999968162729 \tabularnewline
20 & 7.36495777972794e-09 & 1.47299155594559e-08 & 0.999999992635042 \tabularnewline
21 & 1.65153501421082e-09 & 3.30307002842164e-09 & 0.999999998348465 \tabularnewline
22 & 2.60845752376821e-09 & 5.21691504753641e-09 & 0.999999997391542 \tabularnewline
23 & 8.3451220388292e-10 & 1.66902440776584e-09 & 0.999999999165488 \tabularnewline
24 & 4.63368836333475e-09 & 9.2673767266695e-09 & 0.999999995366312 \tabularnewline
25 & 3.84629111608542e-08 & 7.69258223217085e-08 & 0.999999961537089 \tabularnewline
26 & 2.85289248641133e-08 & 5.70578497282266e-08 & 0.999999971471075 \tabularnewline
27 & 1.21863677116986e-08 & 2.43727354233972e-08 & 0.999999987813632 \tabularnewline
28 & 1.00884000737771e-08 & 2.01768001475542e-08 & 0.9999999899116 \tabularnewline
29 & 3.42441948055646e-09 & 6.84883896111291e-09 & 0.99999999657558 \tabularnewline
30 & 2.13301534456684e-08 & 4.26603068913368e-08 & 0.999999978669847 \tabularnewline
31 & 1.08122226767131e-08 & 2.16244453534263e-08 & 0.999999989187777 \tabularnewline
32 & 6.41639559224469e-08 & 1.28327911844894e-07 & 0.999999935836044 \tabularnewline
33 & 1.7959255490078e-07 & 3.5918510980156e-07 & 0.999999820407445 \tabularnewline
34 & 1.43326044127729e-07 & 2.86652088255458e-07 & 0.999999856673956 \tabularnewline
35 & 2.64027998208148e-07 & 5.28055996416296e-07 & 0.999999735972002 \tabularnewline
36 & 8.78684527904906e-07 & 1.75736905580981e-06 & 0.999999121315472 \tabularnewline
37 & 1.03296750538479e-06 & 2.06593501076958e-06 & 0.999998967032495 \tabularnewline
38 & 2.247447933637e-06 & 4.49489586727401e-06 & 0.999997752552066 \tabularnewline
39 & 2.4763178496178e-06 & 4.95263569923559e-06 & 0.99999752368215 \tabularnewline
40 & 2.45301047306088e-06 & 4.90602094612175e-06 & 0.999997546989527 \tabularnewline
41 & 2.58251205908944e-06 & 5.16502411817887e-06 & 0.99999741748794 \tabularnewline
42 & 2.46389006600359e-06 & 4.92778013200717e-06 & 0.999997536109934 \tabularnewline
43 & 2.6206888068709e-06 & 5.24137761374181e-06 & 0.999997379311193 \tabularnewline
44 & 3.64013071788317e-06 & 7.28026143576635e-06 & 0.999996359869282 \tabularnewline
45 & 2.57684500224907e-06 & 5.15369000449814e-06 & 0.999997423154998 \tabularnewline
46 & 2.21888387740262e-06 & 4.43776775480524e-06 & 0.999997781116123 \tabularnewline
47 & 5.47821502813591e-06 & 1.09564300562718e-05 & 0.999994521784972 \tabularnewline
48 & 1.49569963687323e-05 & 2.99139927374645e-05 & 0.999985043003631 \tabularnewline
49 & 3.1274211201965e-05 & 6.25484224039301e-05 & 0.999968725788798 \tabularnewline
50 & 7.80269363135263e-05 & 0.000156053872627053 & 0.999921973063686 \tabularnewline
51 & 0.000132857011300824 & 0.000265714022601648 & 0.9998671429887 \tabularnewline
52 & 0.000329497605177557 & 0.000658995210355114 & 0.999670502394822 \tabularnewline
53 & 0.000566033895891885 & 0.00113206779178377 & 0.999433966104108 \tabularnewline
54 & 0.00103411925205128 & 0.00206823850410256 & 0.998965880747949 \tabularnewline
55 & 0.00197810675648773 & 0.00395621351297547 & 0.998021893243512 \tabularnewline
56 & 0.00516771125669281 & 0.0103354225133856 & 0.994832288743307 \tabularnewline
57 & 0.00952336031796606 & 0.0190467206359321 & 0.990476639682034 \tabularnewline
58 & 0.016786915955704 & 0.0335738319114079 & 0.983213084044296 \tabularnewline
59 & 0.0266654014101618 & 0.0533308028203237 & 0.973334598589838 \tabularnewline
60 & 0.0370059021981374 & 0.0740118043962748 & 0.962994097801863 \tabularnewline
61 & 0.0424451948590305 & 0.084890389718061 & 0.95755480514097 \tabularnewline
62 & 0.077473832859187 & 0.154947665718374 & 0.922526167140813 \tabularnewline
63 & 0.136614028514059 & 0.273228057028117 & 0.863385971485941 \tabularnewline
64 & 0.1760532229669 & 0.352106445933801 & 0.8239467770331 \tabularnewline
65 & 0.280021634489937 & 0.560043268979874 & 0.719978365510063 \tabularnewline
66 & 0.341031505249434 & 0.682063010498868 & 0.658968494750566 \tabularnewline
67 & 0.356949809475211 & 0.713899618950423 & 0.643050190524789 \tabularnewline
68 & 0.460114329730064 & 0.920228659460129 & 0.539885670269936 \tabularnewline
69 & 0.506004100016613 & 0.987991799966773 & 0.493995899983386 \tabularnewline
70 & 0.715962401619482 & 0.568075196761036 & 0.284037598380518 \tabularnewline
71 & 0.786344784678538 & 0.427310430642925 & 0.213655215321462 \tabularnewline
72 & 0.8680340644441 & 0.263931871111801 & 0.131965935555901 \tabularnewline
73 & 0.871958993567137 & 0.256082012865726 & 0.128041006432863 \tabularnewline
74 & 0.875856336662006 & 0.248287326675988 & 0.124143663337994 \tabularnewline
75 & 0.900530581879424 & 0.198938836241151 & 0.0994694181205755 \tabularnewline
76 & 0.933610625043988 & 0.132778749912023 & 0.0663893749560116 \tabularnewline
77 & 0.957630456381262 & 0.0847390872374761 & 0.042369543618738 \tabularnewline
78 & 0.962436452855017 & 0.075127094289965 & 0.0375635471449825 \tabularnewline
79 & 0.974804524519591 & 0.0503909509608172 & 0.0251954754804086 \tabularnewline
80 & 0.983454947362888 & 0.0330901052742248 & 0.0165450526371124 \tabularnewline
81 & 0.991456529130166 & 0.0170869417396686 & 0.00854347086983428 \tabularnewline
82 & 0.997510256791751 & 0.00497948641649751 & 0.00248974320824876 \tabularnewline
83 & 0.998079372355498 & 0.00384125528900439 & 0.00192062764450219 \tabularnewline
84 & 0.998542010082003 & 0.00291597983599461 & 0.0014579899179973 \tabularnewline
85 & 0.99925470147813 & 0.00149059704374092 & 0.00074529852187046 \tabularnewline
86 & 0.999403726071247 & 0.00119254785750575 & 0.000596273928752877 \tabularnewline
87 & 0.999319371369621 & 0.00136125726075772 & 0.000680628630378859 \tabularnewline
88 & 0.999637743321236 & 0.00072451335752749 & 0.000362256678763745 \tabularnewline
89 & 0.999832198109464 & 0.000335603781072169 & 0.000167801890536084 \tabularnewline
90 & 0.999933461502767 & 0.000133076994465295 & 6.65384972326475e-05 \tabularnewline
91 & 0.99994750294038 & 0.000104994119238992 & 5.24970596194959e-05 \tabularnewline
92 & 0.999957558559126 & 8.48828817483165e-05 & 4.24414408741582e-05 \tabularnewline
93 & 0.999968816519192 & 6.23669616161675e-05 & 3.11834808080837e-05 \tabularnewline
94 & 0.999982980569865 & 3.40388602704418e-05 & 1.70194301352209e-05 \tabularnewline
95 & 0.99999210734977 & 1.57853004596289e-05 & 7.89265022981447e-06 \tabularnewline
96 & 0.999992334872635 & 1.5330254730351e-05 & 7.6651273651755e-06 \tabularnewline
97 & 0.999993171349828 & 1.3657300343419e-05 & 6.8286501717095e-06 \tabularnewline
98 & 0.99999708690879 & 5.8261824195093e-06 & 2.91309120975465e-06 \tabularnewline
99 & 0.999997930589638 & 4.13882072370627e-06 & 2.06941036185313e-06 \tabularnewline
100 & 0.999998486616895 & 3.02676621027229e-06 & 1.51338310513614e-06 \tabularnewline
101 & 0.999999332671292 & 1.33465741558478e-06 & 6.67328707792392e-07 \tabularnewline
102 & 0.999999443841854 & 1.11231629133423e-06 & 5.56158145667117e-07 \tabularnewline
103 & 0.999999710734271 & 5.78531458041552e-07 & 2.89265729020776e-07 \tabularnewline
104 & 0.99999985961154 & 2.80776917707143e-07 & 1.40388458853572e-07 \tabularnewline
105 & 0.999999913675621 & 1.72648758096308e-07 & 8.63243790481542e-08 \tabularnewline
106 & 0.999999977175672 & 4.56486554949688e-08 & 2.28243277474844e-08 \tabularnewline
107 & 0.99999997917466 & 4.16506814978661e-08 & 2.08253407489331e-08 \tabularnewline
108 & 0.999999988150414 & 2.36991711814635e-08 & 1.18495855907318e-08 \tabularnewline
109 & 0.999999997443314 & 5.11337175489061e-09 & 2.55668587744531e-09 \tabularnewline
110 & 0.999999995251313 & 9.4973749661805e-09 & 4.74868748309025e-09 \tabularnewline
111 & 0.999999995857563 & 8.28487391371724e-09 & 4.14243695685862e-09 \tabularnewline
112 & 0.99999999231665 & 1.53667018369899e-08 & 7.68335091849496e-09 \tabularnewline
113 & 0.999999991784943 & 1.64301143441858e-08 & 8.21505717209292e-09 \tabularnewline
114 & 0.999999983305987 & 3.3388025144915e-08 & 1.66940125724575e-08 \tabularnewline
115 & 0.999999994322442 & 1.13551161707791e-08 & 5.67755808538955e-09 \tabularnewline
116 & 0.999999999419205 & 1.16158912352791e-09 & 5.80794561763956e-10 \tabularnewline
117 & 0.999999999092364 & 1.8152718439787e-09 & 9.0763592198935e-10 \tabularnewline
118 & 0.999999998337793 & 3.32441348244025e-09 & 1.66220674122012e-09 \tabularnewline
119 & 0.999999995921802 & 8.15639655561647e-09 & 4.07819827780824e-09 \tabularnewline
120 & 0.999999997746326 & 4.50734862614396e-09 & 2.25367431307198e-09 \tabularnewline
121 & 0.999999992278075 & 1.5443849316176e-08 & 7.721924658088e-09 \tabularnewline
122 & 0.999999975152043 & 4.9695913814052e-08 & 2.4847956907026e-08 \tabularnewline
123 & 0.999999968699854 & 6.26002914492694e-08 & 3.13001457246347e-08 \tabularnewline
124 & 0.999999858002157 & 2.83995685262422e-07 & 1.41997842631211e-07 \tabularnewline
125 & 0.999999824877872 & 3.50244256111349e-07 & 1.75122128055674e-07 \tabularnewline
126 & 0.999999995948934 & 8.1021326556373e-09 & 4.05106632781865e-09 \tabularnewline
127 & 0.99999998150052 & 3.6998960234361e-08 & 1.84994801171805e-08 \tabularnewline
128 & 0.99999987899157 & 2.42016859131107e-07 & 1.21008429565553e-07 \tabularnewline
129 & 0.999998951529047 & 2.09694190637656e-06 & 1.04847095318828e-06 \tabularnewline
130 & 0.999991651572966 & 1.66968540675641e-05 & 8.34842703378204e-06 \tabularnewline
131 & 0.999967871467713 & 6.42570645739648e-05 & 3.21285322869824e-05 \tabularnewline
132 & 0.999795632717645 & 0.00040873456471048 & 0.00020436728235524 \tabularnewline
133 & 0.999843640901323 & 0.000312718197353075 & 0.000156359098676538 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145775&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]11[/C][C]0.00223422933728388[/C][C]0.00446845867456777[/C][C]0.997765770662716[/C][/ROW]
[ROW][C]12[/C][C]0.000219611100855067[/C][C]0.000439222201710134[/C][C]0.999780388899145[/C][/ROW]
[ROW][C]13[/C][C]0.000291131685283952[/C][C]0.000582263370567905[/C][C]0.999708868314716[/C][/ROW]
[ROW][C]14[/C][C]4.10818633613399e-05[/C][C]8.21637267226798e-05[/C][C]0.999958918136639[/C][/ROW]
[ROW][C]15[/C][C]2.08441506650273e-05[/C][C]4.16883013300546e-05[/C][C]0.999979155849335[/C][/ROW]
[ROW][C]16[/C][C]3.82063530819471e-06[/C][C]7.64127061638943e-06[/C][C]0.999996179364692[/C][/ROW]
[ROW][C]17[/C][C]6.07312542381477e-07[/C][C]1.21462508476295e-06[/C][C]0.999999392687458[/C][/ROW]
[ROW][C]18[/C][C]2.17507677126454e-07[/C][C]4.35015354252908e-07[/C][C]0.999999782492323[/C][/ROW]
[ROW][C]19[/C][C]3.18372707795643e-08[/C][C]6.36745415591286e-08[/C][C]0.999999968162729[/C][/ROW]
[ROW][C]20[/C][C]7.36495777972794e-09[/C][C]1.47299155594559e-08[/C][C]0.999999992635042[/C][/ROW]
[ROW][C]21[/C][C]1.65153501421082e-09[/C][C]3.30307002842164e-09[/C][C]0.999999998348465[/C][/ROW]
[ROW][C]22[/C][C]2.60845752376821e-09[/C][C]5.21691504753641e-09[/C][C]0.999999997391542[/C][/ROW]
[ROW][C]23[/C][C]8.3451220388292e-10[/C][C]1.66902440776584e-09[/C][C]0.999999999165488[/C][/ROW]
[ROW][C]24[/C][C]4.63368836333475e-09[/C][C]9.2673767266695e-09[/C][C]0.999999995366312[/C][/ROW]
[ROW][C]25[/C][C]3.84629111608542e-08[/C][C]7.69258223217085e-08[/C][C]0.999999961537089[/C][/ROW]
[ROW][C]26[/C][C]2.85289248641133e-08[/C][C]5.70578497282266e-08[/C][C]0.999999971471075[/C][/ROW]
[ROW][C]27[/C][C]1.21863677116986e-08[/C][C]2.43727354233972e-08[/C][C]0.999999987813632[/C][/ROW]
[ROW][C]28[/C][C]1.00884000737771e-08[/C][C]2.01768001475542e-08[/C][C]0.9999999899116[/C][/ROW]
[ROW][C]29[/C][C]3.42441948055646e-09[/C][C]6.84883896111291e-09[/C][C]0.99999999657558[/C][/ROW]
[ROW][C]30[/C][C]2.13301534456684e-08[/C][C]4.26603068913368e-08[/C][C]0.999999978669847[/C][/ROW]
[ROW][C]31[/C][C]1.08122226767131e-08[/C][C]2.16244453534263e-08[/C][C]0.999999989187777[/C][/ROW]
[ROW][C]32[/C][C]6.41639559224469e-08[/C][C]1.28327911844894e-07[/C][C]0.999999935836044[/C][/ROW]
[ROW][C]33[/C][C]1.7959255490078e-07[/C][C]3.5918510980156e-07[/C][C]0.999999820407445[/C][/ROW]
[ROW][C]34[/C][C]1.43326044127729e-07[/C][C]2.86652088255458e-07[/C][C]0.999999856673956[/C][/ROW]
[ROW][C]35[/C][C]2.64027998208148e-07[/C][C]5.28055996416296e-07[/C][C]0.999999735972002[/C][/ROW]
[ROW][C]36[/C][C]8.78684527904906e-07[/C][C]1.75736905580981e-06[/C][C]0.999999121315472[/C][/ROW]
[ROW][C]37[/C][C]1.03296750538479e-06[/C][C]2.06593501076958e-06[/C][C]0.999998967032495[/C][/ROW]
[ROW][C]38[/C][C]2.247447933637e-06[/C][C]4.49489586727401e-06[/C][C]0.999997752552066[/C][/ROW]
[ROW][C]39[/C][C]2.4763178496178e-06[/C][C]4.95263569923559e-06[/C][C]0.99999752368215[/C][/ROW]
[ROW][C]40[/C][C]2.45301047306088e-06[/C][C]4.90602094612175e-06[/C][C]0.999997546989527[/C][/ROW]
[ROW][C]41[/C][C]2.58251205908944e-06[/C][C]5.16502411817887e-06[/C][C]0.99999741748794[/C][/ROW]
[ROW][C]42[/C][C]2.46389006600359e-06[/C][C]4.92778013200717e-06[/C][C]0.999997536109934[/C][/ROW]
[ROW][C]43[/C][C]2.6206888068709e-06[/C][C]5.24137761374181e-06[/C][C]0.999997379311193[/C][/ROW]
[ROW][C]44[/C][C]3.64013071788317e-06[/C][C]7.28026143576635e-06[/C][C]0.999996359869282[/C][/ROW]
[ROW][C]45[/C][C]2.57684500224907e-06[/C][C]5.15369000449814e-06[/C][C]0.999997423154998[/C][/ROW]
[ROW][C]46[/C][C]2.21888387740262e-06[/C][C]4.43776775480524e-06[/C][C]0.999997781116123[/C][/ROW]
[ROW][C]47[/C][C]5.47821502813591e-06[/C][C]1.09564300562718e-05[/C][C]0.999994521784972[/C][/ROW]
[ROW][C]48[/C][C]1.49569963687323e-05[/C][C]2.99139927374645e-05[/C][C]0.999985043003631[/C][/ROW]
[ROW][C]49[/C][C]3.1274211201965e-05[/C][C]6.25484224039301e-05[/C][C]0.999968725788798[/C][/ROW]
[ROW][C]50[/C][C]7.80269363135263e-05[/C][C]0.000156053872627053[/C][C]0.999921973063686[/C][/ROW]
[ROW][C]51[/C][C]0.000132857011300824[/C][C]0.000265714022601648[/C][C]0.9998671429887[/C][/ROW]
[ROW][C]52[/C][C]0.000329497605177557[/C][C]0.000658995210355114[/C][C]0.999670502394822[/C][/ROW]
[ROW][C]53[/C][C]0.000566033895891885[/C][C]0.00113206779178377[/C][C]0.999433966104108[/C][/ROW]
[ROW][C]54[/C][C]0.00103411925205128[/C][C]0.00206823850410256[/C][C]0.998965880747949[/C][/ROW]
[ROW][C]55[/C][C]0.00197810675648773[/C][C]0.00395621351297547[/C][C]0.998021893243512[/C][/ROW]
[ROW][C]56[/C][C]0.00516771125669281[/C][C]0.0103354225133856[/C][C]0.994832288743307[/C][/ROW]
[ROW][C]57[/C][C]0.00952336031796606[/C][C]0.0190467206359321[/C][C]0.990476639682034[/C][/ROW]
[ROW][C]58[/C][C]0.016786915955704[/C][C]0.0335738319114079[/C][C]0.983213084044296[/C][/ROW]
[ROW][C]59[/C][C]0.0266654014101618[/C][C]0.0533308028203237[/C][C]0.973334598589838[/C][/ROW]
[ROW][C]60[/C][C]0.0370059021981374[/C][C]0.0740118043962748[/C][C]0.962994097801863[/C][/ROW]
[ROW][C]61[/C][C]0.0424451948590305[/C][C]0.084890389718061[/C][C]0.95755480514097[/C][/ROW]
[ROW][C]62[/C][C]0.077473832859187[/C][C]0.154947665718374[/C][C]0.922526167140813[/C][/ROW]
[ROW][C]63[/C][C]0.136614028514059[/C][C]0.273228057028117[/C][C]0.863385971485941[/C][/ROW]
[ROW][C]64[/C][C]0.1760532229669[/C][C]0.352106445933801[/C][C]0.8239467770331[/C][/ROW]
[ROW][C]65[/C][C]0.280021634489937[/C][C]0.560043268979874[/C][C]0.719978365510063[/C][/ROW]
[ROW][C]66[/C][C]0.341031505249434[/C][C]0.682063010498868[/C][C]0.658968494750566[/C][/ROW]
[ROW][C]67[/C][C]0.356949809475211[/C][C]0.713899618950423[/C][C]0.643050190524789[/C][/ROW]
[ROW][C]68[/C][C]0.460114329730064[/C][C]0.920228659460129[/C][C]0.539885670269936[/C][/ROW]
[ROW][C]69[/C][C]0.506004100016613[/C][C]0.987991799966773[/C][C]0.493995899983386[/C][/ROW]
[ROW][C]70[/C][C]0.715962401619482[/C][C]0.568075196761036[/C][C]0.284037598380518[/C][/ROW]
[ROW][C]71[/C][C]0.786344784678538[/C][C]0.427310430642925[/C][C]0.213655215321462[/C][/ROW]
[ROW][C]72[/C][C]0.8680340644441[/C][C]0.263931871111801[/C][C]0.131965935555901[/C][/ROW]
[ROW][C]73[/C][C]0.871958993567137[/C][C]0.256082012865726[/C][C]0.128041006432863[/C][/ROW]
[ROW][C]74[/C][C]0.875856336662006[/C][C]0.248287326675988[/C][C]0.124143663337994[/C][/ROW]
[ROW][C]75[/C][C]0.900530581879424[/C][C]0.198938836241151[/C][C]0.0994694181205755[/C][/ROW]
[ROW][C]76[/C][C]0.933610625043988[/C][C]0.132778749912023[/C][C]0.0663893749560116[/C][/ROW]
[ROW][C]77[/C][C]0.957630456381262[/C][C]0.0847390872374761[/C][C]0.042369543618738[/C][/ROW]
[ROW][C]78[/C][C]0.962436452855017[/C][C]0.075127094289965[/C][C]0.0375635471449825[/C][/ROW]
[ROW][C]79[/C][C]0.974804524519591[/C][C]0.0503909509608172[/C][C]0.0251954754804086[/C][/ROW]
[ROW][C]80[/C][C]0.983454947362888[/C][C]0.0330901052742248[/C][C]0.0165450526371124[/C][/ROW]
[ROW][C]81[/C][C]0.991456529130166[/C][C]0.0170869417396686[/C][C]0.00854347086983428[/C][/ROW]
[ROW][C]82[/C][C]0.997510256791751[/C][C]0.00497948641649751[/C][C]0.00248974320824876[/C][/ROW]
[ROW][C]83[/C][C]0.998079372355498[/C][C]0.00384125528900439[/C][C]0.00192062764450219[/C][/ROW]
[ROW][C]84[/C][C]0.998542010082003[/C][C]0.00291597983599461[/C][C]0.0014579899179973[/C][/ROW]
[ROW][C]85[/C][C]0.99925470147813[/C][C]0.00149059704374092[/C][C]0.00074529852187046[/C][/ROW]
[ROW][C]86[/C][C]0.999403726071247[/C][C]0.00119254785750575[/C][C]0.000596273928752877[/C][/ROW]
[ROW][C]87[/C][C]0.999319371369621[/C][C]0.00136125726075772[/C][C]0.000680628630378859[/C][/ROW]
[ROW][C]88[/C][C]0.999637743321236[/C][C]0.00072451335752749[/C][C]0.000362256678763745[/C][/ROW]
[ROW][C]89[/C][C]0.999832198109464[/C][C]0.000335603781072169[/C][C]0.000167801890536084[/C][/ROW]
[ROW][C]90[/C][C]0.999933461502767[/C][C]0.000133076994465295[/C][C]6.65384972326475e-05[/C][/ROW]
[ROW][C]91[/C][C]0.99994750294038[/C][C]0.000104994119238992[/C][C]5.24970596194959e-05[/C][/ROW]
[ROW][C]92[/C][C]0.999957558559126[/C][C]8.48828817483165e-05[/C][C]4.24414408741582e-05[/C][/ROW]
[ROW][C]93[/C][C]0.999968816519192[/C][C]6.23669616161675e-05[/C][C]3.11834808080837e-05[/C][/ROW]
[ROW][C]94[/C][C]0.999982980569865[/C][C]3.40388602704418e-05[/C][C]1.70194301352209e-05[/C][/ROW]
[ROW][C]95[/C][C]0.99999210734977[/C][C]1.57853004596289e-05[/C][C]7.89265022981447e-06[/C][/ROW]
[ROW][C]96[/C][C]0.999992334872635[/C][C]1.5330254730351e-05[/C][C]7.6651273651755e-06[/C][/ROW]
[ROW][C]97[/C][C]0.999993171349828[/C][C]1.3657300343419e-05[/C][C]6.8286501717095e-06[/C][/ROW]
[ROW][C]98[/C][C]0.99999708690879[/C][C]5.8261824195093e-06[/C][C]2.91309120975465e-06[/C][/ROW]
[ROW][C]99[/C][C]0.999997930589638[/C][C]4.13882072370627e-06[/C][C]2.06941036185313e-06[/C][/ROW]
[ROW][C]100[/C][C]0.999998486616895[/C][C]3.02676621027229e-06[/C][C]1.51338310513614e-06[/C][/ROW]
[ROW][C]101[/C][C]0.999999332671292[/C][C]1.33465741558478e-06[/C][C]6.67328707792392e-07[/C][/ROW]
[ROW][C]102[/C][C]0.999999443841854[/C][C]1.11231629133423e-06[/C][C]5.56158145667117e-07[/C][/ROW]
[ROW][C]103[/C][C]0.999999710734271[/C][C]5.78531458041552e-07[/C][C]2.89265729020776e-07[/C][/ROW]
[ROW][C]104[/C][C]0.99999985961154[/C][C]2.80776917707143e-07[/C][C]1.40388458853572e-07[/C][/ROW]
[ROW][C]105[/C][C]0.999999913675621[/C][C]1.72648758096308e-07[/C][C]8.63243790481542e-08[/C][/ROW]
[ROW][C]106[/C][C]0.999999977175672[/C][C]4.56486554949688e-08[/C][C]2.28243277474844e-08[/C][/ROW]
[ROW][C]107[/C][C]0.99999997917466[/C][C]4.16506814978661e-08[/C][C]2.08253407489331e-08[/C][/ROW]
[ROW][C]108[/C][C]0.999999988150414[/C][C]2.36991711814635e-08[/C][C]1.18495855907318e-08[/C][/ROW]
[ROW][C]109[/C][C]0.999999997443314[/C][C]5.11337175489061e-09[/C][C]2.55668587744531e-09[/C][/ROW]
[ROW][C]110[/C][C]0.999999995251313[/C][C]9.4973749661805e-09[/C][C]4.74868748309025e-09[/C][/ROW]
[ROW][C]111[/C][C]0.999999995857563[/C][C]8.28487391371724e-09[/C][C]4.14243695685862e-09[/C][/ROW]
[ROW][C]112[/C][C]0.99999999231665[/C][C]1.53667018369899e-08[/C][C]7.68335091849496e-09[/C][/ROW]
[ROW][C]113[/C][C]0.999999991784943[/C][C]1.64301143441858e-08[/C][C]8.21505717209292e-09[/C][/ROW]
[ROW][C]114[/C][C]0.999999983305987[/C][C]3.3388025144915e-08[/C][C]1.66940125724575e-08[/C][/ROW]
[ROW][C]115[/C][C]0.999999994322442[/C][C]1.13551161707791e-08[/C][C]5.67755808538955e-09[/C][/ROW]
[ROW][C]116[/C][C]0.999999999419205[/C][C]1.16158912352791e-09[/C][C]5.80794561763956e-10[/C][/ROW]
[ROW][C]117[/C][C]0.999999999092364[/C][C]1.8152718439787e-09[/C][C]9.0763592198935e-10[/C][/ROW]
[ROW][C]118[/C][C]0.999999998337793[/C][C]3.32441348244025e-09[/C][C]1.66220674122012e-09[/C][/ROW]
[ROW][C]119[/C][C]0.999999995921802[/C][C]8.15639655561647e-09[/C][C]4.07819827780824e-09[/C][/ROW]
[ROW][C]120[/C][C]0.999999997746326[/C][C]4.50734862614396e-09[/C][C]2.25367431307198e-09[/C][/ROW]
[ROW][C]121[/C][C]0.999999992278075[/C][C]1.5443849316176e-08[/C][C]7.721924658088e-09[/C][/ROW]
[ROW][C]122[/C][C]0.999999975152043[/C][C]4.9695913814052e-08[/C][C]2.4847956907026e-08[/C][/ROW]
[ROW][C]123[/C][C]0.999999968699854[/C][C]6.26002914492694e-08[/C][C]3.13001457246347e-08[/C][/ROW]
[ROW][C]124[/C][C]0.999999858002157[/C][C]2.83995685262422e-07[/C][C]1.41997842631211e-07[/C][/ROW]
[ROW][C]125[/C][C]0.999999824877872[/C][C]3.50244256111349e-07[/C][C]1.75122128055674e-07[/C][/ROW]
[ROW][C]126[/C][C]0.999999995948934[/C][C]8.1021326556373e-09[/C][C]4.05106632781865e-09[/C][/ROW]
[ROW][C]127[/C][C]0.99999998150052[/C][C]3.6998960234361e-08[/C][C]1.84994801171805e-08[/C][/ROW]
[ROW][C]128[/C][C]0.99999987899157[/C][C]2.42016859131107e-07[/C][C]1.21008429565553e-07[/C][/ROW]
[ROW][C]129[/C][C]0.999998951529047[/C][C]2.09694190637656e-06[/C][C]1.04847095318828e-06[/C][/ROW]
[ROW][C]130[/C][C]0.999991651572966[/C][C]1.66968540675641e-05[/C][C]8.34842703378204e-06[/C][/ROW]
[ROW][C]131[/C][C]0.999967871467713[/C][C]6.42570645739648e-05[/C][C]3.21285322869824e-05[/C][/ROW]
[ROW][C]132[/C][C]0.999795632717645[/C][C]0.00040873456471048[/C][C]0.00020436728235524[/C][/ROW]
[ROW][C]133[/C][C]0.999843640901323[/C][C]0.000312718197353075[/C][C]0.000156359098676538[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145775&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145775&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.002234229337283880.004468458674567770.997765770662716
120.0002196111008550670.0004392222017101340.999780388899145
130.0002911316852839520.0005822633705679050.999708868314716
144.10818633613399e-058.21637267226798e-050.999958918136639
152.08441506650273e-054.16883013300546e-050.999979155849335
163.82063530819471e-067.64127061638943e-060.999996179364692
176.07312542381477e-071.21462508476295e-060.999999392687458
182.17507677126454e-074.35015354252908e-070.999999782492323
193.18372707795643e-086.36745415591286e-080.999999968162729
207.36495777972794e-091.47299155594559e-080.999999992635042
211.65153501421082e-093.30307002842164e-090.999999998348465
222.60845752376821e-095.21691504753641e-090.999999997391542
238.3451220388292e-101.66902440776584e-090.999999999165488
244.63368836333475e-099.2673767266695e-090.999999995366312
253.84629111608542e-087.69258223217085e-080.999999961537089
262.85289248641133e-085.70578497282266e-080.999999971471075
271.21863677116986e-082.43727354233972e-080.999999987813632
281.00884000737771e-082.01768001475542e-080.9999999899116
293.42441948055646e-096.84883896111291e-090.99999999657558
302.13301534456684e-084.26603068913368e-080.999999978669847
311.08122226767131e-082.16244453534263e-080.999999989187777
326.41639559224469e-081.28327911844894e-070.999999935836044
331.7959255490078e-073.5918510980156e-070.999999820407445
341.43326044127729e-072.86652088255458e-070.999999856673956
352.64027998208148e-075.28055996416296e-070.999999735972002
368.78684527904906e-071.75736905580981e-060.999999121315472
371.03296750538479e-062.06593501076958e-060.999998967032495
382.247447933637e-064.49489586727401e-060.999997752552066
392.4763178496178e-064.95263569923559e-060.99999752368215
402.45301047306088e-064.90602094612175e-060.999997546989527
412.58251205908944e-065.16502411817887e-060.99999741748794
422.46389006600359e-064.92778013200717e-060.999997536109934
432.6206888068709e-065.24137761374181e-060.999997379311193
443.64013071788317e-067.28026143576635e-060.999996359869282
452.57684500224907e-065.15369000449814e-060.999997423154998
462.21888387740262e-064.43776775480524e-060.999997781116123
475.47821502813591e-061.09564300562718e-050.999994521784972
481.49569963687323e-052.99139927374645e-050.999985043003631
493.1274211201965e-056.25484224039301e-050.999968725788798
507.80269363135263e-050.0001560538726270530.999921973063686
510.0001328570113008240.0002657140226016480.9998671429887
520.0003294976051775570.0006589952103551140.999670502394822
530.0005660338958918850.001132067791783770.999433966104108
540.001034119252051280.002068238504102560.998965880747949
550.001978106756487730.003956213512975470.998021893243512
560.005167711256692810.01033542251338560.994832288743307
570.009523360317966060.01904672063593210.990476639682034
580.0167869159557040.03357383191140790.983213084044296
590.02666540141016180.05333080282032370.973334598589838
600.03700590219813740.07401180439627480.962994097801863
610.04244519485903050.0848903897180610.95755480514097
620.0774738328591870.1549476657183740.922526167140813
630.1366140285140590.2732280570281170.863385971485941
640.17605322296690.3521064459338010.8239467770331
650.2800216344899370.5600432689798740.719978365510063
660.3410315052494340.6820630104988680.658968494750566
670.3569498094752110.7138996189504230.643050190524789
680.4601143297300640.9202286594601290.539885670269936
690.5060041000166130.9879917999667730.493995899983386
700.7159624016194820.5680751967610360.284037598380518
710.7863447846785380.4273104306429250.213655215321462
720.86803406444410.2639318711118010.131965935555901
730.8719589935671370.2560820128657260.128041006432863
740.8758563366620060.2482873266759880.124143663337994
750.9005305818794240.1989388362411510.0994694181205755
760.9336106250439880.1327787499120230.0663893749560116
770.9576304563812620.08473908723747610.042369543618738
780.9624364528550170.0751270942899650.0375635471449825
790.9748045245195910.05039095096081720.0251954754804086
800.9834549473628880.03309010527422480.0165450526371124
810.9914565291301660.01708694173966860.00854347086983428
820.9975102567917510.004979486416497510.00248974320824876
830.9980793723554980.003841255289004390.00192062764450219
840.9985420100820030.002915979835994610.0014579899179973
850.999254701478130.001490597043740920.00074529852187046
860.9994037260712470.001192547857505750.000596273928752877
870.9993193713696210.001361257260757720.000680628630378859
880.9996377433212360.000724513357527490.000362256678763745
890.9998321981094640.0003356037810721690.000167801890536084
900.9999334615027670.0001330769944652956.65384972326475e-05
910.999947502940380.0001049941192389925.24970596194959e-05
920.9999575585591268.48828817483165e-054.24414408741582e-05
930.9999688165191926.23669616161675e-053.11834808080837e-05
940.9999829805698653.40388602704418e-051.70194301352209e-05
950.999992107349771.57853004596289e-057.89265022981447e-06
960.9999923348726351.5330254730351e-057.6651273651755e-06
970.9999931713498281.3657300343419e-056.8286501717095e-06
980.999997086908795.8261824195093e-062.91309120975465e-06
990.9999979305896384.13882072370627e-062.06941036185313e-06
1000.9999984866168953.02676621027229e-061.51338310513614e-06
1010.9999993326712921.33465741558478e-066.67328707792392e-07
1020.9999994438418541.11231629133423e-065.56158145667117e-07
1030.9999997107342715.78531458041552e-072.89265729020776e-07
1040.999999859611542.80776917707143e-071.40388458853572e-07
1050.9999999136756211.72648758096308e-078.63243790481542e-08
1060.9999999771756724.56486554949688e-082.28243277474844e-08
1070.999999979174664.16506814978661e-082.08253407489331e-08
1080.9999999881504142.36991711814635e-081.18495855907318e-08
1090.9999999974433145.11337175489061e-092.55668587744531e-09
1100.9999999952513139.4973749661805e-094.74868748309025e-09
1110.9999999958575638.28487391371724e-094.14243695685862e-09
1120.999999992316651.53667018369899e-087.68335091849496e-09
1130.9999999917849431.64301143441858e-088.21505717209292e-09
1140.9999999833059873.3388025144915e-081.66940125724575e-08
1150.9999999943224421.13551161707791e-085.67755808538955e-09
1160.9999999994192051.16158912352791e-095.80794561763956e-10
1170.9999999990923641.8152718439787e-099.0763592198935e-10
1180.9999999983377933.32441348244025e-091.66220674122012e-09
1190.9999999959218028.15639655561647e-094.07819827780824e-09
1200.9999999977463264.50734862614396e-092.25367431307198e-09
1210.9999999922780751.5443849316176e-087.721924658088e-09
1220.9999999751520434.9695913814052e-082.4847956907026e-08
1230.9999999686998546.26002914492694e-083.13001457246347e-08
1240.9999998580021572.83995685262422e-071.41997842631211e-07
1250.9999998248778723.50244256111349e-071.75122128055674e-07
1260.9999999959489348.1021326556373e-094.05106632781865e-09
1270.999999981500523.6998960234361e-081.84994801171805e-08
1280.999999878991572.42016859131107e-071.21008429565553e-07
1290.9999989515290472.09694190637656e-061.04847095318828e-06
1300.9999916515729661.66968540675641e-058.34842703378204e-06
1310.9999678714677136.42570645739648e-053.21285322869824e-05
1320.9997956327176450.000408734564710480.00020436728235524
1330.9998436409013230.0003127181973530750.000156359098676538







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level970.788617886178862NOK
5% type I error level1020.829268292682927NOK
10% type I error level1080.878048780487805NOK

\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 & 97 & 0.788617886178862 & NOK \tabularnewline
5% type I error level & 102 & 0.829268292682927 & NOK \tabularnewline
10% type I error level & 108 & 0.878048780487805 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145775&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]97[/C][C]0.788617886178862[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]102[/C][C]0.829268292682927[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]108[/C][C]0.878048780487805[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145775&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145775&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level970.788617886178862NOK
5% type I error level1020.829268292682927NOK
10% type I error level1080.878048780487805NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}