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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 10:23:30 -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/t1351952678swxx02nn5jzwt1z.htm/, Retrieved Tue, 09 Aug 2022 20:04:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185736, Retrieved Tue, 09 Aug 2022 20:04:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsHubert Liskiewicz OLS Learning
Estimated Impact100
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Multiple Regressi...] [2012-11-03 14:23:30] [acfd67cb214b61d0a5e0fb4c8c6ef02a] [Current]
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Dataseries X:
41	12	12	13
39	11	11	16
30	15	14	19
31	6	12	15
34	13	21	14
35	10	12	13
39	12	22	19
34	14	11	15
36	12	10	14
37	6	13	15
38	10	10	16
36	12	8	16
38	12	15	16
39	11	14	16
33	15	10	17
32	12	14	15
36	10	14	15
38	12	11	20
39	11	10	18
32	12	13	16
32	11	7	16
31	12	14	16
39	13	12	19
37	11	14	16
39	9	11	17
41	13	9	17
36	10	11	16
33	14	15	15
33	12	14	16
34	10	13	14
31	12	9	15
27	8	15	12
37	10	10	14
34	12	11	16
34	12	13	14
32	7	8	7
29	6	20	10
36	12	12	14
29	10	10	16
35	10	10	16
37	10	9	16
34	12	14	14
38	15	8	20
35	10	14	14
38	10	11	14
37	12	13	11
38	13	9	14
33	11	11	15
36	11	15	16
38	12	11	14
32	14	10	16
32	10	14	14
32	12	18	12
34	13	14	16
32	5	11	9
37	6	12	14
39	12	13	16
29	12	9	16
37	11	10	15
35	10	15	16
30	7	20	12
38	12	12	16
34	14	12	16
31	11	14	14
34	12	13	16
35	13	11	17
36	14	17	18
30	11	12	18
39	12	13	12
35	12	14	16
38	8	13	10
31	11	15	14
34	14	13	18
38	14	10	18
34	12	11	16
39	9	19	17
37	13	13	16
34	11	17	16
28	12	13	13
37	12	9	16
33	12	11	16
37	12	10	20
35	12	9	16
37	12	12	15
32	11	12	15
33	10	13	16
38	9	13	14
33	12	12	16
29	12	15	16
33	12	22	15
31	9	13	12
36	15	15	17
35	12	13	16
32	12	15	15
29	12	10	13
39	10	11	16
37	13	16	16
35	9	11	16
37	12	11	16
32	10	10	14
38	14	10	16
37	11	16	16
36	15	12	20
32	11	11	15
33	11	16	16
40	12	19	13
38	12	11	17
41	12	16	16
36	11	15	16
43	7	24	12
30	12	14	16
31	14	15	16
32	11	11	17
32	11	15	13
37	10	12	12
37	13	10	18
33	13	14	14
34	8	13	14
33	11	9	13
38	12	15	16
33	11	15	13
31	13	14	16
38	12	11	13
37	14	8	16
33	13	11	15
31	15	11	16
39	10	8	15
44	11	10	17
33	9	11	15
35	11	13	12
32	10	11	16
28	11	20	10
40	8	10	16
27	11	15	12
37	12	12	14
32	12	14	15
28	9	23	13
34	11	14	15
30	10	16	11
35	8	11	12
31	9	12	8
32	8	10	16
30	9	14	15
30	15	12	17
31	11	12	16
40	8	11	10
32	13	12	18
36	12	13	13
32	12	11	16
35	9	19	13
38	7	12	10
42	13	17	15
34	9	9	16
35	6	12	16
35	8	19	14
33	8	18	10
36	15	15	17
32	6	14	13
33	9	11	15
34	11	9	16
32	8	18	12
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'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185736&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185736&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185736&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'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 6.45638046344027 + 0.109869875550479Connected[t] + 0.543599269953389Software[t] -0.102140496668171Depression[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  6.45638046344027 +  0.109869875550479Connected[t] +  0.543599269953389Software[t] -0.102140496668171Depression[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185736&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  6.45638046344027 +  0.109869875550479Connected[t] +  0.543599269953389Software[t] -0.102140496668171Depression[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185736&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185736&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] = + 6.45638046344027 + 0.109869875550479Connected[t] + 0.543599269953389Software[t] -0.102140496668171Depression[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.456380463440271.845383.49870.0006080.000304
Connected0.1098698755504790.0432382.5410.0120170.006008
Software0.5435992699533890.0682697.962600
Depression-0.1021404966681710.046427-2.20.0292580.014629

\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) & 6.45638046344027 & 1.84538 & 3.4987 & 0.000608 & 0.000304 \tabularnewline
Connected & 0.109869875550479 & 0.043238 & 2.541 & 0.012017 & 0.006008 \tabularnewline
Software & 0.543599269953389 & 0.068269 & 7.9626 & 0 & 0 \tabularnewline
Depression & -0.102140496668171 & 0.046427 & -2.2 & 0.029258 & 0.014629 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185736&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]6.45638046344027[/C][C]1.84538[/C][C]3.4987[/C][C]0.000608[/C][C]0.000304[/C][/ROW]
[ROW][C]Connected[/C][C]0.109869875550479[/C][C]0.043238[/C][C]2.541[/C][C]0.012017[/C][C]0.006008[/C][/ROW]
[ROW][C]Software[/C][C]0.543599269953389[/C][C]0.068269[/C][C]7.9626[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Depression[/C][C]-0.102140496668171[/C][C]0.046427[/C][C]-2.2[/C][C]0.029258[/C][C]0.014629[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185736&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185736&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)6.456380463440271.845383.49870.0006080.000304
Connected0.1098698755504790.0432382.5410.0120170.006008
Software0.5435992699533890.0682697.962600
Depression-0.1021404966681710.046427-2.20.0292580.014629







Multiple Linear Regression - Regression Statistics
Multiple R0.591595188540567
R-squared0.349984867104349
Adjusted R-squared0.337642807618989
F-TEST (value)28.3570880143208
F-TEST (DF numerator)3
F-TEST (DF denominator)158
p-value9.99200722162641e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.83626549484212
Sum Squared Residuals532.755612872549

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.591595188540567 \tabularnewline
R-squared & 0.349984867104349 \tabularnewline
Adjusted R-squared & 0.337642807618989 \tabularnewline
F-TEST (value) & 28.3570880143208 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 158 \tabularnewline
p-value & 9.99200722162641e-15 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.83626549484212 \tabularnewline
Sum Squared Residuals & 532.755612872549 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185736&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.591595188540567[/C][/ROW]
[ROW][C]R-squared[/C][C]0.349984867104349[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.337642807618989[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]28.3570880143208[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]158[/C][/ROW]
[ROW][C]p-value[/C][C]9.99200722162641e-15[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.83626549484212[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]532.755612872549[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185736&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185736&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 R0.591595188540567
R-squared0.349984867104349
Adjusted R-squared0.337642807618989
F-TEST (value)28.3570880143208
F-TEST (DF numerator)3
F-TEST (DF denominator)158
p-value9.99200722162641e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.83626549484212
Sum Squared Residuals532.755612872549







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.2585506404326-3.25855064043256
21615.59735211604640.402647883953638
31916.47649882590112.52350117409891
41511.89825626520743.10174373479259
51415.113796311519-1.11379631151903
61314.5121328472229-1.51213284722288
71915.01740592264993.98259407735013
81516.6788005481541-1.67880054815413
91415.9134822560165-1.91348225601648
101512.45533502184212.54466497815789
111615.04602346721070.953976532789337
121616.1177632493528-0.117763249352825
131615.62251952377660.377480476223413
141615.29093062604180.709069373958152
151717.2146704392252-0.214670439225213
161515.0654407671419-0.0654407671418822
171514.4177217294370.582278270562979
182016.03108151044933.96891848955073
191815.69949261271452.30050738728547
201615.16758126381010.832418736189947
211615.23682497386570.763175026134311
221614.95557089159141.0444291084086
231916.5824101592852.41758984071503
241615.07119087494090.928809125059111
251714.51015357613962.48984642386042
261717.1085714003904-0.10857140039044
271614.72414321944151.27585678055847
281516.160368685931-1.16036868593097
291615.17531064269240.824689357307638
301414.3001224750042-0.300122475004233
311515.4662733749323-0.466273374932258
321212.2395538129078-0.239553812907758
331414.9361535916602-0.936153591660184
341615.59160200824740.408397991752646
351415.387321014911-1.38732101491101
36712.960287397384-5.96028739738396
371010.8613925407611-0.861392540761082
381415.7092012626801-1.70920126268014
391614.05719458725631.94280541274365
401614.71641384055921.28358615944077
411615.03829408832840.961705911671645
421415.2851805182428-1.28518051824284
432017.9683008103142.03169918968605
441414.3078518538865-0.307851853886541
451414.9438829705425-0.943882970542492
461115.7169306415624-4.71693064156245
471416.778961773739-2.778961773739
481514.93813286274350.0618671372565149
491614.85918050272221.14081949727776
501416.0310815104493-2.03108151044927
511616.5612012937213-0.561201293721345
521413.97824222723510.0217577727648966
531214.6568787804692-2.6568787804692
541615.82877978819620.17122021180377
55911.5666673674727-2.56666736747267
561412.55747551851031.44252448148972
571615.93667039266340.0633296073365918
581615.24653362383130.753466376168701
591515.4797528616136-0.479752861613573
601614.20571135721841.79428864278163
611211.5148616862650.485138313735049
621615.92894101378110.0710589862189001
631616.576660051486-0.576660051485961
641414.411971621638-0.411971621638014
651615.3873210149110.612678985088988
661716.24507115375120.754928846248777
671816.28569731924611.71430268075393
681814.50638273942393.49361726057612
691215.9366703926634-3.93667039266341
701615.39505039379330.60494960620668
711013.6524034372994-3.65240343729937
721414.3098311249698-0.309831124969843
731816.47451955481781.52548044518221
741817.22042054702420.779579452975779
751615.59160200824740.408397991752646
761713.69302960279423.30697039720578
771616.2605299115158-0.260529911515839
781614.43515975828491.56484024171506
791314.7281017616081-1.72810176160814
801616.1254926282351-0.125492628235134
811615.48173213269690.518267867303126
822016.0233521315673.97664786843304
831615.90575287713420.0942471228658251
841515.8190711382306-0.819071138230621
851514.72612249052480.273877509475165
861614.19025259945381.80974740054625
871414.1960027072528-0.196002707252761
881615.37959163602870.620408363971297
891614.63369064382231.36630935617773
901514.3581866693470.641813330653006
911213.4269135783994-1.42691357839941
921717.0335775825358-0.0335775825357964
931615.49719089046150.502809109538509
941514.96330027047370.0366997295262889
951315.1443931271631-2.14439312716313
961615.0537528460930.946247153907029
971615.95410842151130.0458915784886741
981614.07067407393771.92932592606233
991615.92121163489880.0787883651012084
1001414.3868042139078-0.386804213907787
1011617.2204205470242-1.22042054702422
1021614.86690988160451.13309011839545
1032017.33999907254032.66000092745969
1041514.8282629871930.171737012806994
1051614.42743037940261.57256962059737
1061315.4336972882049-2.43369728820486
1071716.03108151044930.968918489550729
1081615.84998865375990.150011346240146
1091614.85918050272221.14081949727776
1101212.5346080817485-0.534608081748497
1111614.84570101604091.15429898395908
1121615.940628934830.0593710651699893
1131714.8282629871932.17173701280699
1141314.4197010005203-1.41970100052032
1151214.7318725983238-2.73187259832384
1161816.56695140152041.43304859847965
1171415.7189099126458-1.71890991264575
1181413.21292393509750.787076064902546
1191315.1424138560798-2.14241385607983
1201615.62251952377660.377480476223413
1211314.5295708760708-1.5295708760708
1221615.49917016154480.500829838455208
1231316.0310815104493-3.03108151044927
1241617.3148316648101-1.31483166481008
1251516.0253314026503-1.02533140265026
1261616.8927901914561-0.892790191456084
1271515.3601743360975-0.360174336097484
1281716.24884199046690.751158009533072
1291513.85093432283671.14906567716329
1301214.9535916205081-2.9535916205081
1311614.28466371723961.71533628276038
1321013.4695190149775-3.46951901497755
1331614.17856467840481.82143532159516
1341213.8703516227679-1.87035162276793
1351415.8190711382306-1.81907113823062
1361515.0654407671419-0.0654407671418822
1371312.07589898506630.924101014933742
1381514.74158124828950.258418751710549
1391113.5542214827978-2.5542214827978
1401213.5270748039843-1.52707480398428
141813.5290540750676-5.52905407506758
1421613.2996056740012.70039432599899
1431513.21490320618081.78509679381924
1441716.68077981923740.319220180762566
1451614.61625261497441.38374738502564
1461014.0764241817367-4.07642418173667
1471815.81332103043162.18667896956839
1481315.607060766012-2.60706076601197
1491615.37186225714640.628137742853605
1501313.2535501005923-0.253550100592297
1511013.2109446640142-3.21094466401415
1521516.4013173025956-1.40131730259555
1531614.16508519172351.83491480827647
1541612.33773576740933.66226423259067
1551412.70995083063891.29004916936109
1561012.5923515762061-2.59235157620612
1571717.0335775825358-0.0335775825357964
1581311.80384514742151.19615485257845
1591513.85093432283671.14906567716329
1601615.25228373163030.747716268369694
1611212.4824817006556-0.482481700655641
1621312.90650244509290.0934975549070586

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.2585506404326 & -3.25855064043256 \tabularnewline
2 & 16 & 15.5973521160464 & 0.402647883953638 \tabularnewline
3 & 19 & 16.4764988259011 & 2.52350117409891 \tabularnewline
4 & 15 & 11.8982562652074 & 3.10174373479259 \tabularnewline
5 & 14 & 15.113796311519 & -1.11379631151903 \tabularnewline
6 & 13 & 14.5121328472229 & -1.51213284722288 \tabularnewline
7 & 19 & 15.0174059226499 & 3.98259407735013 \tabularnewline
8 & 15 & 16.6788005481541 & -1.67880054815413 \tabularnewline
9 & 14 & 15.9134822560165 & -1.91348225601648 \tabularnewline
10 & 15 & 12.4553350218421 & 2.54466497815789 \tabularnewline
11 & 16 & 15.0460234672107 & 0.953976532789337 \tabularnewline
12 & 16 & 16.1177632493528 & -0.117763249352825 \tabularnewline
13 & 16 & 15.6225195237766 & 0.377480476223413 \tabularnewline
14 & 16 & 15.2909306260418 & 0.709069373958152 \tabularnewline
15 & 17 & 17.2146704392252 & -0.214670439225213 \tabularnewline
16 & 15 & 15.0654407671419 & -0.0654407671418822 \tabularnewline
17 & 15 & 14.417721729437 & 0.582278270562979 \tabularnewline
18 & 20 & 16.0310815104493 & 3.96891848955073 \tabularnewline
19 & 18 & 15.6994926127145 & 2.30050738728547 \tabularnewline
20 & 16 & 15.1675812638101 & 0.832418736189947 \tabularnewline
21 & 16 & 15.2368249738657 & 0.763175026134311 \tabularnewline
22 & 16 & 14.9555708915914 & 1.0444291084086 \tabularnewline
23 & 19 & 16.582410159285 & 2.41758984071503 \tabularnewline
24 & 16 & 15.0711908749409 & 0.928809125059111 \tabularnewline
25 & 17 & 14.5101535761396 & 2.48984642386042 \tabularnewline
26 & 17 & 17.1085714003904 & -0.10857140039044 \tabularnewline
27 & 16 & 14.7241432194415 & 1.27585678055847 \tabularnewline
28 & 15 & 16.160368685931 & -1.16036868593097 \tabularnewline
29 & 16 & 15.1753106426924 & 0.824689357307638 \tabularnewline
30 & 14 & 14.3001224750042 & -0.300122475004233 \tabularnewline
31 & 15 & 15.4662733749323 & -0.466273374932258 \tabularnewline
32 & 12 & 12.2395538129078 & -0.239553812907758 \tabularnewline
33 & 14 & 14.9361535916602 & -0.936153591660184 \tabularnewline
34 & 16 & 15.5916020082474 & 0.408397991752646 \tabularnewline
35 & 14 & 15.387321014911 & -1.38732101491101 \tabularnewline
36 & 7 & 12.960287397384 & -5.96028739738396 \tabularnewline
37 & 10 & 10.8613925407611 & -0.861392540761082 \tabularnewline
38 & 14 & 15.7092012626801 & -1.70920126268014 \tabularnewline
39 & 16 & 14.0571945872563 & 1.94280541274365 \tabularnewline
40 & 16 & 14.7164138405592 & 1.28358615944077 \tabularnewline
41 & 16 & 15.0382940883284 & 0.961705911671645 \tabularnewline
42 & 14 & 15.2851805182428 & -1.28518051824284 \tabularnewline
43 & 20 & 17.968300810314 & 2.03169918968605 \tabularnewline
44 & 14 & 14.3078518538865 & -0.307851853886541 \tabularnewline
45 & 14 & 14.9438829705425 & -0.943882970542492 \tabularnewline
46 & 11 & 15.7169306415624 & -4.71693064156245 \tabularnewline
47 & 14 & 16.778961773739 & -2.778961773739 \tabularnewline
48 & 15 & 14.9381328627435 & 0.0618671372565149 \tabularnewline
49 & 16 & 14.8591805027222 & 1.14081949727776 \tabularnewline
50 & 14 & 16.0310815104493 & -2.03108151044927 \tabularnewline
51 & 16 & 16.5612012937213 & -0.561201293721345 \tabularnewline
52 & 14 & 13.9782422272351 & 0.0217577727648966 \tabularnewline
53 & 12 & 14.6568787804692 & -2.6568787804692 \tabularnewline
54 & 16 & 15.8287797881962 & 0.17122021180377 \tabularnewline
55 & 9 & 11.5666673674727 & -2.56666736747267 \tabularnewline
56 & 14 & 12.5574755185103 & 1.44252448148972 \tabularnewline
57 & 16 & 15.9366703926634 & 0.0633296073365918 \tabularnewline
58 & 16 & 15.2465336238313 & 0.753466376168701 \tabularnewline
59 & 15 & 15.4797528616136 & -0.479752861613573 \tabularnewline
60 & 16 & 14.2057113572184 & 1.79428864278163 \tabularnewline
61 & 12 & 11.514861686265 & 0.485138313735049 \tabularnewline
62 & 16 & 15.9289410137811 & 0.0710589862189001 \tabularnewline
63 & 16 & 16.576660051486 & -0.576660051485961 \tabularnewline
64 & 14 & 14.411971621638 & -0.411971621638014 \tabularnewline
65 & 16 & 15.387321014911 & 0.612678985088988 \tabularnewline
66 & 17 & 16.2450711537512 & 0.754928846248777 \tabularnewline
67 & 18 & 16.2856973192461 & 1.71430268075393 \tabularnewline
68 & 18 & 14.5063827394239 & 3.49361726057612 \tabularnewline
69 & 12 & 15.9366703926634 & -3.93667039266341 \tabularnewline
70 & 16 & 15.3950503937933 & 0.60494960620668 \tabularnewline
71 & 10 & 13.6524034372994 & -3.65240343729937 \tabularnewline
72 & 14 & 14.3098311249698 & -0.309831124969843 \tabularnewline
73 & 18 & 16.4745195548178 & 1.52548044518221 \tabularnewline
74 & 18 & 17.2204205470242 & 0.779579452975779 \tabularnewline
75 & 16 & 15.5916020082474 & 0.408397991752646 \tabularnewline
76 & 17 & 13.6930296027942 & 3.30697039720578 \tabularnewline
77 & 16 & 16.2605299115158 & -0.260529911515839 \tabularnewline
78 & 16 & 14.4351597582849 & 1.56484024171506 \tabularnewline
79 & 13 & 14.7281017616081 & -1.72810176160814 \tabularnewline
80 & 16 & 16.1254926282351 & -0.125492628235134 \tabularnewline
81 & 16 & 15.4817321326969 & 0.518267867303126 \tabularnewline
82 & 20 & 16.023352131567 & 3.97664786843304 \tabularnewline
83 & 16 & 15.9057528771342 & 0.0942471228658251 \tabularnewline
84 & 15 & 15.8190711382306 & -0.819071138230621 \tabularnewline
85 & 15 & 14.7261224905248 & 0.273877509475165 \tabularnewline
86 & 16 & 14.1902525994538 & 1.80974740054625 \tabularnewline
87 & 14 & 14.1960027072528 & -0.196002707252761 \tabularnewline
88 & 16 & 15.3795916360287 & 0.620408363971297 \tabularnewline
89 & 16 & 14.6336906438223 & 1.36630935617773 \tabularnewline
90 & 15 & 14.358186669347 & 0.641813330653006 \tabularnewline
91 & 12 & 13.4269135783994 & -1.42691357839941 \tabularnewline
92 & 17 & 17.0335775825358 & -0.0335775825357964 \tabularnewline
93 & 16 & 15.4971908904615 & 0.502809109538509 \tabularnewline
94 & 15 & 14.9633002704737 & 0.0366997295262889 \tabularnewline
95 & 13 & 15.1443931271631 & -2.14439312716313 \tabularnewline
96 & 16 & 15.053752846093 & 0.946247153907029 \tabularnewline
97 & 16 & 15.9541084215113 & 0.0458915784886741 \tabularnewline
98 & 16 & 14.0706740739377 & 1.92932592606233 \tabularnewline
99 & 16 & 15.9212116348988 & 0.0787883651012084 \tabularnewline
100 & 14 & 14.3868042139078 & -0.386804213907787 \tabularnewline
101 & 16 & 17.2204205470242 & -1.22042054702422 \tabularnewline
102 & 16 & 14.8669098816045 & 1.13309011839545 \tabularnewline
103 & 20 & 17.3399990725403 & 2.66000092745969 \tabularnewline
104 & 15 & 14.828262987193 & 0.171737012806994 \tabularnewline
105 & 16 & 14.4274303794026 & 1.57256962059737 \tabularnewline
106 & 13 & 15.4336972882049 & -2.43369728820486 \tabularnewline
107 & 17 & 16.0310815104493 & 0.968918489550729 \tabularnewline
108 & 16 & 15.8499886537599 & 0.150011346240146 \tabularnewline
109 & 16 & 14.8591805027222 & 1.14081949727776 \tabularnewline
110 & 12 & 12.5346080817485 & -0.534608081748497 \tabularnewline
111 & 16 & 14.8457010160409 & 1.15429898395908 \tabularnewline
112 & 16 & 15.94062893483 & 0.0593710651699893 \tabularnewline
113 & 17 & 14.828262987193 & 2.17173701280699 \tabularnewline
114 & 13 & 14.4197010005203 & -1.41970100052032 \tabularnewline
115 & 12 & 14.7318725983238 & -2.73187259832384 \tabularnewline
116 & 18 & 16.5669514015204 & 1.43304859847965 \tabularnewline
117 & 14 & 15.7189099126458 & -1.71890991264575 \tabularnewline
118 & 14 & 13.2129239350975 & 0.787076064902546 \tabularnewline
119 & 13 & 15.1424138560798 & -2.14241385607983 \tabularnewline
120 & 16 & 15.6225195237766 & 0.377480476223413 \tabularnewline
121 & 13 & 14.5295708760708 & -1.5295708760708 \tabularnewline
122 & 16 & 15.4991701615448 & 0.500829838455208 \tabularnewline
123 & 13 & 16.0310815104493 & -3.03108151044927 \tabularnewline
124 & 16 & 17.3148316648101 & -1.31483166481008 \tabularnewline
125 & 15 & 16.0253314026503 & -1.02533140265026 \tabularnewline
126 & 16 & 16.8927901914561 & -0.892790191456084 \tabularnewline
127 & 15 & 15.3601743360975 & -0.360174336097484 \tabularnewline
128 & 17 & 16.2488419904669 & 0.751158009533072 \tabularnewline
129 & 15 & 13.8509343228367 & 1.14906567716329 \tabularnewline
130 & 12 & 14.9535916205081 & -2.9535916205081 \tabularnewline
131 & 16 & 14.2846637172396 & 1.71533628276038 \tabularnewline
132 & 10 & 13.4695190149775 & -3.46951901497755 \tabularnewline
133 & 16 & 14.1785646784048 & 1.82143532159516 \tabularnewline
134 & 12 & 13.8703516227679 & -1.87035162276793 \tabularnewline
135 & 14 & 15.8190711382306 & -1.81907113823062 \tabularnewline
136 & 15 & 15.0654407671419 & -0.0654407671418822 \tabularnewline
137 & 13 & 12.0758989850663 & 0.924101014933742 \tabularnewline
138 & 15 & 14.7415812482895 & 0.258418751710549 \tabularnewline
139 & 11 & 13.5542214827978 & -2.5542214827978 \tabularnewline
140 & 12 & 13.5270748039843 & -1.52707480398428 \tabularnewline
141 & 8 & 13.5290540750676 & -5.52905407506758 \tabularnewline
142 & 16 & 13.299605674001 & 2.70039432599899 \tabularnewline
143 & 15 & 13.2149032061808 & 1.78509679381924 \tabularnewline
144 & 17 & 16.6807798192374 & 0.319220180762566 \tabularnewline
145 & 16 & 14.6162526149744 & 1.38374738502564 \tabularnewline
146 & 10 & 14.0764241817367 & -4.07642418173667 \tabularnewline
147 & 18 & 15.8133210304316 & 2.18667896956839 \tabularnewline
148 & 13 & 15.607060766012 & -2.60706076601197 \tabularnewline
149 & 16 & 15.3718622571464 & 0.628137742853605 \tabularnewline
150 & 13 & 13.2535501005923 & -0.253550100592297 \tabularnewline
151 & 10 & 13.2109446640142 & -3.21094466401415 \tabularnewline
152 & 15 & 16.4013173025956 & -1.40131730259555 \tabularnewline
153 & 16 & 14.1650851917235 & 1.83491480827647 \tabularnewline
154 & 16 & 12.3377357674093 & 3.66226423259067 \tabularnewline
155 & 14 & 12.7099508306389 & 1.29004916936109 \tabularnewline
156 & 10 & 12.5923515762061 & -2.59235157620612 \tabularnewline
157 & 17 & 17.0335775825358 & -0.0335775825357964 \tabularnewline
158 & 13 & 11.8038451474215 & 1.19615485257845 \tabularnewline
159 & 15 & 13.8509343228367 & 1.14906567716329 \tabularnewline
160 & 16 & 15.2522837316303 & 0.747716268369694 \tabularnewline
161 & 12 & 12.4824817006556 & -0.482481700655641 \tabularnewline
162 & 13 & 12.9065024450929 & 0.0934975549070586 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185736&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]16.2585506404326[/C][C]-3.25855064043256[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.5973521160464[/C][C]0.402647883953638[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.4764988259011[/C][C]2.52350117409891[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]11.8982562652074[/C][C]3.10174373479259[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.113796311519[/C][C]-1.11379631151903[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.5121328472229[/C][C]-1.51213284722288[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.0174059226499[/C][C]3.98259407735013[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.6788005481541[/C][C]-1.67880054815413[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.9134822560165[/C][C]-1.91348225601648[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.4553350218421[/C][C]2.54466497815789[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.0460234672107[/C][C]0.953976532789337[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.1177632493528[/C][C]-0.117763249352825[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.6225195237766[/C][C]0.377480476223413[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.2909306260418[/C][C]0.709069373958152[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.2146704392252[/C][C]-0.214670439225213[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.0654407671419[/C][C]-0.0654407671418822[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.417721729437[/C][C]0.582278270562979[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.0310815104493[/C][C]3.96891848955073[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.6994926127145[/C][C]2.30050738728547[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.1675812638101[/C][C]0.832418736189947[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.2368249738657[/C][C]0.763175026134311[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.9555708915914[/C][C]1.0444291084086[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.582410159285[/C][C]2.41758984071503[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]15.0711908749409[/C][C]0.928809125059111[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.5101535761396[/C][C]2.48984642386042[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]17.1085714003904[/C][C]-0.10857140039044[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.7241432194415[/C][C]1.27585678055847[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.160368685931[/C][C]-1.16036868593097[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.1753106426924[/C][C]0.824689357307638[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.3001224750042[/C][C]-0.300122475004233[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.4662733749323[/C][C]-0.466273374932258[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.2395538129078[/C][C]-0.239553812907758[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.9361535916602[/C][C]-0.936153591660184[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.5916020082474[/C][C]0.408397991752646[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.387321014911[/C][C]-1.38732101491101[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]12.960287397384[/C][C]-5.96028739738396[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]10.8613925407611[/C][C]-0.861392540761082[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.7092012626801[/C][C]-1.70920126268014[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.0571945872563[/C][C]1.94280541274365[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.7164138405592[/C][C]1.28358615944077[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.0382940883284[/C][C]0.961705911671645[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.2851805182428[/C][C]-1.28518051824284[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.968300810314[/C][C]2.03169918968605[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.3078518538865[/C][C]-0.307851853886541[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.9438829705425[/C][C]-0.943882970542492[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.7169306415624[/C][C]-4.71693064156245[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.778961773739[/C][C]-2.778961773739[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.9381328627435[/C][C]0.0618671372565149[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]14.8591805027222[/C][C]1.14081949727776[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]16.0310815104493[/C][C]-2.03108151044927[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.5612012937213[/C][C]-0.561201293721345[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.9782422272351[/C][C]0.0217577727648966[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.6568787804692[/C][C]-2.6568787804692[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.8287797881962[/C][C]0.17122021180377[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.5666673674727[/C][C]-2.56666736747267[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.5574755185103[/C][C]1.44252448148972[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.9366703926634[/C][C]0.0633296073365918[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.2465336238313[/C][C]0.753466376168701[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.4797528616136[/C][C]-0.479752861613573[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.2057113572184[/C][C]1.79428864278163[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.514861686265[/C][C]0.485138313735049[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.9289410137811[/C][C]0.0710589862189001[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.576660051486[/C][C]-0.576660051485961[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.411971621638[/C][C]-0.411971621638014[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.387321014911[/C][C]0.612678985088988[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]16.2450711537512[/C][C]0.754928846248777[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.2856973192461[/C][C]1.71430268075393[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.5063827394239[/C][C]3.49361726057612[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.9366703926634[/C][C]-3.93667039266341[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.3950503937933[/C][C]0.60494960620668[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.6524034372994[/C][C]-3.65240343729937[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.3098311249698[/C][C]-0.309831124969843[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.4745195548178[/C][C]1.52548044518221[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.2204205470242[/C][C]0.779579452975779[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.5916020082474[/C][C]0.408397991752646[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.6930296027942[/C][C]3.30697039720578[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.2605299115158[/C][C]-0.260529911515839[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.4351597582849[/C][C]1.56484024171506[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.7281017616081[/C][C]-1.72810176160814[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]16.1254926282351[/C][C]-0.125492628235134[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.4817321326969[/C][C]0.518267867303126[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]16.023352131567[/C][C]3.97664786843304[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.9057528771342[/C][C]0.0942471228658251[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.8190711382306[/C][C]-0.819071138230621[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.7261224905248[/C][C]0.273877509475165[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.1902525994538[/C][C]1.80974740054625[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.1960027072528[/C][C]-0.196002707252761[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.3795916360287[/C][C]0.620408363971297[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.6336906438223[/C][C]1.36630935617773[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.358186669347[/C][C]0.641813330653006[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.4269135783994[/C][C]-1.42691357839941[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]17.0335775825358[/C][C]-0.0335775825357964[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.4971908904615[/C][C]0.502809109538509[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]14.9633002704737[/C][C]0.0366997295262889[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.1443931271631[/C][C]-2.14439312716313[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.053752846093[/C][C]0.946247153907029[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.9541084215113[/C][C]0.0458915784886741[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.0706740739377[/C][C]1.92932592606233[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]15.9212116348988[/C][C]0.0787883651012084[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.3868042139078[/C][C]-0.386804213907787[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.2204205470242[/C][C]-1.22042054702422[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.8669098816045[/C][C]1.13309011839545[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.3399990725403[/C][C]2.66000092745969[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.828262987193[/C][C]0.171737012806994[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.4274303794026[/C][C]1.57256962059737[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.4336972882049[/C][C]-2.43369728820486[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]16.0310815104493[/C][C]0.968918489550729[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.8499886537599[/C][C]0.150011346240146[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.8591805027222[/C][C]1.14081949727776[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.5346080817485[/C][C]-0.534608081748497[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.8457010160409[/C][C]1.15429898395908[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.94062893483[/C][C]0.0593710651699893[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.828262987193[/C][C]2.17173701280699[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.4197010005203[/C][C]-1.41970100052032[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.7318725983238[/C][C]-2.73187259832384[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.5669514015204[/C][C]1.43304859847965[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.7189099126458[/C][C]-1.71890991264575[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.2129239350975[/C][C]0.787076064902546[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]15.1424138560798[/C][C]-2.14241385607983[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.6225195237766[/C][C]0.377480476223413[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.5295708760708[/C][C]-1.5295708760708[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.4991701615448[/C][C]0.500829838455208[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]16.0310815104493[/C][C]-3.03108151044927[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]17.3148316648101[/C][C]-1.31483166481008[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]16.0253314026503[/C][C]-1.02533140265026[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.8927901914561[/C][C]-0.892790191456084[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.3601743360975[/C][C]-0.360174336097484[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.2488419904669[/C][C]0.751158009533072[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.8509343228367[/C][C]1.14906567716329[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]14.9535916205081[/C][C]-2.9535916205081[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.2846637172396[/C][C]1.71533628276038[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.4695190149775[/C][C]-3.46951901497755[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]14.1785646784048[/C][C]1.82143532159516[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.8703516227679[/C][C]-1.87035162276793[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.8190711382306[/C][C]-1.81907113823062[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.0654407671419[/C][C]-0.0654407671418822[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.0758989850663[/C][C]0.924101014933742[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.7415812482895[/C][C]0.258418751710549[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.5542214827978[/C][C]-2.5542214827978[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.5270748039843[/C][C]-1.52707480398428[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.5290540750676[/C][C]-5.52905407506758[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]13.299605674001[/C][C]2.70039432599899[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.2149032061808[/C][C]1.78509679381924[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.6807798192374[/C][C]0.319220180762566[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.6162526149744[/C][C]1.38374738502564[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]14.0764241817367[/C][C]-4.07642418173667[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.8133210304316[/C][C]2.18667896956839[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.607060766012[/C][C]-2.60706076601197[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]15.3718622571464[/C][C]0.628137742853605[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]13.2535501005923[/C][C]-0.253550100592297[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]13.2109446640142[/C][C]-3.21094466401415[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.4013173025956[/C][C]-1.40131730259555[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]14.1650851917235[/C][C]1.83491480827647[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]12.3377357674093[/C][C]3.66226423259067[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.7099508306389[/C][C]1.29004916936109[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.5923515762061[/C][C]-2.59235157620612[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]17.0335775825358[/C][C]-0.0335775825357964[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.8038451474215[/C][C]1.19615485257845[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]13.8509343228367[/C][C]1.14906567716329[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]15.2522837316303[/C][C]0.747716268369694[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.4824817006556[/C][C]-0.482481700655641[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.9065024450929[/C][C]0.0934975549070586[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185736&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185736&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
11316.2585506404326-3.25855064043256
21615.59735211604640.402647883953638
31916.47649882590112.52350117409891
41511.89825626520743.10174373479259
51415.113796311519-1.11379631151903
61314.5121328472229-1.51213284722288
71915.01740592264993.98259407735013
81516.6788005481541-1.67880054815413
91415.9134822560165-1.91348225601648
101512.45533502184212.54466497815789
111615.04602346721070.953976532789337
121616.1177632493528-0.117763249352825
131615.62251952377660.377480476223413
141615.29093062604180.709069373958152
151717.2146704392252-0.214670439225213
161515.0654407671419-0.0654407671418822
171514.4177217294370.582278270562979
182016.03108151044933.96891848955073
191815.69949261271452.30050738728547
201615.16758126381010.832418736189947
211615.23682497386570.763175026134311
221614.95557089159141.0444291084086
231916.5824101592852.41758984071503
241615.07119087494090.928809125059111
251714.51015357613962.48984642386042
261717.1085714003904-0.10857140039044
271614.72414321944151.27585678055847
281516.160368685931-1.16036868593097
291615.17531064269240.824689357307638
301414.3001224750042-0.300122475004233
311515.4662733749323-0.466273374932258
321212.2395538129078-0.239553812907758
331414.9361535916602-0.936153591660184
341615.59160200824740.408397991752646
351415.387321014911-1.38732101491101
36712.960287397384-5.96028739738396
371010.8613925407611-0.861392540761082
381415.7092012626801-1.70920126268014
391614.05719458725631.94280541274365
401614.71641384055921.28358615944077
411615.03829408832840.961705911671645
421415.2851805182428-1.28518051824284
432017.9683008103142.03169918968605
441414.3078518538865-0.307851853886541
451414.9438829705425-0.943882970542492
461115.7169306415624-4.71693064156245
471416.778961773739-2.778961773739
481514.93813286274350.0618671372565149
491614.85918050272221.14081949727776
501416.0310815104493-2.03108151044927
511616.5612012937213-0.561201293721345
521413.97824222723510.0217577727648966
531214.6568787804692-2.6568787804692
541615.82877978819620.17122021180377
55911.5666673674727-2.56666736747267
561412.55747551851031.44252448148972
571615.93667039266340.0633296073365918
581615.24653362383130.753466376168701
591515.4797528616136-0.479752861613573
601614.20571135721841.79428864278163
611211.5148616862650.485138313735049
621615.92894101378110.0710589862189001
631616.576660051486-0.576660051485961
641414.411971621638-0.411971621638014
651615.3873210149110.612678985088988
661716.24507115375120.754928846248777
671816.28569731924611.71430268075393
681814.50638273942393.49361726057612
691215.9366703926634-3.93667039266341
701615.39505039379330.60494960620668
711013.6524034372994-3.65240343729937
721414.3098311249698-0.309831124969843
731816.47451955481781.52548044518221
741817.22042054702420.779579452975779
751615.59160200824740.408397991752646
761713.69302960279423.30697039720578
771616.2605299115158-0.260529911515839
781614.43515975828491.56484024171506
791314.7281017616081-1.72810176160814
801616.1254926282351-0.125492628235134
811615.48173213269690.518267867303126
822016.0233521315673.97664786843304
831615.90575287713420.0942471228658251
841515.8190711382306-0.819071138230621
851514.72612249052480.273877509475165
861614.19025259945381.80974740054625
871414.1960027072528-0.196002707252761
881615.37959163602870.620408363971297
891614.63369064382231.36630935617773
901514.3581866693470.641813330653006
911213.4269135783994-1.42691357839941
921717.0335775825358-0.0335775825357964
931615.49719089046150.502809109538509
941514.96330027047370.0366997295262889
951315.1443931271631-2.14439312716313
961615.0537528460930.946247153907029
971615.95410842151130.0458915784886741
981614.07067407393771.92932592606233
991615.92121163489880.0787883651012084
1001414.3868042139078-0.386804213907787
1011617.2204205470242-1.22042054702422
1021614.86690988160451.13309011839545
1032017.33999907254032.66000092745969
1041514.8282629871930.171737012806994
1051614.42743037940261.57256962059737
1061315.4336972882049-2.43369728820486
1071716.03108151044930.968918489550729
1081615.84998865375990.150011346240146
1091614.85918050272221.14081949727776
1101212.5346080817485-0.534608081748497
1111614.84570101604091.15429898395908
1121615.940628934830.0593710651699893
1131714.8282629871932.17173701280699
1141314.4197010005203-1.41970100052032
1151214.7318725983238-2.73187259832384
1161816.56695140152041.43304859847965
1171415.7189099126458-1.71890991264575
1181413.21292393509750.787076064902546
1191315.1424138560798-2.14241385607983
1201615.62251952377660.377480476223413
1211314.5295708760708-1.5295708760708
1221615.49917016154480.500829838455208
1231316.0310815104493-3.03108151044927
1241617.3148316648101-1.31483166481008
1251516.0253314026503-1.02533140265026
1261616.8927901914561-0.892790191456084
1271515.3601743360975-0.360174336097484
1281716.24884199046690.751158009533072
1291513.85093432283671.14906567716329
1301214.9535916205081-2.9535916205081
1311614.28466371723961.71533628276038
1321013.4695190149775-3.46951901497755
1331614.17856467840481.82143532159516
1341213.8703516227679-1.87035162276793
1351415.8190711382306-1.81907113823062
1361515.0654407671419-0.0654407671418822
1371312.07589898506630.924101014933742
1381514.74158124828950.258418751710549
1391113.5542214827978-2.5542214827978
1401213.5270748039843-1.52707480398428
141813.5290540750676-5.52905407506758
1421613.2996056740012.70039432599899
1431513.21490320618081.78509679381924
1441716.68077981923740.319220180762566
1451614.61625261497441.38374738502564
1461014.0764241817367-4.07642418173667
1471815.81332103043162.18667896956839
1481315.607060766012-2.60706076601197
1491615.37186225714640.628137742853605
1501313.2535501005923-0.253550100592297
1511013.2109446640142-3.21094466401415
1521516.4013173025956-1.40131730259555
1531614.16508519172351.83491480827647
1541612.33773576740933.66226423259067
1551412.70995083063891.29004916936109
1561012.5923515762061-2.59235157620612
1571717.0335775825358-0.0335775825357964
1581311.80384514742151.19615485257845
1591513.85093432283671.14906567716329
1601615.25228373163030.747716268369694
1611212.4824817006556-0.482481700655641
1621312.90650244509290.0934975549070586







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.9680607501140.0638784997720010.0319392498860005
80.9393505541249710.1212988917500580.0606494458750288
90.8978873692957080.2042252614085850.102112630704292
100.855586071913160.2888278561736810.14441392808684
110.8344345948241570.3311308103516860.165565405175843
120.7897866791161730.4204266417676550.210213320883827
130.7143940915725790.5712118168548410.28560590842742
140.6394132060037710.7211735879924580.360586793996229
150.5712980428640670.8574039142718650.428701957135933
160.5114951614574860.9770096770850280.488504838542514
170.429707530315510.8594150606310190.57029246968449
180.7703516230559810.4592967538880380.229648376944019
190.793275472145620.413449055708760.20672452785438
200.7377275360592110.5245449278815780.262272463940789
210.6799320196513610.6401359606972790.320067980348639
220.616180020374750.7676399592504990.38381997962525
230.6608315859606420.6783368280787160.339168414039358
240.5990813532145660.8018372935708680.400918646785434
250.5832587579287740.8334824841424530.416741242071226
260.5175476272445660.9649047455108680.482452372755434
270.4596029120745980.9192058241491970.540397087925402
280.4338864638944260.8677729277888520.566113536105574
290.3747507453437160.7495014906874320.625249254656284
300.3503617712902780.7007235425805570.649638228709722
310.3002942439681670.6005884879363340.699705756031833
320.287170072949580.574340145899160.71282992705042
330.2775030543469010.5550061086938020.722496945653099
340.2307387463632540.4614774927265090.769261253636746
350.2274805418349560.4549610836699130.772519458165044
360.7630033976465080.4739932047069840.236996602353492
370.7502494168651350.499501166269730.249750583134865
380.7524226124376850.495154775124630.247577387562315
390.778151555422130.4436968891557390.22184844457787
400.7543551489097760.4912897021804480.245644851090224
410.7194861333005570.5610277333988870.280513866699443
420.7039531933433570.5920936133132850.296046806656643
430.7094070016159360.5811859967681280.290592998384064
440.6693064192705420.6613871614589150.330693580729458
450.6445038204474540.7109923591050920.355496179552546
460.8707945765746550.2584108468506910.129205423425345
470.8987529951057820.2024940097884360.101247004894218
480.8749586324046390.2500827351907220.125041367595361
490.8547401409974890.2905197180050210.145259859002511
500.8611257436927040.2777485126145930.138874256307296
510.8336722697541260.3326554604917480.166327730245874
520.801356677110810.397286645778380.19864332288919
530.8387979783602480.3224040432795030.161202021639752
540.8077412517659490.3845174964681030.192258748234051
550.83396291862960.33207416274080.1660370813704
560.8181860669544790.3636278660910430.181813933045521
570.785553269902040.4288934601959190.21444673009796
580.7625292712044330.4749414575911330.237470728795567
590.7269529522191670.5460940955616660.273047047780833
600.7198527625469270.5602944749061460.280147237453073
610.6805987061042590.6388025877914810.319401293895741
620.6375771476864110.7248457046271770.362422852313589
630.5962673902059110.8074652195881780.403732609794089
640.5521300567188560.8957398865622890.447869943281144
650.5105893321490920.9788213357018170.489410667850908
660.4724568479614070.9449136959228140.527543152038593
670.4603860525935840.9207721051871670.539613947406416
680.5844340861885460.8311318276229070.415565913811454
690.7372509064076780.5254981871846440.262749093592322
700.7019788296850920.5960423406298170.298021170314908
710.8065133988978110.3869732022043780.193486601102189
720.7748487042260830.4503025915478340.225151295773917
730.7627431308681070.4745137382637870.237256869131893
740.7327568316658310.5344863366683390.267243168334169
750.6956243758504220.6087512482991560.304375624149578
760.7720554758112460.4558890483775090.227944524188754
770.7372130009390610.5255739981218770.262786999060939
780.7261890753106670.5476218493786670.273810924689333
790.7215935414956510.5568129170086980.278406458504349
800.6822032740009910.6355934519980180.317796725999009
810.6439002072809680.7121995854380630.356099792719032
820.7864744295428070.4270511409143860.213525570457193
830.7518226435248770.4963547129502450.248177356475123
840.7216816277324780.5566367445350440.278318372267522
850.6828253733292660.6343492533414690.317174626670734
860.6811202586927870.6377594826144260.318879741307213
870.6400075558431550.7199848883136890.359992444156845
880.6017106078428410.7965787843143180.398289392157159
890.5823216081717670.8353567836564650.417678391828233
900.5521825714697960.8956348570604070.447817428530204
910.5343338053040770.9313323893918460.465666194695923
920.4924677807045790.9849355614091590.507532219295421
930.4516581057443690.9033162114887380.548341894255631
940.4077054920443510.8154109840887010.592294507955649
950.4235089696733280.8470179393466570.576491030326672
960.3910099290894140.7820198581788270.608990070910586
970.3522612988237310.7045225976474620.647738701176269
980.354987519625170.7099750392503410.64501248037483
990.3131759768063750.6263519536127510.686824023193625
1000.27520903306680.55041806613360.7247909669332
1010.2500913012577660.5001826025155330.749908698742234
1020.2342019945586960.4684039891173930.765798005441304
1030.295118575629190.590237151258380.70488142437081
1040.255828392799490.5116567855989810.74417160720051
1050.2541458683728280.5082917367456560.745854131627172
1060.2694016518084290.5388033036168590.730598348191571
1070.247725570575610.495451141151220.75227442942439
1080.2222613535096020.4445227070192050.777738646490397
1090.2114213277126590.4228426554253170.788578672287341
1100.1949193541711760.3898387083423530.805080645828824
1110.177837096835130.355674193670260.82216290316487
1120.1520281386571510.3040562773143020.847971861342849
1130.1634979962472710.3269959924945420.836502003752729
1140.1460155369596560.2920310739193120.853984463040344
1150.169374293258130.338748586516260.83062570674187
1160.1691970337253630.3383940674507250.830802966274637
1170.1539202492227330.3078404984454670.846079750777267
1180.1304220094747020.2608440189494030.869577990525298
1190.1403897778480820.2807795556961640.859610222151918
1200.1269245106410460.2538490212820930.873075489358954
1210.1114590659017590.2229181318035180.888540934098241
1220.09397463120832380.1879492624166480.906025368791676
1230.1145394032434590.2290788064869170.885460596756541
1240.09784252985632770.1956850597126550.902157470143672
1250.08068754612176160.1613750922435230.919312453878238
1260.0648512796808920.1297025593617840.935148720319108
1270.05016419324437170.1003283864887430.949835806755628
1280.04336896472248220.08673792944496430.956631035277518
1290.03451197402367670.06902394804735330.965488025976323
1300.04480158979573290.08960317959146580.955198410204267
1310.03961221290357660.07922442580715320.960387787096423
1320.06352431491454380.1270486298290880.936475685085456
1330.07324480714680890.1464896142936180.926755192853191
1340.09224807036201420.1844961407240280.907751929637986
1350.07761687046750080.1552337409350020.922383129532499
1360.05766606035566580.1153321207113320.942333939644334
1370.04328690931502340.08657381863004670.956713090684977
1380.03124588929068870.06249177858137740.968754110709311
1390.04888738979341390.09777477958682770.951112610206586
1400.04040613335244310.08081226670488610.959593866647557
1410.558152689253650.88369462149270.44184731074635
1420.542107454415650.9157850911686990.45789254558435
1430.4744997636735360.9489995273470720.525500236326464
1440.4417684680600810.8835369361201630.558231531939918
1450.3688937798104760.7377875596209510.631106220189524
1460.4756582907857010.9513165815714020.524341709214299
1470.4473234859228330.8946469718456650.552676514077167
1480.5148193542050170.9703612915899650.485180645794983
1490.4259536014674030.8519072029348060.574046398532597
1500.3339454690950990.6678909381901980.666054530904901
1510.8040963848016820.3918072303966370.195903615198318
1520.9219643835395690.1560712329208620.0780356164604312
1530.857663644205120.284672711589760.14233635579488
1540.7602296498487770.4795407003024470.239770350151223
1550.7193504366646760.5612991266706490.280649563335324

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.968060750114 & 0.063878499772001 & 0.0319392498860005 \tabularnewline
8 & 0.939350554124971 & 0.121298891750058 & 0.0606494458750288 \tabularnewline
9 & 0.897887369295708 & 0.204225261408585 & 0.102112630704292 \tabularnewline
10 & 0.85558607191316 & 0.288827856173681 & 0.14441392808684 \tabularnewline
11 & 0.834434594824157 & 0.331130810351686 & 0.165565405175843 \tabularnewline
12 & 0.789786679116173 & 0.420426641767655 & 0.210213320883827 \tabularnewline
13 & 0.714394091572579 & 0.571211816854841 & 0.28560590842742 \tabularnewline
14 & 0.639413206003771 & 0.721173587992458 & 0.360586793996229 \tabularnewline
15 & 0.571298042864067 & 0.857403914271865 & 0.428701957135933 \tabularnewline
16 & 0.511495161457486 & 0.977009677085028 & 0.488504838542514 \tabularnewline
17 & 0.42970753031551 & 0.859415060631019 & 0.57029246968449 \tabularnewline
18 & 0.770351623055981 & 0.459296753888038 & 0.229648376944019 \tabularnewline
19 & 0.79327547214562 & 0.41344905570876 & 0.20672452785438 \tabularnewline
20 & 0.737727536059211 & 0.524544927881578 & 0.262272463940789 \tabularnewline
21 & 0.679932019651361 & 0.640135960697279 & 0.320067980348639 \tabularnewline
22 & 0.61618002037475 & 0.767639959250499 & 0.38381997962525 \tabularnewline
23 & 0.660831585960642 & 0.678336828078716 & 0.339168414039358 \tabularnewline
24 & 0.599081353214566 & 0.801837293570868 & 0.400918646785434 \tabularnewline
25 & 0.583258757928774 & 0.833482484142453 & 0.416741242071226 \tabularnewline
26 & 0.517547627244566 & 0.964904745510868 & 0.482452372755434 \tabularnewline
27 & 0.459602912074598 & 0.919205824149197 & 0.540397087925402 \tabularnewline
28 & 0.433886463894426 & 0.867772927788852 & 0.566113536105574 \tabularnewline
29 & 0.374750745343716 & 0.749501490687432 & 0.625249254656284 \tabularnewline
30 & 0.350361771290278 & 0.700723542580557 & 0.649638228709722 \tabularnewline
31 & 0.300294243968167 & 0.600588487936334 & 0.699705756031833 \tabularnewline
32 & 0.28717007294958 & 0.57434014589916 & 0.71282992705042 \tabularnewline
33 & 0.277503054346901 & 0.555006108693802 & 0.722496945653099 \tabularnewline
34 & 0.230738746363254 & 0.461477492726509 & 0.769261253636746 \tabularnewline
35 & 0.227480541834956 & 0.454961083669913 & 0.772519458165044 \tabularnewline
36 & 0.763003397646508 & 0.473993204706984 & 0.236996602353492 \tabularnewline
37 & 0.750249416865135 & 0.49950116626973 & 0.249750583134865 \tabularnewline
38 & 0.752422612437685 & 0.49515477512463 & 0.247577387562315 \tabularnewline
39 & 0.77815155542213 & 0.443696889155739 & 0.22184844457787 \tabularnewline
40 & 0.754355148909776 & 0.491289702180448 & 0.245644851090224 \tabularnewline
41 & 0.719486133300557 & 0.561027733398887 & 0.280513866699443 \tabularnewline
42 & 0.703953193343357 & 0.592093613313285 & 0.296046806656643 \tabularnewline
43 & 0.709407001615936 & 0.581185996768128 & 0.290592998384064 \tabularnewline
44 & 0.669306419270542 & 0.661387161458915 & 0.330693580729458 \tabularnewline
45 & 0.644503820447454 & 0.710992359105092 & 0.355496179552546 \tabularnewline
46 & 0.870794576574655 & 0.258410846850691 & 0.129205423425345 \tabularnewline
47 & 0.898752995105782 & 0.202494009788436 & 0.101247004894218 \tabularnewline
48 & 0.874958632404639 & 0.250082735190722 & 0.125041367595361 \tabularnewline
49 & 0.854740140997489 & 0.290519718005021 & 0.145259859002511 \tabularnewline
50 & 0.861125743692704 & 0.277748512614593 & 0.138874256307296 \tabularnewline
51 & 0.833672269754126 & 0.332655460491748 & 0.166327730245874 \tabularnewline
52 & 0.80135667711081 & 0.39728664577838 & 0.19864332288919 \tabularnewline
53 & 0.838797978360248 & 0.322404043279503 & 0.161202021639752 \tabularnewline
54 & 0.807741251765949 & 0.384517496468103 & 0.192258748234051 \tabularnewline
55 & 0.8339629186296 & 0.3320741627408 & 0.1660370813704 \tabularnewline
56 & 0.818186066954479 & 0.363627866091043 & 0.181813933045521 \tabularnewline
57 & 0.78555326990204 & 0.428893460195919 & 0.21444673009796 \tabularnewline
58 & 0.762529271204433 & 0.474941457591133 & 0.237470728795567 \tabularnewline
59 & 0.726952952219167 & 0.546094095561666 & 0.273047047780833 \tabularnewline
60 & 0.719852762546927 & 0.560294474906146 & 0.280147237453073 \tabularnewline
61 & 0.680598706104259 & 0.638802587791481 & 0.319401293895741 \tabularnewline
62 & 0.637577147686411 & 0.724845704627177 & 0.362422852313589 \tabularnewline
63 & 0.596267390205911 & 0.807465219588178 & 0.403732609794089 \tabularnewline
64 & 0.552130056718856 & 0.895739886562289 & 0.447869943281144 \tabularnewline
65 & 0.510589332149092 & 0.978821335701817 & 0.489410667850908 \tabularnewline
66 & 0.472456847961407 & 0.944913695922814 & 0.527543152038593 \tabularnewline
67 & 0.460386052593584 & 0.920772105187167 & 0.539613947406416 \tabularnewline
68 & 0.584434086188546 & 0.831131827622907 & 0.415565913811454 \tabularnewline
69 & 0.737250906407678 & 0.525498187184644 & 0.262749093592322 \tabularnewline
70 & 0.701978829685092 & 0.596042340629817 & 0.298021170314908 \tabularnewline
71 & 0.806513398897811 & 0.386973202204378 & 0.193486601102189 \tabularnewline
72 & 0.774848704226083 & 0.450302591547834 & 0.225151295773917 \tabularnewline
73 & 0.762743130868107 & 0.474513738263787 & 0.237256869131893 \tabularnewline
74 & 0.732756831665831 & 0.534486336668339 & 0.267243168334169 \tabularnewline
75 & 0.695624375850422 & 0.608751248299156 & 0.304375624149578 \tabularnewline
76 & 0.772055475811246 & 0.455889048377509 & 0.227944524188754 \tabularnewline
77 & 0.737213000939061 & 0.525573998121877 & 0.262786999060939 \tabularnewline
78 & 0.726189075310667 & 0.547621849378667 & 0.273810924689333 \tabularnewline
79 & 0.721593541495651 & 0.556812917008698 & 0.278406458504349 \tabularnewline
80 & 0.682203274000991 & 0.635593451998018 & 0.317796725999009 \tabularnewline
81 & 0.643900207280968 & 0.712199585438063 & 0.356099792719032 \tabularnewline
82 & 0.786474429542807 & 0.427051140914386 & 0.213525570457193 \tabularnewline
83 & 0.751822643524877 & 0.496354712950245 & 0.248177356475123 \tabularnewline
84 & 0.721681627732478 & 0.556636744535044 & 0.278318372267522 \tabularnewline
85 & 0.682825373329266 & 0.634349253341469 & 0.317174626670734 \tabularnewline
86 & 0.681120258692787 & 0.637759482614426 & 0.318879741307213 \tabularnewline
87 & 0.640007555843155 & 0.719984888313689 & 0.359992444156845 \tabularnewline
88 & 0.601710607842841 & 0.796578784314318 & 0.398289392157159 \tabularnewline
89 & 0.582321608171767 & 0.835356783656465 & 0.417678391828233 \tabularnewline
90 & 0.552182571469796 & 0.895634857060407 & 0.447817428530204 \tabularnewline
91 & 0.534333805304077 & 0.931332389391846 & 0.465666194695923 \tabularnewline
92 & 0.492467780704579 & 0.984935561409159 & 0.507532219295421 \tabularnewline
93 & 0.451658105744369 & 0.903316211488738 & 0.548341894255631 \tabularnewline
94 & 0.407705492044351 & 0.815410984088701 & 0.592294507955649 \tabularnewline
95 & 0.423508969673328 & 0.847017939346657 & 0.576491030326672 \tabularnewline
96 & 0.391009929089414 & 0.782019858178827 & 0.608990070910586 \tabularnewline
97 & 0.352261298823731 & 0.704522597647462 & 0.647738701176269 \tabularnewline
98 & 0.35498751962517 & 0.709975039250341 & 0.64501248037483 \tabularnewline
99 & 0.313175976806375 & 0.626351953612751 & 0.686824023193625 \tabularnewline
100 & 0.2752090330668 & 0.5504180661336 & 0.7247909669332 \tabularnewline
101 & 0.250091301257766 & 0.500182602515533 & 0.749908698742234 \tabularnewline
102 & 0.234201994558696 & 0.468403989117393 & 0.765798005441304 \tabularnewline
103 & 0.29511857562919 & 0.59023715125838 & 0.70488142437081 \tabularnewline
104 & 0.25582839279949 & 0.511656785598981 & 0.74417160720051 \tabularnewline
105 & 0.254145868372828 & 0.508291736745656 & 0.745854131627172 \tabularnewline
106 & 0.269401651808429 & 0.538803303616859 & 0.730598348191571 \tabularnewline
107 & 0.24772557057561 & 0.49545114115122 & 0.75227442942439 \tabularnewline
108 & 0.222261353509602 & 0.444522707019205 & 0.777738646490397 \tabularnewline
109 & 0.211421327712659 & 0.422842655425317 & 0.788578672287341 \tabularnewline
110 & 0.194919354171176 & 0.389838708342353 & 0.805080645828824 \tabularnewline
111 & 0.17783709683513 & 0.35567419367026 & 0.82216290316487 \tabularnewline
112 & 0.152028138657151 & 0.304056277314302 & 0.847971861342849 \tabularnewline
113 & 0.163497996247271 & 0.326995992494542 & 0.836502003752729 \tabularnewline
114 & 0.146015536959656 & 0.292031073919312 & 0.853984463040344 \tabularnewline
115 & 0.16937429325813 & 0.33874858651626 & 0.83062570674187 \tabularnewline
116 & 0.169197033725363 & 0.338394067450725 & 0.830802966274637 \tabularnewline
117 & 0.153920249222733 & 0.307840498445467 & 0.846079750777267 \tabularnewline
118 & 0.130422009474702 & 0.260844018949403 & 0.869577990525298 \tabularnewline
119 & 0.140389777848082 & 0.280779555696164 & 0.859610222151918 \tabularnewline
120 & 0.126924510641046 & 0.253849021282093 & 0.873075489358954 \tabularnewline
121 & 0.111459065901759 & 0.222918131803518 & 0.888540934098241 \tabularnewline
122 & 0.0939746312083238 & 0.187949262416648 & 0.906025368791676 \tabularnewline
123 & 0.114539403243459 & 0.229078806486917 & 0.885460596756541 \tabularnewline
124 & 0.0978425298563277 & 0.195685059712655 & 0.902157470143672 \tabularnewline
125 & 0.0806875461217616 & 0.161375092243523 & 0.919312453878238 \tabularnewline
126 & 0.064851279680892 & 0.129702559361784 & 0.935148720319108 \tabularnewline
127 & 0.0501641932443717 & 0.100328386488743 & 0.949835806755628 \tabularnewline
128 & 0.0433689647224822 & 0.0867379294449643 & 0.956631035277518 \tabularnewline
129 & 0.0345119740236767 & 0.0690239480473533 & 0.965488025976323 \tabularnewline
130 & 0.0448015897957329 & 0.0896031795914658 & 0.955198410204267 \tabularnewline
131 & 0.0396122129035766 & 0.0792244258071532 & 0.960387787096423 \tabularnewline
132 & 0.0635243149145438 & 0.127048629829088 & 0.936475685085456 \tabularnewline
133 & 0.0732448071468089 & 0.146489614293618 & 0.926755192853191 \tabularnewline
134 & 0.0922480703620142 & 0.184496140724028 & 0.907751929637986 \tabularnewline
135 & 0.0776168704675008 & 0.155233740935002 & 0.922383129532499 \tabularnewline
136 & 0.0576660603556658 & 0.115332120711332 & 0.942333939644334 \tabularnewline
137 & 0.0432869093150234 & 0.0865738186300467 & 0.956713090684977 \tabularnewline
138 & 0.0312458892906887 & 0.0624917785813774 & 0.968754110709311 \tabularnewline
139 & 0.0488873897934139 & 0.0977747795868277 & 0.951112610206586 \tabularnewline
140 & 0.0404061333524431 & 0.0808122667048861 & 0.959593866647557 \tabularnewline
141 & 0.55815268925365 & 0.8836946214927 & 0.44184731074635 \tabularnewline
142 & 0.54210745441565 & 0.915785091168699 & 0.45789254558435 \tabularnewline
143 & 0.474499763673536 & 0.948999527347072 & 0.525500236326464 \tabularnewline
144 & 0.441768468060081 & 0.883536936120163 & 0.558231531939918 \tabularnewline
145 & 0.368893779810476 & 0.737787559620951 & 0.631106220189524 \tabularnewline
146 & 0.475658290785701 & 0.951316581571402 & 0.524341709214299 \tabularnewline
147 & 0.447323485922833 & 0.894646971845665 & 0.552676514077167 \tabularnewline
148 & 0.514819354205017 & 0.970361291589965 & 0.485180645794983 \tabularnewline
149 & 0.425953601467403 & 0.851907202934806 & 0.574046398532597 \tabularnewline
150 & 0.333945469095099 & 0.667890938190198 & 0.666054530904901 \tabularnewline
151 & 0.804096384801682 & 0.391807230396637 & 0.195903615198318 \tabularnewline
152 & 0.921964383539569 & 0.156071232920862 & 0.0780356164604312 \tabularnewline
153 & 0.85766364420512 & 0.28467271158976 & 0.14233635579488 \tabularnewline
154 & 0.760229649848777 & 0.479540700302447 & 0.239770350151223 \tabularnewline
155 & 0.719350436664676 & 0.561299126670649 & 0.280649563335324 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185736&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C]0.968060750114[/C][C]0.063878499772001[/C][C]0.0319392498860005[/C][/ROW]
[ROW][C]8[/C][C]0.939350554124971[/C][C]0.121298891750058[/C][C]0.0606494458750288[/C][/ROW]
[ROW][C]9[/C][C]0.897887369295708[/C][C]0.204225261408585[/C][C]0.102112630704292[/C][/ROW]
[ROW][C]10[/C][C]0.85558607191316[/C][C]0.288827856173681[/C][C]0.14441392808684[/C][/ROW]
[ROW][C]11[/C][C]0.834434594824157[/C][C]0.331130810351686[/C][C]0.165565405175843[/C][/ROW]
[ROW][C]12[/C][C]0.789786679116173[/C][C]0.420426641767655[/C][C]0.210213320883827[/C][/ROW]
[ROW][C]13[/C][C]0.714394091572579[/C][C]0.571211816854841[/C][C]0.28560590842742[/C][/ROW]
[ROW][C]14[/C][C]0.639413206003771[/C][C]0.721173587992458[/C][C]0.360586793996229[/C][/ROW]
[ROW][C]15[/C][C]0.571298042864067[/C][C]0.857403914271865[/C][C]0.428701957135933[/C][/ROW]
[ROW][C]16[/C][C]0.511495161457486[/C][C]0.977009677085028[/C][C]0.488504838542514[/C][/ROW]
[ROW][C]17[/C][C]0.42970753031551[/C][C]0.859415060631019[/C][C]0.57029246968449[/C][/ROW]
[ROW][C]18[/C][C]0.770351623055981[/C][C]0.459296753888038[/C][C]0.229648376944019[/C][/ROW]
[ROW][C]19[/C][C]0.79327547214562[/C][C]0.41344905570876[/C][C]0.20672452785438[/C][/ROW]
[ROW][C]20[/C][C]0.737727536059211[/C][C]0.524544927881578[/C][C]0.262272463940789[/C][/ROW]
[ROW][C]21[/C][C]0.679932019651361[/C][C]0.640135960697279[/C][C]0.320067980348639[/C][/ROW]
[ROW][C]22[/C][C]0.61618002037475[/C][C]0.767639959250499[/C][C]0.38381997962525[/C][/ROW]
[ROW][C]23[/C][C]0.660831585960642[/C][C]0.678336828078716[/C][C]0.339168414039358[/C][/ROW]
[ROW][C]24[/C][C]0.599081353214566[/C][C]0.801837293570868[/C][C]0.400918646785434[/C][/ROW]
[ROW][C]25[/C][C]0.583258757928774[/C][C]0.833482484142453[/C][C]0.416741242071226[/C][/ROW]
[ROW][C]26[/C][C]0.517547627244566[/C][C]0.964904745510868[/C][C]0.482452372755434[/C][/ROW]
[ROW][C]27[/C][C]0.459602912074598[/C][C]0.919205824149197[/C][C]0.540397087925402[/C][/ROW]
[ROW][C]28[/C][C]0.433886463894426[/C][C]0.867772927788852[/C][C]0.566113536105574[/C][/ROW]
[ROW][C]29[/C][C]0.374750745343716[/C][C]0.749501490687432[/C][C]0.625249254656284[/C][/ROW]
[ROW][C]30[/C][C]0.350361771290278[/C][C]0.700723542580557[/C][C]0.649638228709722[/C][/ROW]
[ROW][C]31[/C][C]0.300294243968167[/C][C]0.600588487936334[/C][C]0.699705756031833[/C][/ROW]
[ROW][C]32[/C][C]0.28717007294958[/C][C]0.57434014589916[/C][C]0.71282992705042[/C][/ROW]
[ROW][C]33[/C][C]0.277503054346901[/C][C]0.555006108693802[/C][C]0.722496945653099[/C][/ROW]
[ROW][C]34[/C][C]0.230738746363254[/C][C]0.461477492726509[/C][C]0.769261253636746[/C][/ROW]
[ROW][C]35[/C][C]0.227480541834956[/C][C]0.454961083669913[/C][C]0.772519458165044[/C][/ROW]
[ROW][C]36[/C][C]0.763003397646508[/C][C]0.473993204706984[/C][C]0.236996602353492[/C][/ROW]
[ROW][C]37[/C][C]0.750249416865135[/C][C]0.49950116626973[/C][C]0.249750583134865[/C][/ROW]
[ROW][C]38[/C][C]0.752422612437685[/C][C]0.49515477512463[/C][C]0.247577387562315[/C][/ROW]
[ROW][C]39[/C][C]0.77815155542213[/C][C]0.443696889155739[/C][C]0.22184844457787[/C][/ROW]
[ROW][C]40[/C][C]0.754355148909776[/C][C]0.491289702180448[/C][C]0.245644851090224[/C][/ROW]
[ROW][C]41[/C][C]0.719486133300557[/C][C]0.561027733398887[/C][C]0.280513866699443[/C][/ROW]
[ROW][C]42[/C][C]0.703953193343357[/C][C]0.592093613313285[/C][C]0.296046806656643[/C][/ROW]
[ROW][C]43[/C][C]0.709407001615936[/C][C]0.581185996768128[/C][C]0.290592998384064[/C][/ROW]
[ROW][C]44[/C][C]0.669306419270542[/C][C]0.661387161458915[/C][C]0.330693580729458[/C][/ROW]
[ROW][C]45[/C][C]0.644503820447454[/C][C]0.710992359105092[/C][C]0.355496179552546[/C][/ROW]
[ROW][C]46[/C][C]0.870794576574655[/C][C]0.258410846850691[/C][C]0.129205423425345[/C][/ROW]
[ROW][C]47[/C][C]0.898752995105782[/C][C]0.202494009788436[/C][C]0.101247004894218[/C][/ROW]
[ROW][C]48[/C][C]0.874958632404639[/C][C]0.250082735190722[/C][C]0.125041367595361[/C][/ROW]
[ROW][C]49[/C][C]0.854740140997489[/C][C]0.290519718005021[/C][C]0.145259859002511[/C][/ROW]
[ROW][C]50[/C][C]0.861125743692704[/C][C]0.277748512614593[/C][C]0.138874256307296[/C][/ROW]
[ROW][C]51[/C][C]0.833672269754126[/C][C]0.332655460491748[/C][C]0.166327730245874[/C][/ROW]
[ROW][C]52[/C][C]0.80135667711081[/C][C]0.39728664577838[/C][C]0.19864332288919[/C][/ROW]
[ROW][C]53[/C][C]0.838797978360248[/C][C]0.322404043279503[/C][C]0.161202021639752[/C][/ROW]
[ROW][C]54[/C][C]0.807741251765949[/C][C]0.384517496468103[/C][C]0.192258748234051[/C][/ROW]
[ROW][C]55[/C][C]0.8339629186296[/C][C]0.3320741627408[/C][C]0.1660370813704[/C][/ROW]
[ROW][C]56[/C][C]0.818186066954479[/C][C]0.363627866091043[/C][C]0.181813933045521[/C][/ROW]
[ROW][C]57[/C][C]0.78555326990204[/C][C]0.428893460195919[/C][C]0.21444673009796[/C][/ROW]
[ROW][C]58[/C][C]0.762529271204433[/C][C]0.474941457591133[/C][C]0.237470728795567[/C][/ROW]
[ROW][C]59[/C][C]0.726952952219167[/C][C]0.546094095561666[/C][C]0.273047047780833[/C][/ROW]
[ROW][C]60[/C][C]0.719852762546927[/C][C]0.560294474906146[/C][C]0.280147237453073[/C][/ROW]
[ROW][C]61[/C][C]0.680598706104259[/C][C]0.638802587791481[/C][C]0.319401293895741[/C][/ROW]
[ROW][C]62[/C][C]0.637577147686411[/C][C]0.724845704627177[/C][C]0.362422852313589[/C][/ROW]
[ROW][C]63[/C][C]0.596267390205911[/C][C]0.807465219588178[/C][C]0.403732609794089[/C][/ROW]
[ROW][C]64[/C][C]0.552130056718856[/C][C]0.895739886562289[/C][C]0.447869943281144[/C][/ROW]
[ROW][C]65[/C][C]0.510589332149092[/C][C]0.978821335701817[/C][C]0.489410667850908[/C][/ROW]
[ROW][C]66[/C][C]0.472456847961407[/C][C]0.944913695922814[/C][C]0.527543152038593[/C][/ROW]
[ROW][C]67[/C][C]0.460386052593584[/C][C]0.920772105187167[/C][C]0.539613947406416[/C][/ROW]
[ROW][C]68[/C][C]0.584434086188546[/C][C]0.831131827622907[/C][C]0.415565913811454[/C][/ROW]
[ROW][C]69[/C][C]0.737250906407678[/C][C]0.525498187184644[/C][C]0.262749093592322[/C][/ROW]
[ROW][C]70[/C][C]0.701978829685092[/C][C]0.596042340629817[/C][C]0.298021170314908[/C][/ROW]
[ROW][C]71[/C][C]0.806513398897811[/C][C]0.386973202204378[/C][C]0.193486601102189[/C][/ROW]
[ROW][C]72[/C][C]0.774848704226083[/C][C]0.450302591547834[/C][C]0.225151295773917[/C][/ROW]
[ROW][C]73[/C][C]0.762743130868107[/C][C]0.474513738263787[/C][C]0.237256869131893[/C][/ROW]
[ROW][C]74[/C][C]0.732756831665831[/C][C]0.534486336668339[/C][C]0.267243168334169[/C][/ROW]
[ROW][C]75[/C][C]0.695624375850422[/C][C]0.608751248299156[/C][C]0.304375624149578[/C][/ROW]
[ROW][C]76[/C][C]0.772055475811246[/C][C]0.455889048377509[/C][C]0.227944524188754[/C][/ROW]
[ROW][C]77[/C][C]0.737213000939061[/C][C]0.525573998121877[/C][C]0.262786999060939[/C][/ROW]
[ROW][C]78[/C][C]0.726189075310667[/C][C]0.547621849378667[/C][C]0.273810924689333[/C][/ROW]
[ROW][C]79[/C][C]0.721593541495651[/C][C]0.556812917008698[/C][C]0.278406458504349[/C][/ROW]
[ROW][C]80[/C][C]0.682203274000991[/C][C]0.635593451998018[/C][C]0.317796725999009[/C][/ROW]
[ROW][C]81[/C][C]0.643900207280968[/C][C]0.712199585438063[/C][C]0.356099792719032[/C][/ROW]
[ROW][C]82[/C][C]0.786474429542807[/C][C]0.427051140914386[/C][C]0.213525570457193[/C][/ROW]
[ROW][C]83[/C][C]0.751822643524877[/C][C]0.496354712950245[/C][C]0.248177356475123[/C][/ROW]
[ROW][C]84[/C][C]0.721681627732478[/C][C]0.556636744535044[/C][C]0.278318372267522[/C][/ROW]
[ROW][C]85[/C][C]0.682825373329266[/C][C]0.634349253341469[/C][C]0.317174626670734[/C][/ROW]
[ROW][C]86[/C][C]0.681120258692787[/C][C]0.637759482614426[/C][C]0.318879741307213[/C][/ROW]
[ROW][C]87[/C][C]0.640007555843155[/C][C]0.719984888313689[/C][C]0.359992444156845[/C][/ROW]
[ROW][C]88[/C][C]0.601710607842841[/C][C]0.796578784314318[/C][C]0.398289392157159[/C][/ROW]
[ROW][C]89[/C][C]0.582321608171767[/C][C]0.835356783656465[/C][C]0.417678391828233[/C][/ROW]
[ROW][C]90[/C][C]0.552182571469796[/C][C]0.895634857060407[/C][C]0.447817428530204[/C][/ROW]
[ROW][C]91[/C][C]0.534333805304077[/C][C]0.931332389391846[/C][C]0.465666194695923[/C][/ROW]
[ROW][C]92[/C][C]0.492467780704579[/C][C]0.984935561409159[/C][C]0.507532219295421[/C][/ROW]
[ROW][C]93[/C][C]0.451658105744369[/C][C]0.903316211488738[/C][C]0.548341894255631[/C][/ROW]
[ROW][C]94[/C][C]0.407705492044351[/C][C]0.815410984088701[/C][C]0.592294507955649[/C][/ROW]
[ROW][C]95[/C][C]0.423508969673328[/C][C]0.847017939346657[/C][C]0.576491030326672[/C][/ROW]
[ROW][C]96[/C][C]0.391009929089414[/C][C]0.782019858178827[/C][C]0.608990070910586[/C][/ROW]
[ROW][C]97[/C][C]0.352261298823731[/C][C]0.704522597647462[/C][C]0.647738701176269[/C][/ROW]
[ROW][C]98[/C][C]0.35498751962517[/C][C]0.709975039250341[/C][C]0.64501248037483[/C][/ROW]
[ROW][C]99[/C][C]0.313175976806375[/C][C]0.626351953612751[/C][C]0.686824023193625[/C][/ROW]
[ROW][C]100[/C][C]0.2752090330668[/C][C]0.5504180661336[/C][C]0.7247909669332[/C][/ROW]
[ROW][C]101[/C][C]0.250091301257766[/C][C]0.500182602515533[/C][C]0.749908698742234[/C][/ROW]
[ROW][C]102[/C][C]0.234201994558696[/C][C]0.468403989117393[/C][C]0.765798005441304[/C][/ROW]
[ROW][C]103[/C][C]0.29511857562919[/C][C]0.59023715125838[/C][C]0.70488142437081[/C][/ROW]
[ROW][C]104[/C][C]0.25582839279949[/C][C]0.511656785598981[/C][C]0.74417160720051[/C][/ROW]
[ROW][C]105[/C][C]0.254145868372828[/C][C]0.508291736745656[/C][C]0.745854131627172[/C][/ROW]
[ROW][C]106[/C][C]0.269401651808429[/C][C]0.538803303616859[/C][C]0.730598348191571[/C][/ROW]
[ROW][C]107[/C][C]0.24772557057561[/C][C]0.49545114115122[/C][C]0.75227442942439[/C][/ROW]
[ROW][C]108[/C][C]0.222261353509602[/C][C]0.444522707019205[/C][C]0.777738646490397[/C][/ROW]
[ROW][C]109[/C][C]0.211421327712659[/C][C]0.422842655425317[/C][C]0.788578672287341[/C][/ROW]
[ROW][C]110[/C][C]0.194919354171176[/C][C]0.389838708342353[/C][C]0.805080645828824[/C][/ROW]
[ROW][C]111[/C][C]0.17783709683513[/C][C]0.35567419367026[/C][C]0.82216290316487[/C][/ROW]
[ROW][C]112[/C][C]0.152028138657151[/C][C]0.304056277314302[/C][C]0.847971861342849[/C][/ROW]
[ROW][C]113[/C][C]0.163497996247271[/C][C]0.326995992494542[/C][C]0.836502003752729[/C][/ROW]
[ROW][C]114[/C][C]0.146015536959656[/C][C]0.292031073919312[/C][C]0.853984463040344[/C][/ROW]
[ROW][C]115[/C][C]0.16937429325813[/C][C]0.33874858651626[/C][C]0.83062570674187[/C][/ROW]
[ROW][C]116[/C][C]0.169197033725363[/C][C]0.338394067450725[/C][C]0.830802966274637[/C][/ROW]
[ROW][C]117[/C][C]0.153920249222733[/C][C]0.307840498445467[/C][C]0.846079750777267[/C][/ROW]
[ROW][C]118[/C][C]0.130422009474702[/C][C]0.260844018949403[/C][C]0.869577990525298[/C][/ROW]
[ROW][C]119[/C][C]0.140389777848082[/C][C]0.280779555696164[/C][C]0.859610222151918[/C][/ROW]
[ROW][C]120[/C][C]0.126924510641046[/C][C]0.253849021282093[/C][C]0.873075489358954[/C][/ROW]
[ROW][C]121[/C][C]0.111459065901759[/C][C]0.222918131803518[/C][C]0.888540934098241[/C][/ROW]
[ROW][C]122[/C][C]0.0939746312083238[/C][C]0.187949262416648[/C][C]0.906025368791676[/C][/ROW]
[ROW][C]123[/C][C]0.114539403243459[/C][C]0.229078806486917[/C][C]0.885460596756541[/C][/ROW]
[ROW][C]124[/C][C]0.0978425298563277[/C][C]0.195685059712655[/C][C]0.902157470143672[/C][/ROW]
[ROW][C]125[/C][C]0.0806875461217616[/C][C]0.161375092243523[/C][C]0.919312453878238[/C][/ROW]
[ROW][C]126[/C][C]0.064851279680892[/C][C]0.129702559361784[/C][C]0.935148720319108[/C][/ROW]
[ROW][C]127[/C][C]0.0501641932443717[/C][C]0.100328386488743[/C][C]0.949835806755628[/C][/ROW]
[ROW][C]128[/C][C]0.0433689647224822[/C][C]0.0867379294449643[/C][C]0.956631035277518[/C][/ROW]
[ROW][C]129[/C][C]0.0345119740236767[/C][C]0.0690239480473533[/C][C]0.965488025976323[/C][/ROW]
[ROW][C]130[/C][C]0.0448015897957329[/C][C]0.0896031795914658[/C][C]0.955198410204267[/C][/ROW]
[ROW][C]131[/C][C]0.0396122129035766[/C][C]0.0792244258071532[/C][C]0.960387787096423[/C][/ROW]
[ROW][C]132[/C][C]0.0635243149145438[/C][C]0.127048629829088[/C][C]0.936475685085456[/C][/ROW]
[ROW][C]133[/C][C]0.0732448071468089[/C][C]0.146489614293618[/C][C]0.926755192853191[/C][/ROW]
[ROW][C]134[/C][C]0.0922480703620142[/C][C]0.184496140724028[/C][C]0.907751929637986[/C][/ROW]
[ROW][C]135[/C][C]0.0776168704675008[/C][C]0.155233740935002[/C][C]0.922383129532499[/C][/ROW]
[ROW][C]136[/C][C]0.0576660603556658[/C][C]0.115332120711332[/C][C]0.942333939644334[/C][/ROW]
[ROW][C]137[/C][C]0.0432869093150234[/C][C]0.0865738186300467[/C][C]0.956713090684977[/C][/ROW]
[ROW][C]138[/C][C]0.0312458892906887[/C][C]0.0624917785813774[/C][C]0.968754110709311[/C][/ROW]
[ROW][C]139[/C][C]0.0488873897934139[/C][C]0.0977747795868277[/C][C]0.951112610206586[/C][/ROW]
[ROW][C]140[/C][C]0.0404061333524431[/C][C]0.0808122667048861[/C][C]0.959593866647557[/C][/ROW]
[ROW][C]141[/C][C]0.55815268925365[/C][C]0.8836946214927[/C][C]0.44184731074635[/C][/ROW]
[ROW][C]142[/C][C]0.54210745441565[/C][C]0.915785091168699[/C][C]0.45789254558435[/C][/ROW]
[ROW][C]143[/C][C]0.474499763673536[/C][C]0.948999527347072[/C][C]0.525500236326464[/C][/ROW]
[ROW][C]144[/C][C]0.441768468060081[/C][C]0.883536936120163[/C][C]0.558231531939918[/C][/ROW]
[ROW][C]145[/C][C]0.368893779810476[/C][C]0.737787559620951[/C][C]0.631106220189524[/C][/ROW]
[ROW][C]146[/C][C]0.475658290785701[/C][C]0.951316581571402[/C][C]0.524341709214299[/C][/ROW]
[ROW][C]147[/C][C]0.447323485922833[/C][C]0.894646971845665[/C][C]0.552676514077167[/C][/ROW]
[ROW][C]148[/C][C]0.514819354205017[/C][C]0.970361291589965[/C][C]0.485180645794983[/C][/ROW]
[ROW][C]149[/C][C]0.425953601467403[/C][C]0.851907202934806[/C][C]0.574046398532597[/C][/ROW]
[ROW][C]150[/C][C]0.333945469095099[/C][C]0.667890938190198[/C][C]0.666054530904901[/C][/ROW]
[ROW][C]151[/C][C]0.804096384801682[/C][C]0.391807230396637[/C][C]0.195903615198318[/C][/ROW]
[ROW][C]152[/C][C]0.921964383539569[/C][C]0.156071232920862[/C][C]0.0780356164604312[/C][/ROW]
[ROW][C]153[/C][C]0.85766364420512[/C][C]0.28467271158976[/C][C]0.14233635579488[/C][/ROW]
[ROW][C]154[/C][C]0.760229649848777[/C][C]0.479540700302447[/C][C]0.239770350151223[/C][/ROW]
[ROW][C]155[/C][C]0.719350436664676[/C][C]0.561299126670649[/C][C]0.280649563335324[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185736&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185736&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.9680607501140.0638784997720010.0319392498860005
80.9393505541249710.1212988917500580.0606494458750288
90.8978873692957080.2042252614085850.102112630704292
100.855586071913160.2888278561736810.14441392808684
110.8344345948241570.3311308103516860.165565405175843
120.7897866791161730.4204266417676550.210213320883827
130.7143940915725790.5712118168548410.28560590842742
140.6394132060037710.7211735879924580.360586793996229
150.5712980428640670.8574039142718650.428701957135933
160.5114951614574860.9770096770850280.488504838542514
170.429707530315510.8594150606310190.57029246968449
180.7703516230559810.4592967538880380.229648376944019
190.793275472145620.413449055708760.20672452785438
200.7377275360592110.5245449278815780.262272463940789
210.6799320196513610.6401359606972790.320067980348639
220.616180020374750.7676399592504990.38381997962525
230.6608315859606420.6783368280787160.339168414039358
240.5990813532145660.8018372935708680.400918646785434
250.5832587579287740.8334824841424530.416741242071226
260.5175476272445660.9649047455108680.482452372755434
270.4596029120745980.9192058241491970.540397087925402
280.4338864638944260.8677729277888520.566113536105574
290.3747507453437160.7495014906874320.625249254656284
300.3503617712902780.7007235425805570.649638228709722
310.3002942439681670.6005884879363340.699705756031833
320.287170072949580.574340145899160.71282992705042
330.2775030543469010.5550061086938020.722496945653099
340.2307387463632540.4614774927265090.769261253636746
350.2274805418349560.4549610836699130.772519458165044
360.7630033976465080.4739932047069840.236996602353492
370.7502494168651350.499501166269730.249750583134865
380.7524226124376850.495154775124630.247577387562315
390.778151555422130.4436968891557390.22184844457787
400.7543551489097760.4912897021804480.245644851090224
410.7194861333005570.5610277333988870.280513866699443
420.7039531933433570.5920936133132850.296046806656643
430.7094070016159360.5811859967681280.290592998384064
440.6693064192705420.6613871614589150.330693580729458
450.6445038204474540.7109923591050920.355496179552546
460.8707945765746550.2584108468506910.129205423425345
470.8987529951057820.2024940097884360.101247004894218
480.8749586324046390.2500827351907220.125041367595361
490.8547401409974890.2905197180050210.145259859002511
500.8611257436927040.2777485126145930.138874256307296
510.8336722697541260.3326554604917480.166327730245874
520.801356677110810.397286645778380.19864332288919
530.8387979783602480.3224040432795030.161202021639752
540.8077412517659490.3845174964681030.192258748234051
550.83396291862960.33207416274080.1660370813704
560.8181860669544790.3636278660910430.181813933045521
570.785553269902040.4288934601959190.21444673009796
580.7625292712044330.4749414575911330.237470728795567
590.7269529522191670.5460940955616660.273047047780833
600.7198527625469270.5602944749061460.280147237453073
610.6805987061042590.6388025877914810.319401293895741
620.6375771476864110.7248457046271770.362422852313589
630.5962673902059110.8074652195881780.403732609794089
640.5521300567188560.8957398865622890.447869943281144
650.5105893321490920.9788213357018170.489410667850908
660.4724568479614070.9449136959228140.527543152038593
670.4603860525935840.9207721051871670.539613947406416
680.5844340861885460.8311318276229070.415565913811454
690.7372509064076780.5254981871846440.262749093592322
700.7019788296850920.5960423406298170.298021170314908
710.8065133988978110.3869732022043780.193486601102189
720.7748487042260830.4503025915478340.225151295773917
730.7627431308681070.4745137382637870.237256869131893
740.7327568316658310.5344863366683390.267243168334169
750.6956243758504220.6087512482991560.304375624149578
760.7720554758112460.4558890483775090.227944524188754
770.7372130009390610.5255739981218770.262786999060939
780.7261890753106670.5476218493786670.273810924689333
790.7215935414956510.5568129170086980.278406458504349
800.6822032740009910.6355934519980180.317796725999009
810.6439002072809680.7121995854380630.356099792719032
820.7864744295428070.4270511409143860.213525570457193
830.7518226435248770.4963547129502450.248177356475123
840.7216816277324780.5566367445350440.278318372267522
850.6828253733292660.6343492533414690.317174626670734
860.6811202586927870.6377594826144260.318879741307213
870.6400075558431550.7199848883136890.359992444156845
880.6017106078428410.7965787843143180.398289392157159
890.5823216081717670.8353567836564650.417678391828233
900.5521825714697960.8956348570604070.447817428530204
910.5343338053040770.9313323893918460.465666194695923
920.4924677807045790.9849355614091590.507532219295421
930.4516581057443690.9033162114887380.548341894255631
940.4077054920443510.8154109840887010.592294507955649
950.4235089696733280.8470179393466570.576491030326672
960.3910099290894140.7820198581788270.608990070910586
970.3522612988237310.7045225976474620.647738701176269
980.354987519625170.7099750392503410.64501248037483
990.3131759768063750.6263519536127510.686824023193625
1000.27520903306680.55041806613360.7247909669332
1010.2500913012577660.5001826025155330.749908698742234
1020.2342019945586960.4684039891173930.765798005441304
1030.295118575629190.590237151258380.70488142437081
1040.255828392799490.5116567855989810.74417160720051
1050.2541458683728280.5082917367456560.745854131627172
1060.2694016518084290.5388033036168590.730598348191571
1070.247725570575610.495451141151220.75227442942439
1080.2222613535096020.4445227070192050.777738646490397
1090.2114213277126590.4228426554253170.788578672287341
1100.1949193541711760.3898387083423530.805080645828824
1110.177837096835130.355674193670260.82216290316487
1120.1520281386571510.3040562773143020.847971861342849
1130.1634979962472710.3269959924945420.836502003752729
1140.1460155369596560.2920310739193120.853984463040344
1150.169374293258130.338748586516260.83062570674187
1160.1691970337253630.3383940674507250.830802966274637
1170.1539202492227330.3078404984454670.846079750777267
1180.1304220094747020.2608440189494030.869577990525298
1190.1403897778480820.2807795556961640.859610222151918
1200.1269245106410460.2538490212820930.873075489358954
1210.1114590659017590.2229181318035180.888540934098241
1220.09397463120832380.1879492624166480.906025368791676
1230.1145394032434590.2290788064869170.885460596756541
1240.09784252985632770.1956850597126550.902157470143672
1250.08068754612176160.1613750922435230.919312453878238
1260.0648512796808920.1297025593617840.935148720319108
1270.05016419324437170.1003283864887430.949835806755628
1280.04336896472248220.08673792944496430.956631035277518
1290.03451197402367670.06902394804735330.965488025976323
1300.04480158979573290.08960317959146580.955198410204267
1310.03961221290357660.07922442580715320.960387787096423
1320.06352431491454380.1270486298290880.936475685085456
1330.07324480714680890.1464896142936180.926755192853191
1340.09224807036201420.1844961407240280.907751929637986
1350.07761687046750080.1552337409350020.922383129532499
1360.05766606035566580.1153321207113320.942333939644334
1370.04328690931502340.08657381863004670.956713090684977
1380.03124588929068870.06249177858137740.968754110709311
1390.04888738979341390.09777477958682770.951112610206586
1400.04040613335244310.08081226670488610.959593866647557
1410.558152689253650.88369462149270.44184731074635
1420.542107454415650.9157850911686990.45789254558435
1430.4744997636735360.9489995273470720.525500236326464
1440.4417684680600810.8835369361201630.558231531939918
1450.3688937798104760.7377875596209510.631106220189524
1460.4756582907857010.9513165815714020.524341709214299
1470.4473234859228330.8946469718456650.552676514077167
1480.5148193542050170.9703612915899650.485180645794983
1490.4259536014674030.8519072029348060.574046398532597
1500.3339454690950990.6678909381901980.666054530904901
1510.8040963848016820.3918072303966370.195903615198318
1520.9219643835395690.1560712329208620.0780356164604312
1530.857663644205120.284672711589760.14233635579488
1540.7602296498487770.4795407003024470.239770350151223
1550.7193504366646760.5612991266706490.280649563335324







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 level90.0604026845637584OK

\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 & 9 & 0.0604026845637584 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185736&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]9[/C][C]0.0604026845637584[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185736&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185736&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 level90.0604026845637584OK



Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; 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')
}