Free Statistics

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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 22 Nov 2011 09:33:12 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/22/t1321972427rtu3a77vqt12po4.htm/, Retrieved Fri, 29 Mar 2024 05:47:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146229, Retrieved Fri, 29 Mar 2024 05:47:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact77
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [Workshop 7] [2011-11-22 14:33:12] [240aada53705cc48eae3e230739818e0] [Current]
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Dataseries X:
95556	114468	127	128
54565	88594	90	89
63016	74151	68	68
79774	77921	111	108
31258	53212	51	51
52491	34956	33	33
91256	149703	123	119
22807	6853	5	5
77411	58907	63	63
48821	67067	66	66
52295	110563	99	98
63262	58126	72	71
50466	57113	55	55
62932	77993	116	116
38439	68091	71	71
70817	124676	125	120
105965	109522	123	122
73795	75865	74	74
82043	79746	116	111
74349	77844	117	103
82204	98681	98	98
55709	105531	101	100
37137	51428	43	42
70780	65703	103	100
55027	72562	107	105
56699	81728	77	77
65911	95580	87	83
56316	98278	99	98
26982	46629	46	46
54628	115189	96	95
96750	124865	92	91
53009	59392	96	91
64664	127818	96	94
36990	17821	15	15
85224	154076	147	137
37048	64881	56	56
59635	136506	81	78
42051	66524	69	68
26998	45988	34	34
63717	107445	98	94
55071	102772	82	82
40001	46657	64	63
54506	97563	61	58
35838	36663	45	43
50838	55369	37	36
86997	77921	64	64
33032	56968	21	21
61704	77519	104	104
117986	129805	126	124
56733	72761	104	101
55064	81278	87	85
5950	15049	7	7
84607	113935	130	124
32551	25109	21	21
31701	45824	35	35
71170	89644	97	95
101773	109011	103	102
101653	134245	210	212
81493	136692	151	141
55901	50741	57	54
109104	149510	117	117
114425	147888	152	145
36311	54987	52	50
70027	74467	83	80
73713	100033	87	87
40671	85505	80	78
89041	62426	88	86
57231	82932	83	82
68608	72002	120	119
59155	65469	76	75
55827	63572	70	70
22618	23824	26	25
58425	73831	66	66
65724	63551	89	89
56979	56756	100	99
72369	81399	98	98
79194	117881	109	104
202316	70711	51	48
44970	50495	82	81
49319	53845	65	64
36252	51390	46	44
75741	104953	104	104
38417	65983	36	36
64102	76839	123	120
56622	55792	59	58
15430	25155	27	27
72571	55291	84	84
67271	84279	61	56
43460	99692	46	46
99501	59633	125	119
28340	63249	58	57
76013	82928	152	139
37361	50000	52	51
48204	69455	85	85
76168	84068	95	91
85168	76195	78	79
125410	114634	144	142
123328	139357	149	149
83038	110044	101	96
120087	155118	205	198
91939	83061	61	61
103646	127122	145	145
29467	45653	28	26
43750	19630	49	49
34497	67229	68	68
66477	86060	142	145
71181	88003	82	82
74482	95815	105	102
174949	85499	52	52
46765	27220	56	56
90257	109882	81	80
51370	72579	100	99
1168	5841	11	11
51360	68369	87	87
25162	24610	31	28
21067	30995	67	67
58233	150662	150	150
855	6622	4	4
85903	93694	75	71
14116	13155	39	39
57637	111908	88	87
94137	57550	67	66
62147	16356	24	23
62832	40174	58	56
8773	13983	16	16
63785	52316	49	49
65196	99585	109	108
73087	86271	124	112
72631	131012	115	110
86281	130274	128	126
162365	159051	159	155
56530	76506	75	75
35606	49145	30	30
70111	66398	83	78
92046	127546	135	135
63989	6802	8	8
104911	99509	115	114
43448	43106	60	60
60029	108303	99	99
38650	64167	98	98
47261	8579	36	33
73586	97811	93	93
83042	84365	158	157
37238	10901	16	15
63958	91346	100	98
78956	33660	49	49
99518	93634	89	88
111436	109348	153	151
0	0	0	0
6023	7953	5	5
0	0	0	0
0	0	0	0
0	0	0	0
0	0	0	0
42564	63538	80	80
38885	108281	122	122
0	0	0	0
0	0	0	0
1644	4245	6	6
6179	21509	13	13
3926	7670	3	3
23238	10641	18	18
0	0	0	0
49288	41243	49	48




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146229&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146229&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146229&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Seconds[t] = + 5972.76744903046 + 0.27235037117809Characters[t] + 253.283122990038Hyperlinks[t] + 396.032831098034Blogs[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Seconds[t] =  +  5972.76744903046 +  0.27235037117809Characters[t] +  253.283122990038Hyperlinks[t] +  396.032831098034Blogs[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146229&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Seconds[t] =  +  5972.76744903046 +  0.27235037117809Characters[t] +  253.283122990038Hyperlinks[t] +  396.032831098034Blogs[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146229&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146229&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Seconds[t] = + 5972.76744903046 + 0.27235037117809Characters[t] + 253.283122990038Hyperlinks[t] + 396.032831098034Blogs[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5972.767449030463225.3990041.85180.0658990.032949
Characters0.272350371178090.0631944.30972.8e-051.4e-05
Hyperlinks253.283122990038640.3010080.39560.692950.346475
Blogs396.032831098034653.1618780.60630.5451540.272577

\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) & 5972.76744903046 & 3225.399004 & 1.8518 & 0.065899 & 0.032949 \tabularnewline
Characters & 0.27235037117809 & 0.063194 & 4.3097 & 2.8e-05 & 1.4e-05 \tabularnewline
Hyperlinks & 253.283122990038 & 640.301008 & 0.3956 & 0.69295 & 0.346475 \tabularnewline
Blogs & 396.032831098034 & 653.161878 & 0.6063 & 0.545154 & 0.272577 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146229&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]5972.76744903046[/C][C]3225.399004[/C][C]1.8518[/C][C]0.065899[/C][C]0.032949[/C][/ROW]
[ROW][C]Characters[/C][C]0.27235037117809[/C][C]0.063194[/C][C]4.3097[/C][C]2.8e-05[/C][C]1.4e-05[/C][/ROW]
[ROW][C]Hyperlinks[/C][C]253.283122990038[/C][C]640.301008[/C][C]0.3956[/C][C]0.69295[/C][C]0.346475[/C][/ROW]
[ROW][C]Blogs[/C][C]396.032831098034[/C][C]653.161878[/C][C]0.6063[/C][C]0.545154[/C][C]0.272577[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146229&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146229&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5972.767449030463225.3990041.85180.0658990.032949
Characters0.272350371178090.0631944.30972.8e-051.4e-05
Hyperlinks253.283122990038640.3010080.39560.692950.346475
Blogs396.032831098034653.1618780.60630.5451540.272577







Multiple Linear Regression - Regression Statistics
Multiple R0.879836719961434
R-squared0.774112653792494
Adjusted R-squared0.769877266051103
F-TEST (value)182.772558513924
F-TEST (DF numerator)3
F-TEST (DF denominator)160
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation19128.1219022614
Sum Squared Residuals58541607601.2434

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.879836719961434 \tabularnewline
R-squared & 0.774112653792494 \tabularnewline
Adjusted R-squared & 0.769877266051103 \tabularnewline
F-TEST (value) & 182.772558513924 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 160 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 19128.1219022614 \tabularnewline
Sum Squared Residuals & 58541607601.2434 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146229&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.879836719961434[/C][/ROW]
[ROW][C]R-squared[/C][C]0.774112653792494[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.769877266051103[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]182.772558513924[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]160[/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]19128.1219022614[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]58541607601.2434[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146229&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146229&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.879836719961434
R-squared0.774112653792494
Adjusted R-squared0.769877266051103
F-TEST (value)182.772558513924
F-TEST (DF numerator)3
F-TEST (DF denominator)160
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation19128.1219022614
Sum Squared Residuals58541607601.2434







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1114468114856.638517607-388.638517607226
28859478875.96848919149718.03151080863
37415167288.68331717796862.3166828221
47792198585.2183698733-20664.2183698733
55321247601.00900980695610.99099019314
63495641696.137267446-6740.13726744597
7149703109108.10394969940594.8960503011
8685315430.8421349295-8577.84213492953
95890767962.5871398461-9055.58713984613
106706762124.03789012874942.96210987126
1111056384101.576733409726461.4232665902
125812669556.9124937419-11430.9124937419
135711355429.57875574791683.42124425209
147799398432.9716822263-20439.9716822263
156809162543.07610699825547.92389300184
16124676104444.13379026820231.8662097319
17109522114302.204052652-4780.20405265158
187586574120.24369263491744.75630736506
1979746101657.695470321-21911.6954703207
207784496647.2521886822-18803.2521886822
219868191994.02086198526686.97913801478
2210553186330.012808787919200.9871912121
235142843611.59637816037816.40362183975
246570390941.171498793-25238.171498793
257256289644.1327490748-17082.1327490748
268172871412.089609238510315.9103907615
279558078830.009445019716749.9905549803
289827885196.697575916913081.3024240831
294662943189.8590522093439.14094779101
3011518982789.02228710432399.977712896
3112486591663.700805515333201.2991944847
325939280763.9557117746-21371.9557117746
3312781885126.297781149342691.7022188507
341782125786.7469902291-7965.7469902291
35154076120672.67242227833403.3275777218
366488152424.497429368412456.5025706316
3713650673620.875622075662885.1243779244
386652461832.14090841924691.85909158075
394598835402.42520909110585.574790909
4010744585374.948225623722070.0517743763
4110277274215.282975400928556.717024599
424665758027.2428770638-11370.2428770638
439756359237.671486541738325.3285134583
443666344160.412323078-7497.41232307802
455536943447.173089142811921.8269108572
467792171222.65375204746698.34624795262
475696828604.679945634628363.3200543654
487751990306.7339773628-12787.7339773628
49129805119128.0428957510676.9571042504
507276187764.7817889424-15003.7817889424
518127876667.8906310474610.10936895301
521504912138.46383615662910.5361638434
53113935111050.3923481562884.60765184374
542510928473.679417098-3364.67941709799
554582437332.60495882968491.39504117039
568964487547.5252501222096.47474987797
57109011100174.1922149128836.8077850884
58134245170806.415751088-36561.415751088
59136692122253.79700376514438.202996235
605074157020.3364379829-6279.33643798288
61149510111657.24897434937852.7510256508
62147888133060.25387478414827.7461252159
635498748834.44572726186152.55427273824
647446777749.7725875344-3282.77258753444
6510003382539.018365343317493.9816346567
668550568202.740060064217302.2599399358
676242686570.855146653-24144.855146653
688293275056.84290013577875.15709986434
6972002102180.063374287-30178.0633742874
706546971035.6333356658-5566.63333566581
716357266629.3884069547-3057.38840695473
722382428618.9701195283-4794.97011952834
737383164739.69085492319091.30914507688
746355181661.8431581777-18110.8431581776
755675686026.581826096-29270.581826096
768139989315.4549614487-7916.4549614487
7711788196336.557584217821544.4424157822
787071193000.6203094946-22289.6203094946
795049571068.239045033-20573.239045033
805384561214.3195897893-7369.31958978932
815139044922.48133083386467.51866916618
8210495394129.716137589710823.2838624103
836598339811.026005749726171.9739942503
8476839102108.734801827-25269.7348018271
855579259307.3986259745-3515.39862597449
862515527706.6644366863-2551.66443668633
875529180280.0463791937-24989.0463791937
888427961922.15831243422356.841687566
899969247677.648468481652014.3515315184
9059633111860.199006042-52227.1990060424
916324950955.469474227712293.5305257723
9282928120222.534430503-37294.5344305031
935000049516.4464480968483.553551903214
946945574293.0008387852-4838.00083878522
958406886818.0348348979-2750.03483489792
967619580210.9811114937-4015.98111149369
97114634132837.659224961-18203.659224961
98139357136309.2711848053047.72881519534
9911004492188.944778321817855.0552216782
100155118169016.047243062-13898.0472430622
1018306170620.661424145312440.3385758547
102127122128351.607362925-1229.6073629252
1034565331386.896888805214266.1031111948
1041963049704.5779383874-30074.5779383874
1056722959521.52308154997707.47691845008
10686060117468.767047637-31408.7670476366
1078800378602.847455089400.15254492001
1089581593248.04448107042566.95551892959
1098549987384.6221488459-1885.62214884594
1102722055070.9259861059-27850.9259861059
11110988282752.854350487127129.1456495129
1127257984498.9685941581-11919.9685941581
113584113433.3481775353-7592.34817753526
1146836976451.1705183994-8082.17051839941
1152461031766.3435720497-7156.34357204969
1163099555214.5416425401-24219.5416425401
117150662119229.93972705531432.0602729451
11866228802.89083274002-2180.89083274002
1199369476483.046616555217210.9533834448
1201315535140.5874980152-21985.5874980152
12111190878413.996921274333494.0030787257
1225755074719.1504334251-17169.1504334251
1231635638086.0760336509-21730.0760336509
1244017459953.3456458043-19779.3456458043
1251398318751.152520785-4768.15252078499
1265231655161.1176249405-2845.11762494047
1279958594108.3284128595476.67158714099
12886271101640.823361068-15369.823361068
12913101298445.017822704432566.9821772956
130130274111791.80628572418482.1937142756
131159051151850.0408409727200.95915902763
1327650670067.43048833336438.56951166672
1334914535149.553387839713995.4466121603
1346639876980.5843565173-10582.5843565173
135127546118699.1835163798846.81648362136
136680228594.7229830499-21792.7229830499
13799509108820.619128725-9311.61912872531
1384310656764.8036212604-13658.8036212604
13910830386603.967335199121699.0326648009
1406416780132.0727956947-15965.0727956947
141857941031.5941951547-32452.5941951547
1429781186400.325592732111410.6744072679
14384365130785.174887219-46420.1748872187
1441090126107.5730052713-15206.5730052713
1459134687531.28223544993814.71776455014
1463366059292.9451060833-25632.9451060833
1479363490469.6187706723164.38122932799
148109348134875.678724911-25527.678724911
14905972.76744903046-5972.76744903046
150795310859.7135050765-2906.71350507646
15105972.76744903046-5972.76744903046
15205972.76744903046-5972.76744903046
15305972.76744903046-5972.76744903046
15405972.76744903046-5972.76744903046
1556353869510.3649749004-5972.36497490043
15610828195779.658031035212501.3419689648
15705972.76744903046-5972.76744903046
15805972.76744903046-5972.76744903046
159424510316.4071837757-6071.40718377568
1602150916096.72779568485412.27220431519
16176708989.96286853986-1319.96286853986
1621064123989.3325480522-13348.3325480522
16305972.76744903046-5972.76744903046
1644124350816.8214628737-9573.82146287366

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 114468 & 114856.638517607 & -388.638517607226 \tabularnewline
2 & 88594 & 78875.9684891914 & 9718.03151080863 \tabularnewline
3 & 74151 & 67288.6833171779 & 6862.3166828221 \tabularnewline
4 & 77921 & 98585.2183698733 & -20664.2183698733 \tabularnewline
5 & 53212 & 47601.0090098069 & 5610.99099019314 \tabularnewline
6 & 34956 & 41696.137267446 & -6740.13726744597 \tabularnewline
7 & 149703 & 109108.103949699 & 40594.8960503011 \tabularnewline
8 & 6853 & 15430.8421349295 & -8577.84213492953 \tabularnewline
9 & 58907 & 67962.5871398461 & -9055.58713984613 \tabularnewline
10 & 67067 & 62124.0378901287 & 4942.96210987126 \tabularnewline
11 & 110563 & 84101.5767334097 & 26461.4232665902 \tabularnewline
12 & 58126 & 69556.9124937419 & -11430.9124937419 \tabularnewline
13 & 57113 & 55429.5787557479 & 1683.42124425209 \tabularnewline
14 & 77993 & 98432.9716822263 & -20439.9716822263 \tabularnewline
15 & 68091 & 62543.0761069982 & 5547.92389300184 \tabularnewline
16 & 124676 & 104444.133790268 & 20231.8662097319 \tabularnewline
17 & 109522 & 114302.204052652 & -4780.20405265158 \tabularnewline
18 & 75865 & 74120.2436926349 & 1744.75630736506 \tabularnewline
19 & 79746 & 101657.695470321 & -21911.6954703207 \tabularnewline
20 & 77844 & 96647.2521886822 & -18803.2521886822 \tabularnewline
21 & 98681 & 91994.0208619852 & 6686.97913801478 \tabularnewline
22 & 105531 & 86330.0128087879 & 19200.9871912121 \tabularnewline
23 & 51428 & 43611.5963781603 & 7816.40362183975 \tabularnewline
24 & 65703 & 90941.171498793 & -25238.171498793 \tabularnewline
25 & 72562 & 89644.1327490748 & -17082.1327490748 \tabularnewline
26 & 81728 & 71412.0896092385 & 10315.9103907615 \tabularnewline
27 & 95580 & 78830.0094450197 & 16749.9905549803 \tabularnewline
28 & 98278 & 85196.6975759169 & 13081.3024240831 \tabularnewline
29 & 46629 & 43189.859052209 & 3439.14094779101 \tabularnewline
30 & 115189 & 82789.022287104 & 32399.977712896 \tabularnewline
31 & 124865 & 91663.7008055153 & 33201.2991944847 \tabularnewline
32 & 59392 & 80763.9557117746 & -21371.9557117746 \tabularnewline
33 & 127818 & 85126.2977811493 & 42691.7022188507 \tabularnewline
34 & 17821 & 25786.7469902291 & -7965.7469902291 \tabularnewline
35 & 154076 & 120672.672422278 & 33403.3275777218 \tabularnewline
36 & 64881 & 52424.4974293684 & 12456.5025706316 \tabularnewline
37 & 136506 & 73620.8756220756 & 62885.1243779244 \tabularnewline
38 & 66524 & 61832.1409084192 & 4691.85909158075 \tabularnewline
39 & 45988 & 35402.425209091 & 10585.574790909 \tabularnewline
40 & 107445 & 85374.9482256237 & 22070.0517743763 \tabularnewline
41 & 102772 & 74215.2829754009 & 28556.717024599 \tabularnewline
42 & 46657 & 58027.2428770638 & -11370.2428770638 \tabularnewline
43 & 97563 & 59237.6714865417 & 38325.3285134583 \tabularnewline
44 & 36663 & 44160.412323078 & -7497.41232307802 \tabularnewline
45 & 55369 & 43447.1730891428 & 11921.8269108572 \tabularnewline
46 & 77921 & 71222.6537520474 & 6698.34624795262 \tabularnewline
47 & 56968 & 28604.6799456346 & 28363.3200543654 \tabularnewline
48 & 77519 & 90306.7339773628 & -12787.7339773628 \tabularnewline
49 & 129805 & 119128.04289575 & 10676.9571042504 \tabularnewline
50 & 72761 & 87764.7817889424 & -15003.7817889424 \tabularnewline
51 & 81278 & 76667.890631047 & 4610.10936895301 \tabularnewline
52 & 15049 & 12138.4638361566 & 2910.5361638434 \tabularnewline
53 & 113935 & 111050.392348156 & 2884.60765184374 \tabularnewline
54 & 25109 & 28473.679417098 & -3364.67941709799 \tabularnewline
55 & 45824 & 37332.6049588296 & 8491.39504117039 \tabularnewline
56 & 89644 & 87547.525250122 & 2096.47474987797 \tabularnewline
57 & 109011 & 100174.192214912 & 8836.8077850884 \tabularnewline
58 & 134245 & 170806.415751088 & -36561.415751088 \tabularnewline
59 & 136692 & 122253.797003765 & 14438.202996235 \tabularnewline
60 & 50741 & 57020.3364379829 & -6279.33643798288 \tabularnewline
61 & 149510 & 111657.248974349 & 37852.7510256508 \tabularnewline
62 & 147888 & 133060.253874784 & 14827.7461252159 \tabularnewline
63 & 54987 & 48834.4457272618 & 6152.55427273824 \tabularnewline
64 & 74467 & 77749.7725875344 & -3282.77258753444 \tabularnewline
65 & 100033 & 82539.0183653433 & 17493.9816346567 \tabularnewline
66 & 85505 & 68202.7400600642 & 17302.2599399358 \tabularnewline
67 & 62426 & 86570.855146653 & -24144.855146653 \tabularnewline
68 & 82932 & 75056.8429001357 & 7875.15709986434 \tabularnewline
69 & 72002 & 102180.063374287 & -30178.0633742874 \tabularnewline
70 & 65469 & 71035.6333356658 & -5566.63333566581 \tabularnewline
71 & 63572 & 66629.3884069547 & -3057.38840695473 \tabularnewline
72 & 23824 & 28618.9701195283 & -4794.97011952834 \tabularnewline
73 & 73831 & 64739.6908549231 & 9091.30914507688 \tabularnewline
74 & 63551 & 81661.8431581777 & -18110.8431581776 \tabularnewline
75 & 56756 & 86026.581826096 & -29270.581826096 \tabularnewline
76 & 81399 & 89315.4549614487 & -7916.4549614487 \tabularnewline
77 & 117881 & 96336.5575842178 & 21544.4424157822 \tabularnewline
78 & 70711 & 93000.6203094946 & -22289.6203094946 \tabularnewline
79 & 50495 & 71068.239045033 & -20573.239045033 \tabularnewline
80 & 53845 & 61214.3195897893 & -7369.31958978932 \tabularnewline
81 & 51390 & 44922.4813308338 & 6467.51866916618 \tabularnewline
82 & 104953 & 94129.7161375897 & 10823.2838624103 \tabularnewline
83 & 65983 & 39811.0260057497 & 26171.9739942503 \tabularnewline
84 & 76839 & 102108.734801827 & -25269.7348018271 \tabularnewline
85 & 55792 & 59307.3986259745 & -3515.39862597449 \tabularnewline
86 & 25155 & 27706.6644366863 & -2551.66443668633 \tabularnewline
87 & 55291 & 80280.0463791937 & -24989.0463791937 \tabularnewline
88 & 84279 & 61922.158312434 & 22356.841687566 \tabularnewline
89 & 99692 & 47677.6484684816 & 52014.3515315184 \tabularnewline
90 & 59633 & 111860.199006042 & -52227.1990060424 \tabularnewline
91 & 63249 & 50955.4694742277 & 12293.5305257723 \tabularnewline
92 & 82928 & 120222.534430503 & -37294.5344305031 \tabularnewline
93 & 50000 & 49516.4464480968 & 483.553551903214 \tabularnewline
94 & 69455 & 74293.0008387852 & -4838.00083878522 \tabularnewline
95 & 84068 & 86818.0348348979 & -2750.03483489792 \tabularnewline
96 & 76195 & 80210.9811114937 & -4015.98111149369 \tabularnewline
97 & 114634 & 132837.659224961 & -18203.659224961 \tabularnewline
98 & 139357 & 136309.271184805 & 3047.72881519534 \tabularnewline
99 & 110044 & 92188.9447783218 & 17855.0552216782 \tabularnewline
100 & 155118 & 169016.047243062 & -13898.0472430622 \tabularnewline
101 & 83061 & 70620.6614241453 & 12440.3385758547 \tabularnewline
102 & 127122 & 128351.607362925 & -1229.6073629252 \tabularnewline
103 & 45653 & 31386.8968888052 & 14266.1031111948 \tabularnewline
104 & 19630 & 49704.5779383874 & -30074.5779383874 \tabularnewline
105 & 67229 & 59521.5230815499 & 7707.47691845008 \tabularnewline
106 & 86060 & 117468.767047637 & -31408.7670476366 \tabularnewline
107 & 88003 & 78602.84745508 & 9400.15254492001 \tabularnewline
108 & 95815 & 93248.0444810704 & 2566.95551892959 \tabularnewline
109 & 85499 & 87384.6221488459 & -1885.62214884594 \tabularnewline
110 & 27220 & 55070.9259861059 & -27850.9259861059 \tabularnewline
111 & 109882 & 82752.8543504871 & 27129.1456495129 \tabularnewline
112 & 72579 & 84498.9685941581 & -11919.9685941581 \tabularnewline
113 & 5841 & 13433.3481775353 & -7592.34817753526 \tabularnewline
114 & 68369 & 76451.1705183994 & -8082.17051839941 \tabularnewline
115 & 24610 & 31766.3435720497 & -7156.34357204969 \tabularnewline
116 & 30995 & 55214.5416425401 & -24219.5416425401 \tabularnewline
117 & 150662 & 119229.939727055 & 31432.0602729451 \tabularnewline
118 & 6622 & 8802.89083274002 & -2180.89083274002 \tabularnewline
119 & 93694 & 76483.0466165552 & 17210.9533834448 \tabularnewline
120 & 13155 & 35140.5874980152 & -21985.5874980152 \tabularnewline
121 & 111908 & 78413.9969212743 & 33494.0030787257 \tabularnewline
122 & 57550 & 74719.1504334251 & -17169.1504334251 \tabularnewline
123 & 16356 & 38086.0760336509 & -21730.0760336509 \tabularnewline
124 & 40174 & 59953.3456458043 & -19779.3456458043 \tabularnewline
125 & 13983 & 18751.152520785 & -4768.15252078499 \tabularnewline
126 & 52316 & 55161.1176249405 & -2845.11762494047 \tabularnewline
127 & 99585 & 94108.328412859 & 5476.67158714099 \tabularnewline
128 & 86271 & 101640.823361068 & -15369.823361068 \tabularnewline
129 & 131012 & 98445.0178227044 & 32566.9821772956 \tabularnewline
130 & 130274 & 111791.806285724 & 18482.1937142756 \tabularnewline
131 & 159051 & 151850.040840972 & 7200.95915902763 \tabularnewline
132 & 76506 & 70067.4304883333 & 6438.56951166672 \tabularnewline
133 & 49145 & 35149.5533878397 & 13995.4466121603 \tabularnewline
134 & 66398 & 76980.5843565173 & -10582.5843565173 \tabularnewline
135 & 127546 & 118699.183516379 & 8846.81648362136 \tabularnewline
136 & 6802 & 28594.7229830499 & -21792.7229830499 \tabularnewline
137 & 99509 & 108820.619128725 & -9311.61912872531 \tabularnewline
138 & 43106 & 56764.8036212604 & -13658.8036212604 \tabularnewline
139 & 108303 & 86603.9673351991 & 21699.0326648009 \tabularnewline
140 & 64167 & 80132.0727956947 & -15965.0727956947 \tabularnewline
141 & 8579 & 41031.5941951547 & -32452.5941951547 \tabularnewline
142 & 97811 & 86400.3255927321 & 11410.6744072679 \tabularnewline
143 & 84365 & 130785.174887219 & -46420.1748872187 \tabularnewline
144 & 10901 & 26107.5730052713 & -15206.5730052713 \tabularnewline
145 & 91346 & 87531.2822354499 & 3814.71776455014 \tabularnewline
146 & 33660 & 59292.9451060833 & -25632.9451060833 \tabularnewline
147 & 93634 & 90469.618770672 & 3164.38122932799 \tabularnewline
148 & 109348 & 134875.678724911 & -25527.678724911 \tabularnewline
149 & 0 & 5972.76744903046 & -5972.76744903046 \tabularnewline
150 & 7953 & 10859.7135050765 & -2906.71350507646 \tabularnewline
151 & 0 & 5972.76744903046 & -5972.76744903046 \tabularnewline
152 & 0 & 5972.76744903046 & -5972.76744903046 \tabularnewline
153 & 0 & 5972.76744903046 & -5972.76744903046 \tabularnewline
154 & 0 & 5972.76744903046 & -5972.76744903046 \tabularnewline
155 & 63538 & 69510.3649749004 & -5972.36497490043 \tabularnewline
156 & 108281 & 95779.6580310352 & 12501.3419689648 \tabularnewline
157 & 0 & 5972.76744903046 & -5972.76744903046 \tabularnewline
158 & 0 & 5972.76744903046 & -5972.76744903046 \tabularnewline
159 & 4245 & 10316.4071837757 & -6071.40718377568 \tabularnewline
160 & 21509 & 16096.7277956848 & 5412.27220431519 \tabularnewline
161 & 7670 & 8989.96286853986 & -1319.96286853986 \tabularnewline
162 & 10641 & 23989.3325480522 & -13348.3325480522 \tabularnewline
163 & 0 & 5972.76744903046 & -5972.76744903046 \tabularnewline
164 & 41243 & 50816.8214628737 & -9573.82146287366 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146229&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]114468[/C][C]114856.638517607[/C][C]-388.638517607226[/C][/ROW]
[ROW][C]2[/C][C]88594[/C][C]78875.9684891914[/C][C]9718.03151080863[/C][/ROW]
[ROW][C]3[/C][C]74151[/C][C]67288.6833171779[/C][C]6862.3166828221[/C][/ROW]
[ROW][C]4[/C][C]77921[/C][C]98585.2183698733[/C][C]-20664.2183698733[/C][/ROW]
[ROW][C]5[/C][C]53212[/C][C]47601.0090098069[/C][C]5610.99099019314[/C][/ROW]
[ROW][C]6[/C][C]34956[/C][C]41696.137267446[/C][C]-6740.13726744597[/C][/ROW]
[ROW][C]7[/C][C]149703[/C][C]109108.103949699[/C][C]40594.8960503011[/C][/ROW]
[ROW][C]8[/C][C]6853[/C][C]15430.8421349295[/C][C]-8577.84213492953[/C][/ROW]
[ROW][C]9[/C][C]58907[/C][C]67962.5871398461[/C][C]-9055.58713984613[/C][/ROW]
[ROW][C]10[/C][C]67067[/C][C]62124.0378901287[/C][C]4942.96210987126[/C][/ROW]
[ROW][C]11[/C][C]110563[/C][C]84101.5767334097[/C][C]26461.4232665902[/C][/ROW]
[ROW][C]12[/C][C]58126[/C][C]69556.9124937419[/C][C]-11430.9124937419[/C][/ROW]
[ROW][C]13[/C][C]57113[/C][C]55429.5787557479[/C][C]1683.42124425209[/C][/ROW]
[ROW][C]14[/C][C]77993[/C][C]98432.9716822263[/C][C]-20439.9716822263[/C][/ROW]
[ROW][C]15[/C][C]68091[/C][C]62543.0761069982[/C][C]5547.92389300184[/C][/ROW]
[ROW][C]16[/C][C]124676[/C][C]104444.133790268[/C][C]20231.8662097319[/C][/ROW]
[ROW][C]17[/C][C]109522[/C][C]114302.204052652[/C][C]-4780.20405265158[/C][/ROW]
[ROW][C]18[/C][C]75865[/C][C]74120.2436926349[/C][C]1744.75630736506[/C][/ROW]
[ROW][C]19[/C][C]79746[/C][C]101657.695470321[/C][C]-21911.6954703207[/C][/ROW]
[ROW][C]20[/C][C]77844[/C][C]96647.2521886822[/C][C]-18803.2521886822[/C][/ROW]
[ROW][C]21[/C][C]98681[/C][C]91994.0208619852[/C][C]6686.97913801478[/C][/ROW]
[ROW][C]22[/C][C]105531[/C][C]86330.0128087879[/C][C]19200.9871912121[/C][/ROW]
[ROW][C]23[/C][C]51428[/C][C]43611.5963781603[/C][C]7816.40362183975[/C][/ROW]
[ROW][C]24[/C][C]65703[/C][C]90941.171498793[/C][C]-25238.171498793[/C][/ROW]
[ROW][C]25[/C][C]72562[/C][C]89644.1327490748[/C][C]-17082.1327490748[/C][/ROW]
[ROW][C]26[/C][C]81728[/C][C]71412.0896092385[/C][C]10315.9103907615[/C][/ROW]
[ROW][C]27[/C][C]95580[/C][C]78830.0094450197[/C][C]16749.9905549803[/C][/ROW]
[ROW][C]28[/C][C]98278[/C][C]85196.6975759169[/C][C]13081.3024240831[/C][/ROW]
[ROW][C]29[/C][C]46629[/C][C]43189.859052209[/C][C]3439.14094779101[/C][/ROW]
[ROW][C]30[/C][C]115189[/C][C]82789.022287104[/C][C]32399.977712896[/C][/ROW]
[ROW][C]31[/C][C]124865[/C][C]91663.7008055153[/C][C]33201.2991944847[/C][/ROW]
[ROW][C]32[/C][C]59392[/C][C]80763.9557117746[/C][C]-21371.9557117746[/C][/ROW]
[ROW][C]33[/C][C]127818[/C][C]85126.2977811493[/C][C]42691.7022188507[/C][/ROW]
[ROW][C]34[/C][C]17821[/C][C]25786.7469902291[/C][C]-7965.7469902291[/C][/ROW]
[ROW][C]35[/C][C]154076[/C][C]120672.672422278[/C][C]33403.3275777218[/C][/ROW]
[ROW][C]36[/C][C]64881[/C][C]52424.4974293684[/C][C]12456.5025706316[/C][/ROW]
[ROW][C]37[/C][C]136506[/C][C]73620.8756220756[/C][C]62885.1243779244[/C][/ROW]
[ROW][C]38[/C][C]66524[/C][C]61832.1409084192[/C][C]4691.85909158075[/C][/ROW]
[ROW][C]39[/C][C]45988[/C][C]35402.425209091[/C][C]10585.574790909[/C][/ROW]
[ROW][C]40[/C][C]107445[/C][C]85374.9482256237[/C][C]22070.0517743763[/C][/ROW]
[ROW][C]41[/C][C]102772[/C][C]74215.2829754009[/C][C]28556.717024599[/C][/ROW]
[ROW][C]42[/C][C]46657[/C][C]58027.2428770638[/C][C]-11370.2428770638[/C][/ROW]
[ROW][C]43[/C][C]97563[/C][C]59237.6714865417[/C][C]38325.3285134583[/C][/ROW]
[ROW][C]44[/C][C]36663[/C][C]44160.412323078[/C][C]-7497.41232307802[/C][/ROW]
[ROW][C]45[/C][C]55369[/C][C]43447.1730891428[/C][C]11921.8269108572[/C][/ROW]
[ROW][C]46[/C][C]77921[/C][C]71222.6537520474[/C][C]6698.34624795262[/C][/ROW]
[ROW][C]47[/C][C]56968[/C][C]28604.6799456346[/C][C]28363.3200543654[/C][/ROW]
[ROW][C]48[/C][C]77519[/C][C]90306.7339773628[/C][C]-12787.7339773628[/C][/ROW]
[ROW][C]49[/C][C]129805[/C][C]119128.04289575[/C][C]10676.9571042504[/C][/ROW]
[ROW][C]50[/C][C]72761[/C][C]87764.7817889424[/C][C]-15003.7817889424[/C][/ROW]
[ROW][C]51[/C][C]81278[/C][C]76667.890631047[/C][C]4610.10936895301[/C][/ROW]
[ROW][C]52[/C][C]15049[/C][C]12138.4638361566[/C][C]2910.5361638434[/C][/ROW]
[ROW][C]53[/C][C]113935[/C][C]111050.392348156[/C][C]2884.60765184374[/C][/ROW]
[ROW][C]54[/C][C]25109[/C][C]28473.679417098[/C][C]-3364.67941709799[/C][/ROW]
[ROW][C]55[/C][C]45824[/C][C]37332.6049588296[/C][C]8491.39504117039[/C][/ROW]
[ROW][C]56[/C][C]89644[/C][C]87547.525250122[/C][C]2096.47474987797[/C][/ROW]
[ROW][C]57[/C][C]109011[/C][C]100174.192214912[/C][C]8836.8077850884[/C][/ROW]
[ROW][C]58[/C][C]134245[/C][C]170806.415751088[/C][C]-36561.415751088[/C][/ROW]
[ROW][C]59[/C][C]136692[/C][C]122253.797003765[/C][C]14438.202996235[/C][/ROW]
[ROW][C]60[/C][C]50741[/C][C]57020.3364379829[/C][C]-6279.33643798288[/C][/ROW]
[ROW][C]61[/C][C]149510[/C][C]111657.248974349[/C][C]37852.7510256508[/C][/ROW]
[ROW][C]62[/C][C]147888[/C][C]133060.253874784[/C][C]14827.7461252159[/C][/ROW]
[ROW][C]63[/C][C]54987[/C][C]48834.4457272618[/C][C]6152.55427273824[/C][/ROW]
[ROW][C]64[/C][C]74467[/C][C]77749.7725875344[/C][C]-3282.77258753444[/C][/ROW]
[ROW][C]65[/C][C]100033[/C][C]82539.0183653433[/C][C]17493.9816346567[/C][/ROW]
[ROW][C]66[/C][C]85505[/C][C]68202.7400600642[/C][C]17302.2599399358[/C][/ROW]
[ROW][C]67[/C][C]62426[/C][C]86570.855146653[/C][C]-24144.855146653[/C][/ROW]
[ROW][C]68[/C][C]82932[/C][C]75056.8429001357[/C][C]7875.15709986434[/C][/ROW]
[ROW][C]69[/C][C]72002[/C][C]102180.063374287[/C][C]-30178.0633742874[/C][/ROW]
[ROW][C]70[/C][C]65469[/C][C]71035.6333356658[/C][C]-5566.63333566581[/C][/ROW]
[ROW][C]71[/C][C]63572[/C][C]66629.3884069547[/C][C]-3057.38840695473[/C][/ROW]
[ROW][C]72[/C][C]23824[/C][C]28618.9701195283[/C][C]-4794.97011952834[/C][/ROW]
[ROW][C]73[/C][C]73831[/C][C]64739.6908549231[/C][C]9091.30914507688[/C][/ROW]
[ROW][C]74[/C][C]63551[/C][C]81661.8431581777[/C][C]-18110.8431581776[/C][/ROW]
[ROW][C]75[/C][C]56756[/C][C]86026.581826096[/C][C]-29270.581826096[/C][/ROW]
[ROW][C]76[/C][C]81399[/C][C]89315.4549614487[/C][C]-7916.4549614487[/C][/ROW]
[ROW][C]77[/C][C]117881[/C][C]96336.5575842178[/C][C]21544.4424157822[/C][/ROW]
[ROW][C]78[/C][C]70711[/C][C]93000.6203094946[/C][C]-22289.6203094946[/C][/ROW]
[ROW][C]79[/C][C]50495[/C][C]71068.239045033[/C][C]-20573.239045033[/C][/ROW]
[ROW][C]80[/C][C]53845[/C][C]61214.3195897893[/C][C]-7369.31958978932[/C][/ROW]
[ROW][C]81[/C][C]51390[/C][C]44922.4813308338[/C][C]6467.51866916618[/C][/ROW]
[ROW][C]82[/C][C]104953[/C][C]94129.7161375897[/C][C]10823.2838624103[/C][/ROW]
[ROW][C]83[/C][C]65983[/C][C]39811.0260057497[/C][C]26171.9739942503[/C][/ROW]
[ROW][C]84[/C][C]76839[/C][C]102108.734801827[/C][C]-25269.7348018271[/C][/ROW]
[ROW][C]85[/C][C]55792[/C][C]59307.3986259745[/C][C]-3515.39862597449[/C][/ROW]
[ROW][C]86[/C][C]25155[/C][C]27706.6644366863[/C][C]-2551.66443668633[/C][/ROW]
[ROW][C]87[/C][C]55291[/C][C]80280.0463791937[/C][C]-24989.0463791937[/C][/ROW]
[ROW][C]88[/C][C]84279[/C][C]61922.158312434[/C][C]22356.841687566[/C][/ROW]
[ROW][C]89[/C][C]99692[/C][C]47677.6484684816[/C][C]52014.3515315184[/C][/ROW]
[ROW][C]90[/C][C]59633[/C][C]111860.199006042[/C][C]-52227.1990060424[/C][/ROW]
[ROW][C]91[/C][C]63249[/C][C]50955.4694742277[/C][C]12293.5305257723[/C][/ROW]
[ROW][C]92[/C][C]82928[/C][C]120222.534430503[/C][C]-37294.5344305031[/C][/ROW]
[ROW][C]93[/C][C]50000[/C][C]49516.4464480968[/C][C]483.553551903214[/C][/ROW]
[ROW][C]94[/C][C]69455[/C][C]74293.0008387852[/C][C]-4838.00083878522[/C][/ROW]
[ROW][C]95[/C][C]84068[/C][C]86818.0348348979[/C][C]-2750.03483489792[/C][/ROW]
[ROW][C]96[/C][C]76195[/C][C]80210.9811114937[/C][C]-4015.98111149369[/C][/ROW]
[ROW][C]97[/C][C]114634[/C][C]132837.659224961[/C][C]-18203.659224961[/C][/ROW]
[ROW][C]98[/C][C]139357[/C][C]136309.271184805[/C][C]3047.72881519534[/C][/ROW]
[ROW][C]99[/C][C]110044[/C][C]92188.9447783218[/C][C]17855.0552216782[/C][/ROW]
[ROW][C]100[/C][C]155118[/C][C]169016.047243062[/C][C]-13898.0472430622[/C][/ROW]
[ROW][C]101[/C][C]83061[/C][C]70620.6614241453[/C][C]12440.3385758547[/C][/ROW]
[ROW][C]102[/C][C]127122[/C][C]128351.607362925[/C][C]-1229.6073629252[/C][/ROW]
[ROW][C]103[/C][C]45653[/C][C]31386.8968888052[/C][C]14266.1031111948[/C][/ROW]
[ROW][C]104[/C][C]19630[/C][C]49704.5779383874[/C][C]-30074.5779383874[/C][/ROW]
[ROW][C]105[/C][C]67229[/C][C]59521.5230815499[/C][C]7707.47691845008[/C][/ROW]
[ROW][C]106[/C][C]86060[/C][C]117468.767047637[/C][C]-31408.7670476366[/C][/ROW]
[ROW][C]107[/C][C]88003[/C][C]78602.84745508[/C][C]9400.15254492001[/C][/ROW]
[ROW][C]108[/C][C]95815[/C][C]93248.0444810704[/C][C]2566.95551892959[/C][/ROW]
[ROW][C]109[/C][C]85499[/C][C]87384.6221488459[/C][C]-1885.62214884594[/C][/ROW]
[ROW][C]110[/C][C]27220[/C][C]55070.9259861059[/C][C]-27850.9259861059[/C][/ROW]
[ROW][C]111[/C][C]109882[/C][C]82752.8543504871[/C][C]27129.1456495129[/C][/ROW]
[ROW][C]112[/C][C]72579[/C][C]84498.9685941581[/C][C]-11919.9685941581[/C][/ROW]
[ROW][C]113[/C][C]5841[/C][C]13433.3481775353[/C][C]-7592.34817753526[/C][/ROW]
[ROW][C]114[/C][C]68369[/C][C]76451.1705183994[/C][C]-8082.17051839941[/C][/ROW]
[ROW][C]115[/C][C]24610[/C][C]31766.3435720497[/C][C]-7156.34357204969[/C][/ROW]
[ROW][C]116[/C][C]30995[/C][C]55214.5416425401[/C][C]-24219.5416425401[/C][/ROW]
[ROW][C]117[/C][C]150662[/C][C]119229.939727055[/C][C]31432.0602729451[/C][/ROW]
[ROW][C]118[/C][C]6622[/C][C]8802.89083274002[/C][C]-2180.89083274002[/C][/ROW]
[ROW][C]119[/C][C]93694[/C][C]76483.0466165552[/C][C]17210.9533834448[/C][/ROW]
[ROW][C]120[/C][C]13155[/C][C]35140.5874980152[/C][C]-21985.5874980152[/C][/ROW]
[ROW][C]121[/C][C]111908[/C][C]78413.9969212743[/C][C]33494.0030787257[/C][/ROW]
[ROW][C]122[/C][C]57550[/C][C]74719.1504334251[/C][C]-17169.1504334251[/C][/ROW]
[ROW][C]123[/C][C]16356[/C][C]38086.0760336509[/C][C]-21730.0760336509[/C][/ROW]
[ROW][C]124[/C][C]40174[/C][C]59953.3456458043[/C][C]-19779.3456458043[/C][/ROW]
[ROW][C]125[/C][C]13983[/C][C]18751.152520785[/C][C]-4768.15252078499[/C][/ROW]
[ROW][C]126[/C][C]52316[/C][C]55161.1176249405[/C][C]-2845.11762494047[/C][/ROW]
[ROW][C]127[/C][C]99585[/C][C]94108.328412859[/C][C]5476.67158714099[/C][/ROW]
[ROW][C]128[/C][C]86271[/C][C]101640.823361068[/C][C]-15369.823361068[/C][/ROW]
[ROW][C]129[/C][C]131012[/C][C]98445.0178227044[/C][C]32566.9821772956[/C][/ROW]
[ROW][C]130[/C][C]130274[/C][C]111791.806285724[/C][C]18482.1937142756[/C][/ROW]
[ROW][C]131[/C][C]159051[/C][C]151850.040840972[/C][C]7200.95915902763[/C][/ROW]
[ROW][C]132[/C][C]76506[/C][C]70067.4304883333[/C][C]6438.56951166672[/C][/ROW]
[ROW][C]133[/C][C]49145[/C][C]35149.5533878397[/C][C]13995.4466121603[/C][/ROW]
[ROW][C]134[/C][C]66398[/C][C]76980.5843565173[/C][C]-10582.5843565173[/C][/ROW]
[ROW][C]135[/C][C]127546[/C][C]118699.183516379[/C][C]8846.81648362136[/C][/ROW]
[ROW][C]136[/C][C]6802[/C][C]28594.7229830499[/C][C]-21792.7229830499[/C][/ROW]
[ROW][C]137[/C][C]99509[/C][C]108820.619128725[/C][C]-9311.61912872531[/C][/ROW]
[ROW][C]138[/C][C]43106[/C][C]56764.8036212604[/C][C]-13658.8036212604[/C][/ROW]
[ROW][C]139[/C][C]108303[/C][C]86603.9673351991[/C][C]21699.0326648009[/C][/ROW]
[ROW][C]140[/C][C]64167[/C][C]80132.0727956947[/C][C]-15965.0727956947[/C][/ROW]
[ROW][C]141[/C][C]8579[/C][C]41031.5941951547[/C][C]-32452.5941951547[/C][/ROW]
[ROW][C]142[/C][C]97811[/C][C]86400.3255927321[/C][C]11410.6744072679[/C][/ROW]
[ROW][C]143[/C][C]84365[/C][C]130785.174887219[/C][C]-46420.1748872187[/C][/ROW]
[ROW][C]144[/C][C]10901[/C][C]26107.5730052713[/C][C]-15206.5730052713[/C][/ROW]
[ROW][C]145[/C][C]91346[/C][C]87531.2822354499[/C][C]3814.71776455014[/C][/ROW]
[ROW][C]146[/C][C]33660[/C][C]59292.9451060833[/C][C]-25632.9451060833[/C][/ROW]
[ROW][C]147[/C][C]93634[/C][C]90469.618770672[/C][C]3164.38122932799[/C][/ROW]
[ROW][C]148[/C][C]109348[/C][C]134875.678724911[/C][C]-25527.678724911[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]5972.76744903046[/C][C]-5972.76744903046[/C][/ROW]
[ROW][C]150[/C][C]7953[/C][C]10859.7135050765[/C][C]-2906.71350507646[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]5972.76744903046[/C][C]-5972.76744903046[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]5972.76744903046[/C][C]-5972.76744903046[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]5972.76744903046[/C][C]-5972.76744903046[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]5972.76744903046[/C][C]-5972.76744903046[/C][/ROW]
[ROW][C]155[/C][C]63538[/C][C]69510.3649749004[/C][C]-5972.36497490043[/C][/ROW]
[ROW][C]156[/C][C]108281[/C][C]95779.6580310352[/C][C]12501.3419689648[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]5972.76744903046[/C][C]-5972.76744903046[/C][/ROW]
[ROW][C]158[/C][C]0[/C][C]5972.76744903046[/C][C]-5972.76744903046[/C][/ROW]
[ROW][C]159[/C][C]4245[/C][C]10316.4071837757[/C][C]-6071.40718377568[/C][/ROW]
[ROW][C]160[/C][C]21509[/C][C]16096.7277956848[/C][C]5412.27220431519[/C][/ROW]
[ROW][C]161[/C][C]7670[/C][C]8989.96286853986[/C][C]-1319.96286853986[/C][/ROW]
[ROW][C]162[/C][C]10641[/C][C]23989.3325480522[/C][C]-13348.3325480522[/C][/ROW]
[ROW][C]163[/C][C]0[/C][C]5972.76744903046[/C][C]-5972.76744903046[/C][/ROW]
[ROW][C]164[/C][C]41243[/C][C]50816.8214628737[/C][C]-9573.82146287366[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146229&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146229&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1114468114856.638517607-388.638517607226
28859478875.96848919149718.03151080863
37415167288.68331717796862.3166828221
47792198585.2183698733-20664.2183698733
55321247601.00900980695610.99099019314
63495641696.137267446-6740.13726744597
7149703109108.10394969940594.8960503011
8685315430.8421349295-8577.84213492953
95890767962.5871398461-9055.58713984613
106706762124.03789012874942.96210987126
1111056384101.576733409726461.4232665902
125812669556.9124937419-11430.9124937419
135711355429.57875574791683.42124425209
147799398432.9716822263-20439.9716822263
156809162543.07610699825547.92389300184
16124676104444.13379026820231.8662097319
17109522114302.204052652-4780.20405265158
187586574120.24369263491744.75630736506
1979746101657.695470321-21911.6954703207
207784496647.2521886822-18803.2521886822
219868191994.02086198526686.97913801478
2210553186330.012808787919200.9871912121
235142843611.59637816037816.40362183975
246570390941.171498793-25238.171498793
257256289644.1327490748-17082.1327490748
268172871412.089609238510315.9103907615
279558078830.009445019716749.9905549803
289827885196.697575916913081.3024240831
294662943189.8590522093439.14094779101
3011518982789.02228710432399.977712896
3112486591663.700805515333201.2991944847
325939280763.9557117746-21371.9557117746
3312781885126.297781149342691.7022188507
341782125786.7469902291-7965.7469902291
35154076120672.67242227833403.3275777218
366488152424.497429368412456.5025706316
3713650673620.875622075662885.1243779244
386652461832.14090841924691.85909158075
394598835402.42520909110585.574790909
4010744585374.948225623722070.0517743763
4110277274215.282975400928556.717024599
424665758027.2428770638-11370.2428770638
439756359237.671486541738325.3285134583
443666344160.412323078-7497.41232307802
455536943447.173089142811921.8269108572
467792171222.65375204746698.34624795262
475696828604.679945634628363.3200543654
487751990306.7339773628-12787.7339773628
49129805119128.0428957510676.9571042504
507276187764.7817889424-15003.7817889424
518127876667.8906310474610.10936895301
521504912138.46383615662910.5361638434
53113935111050.3923481562884.60765184374
542510928473.679417098-3364.67941709799
554582437332.60495882968491.39504117039
568964487547.5252501222096.47474987797
57109011100174.1922149128836.8077850884
58134245170806.415751088-36561.415751088
59136692122253.79700376514438.202996235
605074157020.3364379829-6279.33643798288
61149510111657.24897434937852.7510256508
62147888133060.25387478414827.7461252159
635498748834.44572726186152.55427273824
647446777749.7725875344-3282.77258753444
6510003382539.018365343317493.9816346567
668550568202.740060064217302.2599399358
676242686570.855146653-24144.855146653
688293275056.84290013577875.15709986434
6972002102180.063374287-30178.0633742874
706546971035.6333356658-5566.63333566581
716357266629.3884069547-3057.38840695473
722382428618.9701195283-4794.97011952834
737383164739.69085492319091.30914507688
746355181661.8431581777-18110.8431581776
755675686026.581826096-29270.581826096
768139989315.4549614487-7916.4549614487
7711788196336.557584217821544.4424157822
787071193000.6203094946-22289.6203094946
795049571068.239045033-20573.239045033
805384561214.3195897893-7369.31958978932
815139044922.48133083386467.51866916618
8210495394129.716137589710823.2838624103
836598339811.026005749726171.9739942503
8476839102108.734801827-25269.7348018271
855579259307.3986259745-3515.39862597449
862515527706.6644366863-2551.66443668633
875529180280.0463791937-24989.0463791937
888427961922.15831243422356.841687566
899969247677.648468481652014.3515315184
9059633111860.199006042-52227.1990060424
916324950955.469474227712293.5305257723
9282928120222.534430503-37294.5344305031
935000049516.4464480968483.553551903214
946945574293.0008387852-4838.00083878522
958406886818.0348348979-2750.03483489792
967619580210.9811114937-4015.98111149369
97114634132837.659224961-18203.659224961
98139357136309.2711848053047.72881519534
9911004492188.944778321817855.0552216782
100155118169016.047243062-13898.0472430622
1018306170620.661424145312440.3385758547
102127122128351.607362925-1229.6073629252
1034565331386.896888805214266.1031111948
1041963049704.5779383874-30074.5779383874
1056722959521.52308154997707.47691845008
10686060117468.767047637-31408.7670476366
1078800378602.847455089400.15254492001
1089581593248.04448107042566.95551892959
1098549987384.6221488459-1885.62214884594
1102722055070.9259861059-27850.9259861059
11110988282752.854350487127129.1456495129
1127257984498.9685941581-11919.9685941581
113584113433.3481775353-7592.34817753526
1146836976451.1705183994-8082.17051839941
1152461031766.3435720497-7156.34357204969
1163099555214.5416425401-24219.5416425401
117150662119229.93972705531432.0602729451
11866228802.89083274002-2180.89083274002
1199369476483.046616555217210.9533834448
1201315535140.5874980152-21985.5874980152
12111190878413.996921274333494.0030787257
1225755074719.1504334251-17169.1504334251
1231635638086.0760336509-21730.0760336509
1244017459953.3456458043-19779.3456458043
1251398318751.152520785-4768.15252078499
1265231655161.1176249405-2845.11762494047
1279958594108.3284128595476.67158714099
12886271101640.823361068-15369.823361068
12913101298445.017822704432566.9821772956
130130274111791.80628572418482.1937142756
131159051151850.0408409727200.95915902763
1327650670067.43048833336438.56951166672
1334914535149.553387839713995.4466121603
1346639876980.5843565173-10582.5843565173
135127546118699.1835163798846.81648362136
136680228594.7229830499-21792.7229830499
13799509108820.619128725-9311.61912872531
1384310656764.8036212604-13658.8036212604
13910830386603.967335199121699.0326648009
1406416780132.0727956947-15965.0727956947
141857941031.5941951547-32452.5941951547
1429781186400.325592732111410.6744072679
14384365130785.174887219-46420.1748872187
1441090126107.5730052713-15206.5730052713
1459134687531.28223544993814.71776455014
1463366059292.9451060833-25632.9451060833
1479363490469.6187706723164.38122932799
148109348134875.678724911-25527.678724911
14905972.76744903046-5972.76744903046
150795310859.7135050765-2906.71350507646
15105972.76744903046-5972.76744903046
15205972.76744903046-5972.76744903046
15305972.76744903046-5972.76744903046
15405972.76744903046-5972.76744903046
1556353869510.3649749004-5972.36497490043
15610828195779.658031035212501.3419689648
15705972.76744903046-5972.76744903046
15805972.76744903046-5972.76744903046
159424510316.4071837757-6071.40718377568
1602150916096.72779568485412.27220431519
16176708989.96286853986-1319.96286853986
1621064123989.3325480522-13348.3325480522
16305972.76744903046-5972.76744903046
1644124350816.8214628737-9573.82146287366







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.8169160463958720.3661679072082570.183083953604128
80.7007386708249350.598522658350130.299261329175065
90.57496307803040.8500738439391990.425036921969599
100.4497070583885450.899414116777090.550292941611455
110.3898837566750410.7797675133500830.610116243324959
120.3400856775115560.6801713550231130.659914322488444
130.2492216595504240.4984433191008490.750778340449576
140.3797260442226680.7594520884453370.620273955777332
150.2947932758551950.5895865517103910.705206724144805
160.2279547240542240.4559094481084490.772045275945776
170.1676906646720150.335381329344030.832309335327985
180.1240744361583870.2481488723167750.875925563841613
190.2600000630729910.5200001261459810.739999936927009
200.2487799845773710.4975599691547410.75122001542263
210.1971638320492930.3943276640985860.802836167950707
220.169502692801230.3390053856024610.83049730719877
230.1346426089221810.2692852178443610.865357391077819
240.185273723637790.370547447275580.81472627636221
250.2110246070040410.4220492140080830.788975392995959
260.1740609003070450.3481218006140890.825939099692955
270.1801729711502990.3603459423005980.819827028849701
280.1484994800444360.2969989600888720.851500519955564
290.113867898590520.2277357971810410.88613210140948
300.1571052323589660.3142104647179320.842894767641034
310.2715564171558130.5431128343116260.728443582844187
320.2835047954681680.5670095909363370.716495204531832
330.4766038799015190.9532077598030380.523396120098481
340.4249093000940480.8498186001880950.575090699905952
350.5335457500150390.9329084999699230.466454249984961
360.4933824699582140.9867649399164290.506617530041786
370.879015315108540.2419693697829210.12098468489146
380.850581728444770.2988365431104590.14941827155523
390.8245289588333020.3509420823333950.175471041166698
400.8201515451920850.3596969096158310.179848454807915
410.8364699560174990.3270600879650020.163530043982501
420.8249716356936650.350056728612670.175028364306335
430.8959764274519340.2080471450961330.104023572548066
440.8779170347544580.2441659304910840.122082965245542
450.8590376799176190.2819246401647610.140962320082381
460.8308305415209860.3383389169580280.169169458479014
470.8573984706936020.2852030586127970.142601529306398
480.8548469528032370.2903060943935250.145153047196763
490.8304340696171310.3391318607657380.169565930382869
500.829962517938450.3400749641231010.17003748206155
510.7992360615211460.4015278769577080.200763938478854
520.7642469088193860.4715061823612280.235753091180614
530.7284710607246610.5430578785506780.271528939275339
540.6925689200542480.6148621598915040.307431079945752
550.6550574387394680.6898851225210640.344942561260532
560.6130852657252920.7738294685494160.386914734274708
570.5732841415484720.8534317169030570.426715858451528
580.6839948980117950.632010203976410.316005101988205
590.6663941914593740.6672116170812530.333605808540626
600.641660524030710.716678951938580.35833947596929
610.7378985096125540.5242029807748930.262101490387446
620.7224941525971890.5550116948056220.277505847402811
630.6872041167630630.6255917664738730.312795883236937
640.6555467603190550.6889064793618910.344453239680945
650.6424069288424120.7151861423151770.357593071157588
660.6386904309531890.7226191380936230.361309569046811
670.6955703441831990.6088593116336030.304429655816801
680.6619630134494080.6760739731011830.338036986550592
690.7242858696866690.5514282606266620.275714130313331
700.6923902483944450.615219503211110.307609751605555
710.6548804168845840.6902391662308320.345119583115416
720.6194430021628710.7611139956742570.380556997837129
730.5846980012194250.8306039975611490.415301998780575
740.5855493821357980.8289012357284040.414450617864202
750.6415662758730140.7168674482539720.358433724126986
760.6069506354310820.7860987291378360.393049364568918
770.625344147084840.749311705830320.37465585291516
780.6769364953608110.6461270092783790.323063504639189
790.6870332553041310.6259334893917380.312966744695869
800.6541348527892020.6917302944215950.345865147210798
810.6190866476594270.7618267046811470.380913352340573
820.591073955585230.8178520888295390.40892604441477
830.6290068254289490.7419863491421020.370993174571051
840.657821757433510.684356485132980.34217824256649
850.6185387209416870.7629225581166250.381461279058313
860.5783658919448920.8432682161102160.421634108055108
870.6089929406203190.7820141187593620.391007059379681
880.6410454548999590.7179090902000820.358954545100041
890.8747508875377940.2504982249244120.125249112462206
900.9677273605737370.06454527885252560.0322726394262628
910.9642997825387330.07140043492253410.0357002174612671
920.9817669301444910.03646613971101870.0182330698555093
930.97638515128250.04722969743500080.0236148487175004
940.9695863499977510.06082730000449780.0304136500022489
950.960775333911070.07844933217786040.0392246660889302
960.9502284046433830.09954319071323420.0497715953566171
970.9479752511723490.1040494976553020.0520247488276509
980.935391305363640.1292173892727190.0646086946363596
990.9365647971454840.1268704057090310.0634352028545155
1000.9308844429643770.1382311140712450.0691155570356225
1010.9260868472939630.1478263054120740.0739131527060369
1020.9079959277995240.1840081444009520.092004072200476
1030.9059740132910930.1880519734178140.094025986708907
1040.9316929822783380.1366140354433250.0683070177216624
1050.9197743267572250.1604513464855490.0802256732427746
1060.9535925496699960.09281490066000910.0464074503300045
1070.9451272728443350.1097454543113310.0548727271556654
1080.9307346568317270.1385306863365460.0692653431682732
1090.9165963954155760.1668072091688480.0834036045844238
1100.9358620106282630.1282759787434730.0641379893717366
1110.9615350001684560.07692999966308880.0384649998315444
1120.9560591034018590.08788179319628210.043940896598141
1130.9456013280821790.1087973438356420.0543986719178208
1140.9335062138166890.1329875723666230.0664937861833114
1150.9180010523395640.1639978953208720.081998947660436
1160.9371079122455920.1257841755088160.0628920877544078
1170.9528240353831460.09435192923370820.0471759646168541
1180.9396385382136870.1207229235726250.0603614617863126
1190.9521756752150860.09564864956982780.0478243247849139
1200.9576104372190430.08477912556191480.0423895627809574
1210.9838105666532640.0323788666934720.016189433346736
1220.9796917441390140.04061651172197260.0203082558609863
1230.977285171391470.04542965721706020.0227148286085301
1240.9748855352487310.05022892950253790.025114464751269
1250.9656110132379610.06877797352407840.0343889867620392
1260.9538025750089190.09239484998216190.046197424991081
1270.9401167062416510.1197665875166970.0598832937583486
1280.9358490590590940.1283018818818120.064150940940906
1290.9767543689132850.04649126217342920.0232456310867146
1300.9840970086933190.03180598261336280.0159029913066814
1310.9900681552663250.01986368946735070.00993184473367535
1320.9873546109134560.02529077817308750.0126453890865437
1330.9897832743667670.02043345126646570.0102167256332329
1340.9916618037438560.01667639251228770.00833819625614387
1350.9898791319479510.02024173610409810.0101208680520491
1360.9886979482304780.02260410353904440.0113020517695222
1370.9828264133599010.03434717328019810.0171735866400991
1380.9775312738099090.04493745238018270.0224687261900914
1390.9898966156554340.02020676868913220.0101033843445661
1400.9875001312109580.02499973757808430.0124998687890422
1410.9850471560282460.0299056879435090.0149528439717545
1420.9924858569478160.01502828610436890.00751414305218444
1430.9999699228518296.0154296341945e-053.00771481709725e-05
1440.9999279635450840.000144072909832367.20364549161802e-05
1450.9999149378085230.0001701243829537988.50621914768988e-05
1460.999991149413551.77011728992095e-058.85058644960477e-06
1470.9999998266433913.46713218000892e-071.73356609000446e-07
1480.9999999366245361.26750927932891e-076.33754639664455e-08
1490.9999996678727156.64254570136151e-073.32127285068075e-07
1500.9999988085178512.3829642976573e-061.19148214882865e-06
1510.9999938538107721.22923784569274e-056.14618922846372e-06
1520.9999699497543266.01004913482587e-053.00502456741294e-05
1530.9998616834458150.0002766331083700540.000138316554185027
1540.9994061174009660.001187765198068470.000593882599034233
1550.9975018214639610.004996357072078380.00249817853603919
1560.9958006331574620.008398733685075770.00419936684253789
1570.9794085683350670.0411828633298670.0205914316649335

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.816916046395872 & 0.366167907208257 & 0.183083953604128 \tabularnewline
8 & 0.700738670824935 & 0.59852265835013 & 0.299261329175065 \tabularnewline
9 & 0.5749630780304 & 0.850073843939199 & 0.425036921969599 \tabularnewline
10 & 0.449707058388545 & 0.89941411677709 & 0.550292941611455 \tabularnewline
11 & 0.389883756675041 & 0.779767513350083 & 0.610116243324959 \tabularnewline
12 & 0.340085677511556 & 0.680171355023113 & 0.659914322488444 \tabularnewline
13 & 0.249221659550424 & 0.498443319100849 & 0.750778340449576 \tabularnewline
14 & 0.379726044222668 & 0.759452088445337 & 0.620273955777332 \tabularnewline
15 & 0.294793275855195 & 0.589586551710391 & 0.705206724144805 \tabularnewline
16 & 0.227954724054224 & 0.455909448108449 & 0.772045275945776 \tabularnewline
17 & 0.167690664672015 & 0.33538132934403 & 0.832309335327985 \tabularnewline
18 & 0.124074436158387 & 0.248148872316775 & 0.875925563841613 \tabularnewline
19 & 0.260000063072991 & 0.520000126145981 & 0.739999936927009 \tabularnewline
20 & 0.248779984577371 & 0.497559969154741 & 0.75122001542263 \tabularnewline
21 & 0.197163832049293 & 0.394327664098586 & 0.802836167950707 \tabularnewline
22 & 0.16950269280123 & 0.339005385602461 & 0.83049730719877 \tabularnewline
23 & 0.134642608922181 & 0.269285217844361 & 0.865357391077819 \tabularnewline
24 & 0.18527372363779 & 0.37054744727558 & 0.81472627636221 \tabularnewline
25 & 0.211024607004041 & 0.422049214008083 & 0.788975392995959 \tabularnewline
26 & 0.174060900307045 & 0.348121800614089 & 0.825939099692955 \tabularnewline
27 & 0.180172971150299 & 0.360345942300598 & 0.819827028849701 \tabularnewline
28 & 0.148499480044436 & 0.296998960088872 & 0.851500519955564 \tabularnewline
29 & 0.11386789859052 & 0.227735797181041 & 0.88613210140948 \tabularnewline
30 & 0.157105232358966 & 0.314210464717932 & 0.842894767641034 \tabularnewline
31 & 0.271556417155813 & 0.543112834311626 & 0.728443582844187 \tabularnewline
32 & 0.283504795468168 & 0.567009590936337 & 0.716495204531832 \tabularnewline
33 & 0.476603879901519 & 0.953207759803038 & 0.523396120098481 \tabularnewline
34 & 0.424909300094048 & 0.849818600188095 & 0.575090699905952 \tabularnewline
35 & 0.533545750015039 & 0.932908499969923 & 0.466454249984961 \tabularnewline
36 & 0.493382469958214 & 0.986764939916429 & 0.506617530041786 \tabularnewline
37 & 0.87901531510854 & 0.241969369782921 & 0.12098468489146 \tabularnewline
38 & 0.85058172844477 & 0.298836543110459 & 0.14941827155523 \tabularnewline
39 & 0.824528958833302 & 0.350942082333395 & 0.175471041166698 \tabularnewline
40 & 0.820151545192085 & 0.359696909615831 & 0.179848454807915 \tabularnewline
41 & 0.836469956017499 & 0.327060087965002 & 0.163530043982501 \tabularnewline
42 & 0.824971635693665 & 0.35005672861267 & 0.175028364306335 \tabularnewline
43 & 0.895976427451934 & 0.208047145096133 & 0.104023572548066 \tabularnewline
44 & 0.877917034754458 & 0.244165930491084 & 0.122082965245542 \tabularnewline
45 & 0.859037679917619 & 0.281924640164761 & 0.140962320082381 \tabularnewline
46 & 0.830830541520986 & 0.338338916958028 & 0.169169458479014 \tabularnewline
47 & 0.857398470693602 & 0.285203058612797 & 0.142601529306398 \tabularnewline
48 & 0.854846952803237 & 0.290306094393525 & 0.145153047196763 \tabularnewline
49 & 0.830434069617131 & 0.339131860765738 & 0.169565930382869 \tabularnewline
50 & 0.82996251793845 & 0.340074964123101 & 0.17003748206155 \tabularnewline
51 & 0.799236061521146 & 0.401527876957708 & 0.200763938478854 \tabularnewline
52 & 0.764246908819386 & 0.471506182361228 & 0.235753091180614 \tabularnewline
53 & 0.728471060724661 & 0.543057878550678 & 0.271528939275339 \tabularnewline
54 & 0.692568920054248 & 0.614862159891504 & 0.307431079945752 \tabularnewline
55 & 0.655057438739468 & 0.689885122521064 & 0.344942561260532 \tabularnewline
56 & 0.613085265725292 & 0.773829468549416 & 0.386914734274708 \tabularnewline
57 & 0.573284141548472 & 0.853431716903057 & 0.426715858451528 \tabularnewline
58 & 0.683994898011795 & 0.63201020397641 & 0.316005101988205 \tabularnewline
59 & 0.666394191459374 & 0.667211617081253 & 0.333605808540626 \tabularnewline
60 & 0.64166052403071 & 0.71667895193858 & 0.35833947596929 \tabularnewline
61 & 0.737898509612554 & 0.524202980774893 & 0.262101490387446 \tabularnewline
62 & 0.722494152597189 & 0.555011694805622 & 0.277505847402811 \tabularnewline
63 & 0.687204116763063 & 0.625591766473873 & 0.312795883236937 \tabularnewline
64 & 0.655546760319055 & 0.688906479361891 & 0.344453239680945 \tabularnewline
65 & 0.642406928842412 & 0.715186142315177 & 0.357593071157588 \tabularnewline
66 & 0.638690430953189 & 0.722619138093623 & 0.361309569046811 \tabularnewline
67 & 0.695570344183199 & 0.608859311633603 & 0.304429655816801 \tabularnewline
68 & 0.661963013449408 & 0.676073973101183 & 0.338036986550592 \tabularnewline
69 & 0.724285869686669 & 0.551428260626662 & 0.275714130313331 \tabularnewline
70 & 0.692390248394445 & 0.61521950321111 & 0.307609751605555 \tabularnewline
71 & 0.654880416884584 & 0.690239166230832 & 0.345119583115416 \tabularnewline
72 & 0.619443002162871 & 0.761113995674257 & 0.380556997837129 \tabularnewline
73 & 0.584698001219425 & 0.830603997561149 & 0.415301998780575 \tabularnewline
74 & 0.585549382135798 & 0.828901235728404 & 0.414450617864202 \tabularnewline
75 & 0.641566275873014 & 0.716867448253972 & 0.358433724126986 \tabularnewline
76 & 0.606950635431082 & 0.786098729137836 & 0.393049364568918 \tabularnewline
77 & 0.62534414708484 & 0.74931170583032 & 0.37465585291516 \tabularnewline
78 & 0.676936495360811 & 0.646127009278379 & 0.323063504639189 \tabularnewline
79 & 0.687033255304131 & 0.625933489391738 & 0.312966744695869 \tabularnewline
80 & 0.654134852789202 & 0.691730294421595 & 0.345865147210798 \tabularnewline
81 & 0.619086647659427 & 0.761826704681147 & 0.380913352340573 \tabularnewline
82 & 0.59107395558523 & 0.817852088829539 & 0.40892604441477 \tabularnewline
83 & 0.629006825428949 & 0.741986349142102 & 0.370993174571051 \tabularnewline
84 & 0.65782175743351 & 0.68435648513298 & 0.34217824256649 \tabularnewline
85 & 0.618538720941687 & 0.762922558116625 & 0.381461279058313 \tabularnewline
86 & 0.578365891944892 & 0.843268216110216 & 0.421634108055108 \tabularnewline
87 & 0.608992940620319 & 0.782014118759362 & 0.391007059379681 \tabularnewline
88 & 0.641045454899959 & 0.717909090200082 & 0.358954545100041 \tabularnewline
89 & 0.874750887537794 & 0.250498224924412 & 0.125249112462206 \tabularnewline
90 & 0.967727360573737 & 0.0645452788525256 & 0.0322726394262628 \tabularnewline
91 & 0.964299782538733 & 0.0714004349225341 & 0.0357002174612671 \tabularnewline
92 & 0.981766930144491 & 0.0364661397110187 & 0.0182330698555093 \tabularnewline
93 & 0.9763851512825 & 0.0472296974350008 & 0.0236148487175004 \tabularnewline
94 & 0.969586349997751 & 0.0608273000044978 & 0.0304136500022489 \tabularnewline
95 & 0.96077533391107 & 0.0784493321778604 & 0.0392246660889302 \tabularnewline
96 & 0.950228404643383 & 0.0995431907132342 & 0.0497715953566171 \tabularnewline
97 & 0.947975251172349 & 0.104049497655302 & 0.0520247488276509 \tabularnewline
98 & 0.93539130536364 & 0.129217389272719 & 0.0646086946363596 \tabularnewline
99 & 0.936564797145484 & 0.126870405709031 & 0.0634352028545155 \tabularnewline
100 & 0.930884442964377 & 0.138231114071245 & 0.0691155570356225 \tabularnewline
101 & 0.926086847293963 & 0.147826305412074 & 0.0739131527060369 \tabularnewline
102 & 0.907995927799524 & 0.184008144400952 & 0.092004072200476 \tabularnewline
103 & 0.905974013291093 & 0.188051973417814 & 0.094025986708907 \tabularnewline
104 & 0.931692982278338 & 0.136614035443325 & 0.0683070177216624 \tabularnewline
105 & 0.919774326757225 & 0.160451346485549 & 0.0802256732427746 \tabularnewline
106 & 0.953592549669996 & 0.0928149006600091 & 0.0464074503300045 \tabularnewline
107 & 0.945127272844335 & 0.109745454311331 & 0.0548727271556654 \tabularnewline
108 & 0.930734656831727 & 0.138530686336546 & 0.0692653431682732 \tabularnewline
109 & 0.916596395415576 & 0.166807209168848 & 0.0834036045844238 \tabularnewline
110 & 0.935862010628263 & 0.128275978743473 & 0.0641379893717366 \tabularnewline
111 & 0.961535000168456 & 0.0769299996630888 & 0.0384649998315444 \tabularnewline
112 & 0.956059103401859 & 0.0878817931962821 & 0.043940896598141 \tabularnewline
113 & 0.945601328082179 & 0.108797343835642 & 0.0543986719178208 \tabularnewline
114 & 0.933506213816689 & 0.132987572366623 & 0.0664937861833114 \tabularnewline
115 & 0.918001052339564 & 0.163997895320872 & 0.081998947660436 \tabularnewline
116 & 0.937107912245592 & 0.125784175508816 & 0.0628920877544078 \tabularnewline
117 & 0.952824035383146 & 0.0943519292337082 & 0.0471759646168541 \tabularnewline
118 & 0.939638538213687 & 0.120722923572625 & 0.0603614617863126 \tabularnewline
119 & 0.952175675215086 & 0.0956486495698278 & 0.0478243247849139 \tabularnewline
120 & 0.957610437219043 & 0.0847791255619148 & 0.0423895627809574 \tabularnewline
121 & 0.983810566653264 & 0.032378866693472 & 0.016189433346736 \tabularnewline
122 & 0.979691744139014 & 0.0406165117219726 & 0.0203082558609863 \tabularnewline
123 & 0.97728517139147 & 0.0454296572170602 & 0.0227148286085301 \tabularnewline
124 & 0.974885535248731 & 0.0502289295025379 & 0.025114464751269 \tabularnewline
125 & 0.965611013237961 & 0.0687779735240784 & 0.0343889867620392 \tabularnewline
126 & 0.953802575008919 & 0.0923948499821619 & 0.046197424991081 \tabularnewline
127 & 0.940116706241651 & 0.119766587516697 & 0.0598832937583486 \tabularnewline
128 & 0.935849059059094 & 0.128301881881812 & 0.064150940940906 \tabularnewline
129 & 0.976754368913285 & 0.0464912621734292 & 0.0232456310867146 \tabularnewline
130 & 0.984097008693319 & 0.0318059826133628 & 0.0159029913066814 \tabularnewline
131 & 0.990068155266325 & 0.0198636894673507 & 0.00993184473367535 \tabularnewline
132 & 0.987354610913456 & 0.0252907781730875 & 0.0126453890865437 \tabularnewline
133 & 0.989783274366767 & 0.0204334512664657 & 0.0102167256332329 \tabularnewline
134 & 0.991661803743856 & 0.0166763925122877 & 0.00833819625614387 \tabularnewline
135 & 0.989879131947951 & 0.0202417361040981 & 0.0101208680520491 \tabularnewline
136 & 0.988697948230478 & 0.0226041035390444 & 0.0113020517695222 \tabularnewline
137 & 0.982826413359901 & 0.0343471732801981 & 0.0171735866400991 \tabularnewline
138 & 0.977531273809909 & 0.0449374523801827 & 0.0224687261900914 \tabularnewline
139 & 0.989896615655434 & 0.0202067686891322 & 0.0101033843445661 \tabularnewline
140 & 0.987500131210958 & 0.0249997375780843 & 0.0124998687890422 \tabularnewline
141 & 0.985047156028246 & 0.029905687943509 & 0.0149528439717545 \tabularnewline
142 & 0.992485856947816 & 0.0150282861043689 & 0.00751414305218444 \tabularnewline
143 & 0.999969922851829 & 6.0154296341945e-05 & 3.00771481709725e-05 \tabularnewline
144 & 0.999927963545084 & 0.00014407290983236 & 7.20364549161802e-05 \tabularnewline
145 & 0.999914937808523 & 0.000170124382953798 & 8.50621914768988e-05 \tabularnewline
146 & 0.99999114941355 & 1.77011728992095e-05 & 8.85058644960477e-06 \tabularnewline
147 & 0.999999826643391 & 3.46713218000892e-07 & 1.73356609000446e-07 \tabularnewline
148 & 0.999999936624536 & 1.26750927932891e-07 & 6.33754639664455e-08 \tabularnewline
149 & 0.999999667872715 & 6.64254570136151e-07 & 3.32127285068075e-07 \tabularnewline
150 & 0.999998808517851 & 2.3829642976573e-06 & 1.19148214882865e-06 \tabularnewline
151 & 0.999993853810772 & 1.22923784569274e-05 & 6.14618922846372e-06 \tabularnewline
152 & 0.999969949754326 & 6.01004913482587e-05 & 3.00502456741294e-05 \tabularnewline
153 & 0.999861683445815 & 0.000276633108370054 & 0.000138316554185027 \tabularnewline
154 & 0.999406117400966 & 0.00118776519806847 & 0.000593882599034233 \tabularnewline
155 & 0.997501821463961 & 0.00499635707207838 & 0.00249817853603919 \tabularnewline
156 & 0.995800633157462 & 0.00839873368507577 & 0.00419936684253789 \tabularnewline
157 & 0.979408568335067 & 0.041182863329867 & 0.0205914316649335 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146229&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]7[/C][C]0.816916046395872[/C][C]0.366167907208257[/C][C]0.183083953604128[/C][/ROW]
[ROW][C]8[/C][C]0.700738670824935[/C][C]0.59852265835013[/C][C]0.299261329175065[/C][/ROW]
[ROW][C]9[/C][C]0.5749630780304[/C][C]0.850073843939199[/C][C]0.425036921969599[/C][/ROW]
[ROW][C]10[/C][C]0.449707058388545[/C][C]0.89941411677709[/C][C]0.550292941611455[/C][/ROW]
[ROW][C]11[/C][C]0.389883756675041[/C][C]0.779767513350083[/C][C]0.610116243324959[/C][/ROW]
[ROW][C]12[/C][C]0.340085677511556[/C][C]0.680171355023113[/C][C]0.659914322488444[/C][/ROW]
[ROW][C]13[/C][C]0.249221659550424[/C][C]0.498443319100849[/C][C]0.750778340449576[/C][/ROW]
[ROW][C]14[/C][C]0.379726044222668[/C][C]0.759452088445337[/C][C]0.620273955777332[/C][/ROW]
[ROW][C]15[/C][C]0.294793275855195[/C][C]0.589586551710391[/C][C]0.705206724144805[/C][/ROW]
[ROW][C]16[/C][C]0.227954724054224[/C][C]0.455909448108449[/C][C]0.772045275945776[/C][/ROW]
[ROW][C]17[/C][C]0.167690664672015[/C][C]0.33538132934403[/C][C]0.832309335327985[/C][/ROW]
[ROW][C]18[/C][C]0.124074436158387[/C][C]0.248148872316775[/C][C]0.875925563841613[/C][/ROW]
[ROW][C]19[/C][C]0.260000063072991[/C][C]0.520000126145981[/C][C]0.739999936927009[/C][/ROW]
[ROW][C]20[/C][C]0.248779984577371[/C][C]0.497559969154741[/C][C]0.75122001542263[/C][/ROW]
[ROW][C]21[/C][C]0.197163832049293[/C][C]0.394327664098586[/C][C]0.802836167950707[/C][/ROW]
[ROW][C]22[/C][C]0.16950269280123[/C][C]0.339005385602461[/C][C]0.83049730719877[/C][/ROW]
[ROW][C]23[/C][C]0.134642608922181[/C][C]0.269285217844361[/C][C]0.865357391077819[/C][/ROW]
[ROW][C]24[/C][C]0.18527372363779[/C][C]0.37054744727558[/C][C]0.81472627636221[/C][/ROW]
[ROW][C]25[/C][C]0.211024607004041[/C][C]0.422049214008083[/C][C]0.788975392995959[/C][/ROW]
[ROW][C]26[/C][C]0.174060900307045[/C][C]0.348121800614089[/C][C]0.825939099692955[/C][/ROW]
[ROW][C]27[/C][C]0.180172971150299[/C][C]0.360345942300598[/C][C]0.819827028849701[/C][/ROW]
[ROW][C]28[/C][C]0.148499480044436[/C][C]0.296998960088872[/C][C]0.851500519955564[/C][/ROW]
[ROW][C]29[/C][C]0.11386789859052[/C][C]0.227735797181041[/C][C]0.88613210140948[/C][/ROW]
[ROW][C]30[/C][C]0.157105232358966[/C][C]0.314210464717932[/C][C]0.842894767641034[/C][/ROW]
[ROW][C]31[/C][C]0.271556417155813[/C][C]0.543112834311626[/C][C]0.728443582844187[/C][/ROW]
[ROW][C]32[/C][C]0.283504795468168[/C][C]0.567009590936337[/C][C]0.716495204531832[/C][/ROW]
[ROW][C]33[/C][C]0.476603879901519[/C][C]0.953207759803038[/C][C]0.523396120098481[/C][/ROW]
[ROW][C]34[/C][C]0.424909300094048[/C][C]0.849818600188095[/C][C]0.575090699905952[/C][/ROW]
[ROW][C]35[/C][C]0.533545750015039[/C][C]0.932908499969923[/C][C]0.466454249984961[/C][/ROW]
[ROW][C]36[/C][C]0.493382469958214[/C][C]0.986764939916429[/C][C]0.506617530041786[/C][/ROW]
[ROW][C]37[/C][C]0.87901531510854[/C][C]0.241969369782921[/C][C]0.12098468489146[/C][/ROW]
[ROW][C]38[/C][C]0.85058172844477[/C][C]0.298836543110459[/C][C]0.14941827155523[/C][/ROW]
[ROW][C]39[/C][C]0.824528958833302[/C][C]0.350942082333395[/C][C]0.175471041166698[/C][/ROW]
[ROW][C]40[/C][C]0.820151545192085[/C][C]0.359696909615831[/C][C]0.179848454807915[/C][/ROW]
[ROW][C]41[/C][C]0.836469956017499[/C][C]0.327060087965002[/C][C]0.163530043982501[/C][/ROW]
[ROW][C]42[/C][C]0.824971635693665[/C][C]0.35005672861267[/C][C]0.175028364306335[/C][/ROW]
[ROW][C]43[/C][C]0.895976427451934[/C][C]0.208047145096133[/C][C]0.104023572548066[/C][/ROW]
[ROW][C]44[/C][C]0.877917034754458[/C][C]0.244165930491084[/C][C]0.122082965245542[/C][/ROW]
[ROW][C]45[/C][C]0.859037679917619[/C][C]0.281924640164761[/C][C]0.140962320082381[/C][/ROW]
[ROW][C]46[/C][C]0.830830541520986[/C][C]0.338338916958028[/C][C]0.169169458479014[/C][/ROW]
[ROW][C]47[/C][C]0.857398470693602[/C][C]0.285203058612797[/C][C]0.142601529306398[/C][/ROW]
[ROW][C]48[/C][C]0.854846952803237[/C][C]0.290306094393525[/C][C]0.145153047196763[/C][/ROW]
[ROW][C]49[/C][C]0.830434069617131[/C][C]0.339131860765738[/C][C]0.169565930382869[/C][/ROW]
[ROW][C]50[/C][C]0.82996251793845[/C][C]0.340074964123101[/C][C]0.17003748206155[/C][/ROW]
[ROW][C]51[/C][C]0.799236061521146[/C][C]0.401527876957708[/C][C]0.200763938478854[/C][/ROW]
[ROW][C]52[/C][C]0.764246908819386[/C][C]0.471506182361228[/C][C]0.235753091180614[/C][/ROW]
[ROW][C]53[/C][C]0.728471060724661[/C][C]0.543057878550678[/C][C]0.271528939275339[/C][/ROW]
[ROW][C]54[/C][C]0.692568920054248[/C][C]0.614862159891504[/C][C]0.307431079945752[/C][/ROW]
[ROW][C]55[/C][C]0.655057438739468[/C][C]0.689885122521064[/C][C]0.344942561260532[/C][/ROW]
[ROW][C]56[/C][C]0.613085265725292[/C][C]0.773829468549416[/C][C]0.386914734274708[/C][/ROW]
[ROW][C]57[/C][C]0.573284141548472[/C][C]0.853431716903057[/C][C]0.426715858451528[/C][/ROW]
[ROW][C]58[/C][C]0.683994898011795[/C][C]0.63201020397641[/C][C]0.316005101988205[/C][/ROW]
[ROW][C]59[/C][C]0.666394191459374[/C][C]0.667211617081253[/C][C]0.333605808540626[/C][/ROW]
[ROW][C]60[/C][C]0.64166052403071[/C][C]0.71667895193858[/C][C]0.35833947596929[/C][/ROW]
[ROW][C]61[/C][C]0.737898509612554[/C][C]0.524202980774893[/C][C]0.262101490387446[/C][/ROW]
[ROW][C]62[/C][C]0.722494152597189[/C][C]0.555011694805622[/C][C]0.277505847402811[/C][/ROW]
[ROW][C]63[/C][C]0.687204116763063[/C][C]0.625591766473873[/C][C]0.312795883236937[/C][/ROW]
[ROW][C]64[/C][C]0.655546760319055[/C][C]0.688906479361891[/C][C]0.344453239680945[/C][/ROW]
[ROW][C]65[/C][C]0.642406928842412[/C][C]0.715186142315177[/C][C]0.357593071157588[/C][/ROW]
[ROW][C]66[/C][C]0.638690430953189[/C][C]0.722619138093623[/C][C]0.361309569046811[/C][/ROW]
[ROW][C]67[/C][C]0.695570344183199[/C][C]0.608859311633603[/C][C]0.304429655816801[/C][/ROW]
[ROW][C]68[/C][C]0.661963013449408[/C][C]0.676073973101183[/C][C]0.338036986550592[/C][/ROW]
[ROW][C]69[/C][C]0.724285869686669[/C][C]0.551428260626662[/C][C]0.275714130313331[/C][/ROW]
[ROW][C]70[/C][C]0.692390248394445[/C][C]0.61521950321111[/C][C]0.307609751605555[/C][/ROW]
[ROW][C]71[/C][C]0.654880416884584[/C][C]0.690239166230832[/C][C]0.345119583115416[/C][/ROW]
[ROW][C]72[/C][C]0.619443002162871[/C][C]0.761113995674257[/C][C]0.380556997837129[/C][/ROW]
[ROW][C]73[/C][C]0.584698001219425[/C][C]0.830603997561149[/C][C]0.415301998780575[/C][/ROW]
[ROW][C]74[/C][C]0.585549382135798[/C][C]0.828901235728404[/C][C]0.414450617864202[/C][/ROW]
[ROW][C]75[/C][C]0.641566275873014[/C][C]0.716867448253972[/C][C]0.358433724126986[/C][/ROW]
[ROW][C]76[/C][C]0.606950635431082[/C][C]0.786098729137836[/C][C]0.393049364568918[/C][/ROW]
[ROW][C]77[/C][C]0.62534414708484[/C][C]0.74931170583032[/C][C]0.37465585291516[/C][/ROW]
[ROW][C]78[/C][C]0.676936495360811[/C][C]0.646127009278379[/C][C]0.323063504639189[/C][/ROW]
[ROW][C]79[/C][C]0.687033255304131[/C][C]0.625933489391738[/C][C]0.312966744695869[/C][/ROW]
[ROW][C]80[/C][C]0.654134852789202[/C][C]0.691730294421595[/C][C]0.345865147210798[/C][/ROW]
[ROW][C]81[/C][C]0.619086647659427[/C][C]0.761826704681147[/C][C]0.380913352340573[/C][/ROW]
[ROW][C]82[/C][C]0.59107395558523[/C][C]0.817852088829539[/C][C]0.40892604441477[/C][/ROW]
[ROW][C]83[/C][C]0.629006825428949[/C][C]0.741986349142102[/C][C]0.370993174571051[/C][/ROW]
[ROW][C]84[/C][C]0.65782175743351[/C][C]0.68435648513298[/C][C]0.34217824256649[/C][/ROW]
[ROW][C]85[/C][C]0.618538720941687[/C][C]0.762922558116625[/C][C]0.381461279058313[/C][/ROW]
[ROW][C]86[/C][C]0.578365891944892[/C][C]0.843268216110216[/C][C]0.421634108055108[/C][/ROW]
[ROW][C]87[/C][C]0.608992940620319[/C][C]0.782014118759362[/C][C]0.391007059379681[/C][/ROW]
[ROW][C]88[/C][C]0.641045454899959[/C][C]0.717909090200082[/C][C]0.358954545100041[/C][/ROW]
[ROW][C]89[/C][C]0.874750887537794[/C][C]0.250498224924412[/C][C]0.125249112462206[/C][/ROW]
[ROW][C]90[/C][C]0.967727360573737[/C][C]0.0645452788525256[/C][C]0.0322726394262628[/C][/ROW]
[ROW][C]91[/C][C]0.964299782538733[/C][C]0.0714004349225341[/C][C]0.0357002174612671[/C][/ROW]
[ROW][C]92[/C][C]0.981766930144491[/C][C]0.0364661397110187[/C][C]0.0182330698555093[/C][/ROW]
[ROW][C]93[/C][C]0.9763851512825[/C][C]0.0472296974350008[/C][C]0.0236148487175004[/C][/ROW]
[ROW][C]94[/C][C]0.969586349997751[/C][C]0.0608273000044978[/C][C]0.0304136500022489[/C][/ROW]
[ROW][C]95[/C][C]0.96077533391107[/C][C]0.0784493321778604[/C][C]0.0392246660889302[/C][/ROW]
[ROW][C]96[/C][C]0.950228404643383[/C][C]0.0995431907132342[/C][C]0.0497715953566171[/C][/ROW]
[ROW][C]97[/C][C]0.947975251172349[/C][C]0.104049497655302[/C][C]0.0520247488276509[/C][/ROW]
[ROW][C]98[/C][C]0.93539130536364[/C][C]0.129217389272719[/C][C]0.0646086946363596[/C][/ROW]
[ROW][C]99[/C][C]0.936564797145484[/C][C]0.126870405709031[/C][C]0.0634352028545155[/C][/ROW]
[ROW][C]100[/C][C]0.930884442964377[/C][C]0.138231114071245[/C][C]0.0691155570356225[/C][/ROW]
[ROW][C]101[/C][C]0.926086847293963[/C][C]0.147826305412074[/C][C]0.0739131527060369[/C][/ROW]
[ROW][C]102[/C][C]0.907995927799524[/C][C]0.184008144400952[/C][C]0.092004072200476[/C][/ROW]
[ROW][C]103[/C][C]0.905974013291093[/C][C]0.188051973417814[/C][C]0.094025986708907[/C][/ROW]
[ROW][C]104[/C][C]0.931692982278338[/C][C]0.136614035443325[/C][C]0.0683070177216624[/C][/ROW]
[ROW][C]105[/C][C]0.919774326757225[/C][C]0.160451346485549[/C][C]0.0802256732427746[/C][/ROW]
[ROW][C]106[/C][C]0.953592549669996[/C][C]0.0928149006600091[/C][C]0.0464074503300045[/C][/ROW]
[ROW][C]107[/C][C]0.945127272844335[/C][C]0.109745454311331[/C][C]0.0548727271556654[/C][/ROW]
[ROW][C]108[/C][C]0.930734656831727[/C][C]0.138530686336546[/C][C]0.0692653431682732[/C][/ROW]
[ROW][C]109[/C][C]0.916596395415576[/C][C]0.166807209168848[/C][C]0.0834036045844238[/C][/ROW]
[ROW][C]110[/C][C]0.935862010628263[/C][C]0.128275978743473[/C][C]0.0641379893717366[/C][/ROW]
[ROW][C]111[/C][C]0.961535000168456[/C][C]0.0769299996630888[/C][C]0.0384649998315444[/C][/ROW]
[ROW][C]112[/C][C]0.956059103401859[/C][C]0.0878817931962821[/C][C]0.043940896598141[/C][/ROW]
[ROW][C]113[/C][C]0.945601328082179[/C][C]0.108797343835642[/C][C]0.0543986719178208[/C][/ROW]
[ROW][C]114[/C][C]0.933506213816689[/C][C]0.132987572366623[/C][C]0.0664937861833114[/C][/ROW]
[ROW][C]115[/C][C]0.918001052339564[/C][C]0.163997895320872[/C][C]0.081998947660436[/C][/ROW]
[ROW][C]116[/C][C]0.937107912245592[/C][C]0.125784175508816[/C][C]0.0628920877544078[/C][/ROW]
[ROW][C]117[/C][C]0.952824035383146[/C][C]0.0943519292337082[/C][C]0.0471759646168541[/C][/ROW]
[ROW][C]118[/C][C]0.939638538213687[/C][C]0.120722923572625[/C][C]0.0603614617863126[/C][/ROW]
[ROW][C]119[/C][C]0.952175675215086[/C][C]0.0956486495698278[/C][C]0.0478243247849139[/C][/ROW]
[ROW][C]120[/C][C]0.957610437219043[/C][C]0.0847791255619148[/C][C]0.0423895627809574[/C][/ROW]
[ROW][C]121[/C][C]0.983810566653264[/C][C]0.032378866693472[/C][C]0.016189433346736[/C][/ROW]
[ROW][C]122[/C][C]0.979691744139014[/C][C]0.0406165117219726[/C][C]0.0203082558609863[/C][/ROW]
[ROW][C]123[/C][C]0.97728517139147[/C][C]0.0454296572170602[/C][C]0.0227148286085301[/C][/ROW]
[ROW][C]124[/C][C]0.974885535248731[/C][C]0.0502289295025379[/C][C]0.025114464751269[/C][/ROW]
[ROW][C]125[/C][C]0.965611013237961[/C][C]0.0687779735240784[/C][C]0.0343889867620392[/C][/ROW]
[ROW][C]126[/C][C]0.953802575008919[/C][C]0.0923948499821619[/C][C]0.046197424991081[/C][/ROW]
[ROW][C]127[/C][C]0.940116706241651[/C][C]0.119766587516697[/C][C]0.0598832937583486[/C][/ROW]
[ROW][C]128[/C][C]0.935849059059094[/C][C]0.128301881881812[/C][C]0.064150940940906[/C][/ROW]
[ROW][C]129[/C][C]0.976754368913285[/C][C]0.0464912621734292[/C][C]0.0232456310867146[/C][/ROW]
[ROW][C]130[/C][C]0.984097008693319[/C][C]0.0318059826133628[/C][C]0.0159029913066814[/C][/ROW]
[ROW][C]131[/C][C]0.990068155266325[/C][C]0.0198636894673507[/C][C]0.00993184473367535[/C][/ROW]
[ROW][C]132[/C][C]0.987354610913456[/C][C]0.0252907781730875[/C][C]0.0126453890865437[/C][/ROW]
[ROW][C]133[/C][C]0.989783274366767[/C][C]0.0204334512664657[/C][C]0.0102167256332329[/C][/ROW]
[ROW][C]134[/C][C]0.991661803743856[/C][C]0.0166763925122877[/C][C]0.00833819625614387[/C][/ROW]
[ROW][C]135[/C][C]0.989879131947951[/C][C]0.0202417361040981[/C][C]0.0101208680520491[/C][/ROW]
[ROW][C]136[/C][C]0.988697948230478[/C][C]0.0226041035390444[/C][C]0.0113020517695222[/C][/ROW]
[ROW][C]137[/C][C]0.982826413359901[/C][C]0.0343471732801981[/C][C]0.0171735866400991[/C][/ROW]
[ROW][C]138[/C][C]0.977531273809909[/C][C]0.0449374523801827[/C][C]0.0224687261900914[/C][/ROW]
[ROW][C]139[/C][C]0.989896615655434[/C][C]0.0202067686891322[/C][C]0.0101033843445661[/C][/ROW]
[ROW][C]140[/C][C]0.987500131210958[/C][C]0.0249997375780843[/C][C]0.0124998687890422[/C][/ROW]
[ROW][C]141[/C][C]0.985047156028246[/C][C]0.029905687943509[/C][C]0.0149528439717545[/C][/ROW]
[ROW][C]142[/C][C]0.992485856947816[/C][C]0.0150282861043689[/C][C]0.00751414305218444[/C][/ROW]
[ROW][C]143[/C][C]0.999969922851829[/C][C]6.0154296341945e-05[/C][C]3.00771481709725e-05[/C][/ROW]
[ROW][C]144[/C][C]0.999927963545084[/C][C]0.00014407290983236[/C][C]7.20364549161802e-05[/C][/ROW]
[ROW][C]145[/C][C]0.999914937808523[/C][C]0.000170124382953798[/C][C]8.50621914768988e-05[/C][/ROW]
[ROW][C]146[/C][C]0.99999114941355[/C][C]1.77011728992095e-05[/C][C]8.85058644960477e-06[/C][/ROW]
[ROW][C]147[/C][C]0.999999826643391[/C][C]3.46713218000892e-07[/C][C]1.73356609000446e-07[/C][/ROW]
[ROW][C]148[/C][C]0.999999936624536[/C][C]1.26750927932891e-07[/C][C]6.33754639664455e-08[/C][/ROW]
[ROW][C]149[/C][C]0.999999667872715[/C][C]6.64254570136151e-07[/C][C]3.32127285068075e-07[/C][/ROW]
[ROW][C]150[/C][C]0.999998808517851[/C][C]2.3829642976573e-06[/C][C]1.19148214882865e-06[/C][/ROW]
[ROW][C]151[/C][C]0.999993853810772[/C][C]1.22923784569274e-05[/C][C]6.14618922846372e-06[/C][/ROW]
[ROW][C]152[/C][C]0.999969949754326[/C][C]6.01004913482587e-05[/C][C]3.00502456741294e-05[/C][/ROW]
[ROW][C]153[/C][C]0.999861683445815[/C][C]0.000276633108370054[/C][C]0.000138316554185027[/C][/ROW]
[ROW][C]154[/C][C]0.999406117400966[/C][C]0.00118776519806847[/C][C]0.000593882599034233[/C][/ROW]
[ROW][C]155[/C][C]0.997501821463961[/C][C]0.00499635707207838[/C][C]0.00249817853603919[/C][/ROW]
[ROW][C]156[/C][C]0.995800633157462[/C][C]0.00839873368507577[/C][C]0.00419936684253789[/C][/ROW]
[ROW][C]157[/C][C]0.979408568335067[/C][C]0.041182863329867[/C][C]0.0205914316649335[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146229&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146229&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.8169160463958720.3661679072082570.183083953604128
80.7007386708249350.598522658350130.299261329175065
90.57496307803040.8500738439391990.425036921969599
100.4497070583885450.899414116777090.550292941611455
110.3898837566750410.7797675133500830.610116243324959
120.3400856775115560.6801713550231130.659914322488444
130.2492216595504240.4984433191008490.750778340449576
140.3797260442226680.7594520884453370.620273955777332
150.2947932758551950.5895865517103910.705206724144805
160.2279547240542240.4559094481084490.772045275945776
170.1676906646720150.335381329344030.832309335327985
180.1240744361583870.2481488723167750.875925563841613
190.2600000630729910.5200001261459810.739999936927009
200.2487799845773710.4975599691547410.75122001542263
210.1971638320492930.3943276640985860.802836167950707
220.169502692801230.3390053856024610.83049730719877
230.1346426089221810.2692852178443610.865357391077819
240.185273723637790.370547447275580.81472627636221
250.2110246070040410.4220492140080830.788975392995959
260.1740609003070450.3481218006140890.825939099692955
270.1801729711502990.3603459423005980.819827028849701
280.1484994800444360.2969989600888720.851500519955564
290.113867898590520.2277357971810410.88613210140948
300.1571052323589660.3142104647179320.842894767641034
310.2715564171558130.5431128343116260.728443582844187
320.2835047954681680.5670095909363370.716495204531832
330.4766038799015190.9532077598030380.523396120098481
340.4249093000940480.8498186001880950.575090699905952
350.5335457500150390.9329084999699230.466454249984961
360.4933824699582140.9867649399164290.506617530041786
370.879015315108540.2419693697829210.12098468489146
380.850581728444770.2988365431104590.14941827155523
390.8245289588333020.3509420823333950.175471041166698
400.8201515451920850.3596969096158310.179848454807915
410.8364699560174990.3270600879650020.163530043982501
420.8249716356936650.350056728612670.175028364306335
430.8959764274519340.2080471450961330.104023572548066
440.8779170347544580.2441659304910840.122082965245542
450.8590376799176190.2819246401647610.140962320082381
460.8308305415209860.3383389169580280.169169458479014
470.8573984706936020.2852030586127970.142601529306398
480.8548469528032370.2903060943935250.145153047196763
490.8304340696171310.3391318607657380.169565930382869
500.829962517938450.3400749641231010.17003748206155
510.7992360615211460.4015278769577080.200763938478854
520.7642469088193860.4715061823612280.235753091180614
530.7284710607246610.5430578785506780.271528939275339
540.6925689200542480.6148621598915040.307431079945752
550.6550574387394680.6898851225210640.344942561260532
560.6130852657252920.7738294685494160.386914734274708
570.5732841415484720.8534317169030570.426715858451528
580.6839948980117950.632010203976410.316005101988205
590.6663941914593740.6672116170812530.333605808540626
600.641660524030710.716678951938580.35833947596929
610.7378985096125540.5242029807748930.262101490387446
620.7224941525971890.5550116948056220.277505847402811
630.6872041167630630.6255917664738730.312795883236937
640.6555467603190550.6889064793618910.344453239680945
650.6424069288424120.7151861423151770.357593071157588
660.6386904309531890.7226191380936230.361309569046811
670.6955703441831990.6088593116336030.304429655816801
680.6619630134494080.6760739731011830.338036986550592
690.7242858696866690.5514282606266620.275714130313331
700.6923902483944450.615219503211110.307609751605555
710.6548804168845840.6902391662308320.345119583115416
720.6194430021628710.7611139956742570.380556997837129
730.5846980012194250.8306039975611490.415301998780575
740.5855493821357980.8289012357284040.414450617864202
750.6415662758730140.7168674482539720.358433724126986
760.6069506354310820.7860987291378360.393049364568918
770.625344147084840.749311705830320.37465585291516
780.6769364953608110.6461270092783790.323063504639189
790.6870332553041310.6259334893917380.312966744695869
800.6541348527892020.6917302944215950.345865147210798
810.6190866476594270.7618267046811470.380913352340573
820.591073955585230.8178520888295390.40892604441477
830.6290068254289490.7419863491421020.370993174571051
840.657821757433510.684356485132980.34217824256649
850.6185387209416870.7629225581166250.381461279058313
860.5783658919448920.8432682161102160.421634108055108
870.6089929406203190.7820141187593620.391007059379681
880.6410454548999590.7179090902000820.358954545100041
890.8747508875377940.2504982249244120.125249112462206
900.9677273605737370.06454527885252560.0322726394262628
910.9642997825387330.07140043492253410.0357002174612671
920.9817669301444910.03646613971101870.0182330698555093
930.97638515128250.04722969743500080.0236148487175004
940.9695863499977510.06082730000449780.0304136500022489
950.960775333911070.07844933217786040.0392246660889302
960.9502284046433830.09954319071323420.0497715953566171
970.9479752511723490.1040494976553020.0520247488276509
980.935391305363640.1292173892727190.0646086946363596
990.9365647971454840.1268704057090310.0634352028545155
1000.9308844429643770.1382311140712450.0691155570356225
1010.9260868472939630.1478263054120740.0739131527060369
1020.9079959277995240.1840081444009520.092004072200476
1030.9059740132910930.1880519734178140.094025986708907
1040.9316929822783380.1366140354433250.0683070177216624
1050.9197743267572250.1604513464855490.0802256732427746
1060.9535925496699960.09281490066000910.0464074503300045
1070.9451272728443350.1097454543113310.0548727271556654
1080.9307346568317270.1385306863365460.0692653431682732
1090.9165963954155760.1668072091688480.0834036045844238
1100.9358620106282630.1282759787434730.0641379893717366
1110.9615350001684560.07692999966308880.0384649998315444
1120.9560591034018590.08788179319628210.043940896598141
1130.9456013280821790.1087973438356420.0543986719178208
1140.9335062138166890.1329875723666230.0664937861833114
1150.9180010523395640.1639978953208720.081998947660436
1160.9371079122455920.1257841755088160.0628920877544078
1170.9528240353831460.09435192923370820.0471759646168541
1180.9396385382136870.1207229235726250.0603614617863126
1190.9521756752150860.09564864956982780.0478243247849139
1200.9576104372190430.08477912556191480.0423895627809574
1210.9838105666532640.0323788666934720.016189433346736
1220.9796917441390140.04061651172197260.0203082558609863
1230.977285171391470.04542965721706020.0227148286085301
1240.9748855352487310.05022892950253790.025114464751269
1250.9656110132379610.06877797352407840.0343889867620392
1260.9538025750089190.09239484998216190.046197424991081
1270.9401167062416510.1197665875166970.0598832937583486
1280.9358490590590940.1283018818818120.064150940940906
1290.9767543689132850.04649126217342920.0232456310867146
1300.9840970086933190.03180598261336280.0159029913066814
1310.9900681552663250.01986368946735070.00993184473367535
1320.9873546109134560.02529077817308750.0126453890865437
1330.9897832743667670.02043345126646570.0102167256332329
1340.9916618037438560.01667639251228770.00833819625614387
1350.9898791319479510.02024173610409810.0101208680520491
1360.9886979482304780.02260410353904440.0113020517695222
1370.9828264133599010.03434717328019810.0171735866400991
1380.9775312738099090.04493745238018270.0224687261900914
1390.9898966156554340.02020676868913220.0101033843445661
1400.9875001312109580.02499973757808430.0124998687890422
1410.9850471560282460.0299056879435090.0149528439717545
1420.9924858569478160.01502828610436890.00751414305218444
1430.9999699228518296.0154296341945e-053.00771481709725e-05
1440.9999279635450840.000144072909832367.20364549161802e-05
1450.9999149378085230.0001701243829537988.50621914768988e-05
1460.999991149413551.77011728992095e-058.85058644960477e-06
1470.9999998266433913.46713218000892e-071.73356609000446e-07
1480.9999999366245361.26750927932891e-076.33754639664455e-08
1490.9999996678727156.64254570136151e-073.32127285068075e-07
1500.9999988085178512.3829642976573e-061.19148214882865e-06
1510.9999938538107721.22923784569274e-056.14618922846372e-06
1520.9999699497543266.01004913482587e-053.00502456741294e-05
1530.9998616834458150.0002766331083700540.000138316554185027
1540.9994061174009660.001187765198068470.000593882599034233
1550.9975018214639610.004996357072078380.00249817853603919
1560.9958006331574620.008398733685075770.00419936684253789
1570.9794085683350670.0411828633298670.0205914316649335







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level140.0927152317880795NOK
5% type I error level340.225165562913907NOK
10% type I error level480.317880794701987NOK

\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 & 14 & 0.0927152317880795 & NOK \tabularnewline
5% type I error level & 34 & 0.225165562913907 & NOK \tabularnewline
10% type I error level & 48 & 0.317880794701987 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146229&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]14[/C][C]0.0927152317880795[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]34[/C][C]0.225165562913907[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]48[/C][C]0.317880794701987[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146229&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146229&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level140.0927152317880795NOK
5% type I error level340.225165562913907NOK
10% type I error level480.317880794701987NOK



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