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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 computationSat, 03 Nov 2012 10:19:13 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/03/t1351952433ciywsgjxti46haa.htm/, Retrieved Sun, 03 Jul 2022 15:21:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185735, Retrieved Sun, 03 Jul 2022 15:21:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsHubert Liskiewicz OLS Learning
Estimated Impact96
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Hubert Liskiewicz...] [2012-11-03 14:19:13] [acfd67cb214b61d0a5e0fb4c8c6ef02a] [Current]
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Dataseries X:
1	41	12	12	13
2	39	11	11	16
3	30	15	14	19
4	31	6	12	15
5	34	13	21	14
6	35	10	12	13
7	39	12	22	19
8	34	14	11	15
9	36	12	10	14
10	37	6	13	15
11	38	10	10	16
12	36	12	8	16
13	38	12	15	16
14	39	11	14	16
15	33	15	10	17
16	32	12	14	15
17	36	10	14	15
18	38	12	11	20
19	39	11	10	18
20	32	12	13	16
21	32	11	7	16
22	31	12	14	16
23	39	13	12	19
24	37	11	14	16
25	39	9	11	17
26	41	13	9	17
27	36	10	11	16
28	33	14	15	15
29	33	12	14	16
30	34	10	13	14
31	31	12	9	15
32	27	8	15	12
33	37	10	10	14
34	34	12	11	16
35	34	12	13	14
36	32	7	8	7
37	29	6	20	10
38	36	12	12	14
39	29	10	10	16
40	35	10	10	16
41	37	10	9	16
42	34	12	14	14
43	38	15	8	20
44	35	10	14	14
45	38	10	11	14
46	37	12	13	11
47	38	13	9	14
48	33	11	11	15
49	36	11	15	16
50	38	12	11	14
51	32	14	10	16
52	32	10	14	14
53	32	12	18	12
54	34	13	14	16
55	32	5	11	9
56	37	6	12	14
57	39	12	13	16
58	29	12	9	16
59	37	11	10	15
60	35	10	15	16
61	30	7	20	12
62	38	12	12	16
63	34	14	12	16
64	31	11	14	14
65	34	12	13	16
66	35	13	11	17
67	36	14	17	18
68	30	11	12	18
69	39	12	13	12
70	35	12	14	16
71	38	8	13	10
72	31	11	15	14
73	34	14	13	18
74	38	14	10	18
75	34	12	11	16
76	39	9	19	17
77	37	13	13	16
78	34	11	17	16
79	28	12	13	13
80	37	12	9	16
81	33	12	11	16
82	37	12	10	20
83	35	12	9	16
84	37	12	12	15
85	32	11	12	15
86	33	10	13	16
87	38	9	13	14
88	33	12	12	16
89	29	12	15	16
90	33	12	22	15
91	31	9	13	12
92	36	15	15	17
93	35	12	13	16
94	32	12	15	15
95	29	12	10	13
96	39	10	11	16
97	37	13	16	16
98	35	9	11	16
99	37	12	11	16
100	32	10	10	14
101	38	14	10	16
102	37	11	16	16
103	36	15	12	20
104	32	11	11	15
105	33	11	16	16
106	40	12	19	13
107	38	12	11	17
108	41	12	16	16
109	36	11	15	16
110	43	7	24	12
111	30	12	14	16
112	31	14	15	16
113	32	11	11	17
114	32	11	15	13
115	37	10	12	12
116	37	13	10	18
117	33	13	14	14
118	34	8	13	14
119	33	11	9	13
120	38	12	15	16
121	33	11	15	13
122	31	13	14	16
123	38	12	11	13
124	37	14	8	16
125	33	13	11	15
126	31	15	11	16
127	39	10	8	15
128	44	11	10	17
129	33	9	11	15
130	35	11	13	12
131	32	10	11	16
132	28	11	20	10
133	40	8	10	16
134	27	11	15	12
135	37	12	12	14
136	32	12	14	15
137	28	9	23	13
138	34	11	14	15
139	30	10	16	11
140	35	8	11	12
141	31	9	12	8
142	32	8	10	16
143	30	9	14	15
144	30	15	12	17
145	31	11	12	16
146	40	8	11	10
147	32	13	12	18
148	36	12	13	13
149	32	12	11	16
150	35	9	19	13
151	38	7	12	10
152	42	13	17	15
153	34	9	9	16
154	35	6	12	16
155	35	8	19	14
156	33	8	18	10
157	36	15	15	17
158	32	6	14	13
159	33	9	11	15
160	34	11	9	16
161	32	8	18	12
162	34	8	16	13




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 9 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185735&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185735&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185735&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 time9 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 7.02339996521227 -0.00404340643189375t + 0.104338976442402Connected[t] + 0.532415969150616Software[t] -0.0961299660096882Depression[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  7.02339996521227 -0.00404340643189375t +  0.104338976442402Connected[t] +  0.532415969150616Software[t] -0.0961299660096882Depression[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185735&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  7.02339996521227 -0.00404340643189375t +  0.104338976442402Connected[t] +  0.532415969150616Software[t] -0.0961299660096882Depression[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185735&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185735&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
Learning[t] = + 7.02339996521227 -0.00404340643189375t + 0.104338976442402Connected[t] + 0.532415969150616Software[t] -0.0961299660096882Depression[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.023399965212271.8937383.70880.0002880.000144
t-0.004043406431893750.003147-1.28480.2007650.100383
Connected0.1043389764424020.0433642.40610.0172850.008642
Software0.5324159691506160.0686837.751800
Depression-0.09612996600968820.046568-2.06430.0406340.020317

\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) & 7.02339996521227 & 1.893738 & 3.7088 & 0.000288 & 0.000144 \tabularnewline
t & -0.00404340643189375 & 0.003147 & -1.2848 & 0.200765 & 0.100383 \tabularnewline
Connected & 0.104338976442402 & 0.043364 & 2.4061 & 0.017285 & 0.008642 \tabularnewline
Software & 0.532415969150616 & 0.068683 & 7.7518 & 0 & 0 \tabularnewline
Depression & -0.0961299660096882 & 0.046568 & -2.0643 & 0.040634 & 0.020317 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185735&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]7.02339996521227[/C][C]1.893738[/C][C]3.7088[/C][C]0.000288[/C][C]0.000144[/C][/ROW]
[ROW][C]t[/C][C]-0.00404340643189375[/C][C]0.003147[/C][C]-1.2848[/C][C]0.200765[/C][C]0.100383[/C][/ROW]
[ROW][C]Connected[/C][C]0.104338976442402[/C][C]0.043364[/C][C]2.4061[/C][C]0.017285[/C][C]0.008642[/C][/ROW]
[ROW][C]Software[/C][C]0.532415969150616[/C][C]0.068683[/C][C]7.7518[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0961299660096882[/C][C]0.046568[/C][C]-2.0643[/C][C]0.040634[/C][C]0.020317[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185735&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185735&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)7.023399965212271.8937383.70880.0002880.000144
t-0.004043406431893750.003147-1.28480.2007650.100383
Connected0.1043389764424020.0433642.40610.0172850.008642
Software0.5324159691506160.0686837.751800
Depression-0.09612996600968820.046568-2.06430.0406340.020317







Multiple Linear Regression - Regression Statistics
Multiple R0.597283647877422
R-squared0.35674775602176
Adjusted R-squared0.340359163818493
F-TEST (value)21.7680537533076
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value2.63122856836162e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.83249632340132
Sum Squared Residuals527.212715718857

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.597283647877422 \tabularnewline
R-squared & 0.35674775602176 \tabularnewline
Adjusted R-squared & 0.340359163818493 \tabularnewline
F-TEST (value) & 21.7680537533076 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value & 2.63122856836162e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.83249632340132 \tabularnewline
Sum Squared Residuals & 527.212715718857 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185735&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.597283647877422[/C][/ROW]
[ROW][C]R-squared[/C][C]0.35674775602176[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.340359163818493[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]21.7680537533076[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]157[/C][/ROW]
[ROW][C]p-value[/C][C]2.63122856836162e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.83249632340132[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]527.212715718857[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185735&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185735&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 R0.597283647877422
R-squared0.35674775602176
Adjusted R-squared0.340359163818493
F-TEST (value)21.7680537533076
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value2.63122856836162e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.83249632340132
Sum Squared Residuals527.212715718857







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.53268663061-3.53268663060999
21615.88367926815230.116320731847656
31916.78185905231222.21814094768777
41512.28267083198662.71732916801342
51415.453386444849-1.453386444849
61314.8216038014949-1.82160380149486
71915.33844857903693.66155142096308
81516.9349718548008-1.93497185480082
91416.1709044289622-2.17090442896219
101512.78831428603992.21168571396006
111615.3066636306820.693336369318029
121616.3510341416859-0.351034141685882
131615.8827589260710.117241073929026
141615.54676849294060.453231507059446
151717.4308749684955-0.430874968495467
161515.3407248141306-0.340724814130572
171514.68920537516710.310794624832947
182016.24706175795033.75293824204974
191815.91107132481982.08892867518016
201615.42068115441270.579318845587315
211615.46100157488830.538998425111695
221615.21212539909680.787874600903192
231916.76746970537412.23253029462588
241615.29765647573680.702343524263186
251714.72584898191762.27415101808244
261717.2524073369923-0.252407336992304
271614.93716120887721.06283879112282
281516.3652448856818-1.36524488568179
291615.39249950695840.607500493041645
301414.5240931046773-0.524093104677319
311515.6563845712582-0.656384571258205
321212.5285415863961-0.528541586396114
331415.1133697127379-1.11336971273791
341615.76501134927040.234988650729648
351415.5687080108191-1.56870801081908
36713.1745566357977-6.17455663579775
371011.1715207387718-1.17152073877178
381415.8613857104179-1.86138571041789
391614.25439746260731.74560253739267
401614.87638791482981.12361208517015
411615.17715242729240.822847572707555
421415.4442741997861-1.44427419978614
432018.03161440263381.96838559736617
441414.4756944250635-0.47569442506352
451415.0730578459879-1.07305784598789
461115.8372474693955-4.83724746939546
471416.8544788725953-2.85447887259533
481515.0716487136308-0.0716487136308224
491614.99610237248741.00389762751262
501416.1176727521297-2.11767275212966
511616.6485573913543-0.648557391354275
521414.1303302442812-0.130330244281166
531214.8065989121118-2.80659891211175
541615.9281692917540.0718307082459716
55911.7445100772615-2.74451007726147
561412.69844755618251.30155244381749
571616.0014479515294-0.00144795152942706
581615.33853464471230.661465355287729
591515.5406571146593-0.540657114659285
601614.31486995614351.68513004385647
611211.71123392999930.288766070000657
621615.97302190893720.026978091062755
631616.616454535037-0.616454535036977
641414.5098863598067-0.509886359806655
651615.44740581786230.55259418213773
661716.27237728904280.727622710957231
671816.32830903214581.67169096785424
681814.58163368965613.41836631034395
691215.9529270743467-3.9529270743467
701615.43539779613550.564602203864486
711013.7108374084381-3.71083740843805
721414.3814091423418-0.381409142341817
731816.47989050470841.52010949529165
741817.18159290207510.818407097924872
751615.59923168556270.400768314437292
761713.75059552581353.24940447418653
771616.2443178391574-0.244317839157365
781614.47790570105831.52209429894172
791314.7647642691613-1.76476426916135
801616.0842915147498-0.0842915147498202
811615.47063227052890.529367729471056
822015.98007473587634.01992526412366
831615.86348334256930.136516657430664
841515.7797279909932-0.779727990993181
851514.72157373319870.278426266801336
861614.19332336804891.80667663195113
871414.1785588746784-0.178558874678366
881615.3461984594960.653801540504
891614.63640924926541.36359075073456
901514.37681198653530.623188013464669
911213.432012413854-1.43201241385398
921716.95189977251840.0481002274815878
931615.43852941421160.561470585788354
941514.92920914643320.0707908535668285
951315.0927986407225-2.09279864072251
961614.97118309440371.02881690559628
971615.87505981249040.124940187509575
981614.01332440661971.98667559338029
991615.81520686052450.184793139475537
1001414.320766599589-0.320766599589018
1011617.072420928414-1.072420928414
1021614.79001084202971.20998915797027
1032017.19581219979662.80418780020335
1041514.74087897700240.259121022997629
1051614.36052471696441.63947528303556
1061315.3308802167509-2.33088021675091
1071715.88719858551171.11280141448829
1081615.71552227835860.284477721641416
1091614.75349798657381.24650201342624
1101212.484993844549-0.484993844549016
1111614.74792325021591.25207674978414
1121615.81692079251790.183079207482086
1131714.70448831911532.29551168088467
1141314.3159250486447-1.31592504864468
1151214.5895504533032-2.58955045330324
1161816.37501488634261.62498511365743
1171415.5690957101023-1.56909571010232
1181413.10344140036940.896558599630562
1191314.9768267889857-1.97682678898574
1201615.45011443785830.549885562141657
1211314.3919601800638-1.39196018006383
1221615.3402007250580.659799274941951
1231315.8225040826014-2.82250408260141
1241617.0673435360574-1.06734353605742
1251515.8251383566762-0.825138356676235
1261616.6772489356608-0.67724893566077
1271515.1342273930441-0.134227393044074
1281715.99203490595541.00796509404457
1291513.67930085434621.3206991456538
1301214.756507407081-2.75650740708096
1311614.09929103419061.90070896580938
1321013.3451379970525-3.34513799705255
1331613.95721406057452.0427859394255
1341213.7133620377948-1.7133620377948
1351415.5735142629666-1.5735142629666
1361514.85551604230330.144483957696678
1371311.97169912856281.02830087143722
1381514.52369121317370.476308786826278
1391113.3776159998022-2.37761599980223
1401213.3110853673296-1.31108536732955
141813.325972058269-5.32597205826898
1421613.08611159114822.91388840885175
1431513.02128633694341.97871366305658
1441716.40399867743460.596001322565408
1451614.37463037084261.62536962915736
1461013.8085198109502-3.8085198109502
1471815.53571447272252.46428552727752
1481315.3204810368999-2.32048103689989
1491615.09134165671780.908658343282232
1501313.0340275440837-0.0340275440837263
1511012.9510788907456-2.95107889074562
1521516.0782373749386-1.07823737493859
1531613.87885800844252.12114199155747
1541612.09351577297213.90648422702788
1551412.48139454277361.51860545722636
1561012.3648031494666-2.36480314946663
1571716.68907835444530.310921645554682
1581311.5720652858981.42793471410203
1591513.55799866138941.44200133861062
1601614.91538610172051.0846138982795
1611212.2402471408648-0.240247140864763
1621312.6371416193370.362858380662951

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.53268663061 & -3.53268663060999 \tabularnewline
2 & 16 & 15.8836792681523 & 0.116320731847656 \tabularnewline
3 & 19 & 16.7818590523122 & 2.21814094768777 \tabularnewline
4 & 15 & 12.2826708319866 & 2.71732916801342 \tabularnewline
5 & 14 & 15.453386444849 & -1.453386444849 \tabularnewline
6 & 13 & 14.8216038014949 & -1.82160380149486 \tabularnewline
7 & 19 & 15.3384485790369 & 3.66155142096308 \tabularnewline
8 & 15 & 16.9349718548008 & -1.93497185480082 \tabularnewline
9 & 14 & 16.1709044289622 & -2.17090442896219 \tabularnewline
10 & 15 & 12.7883142860399 & 2.21168571396006 \tabularnewline
11 & 16 & 15.306663630682 & 0.693336369318029 \tabularnewline
12 & 16 & 16.3510341416859 & -0.351034141685882 \tabularnewline
13 & 16 & 15.882758926071 & 0.117241073929026 \tabularnewline
14 & 16 & 15.5467684929406 & 0.453231507059446 \tabularnewline
15 & 17 & 17.4308749684955 & -0.430874968495467 \tabularnewline
16 & 15 & 15.3407248141306 & -0.340724814130572 \tabularnewline
17 & 15 & 14.6892053751671 & 0.310794624832947 \tabularnewline
18 & 20 & 16.2470617579503 & 3.75293824204974 \tabularnewline
19 & 18 & 15.9110713248198 & 2.08892867518016 \tabularnewline
20 & 16 & 15.4206811544127 & 0.579318845587315 \tabularnewline
21 & 16 & 15.4610015748883 & 0.538998425111695 \tabularnewline
22 & 16 & 15.2121253990968 & 0.787874600903192 \tabularnewline
23 & 19 & 16.7674697053741 & 2.23253029462588 \tabularnewline
24 & 16 & 15.2976564757368 & 0.702343524263186 \tabularnewline
25 & 17 & 14.7258489819176 & 2.27415101808244 \tabularnewline
26 & 17 & 17.2524073369923 & -0.252407336992304 \tabularnewline
27 & 16 & 14.9371612088772 & 1.06283879112282 \tabularnewline
28 & 15 & 16.3652448856818 & -1.36524488568179 \tabularnewline
29 & 16 & 15.3924995069584 & 0.607500493041645 \tabularnewline
30 & 14 & 14.5240931046773 & -0.524093104677319 \tabularnewline
31 & 15 & 15.6563845712582 & -0.656384571258205 \tabularnewline
32 & 12 & 12.5285415863961 & -0.528541586396114 \tabularnewline
33 & 14 & 15.1133697127379 & -1.11336971273791 \tabularnewline
34 & 16 & 15.7650113492704 & 0.234988650729648 \tabularnewline
35 & 14 & 15.5687080108191 & -1.56870801081908 \tabularnewline
36 & 7 & 13.1745566357977 & -6.17455663579775 \tabularnewline
37 & 10 & 11.1715207387718 & -1.17152073877178 \tabularnewline
38 & 14 & 15.8613857104179 & -1.86138571041789 \tabularnewline
39 & 16 & 14.2543974626073 & 1.74560253739267 \tabularnewline
40 & 16 & 14.8763879148298 & 1.12361208517015 \tabularnewline
41 & 16 & 15.1771524272924 & 0.822847572707555 \tabularnewline
42 & 14 & 15.4442741997861 & -1.44427419978614 \tabularnewline
43 & 20 & 18.0316144026338 & 1.96838559736617 \tabularnewline
44 & 14 & 14.4756944250635 & -0.47569442506352 \tabularnewline
45 & 14 & 15.0730578459879 & -1.07305784598789 \tabularnewline
46 & 11 & 15.8372474693955 & -4.83724746939546 \tabularnewline
47 & 14 & 16.8544788725953 & -2.85447887259533 \tabularnewline
48 & 15 & 15.0716487136308 & -0.0716487136308224 \tabularnewline
49 & 16 & 14.9961023724874 & 1.00389762751262 \tabularnewline
50 & 14 & 16.1176727521297 & -2.11767275212966 \tabularnewline
51 & 16 & 16.6485573913543 & -0.648557391354275 \tabularnewline
52 & 14 & 14.1303302442812 & -0.130330244281166 \tabularnewline
53 & 12 & 14.8065989121118 & -2.80659891211175 \tabularnewline
54 & 16 & 15.928169291754 & 0.0718307082459716 \tabularnewline
55 & 9 & 11.7445100772615 & -2.74451007726147 \tabularnewline
56 & 14 & 12.6984475561825 & 1.30155244381749 \tabularnewline
57 & 16 & 16.0014479515294 & -0.00144795152942706 \tabularnewline
58 & 16 & 15.3385346447123 & 0.661465355287729 \tabularnewline
59 & 15 & 15.5406571146593 & -0.540657114659285 \tabularnewline
60 & 16 & 14.3148699561435 & 1.68513004385647 \tabularnewline
61 & 12 & 11.7112339299993 & 0.288766070000657 \tabularnewline
62 & 16 & 15.9730219089372 & 0.026978091062755 \tabularnewline
63 & 16 & 16.616454535037 & -0.616454535036977 \tabularnewline
64 & 14 & 14.5098863598067 & -0.509886359806655 \tabularnewline
65 & 16 & 15.4474058178623 & 0.55259418213773 \tabularnewline
66 & 17 & 16.2723772890428 & 0.727622710957231 \tabularnewline
67 & 18 & 16.3283090321458 & 1.67169096785424 \tabularnewline
68 & 18 & 14.5816336896561 & 3.41836631034395 \tabularnewline
69 & 12 & 15.9529270743467 & -3.9529270743467 \tabularnewline
70 & 16 & 15.4353977961355 & 0.564602203864486 \tabularnewline
71 & 10 & 13.7108374084381 & -3.71083740843805 \tabularnewline
72 & 14 & 14.3814091423418 & -0.381409142341817 \tabularnewline
73 & 18 & 16.4798905047084 & 1.52010949529165 \tabularnewline
74 & 18 & 17.1815929020751 & 0.818407097924872 \tabularnewline
75 & 16 & 15.5992316855627 & 0.400768314437292 \tabularnewline
76 & 17 & 13.7505955258135 & 3.24940447418653 \tabularnewline
77 & 16 & 16.2443178391574 & -0.244317839157365 \tabularnewline
78 & 16 & 14.4779057010583 & 1.52209429894172 \tabularnewline
79 & 13 & 14.7647642691613 & -1.76476426916135 \tabularnewline
80 & 16 & 16.0842915147498 & -0.0842915147498202 \tabularnewline
81 & 16 & 15.4706322705289 & 0.529367729471056 \tabularnewline
82 & 20 & 15.9800747358763 & 4.01992526412366 \tabularnewline
83 & 16 & 15.8634833425693 & 0.136516657430664 \tabularnewline
84 & 15 & 15.7797279909932 & -0.779727990993181 \tabularnewline
85 & 15 & 14.7215737331987 & 0.278426266801336 \tabularnewline
86 & 16 & 14.1933233680489 & 1.80667663195113 \tabularnewline
87 & 14 & 14.1785588746784 & -0.178558874678366 \tabularnewline
88 & 16 & 15.346198459496 & 0.653801540504 \tabularnewline
89 & 16 & 14.6364092492654 & 1.36359075073456 \tabularnewline
90 & 15 & 14.3768119865353 & 0.623188013464669 \tabularnewline
91 & 12 & 13.432012413854 & -1.43201241385398 \tabularnewline
92 & 17 & 16.9518997725184 & 0.0481002274815878 \tabularnewline
93 & 16 & 15.4385294142116 & 0.561470585788354 \tabularnewline
94 & 15 & 14.9292091464332 & 0.0707908535668285 \tabularnewline
95 & 13 & 15.0927986407225 & -2.09279864072251 \tabularnewline
96 & 16 & 14.9711830944037 & 1.02881690559628 \tabularnewline
97 & 16 & 15.8750598124904 & 0.124940187509575 \tabularnewline
98 & 16 & 14.0133244066197 & 1.98667559338029 \tabularnewline
99 & 16 & 15.8152068605245 & 0.184793139475537 \tabularnewline
100 & 14 & 14.320766599589 & -0.320766599589018 \tabularnewline
101 & 16 & 17.072420928414 & -1.072420928414 \tabularnewline
102 & 16 & 14.7900108420297 & 1.20998915797027 \tabularnewline
103 & 20 & 17.1958121997966 & 2.80418780020335 \tabularnewline
104 & 15 & 14.7408789770024 & 0.259121022997629 \tabularnewline
105 & 16 & 14.3605247169644 & 1.63947528303556 \tabularnewline
106 & 13 & 15.3308802167509 & -2.33088021675091 \tabularnewline
107 & 17 & 15.8871985855117 & 1.11280141448829 \tabularnewline
108 & 16 & 15.7155222783586 & 0.284477721641416 \tabularnewline
109 & 16 & 14.7534979865738 & 1.24650201342624 \tabularnewline
110 & 12 & 12.484993844549 & -0.484993844549016 \tabularnewline
111 & 16 & 14.7479232502159 & 1.25207674978414 \tabularnewline
112 & 16 & 15.8169207925179 & 0.183079207482086 \tabularnewline
113 & 17 & 14.7044883191153 & 2.29551168088467 \tabularnewline
114 & 13 & 14.3159250486447 & -1.31592504864468 \tabularnewline
115 & 12 & 14.5895504533032 & -2.58955045330324 \tabularnewline
116 & 18 & 16.3750148863426 & 1.62498511365743 \tabularnewline
117 & 14 & 15.5690957101023 & -1.56909571010232 \tabularnewline
118 & 14 & 13.1034414003694 & 0.896558599630562 \tabularnewline
119 & 13 & 14.9768267889857 & -1.97682678898574 \tabularnewline
120 & 16 & 15.4501144378583 & 0.549885562141657 \tabularnewline
121 & 13 & 14.3919601800638 & -1.39196018006383 \tabularnewline
122 & 16 & 15.340200725058 & 0.659799274941951 \tabularnewline
123 & 13 & 15.8225040826014 & -2.82250408260141 \tabularnewline
124 & 16 & 17.0673435360574 & -1.06734353605742 \tabularnewline
125 & 15 & 15.8251383566762 & -0.825138356676235 \tabularnewline
126 & 16 & 16.6772489356608 & -0.67724893566077 \tabularnewline
127 & 15 & 15.1342273930441 & -0.134227393044074 \tabularnewline
128 & 17 & 15.9920349059554 & 1.00796509404457 \tabularnewline
129 & 15 & 13.6793008543462 & 1.3206991456538 \tabularnewline
130 & 12 & 14.756507407081 & -2.75650740708096 \tabularnewline
131 & 16 & 14.0992910341906 & 1.90070896580938 \tabularnewline
132 & 10 & 13.3451379970525 & -3.34513799705255 \tabularnewline
133 & 16 & 13.9572140605745 & 2.0427859394255 \tabularnewline
134 & 12 & 13.7133620377948 & -1.7133620377948 \tabularnewline
135 & 14 & 15.5735142629666 & -1.5735142629666 \tabularnewline
136 & 15 & 14.8555160423033 & 0.144483957696678 \tabularnewline
137 & 13 & 11.9716991285628 & 1.02830087143722 \tabularnewline
138 & 15 & 14.5236912131737 & 0.476308786826278 \tabularnewline
139 & 11 & 13.3776159998022 & -2.37761599980223 \tabularnewline
140 & 12 & 13.3110853673296 & -1.31108536732955 \tabularnewline
141 & 8 & 13.325972058269 & -5.32597205826898 \tabularnewline
142 & 16 & 13.0861115911482 & 2.91388840885175 \tabularnewline
143 & 15 & 13.0212863369434 & 1.97871366305658 \tabularnewline
144 & 17 & 16.4039986774346 & 0.596001322565408 \tabularnewline
145 & 16 & 14.3746303708426 & 1.62536962915736 \tabularnewline
146 & 10 & 13.8085198109502 & -3.8085198109502 \tabularnewline
147 & 18 & 15.5357144727225 & 2.46428552727752 \tabularnewline
148 & 13 & 15.3204810368999 & -2.32048103689989 \tabularnewline
149 & 16 & 15.0913416567178 & 0.908658343282232 \tabularnewline
150 & 13 & 13.0340275440837 & -0.0340275440837263 \tabularnewline
151 & 10 & 12.9510788907456 & -2.95107889074562 \tabularnewline
152 & 15 & 16.0782373749386 & -1.07823737493859 \tabularnewline
153 & 16 & 13.8788580084425 & 2.12114199155747 \tabularnewline
154 & 16 & 12.0935157729721 & 3.90648422702788 \tabularnewline
155 & 14 & 12.4813945427736 & 1.51860545722636 \tabularnewline
156 & 10 & 12.3648031494666 & -2.36480314946663 \tabularnewline
157 & 17 & 16.6890783544453 & 0.310921645554682 \tabularnewline
158 & 13 & 11.572065285898 & 1.42793471410203 \tabularnewline
159 & 15 & 13.5579986613894 & 1.44200133861062 \tabularnewline
160 & 16 & 14.9153861017205 & 1.0846138982795 \tabularnewline
161 & 12 & 12.2402471408648 & -0.240247140864763 \tabularnewline
162 & 13 & 12.637141619337 & 0.362858380662951 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185735&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]13[/C][C]16.53268663061[/C][C]-3.53268663060999[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.8836792681523[/C][C]0.116320731847656[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.7818590523122[/C][C]2.21814094768777[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.2826708319866[/C][C]2.71732916801342[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.453386444849[/C][C]-1.453386444849[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.8216038014949[/C][C]-1.82160380149486[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.3384485790369[/C][C]3.66155142096308[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.9349718548008[/C][C]-1.93497185480082[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]16.1709044289622[/C][C]-2.17090442896219[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.7883142860399[/C][C]2.21168571396006[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.306663630682[/C][C]0.693336369318029[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.3510341416859[/C][C]-0.351034141685882[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.882758926071[/C][C]0.117241073929026[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.5467684929406[/C][C]0.453231507059446[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.4308749684955[/C][C]-0.430874968495467[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.3407248141306[/C][C]-0.340724814130572[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.6892053751671[/C][C]0.310794624832947[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.2470617579503[/C][C]3.75293824204974[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.9110713248198[/C][C]2.08892867518016[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.4206811544127[/C][C]0.579318845587315[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.4610015748883[/C][C]0.538998425111695[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]15.2121253990968[/C][C]0.787874600903192[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.7674697053741[/C][C]2.23253029462588[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]15.2976564757368[/C][C]0.702343524263186[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.7258489819176[/C][C]2.27415101808244[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]17.2524073369923[/C][C]-0.252407336992304[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.9371612088772[/C][C]1.06283879112282[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.3652448856818[/C][C]-1.36524488568179[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.3924995069584[/C][C]0.607500493041645[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.5240931046773[/C][C]-0.524093104677319[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.6563845712582[/C][C]-0.656384571258205[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.5285415863961[/C][C]-0.528541586396114[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.1133697127379[/C][C]-1.11336971273791[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.7650113492704[/C][C]0.234988650729648[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.5687080108191[/C][C]-1.56870801081908[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]13.1745566357977[/C][C]-6.17455663579775[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]11.1715207387718[/C][C]-1.17152073877178[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.8613857104179[/C][C]-1.86138571041789[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.2543974626073[/C][C]1.74560253739267[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.8763879148298[/C][C]1.12361208517015[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.1771524272924[/C][C]0.822847572707555[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.4442741997861[/C][C]-1.44427419978614[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]18.0316144026338[/C][C]1.96838559736617[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.4756944250635[/C][C]-0.47569442506352[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]15.0730578459879[/C][C]-1.07305784598789[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.8372474693955[/C][C]-4.83724746939546[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.8544788725953[/C][C]-2.85447887259533[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.0716487136308[/C][C]-0.0716487136308224[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]14.9961023724874[/C][C]1.00389762751262[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]16.1176727521297[/C][C]-2.11767275212966[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.6485573913543[/C][C]-0.648557391354275[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.1303302442812[/C][C]-0.130330244281166[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.8065989121118[/C][C]-2.80659891211175[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.928169291754[/C][C]0.0718307082459716[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.7445100772615[/C][C]-2.74451007726147[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.6984475561825[/C][C]1.30155244381749[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.0014479515294[/C][C]-0.00144795152942706[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.3385346447123[/C][C]0.661465355287729[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.5406571146593[/C][C]-0.540657114659285[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.3148699561435[/C][C]1.68513004385647[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.7112339299993[/C][C]0.288766070000657[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.9730219089372[/C][C]0.026978091062755[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.616454535037[/C][C]-0.616454535036977[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.5098863598067[/C][C]-0.509886359806655[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.4474058178623[/C][C]0.55259418213773[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]16.2723772890428[/C][C]0.727622710957231[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.3283090321458[/C][C]1.67169096785424[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.5816336896561[/C][C]3.41836631034395[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.9529270743467[/C][C]-3.9529270743467[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.4353977961355[/C][C]0.564602203864486[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.7108374084381[/C][C]-3.71083740843805[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.3814091423418[/C][C]-0.381409142341817[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.4798905047084[/C][C]1.52010949529165[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.1815929020751[/C][C]0.818407097924872[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.5992316855627[/C][C]0.400768314437292[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.7505955258135[/C][C]3.24940447418653[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.2443178391574[/C][C]-0.244317839157365[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.4779057010583[/C][C]1.52209429894172[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.7647642691613[/C][C]-1.76476426916135[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]16.0842915147498[/C][C]-0.0842915147498202[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.4706322705289[/C][C]0.529367729471056[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]15.9800747358763[/C][C]4.01992526412366[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.8634833425693[/C][C]0.136516657430664[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.7797279909932[/C][C]-0.779727990993181[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.7215737331987[/C][C]0.278426266801336[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.1933233680489[/C][C]1.80667663195113[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.1785588746784[/C][C]-0.178558874678366[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.346198459496[/C][C]0.653801540504[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.6364092492654[/C][C]1.36359075073456[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.3768119865353[/C][C]0.623188013464669[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.432012413854[/C][C]-1.43201241385398[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]16.9518997725184[/C][C]0.0481002274815878[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.4385294142116[/C][C]0.561470585788354[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]14.9292091464332[/C][C]0.0707908535668285[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.0927986407225[/C][C]-2.09279864072251[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]14.9711830944037[/C][C]1.02881690559628[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.8750598124904[/C][C]0.124940187509575[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.0133244066197[/C][C]1.98667559338029[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]15.8152068605245[/C][C]0.184793139475537[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.320766599589[/C][C]-0.320766599589018[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.072420928414[/C][C]-1.072420928414[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.7900108420297[/C][C]1.20998915797027[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.1958121997966[/C][C]2.80418780020335[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.7408789770024[/C][C]0.259121022997629[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.3605247169644[/C][C]1.63947528303556[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.3308802167509[/C][C]-2.33088021675091[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]15.8871985855117[/C][C]1.11280141448829[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.7155222783586[/C][C]0.284477721641416[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.7534979865738[/C][C]1.24650201342624[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.484993844549[/C][C]-0.484993844549016[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.7479232502159[/C][C]1.25207674978414[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.8169207925179[/C][C]0.183079207482086[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.7044883191153[/C][C]2.29551168088467[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.3159250486447[/C][C]-1.31592504864468[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.5895504533032[/C][C]-2.58955045330324[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.3750148863426[/C][C]1.62498511365743[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.5690957101023[/C][C]-1.56909571010232[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.1034414003694[/C][C]0.896558599630562[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.9768267889857[/C][C]-1.97682678898574[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.4501144378583[/C][C]0.549885562141657[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.3919601800638[/C][C]-1.39196018006383[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.340200725058[/C][C]0.659799274941951[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.8225040826014[/C][C]-2.82250408260141[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]17.0673435360574[/C][C]-1.06734353605742[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.8251383566762[/C][C]-0.825138356676235[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.6772489356608[/C][C]-0.67724893566077[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.1342273930441[/C][C]-0.134227393044074[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]15.9920349059554[/C][C]1.00796509404457[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.6793008543462[/C][C]1.3206991456538[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]14.756507407081[/C][C]-2.75650740708096[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.0992910341906[/C][C]1.90070896580938[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.3451379970525[/C][C]-3.34513799705255[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]13.9572140605745[/C][C]2.0427859394255[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.7133620377948[/C][C]-1.7133620377948[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.5735142629666[/C][C]-1.5735142629666[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]14.8555160423033[/C][C]0.144483957696678[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]11.9716991285628[/C][C]1.02830087143722[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.5236912131737[/C][C]0.476308786826278[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.3776159998022[/C][C]-2.37761599980223[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.3110853673296[/C][C]-1.31108536732955[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.325972058269[/C][C]-5.32597205826898[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]13.0861115911482[/C][C]2.91388840885175[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.0212863369434[/C][C]1.97871366305658[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.4039986774346[/C][C]0.596001322565408[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.3746303708426[/C][C]1.62536962915736[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]13.8085198109502[/C][C]-3.8085198109502[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.5357144727225[/C][C]2.46428552727752[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.3204810368999[/C][C]-2.32048103689989[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]15.0913416567178[/C][C]0.908658343282232[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]13.0340275440837[/C][C]-0.0340275440837263[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]12.9510788907456[/C][C]-2.95107889074562[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.0782373749386[/C][C]-1.07823737493859[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]13.8788580084425[/C][C]2.12114199155747[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]12.0935157729721[/C][C]3.90648422702788[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.4813945427736[/C][C]1.51860545722636[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.3648031494666[/C][C]-2.36480314946663[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]16.6890783544453[/C][C]0.310921645554682[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.572065285898[/C][C]1.42793471410203[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]13.5579986613894[/C][C]1.44200133861062[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]14.9153861017205[/C][C]1.0846138982795[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.2402471408648[/C][C]-0.240247140864763[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.637141619337[/C][C]0.362858380662951[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185735&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185735&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
11316.53268663061-3.53268663060999
21615.88367926815230.116320731847656
31916.78185905231222.21814094768777
41512.28267083198662.71732916801342
51415.453386444849-1.453386444849
61314.8216038014949-1.82160380149486
71915.33844857903693.66155142096308
81516.9349718548008-1.93497185480082
91416.1709044289622-2.17090442896219
101512.78831428603992.21168571396006
111615.3066636306820.693336369318029
121616.3510341416859-0.351034141685882
131615.8827589260710.117241073929026
141615.54676849294060.453231507059446
151717.4308749684955-0.430874968495467
161515.3407248141306-0.340724814130572
171514.68920537516710.310794624832947
182016.24706175795033.75293824204974
191815.91107132481982.08892867518016
201615.42068115441270.579318845587315
211615.46100157488830.538998425111695
221615.21212539909680.787874600903192
231916.76746970537412.23253029462588
241615.29765647573680.702343524263186
251714.72584898191762.27415101808244
261717.2524073369923-0.252407336992304
271614.93716120887721.06283879112282
281516.3652448856818-1.36524488568179
291615.39249950695840.607500493041645
301414.5240931046773-0.524093104677319
311515.6563845712582-0.656384571258205
321212.5285415863961-0.528541586396114
331415.1133697127379-1.11336971273791
341615.76501134927040.234988650729648
351415.5687080108191-1.56870801081908
36713.1745566357977-6.17455663579775
371011.1715207387718-1.17152073877178
381415.8613857104179-1.86138571041789
391614.25439746260731.74560253739267
401614.87638791482981.12361208517015
411615.17715242729240.822847572707555
421415.4442741997861-1.44427419978614
432018.03161440263381.96838559736617
441414.4756944250635-0.47569442506352
451415.0730578459879-1.07305784598789
461115.8372474693955-4.83724746939546
471416.8544788725953-2.85447887259533
481515.0716487136308-0.0716487136308224
491614.99610237248741.00389762751262
501416.1176727521297-2.11767275212966
511616.6485573913543-0.648557391354275
521414.1303302442812-0.130330244281166
531214.8065989121118-2.80659891211175
541615.9281692917540.0718307082459716
55911.7445100772615-2.74451007726147
561412.69844755618251.30155244381749
571616.0014479515294-0.00144795152942706
581615.33853464471230.661465355287729
591515.5406571146593-0.540657114659285
601614.31486995614351.68513004385647
611211.71123392999930.288766070000657
621615.97302190893720.026978091062755
631616.616454535037-0.616454535036977
641414.5098863598067-0.509886359806655
651615.44740581786230.55259418213773
661716.27237728904280.727622710957231
671816.32830903214581.67169096785424
681814.58163368965613.41836631034395
691215.9529270743467-3.9529270743467
701615.43539779613550.564602203864486
711013.7108374084381-3.71083740843805
721414.3814091423418-0.381409142341817
731816.47989050470841.52010949529165
741817.18159290207510.818407097924872
751615.59923168556270.400768314437292
761713.75059552581353.24940447418653
771616.2443178391574-0.244317839157365
781614.47790570105831.52209429894172
791314.7647642691613-1.76476426916135
801616.0842915147498-0.0842915147498202
811615.47063227052890.529367729471056
822015.98007473587634.01992526412366
831615.86348334256930.136516657430664
841515.7797279909932-0.779727990993181
851514.72157373319870.278426266801336
861614.19332336804891.80667663195113
871414.1785588746784-0.178558874678366
881615.3461984594960.653801540504
891614.63640924926541.36359075073456
901514.37681198653530.623188013464669
911213.432012413854-1.43201241385398
921716.95189977251840.0481002274815878
931615.43852941421160.561470585788354
941514.92920914643320.0707908535668285
951315.0927986407225-2.09279864072251
961614.97118309440371.02881690559628
971615.87505981249040.124940187509575
981614.01332440661971.98667559338029
991615.81520686052450.184793139475537
1001414.320766599589-0.320766599589018
1011617.072420928414-1.072420928414
1021614.79001084202971.20998915797027
1032017.19581219979662.80418780020335
1041514.74087897700240.259121022997629
1051614.36052471696441.63947528303556
1061315.3308802167509-2.33088021675091
1071715.88719858551171.11280141448829
1081615.71552227835860.284477721641416
1091614.75349798657381.24650201342624
1101212.484993844549-0.484993844549016
1111614.74792325021591.25207674978414
1121615.81692079251790.183079207482086
1131714.70448831911532.29551168088467
1141314.3159250486447-1.31592504864468
1151214.5895504533032-2.58955045330324
1161816.37501488634261.62498511365743
1171415.5690957101023-1.56909571010232
1181413.10344140036940.896558599630562
1191314.9768267889857-1.97682678898574
1201615.45011443785830.549885562141657
1211314.3919601800638-1.39196018006383
1221615.3402007250580.659799274941951
1231315.8225040826014-2.82250408260141
1241617.0673435360574-1.06734353605742
1251515.8251383566762-0.825138356676235
1261616.6772489356608-0.67724893566077
1271515.1342273930441-0.134227393044074
1281715.99203490595541.00796509404457
1291513.67930085434621.3206991456538
1301214.756507407081-2.75650740708096
1311614.09929103419061.90070896580938
1321013.3451379970525-3.34513799705255
1331613.95721406057452.0427859394255
1341213.7133620377948-1.7133620377948
1351415.5735142629666-1.5735142629666
1361514.85551604230330.144483957696678
1371311.97169912856281.02830087143722
1381514.52369121317370.476308786826278
1391113.3776159998022-2.37761599980223
1401213.3110853673296-1.31108536732955
141813.325972058269-5.32597205826898
1421613.08611159114822.91388840885175
1431513.02128633694341.97871366305658
1441716.40399867743460.596001322565408
1451614.37463037084261.62536962915736
1461013.8085198109502-3.8085198109502
1471815.53571447272252.46428552727752
1481315.3204810368999-2.32048103689989
1491615.09134165671780.908658343282232
1501313.0340275440837-0.0340275440837263
1511012.9510788907456-2.95107889074562
1521516.0782373749386-1.07823737493859
1531613.87885800844252.12114199155747
1541612.09351577297213.90648422702788
1551412.48139454277361.51860545722636
1561012.3648031494666-2.36480314946663
1571716.68907835444530.310921645554682
1581311.5720652858981.42793471410203
1591513.55799866138941.44200133861062
1601614.91538610172051.0846138982795
1611212.2402471408648-0.240247140864763
1621312.6371416193370.362858380662951







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.9697098443227420.06058031135451670.0302901556772583
90.93885842258960.1222831548207990.0611415774103997
100.9099774026874180.1800451946251630.0900225973125817
110.8851546303767360.2296907392465280.114845369623264
120.832504701265170.334990597469660.16749529873483
130.7604814994412570.4790370011174860.239518500558743
140.677028540182450.64594291963510.32297145981755
150.5882813166839980.8234373666320030.411718683316002
160.5687591063403920.8624817873192150.431240893659608
170.4940400896616860.9880801793233720.505959910338314
180.7392187621773850.5215624756452290.260781237822615
190.7108697257019920.5782605485960160.289130274298008
200.6628001023892430.6743997952215150.337199897610757
210.5936792048558810.8126415902882370.406320795144119
220.5393890699308920.9212218601382160.460610930069108
230.5102392234305230.9795215531389530.489760776569477
240.4761134544865430.9522269089730860.523886545513457
250.4275140259026720.8550280518053440.572485974097328
260.3761163520464390.7522327040928790.623883647953561
270.3313540181260250.662708036252050.668645981873975
280.3791626524724790.7583253049449580.620837347527521
290.3294763309222410.6589526618444830.670523669077759
300.3454827871597340.6909655743194680.654517212840266
310.3042494339624120.6084988679248230.695750566037588
320.308598315807020.617196631614040.69140168419298
330.3093163415035610.6186326830071220.690683658496439
340.2589288095315510.5178576190631020.741071190468449
350.2573412022340990.5146824044681970.742658797765901
360.775187328859280.4496253422814410.22481267114072
370.7530126508592210.4939746982815570.246987349140779
380.7365936068358570.5268127863282870.263406393164144
390.7776087282625450.4447825434749090.222391271737455
400.7620566018179250.475886796364150.237943398182075
410.7321256477629590.5357487044740820.267874352237041
420.7064208391275390.5871583217449210.293579160872461
430.7243398446918070.5513203106163850.275660155308193
440.681370977472680.6372580450546390.31862902252732
450.648162026340830.703675947318340.35183797365917
460.8424905242340590.3150189515318830.157509475765941
470.857949379804440.2841012403911210.142050620195561
480.8332425050687520.3335149898624960.166757494931248
490.8208653135025680.3582693729948640.179134686497432
500.8140466835764830.3719066328470330.185953316423517
510.7844612150218790.4310775699562410.21553878497812
520.7505659690084840.4988680619830310.249434030991516
530.7713920635586440.4572158728827120.228607936441356
540.7430417728571310.5139164542857390.256958227142869
550.7695215610905010.4609568778189970.230478438909499
560.7664539192147530.4670921615704930.233546080785247
570.7322111828404940.5355776343190120.267788817159506
580.7225737196609260.5548525606781490.277426280339074
590.6862783020658920.6274433958682160.313721697934108
600.6948951031247560.6102097937504890.305104896875244
610.6561769497735820.6876461004528360.343823050226418
620.614765171110960.770469657778080.38523482888904
630.5740346519865710.8519306960268580.425965348013429
640.5333056963132460.9333886073735070.466694303686754
650.4991342180534070.9982684361068130.500865781946593
660.4693696651857990.9387393303715980.530630334814201
670.4649838741698520.9299677483397040.535016125830148
680.5934081402868990.8131837194262030.406591859713101
690.7360580814048510.5278838371902980.263941918595149
700.7026051711175110.5947896577649780.297394828882489
710.8128454010473860.3743091979052280.187154598952614
720.7840251038682240.4319497922635510.215974896131776
730.7762502665491740.4474994669016520.223749733450826
740.7510713101877810.4978573796244370.248928689812219
750.717456945422130.565086109155740.28254305457787
760.7857433719750530.4285132560498940.214256628024947
770.7516782256311040.4966435487377910.248321774368896
780.7364930715483930.5270138569032130.263506928451606
790.7371558837238430.5256882325523140.262844116276157
800.7015937835239910.5968124329520170.298406216476009
810.6654116259678550.669176748064290.334588374032145
820.7980610730890770.4038778538218460.201938926910923
830.764855645289280.470288709421440.23514435471072
840.7369484383485020.5261031233029960.263051561651498
850.6989963157212510.6020073685574970.301003684278749
860.6905823324891210.6188353350217580.309417667510879
870.6502086607344460.6995826785311080.349791339265554
880.6100654741143920.7798690517712160.389934525885608
890.5855619817446170.8288760365107650.414438018255383
900.5512416363871920.8975167272256160.448758363612808
910.5391827219880640.9216345560238710.460817278011936
920.4952510928770630.9905021857541260.504748907122937
930.4521619914535490.9043239829070990.547838008546451
940.4066602032799760.8133204065599510.593339796720024
950.432195669183440.864391338366880.56780433081656
960.3961435003134360.7922870006268730.603856499686564
970.3542276852378230.7084553704756460.645772314762177
980.3509009515644830.7018019031289660.649099048435517
990.3083881624224650.6167763248449290.691611837577535
1000.2728211930684540.5456423861369080.727178806931546
1010.2483802436034440.4967604872068870.751619756396556
1020.228620136835190.4572402736703790.77137986316481
1030.2856196923931340.5712393847862670.714380307606866
1040.2466013243954750.493202648790950.753398675604525
1050.2435680903702370.4871361807404750.756431909629763
1060.256979190433120.513958380866240.74302080956688
1070.2362256508746070.4724513017492140.763774349125393
1080.2119877027365460.4239754054730910.788012297263454
1090.2051679769317540.4103359538635080.794832023068246
1100.197262701768020.3945254035360390.80273729823198
1110.1879227428492470.3758454856984940.812077257150753
1120.1656847336865010.3313694673730020.834315266313499
1130.1966960956177230.3933921912354450.803303904382277
1140.1722089254226190.3444178508452370.827791074577382
1150.1801419551072550.3602839102145090.819858044892745
1160.1936086512442150.387217302488430.806391348755785
1170.1713037748556880.3426075497113760.828696225144312
1180.1594654032593360.3189308065186720.840534596740664
1190.1551509137252380.3103018274504760.844849086274762
1200.157533834335840.3150676686716810.84246616566416
1210.133487990385620.266975980771240.86651200961438
1220.1242582826310440.2485165652620890.875741717368956
1230.1301528838900280.2603057677800560.869847116109972
1240.1071488772988410.2142977545976820.892851122701159
1250.08534975992498690.1706995198499740.914650240075013
1260.06666328432525030.1333265686505010.93333671567475
1270.05053793690301870.1010758738060370.949462063096981
1280.05369624930716390.1073924986143280.946303750692836
1290.05036291443652880.1007258288730580.949637085563471
1300.05052642754312560.1010528550862510.949473572456874
1310.05488872268080060.1097774453616010.945111277319199
1320.0675991678454380.1351983356908760.932400832154562
1330.1349812525155470.2699625050310950.865018747484453
1340.1371019405749250.2742038811498490.862898059425075
1350.1098032510773310.2196065021546610.890196748922669
1360.08556016430263930.1711203286052790.914439835697361
1370.08026933529518370.1605386705903670.919730664704816
1380.0777250111448470.1554500222896940.922274988855153
1390.07345005324188010.146900106483760.92654994675812
1400.05346338272544090.1069267654508820.946536617274559
1410.4792518817265550.9585037634531090.520748118273445
1420.4939529589261010.9879059178522030.506047041073899
1430.4442996764145260.8885993528290520.555700323585474
1440.3891288905477230.7782577810954450.610871109452277
1450.3201357067654490.6402714135308980.679864293234551
1460.3871660690049950.7743321380099890.612833930995005
1470.3876003052689120.7752006105378240.612399694731088
1480.4143442422569420.8286884845138840.585655757743058
1490.3278457142822470.6556914285644950.672154285717753
1500.2408788044271330.4817576088542660.759121195572867
1510.7027552144804020.5944895710391960.297244785519598
1520.8511404739926720.2977190520146570.148859526007328
1530.743793300692780.5124133986144410.25620669930722
1540.6012876266995160.7974247466009680.398712373300484

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.969709844322742 & 0.0605803113545167 & 0.0302901556772583 \tabularnewline
9 & 0.9388584225896 & 0.122283154820799 & 0.0611415774103997 \tabularnewline
10 & 0.909977402687418 & 0.180045194625163 & 0.0900225973125817 \tabularnewline
11 & 0.885154630376736 & 0.229690739246528 & 0.114845369623264 \tabularnewline
12 & 0.83250470126517 & 0.33499059746966 & 0.16749529873483 \tabularnewline
13 & 0.760481499441257 & 0.479037001117486 & 0.239518500558743 \tabularnewline
14 & 0.67702854018245 & 0.6459429196351 & 0.32297145981755 \tabularnewline
15 & 0.588281316683998 & 0.823437366632003 & 0.411718683316002 \tabularnewline
16 & 0.568759106340392 & 0.862481787319215 & 0.431240893659608 \tabularnewline
17 & 0.494040089661686 & 0.988080179323372 & 0.505959910338314 \tabularnewline
18 & 0.739218762177385 & 0.521562475645229 & 0.260781237822615 \tabularnewline
19 & 0.710869725701992 & 0.578260548596016 & 0.289130274298008 \tabularnewline
20 & 0.662800102389243 & 0.674399795221515 & 0.337199897610757 \tabularnewline
21 & 0.593679204855881 & 0.812641590288237 & 0.406320795144119 \tabularnewline
22 & 0.539389069930892 & 0.921221860138216 & 0.460610930069108 \tabularnewline
23 & 0.510239223430523 & 0.979521553138953 & 0.489760776569477 \tabularnewline
24 & 0.476113454486543 & 0.952226908973086 & 0.523886545513457 \tabularnewline
25 & 0.427514025902672 & 0.855028051805344 & 0.572485974097328 \tabularnewline
26 & 0.376116352046439 & 0.752232704092879 & 0.623883647953561 \tabularnewline
27 & 0.331354018126025 & 0.66270803625205 & 0.668645981873975 \tabularnewline
28 & 0.379162652472479 & 0.758325304944958 & 0.620837347527521 \tabularnewline
29 & 0.329476330922241 & 0.658952661844483 & 0.670523669077759 \tabularnewline
30 & 0.345482787159734 & 0.690965574319468 & 0.654517212840266 \tabularnewline
31 & 0.304249433962412 & 0.608498867924823 & 0.695750566037588 \tabularnewline
32 & 0.30859831580702 & 0.61719663161404 & 0.69140168419298 \tabularnewline
33 & 0.309316341503561 & 0.618632683007122 & 0.690683658496439 \tabularnewline
34 & 0.258928809531551 & 0.517857619063102 & 0.741071190468449 \tabularnewline
35 & 0.257341202234099 & 0.514682404468197 & 0.742658797765901 \tabularnewline
36 & 0.77518732885928 & 0.449625342281441 & 0.22481267114072 \tabularnewline
37 & 0.753012650859221 & 0.493974698281557 & 0.246987349140779 \tabularnewline
38 & 0.736593606835857 & 0.526812786328287 & 0.263406393164144 \tabularnewline
39 & 0.777608728262545 & 0.444782543474909 & 0.222391271737455 \tabularnewline
40 & 0.762056601817925 & 0.47588679636415 & 0.237943398182075 \tabularnewline
41 & 0.732125647762959 & 0.535748704474082 & 0.267874352237041 \tabularnewline
42 & 0.706420839127539 & 0.587158321744921 & 0.293579160872461 \tabularnewline
43 & 0.724339844691807 & 0.551320310616385 & 0.275660155308193 \tabularnewline
44 & 0.68137097747268 & 0.637258045054639 & 0.31862902252732 \tabularnewline
45 & 0.64816202634083 & 0.70367594731834 & 0.35183797365917 \tabularnewline
46 & 0.842490524234059 & 0.315018951531883 & 0.157509475765941 \tabularnewline
47 & 0.85794937980444 & 0.284101240391121 & 0.142050620195561 \tabularnewline
48 & 0.833242505068752 & 0.333514989862496 & 0.166757494931248 \tabularnewline
49 & 0.820865313502568 & 0.358269372994864 & 0.179134686497432 \tabularnewline
50 & 0.814046683576483 & 0.371906632847033 & 0.185953316423517 \tabularnewline
51 & 0.784461215021879 & 0.431077569956241 & 0.21553878497812 \tabularnewline
52 & 0.750565969008484 & 0.498868061983031 & 0.249434030991516 \tabularnewline
53 & 0.771392063558644 & 0.457215872882712 & 0.228607936441356 \tabularnewline
54 & 0.743041772857131 & 0.513916454285739 & 0.256958227142869 \tabularnewline
55 & 0.769521561090501 & 0.460956877818997 & 0.230478438909499 \tabularnewline
56 & 0.766453919214753 & 0.467092161570493 & 0.233546080785247 \tabularnewline
57 & 0.732211182840494 & 0.535577634319012 & 0.267788817159506 \tabularnewline
58 & 0.722573719660926 & 0.554852560678149 & 0.277426280339074 \tabularnewline
59 & 0.686278302065892 & 0.627443395868216 & 0.313721697934108 \tabularnewline
60 & 0.694895103124756 & 0.610209793750489 & 0.305104896875244 \tabularnewline
61 & 0.656176949773582 & 0.687646100452836 & 0.343823050226418 \tabularnewline
62 & 0.61476517111096 & 0.77046965777808 & 0.38523482888904 \tabularnewline
63 & 0.574034651986571 & 0.851930696026858 & 0.425965348013429 \tabularnewline
64 & 0.533305696313246 & 0.933388607373507 & 0.466694303686754 \tabularnewline
65 & 0.499134218053407 & 0.998268436106813 & 0.500865781946593 \tabularnewline
66 & 0.469369665185799 & 0.938739330371598 & 0.530630334814201 \tabularnewline
67 & 0.464983874169852 & 0.929967748339704 & 0.535016125830148 \tabularnewline
68 & 0.593408140286899 & 0.813183719426203 & 0.406591859713101 \tabularnewline
69 & 0.736058081404851 & 0.527883837190298 & 0.263941918595149 \tabularnewline
70 & 0.702605171117511 & 0.594789657764978 & 0.297394828882489 \tabularnewline
71 & 0.812845401047386 & 0.374309197905228 & 0.187154598952614 \tabularnewline
72 & 0.784025103868224 & 0.431949792263551 & 0.215974896131776 \tabularnewline
73 & 0.776250266549174 & 0.447499466901652 & 0.223749733450826 \tabularnewline
74 & 0.751071310187781 & 0.497857379624437 & 0.248928689812219 \tabularnewline
75 & 0.71745694542213 & 0.56508610915574 & 0.28254305457787 \tabularnewline
76 & 0.785743371975053 & 0.428513256049894 & 0.214256628024947 \tabularnewline
77 & 0.751678225631104 & 0.496643548737791 & 0.248321774368896 \tabularnewline
78 & 0.736493071548393 & 0.527013856903213 & 0.263506928451606 \tabularnewline
79 & 0.737155883723843 & 0.525688232552314 & 0.262844116276157 \tabularnewline
80 & 0.701593783523991 & 0.596812432952017 & 0.298406216476009 \tabularnewline
81 & 0.665411625967855 & 0.66917674806429 & 0.334588374032145 \tabularnewline
82 & 0.798061073089077 & 0.403877853821846 & 0.201938926910923 \tabularnewline
83 & 0.76485564528928 & 0.47028870942144 & 0.23514435471072 \tabularnewline
84 & 0.736948438348502 & 0.526103123302996 & 0.263051561651498 \tabularnewline
85 & 0.698996315721251 & 0.602007368557497 & 0.301003684278749 \tabularnewline
86 & 0.690582332489121 & 0.618835335021758 & 0.309417667510879 \tabularnewline
87 & 0.650208660734446 & 0.699582678531108 & 0.349791339265554 \tabularnewline
88 & 0.610065474114392 & 0.779869051771216 & 0.389934525885608 \tabularnewline
89 & 0.585561981744617 & 0.828876036510765 & 0.414438018255383 \tabularnewline
90 & 0.551241636387192 & 0.897516727225616 & 0.448758363612808 \tabularnewline
91 & 0.539182721988064 & 0.921634556023871 & 0.460817278011936 \tabularnewline
92 & 0.495251092877063 & 0.990502185754126 & 0.504748907122937 \tabularnewline
93 & 0.452161991453549 & 0.904323982907099 & 0.547838008546451 \tabularnewline
94 & 0.406660203279976 & 0.813320406559951 & 0.593339796720024 \tabularnewline
95 & 0.43219566918344 & 0.86439133836688 & 0.56780433081656 \tabularnewline
96 & 0.396143500313436 & 0.792287000626873 & 0.603856499686564 \tabularnewline
97 & 0.354227685237823 & 0.708455370475646 & 0.645772314762177 \tabularnewline
98 & 0.350900951564483 & 0.701801903128966 & 0.649099048435517 \tabularnewline
99 & 0.308388162422465 & 0.616776324844929 & 0.691611837577535 \tabularnewline
100 & 0.272821193068454 & 0.545642386136908 & 0.727178806931546 \tabularnewline
101 & 0.248380243603444 & 0.496760487206887 & 0.751619756396556 \tabularnewline
102 & 0.22862013683519 & 0.457240273670379 & 0.77137986316481 \tabularnewline
103 & 0.285619692393134 & 0.571239384786267 & 0.714380307606866 \tabularnewline
104 & 0.246601324395475 & 0.49320264879095 & 0.753398675604525 \tabularnewline
105 & 0.243568090370237 & 0.487136180740475 & 0.756431909629763 \tabularnewline
106 & 0.25697919043312 & 0.51395838086624 & 0.74302080956688 \tabularnewline
107 & 0.236225650874607 & 0.472451301749214 & 0.763774349125393 \tabularnewline
108 & 0.211987702736546 & 0.423975405473091 & 0.788012297263454 \tabularnewline
109 & 0.205167976931754 & 0.410335953863508 & 0.794832023068246 \tabularnewline
110 & 0.19726270176802 & 0.394525403536039 & 0.80273729823198 \tabularnewline
111 & 0.187922742849247 & 0.375845485698494 & 0.812077257150753 \tabularnewline
112 & 0.165684733686501 & 0.331369467373002 & 0.834315266313499 \tabularnewline
113 & 0.196696095617723 & 0.393392191235445 & 0.803303904382277 \tabularnewline
114 & 0.172208925422619 & 0.344417850845237 & 0.827791074577382 \tabularnewline
115 & 0.180141955107255 & 0.360283910214509 & 0.819858044892745 \tabularnewline
116 & 0.193608651244215 & 0.38721730248843 & 0.806391348755785 \tabularnewline
117 & 0.171303774855688 & 0.342607549711376 & 0.828696225144312 \tabularnewline
118 & 0.159465403259336 & 0.318930806518672 & 0.840534596740664 \tabularnewline
119 & 0.155150913725238 & 0.310301827450476 & 0.844849086274762 \tabularnewline
120 & 0.15753383433584 & 0.315067668671681 & 0.84246616566416 \tabularnewline
121 & 0.13348799038562 & 0.26697598077124 & 0.86651200961438 \tabularnewline
122 & 0.124258282631044 & 0.248516565262089 & 0.875741717368956 \tabularnewline
123 & 0.130152883890028 & 0.260305767780056 & 0.869847116109972 \tabularnewline
124 & 0.107148877298841 & 0.214297754597682 & 0.892851122701159 \tabularnewline
125 & 0.0853497599249869 & 0.170699519849974 & 0.914650240075013 \tabularnewline
126 & 0.0666632843252503 & 0.133326568650501 & 0.93333671567475 \tabularnewline
127 & 0.0505379369030187 & 0.101075873806037 & 0.949462063096981 \tabularnewline
128 & 0.0536962493071639 & 0.107392498614328 & 0.946303750692836 \tabularnewline
129 & 0.0503629144365288 & 0.100725828873058 & 0.949637085563471 \tabularnewline
130 & 0.0505264275431256 & 0.101052855086251 & 0.949473572456874 \tabularnewline
131 & 0.0548887226808006 & 0.109777445361601 & 0.945111277319199 \tabularnewline
132 & 0.067599167845438 & 0.135198335690876 & 0.932400832154562 \tabularnewline
133 & 0.134981252515547 & 0.269962505031095 & 0.865018747484453 \tabularnewline
134 & 0.137101940574925 & 0.274203881149849 & 0.862898059425075 \tabularnewline
135 & 0.109803251077331 & 0.219606502154661 & 0.890196748922669 \tabularnewline
136 & 0.0855601643026393 & 0.171120328605279 & 0.914439835697361 \tabularnewline
137 & 0.0802693352951837 & 0.160538670590367 & 0.919730664704816 \tabularnewline
138 & 0.077725011144847 & 0.155450022289694 & 0.922274988855153 \tabularnewline
139 & 0.0734500532418801 & 0.14690010648376 & 0.92654994675812 \tabularnewline
140 & 0.0534633827254409 & 0.106926765450882 & 0.946536617274559 \tabularnewline
141 & 0.479251881726555 & 0.958503763453109 & 0.520748118273445 \tabularnewline
142 & 0.493952958926101 & 0.987905917852203 & 0.506047041073899 \tabularnewline
143 & 0.444299676414526 & 0.888599352829052 & 0.555700323585474 \tabularnewline
144 & 0.389128890547723 & 0.778257781095445 & 0.610871109452277 \tabularnewline
145 & 0.320135706765449 & 0.640271413530898 & 0.679864293234551 \tabularnewline
146 & 0.387166069004995 & 0.774332138009989 & 0.612833930995005 \tabularnewline
147 & 0.387600305268912 & 0.775200610537824 & 0.612399694731088 \tabularnewline
148 & 0.414344242256942 & 0.828688484513884 & 0.585655757743058 \tabularnewline
149 & 0.327845714282247 & 0.655691428564495 & 0.672154285717753 \tabularnewline
150 & 0.240878804427133 & 0.481757608854266 & 0.759121195572867 \tabularnewline
151 & 0.702755214480402 & 0.594489571039196 & 0.297244785519598 \tabularnewline
152 & 0.851140473992672 & 0.297719052014657 & 0.148859526007328 \tabularnewline
153 & 0.74379330069278 & 0.512413398614441 & 0.25620669930722 \tabularnewline
154 & 0.601287626699516 & 0.797424746600968 & 0.398712373300484 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185735&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]8[/C][C]0.969709844322742[/C][C]0.0605803113545167[/C][C]0.0302901556772583[/C][/ROW]
[ROW][C]9[/C][C]0.9388584225896[/C][C]0.122283154820799[/C][C]0.0611415774103997[/C][/ROW]
[ROW][C]10[/C][C]0.909977402687418[/C][C]0.180045194625163[/C][C]0.0900225973125817[/C][/ROW]
[ROW][C]11[/C][C]0.885154630376736[/C][C]0.229690739246528[/C][C]0.114845369623264[/C][/ROW]
[ROW][C]12[/C][C]0.83250470126517[/C][C]0.33499059746966[/C][C]0.16749529873483[/C][/ROW]
[ROW][C]13[/C][C]0.760481499441257[/C][C]0.479037001117486[/C][C]0.239518500558743[/C][/ROW]
[ROW][C]14[/C][C]0.67702854018245[/C][C]0.6459429196351[/C][C]0.32297145981755[/C][/ROW]
[ROW][C]15[/C][C]0.588281316683998[/C][C]0.823437366632003[/C][C]0.411718683316002[/C][/ROW]
[ROW][C]16[/C][C]0.568759106340392[/C][C]0.862481787319215[/C][C]0.431240893659608[/C][/ROW]
[ROW][C]17[/C][C]0.494040089661686[/C][C]0.988080179323372[/C][C]0.505959910338314[/C][/ROW]
[ROW][C]18[/C][C]0.739218762177385[/C][C]0.521562475645229[/C][C]0.260781237822615[/C][/ROW]
[ROW][C]19[/C][C]0.710869725701992[/C][C]0.578260548596016[/C][C]0.289130274298008[/C][/ROW]
[ROW][C]20[/C][C]0.662800102389243[/C][C]0.674399795221515[/C][C]0.337199897610757[/C][/ROW]
[ROW][C]21[/C][C]0.593679204855881[/C][C]0.812641590288237[/C][C]0.406320795144119[/C][/ROW]
[ROW][C]22[/C][C]0.539389069930892[/C][C]0.921221860138216[/C][C]0.460610930069108[/C][/ROW]
[ROW][C]23[/C][C]0.510239223430523[/C][C]0.979521553138953[/C][C]0.489760776569477[/C][/ROW]
[ROW][C]24[/C][C]0.476113454486543[/C][C]0.952226908973086[/C][C]0.523886545513457[/C][/ROW]
[ROW][C]25[/C][C]0.427514025902672[/C][C]0.855028051805344[/C][C]0.572485974097328[/C][/ROW]
[ROW][C]26[/C][C]0.376116352046439[/C][C]0.752232704092879[/C][C]0.623883647953561[/C][/ROW]
[ROW][C]27[/C][C]0.331354018126025[/C][C]0.66270803625205[/C][C]0.668645981873975[/C][/ROW]
[ROW][C]28[/C][C]0.379162652472479[/C][C]0.758325304944958[/C][C]0.620837347527521[/C][/ROW]
[ROW][C]29[/C][C]0.329476330922241[/C][C]0.658952661844483[/C][C]0.670523669077759[/C][/ROW]
[ROW][C]30[/C][C]0.345482787159734[/C][C]0.690965574319468[/C][C]0.654517212840266[/C][/ROW]
[ROW][C]31[/C][C]0.304249433962412[/C][C]0.608498867924823[/C][C]0.695750566037588[/C][/ROW]
[ROW][C]32[/C][C]0.30859831580702[/C][C]0.61719663161404[/C][C]0.69140168419298[/C][/ROW]
[ROW][C]33[/C][C]0.309316341503561[/C][C]0.618632683007122[/C][C]0.690683658496439[/C][/ROW]
[ROW][C]34[/C][C]0.258928809531551[/C][C]0.517857619063102[/C][C]0.741071190468449[/C][/ROW]
[ROW][C]35[/C][C]0.257341202234099[/C][C]0.514682404468197[/C][C]0.742658797765901[/C][/ROW]
[ROW][C]36[/C][C]0.77518732885928[/C][C]0.449625342281441[/C][C]0.22481267114072[/C][/ROW]
[ROW][C]37[/C][C]0.753012650859221[/C][C]0.493974698281557[/C][C]0.246987349140779[/C][/ROW]
[ROW][C]38[/C][C]0.736593606835857[/C][C]0.526812786328287[/C][C]0.263406393164144[/C][/ROW]
[ROW][C]39[/C][C]0.777608728262545[/C][C]0.444782543474909[/C][C]0.222391271737455[/C][/ROW]
[ROW][C]40[/C][C]0.762056601817925[/C][C]0.47588679636415[/C][C]0.237943398182075[/C][/ROW]
[ROW][C]41[/C][C]0.732125647762959[/C][C]0.535748704474082[/C][C]0.267874352237041[/C][/ROW]
[ROW][C]42[/C][C]0.706420839127539[/C][C]0.587158321744921[/C][C]0.293579160872461[/C][/ROW]
[ROW][C]43[/C][C]0.724339844691807[/C][C]0.551320310616385[/C][C]0.275660155308193[/C][/ROW]
[ROW][C]44[/C][C]0.68137097747268[/C][C]0.637258045054639[/C][C]0.31862902252732[/C][/ROW]
[ROW][C]45[/C][C]0.64816202634083[/C][C]0.70367594731834[/C][C]0.35183797365917[/C][/ROW]
[ROW][C]46[/C][C]0.842490524234059[/C][C]0.315018951531883[/C][C]0.157509475765941[/C][/ROW]
[ROW][C]47[/C][C]0.85794937980444[/C][C]0.284101240391121[/C][C]0.142050620195561[/C][/ROW]
[ROW][C]48[/C][C]0.833242505068752[/C][C]0.333514989862496[/C][C]0.166757494931248[/C][/ROW]
[ROW][C]49[/C][C]0.820865313502568[/C][C]0.358269372994864[/C][C]0.179134686497432[/C][/ROW]
[ROW][C]50[/C][C]0.814046683576483[/C][C]0.371906632847033[/C][C]0.185953316423517[/C][/ROW]
[ROW][C]51[/C][C]0.784461215021879[/C][C]0.431077569956241[/C][C]0.21553878497812[/C][/ROW]
[ROW][C]52[/C][C]0.750565969008484[/C][C]0.498868061983031[/C][C]0.249434030991516[/C][/ROW]
[ROW][C]53[/C][C]0.771392063558644[/C][C]0.457215872882712[/C][C]0.228607936441356[/C][/ROW]
[ROW][C]54[/C][C]0.743041772857131[/C][C]0.513916454285739[/C][C]0.256958227142869[/C][/ROW]
[ROW][C]55[/C][C]0.769521561090501[/C][C]0.460956877818997[/C][C]0.230478438909499[/C][/ROW]
[ROW][C]56[/C][C]0.766453919214753[/C][C]0.467092161570493[/C][C]0.233546080785247[/C][/ROW]
[ROW][C]57[/C][C]0.732211182840494[/C][C]0.535577634319012[/C][C]0.267788817159506[/C][/ROW]
[ROW][C]58[/C][C]0.722573719660926[/C][C]0.554852560678149[/C][C]0.277426280339074[/C][/ROW]
[ROW][C]59[/C][C]0.686278302065892[/C][C]0.627443395868216[/C][C]0.313721697934108[/C][/ROW]
[ROW][C]60[/C][C]0.694895103124756[/C][C]0.610209793750489[/C][C]0.305104896875244[/C][/ROW]
[ROW][C]61[/C][C]0.656176949773582[/C][C]0.687646100452836[/C][C]0.343823050226418[/C][/ROW]
[ROW][C]62[/C][C]0.61476517111096[/C][C]0.77046965777808[/C][C]0.38523482888904[/C][/ROW]
[ROW][C]63[/C][C]0.574034651986571[/C][C]0.851930696026858[/C][C]0.425965348013429[/C][/ROW]
[ROW][C]64[/C][C]0.533305696313246[/C][C]0.933388607373507[/C][C]0.466694303686754[/C][/ROW]
[ROW][C]65[/C][C]0.499134218053407[/C][C]0.998268436106813[/C][C]0.500865781946593[/C][/ROW]
[ROW][C]66[/C][C]0.469369665185799[/C][C]0.938739330371598[/C][C]0.530630334814201[/C][/ROW]
[ROW][C]67[/C][C]0.464983874169852[/C][C]0.929967748339704[/C][C]0.535016125830148[/C][/ROW]
[ROW][C]68[/C][C]0.593408140286899[/C][C]0.813183719426203[/C][C]0.406591859713101[/C][/ROW]
[ROW][C]69[/C][C]0.736058081404851[/C][C]0.527883837190298[/C][C]0.263941918595149[/C][/ROW]
[ROW][C]70[/C][C]0.702605171117511[/C][C]0.594789657764978[/C][C]0.297394828882489[/C][/ROW]
[ROW][C]71[/C][C]0.812845401047386[/C][C]0.374309197905228[/C][C]0.187154598952614[/C][/ROW]
[ROW][C]72[/C][C]0.784025103868224[/C][C]0.431949792263551[/C][C]0.215974896131776[/C][/ROW]
[ROW][C]73[/C][C]0.776250266549174[/C][C]0.447499466901652[/C][C]0.223749733450826[/C][/ROW]
[ROW][C]74[/C][C]0.751071310187781[/C][C]0.497857379624437[/C][C]0.248928689812219[/C][/ROW]
[ROW][C]75[/C][C]0.71745694542213[/C][C]0.56508610915574[/C][C]0.28254305457787[/C][/ROW]
[ROW][C]76[/C][C]0.785743371975053[/C][C]0.428513256049894[/C][C]0.214256628024947[/C][/ROW]
[ROW][C]77[/C][C]0.751678225631104[/C][C]0.496643548737791[/C][C]0.248321774368896[/C][/ROW]
[ROW][C]78[/C][C]0.736493071548393[/C][C]0.527013856903213[/C][C]0.263506928451606[/C][/ROW]
[ROW][C]79[/C][C]0.737155883723843[/C][C]0.525688232552314[/C][C]0.262844116276157[/C][/ROW]
[ROW][C]80[/C][C]0.701593783523991[/C][C]0.596812432952017[/C][C]0.298406216476009[/C][/ROW]
[ROW][C]81[/C][C]0.665411625967855[/C][C]0.66917674806429[/C][C]0.334588374032145[/C][/ROW]
[ROW][C]82[/C][C]0.798061073089077[/C][C]0.403877853821846[/C][C]0.201938926910923[/C][/ROW]
[ROW][C]83[/C][C]0.76485564528928[/C][C]0.47028870942144[/C][C]0.23514435471072[/C][/ROW]
[ROW][C]84[/C][C]0.736948438348502[/C][C]0.526103123302996[/C][C]0.263051561651498[/C][/ROW]
[ROW][C]85[/C][C]0.698996315721251[/C][C]0.602007368557497[/C][C]0.301003684278749[/C][/ROW]
[ROW][C]86[/C][C]0.690582332489121[/C][C]0.618835335021758[/C][C]0.309417667510879[/C][/ROW]
[ROW][C]87[/C][C]0.650208660734446[/C][C]0.699582678531108[/C][C]0.349791339265554[/C][/ROW]
[ROW][C]88[/C][C]0.610065474114392[/C][C]0.779869051771216[/C][C]0.389934525885608[/C][/ROW]
[ROW][C]89[/C][C]0.585561981744617[/C][C]0.828876036510765[/C][C]0.414438018255383[/C][/ROW]
[ROW][C]90[/C][C]0.551241636387192[/C][C]0.897516727225616[/C][C]0.448758363612808[/C][/ROW]
[ROW][C]91[/C][C]0.539182721988064[/C][C]0.921634556023871[/C][C]0.460817278011936[/C][/ROW]
[ROW][C]92[/C][C]0.495251092877063[/C][C]0.990502185754126[/C][C]0.504748907122937[/C][/ROW]
[ROW][C]93[/C][C]0.452161991453549[/C][C]0.904323982907099[/C][C]0.547838008546451[/C][/ROW]
[ROW][C]94[/C][C]0.406660203279976[/C][C]0.813320406559951[/C][C]0.593339796720024[/C][/ROW]
[ROW][C]95[/C][C]0.43219566918344[/C][C]0.86439133836688[/C][C]0.56780433081656[/C][/ROW]
[ROW][C]96[/C][C]0.396143500313436[/C][C]0.792287000626873[/C][C]0.603856499686564[/C][/ROW]
[ROW][C]97[/C][C]0.354227685237823[/C][C]0.708455370475646[/C][C]0.645772314762177[/C][/ROW]
[ROW][C]98[/C][C]0.350900951564483[/C][C]0.701801903128966[/C][C]0.649099048435517[/C][/ROW]
[ROW][C]99[/C][C]0.308388162422465[/C][C]0.616776324844929[/C][C]0.691611837577535[/C][/ROW]
[ROW][C]100[/C][C]0.272821193068454[/C][C]0.545642386136908[/C][C]0.727178806931546[/C][/ROW]
[ROW][C]101[/C][C]0.248380243603444[/C][C]0.496760487206887[/C][C]0.751619756396556[/C][/ROW]
[ROW][C]102[/C][C]0.22862013683519[/C][C]0.457240273670379[/C][C]0.77137986316481[/C][/ROW]
[ROW][C]103[/C][C]0.285619692393134[/C][C]0.571239384786267[/C][C]0.714380307606866[/C][/ROW]
[ROW][C]104[/C][C]0.246601324395475[/C][C]0.49320264879095[/C][C]0.753398675604525[/C][/ROW]
[ROW][C]105[/C][C]0.243568090370237[/C][C]0.487136180740475[/C][C]0.756431909629763[/C][/ROW]
[ROW][C]106[/C][C]0.25697919043312[/C][C]0.51395838086624[/C][C]0.74302080956688[/C][/ROW]
[ROW][C]107[/C][C]0.236225650874607[/C][C]0.472451301749214[/C][C]0.763774349125393[/C][/ROW]
[ROW][C]108[/C][C]0.211987702736546[/C][C]0.423975405473091[/C][C]0.788012297263454[/C][/ROW]
[ROW][C]109[/C][C]0.205167976931754[/C][C]0.410335953863508[/C][C]0.794832023068246[/C][/ROW]
[ROW][C]110[/C][C]0.19726270176802[/C][C]0.394525403536039[/C][C]0.80273729823198[/C][/ROW]
[ROW][C]111[/C][C]0.187922742849247[/C][C]0.375845485698494[/C][C]0.812077257150753[/C][/ROW]
[ROW][C]112[/C][C]0.165684733686501[/C][C]0.331369467373002[/C][C]0.834315266313499[/C][/ROW]
[ROW][C]113[/C][C]0.196696095617723[/C][C]0.393392191235445[/C][C]0.803303904382277[/C][/ROW]
[ROW][C]114[/C][C]0.172208925422619[/C][C]0.344417850845237[/C][C]0.827791074577382[/C][/ROW]
[ROW][C]115[/C][C]0.180141955107255[/C][C]0.360283910214509[/C][C]0.819858044892745[/C][/ROW]
[ROW][C]116[/C][C]0.193608651244215[/C][C]0.38721730248843[/C][C]0.806391348755785[/C][/ROW]
[ROW][C]117[/C][C]0.171303774855688[/C][C]0.342607549711376[/C][C]0.828696225144312[/C][/ROW]
[ROW][C]118[/C][C]0.159465403259336[/C][C]0.318930806518672[/C][C]0.840534596740664[/C][/ROW]
[ROW][C]119[/C][C]0.155150913725238[/C][C]0.310301827450476[/C][C]0.844849086274762[/C][/ROW]
[ROW][C]120[/C][C]0.15753383433584[/C][C]0.315067668671681[/C][C]0.84246616566416[/C][/ROW]
[ROW][C]121[/C][C]0.13348799038562[/C][C]0.26697598077124[/C][C]0.86651200961438[/C][/ROW]
[ROW][C]122[/C][C]0.124258282631044[/C][C]0.248516565262089[/C][C]0.875741717368956[/C][/ROW]
[ROW][C]123[/C][C]0.130152883890028[/C][C]0.260305767780056[/C][C]0.869847116109972[/C][/ROW]
[ROW][C]124[/C][C]0.107148877298841[/C][C]0.214297754597682[/C][C]0.892851122701159[/C][/ROW]
[ROW][C]125[/C][C]0.0853497599249869[/C][C]0.170699519849974[/C][C]0.914650240075013[/C][/ROW]
[ROW][C]126[/C][C]0.0666632843252503[/C][C]0.133326568650501[/C][C]0.93333671567475[/C][/ROW]
[ROW][C]127[/C][C]0.0505379369030187[/C][C]0.101075873806037[/C][C]0.949462063096981[/C][/ROW]
[ROW][C]128[/C][C]0.0536962493071639[/C][C]0.107392498614328[/C][C]0.946303750692836[/C][/ROW]
[ROW][C]129[/C][C]0.0503629144365288[/C][C]0.100725828873058[/C][C]0.949637085563471[/C][/ROW]
[ROW][C]130[/C][C]0.0505264275431256[/C][C]0.101052855086251[/C][C]0.949473572456874[/C][/ROW]
[ROW][C]131[/C][C]0.0548887226808006[/C][C]0.109777445361601[/C][C]0.945111277319199[/C][/ROW]
[ROW][C]132[/C][C]0.067599167845438[/C][C]0.135198335690876[/C][C]0.932400832154562[/C][/ROW]
[ROW][C]133[/C][C]0.134981252515547[/C][C]0.269962505031095[/C][C]0.865018747484453[/C][/ROW]
[ROW][C]134[/C][C]0.137101940574925[/C][C]0.274203881149849[/C][C]0.862898059425075[/C][/ROW]
[ROW][C]135[/C][C]0.109803251077331[/C][C]0.219606502154661[/C][C]0.890196748922669[/C][/ROW]
[ROW][C]136[/C][C]0.0855601643026393[/C][C]0.171120328605279[/C][C]0.914439835697361[/C][/ROW]
[ROW][C]137[/C][C]0.0802693352951837[/C][C]0.160538670590367[/C][C]0.919730664704816[/C][/ROW]
[ROW][C]138[/C][C]0.077725011144847[/C][C]0.155450022289694[/C][C]0.922274988855153[/C][/ROW]
[ROW][C]139[/C][C]0.0734500532418801[/C][C]0.14690010648376[/C][C]0.92654994675812[/C][/ROW]
[ROW][C]140[/C][C]0.0534633827254409[/C][C]0.106926765450882[/C][C]0.946536617274559[/C][/ROW]
[ROW][C]141[/C][C]0.479251881726555[/C][C]0.958503763453109[/C][C]0.520748118273445[/C][/ROW]
[ROW][C]142[/C][C]0.493952958926101[/C][C]0.987905917852203[/C][C]0.506047041073899[/C][/ROW]
[ROW][C]143[/C][C]0.444299676414526[/C][C]0.888599352829052[/C][C]0.555700323585474[/C][/ROW]
[ROW][C]144[/C][C]0.389128890547723[/C][C]0.778257781095445[/C][C]0.610871109452277[/C][/ROW]
[ROW][C]145[/C][C]0.320135706765449[/C][C]0.640271413530898[/C][C]0.679864293234551[/C][/ROW]
[ROW][C]146[/C][C]0.387166069004995[/C][C]0.774332138009989[/C][C]0.612833930995005[/C][/ROW]
[ROW][C]147[/C][C]0.387600305268912[/C][C]0.775200610537824[/C][C]0.612399694731088[/C][/ROW]
[ROW][C]148[/C][C]0.414344242256942[/C][C]0.828688484513884[/C][C]0.585655757743058[/C][/ROW]
[ROW][C]149[/C][C]0.327845714282247[/C][C]0.655691428564495[/C][C]0.672154285717753[/C][/ROW]
[ROW][C]150[/C][C]0.240878804427133[/C][C]0.481757608854266[/C][C]0.759121195572867[/C][/ROW]
[ROW][C]151[/C][C]0.702755214480402[/C][C]0.594489571039196[/C][C]0.297244785519598[/C][/ROW]
[ROW][C]152[/C][C]0.851140473992672[/C][C]0.297719052014657[/C][C]0.148859526007328[/C][/ROW]
[ROW][C]153[/C][C]0.74379330069278[/C][C]0.512413398614441[/C][C]0.25620669930722[/C][/ROW]
[ROW][C]154[/C][C]0.601287626699516[/C][C]0.797424746600968[/C][C]0.398712373300484[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185735&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185735&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
80.9697098443227420.06058031135451670.0302901556772583
90.93885842258960.1222831548207990.0611415774103997
100.9099774026874180.1800451946251630.0900225973125817
110.8851546303767360.2296907392465280.114845369623264
120.832504701265170.334990597469660.16749529873483
130.7604814994412570.4790370011174860.239518500558743
140.677028540182450.64594291963510.32297145981755
150.5882813166839980.8234373666320030.411718683316002
160.5687591063403920.8624817873192150.431240893659608
170.4940400896616860.9880801793233720.505959910338314
180.7392187621773850.5215624756452290.260781237822615
190.7108697257019920.5782605485960160.289130274298008
200.6628001023892430.6743997952215150.337199897610757
210.5936792048558810.8126415902882370.406320795144119
220.5393890699308920.9212218601382160.460610930069108
230.5102392234305230.9795215531389530.489760776569477
240.4761134544865430.9522269089730860.523886545513457
250.4275140259026720.8550280518053440.572485974097328
260.3761163520464390.7522327040928790.623883647953561
270.3313540181260250.662708036252050.668645981873975
280.3791626524724790.7583253049449580.620837347527521
290.3294763309222410.6589526618444830.670523669077759
300.3454827871597340.6909655743194680.654517212840266
310.3042494339624120.6084988679248230.695750566037588
320.308598315807020.617196631614040.69140168419298
330.3093163415035610.6186326830071220.690683658496439
340.2589288095315510.5178576190631020.741071190468449
350.2573412022340990.5146824044681970.742658797765901
360.775187328859280.4496253422814410.22481267114072
370.7530126508592210.4939746982815570.246987349140779
380.7365936068358570.5268127863282870.263406393164144
390.7776087282625450.4447825434749090.222391271737455
400.7620566018179250.475886796364150.237943398182075
410.7321256477629590.5357487044740820.267874352237041
420.7064208391275390.5871583217449210.293579160872461
430.7243398446918070.5513203106163850.275660155308193
440.681370977472680.6372580450546390.31862902252732
450.648162026340830.703675947318340.35183797365917
460.8424905242340590.3150189515318830.157509475765941
470.857949379804440.2841012403911210.142050620195561
480.8332425050687520.3335149898624960.166757494931248
490.8208653135025680.3582693729948640.179134686497432
500.8140466835764830.3719066328470330.185953316423517
510.7844612150218790.4310775699562410.21553878497812
520.7505659690084840.4988680619830310.249434030991516
530.7713920635586440.4572158728827120.228607936441356
540.7430417728571310.5139164542857390.256958227142869
550.7695215610905010.4609568778189970.230478438909499
560.7664539192147530.4670921615704930.233546080785247
570.7322111828404940.5355776343190120.267788817159506
580.7225737196609260.5548525606781490.277426280339074
590.6862783020658920.6274433958682160.313721697934108
600.6948951031247560.6102097937504890.305104896875244
610.6561769497735820.6876461004528360.343823050226418
620.614765171110960.770469657778080.38523482888904
630.5740346519865710.8519306960268580.425965348013429
640.5333056963132460.9333886073735070.466694303686754
650.4991342180534070.9982684361068130.500865781946593
660.4693696651857990.9387393303715980.530630334814201
670.4649838741698520.9299677483397040.535016125830148
680.5934081402868990.8131837194262030.406591859713101
690.7360580814048510.5278838371902980.263941918595149
700.7026051711175110.5947896577649780.297394828882489
710.8128454010473860.3743091979052280.187154598952614
720.7840251038682240.4319497922635510.215974896131776
730.7762502665491740.4474994669016520.223749733450826
740.7510713101877810.4978573796244370.248928689812219
750.717456945422130.565086109155740.28254305457787
760.7857433719750530.4285132560498940.214256628024947
770.7516782256311040.4966435487377910.248321774368896
780.7364930715483930.5270138569032130.263506928451606
790.7371558837238430.5256882325523140.262844116276157
800.7015937835239910.5968124329520170.298406216476009
810.6654116259678550.669176748064290.334588374032145
820.7980610730890770.4038778538218460.201938926910923
830.764855645289280.470288709421440.23514435471072
840.7369484383485020.5261031233029960.263051561651498
850.6989963157212510.6020073685574970.301003684278749
860.6905823324891210.6188353350217580.309417667510879
870.6502086607344460.6995826785311080.349791339265554
880.6100654741143920.7798690517712160.389934525885608
890.5855619817446170.8288760365107650.414438018255383
900.5512416363871920.8975167272256160.448758363612808
910.5391827219880640.9216345560238710.460817278011936
920.4952510928770630.9905021857541260.504748907122937
930.4521619914535490.9043239829070990.547838008546451
940.4066602032799760.8133204065599510.593339796720024
950.432195669183440.864391338366880.56780433081656
960.3961435003134360.7922870006268730.603856499686564
970.3542276852378230.7084553704756460.645772314762177
980.3509009515644830.7018019031289660.649099048435517
990.3083881624224650.6167763248449290.691611837577535
1000.2728211930684540.5456423861369080.727178806931546
1010.2483802436034440.4967604872068870.751619756396556
1020.228620136835190.4572402736703790.77137986316481
1030.2856196923931340.5712393847862670.714380307606866
1040.2466013243954750.493202648790950.753398675604525
1050.2435680903702370.4871361807404750.756431909629763
1060.256979190433120.513958380866240.74302080956688
1070.2362256508746070.4724513017492140.763774349125393
1080.2119877027365460.4239754054730910.788012297263454
1090.2051679769317540.4103359538635080.794832023068246
1100.197262701768020.3945254035360390.80273729823198
1110.1879227428492470.3758454856984940.812077257150753
1120.1656847336865010.3313694673730020.834315266313499
1130.1966960956177230.3933921912354450.803303904382277
1140.1722089254226190.3444178508452370.827791074577382
1150.1801419551072550.3602839102145090.819858044892745
1160.1936086512442150.387217302488430.806391348755785
1170.1713037748556880.3426075497113760.828696225144312
1180.1594654032593360.3189308065186720.840534596740664
1190.1551509137252380.3103018274504760.844849086274762
1200.157533834335840.3150676686716810.84246616566416
1210.133487990385620.266975980771240.86651200961438
1220.1242582826310440.2485165652620890.875741717368956
1230.1301528838900280.2603057677800560.869847116109972
1240.1071488772988410.2142977545976820.892851122701159
1250.08534975992498690.1706995198499740.914650240075013
1260.06666328432525030.1333265686505010.93333671567475
1270.05053793690301870.1010758738060370.949462063096981
1280.05369624930716390.1073924986143280.946303750692836
1290.05036291443652880.1007258288730580.949637085563471
1300.05052642754312560.1010528550862510.949473572456874
1310.05488872268080060.1097774453616010.945111277319199
1320.0675991678454380.1351983356908760.932400832154562
1330.1349812525155470.2699625050310950.865018747484453
1340.1371019405749250.2742038811498490.862898059425075
1350.1098032510773310.2196065021546610.890196748922669
1360.08556016430263930.1711203286052790.914439835697361
1370.08026933529518370.1605386705903670.919730664704816
1380.0777250111448470.1554500222896940.922274988855153
1390.07345005324188010.146900106483760.92654994675812
1400.05346338272544090.1069267654508820.946536617274559
1410.4792518817265550.9585037634531090.520748118273445
1420.4939529589261010.9879059178522030.506047041073899
1430.4442996764145260.8885993528290520.555700323585474
1440.3891288905477230.7782577810954450.610871109452277
1450.3201357067654490.6402714135308980.679864293234551
1460.3871660690049950.7743321380099890.612833930995005
1470.3876003052689120.7752006105378240.612399694731088
1480.4143442422569420.8286884845138840.585655757743058
1490.3278457142822470.6556914285644950.672154285717753
1500.2408788044271330.4817576088542660.759121195572867
1510.7027552144804020.5944895710391960.297244785519598
1520.8511404739926720.2977190520146570.148859526007328
1530.743793300692780.5124133986144410.25620669930722
1540.6012876266995160.7974247466009680.398712373300484







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.00680272108843537OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.00680272108843537 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185735&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.00680272108843537[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185735&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185735&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 level00OK
5% type I error level00OK
10% type I error level10.00680272108843537OK



Parameters (Session):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 5 ; 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')
}