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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 21 Dec 2011 11:43:50 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/21/t1324485878o1sdmcr69uh13ms.htm/, Retrieved Fri, 03 May 2024 20:39:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=158880, Retrieved Fri, 03 May 2024 20:39:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact88

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2011-12-17 15:29:01] [ee8c3a74bf3b349877806e9a50913c60]
-    D    [Multiple Regression] [] [2011-12-21 16:43:50] [539ae27d3016cec7ecb6ecd6e9a1efc7] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
41	1785	276257	93	492	140824	165
34	1227	180480	63	436	110459	135
44	1944	229345	62	697	105079	121
38	2683	218443	135	1137	112098	148
27	1247	171533	48	380	43929	73
35	631	70849	46	179	76173	49
33	4845	536497	109	2354	187326	185
18	381	33186	46	111	22807	5
34	2066	217320	75	740	144408	125
33	1759	213274	72	595	66485	93
46	2157	310843	82	809	79089	154
57	2211	242788	63	693	81625	98
37	1967	195022	60	738	68788	70
55	3005	367785	117	1184	103297	148
44	2228	261990	50	713	69446	100
62	5009	392509	132	1729	114948	150
40	2353	335528	63	844	167949	197
39	3278	376673	90	1298	125081	114
32	1473	181980	44	514	125818	169
51	2347	266736	64	689	136588	200
49	2523	278265	103	837	112431	148
39	2987	461287	101	1330	103037	140
25	1523	195786	62	491	82317	74
56	1761	197058	69	622	118906	128
45	3660	250284	159	1332	83515	140
38	2949	250092	74	1046	104581	116
45	2914	274121	101	1082	103129	147
43	2009	278027	73	636	83243	132
32	1555	165597	53	586	37110	70
41	3050	371452	140	1170	113344	144
50	2438	296386	54	976	139165	155
50	2092	248320	84	721	86652	165
51	2404	351980	56	863	112302	161
37	1018	101014	40	343	69652	31
44	3462	412722	87	1278	119442	199
42	2764	273950	87	1186	69867	78
44	3710	425531	77	1334	101629	121
36	1947	231912	67	652	70168	112
17	947	115658	36	284	31081	41
43	3609	376008	51	1273	103925	158
41	3324	335039	44	1518	92622	123
41	1842	194127	75	715	79011	104
38	1869	206947	85	671	93487	94
49	1813	182286	97	486	64520	73
45	2496	153778	82	1022	93473	52
42	5557	457592	860	2084	114360	71
26	918	78800	57	330	33032	21
52	2254	208277	80	658	96125	155
50	4103	359144	120	1385	151911	174
45	2241	184648	68	930	89256	136
40	1943	234078	48	620	95676	128
4	496	24188	20	218	5950	7
44	2594	380576	81	840	149695	165
18	744	65029	21	255	32551	21
14	1161	101097	70	454	31701	35
38	3121	288327	128	1149	100087	137
61	2621	334829	86	684	169707	174
39	3906	359400	222	1190	150491	257
42	2722	369577	63	1079	120192	207
40	2312	269961	86	883	95893	103
51	4061	389738	70	1331	151715	171
28	3195	309474	146	1159	176225	279
43	3041	282769	72	1217	59900	83
42	2644	269324	59	946	104767	130
37	1496	234773	58	579	114799	131
30	1835	237561	58	474	72128	126
39	2090	214916	60	630	143592	158
44	2772	240376	89	843	89626	138
36	2440	247447	78	893	131072	200
28	1718	181550	62	633	126817	104
47	2566	242152	67	873	81351	111
23	893	73566	39	385	22618	26
48	2227	246125	60	729	88977	115
38	1878	199565	94	774	92059	127
42	2177	222676	67	769	81897	140
46	2352	363558	96	996	108146	121
37	2981	365442	51	1194	126372	183
41	1926	217036	50	575	249771	68
42	2400	213466	62	725	71154	112
41	2034	204477	71	706	71571	103
36	1800	169080	49	665	55918	63
45	4168	478396	114	1259	160141	166
26	1369	145943	45	653	38692	38
44	2403	288626	57	694	102812	163
8	870	80953	31	437	56622	59
27	1968	150221	175	822	15986	27
38	1569	179317	70	458	123534	108
38	3962	395368	282	1545	108535	88
57	2928	349460	91	987	93879	92
45	3098	180679	72	1051	144551	170
40	2557	286578	62	838	56750	98
42	2329	274477	75	703	127654	205
31	1858	195623	86	613	65594	96
36	3146	282361	89	1128	59938	107
40	2582	329121	138	967	146975	150
40	1824	198658	68	617	165904	138
35	1938	264817	72	654	169265	177
39	2210	300481	61	805	183500	213
65	4119	403404	130	1355	165986	208
33	3844	406613	85	1456	184923	307
51	2791	294371	83	878	140358	125
42	2425	313267	86	887	149959	208
36	2052	189276	116	663	57224	73
19	602	43287	43	214	43750	49
25	2195	189520	87	733	48029	82
44	2413	250254	80	830	104978	206
40	2632	270832	110	1174	100046	112
44	2759	314153	59	1068	101047	139
30	1268	160308	44	413	197426	60
45	3233	162843	85	946	160902	70
42	2025	344925	60	657	147172	112
45	2310	300526	70	690	109432	142
1	398	23623	9	156	1168	11
40	2205	195817	54	779	83248	130
11	530	61857	25	192	25162	31
45	1611	163931	107	461	45724	132
38	3153	428191	63	1213	110529	219
0	387	21054	2	146	855	4
30	2137	252805	67	866	101382	102
8	492	31961	22	200	14116	39
41	3674	335888	155	1290	89506	125
48	2136	246100	79	715	135356	121
48	1748	180591	112	514	116066	42
32	1790	163400	50	697	144244	111
8	568	38214	52	276	8773	16
43	2191	224597	113	752	102153	70
52	2792	357602	115	1021	117440	162
53	1420	202565	78	494	104128	173
49	3718	424398	135	1626	134238	171
48	2554	348017	120	884	134047	172
56	3460	421610	122	1187	279488	254
40	1284	192170	49	488	79756	90
36	1130	102510	56	403	66089	50
44	2974	302158	155	977	102070	113
46	4346	444599	155	1525	146760	187
43	1785	148707	107	551	154771	16
46	4350	407736	146	1807	165933	175
39	1920	164406	76	723	64593	90
41	1923	278077	83	632	92280	140
46	2541	282461	192	898	67150	145
32	1845	219544	69	621	128692	141
45	4081	384177	131	1606	124089	125
39	2339	246963	67	811	125386	241
21	2035	173260	37	716	37238	16
49	3108	336715	60	1001	140015	175
55	1974	176654	127	732	150047	132
36	2651	253341	52	1024	154451	154
48	2702	307133	71	831	156349	198
0	2	1	0	0	0	0
0	207	14688	0	85	6023	5
0	5	98	0	0	0	0
0	8	455	0	0	0	0
0	0	0	0	0	0	0
0	0	0	0	0	0	0
43	2255	260901	67	773	84601	125
52	3447	409280	123	1128	68946	174
0	0	0	0	0	0	0
0	4	203	0	0	0	0
0	151	7199	0	74	1644	6
5	474	46660	7	259	6179	13
1	141	17547	3	69	3926	3
45	1111	118589	102	301	52789	35
0	29	969	0	0	0	0
34	2011	233108	53	668	100350	80

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'AstonUniversity' @ aston.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158880&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]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158880&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158880&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'AstonUniversity' @ aston.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
pr[t] = + 11.2519485957141 + 0.0154685333902034pgviews[t] + 4.21335961704024e-05timerfc[t] -0.00412545059060588compviewpronly[t] -0.0319122815828951compview[t] + 6.20323283192624e-05char[t] + 0.00684485454646724`hyperlinks `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
pr[t] =  +  11.2519485957141 +  0.0154685333902034pgviews[t] +  4.21335961704024e-05timerfc[t] -0.00412545059060588compviewpronly[t] -0.0319122815828951compview[t] +  6.20323283192624e-05char[t] +  0.00684485454646724`hyperlinks
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158880&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]pr[t] =  +  11.2519485957141 +  0.0154685333902034pgviews[t] +  4.21335961704024e-05timerfc[t] -0.00412545059060588compviewpronly[t] -0.0319122815828951compview[t] +  6.20323283192624e-05char[t] +  0.00684485454646724`hyperlinks
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158880&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158880&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
pr[t] = + 11.2519485957141 + 0.0154685333902034pgviews[t] + 4.21335961704024e-05timerfc[t] -0.00412545059060588compviewpronly[t] -0.0319122815828951compview[t] + 6.20323283192624e-05char[t] + 0.00684485454646724`hyperlinks `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)11.25194859571411.5774117.133200
pgviews0.01546853339020340.002955.2431e-060
timerfc4.21335961704024e-051.7e-052.54060.0120360.006018
compviewpronly-0.004125450590605880.01195-0.34520.7303880.365194
compview-0.03191228158289510.006597-4.83773e-062e-06
char6.20323283192624e-052.1e-052.91520.0040740.002037
`hyperlinks `0.006844854546467240.0201490.33970.7345250.367263

\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) & 11.2519485957141 & 1.577411 & 7.1332 & 0 & 0 \tabularnewline
pgviews & 0.0154685333902034 & 0.00295 & 5.243 & 1e-06 & 0 \tabularnewline
timerfc & 4.21335961704024e-05 & 1.7e-05 & 2.5406 & 0.012036 & 0.006018 \tabularnewline
compviewpronly & -0.00412545059060588 & 0.01195 & -0.3452 & 0.730388 & 0.365194 \tabularnewline
compview & -0.0319122815828951 & 0.006597 & -4.8377 & 3e-06 & 2e-06 \tabularnewline
char & 6.20323283192624e-05 & 2.1e-05 & 2.9152 & 0.004074 & 0.002037 \tabularnewline
`hyperlinks
` & 0.00684485454646724 & 0.020149 & 0.3397 & 0.734525 & 0.367263 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158880&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]11.2519485957141[/C][C]1.577411[/C][C]7.1332[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]pgviews[/C][C]0.0154685333902034[/C][C]0.00295[/C][C]5.243[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]timerfc[/C][C]4.21335961704024e-05[/C][C]1.7e-05[/C][C]2.5406[/C][C]0.012036[/C][C]0.006018[/C][/ROW]
[ROW][C]compviewpronly[/C][C]-0.00412545059060588[/C][C]0.01195[/C][C]-0.3452[/C][C]0.730388[/C][C]0.365194[/C][/ROW]
[ROW][C]compview[/C][C]-0.0319122815828951[/C][C]0.006597[/C][C]-4.8377[/C][C]3e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]char[/C][C]6.20323283192624e-05[/C][C]2.1e-05[/C][C]2.9152[/C][C]0.004074[/C][C]0.002037[/C][/ROW]
[ROW][C]`hyperlinks
`[/C][C]0.00684485454646724[/C][C]0.020149[/C][C]0.3397[/C][C]0.734525[/C][C]0.367263[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158880&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158880&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)11.25194859571411.5774117.133200
pgviews0.01546853339020340.002955.2431e-060
timerfc4.21335961704024e-051.7e-052.54060.0120360.006018
compviewpronly-0.004125450590605880.01195-0.34520.7303880.365194
compview-0.03191228158289510.006597-4.83773e-062e-06
char6.20323283192624e-052.1e-052.91520.0040740.002037
`hyperlinks `0.006844854546467240.0201490.33970.7345250.367263






Multiple Linear Regression - Regression Statistics
Multiple R0.82120045426018
R-squared0.674370186077125
Adjusted R-squared0.661925734589627
F-TEST (value)54.1904307115981
F-TEST (DF numerator)6
F-TEST (DF denominator)157
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.70129580705954
Sum Squared Residuals11886.8701493465

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.82120045426018 \tabularnewline
R-squared & 0.674370186077125 \tabularnewline
Adjusted R-squared & 0.661925734589627 \tabularnewline
F-TEST (value) & 54.1904307115981 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 8.70129580705954 \tabularnewline
Sum Squared Residuals & 11886.8701493465 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158880&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.82120045426018[/C][/ROW]
[ROW][C]R-squared[/C][C]0.674370186077125[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.661925734589627[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]54.1904307115981[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]157[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]8.70129580705954[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]11886.8701493465[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158880&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158880&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.82120045426018
R-squared0.674370186077125
Adjusted R-squared0.661925734589627
F-TEST (value)54.1904307115981
F-TEST (DF numerator)6
F-TEST (DF denominator)157
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.70129580705954
Sum Squared Residuals11886.8701493465






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14144.2835137341622-3.28351373416218
23431.4385366625682.561463337432
34435.83379134765738.16620865234267
43833.08335125320744.91664874679263
52728.6685157879752-1.66851578797515
63523.156233607343311.8437663926567
73346.1169218812015-13.1169218812015
81816.26066694173311.73933305826687
93438.2554858202248-4.25548582022483
103332.92305025550970.0769497444902889
114637.51936821838558.48063178161447
125739.041477488932617.9585225110674
133730.84296074165366.15703925834638
145542.384968878830112.6150311211699
154438.78707507874275.21292492125726
166257.70797396931854.29202603068152
174046.3592437300997-6.35924373009971
183944.5743361481414-5.57433614814141
193234.0817014538047-2.08170145380474
205146.38539509198134.61460490801869
214942.85525755997896.14474244002114
223941.3820376435437-2.38203764354368
232532.7478194216885-7.74781942168854
245634.912860267774921.0871397322251
254541.38794961153763.61205038846244
263841.0018050728061-3.0018050728061
274540.33472483381794.66527516618209
284339.51241844567973.48758155432026
293226.14462877581245.85537122418757
304144.1833027415605-3.18330274156048
315039.776561304045110.2234386959549
325037.224068491863512.7759315081365
335143.56553792367.43446207640004
343724.676934286974712.3230657130253
354449.8220546207728-5.82205462077279
364232.21050473572819.78949526427188
374450.8132263531784-6.81322635317838
383635.17656500260260.82343499739736
391723.7707988242053-6.77079882420528
404349.6139191335737-6.61391913357366
414134.74886366606656.25113633393354
424130.410666763840910.5893332361591
433833.56088719130724.43911280869276
444935.56922699214613.430773007854
454529.542269624808215.4577303751918
464254.0175045965968-12.0175045965968
472620.19877983464635.80122016535367
485240.588974552413611.4110254475864
495055.772203013952-5.77220301395201
504530.205521391362414.7944786086376
514037.99736711373232.00263288626771
52413.3210885198663-9.32108851986635
534450.6872120664969-6.68721206649692
541819.4391330619457-1.4391330619457
551420.899595402978-6.89959540297795
563841.6286001925746-3.62860019257455
576155.43806016324695.5619398367531
583959.0178241062677-20.0178241062677
594243.1083238370321-1.10832383703211
604036.50977823893913.49022176106093
615158.3084026878174-7.30840268781742
622848.9658766543756-20.9658766543756
634335.35541375560487.64458624439517
644240.45469160420981.54530839579024
653733.58614335473113.41385664526889
663039.6170284518935-9.61702845189353
673942.2729360094985-3.27293600949851
684443.49370937482480.506290625175214
693640.1012216865512-4.10122168655121
702833.5986098195265-5.59860981952645
714738.81728363915158.18271636084851
722317.29884148672265.70115851327743
734838.86553225347729.13446774652275
743830.2022777617297.79772223827097
754235.53067794907866.46932205092143
764638.30804495126757.69195504873251
773743.5391278698823-6.53912786988234
784147.5923434297806-6.59234342978061
794239.158808886532.84119111346999
804133.65205485449037.34794514550969
813628.69539237839097.3046076216091
824566.3036407035123-21.3036407035123
832620.21336840268655.78663159731348
844445.6947905886919-1.69479058869188
85817.9630985482746-9.96309854827457
862722.2459838167194.75401618328102
873836.57508698073321.42491301926684
883846.0637273804961-8.06372738049613
895745.848242522434911.1517574775651
904543.07974103866711.92025896133286
914040.0724106723741-0.072410672374065
924245.4209932117836-3.42099321178359
933133.0438213374824-2.04382133748242
943639.8991133927674-3.89911339276736
954043.7741942789488-3.77419427894882
964039.10232239554880.897677604451164
973542.9314355529334-7.9314355529334
983944.997599603667-5.99759960366698
996559.90647653195165.09352346804842
1003354.6026881979435-21.6026881979435
1015148.02847885379062.97152114620942
1024244.0272604917442-2.02726049174421
1033633.38127504082442.61872495917557
1041918.43053227665380.569467723346196
1052532.9805507014366-7.9805507014366
1064440.22646068012323.77353931987682
1074032.43014648169977.56985351830033
1084440.05992500326353.94007499673653
1093036.9165950732013-6.9165950732013
1104548.043462079277-3.04346207927694
1114245.7908088671604-3.79080886716044
1124545.0985371144822-0.0985371144821614
113113.5360490045894-12.5360490045894
1144034.5819957964075.41800420359299
1151117.5992827582633-6.59928275826327
1164531.665660398436213.3343396015638
1173847.4513544680424-9.45135446804237
118013.5383247981094-13.5383247981094
1193034.0344838634827-4.03448386348274
120814.878480335191-6.87848033519101
1214146.8370939351792-5.83709393517925
1224840.74329723840147.25670276159863
1234836.52225813769811.477741862302
1243233.0836902063553-1.08369020635534
125813.2795829469032-5.27958294690323
1264336.95830113870846.04169886129162
1275244.84422883917667.15577116082345
1285333.309067789810119.6909322101899
1294943.69662982086465.30337017913537
1304846.20884211878111.79115788121891
1315663.2297208283675-7.22972082836753
1324028.998505440022211.0014945599778
1333624.400728832706411.5992711672936
1344445.273734417101-1.27373441710097
1354658.2889274821682-12.2889274821682
1364336.81407417661326.18592582338677
1374648.9427040853461-2.94270408534614
1383929.11532597988489.8846740201152
1394138.88597086174922.11402913825079
1404638.16724902914917.83275097085092
1413237.887576871659-5.88757687165896
1424547.3273700947093-2.32737009470934
1433941.1086174174701-2.10861741747008
1442129.4481231467528-8.44812314675282
1454951.2067493023223-2.20674930232227
1465535.557465029629819.4425349703702
1473640.6755609703475-4.67556097034752
1484850.2305043219179-2.23050432191793
149011.2829277960907-11.2829277960907
150012.7680943196903-12.7680943196903
151011.3334203550898-11.3334203550898
152011.3948676490933-11.3948676490933
153011.2519485957141-11.2519485957141
154011.2519485957141-11.2519485957141
1554338.28519373837484.71480626162524
1565250.77982298360251.2201770163975
157011.2519485957141-11.2519485957141
158011.3223758492975-11.3223758492975
159011.672558334367-11.672558334367
160512.7281088016662-7.72810880166624
161112.2220797193641-11.2220797193641
1624526.921872000560618.0781279994394
163011.7413635187191-11.7413635187191
1643437.4173271113826-3.41732711138265

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 41 & 44.2835137341622 & -3.28351373416218 \tabularnewline
2 & 34 & 31.438536662568 & 2.561463337432 \tabularnewline
3 & 44 & 35.8337913476573 & 8.16620865234267 \tabularnewline
4 & 38 & 33.0833512532074 & 4.91664874679263 \tabularnewline
5 & 27 & 28.6685157879752 & -1.66851578797515 \tabularnewline
6 & 35 & 23.1562336073433 & 11.8437663926567 \tabularnewline
7 & 33 & 46.1169218812015 & -13.1169218812015 \tabularnewline
8 & 18 & 16.2606669417331 & 1.73933305826687 \tabularnewline
9 & 34 & 38.2554858202248 & -4.25548582022483 \tabularnewline
10 & 33 & 32.9230502555097 & 0.0769497444902889 \tabularnewline
11 & 46 & 37.5193682183855 & 8.48063178161447 \tabularnewline
12 & 57 & 39.0414774889326 & 17.9585225110674 \tabularnewline
13 & 37 & 30.8429607416536 & 6.15703925834638 \tabularnewline
14 & 55 & 42.3849688788301 & 12.6150311211699 \tabularnewline
15 & 44 & 38.7870750787427 & 5.21292492125726 \tabularnewline
16 & 62 & 57.7079739693185 & 4.29202603068152 \tabularnewline
17 & 40 & 46.3592437300997 & -6.35924373009971 \tabularnewline
18 & 39 & 44.5743361481414 & -5.57433614814141 \tabularnewline
19 & 32 & 34.0817014538047 & -2.08170145380474 \tabularnewline
20 & 51 & 46.3853950919813 & 4.61460490801869 \tabularnewline
21 & 49 & 42.8552575599789 & 6.14474244002114 \tabularnewline
22 & 39 & 41.3820376435437 & -2.38203764354368 \tabularnewline
23 & 25 & 32.7478194216885 & -7.74781942168854 \tabularnewline
24 & 56 & 34.9128602677749 & 21.0871397322251 \tabularnewline
25 & 45 & 41.3879496115376 & 3.61205038846244 \tabularnewline
26 & 38 & 41.0018050728061 & -3.0018050728061 \tabularnewline
27 & 45 & 40.3347248338179 & 4.66527516618209 \tabularnewline
28 & 43 & 39.5124184456797 & 3.48758155432026 \tabularnewline
29 & 32 & 26.1446287758124 & 5.85537122418757 \tabularnewline
30 & 41 & 44.1833027415605 & -3.18330274156048 \tabularnewline
31 & 50 & 39.7765613040451 & 10.2234386959549 \tabularnewline
32 & 50 & 37.2240684918635 & 12.7759315081365 \tabularnewline
33 & 51 & 43.5655379236 & 7.43446207640004 \tabularnewline
34 & 37 & 24.6769342869747 & 12.3230657130253 \tabularnewline
35 & 44 & 49.8220546207728 & -5.82205462077279 \tabularnewline
36 & 42 & 32.2105047357281 & 9.78949526427188 \tabularnewline
37 & 44 & 50.8132263531784 & -6.81322635317838 \tabularnewline
38 & 36 & 35.1765650026026 & 0.82343499739736 \tabularnewline
39 & 17 & 23.7707988242053 & -6.77079882420528 \tabularnewline
40 & 43 & 49.6139191335737 & -6.61391913357366 \tabularnewline
41 & 41 & 34.7488636660665 & 6.25113633393354 \tabularnewline
42 & 41 & 30.4106667638409 & 10.5893332361591 \tabularnewline
43 & 38 & 33.5608871913072 & 4.43911280869276 \tabularnewline
44 & 49 & 35.569226992146 & 13.430773007854 \tabularnewline
45 & 45 & 29.5422696248082 & 15.4577303751918 \tabularnewline
46 & 42 & 54.0175045965968 & -12.0175045965968 \tabularnewline
47 & 26 & 20.1987798346463 & 5.80122016535367 \tabularnewline
48 & 52 & 40.5889745524136 & 11.4110254475864 \tabularnewline
49 & 50 & 55.772203013952 & -5.77220301395201 \tabularnewline
50 & 45 & 30.2055213913624 & 14.7944786086376 \tabularnewline
51 & 40 & 37.9973671137323 & 2.00263288626771 \tabularnewline
52 & 4 & 13.3210885198663 & -9.32108851986635 \tabularnewline
53 & 44 & 50.6872120664969 & -6.68721206649692 \tabularnewline
54 & 18 & 19.4391330619457 & -1.4391330619457 \tabularnewline
55 & 14 & 20.899595402978 & -6.89959540297795 \tabularnewline
56 & 38 & 41.6286001925746 & -3.62860019257455 \tabularnewline
57 & 61 & 55.4380601632469 & 5.5619398367531 \tabularnewline
58 & 39 & 59.0178241062677 & -20.0178241062677 \tabularnewline
59 & 42 & 43.1083238370321 & -1.10832383703211 \tabularnewline
60 & 40 & 36.5097782389391 & 3.49022176106093 \tabularnewline
61 & 51 & 58.3084026878174 & -7.30840268781742 \tabularnewline
62 & 28 & 48.9658766543756 & -20.9658766543756 \tabularnewline
63 & 43 & 35.3554137556048 & 7.64458624439517 \tabularnewline
64 & 42 & 40.4546916042098 & 1.54530839579024 \tabularnewline
65 & 37 & 33.5861433547311 & 3.41385664526889 \tabularnewline
66 & 30 & 39.6170284518935 & -9.61702845189353 \tabularnewline
67 & 39 & 42.2729360094985 & -3.27293600949851 \tabularnewline
68 & 44 & 43.4937093748248 & 0.506290625175214 \tabularnewline
69 & 36 & 40.1012216865512 & -4.10122168655121 \tabularnewline
70 & 28 & 33.5986098195265 & -5.59860981952645 \tabularnewline
71 & 47 & 38.8172836391515 & 8.18271636084851 \tabularnewline
72 & 23 & 17.2988414867226 & 5.70115851327743 \tabularnewline
73 & 48 & 38.8655322534772 & 9.13446774652275 \tabularnewline
74 & 38 & 30.202277761729 & 7.79772223827097 \tabularnewline
75 & 42 & 35.5306779490786 & 6.46932205092143 \tabularnewline
76 & 46 & 38.3080449512675 & 7.69195504873251 \tabularnewline
77 & 37 & 43.5391278698823 & -6.53912786988234 \tabularnewline
78 & 41 & 47.5923434297806 & -6.59234342978061 \tabularnewline
79 & 42 & 39.15880888653 & 2.84119111346999 \tabularnewline
80 & 41 & 33.6520548544903 & 7.34794514550969 \tabularnewline
81 & 36 & 28.6953923783909 & 7.3046076216091 \tabularnewline
82 & 45 & 66.3036407035123 & -21.3036407035123 \tabularnewline
83 & 26 & 20.2133684026865 & 5.78663159731348 \tabularnewline
84 & 44 & 45.6947905886919 & -1.69479058869188 \tabularnewline
85 & 8 & 17.9630985482746 & -9.96309854827457 \tabularnewline
86 & 27 & 22.245983816719 & 4.75401618328102 \tabularnewline
87 & 38 & 36.5750869807332 & 1.42491301926684 \tabularnewline
88 & 38 & 46.0637273804961 & -8.06372738049613 \tabularnewline
89 & 57 & 45.8482425224349 & 11.1517574775651 \tabularnewline
90 & 45 & 43.0797410386671 & 1.92025896133286 \tabularnewline
91 & 40 & 40.0724106723741 & -0.072410672374065 \tabularnewline
92 & 42 & 45.4209932117836 & -3.42099321178359 \tabularnewline
93 & 31 & 33.0438213374824 & -2.04382133748242 \tabularnewline
94 & 36 & 39.8991133927674 & -3.89911339276736 \tabularnewline
95 & 40 & 43.7741942789488 & -3.77419427894882 \tabularnewline
96 & 40 & 39.1023223955488 & 0.897677604451164 \tabularnewline
97 & 35 & 42.9314355529334 & -7.9314355529334 \tabularnewline
98 & 39 & 44.997599603667 & -5.99759960366698 \tabularnewline
99 & 65 & 59.9064765319516 & 5.09352346804842 \tabularnewline
100 & 33 & 54.6026881979435 & -21.6026881979435 \tabularnewline
101 & 51 & 48.0284788537906 & 2.97152114620942 \tabularnewline
102 & 42 & 44.0272604917442 & -2.02726049174421 \tabularnewline
103 & 36 & 33.3812750408244 & 2.61872495917557 \tabularnewline
104 & 19 & 18.4305322766538 & 0.569467723346196 \tabularnewline
105 & 25 & 32.9805507014366 & -7.9805507014366 \tabularnewline
106 & 44 & 40.2264606801232 & 3.77353931987682 \tabularnewline
107 & 40 & 32.4301464816997 & 7.56985351830033 \tabularnewline
108 & 44 & 40.0599250032635 & 3.94007499673653 \tabularnewline
109 & 30 & 36.9165950732013 & -6.9165950732013 \tabularnewline
110 & 45 & 48.043462079277 & -3.04346207927694 \tabularnewline
111 & 42 & 45.7908088671604 & -3.79080886716044 \tabularnewline
112 & 45 & 45.0985371144822 & -0.0985371144821614 \tabularnewline
113 & 1 & 13.5360490045894 & -12.5360490045894 \tabularnewline
114 & 40 & 34.581995796407 & 5.41800420359299 \tabularnewline
115 & 11 & 17.5992827582633 & -6.59928275826327 \tabularnewline
116 & 45 & 31.6656603984362 & 13.3343396015638 \tabularnewline
117 & 38 & 47.4513544680424 & -9.45135446804237 \tabularnewline
118 & 0 & 13.5383247981094 & -13.5383247981094 \tabularnewline
119 & 30 & 34.0344838634827 & -4.03448386348274 \tabularnewline
120 & 8 & 14.878480335191 & -6.87848033519101 \tabularnewline
121 & 41 & 46.8370939351792 & -5.83709393517925 \tabularnewline
122 & 48 & 40.7432972384014 & 7.25670276159863 \tabularnewline
123 & 48 & 36.522258137698 & 11.477741862302 \tabularnewline
124 & 32 & 33.0836902063553 & -1.08369020635534 \tabularnewline
125 & 8 & 13.2795829469032 & -5.27958294690323 \tabularnewline
126 & 43 & 36.9583011387084 & 6.04169886129162 \tabularnewline
127 & 52 & 44.8442288391766 & 7.15577116082345 \tabularnewline
128 & 53 & 33.3090677898101 & 19.6909322101899 \tabularnewline
129 & 49 & 43.6966298208646 & 5.30337017913537 \tabularnewline
130 & 48 & 46.2088421187811 & 1.79115788121891 \tabularnewline
131 & 56 & 63.2297208283675 & -7.22972082836753 \tabularnewline
132 & 40 & 28.9985054400222 & 11.0014945599778 \tabularnewline
133 & 36 & 24.4007288327064 & 11.5992711672936 \tabularnewline
134 & 44 & 45.273734417101 & -1.27373441710097 \tabularnewline
135 & 46 & 58.2889274821682 & -12.2889274821682 \tabularnewline
136 & 43 & 36.8140741766132 & 6.18592582338677 \tabularnewline
137 & 46 & 48.9427040853461 & -2.94270408534614 \tabularnewline
138 & 39 & 29.1153259798848 & 9.8846740201152 \tabularnewline
139 & 41 & 38.8859708617492 & 2.11402913825079 \tabularnewline
140 & 46 & 38.1672490291491 & 7.83275097085092 \tabularnewline
141 & 32 & 37.887576871659 & -5.88757687165896 \tabularnewline
142 & 45 & 47.3273700947093 & -2.32737009470934 \tabularnewline
143 & 39 & 41.1086174174701 & -2.10861741747008 \tabularnewline
144 & 21 & 29.4481231467528 & -8.44812314675282 \tabularnewline
145 & 49 & 51.2067493023223 & -2.20674930232227 \tabularnewline
146 & 55 & 35.5574650296298 & 19.4425349703702 \tabularnewline
147 & 36 & 40.6755609703475 & -4.67556097034752 \tabularnewline
148 & 48 & 50.2305043219179 & -2.23050432191793 \tabularnewline
149 & 0 & 11.2829277960907 & -11.2829277960907 \tabularnewline
150 & 0 & 12.7680943196903 & -12.7680943196903 \tabularnewline
151 & 0 & 11.3334203550898 & -11.3334203550898 \tabularnewline
152 & 0 & 11.3948676490933 & -11.3948676490933 \tabularnewline
153 & 0 & 11.2519485957141 & -11.2519485957141 \tabularnewline
154 & 0 & 11.2519485957141 & -11.2519485957141 \tabularnewline
155 & 43 & 38.2851937383748 & 4.71480626162524 \tabularnewline
156 & 52 & 50.7798229836025 & 1.2201770163975 \tabularnewline
157 & 0 & 11.2519485957141 & -11.2519485957141 \tabularnewline
158 & 0 & 11.3223758492975 & -11.3223758492975 \tabularnewline
159 & 0 & 11.672558334367 & -11.672558334367 \tabularnewline
160 & 5 & 12.7281088016662 & -7.72810880166624 \tabularnewline
161 & 1 & 12.2220797193641 & -11.2220797193641 \tabularnewline
162 & 45 & 26.9218720005606 & 18.0781279994394 \tabularnewline
163 & 0 & 11.7413635187191 & -11.7413635187191 \tabularnewline
164 & 34 & 37.4173271113826 & -3.41732711138265 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158880&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]41[/C][C]44.2835137341622[/C][C]-3.28351373416218[/C][/ROW]
[ROW][C]2[/C][C]34[/C][C]31.438536662568[/C][C]2.561463337432[/C][/ROW]
[ROW][C]3[/C][C]44[/C][C]35.8337913476573[/C][C]8.16620865234267[/C][/ROW]
[ROW][C]4[/C][C]38[/C][C]33.0833512532074[/C][C]4.91664874679263[/C][/ROW]
[ROW][C]5[/C][C]27[/C][C]28.6685157879752[/C][C]-1.66851578797515[/C][/ROW]
[ROW][C]6[/C][C]35[/C][C]23.1562336073433[/C][C]11.8437663926567[/C][/ROW]
[ROW][C]7[/C][C]33[/C][C]46.1169218812015[/C][C]-13.1169218812015[/C][/ROW]
[ROW][C]8[/C][C]18[/C][C]16.2606669417331[/C][C]1.73933305826687[/C][/ROW]
[ROW][C]9[/C][C]34[/C][C]38.2554858202248[/C][C]-4.25548582022483[/C][/ROW]
[ROW][C]10[/C][C]33[/C][C]32.9230502555097[/C][C]0.0769497444902889[/C][/ROW]
[ROW][C]11[/C][C]46[/C][C]37.5193682183855[/C][C]8.48063178161447[/C][/ROW]
[ROW][C]12[/C][C]57[/C][C]39.0414774889326[/C][C]17.9585225110674[/C][/ROW]
[ROW][C]13[/C][C]37[/C][C]30.8429607416536[/C][C]6.15703925834638[/C][/ROW]
[ROW][C]14[/C][C]55[/C][C]42.3849688788301[/C][C]12.6150311211699[/C][/ROW]
[ROW][C]15[/C][C]44[/C][C]38.7870750787427[/C][C]5.21292492125726[/C][/ROW]
[ROW][C]16[/C][C]62[/C][C]57.7079739693185[/C][C]4.29202603068152[/C][/ROW]
[ROW][C]17[/C][C]40[/C][C]46.3592437300997[/C][C]-6.35924373009971[/C][/ROW]
[ROW][C]18[/C][C]39[/C][C]44.5743361481414[/C][C]-5.57433614814141[/C][/ROW]
[ROW][C]19[/C][C]32[/C][C]34.0817014538047[/C][C]-2.08170145380474[/C][/ROW]
[ROW][C]20[/C][C]51[/C][C]46.3853950919813[/C][C]4.61460490801869[/C][/ROW]
[ROW][C]21[/C][C]49[/C][C]42.8552575599789[/C][C]6.14474244002114[/C][/ROW]
[ROW][C]22[/C][C]39[/C][C]41.3820376435437[/C][C]-2.38203764354368[/C][/ROW]
[ROW][C]23[/C][C]25[/C][C]32.7478194216885[/C][C]-7.74781942168854[/C][/ROW]
[ROW][C]24[/C][C]56[/C][C]34.9128602677749[/C][C]21.0871397322251[/C][/ROW]
[ROW][C]25[/C][C]45[/C][C]41.3879496115376[/C][C]3.61205038846244[/C][/ROW]
[ROW][C]26[/C][C]38[/C][C]41.0018050728061[/C][C]-3.0018050728061[/C][/ROW]
[ROW][C]27[/C][C]45[/C][C]40.3347248338179[/C][C]4.66527516618209[/C][/ROW]
[ROW][C]28[/C][C]43[/C][C]39.5124184456797[/C][C]3.48758155432026[/C][/ROW]
[ROW][C]29[/C][C]32[/C][C]26.1446287758124[/C][C]5.85537122418757[/C][/ROW]
[ROW][C]30[/C][C]41[/C][C]44.1833027415605[/C][C]-3.18330274156048[/C][/ROW]
[ROW][C]31[/C][C]50[/C][C]39.7765613040451[/C][C]10.2234386959549[/C][/ROW]
[ROW][C]32[/C][C]50[/C][C]37.2240684918635[/C][C]12.7759315081365[/C][/ROW]
[ROW][C]33[/C][C]51[/C][C]43.5655379236[/C][C]7.43446207640004[/C][/ROW]
[ROW][C]34[/C][C]37[/C][C]24.6769342869747[/C][C]12.3230657130253[/C][/ROW]
[ROW][C]35[/C][C]44[/C][C]49.8220546207728[/C][C]-5.82205462077279[/C][/ROW]
[ROW][C]36[/C][C]42[/C][C]32.2105047357281[/C][C]9.78949526427188[/C][/ROW]
[ROW][C]37[/C][C]44[/C][C]50.8132263531784[/C][C]-6.81322635317838[/C][/ROW]
[ROW][C]38[/C][C]36[/C][C]35.1765650026026[/C][C]0.82343499739736[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]23.7707988242053[/C][C]-6.77079882420528[/C][/ROW]
[ROW][C]40[/C][C]43[/C][C]49.6139191335737[/C][C]-6.61391913357366[/C][/ROW]
[ROW][C]41[/C][C]41[/C][C]34.7488636660665[/C][C]6.25113633393354[/C][/ROW]
[ROW][C]42[/C][C]41[/C][C]30.4106667638409[/C][C]10.5893332361591[/C][/ROW]
[ROW][C]43[/C][C]38[/C][C]33.5608871913072[/C][C]4.43911280869276[/C][/ROW]
[ROW][C]44[/C][C]49[/C][C]35.569226992146[/C][C]13.430773007854[/C][/ROW]
[ROW][C]45[/C][C]45[/C][C]29.5422696248082[/C][C]15.4577303751918[/C][/ROW]
[ROW][C]46[/C][C]42[/C][C]54.0175045965968[/C][C]-12.0175045965968[/C][/ROW]
[ROW][C]47[/C][C]26[/C][C]20.1987798346463[/C][C]5.80122016535367[/C][/ROW]
[ROW][C]48[/C][C]52[/C][C]40.5889745524136[/C][C]11.4110254475864[/C][/ROW]
[ROW][C]49[/C][C]50[/C][C]55.772203013952[/C][C]-5.77220301395201[/C][/ROW]
[ROW][C]50[/C][C]45[/C][C]30.2055213913624[/C][C]14.7944786086376[/C][/ROW]
[ROW][C]51[/C][C]40[/C][C]37.9973671137323[/C][C]2.00263288626771[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]13.3210885198663[/C][C]-9.32108851986635[/C][/ROW]
[ROW][C]53[/C][C]44[/C][C]50.6872120664969[/C][C]-6.68721206649692[/C][/ROW]
[ROW][C]54[/C][C]18[/C][C]19.4391330619457[/C][C]-1.4391330619457[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]20.899595402978[/C][C]-6.89959540297795[/C][/ROW]
[ROW][C]56[/C][C]38[/C][C]41.6286001925746[/C][C]-3.62860019257455[/C][/ROW]
[ROW][C]57[/C][C]61[/C][C]55.4380601632469[/C][C]5.5619398367531[/C][/ROW]
[ROW][C]58[/C][C]39[/C][C]59.0178241062677[/C][C]-20.0178241062677[/C][/ROW]
[ROW][C]59[/C][C]42[/C][C]43.1083238370321[/C][C]-1.10832383703211[/C][/ROW]
[ROW][C]60[/C][C]40[/C][C]36.5097782389391[/C][C]3.49022176106093[/C][/ROW]
[ROW][C]61[/C][C]51[/C][C]58.3084026878174[/C][C]-7.30840268781742[/C][/ROW]
[ROW][C]62[/C][C]28[/C][C]48.9658766543756[/C][C]-20.9658766543756[/C][/ROW]
[ROW][C]63[/C][C]43[/C][C]35.3554137556048[/C][C]7.64458624439517[/C][/ROW]
[ROW][C]64[/C][C]42[/C][C]40.4546916042098[/C][C]1.54530839579024[/C][/ROW]
[ROW][C]65[/C][C]37[/C][C]33.5861433547311[/C][C]3.41385664526889[/C][/ROW]
[ROW][C]66[/C][C]30[/C][C]39.6170284518935[/C][C]-9.61702845189353[/C][/ROW]
[ROW][C]67[/C][C]39[/C][C]42.2729360094985[/C][C]-3.27293600949851[/C][/ROW]
[ROW][C]68[/C][C]44[/C][C]43.4937093748248[/C][C]0.506290625175214[/C][/ROW]
[ROW][C]69[/C][C]36[/C][C]40.1012216865512[/C][C]-4.10122168655121[/C][/ROW]
[ROW][C]70[/C][C]28[/C][C]33.5986098195265[/C][C]-5.59860981952645[/C][/ROW]
[ROW][C]71[/C][C]47[/C][C]38.8172836391515[/C][C]8.18271636084851[/C][/ROW]
[ROW][C]72[/C][C]23[/C][C]17.2988414867226[/C][C]5.70115851327743[/C][/ROW]
[ROW][C]73[/C][C]48[/C][C]38.8655322534772[/C][C]9.13446774652275[/C][/ROW]
[ROW][C]74[/C][C]38[/C][C]30.202277761729[/C][C]7.79772223827097[/C][/ROW]
[ROW][C]75[/C][C]42[/C][C]35.5306779490786[/C][C]6.46932205092143[/C][/ROW]
[ROW][C]76[/C][C]46[/C][C]38.3080449512675[/C][C]7.69195504873251[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]43.5391278698823[/C][C]-6.53912786988234[/C][/ROW]
[ROW][C]78[/C][C]41[/C][C]47.5923434297806[/C][C]-6.59234342978061[/C][/ROW]
[ROW][C]79[/C][C]42[/C][C]39.15880888653[/C][C]2.84119111346999[/C][/ROW]
[ROW][C]80[/C][C]41[/C][C]33.6520548544903[/C][C]7.34794514550969[/C][/ROW]
[ROW][C]81[/C][C]36[/C][C]28.6953923783909[/C][C]7.3046076216091[/C][/ROW]
[ROW][C]82[/C][C]45[/C][C]66.3036407035123[/C][C]-21.3036407035123[/C][/ROW]
[ROW][C]83[/C][C]26[/C][C]20.2133684026865[/C][C]5.78663159731348[/C][/ROW]
[ROW][C]84[/C][C]44[/C][C]45.6947905886919[/C][C]-1.69479058869188[/C][/ROW]
[ROW][C]85[/C][C]8[/C][C]17.9630985482746[/C][C]-9.96309854827457[/C][/ROW]
[ROW][C]86[/C][C]27[/C][C]22.245983816719[/C][C]4.75401618328102[/C][/ROW]
[ROW][C]87[/C][C]38[/C][C]36.5750869807332[/C][C]1.42491301926684[/C][/ROW]
[ROW][C]88[/C][C]38[/C][C]46.0637273804961[/C][C]-8.06372738049613[/C][/ROW]
[ROW][C]89[/C][C]57[/C][C]45.8482425224349[/C][C]11.1517574775651[/C][/ROW]
[ROW][C]90[/C][C]45[/C][C]43.0797410386671[/C][C]1.92025896133286[/C][/ROW]
[ROW][C]91[/C][C]40[/C][C]40.0724106723741[/C][C]-0.072410672374065[/C][/ROW]
[ROW][C]92[/C][C]42[/C][C]45.4209932117836[/C][C]-3.42099321178359[/C][/ROW]
[ROW][C]93[/C][C]31[/C][C]33.0438213374824[/C][C]-2.04382133748242[/C][/ROW]
[ROW][C]94[/C][C]36[/C][C]39.8991133927674[/C][C]-3.89911339276736[/C][/ROW]
[ROW][C]95[/C][C]40[/C][C]43.7741942789488[/C][C]-3.77419427894882[/C][/ROW]
[ROW][C]96[/C][C]40[/C][C]39.1023223955488[/C][C]0.897677604451164[/C][/ROW]
[ROW][C]97[/C][C]35[/C][C]42.9314355529334[/C][C]-7.9314355529334[/C][/ROW]
[ROW][C]98[/C][C]39[/C][C]44.997599603667[/C][C]-5.99759960366698[/C][/ROW]
[ROW][C]99[/C][C]65[/C][C]59.9064765319516[/C][C]5.09352346804842[/C][/ROW]
[ROW][C]100[/C][C]33[/C][C]54.6026881979435[/C][C]-21.6026881979435[/C][/ROW]
[ROW][C]101[/C][C]51[/C][C]48.0284788537906[/C][C]2.97152114620942[/C][/ROW]
[ROW][C]102[/C][C]42[/C][C]44.0272604917442[/C][C]-2.02726049174421[/C][/ROW]
[ROW][C]103[/C][C]36[/C][C]33.3812750408244[/C][C]2.61872495917557[/C][/ROW]
[ROW][C]104[/C][C]19[/C][C]18.4305322766538[/C][C]0.569467723346196[/C][/ROW]
[ROW][C]105[/C][C]25[/C][C]32.9805507014366[/C][C]-7.9805507014366[/C][/ROW]
[ROW][C]106[/C][C]44[/C][C]40.2264606801232[/C][C]3.77353931987682[/C][/ROW]
[ROW][C]107[/C][C]40[/C][C]32.4301464816997[/C][C]7.56985351830033[/C][/ROW]
[ROW][C]108[/C][C]44[/C][C]40.0599250032635[/C][C]3.94007499673653[/C][/ROW]
[ROW][C]109[/C][C]30[/C][C]36.9165950732013[/C][C]-6.9165950732013[/C][/ROW]
[ROW][C]110[/C][C]45[/C][C]48.043462079277[/C][C]-3.04346207927694[/C][/ROW]
[ROW][C]111[/C][C]42[/C][C]45.7908088671604[/C][C]-3.79080886716044[/C][/ROW]
[ROW][C]112[/C][C]45[/C][C]45.0985371144822[/C][C]-0.0985371144821614[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]13.5360490045894[/C][C]-12.5360490045894[/C][/ROW]
[ROW][C]114[/C][C]40[/C][C]34.581995796407[/C][C]5.41800420359299[/C][/ROW]
[ROW][C]115[/C][C]11[/C][C]17.5992827582633[/C][C]-6.59928275826327[/C][/ROW]
[ROW][C]116[/C][C]45[/C][C]31.6656603984362[/C][C]13.3343396015638[/C][/ROW]
[ROW][C]117[/C][C]38[/C][C]47.4513544680424[/C][C]-9.45135446804237[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]13.5383247981094[/C][C]-13.5383247981094[/C][/ROW]
[ROW][C]119[/C][C]30[/C][C]34.0344838634827[/C][C]-4.03448386348274[/C][/ROW]
[ROW][C]120[/C][C]8[/C][C]14.878480335191[/C][C]-6.87848033519101[/C][/ROW]
[ROW][C]121[/C][C]41[/C][C]46.8370939351792[/C][C]-5.83709393517925[/C][/ROW]
[ROW][C]122[/C][C]48[/C][C]40.7432972384014[/C][C]7.25670276159863[/C][/ROW]
[ROW][C]123[/C][C]48[/C][C]36.522258137698[/C][C]11.477741862302[/C][/ROW]
[ROW][C]124[/C][C]32[/C][C]33.0836902063553[/C][C]-1.08369020635534[/C][/ROW]
[ROW][C]125[/C][C]8[/C][C]13.2795829469032[/C][C]-5.27958294690323[/C][/ROW]
[ROW][C]126[/C][C]43[/C][C]36.9583011387084[/C][C]6.04169886129162[/C][/ROW]
[ROW][C]127[/C][C]52[/C][C]44.8442288391766[/C][C]7.15577116082345[/C][/ROW]
[ROW][C]128[/C][C]53[/C][C]33.3090677898101[/C][C]19.6909322101899[/C][/ROW]
[ROW][C]129[/C][C]49[/C][C]43.6966298208646[/C][C]5.30337017913537[/C][/ROW]
[ROW][C]130[/C][C]48[/C][C]46.2088421187811[/C][C]1.79115788121891[/C][/ROW]
[ROW][C]131[/C][C]56[/C][C]63.2297208283675[/C][C]-7.22972082836753[/C][/ROW]
[ROW][C]132[/C][C]40[/C][C]28.9985054400222[/C][C]11.0014945599778[/C][/ROW]
[ROW][C]133[/C][C]36[/C][C]24.4007288327064[/C][C]11.5992711672936[/C][/ROW]
[ROW][C]134[/C][C]44[/C][C]45.273734417101[/C][C]-1.27373441710097[/C][/ROW]
[ROW][C]135[/C][C]46[/C][C]58.2889274821682[/C][C]-12.2889274821682[/C][/ROW]
[ROW][C]136[/C][C]43[/C][C]36.8140741766132[/C][C]6.18592582338677[/C][/ROW]
[ROW][C]137[/C][C]46[/C][C]48.9427040853461[/C][C]-2.94270408534614[/C][/ROW]
[ROW][C]138[/C][C]39[/C][C]29.1153259798848[/C][C]9.8846740201152[/C][/ROW]
[ROW][C]139[/C][C]41[/C][C]38.8859708617492[/C][C]2.11402913825079[/C][/ROW]
[ROW][C]140[/C][C]46[/C][C]38.1672490291491[/C][C]7.83275097085092[/C][/ROW]
[ROW][C]141[/C][C]32[/C][C]37.887576871659[/C][C]-5.88757687165896[/C][/ROW]
[ROW][C]142[/C][C]45[/C][C]47.3273700947093[/C][C]-2.32737009470934[/C][/ROW]
[ROW][C]143[/C][C]39[/C][C]41.1086174174701[/C][C]-2.10861741747008[/C][/ROW]
[ROW][C]144[/C][C]21[/C][C]29.4481231467528[/C][C]-8.44812314675282[/C][/ROW]
[ROW][C]145[/C][C]49[/C][C]51.2067493023223[/C][C]-2.20674930232227[/C][/ROW]
[ROW][C]146[/C][C]55[/C][C]35.5574650296298[/C][C]19.4425349703702[/C][/ROW]
[ROW][C]147[/C][C]36[/C][C]40.6755609703475[/C][C]-4.67556097034752[/C][/ROW]
[ROW][C]148[/C][C]48[/C][C]50.2305043219179[/C][C]-2.23050432191793[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]11.2829277960907[/C][C]-11.2829277960907[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]12.7680943196903[/C][C]-12.7680943196903[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]11.3334203550898[/C][C]-11.3334203550898[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]11.3948676490933[/C][C]-11.3948676490933[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]11.2519485957141[/C][C]-11.2519485957141[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]11.2519485957141[/C][C]-11.2519485957141[/C][/ROW]
[ROW][C]155[/C][C]43[/C][C]38.2851937383748[/C][C]4.71480626162524[/C][/ROW]
[ROW][C]156[/C][C]52[/C][C]50.7798229836025[/C][C]1.2201770163975[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]11.2519485957141[/C][C]-11.2519485957141[/C][/ROW]
[ROW][C]158[/C][C]0[/C][C]11.3223758492975[/C][C]-11.3223758492975[/C][/ROW]
[ROW][C]159[/C][C]0[/C][C]11.672558334367[/C][C]-11.672558334367[/C][/ROW]
[ROW][C]160[/C][C]5[/C][C]12.7281088016662[/C][C]-7.72810880166624[/C][/ROW]
[ROW][C]161[/C][C]1[/C][C]12.2220797193641[/C][C]-11.2220797193641[/C][/ROW]
[ROW][C]162[/C][C]45[/C][C]26.9218720005606[/C][C]18.0781279994394[/C][/ROW]
[ROW][C]163[/C][C]0[/C][C]11.7413635187191[/C][C]-11.7413635187191[/C][/ROW]
[ROW][C]164[/C][C]34[/C][C]37.4173271113826[/C][C]-3.41732711138265[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158880&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158880&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14144.2835137341622-3.28351373416218
23431.4385366625682.561463337432
34435.83379134765738.16620865234267
43833.08335125320744.91664874679263
52728.6685157879752-1.66851578797515
63523.156233607343311.8437663926567
73346.1169218812015-13.1169218812015
81816.26066694173311.73933305826687
93438.2554858202248-4.25548582022483
103332.92305025550970.0769497444902889
114637.51936821838558.48063178161447
125739.041477488932617.9585225110674
133730.84296074165366.15703925834638
145542.384968878830112.6150311211699
154438.78707507874275.21292492125726
166257.70797396931854.29202603068152
174046.3592437300997-6.35924373009971
183944.5743361481414-5.57433614814141
193234.0817014538047-2.08170145380474
205146.38539509198134.61460490801869
214942.85525755997896.14474244002114
223941.3820376435437-2.38203764354368
232532.7478194216885-7.74781942168854
245634.912860267774921.0871397322251
254541.38794961153763.61205038846244
263841.0018050728061-3.0018050728061
274540.33472483381794.66527516618209
284339.51241844567973.48758155432026
293226.14462877581245.85537122418757
304144.1833027415605-3.18330274156048
315039.776561304045110.2234386959549
325037.224068491863512.7759315081365
335143.56553792367.43446207640004
343724.676934286974712.3230657130253
354449.8220546207728-5.82205462077279
364232.21050473572819.78949526427188
374450.8132263531784-6.81322635317838
383635.17656500260260.82343499739736
391723.7707988242053-6.77079882420528
404349.6139191335737-6.61391913357366
414134.74886366606656.25113633393354
424130.410666763840910.5893332361591
433833.56088719130724.43911280869276
444935.56922699214613.430773007854
454529.542269624808215.4577303751918
464254.0175045965968-12.0175045965968
472620.19877983464635.80122016535367
485240.588974552413611.4110254475864
495055.772203013952-5.77220301395201
504530.205521391362414.7944786086376
514037.99736711373232.00263288626771
52413.3210885198663-9.32108851986635
534450.6872120664969-6.68721206649692
541819.4391330619457-1.4391330619457
551420.899595402978-6.89959540297795
563841.6286001925746-3.62860019257455
576155.43806016324695.5619398367531
583959.0178241062677-20.0178241062677
594243.1083238370321-1.10832383703211
604036.50977823893913.49022176106093
615158.3084026878174-7.30840268781742
622848.9658766543756-20.9658766543756
634335.35541375560487.64458624439517
644240.45469160420981.54530839579024
653733.58614335473113.41385664526889
663039.6170284518935-9.61702845189353
673942.2729360094985-3.27293600949851
684443.49370937482480.506290625175214
693640.1012216865512-4.10122168655121
702833.5986098195265-5.59860981952645
714738.81728363915158.18271636084851
722317.29884148672265.70115851327743
734838.86553225347729.13446774652275
743830.2022777617297.79772223827097
754235.53067794907866.46932205092143
764638.30804495126757.69195504873251
773743.5391278698823-6.53912786988234
784147.5923434297806-6.59234342978061
794239.158808886532.84119111346999
804133.65205485449037.34794514550969
813628.69539237839097.3046076216091
824566.3036407035123-21.3036407035123
832620.21336840268655.78663159731348
844445.6947905886919-1.69479058869188
85817.9630985482746-9.96309854827457
862722.2459838167194.75401618328102
873836.57508698073321.42491301926684
883846.0637273804961-8.06372738049613
895745.848242522434911.1517574775651
904543.07974103866711.92025896133286
914040.0724106723741-0.072410672374065
924245.4209932117836-3.42099321178359
933133.0438213374824-2.04382133748242
943639.8991133927674-3.89911339276736
954043.7741942789488-3.77419427894882
964039.10232239554880.897677604451164
973542.9314355529334-7.9314355529334
983944.997599603667-5.99759960366698
996559.90647653195165.09352346804842
1003354.6026881979435-21.6026881979435
1015148.02847885379062.97152114620942
1024244.0272604917442-2.02726049174421
1033633.38127504082442.61872495917557
1041918.43053227665380.569467723346196
1052532.9805507014366-7.9805507014366
1064440.22646068012323.77353931987682
1074032.43014648169977.56985351830033
1084440.05992500326353.94007499673653
1093036.9165950732013-6.9165950732013
1104548.043462079277-3.04346207927694
1114245.7908088671604-3.79080886716044
1124545.0985371144822-0.0985371144821614
113113.5360490045894-12.5360490045894
1144034.5819957964075.41800420359299
1151117.5992827582633-6.59928275826327
1164531.665660398436213.3343396015638
1173847.4513544680424-9.45135446804237
118013.5383247981094-13.5383247981094
1193034.0344838634827-4.03448386348274
120814.878480335191-6.87848033519101
1214146.8370939351792-5.83709393517925
1224840.74329723840147.25670276159863
1234836.52225813769811.477741862302
1243233.0836902063553-1.08369020635534
125813.2795829469032-5.27958294690323
1264336.95830113870846.04169886129162
1275244.84422883917667.15577116082345
1285333.309067789810119.6909322101899
1294943.69662982086465.30337017913537
1304846.20884211878111.79115788121891
1315663.2297208283675-7.22972082836753
1324028.998505440022211.0014945599778
1333624.400728832706411.5992711672936
1344445.273734417101-1.27373441710097
1354658.2889274821682-12.2889274821682
1364336.81407417661326.18592582338677
1374648.9427040853461-2.94270408534614
1383929.11532597988489.8846740201152
1394138.88597086174922.11402913825079
1404638.16724902914917.83275097085092
1413237.887576871659-5.88757687165896
1424547.3273700947093-2.32737009470934
1433941.1086174174701-2.10861741747008
1442129.4481231467528-8.44812314675282
1454951.2067493023223-2.20674930232227
1465535.557465029629819.4425349703702
1473640.6755609703475-4.67556097034752
1484850.2305043219179-2.23050432191793
149011.2829277960907-11.2829277960907
150012.7680943196903-12.7680943196903
151011.3334203550898-11.3334203550898
152011.3948676490933-11.3948676490933
153011.2519485957141-11.2519485957141
154011.2519485957141-11.2519485957141
1554338.28519373837484.71480626162524
1565250.77982298360251.2201770163975
157011.2519485957141-11.2519485957141
158011.3223758492975-11.3223758492975
159011.672558334367-11.672558334367
160512.7281088016662-7.72810880166624
161112.2220797193641-11.2220797193641
1624526.921872000560618.0781279994394
163011.7413635187191-11.7413635187191
1643437.4173271113826-3.41732711138265






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.38724234898620.77448469797240.6127576510138
110.3550537551101360.7101075102202720.644946244889864
120.5009921261583250.998015747683350.499007873841675
130.3745796188528990.7491592377057980.625420381147101
140.4925403885807510.9850807771615030.507459611419249
150.421634771336270.843269542672540.57836522866373
160.4001839967250180.8003679934500350.599816003274982
170.3269447996808360.6538895993616730.673055200319164
180.2653421611002550.530684322200510.734657838899745
190.2035714199830960.4071428399661910.796428580016904
200.1492449725336680.2984899450673350.850755027466332
210.1059056573593330.2118113147186660.894094342640667
220.07333378903637880.1466675780727580.926666210963621
230.09608813179867720.1921762635973540.903911868201323
240.3643671114576070.7287342229152140.635632888542393
250.3477813562657540.6955627125315070.652218643734246
260.3132599530080140.6265199060160280.686740046991986
270.2549769201134960.5099538402269920.745023079886504
280.2055638594103510.4111277188207010.79443614058965
290.1629126239365070.3258252478730140.837087376063493
300.1353433050478730.2706866100957470.864656694952126
310.159606893453820.3192137869076390.84039310654618
320.1469281323034480.2938562646068950.853071867696553
330.1245565314151610.2491130628303220.875443468584839
340.1419489460443130.2838978920886260.858051053955687
350.1463768372550710.2927536745101420.85362316274493
360.138976223544230.2779524470884590.86102377645577
370.1255205313915680.2510410627831360.874479468608432
380.1067666181249010.2135332362498030.893233381875099
390.1565452014950730.3130904029901460.843454798504927
400.1460419622979580.2920839245959170.853958037702042
410.1296220657431710.2592441314863430.870377934256829
420.1194960959450750.2389921918901510.880503904054925
430.09563105913129320.1912621182625860.904368940868707
440.1071141924348630.2142283848697250.892885807565137
450.1320780943552230.2641561887104470.867921905644777
460.1670829535983140.3341659071966270.832917046401686
470.1428983438502530.2857966877005050.857101656149747
480.13951333791920.2790266758383990.8604866620808
490.128551247815260.257102495630520.87144875218474
500.1419981391031280.2839962782062550.858001860896872
510.1187974449136860.2375948898273710.881202555086314
520.2558517937221470.5117035874442950.744148206277853
530.2297343600491310.4594687200982620.770265639950869
540.2199121516530570.4398243033061150.780087848346943
550.2545937421500870.5091874843001750.745406257849913
560.2390375607667760.4780751215335510.760962439233224
570.2207395758542640.4414791517085290.779260424145736
580.5503772405361480.8992455189277040.449622759463852
590.503963864423510.992072271152980.49603613557649
600.4608585949433590.9217171898867170.539141405056642
610.436484416718460.8729688334369190.56351558328154
620.7248193639080180.5503612721839640.275180636091982
630.7273685245877770.5452629508244460.272631475412223
640.6945955606533190.6108088786933630.305404439346681
650.6582338952347030.6835322095305940.341766104765297
660.6841534452433220.6316931095133560.315846554756678
670.6513693165923650.697261366815270.348630683407635
680.6071074726325020.7857850547349950.392892527367498
690.5730992845013680.8538014309972630.426900715498632
700.5626108118293670.8747783763412650.437389188170633
710.5682821409120820.8634357181758360.431717859087918
720.5480137841086350.903972431782730.451986215891365
730.5687091663828780.8625816672342440.431290833617122
740.5481499897890550.9037000204218890.451850010210945
750.5321257988692280.9357484022615440.467874201130772
760.5365037330440560.9269925339118890.463496266955944
770.5069459693798050.986108061240390.493054030620195
780.4761960254015250.952392050803050.523803974598475
790.4430531095385590.8861062190771170.556946890461441
800.4343780591274580.8687561182549170.565621940872542
810.4447965618220070.8895931236440140.555203438177993
820.636436993512470.7271260129750610.363563006487531
830.6550515597923840.6898968804152320.344948440207616
840.6121108889947250.775778222010550.387889111005275
850.6760409175693120.6479181648613760.323959082430688
860.6336436391738680.7327127216522630.366356360826132
870.5892186376604510.8215627246790980.410781362339549
880.8016391841554010.3967216316891990.198360815844599
890.856871400092670.286257199814660.14312859990733
900.8411404249668990.3177191500662020.158859575033101
910.8282649497020950.3434701005958110.171735050297906
920.800469364590440.399061270819120.19953063540956
930.7759503215156870.4480993569686270.224049678484313
940.7510436260972120.4979127478055760.248956373902788
950.7730851832582040.4538296334835930.226914816741796
960.7364314505779910.5271370988440180.263568549422009
970.7463166670576450.507366665884710.253683332942355
980.721854649275070.556290701449860.27814535072493
990.7177774992658760.5644450014682480.282222500734124
1000.8795604631262540.2408790737474930.120439536873746
1010.8671932302653120.2656135394693760.132806769734688
1020.8527078293107180.2945843413785630.147292170689282
1030.8234089204224590.3531821591550820.176591079577541
1040.7973731376874030.4052537246251940.202626862312597
1050.8077441892123830.3845116215752340.192255810787617
1060.7777200149665970.4445599700668060.222279985033403
1070.7646474187111070.4707051625777870.235352581288893
1080.7983564971430450.4032870057139110.201643502856955
1090.7947205645629980.4105588708740030.205279435437002
1100.7594462432429970.4811075135140050.240553756757003
1110.7204838096302060.5590323807395890.279516190369794
1120.6791946728614250.641610654277150.320805327138575
1130.741476087155950.51704782568810.25852391284405
1140.7765345208712560.4469309582574880.223465479128744
1150.7667673243715750.466465351256850.233232675628425
1160.7801623729233910.4396752541532190.219837627076609
1170.7551298624128640.4897402751742720.244870137587136
1180.7897246711353590.4205506577292820.210275328864641
1190.7544011243710290.4911977512579420.245598875628971
1200.7345634458993070.5308731082013850.265436554100693
1210.7213493523426340.5573012953147320.278650647657366
1220.7096370409127240.5807259181745520.290362959087276
1230.6986616181563680.6026767636872640.301338381843632
1240.649039294012290.7019214119754210.35096070598771
1250.6285252660632040.7429494678735910.371474733936796
1260.5828738420276440.8342523159447130.417126157972356
1270.5490061875861730.9019876248276540.450993812413827
1280.7238372625518780.5523254748962440.276162737448122
1290.6979186565582760.6041626868834480.302081343441724
1300.6452132510657420.7095734978685160.354786748934258
1310.8011248533793240.3977502932413520.198875146620676
1320.865228321014250.2695433579715020.134771678985751
1330.922500810906320.154998378187360.07749918909368
1340.933566742334210.1328665153315820.0664332576657908
1350.9933529045046210.0132941909907580.00664709549537898
1360.9959866340940650.008026731811869680.00401336590593484
1370.9940354156169670.01192916876606570.00596458438303287
1380.998267354629470.003465290741059120.00173264537052956
1390.9972960729243880.005407854151223890.00270392707561195
1400.9996350458715610.0007299082568772470.000364954128438623
1410.9999317800364990.0001364399270023076.82199635011534e-05
1420.999988537045452.29259091017935e-051.14629545508968e-05
1430.9999719293970185.61412059640583e-052.80706029820291e-05
1440.9999324134937020.0001351730125961466.7586506298073e-05
1450.9999510808844339.78382311334906e-054.89191155667453e-05
1460.9999969284205026.14315899495622e-063.07157949747811e-06
1470.9999957975793398.40484132284976e-064.20242066142488e-06
1480.9999999998982252.03550336220059e-101.0177516811003e-10
1490.9999999983588023.28239669182121e-091.6411983459106e-09
1500.9999999761328874.77342263039911e-082.38671131519956e-08
1510.9999996311056647.37788672730723e-073.68894336365362e-07
1520.9999947916111961.04167776089052e-055.20838880445262e-06
1530.9999250294676750.0001499410646495597.49705323247793e-05
1540.9990037964363270.001992407127345380.000996203563672692

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.3872423489862 & 0.7744846979724 & 0.6127576510138 \tabularnewline
11 & 0.355053755110136 & 0.710107510220272 & 0.644946244889864 \tabularnewline
12 & 0.500992126158325 & 0.99801574768335 & 0.499007873841675 \tabularnewline
13 & 0.374579618852899 & 0.749159237705798 & 0.625420381147101 \tabularnewline
14 & 0.492540388580751 & 0.985080777161503 & 0.507459611419249 \tabularnewline
15 & 0.42163477133627 & 0.84326954267254 & 0.57836522866373 \tabularnewline
16 & 0.400183996725018 & 0.800367993450035 & 0.599816003274982 \tabularnewline
17 & 0.326944799680836 & 0.653889599361673 & 0.673055200319164 \tabularnewline
18 & 0.265342161100255 & 0.53068432220051 & 0.734657838899745 \tabularnewline
19 & 0.203571419983096 & 0.407142839966191 & 0.796428580016904 \tabularnewline
20 & 0.149244972533668 & 0.298489945067335 & 0.850755027466332 \tabularnewline
21 & 0.105905657359333 & 0.211811314718666 & 0.894094342640667 \tabularnewline
22 & 0.0733337890363788 & 0.146667578072758 & 0.926666210963621 \tabularnewline
23 & 0.0960881317986772 & 0.192176263597354 & 0.903911868201323 \tabularnewline
24 & 0.364367111457607 & 0.728734222915214 & 0.635632888542393 \tabularnewline
25 & 0.347781356265754 & 0.695562712531507 & 0.652218643734246 \tabularnewline
26 & 0.313259953008014 & 0.626519906016028 & 0.686740046991986 \tabularnewline
27 & 0.254976920113496 & 0.509953840226992 & 0.745023079886504 \tabularnewline
28 & 0.205563859410351 & 0.411127718820701 & 0.79443614058965 \tabularnewline
29 & 0.162912623936507 & 0.325825247873014 & 0.837087376063493 \tabularnewline
30 & 0.135343305047873 & 0.270686610095747 & 0.864656694952126 \tabularnewline
31 & 0.15960689345382 & 0.319213786907639 & 0.84039310654618 \tabularnewline
32 & 0.146928132303448 & 0.293856264606895 & 0.853071867696553 \tabularnewline
33 & 0.124556531415161 & 0.249113062830322 & 0.875443468584839 \tabularnewline
34 & 0.141948946044313 & 0.283897892088626 & 0.858051053955687 \tabularnewline
35 & 0.146376837255071 & 0.292753674510142 & 0.85362316274493 \tabularnewline
36 & 0.13897622354423 & 0.277952447088459 & 0.86102377645577 \tabularnewline
37 & 0.125520531391568 & 0.251041062783136 & 0.874479468608432 \tabularnewline
38 & 0.106766618124901 & 0.213533236249803 & 0.893233381875099 \tabularnewline
39 & 0.156545201495073 & 0.313090402990146 & 0.843454798504927 \tabularnewline
40 & 0.146041962297958 & 0.292083924595917 & 0.853958037702042 \tabularnewline
41 & 0.129622065743171 & 0.259244131486343 & 0.870377934256829 \tabularnewline
42 & 0.119496095945075 & 0.238992191890151 & 0.880503904054925 \tabularnewline
43 & 0.0956310591312932 & 0.191262118262586 & 0.904368940868707 \tabularnewline
44 & 0.107114192434863 & 0.214228384869725 & 0.892885807565137 \tabularnewline
45 & 0.132078094355223 & 0.264156188710447 & 0.867921905644777 \tabularnewline
46 & 0.167082953598314 & 0.334165907196627 & 0.832917046401686 \tabularnewline
47 & 0.142898343850253 & 0.285796687700505 & 0.857101656149747 \tabularnewline
48 & 0.1395133379192 & 0.279026675838399 & 0.8604866620808 \tabularnewline
49 & 0.12855124781526 & 0.25710249563052 & 0.87144875218474 \tabularnewline
50 & 0.141998139103128 & 0.283996278206255 & 0.858001860896872 \tabularnewline
51 & 0.118797444913686 & 0.237594889827371 & 0.881202555086314 \tabularnewline
52 & 0.255851793722147 & 0.511703587444295 & 0.744148206277853 \tabularnewline
53 & 0.229734360049131 & 0.459468720098262 & 0.770265639950869 \tabularnewline
54 & 0.219912151653057 & 0.439824303306115 & 0.780087848346943 \tabularnewline
55 & 0.254593742150087 & 0.509187484300175 & 0.745406257849913 \tabularnewline
56 & 0.239037560766776 & 0.478075121533551 & 0.760962439233224 \tabularnewline
57 & 0.220739575854264 & 0.441479151708529 & 0.779260424145736 \tabularnewline
58 & 0.550377240536148 & 0.899245518927704 & 0.449622759463852 \tabularnewline
59 & 0.50396386442351 & 0.99207227115298 & 0.49603613557649 \tabularnewline
60 & 0.460858594943359 & 0.921717189886717 & 0.539141405056642 \tabularnewline
61 & 0.43648441671846 & 0.872968833436919 & 0.56351558328154 \tabularnewline
62 & 0.724819363908018 & 0.550361272183964 & 0.275180636091982 \tabularnewline
63 & 0.727368524587777 & 0.545262950824446 & 0.272631475412223 \tabularnewline
64 & 0.694595560653319 & 0.610808878693363 & 0.305404439346681 \tabularnewline
65 & 0.658233895234703 & 0.683532209530594 & 0.341766104765297 \tabularnewline
66 & 0.684153445243322 & 0.631693109513356 & 0.315846554756678 \tabularnewline
67 & 0.651369316592365 & 0.69726136681527 & 0.348630683407635 \tabularnewline
68 & 0.607107472632502 & 0.785785054734995 & 0.392892527367498 \tabularnewline
69 & 0.573099284501368 & 0.853801430997263 & 0.426900715498632 \tabularnewline
70 & 0.562610811829367 & 0.874778376341265 & 0.437389188170633 \tabularnewline
71 & 0.568282140912082 & 0.863435718175836 & 0.431717859087918 \tabularnewline
72 & 0.548013784108635 & 0.90397243178273 & 0.451986215891365 \tabularnewline
73 & 0.568709166382878 & 0.862581667234244 & 0.431290833617122 \tabularnewline
74 & 0.548149989789055 & 0.903700020421889 & 0.451850010210945 \tabularnewline
75 & 0.532125798869228 & 0.935748402261544 & 0.467874201130772 \tabularnewline
76 & 0.536503733044056 & 0.926992533911889 & 0.463496266955944 \tabularnewline
77 & 0.506945969379805 & 0.98610806124039 & 0.493054030620195 \tabularnewline
78 & 0.476196025401525 & 0.95239205080305 & 0.523803974598475 \tabularnewline
79 & 0.443053109538559 & 0.886106219077117 & 0.556946890461441 \tabularnewline
80 & 0.434378059127458 & 0.868756118254917 & 0.565621940872542 \tabularnewline
81 & 0.444796561822007 & 0.889593123644014 & 0.555203438177993 \tabularnewline
82 & 0.63643699351247 & 0.727126012975061 & 0.363563006487531 \tabularnewline
83 & 0.655051559792384 & 0.689896880415232 & 0.344948440207616 \tabularnewline
84 & 0.612110888994725 & 0.77577822201055 & 0.387889111005275 \tabularnewline
85 & 0.676040917569312 & 0.647918164861376 & 0.323959082430688 \tabularnewline
86 & 0.633643639173868 & 0.732712721652263 & 0.366356360826132 \tabularnewline
87 & 0.589218637660451 & 0.821562724679098 & 0.410781362339549 \tabularnewline
88 & 0.801639184155401 & 0.396721631689199 & 0.198360815844599 \tabularnewline
89 & 0.85687140009267 & 0.28625719981466 & 0.14312859990733 \tabularnewline
90 & 0.841140424966899 & 0.317719150066202 & 0.158859575033101 \tabularnewline
91 & 0.828264949702095 & 0.343470100595811 & 0.171735050297906 \tabularnewline
92 & 0.80046936459044 & 0.39906127081912 & 0.19953063540956 \tabularnewline
93 & 0.775950321515687 & 0.448099356968627 & 0.224049678484313 \tabularnewline
94 & 0.751043626097212 & 0.497912747805576 & 0.248956373902788 \tabularnewline
95 & 0.773085183258204 & 0.453829633483593 & 0.226914816741796 \tabularnewline
96 & 0.736431450577991 & 0.527137098844018 & 0.263568549422009 \tabularnewline
97 & 0.746316667057645 & 0.50736666588471 & 0.253683332942355 \tabularnewline
98 & 0.72185464927507 & 0.55629070144986 & 0.27814535072493 \tabularnewline
99 & 0.717777499265876 & 0.564445001468248 & 0.282222500734124 \tabularnewline
100 & 0.879560463126254 & 0.240879073747493 & 0.120439536873746 \tabularnewline
101 & 0.867193230265312 & 0.265613539469376 & 0.132806769734688 \tabularnewline
102 & 0.852707829310718 & 0.294584341378563 & 0.147292170689282 \tabularnewline
103 & 0.823408920422459 & 0.353182159155082 & 0.176591079577541 \tabularnewline
104 & 0.797373137687403 & 0.405253724625194 & 0.202626862312597 \tabularnewline
105 & 0.807744189212383 & 0.384511621575234 & 0.192255810787617 \tabularnewline
106 & 0.777720014966597 & 0.444559970066806 & 0.222279985033403 \tabularnewline
107 & 0.764647418711107 & 0.470705162577787 & 0.235352581288893 \tabularnewline
108 & 0.798356497143045 & 0.403287005713911 & 0.201643502856955 \tabularnewline
109 & 0.794720564562998 & 0.410558870874003 & 0.205279435437002 \tabularnewline
110 & 0.759446243242997 & 0.481107513514005 & 0.240553756757003 \tabularnewline
111 & 0.720483809630206 & 0.559032380739589 & 0.279516190369794 \tabularnewline
112 & 0.679194672861425 & 0.64161065427715 & 0.320805327138575 \tabularnewline
113 & 0.74147608715595 & 0.5170478256881 & 0.25852391284405 \tabularnewline
114 & 0.776534520871256 & 0.446930958257488 & 0.223465479128744 \tabularnewline
115 & 0.766767324371575 & 0.46646535125685 & 0.233232675628425 \tabularnewline
116 & 0.780162372923391 & 0.439675254153219 & 0.219837627076609 \tabularnewline
117 & 0.755129862412864 & 0.489740275174272 & 0.244870137587136 \tabularnewline
118 & 0.789724671135359 & 0.420550657729282 & 0.210275328864641 \tabularnewline
119 & 0.754401124371029 & 0.491197751257942 & 0.245598875628971 \tabularnewline
120 & 0.734563445899307 & 0.530873108201385 & 0.265436554100693 \tabularnewline
121 & 0.721349352342634 & 0.557301295314732 & 0.278650647657366 \tabularnewline
122 & 0.709637040912724 & 0.580725918174552 & 0.290362959087276 \tabularnewline
123 & 0.698661618156368 & 0.602676763687264 & 0.301338381843632 \tabularnewline
124 & 0.64903929401229 & 0.701921411975421 & 0.35096070598771 \tabularnewline
125 & 0.628525266063204 & 0.742949467873591 & 0.371474733936796 \tabularnewline
126 & 0.582873842027644 & 0.834252315944713 & 0.417126157972356 \tabularnewline
127 & 0.549006187586173 & 0.901987624827654 & 0.450993812413827 \tabularnewline
128 & 0.723837262551878 & 0.552325474896244 & 0.276162737448122 \tabularnewline
129 & 0.697918656558276 & 0.604162686883448 & 0.302081343441724 \tabularnewline
130 & 0.645213251065742 & 0.709573497868516 & 0.354786748934258 \tabularnewline
131 & 0.801124853379324 & 0.397750293241352 & 0.198875146620676 \tabularnewline
132 & 0.86522832101425 & 0.269543357971502 & 0.134771678985751 \tabularnewline
133 & 0.92250081090632 & 0.15499837818736 & 0.07749918909368 \tabularnewline
134 & 0.93356674233421 & 0.132866515331582 & 0.0664332576657908 \tabularnewline
135 & 0.993352904504621 & 0.013294190990758 & 0.00664709549537898 \tabularnewline
136 & 0.995986634094065 & 0.00802673181186968 & 0.00401336590593484 \tabularnewline
137 & 0.994035415616967 & 0.0119291687660657 & 0.00596458438303287 \tabularnewline
138 & 0.99826735462947 & 0.00346529074105912 & 0.00173264537052956 \tabularnewline
139 & 0.997296072924388 & 0.00540785415122389 & 0.00270392707561195 \tabularnewline
140 & 0.999635045871561 & 0.000729908256877247 & 0.000364954128438623 \tabularnewline
141 & 0.999931780036499 & 0.000136439927002307 & 6.82199635011534e-05 \tabularnewline
142 & 0.99998853704545 & 2.29259091017935e-05 & 1.14629545508968e-05 \tabularnewline
143 & 0.999971929397018 & 5.61412059640583e-05 & 2.80706029820291e-05 \tabularnewline
144 & 0.999932413493702 & 0.000135173012596146 & 6.7586506298073e-05 \tabularnewline
145 & 0.999951080884433 & 9.78382311334906e-05 & 4.89191155667453e-05 \tabularnewline
146 & 0.999996928420502 & 6.14315899495622e-06 & 3.07157949747811e-06 \tabularnewline
147 & 0.999995797579339 & 8.40484132284976e-06 & 4.20242066142488e-06 \tabularnewline
148 & 0.999999999898225 & 2.03550336220059e-10 & 1.0177516811003e-10 \tabularnewline
149 & 0.999999998358802 & 3.28239669182121e-09 & 1.6411983459106e-09 \tabularnewline
150 & 0.999999976132887 & 4.77342263039911e-08 & 2.38671131519956e-08 \tabularnewline
151 & 0.999999631105664 & 7.37788672730723e-07 & 3.68894336365362e-07 \tabularnewline
152 & 0.999994791611196 & 1.04167776089052e-05 & 5.20838880445262e-06 \tabularnewline
153 & 0.999925029467675 & 0.000149941064649559 & 7.49705323247793e-05 \tabularnewline
154 & 0.999003796436327 & 0.00199240712734538 & 0.000996203563672692 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158880&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]10[/C][C]0.3872423489862[/C][C]0.7744846979724[/C][C]0.6127576510138[/C][/ROW]
[ROW][C]11[/C][C]0.355053755110136[/C][C]0.710107510220272[/C][C]0.644946244889864[/C][/ROW]
[ROW][C]12[/C][C]0.500992126158325[/C][C]0.99801574768335[/C][C]0.499007873841675[/C][/ROW]
[ROW][C]13[/C][C]0.374579618852899[/C][C]0.749159237705798[/C][C]0.625420381147101[/C][/ROW]
[ROW][C]14[/C][C]0.492540388580751[/C][C]0.985080777161503[/C][C]0.507459611419249[/C][/ROW]
[ROW][C]15[/C][C]0.42163477133627[/C][C]0.84326954267254[/C][C]0.57836522866373[/C][/ROW]
[ROW][C]16[/C][C]0.400183996725018[/C][C]0.800367993450035[/C][C]0.599816003274982[/C][/ROW]
[ROW][C]17[/C][C]0.326944799680836[/C][C]0.653889599361673[/C][C]0.673055200319164[/C][/ROW]
[ROW][C]18[/C][C]0.265342161100255[/C][C]0.53068432220051[/C][C]0.734657838899745[/C][/ROW]
[ROW][C]19[/C][C]0.203571419983096[/C][C]0.407142839966191[/C][C]0.796428580016904[/C][/ROW]
[ROW][C]20[/C][C]0.149244972533668[/C][C]0.298489945067335[/C][C]0.850755027466332[/C][/ROW]
[ROW][C]21[/C][C]0.105905657359333[/C][C]0.211811314718666[/C][C]0.894094342640667[/C][/ROW]
[ROW][C]22[/C][C]0.0733337890363788[/C][C]0.146667578072758[/C][C]0.926666210963621[/C][/ROW]
[ROW][C]23[/C][C]0.0960881317986772[/C][C]0.192176263597354[/C][C]0.903911868201323[/C][/ROW]
[ROW][C]24[/C][C]0.364367111457607[/C][C]0.728734222915214[/C][C]0.635632888542393[/C][/ROW]
[ROW][C]25[/C][C]0.347781356265754[/C][C]0.695562712531507[/C][C]0.652218643734246[/C][/ROW]
[ROW][C]26[/C][C]0.313259953008014[/C][C]0.626519906016028[/C][C]0.686740046991986[/C][/ROW]
[ROW][C]27[/C][C]0.254976920113496[/C][C]0.509953840226992[/C][C]0.745023079886504[/C][/ROW]
[ROW][C]28[/C][C]0.205563859410351[/C][C]0.411127718820701[/C][C]0.79443614058965[/C][/ROW]
[ROW][C]29[/C][C]0.162912623936507[/C][C]0.325825247873014[/C][C]0.837087376063493[/C][/ROW]
[ROW][C]30[/C][C]0.135343305047873[/C][C]0.270686610095747[/C][C]0.864656694952126[/C][/ROW]
[ROW][C]31[/C][C]0.15960689345382[/C][C]0.319213786907639[/C][C]0.84039310654618[/C][/ROW]
[ROW][C]32[/C][C]0.146928132303448[/C][C]0.293856264606895[/C][C]0.853071867696553[/C][/ROW]
[ROW][C]33[/C][C]0.124556531415161[/C][C]0.249113062830322[/C][C]0.875443468584839[/C][/ROW]
[ROW][C]34[/C][C]0.141948946044313[/C][C]0.283897892088626[/C][C]0.858051053955687[/C][/ROW]
[ROW][C]35[/C][C]0.146376837255071[/C][C]0.292753674510142[/C][C]0.85362316274493[/C][/ROW]
[ROW][C]36[/C][C]0.13897622354423[/C][C]0.277952447088459[/C][C]0.86102377645577[/C][/ROW]
[ROW][C]37[/C][C]0.125520531391568[/C][C]0.251041062783136[/C][C]0.874479468608432[/C][/ROW]
[ROW][C]38[/C][C]0.106766618124901[/C][C]0.213533236249803[/C][C]0.893233381875099[/C][/ROW]
[ROW][C]39[/C][C]0.156545201495073[/C][C]0.313090402990146[/C][C]0.843454798504927[/C][/ROW]
[ROW][C]40[/C][C]0.146041962297958[/C][C]0.292083924595917[/C][C]0.853958037702042[/C][/ROW]
[ROW][C]41[/C][C]0.129622065743171[/C][C]0.259244131486343[/C][C]0.870377934256829[/C][/ROW]
[ROW][C]42[/C][C]0.119496095945075[/C][C]0.238992191890151[/C][C]0.880503904054925[/C][/ROW]
[ROW][C]43[/C][C]0.0956310591312932[/C][C]0.191262118262586[/C][C]0.904368940868707[/C][/ROW]
[ROW][C]44[/C][C]0.107114192434863[/C][C]0.214228384869725[/C][C]0.892885807565137[/C][/ROW]
[ROW][C]45[/C][C]0.132078094355223[/C][C]0.264156188710447[/C][C]0.867921905644777[/C][/ROW]
[ROW][C]46[/C][C]0.167082953598314[/C][C]0.334165907196627[/C][C]0.832917046401686[/C][/ROW]
[ROW][C]47[/C][C]0.142898343850253[/C][C]0.285796687700505[/C][C]0.857101656149747[/C][/ROW]
[ROW][C]48[/C][C]0.1395133379192[/C][C]0.279026675838399[/C][C]0.8604866620808[/C][/ROW]
[ROW][C]49[/C][C]0.12855124781526[/C][C]0.25710249563052[/C][C]0.87144875218474[/C][/ROW]
[ROW][C]50[/C][C]0.141998139103128[/C][C]0.283996278206255[/C][C]0.858001860896872[/C][/ROW]
[ROW][C]51[/C][C]0.118797444913686[/C][C]0.237594889827371[/C][C]0.881202555086314[/C][/ROW]
[ROW][C]52[/C][C]0.255851793722147[/C][C]0.511703587444295[/C][C]0.744148206277853[/C][/ROW]
[ROW][C]53[/C][C]0.229734360049131[/C][C]0.459468720098262[/C][C]0.770265639950869[/C][/ROW]
[ROW][C]54[/C][C]0.219912151653057[/C][C]0.439824303306115[/C][C]0.780087848346943[/C][/ROW]
[ROW][C]55[/C][C]0.254593742150087[/C][C]0.509187484300175[/C][C]0.745406257849913[/C][/ROW]
[ROW][C]56[/C][C]0.239037560766776[/C][C]0.478075121533551[/C][C]0.760962439233224[/C][/ROW]
[ROW][C]57[/C][C]0.220739575854264[/C][C]0.441479151708529[/C][C]0.779260424145736[/C][/ROW]
[ROW][C]58[/C][C]0.550377240536148[/C][C]0.899245518927704[/C][C]0.449622759463852[/C][/ROW]
[ROW][C]59[/C][C]0.50396386442351[/C][C]0.99207227115298[/C][C]0.49603613557649[/C][/ROW]
[ROW][C]60[/C][C]0.460858594943359[/C][C]0.921717189886717[/C][C]0.539141405056642[/C][/ROW]
[ROW][C]61[/C][C]0.43648441671846[/C][C]0.872968833436919[/C][C]0.56351558328154[/C][/ROW]
[ROW][C]62[/C][C]0.724819363908018[/C][C]0.550361272183964[/C][C]0.275180636091982[/C][/ROW]
[ROW][C]63[/C][C]0.727368524587777[/C][C]0.545262950824446[/C][C]0.272631475412223[/C][/ROW]
[ROW][C]64[/C][C]0.694595560653319[/C][C]0.610808878693363[/C][C]0.305404439346681[/C][/ROW]
[ROW][C]65[/C][C]0.658233895234703[/C][C]0.683532209530594[/C][C]0.341766104765297[/C][/ROW]
[ROW][C]66[/C][C]0.684153445243322[/C][C]0.631693109513356[/C][C]0.315846554756678[/C][/ROW]
[ROW][C]67[/C][C]0.651369316592365[/C][C]0.69726136681527[/C][C]0.348630683407635[/C][/ROW]
[ROW][C]68[/C][C]0.607107472632502[/C][C]0.785785054734995[/C][C]0.392892527367498[/C][/ROW]
[ROW][C]69[/C][C]0.573099284501368[/C][C]0.853801430997263[/C][C]0.426900715498632[/C][/ROW]
[ROW][C]70[/C][C]0.562610811829367[/C][C]0.874778376341265[/C][C]0.437389188170633[/C][/ROW]
[ROW][C]71[/C][C]0.568282140912082[/C][C]0.863435718175836[/C][C]0.431717859087918[/C][/ROW]
[ROW][C]72[/C][C]0.548013784108635[/C][C]0.90397243178273[/C][C]0.451986215891365[/C][/ROW]
[ROW][C]73[/C][C]0.568709166382878[/C][C]0.862581667234244[/C][C]0.431290833617122[/C][/ROW]
[ROW][C]74[/C][C]0.548149989789055[/C][C]0.903700020421889[/C][C]0.451850010210945[/C][/ROW]
[ROW][C]75[/C][C]0.532125798869228[/C][C]0.935748402261544[/C][C]0.467874201130772[/C][/ROW]
[ROW][C]76[/C][C]0.536503733044056[/C][C]0.926992533911889[/C][C]0.463496266955944[/C][/ROW]
[ROW][C]77[/C][C]0.506945969379805[/C][C]0.98610806124039[/C][C]0.493054030620195[/C][/ROW]
[ROW][C]78[/C][C]0.476196025401525[/C][C]0.95239205080305[/C][C]0.523803974598475[/C][/ROW]
[ROW][C]79[/C][C]0.443053109538559[/C][C]0.886106219077117[/C][C]0.556946890461441[/C][/ROW]
[ROW][C]80[/C][C]0.434378059127458[/C][C]0.868756118254917[/C][C]0.565621940872542[/C][/ROW]
[ROW][C]81[/C][C]0.444796561822007[/C][C]0.889593123644014[/C][C]0.555203438177993[/C][/ROW]
[ROW][C]82[/C][C]0.63643699351247[/C][C]0.727126012975061[/C][C]0.363563006487531[/C][/ROW]
[ROW][C]83[/C][C]0.655051559792384[/C][C]0.689896880415232[/C][C]0.344948440207616[/C][/ROW]
[ROW][C]84[/C][C]0.612110888994725[/C][C]0.77577822201055[/C][C]0.387889111005275[/C][/ROW]
[ROW][C]85[/C][C]0.676040917569312[/C][C]0.647918164861376[/C][C]0.323959082430688[/C][/ROW]
[ROW][C]86[/C][C]0.633643639173868[/C][C]0.732712721652263[/C][C]0.366356360826132[/C][/ROW]
[ROW][C]87[/C][C]0.589218637660451[/C][C]0.821562724679098[/C][C]0.410781362339549[/C][/ROW]
[ROW][C]88[/C][C]0.801639184155401[/C][C]0.396721631689199[/C][C]0.198360815844599[/C][/ROW]
[ROW][C]89[/C][C]0.85687140009267[/C][C]0.28625719981466[/C][C]0.14312859990733[/C][/ROW]
[ROW][C]90[/C][C]0.841140424966899[/C][C]0.317719150066202[/C][C]0.158859575033101[/C][/ROW]
[ROW][C]91[/C][C]0.828264949702095[/C][C]0.343470100595811[/C][C]0.171735050297906[/C][/ROW]
[ROW][C]92[/C][C]0.80046936459044[/C][C]0.39906127081912[/C][C]0.19953063540956[/C][/ROW]
[ROW][C]93[/C][C]0.775950321515687[/C][C]0.448099356968627[/C][C]0.224049678484313[/C][/ROW]
[ROW][C]94[/C][C]0.751043626097212[/C][C]0.497912747805576[/C][C]0.248956373902788[/C][/ROW]
[ROW][C]95[/C][C]0.773085183258204[/C][C]0.453829633483593[/C][C]0.226914816741796[/C][/ROW]
[ROW][C]96[/C][C]0.736431450577991[/C][C]0.527137098844018[/C][C]0.263568549422009[/C][/ROW]
[ROW][C]97[/C][C]0.746316667057645[/C][C]0.50736666588471[/C][C]0.253683332942355[/C][/ROW]
[ROW][C]98[/C][C]0.72185464927507[/C][C]0.55629070144986[/C][C]0.27814535072493[/C][/ROW]
[ROW][C]99[/C][C]0.717777499265876[/C][C]0.564445001468248[/C][C]0.282222500734124[/C][/ROW]
[ROW][C]100[/C][C]0.879560463126254[/C][C]0.240879073747493[/C][C]0.120439536873746[/C][/ROW]
[ROW][C]101[/C][C]0.867193230265312[/C][C]0.265613539469376[/C][C]0.132806769734688[/C][/ROW]
[ROW][C]102[/C][C]0.852707829310718[/C][C]0.294584341378563[/C][C]0.147292170689282[/C][/ROW]
[ROW][C]103[/C][C]0.823408920422459[/C][C]0.353182159155082[/C][C]0.176591079577541[/C][/ROW]
[ROW][C]104[/C][C]0.797373137687403[/C][C]0.405253724625194[/C][C]0.202626862312597[/C][/ROW]
[ROW][C]105[/C][C]0.807744189212383[/C][C]0.384511621575234[/C][C]0.192255810787617[/C][/ROW]
[ROW][C]106[/C][C]0.777720014966597[/C][C]0.444559970066806[/C][C]0.222279985033403[/C][/ROW]
[ROW][C]107[/C][C]0.764647418711107[/C][C]0.470705162577787[/C][C]0.235352581288893[/C][/ROW]
[ROW][C]108[/C][C]0.798356497143045[/C][C]0.403287005713911[/C][C]0.201643502856955[/C][/ROW]
[ROW][C]109[/C][C]0.794720564562998[/C][C]0.410558870874003[/C][C]0.205279435437002[/C][/ROW]
[ROW][C]110[/C][C]0.759446243242997[/C][C]0.481107513514005[/C][C]0.240553756757003[/C][/ROW]
[ROW][C]111[/C][C]0.720483809630206[/C][C]0.559032380739589[/C][C]0.279516190369794[/C][/ROW]
[ROW][C]112[/C][C]0.679194672861425[/C][C]0.64161065427715[/C][C]0.320805327138575[/C][/ROW]
[ROW][C]113[/C][C]0.74147608715595[/C][C]0.5170478256881[/C][C]0.25852391284405[/C][/ROW]
[ROW][C]114[/C][C]0.776534520871256[/C][C]0.446930958257488[/C][C]0.223465479128744[/C][/ROW]
[ROW][C]115[/C][C]0.766767324371575[/C][C]0.46646535125685[/C][C]0.233232675628425[/C][/ROW]
[ROW][C]116[/C][C]0.780162372923391[/C][C]0.439675254153219[/C][C]0.219837627076609[/C][/ROW]
[ROW][C]117[/C][C]0.755129862412864[/C][C]0.489740275174272[/C][C]0.244870137587136[/C][/ROW]
[ROW][C]118[/C][C]0.789724671135359[/C][C]0.420550657729282[/C][C]0.210275328864641[/C][/ROW]
[ROW][C]119[/C][C]0.754401124371029[/C][C]0.491197751257942[/C][C]0.245598875628971[/C][/ROW]
[ROW][C]120[/C][C]0.734563445899307[/C][C]0.530873108201385[/C][C]0.265436554100693[/C][/ROW]
[ROW][C]121[/C][C]0.721349352342634[/C][C]0.557301295314732[/C][C]0.278650647657366[/C][/ROW]
[ROW][C]122[/C][C]0.709637040912724[/C][C]0.580725918174552[/C][C]0.290362959087276[/C][/ROW]
[ROW][C]123[/C][C]0.698661618156368[/C][C]0.602676763687264[/C][C]0.301338381843632[/C][/ROW]
[ROW][C]124[/C][C]0.64903929401229[/C][C]0.701921411975421[/C][C]0.35096070598771[/C][/ROW]
[ROW][C]125[/C][C]0.628525266063204[/C][C]0.742949467873591[/C][C]0.371474733936796[/C][/ROW]
[ROW][C]126[/C][C]0.582873842027644[/C][C]0.834252315944713[/C][C]0.417126157972356[/C][/ROW]
[ROW][C]127[/C][C]0.549006187586173[/C][C]0.901987624827654[/C][C]0.450993812413827[/C][/ROW]
[ROW][C]128[/C][C]0.723837262551878[/C][C]0.552325474896244[/C][C]0.276162737448122[/C][/ROW]
[ROW][C]129[/C][C]0.697918656558276[/C][C]0.604162686883448[/C][C]0.302081343441724[/C][/ROW]
[ROW][C]130[/C][C]0.645213251065742[/C][C]0.709573497868516[/C][C]0.354786748934258[/C][/ROW]
[ROW][C]131[/C][C]0.801124853379324[/C][C]0.397750293241352[/C][C]0.198875146620676[/C][/ROW]
[ROW][C]132[/C][C]0.86522832101425[/C][C]0.269543357971502[/C][C]0.134771678985751[/C][/ROW]
[ROW][C]133[/C][C]0.92250081090632[/C][C]0.15499837818736[/C][C]0.07749918909368[/C][/ROW]
[ROW][C]134[/C][C]0.93356674233421[/C][C]0.132866515331582[/C][C]0.0664332576657908[/C][/ROW]
[ROW][C]135[/C][C]0.993352904504621[/C][C]0.013294190990758[/C][C]0.00664709549537898[/C][/ROW]
[ROW][C]136[/C][C]0.995986634094065[/C][C]0.00802673181186968[/C][C]0.00401336590593484[/C][/ROW]
[ROW][C]137[/C][C]0.994035415616967[/C][C]0.0119291687660657[/C][C]0.00596458438303287[/C][/ROW]
[ROW][C]138[/C][C]0.99826735462947[/C][C]0.00346529074105912[/C][C]0.00173264537052956[/C][/ROW]
[ROW][C]139[/C][C]0.997296072924388[/C][C]0.00540785415122389[/C][C]0.00270392707561195[/C][/ROW]
[ROW][C]140[/C][C]0.999635045871561[/C][C]0.000729908256877247[/C][C]0.000364954128438623[/C][/ROW]
[ROW][C]141[/C][C]0.999931780036499[/C][C]0.000136439927002307[/C][C]6.82199635011534e-05[/C][/ROW]
[ROW][C]142[/C][C]0.99998853704545[/C][C]2.29259091017935e-05[/C][C]1.14629545508968e-05[/C][/ROW]
[ROW][C]143[/C][C]0.999971929397018[/C][C]5.61412059640583e-05[/C][C]2.80706029820291e-05[/C][/ROW]
[ROW][C]144[/C][C]0.999932413493702[/C][C]0.000135173012596146[/C][C]6.7586506298073e-05[/C][/ROW]
[ROW][C]145[/C][C]0.999951080884433[/C][C]9.78382311334906e-05[/C][C]4.89191155667453e-05[/C][/ROW]
[ROW][C]146[/C][C]0.999996928420502[/C][C]6.14315899495622e-06[/C][C]3.07157949747811e-06[/C][/ROW]
[ROW][C]147[/C][C]0.999995797579339[/C][C]8.40484132284976e-06[/C][C]4.20242066142488e-06[/C][/ROW]
[ROW][C]148[/C][C]0.999999999898225[/C][C]2.03550336220059e-10[/C][C]1.0177516811003e-10[/C][/ROW]
[ROW][C]149[/C][C]0.999999998358802[/C][C]3.28239669182121e-09[/C][C]1.6411983459106e-09[/C][/ROW]
[ROW][C]150[/C][C]0.999999976132887[/C][C]4.77342263039911e-08[/C][C]2.38671131519956e-08[/C][/ROW]
[ROW][C]151[/C][C]0.999999631105664[/C][C]7.37788672730723e-07[/C][C]3.68894336365362e-07[/C][/ROW]
[ROW][C]152[/C][C]0.999994791611196[/C][C]1.04167776089052e-05[/C][C]5.20838880445262e-06[/C][/ROW]
[ROW][C]153[/C][C]0.999925029467675[/C][C]0.000149941064649559[/C][C]7.49705323247793e-05[/C][/ROW]
[ROW][C]154[/C][C]0.999003796436327[/C][C]0.00199240712734538[/C][C]0.000996203563672692[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158880&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158880&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.38724234898620.77448469797240.6127576510138
110.3550537551101360.7101075102202720.644946244889864
120.5009921261583250.998015747683350.499007873841675
130.3745796188528990.7491592377057980.625420381147101
140.4925403885807510.9850807771615030.507459611419249
150.421634771336270.843269542672540.57836522866373
160.4001839967250180.8003679934500350.599816003274982
170.3269447996808360.6538895993616730.673055200319164
180.2653421611002550.530684322200510.734657838899745
190.2035714199830960.4071428399661910.796428580016904
200.1492449725336680.2984899450673350.850755027466332
210.1059056573593330.2118113147186660.894094342640667
220.07333378903637880.1466675780727580.926666210963621
230.09608813179867720.1921762635973540.903911868201323
240.3643671114576070.7287342229152140.635632888542393
250.3477813562657540.6955627125315070.652218643734246
260.3132599530080140.6265199060160280.686740046991986
270.2549769201134960.5099538402269920.745023079886504
280.2055638594103510.4111277188207010.79443614058965
290.1629126239365070.3258252478730140.837087376063493
300.1353433050478730.2706866100957470.864656694952126
310.159606893453820.3192137869076390.84039310654618
320.1469281323034480.2938562646068950.853071867696553
330.1245565314151610.2491130628303220.875443468584839
340.1419489460443130.2838978920886260.858051053955687
350.1463768372550710.2927536745101420.85362316274493
360.138976223544230.2779524470884590.86102377645577
370.1255205313915680.2510410627831360.874479468608432
380.1067666181249010.2135332362498030.893233381875099
390.1565452014950730.3130904029901460.843454798504927
400.1460419622979580.2920839245959170.853958037702042
410.1296220657431710.2592441314863430.870377934256829
420.1194960959450750.2389921918901510.880503904054925
430.09563105913129320.1912621182625860.904368940868707
440.1071141924348630.2142283848697250.892885807565137
450.1320780943552230.2641561887104470.867921905644777
460.1670829535983140.3341659071966270.832917046401686
470.1428983438502530.2857966877005050.857101656149747
480.13951333791920.2790266758383990.8604866620808
490.128551247815260.257102495630520.87144875218474
500.1419981391031280.2839962782062550.858001860896872
510.1187974449136860.2375948898273710.881202555086314
520.2558517937221470.5117035874442950.744148206277853
530.2297343600491310.4594687200982620.770265639950869
540.2199121516530570.4398243033061150.780087848346943
550.2545937421500870.5091874843001750.745406257849913
560.2390375607667760.4780751215335510.760962439233224
570.2207395758542640.4414791517085290.779260424145736
580.5503772405361480.8992455189277040.449622759463852
590.503963864423510.992072271152980.49603613557649
600.4608585949433590.9217171898867170.539141405056642
610.436484416718460.8729688334369190.56351558328154
620.7248193639080180.5503612721839640.275180636091982
630.7273685245877770.5452629508244460.272631475412223
640.6945955606533190.6108088786933630.305404439346681
650.6582338952347030.6835322095305940.341766104765297
660.6841534452433220.6316931095133560.315846554756678
670.6513693165923650.697261366815270.348630683407635
680.6071074726325020.7857850547349950.392892527367498
690.5730992845013680.8538014309972630.426900715498632
700.5626108118293670.8747783763412650.437389188170633
710.5682821409120820.8634357181758360.431717859087918
720.5480137841086350.903972431782730.451986215891365
730.5687091663828780.8625816672342440.431290833617122
740.5481499897890550.9037000204218890.451850010210945
750.5321257988692280.9357484022615440.467874201130772
760.5365037330440560.9269925339118890.463496266955944
770.5069459693798050.986108061240390.493054030620195
780.4761960254015250.952392050803050.523803974598475
790.4430531095385590.8861062190771170.556946890461441
800.4343780591274580.8687561182549170.565621940872542
810.4447965618220070.8895931236440140.555203438177993
820.636436993512470.7271260129750610.363563006487531
830.6550515597923840.6898968804152320.344948440207616
840.6121108889947250.775778222010550.387889111005275
850.6760409175693120.6479181648613760.323959082430688
860.6336436391738680.7327127216522630.366356360826132
870.5892186376604510.8215627246790980.410781362339549
880.8016391841554010.3967216316891990.198360815844599
890.856871400092670.286257199814660.14312859990733
900.8411404249668990.3177191500662020.158859575033101
910.8282649497020950.3434701005958110.171735050297906
920.800469364590440.399061270819120.19953063540956
930.7759503215156870.4480993569686270.224049678484313
940.7510436260972120.4979127478055760.248956373902788
950.7730851832582040.4538296334835930.226914816741796
960.7364314505779910.5271370988440180.263568549422009
970.7463166670576450.507366665884710.253683332942355
980.721854649275070.556290701449860.27814535072493
990.7177774992658760.5644450014682480.282222500734124
1000.8795604631262540.2408790737474930.120439536873746
1010.8671932302653120.2656135394693760.132806769734688
1020.8527078293107180.2945843413785630.147292170689282
1030.8234089204224590.3531821591550820.176591079577541
1040.7973731376874030.4052537246251940.202626862312597
1050.8077441892123830.3845116215752340.192255810787617
1060.7777200149665970.4445599700668060.222279985033403
1070.7646474187111070.4707051625777870.235352581288893
1080.7983564971430450.4032870057139110.201643502856955
1090.7947205645629980.4105588708740030.205279435437002
1100.7594462432429970.4811075135140050.240553756757003
1110.7204838096302060.5590323807395890.279516190369794
1120.6791946728614250.641610654277150.320805327138575
1130.741476087155950.51704782568810.25852391284405
1140.7765345208712560.4469309582574880.223465479128744
1150.7667673243715750.466465351256850.233232675628425
1160.7801623729233910.4396752541532190.219837627076609
1170.7551298624128640.4897402751742720.244870137587136
1180.7897246711353590.4205506577292820.210275328864641
1190.7544011243710290.4911977512579420.245598875628971
1200.7345634458993070.5308731082013850.265436554100693
1210.7213493523426340.5573012953147320.278650647657366
1220.7096370409127240.5807259181745520.290362959087276
1230.6986616181563680.6026767636872640.301338381843632
1240.649039294012290.7019214119754210.35096070598771
1250.6285252660632040.7429494678735910.371474733936796
1260.5828738420276440.8342523159447130.417126157972356
1270.5490061875861730.9019876248276540.450993812413827
1280.7238372625518780.5523254748962440.276162737448122
1290.6979186565582760.6041626868834480.302081343441724
1300.6452132510657420.7095734978685160.354786748934258
1310.8011248533793240.3977502932413520.198875146620676
1320.865228321014250.2695433579715020.134771678985751
1330.922500810906320.154998378187360.07749918909368
1340.933566742334210.1328665153315820.0664332576657908
1350.9933529045046210.0132941909907580.00664709549537898
1360.9959866340940650.008026731811869680.00401336590593484
1370.9940354156169670.01192916876606570.00596458438303287
1380.998267354629470.003465290741059120.00173264537052956
1390.9972960729243880.005407854151223890.00270392707561195
1400.9996350458715610.0007299082568772470.000364954128438623
1410.9999317800364990.0001364399270023076.82199635011534e-05
1420.999988537045452.29259091017935e-051.14629545508968e-05
1430.9999719293970185.61412059640583e-052.80706029820291e-05
1440.9999324134937020.0001351730125961466.7586506298073e-05
1450.9999510808844339.78382311334906e-054.89191155667453e-05
1460.9999969284205026.14315899495622e-063.07157949747811e-06
1470.9999957975793398.40484132284976e-064.20242066142488e-06
1480.9999999998982252.03550336220059e-101.0177516811003e-10
1490.9999999983588023.28239669182121e-091.6411983459106e-09
1500.9999999761328874.77342263039911e-082.38671131519956e-08
1510.9999996311056647.37788672730723e-073.68894336365362e-07
1520.9999947916111961.04167776089052e-055.20838880445262e-06
1530.9999250294676750.0001499410646495597.49705323247793e-05
1540.9990037964363270.001992407127345380.000996203563672692






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level180.124137931034483NOK
5% type I error level200.137931034482759NOK
10% type I error level200.137931034482759NOK

\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 & 18 & 0.124137931034483 & NOK \tabularnewline
5% type I error level & 20 & 0.137931034482759 & NOK \tabularnewline
10% type I error level & 20 & 0.137931034482759 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158880&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]18[/C][C]0.124137931034483[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]20[/C][C]0.137931034482759[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]20[/C][C]0.137931034482759[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158880&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158880&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level180.124137931034483NOK
5% type I error level200.137931034482759NOK
10% type I error level200.137931034482759NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 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')
}