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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 23 Dec 2011 09:27:55 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/23/t13246505138ppk11vn4fd27wh.htm/, Retrieved Mon, 29 Apr 2024 21:49:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=160449, Retrieved Mon, 29 Apr 2024 21:49:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact134

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [ARIMA Forecasting] [Paper - Deel 2 - ...] [2011-12-20 08:59:21] [4cb29c3614c5a92c2f34c46f79abd839]
- RMPD  [Multiple Regression] [Paper - Deel 1 - ...] [2011-12-20 10:06:10] [4cb29c3614c5a92c2f34c46f79abd839]
- R         [Multiple Regression] [Paper - Deel 1 - ...] [2011-12-23 14:27:55] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
41	38	13	14	12
39	32	16	18	11
30	35	19	11	14
31	33	15	12	12
34	37	14	16	21
35	29	13	18	12
39	31	19	14	22
34	36	15	14	11
36	35	14	15	10
37	38	15	15	13
38	31	16	17	10
36	34	16	19	8
38	35	16	10	15
39	38	16	16	14
33	37	17	18	10
32	33	15	14	14
36	32	15	14	14
38	38	20	17	11
39	38	18	14	10
32	32	16	16	13
32	33	16	18	7
31	31	16	11	14
39	38	19	14	12
37	39	16	12	14
39	32	17	17	11
41	32	17	9	9
36	35	16	16	11
33	37	15	14	15
33	33	16	15	14
34	33	14	11	13
31	28	15	16	9
27	32	12	13	15
37	31	14	17	10
34	37	16	15	11
34	30	14	14	13
32	33	7	16	8
29	31	10	9	20
36	33	14	15	12
29	31	16	17	10
35	33	16	13	10
37	32	16	15	9
34	33	14	16	14
38	32	20	16	8
35	33	14	12	14
38	28	14	12	11
37	35	11	11	13
38	39	14	15	9
33	34	15	15	11
36	38	16	17	15
38	32	14	13	11
32	38	16	16	10
32	30	14	14	14
32	33	12	11	18
34	38	16	12	14
32	32	9	12	11
37	32	14	15	12
39	34	16	16	13
29	34	16	15	9
37	36	15	12	10
35	34	16	12	15
30	28	12	8	20
38	34	16	13	12
34	35	16	11	12
31	35	14	14	14
34	31	16	15	13
35	37	17	10	11
36	35	18	11	17
30	27	18	12	12
39	40	12	15	13
35	37	16	15	14
38	36	10	14	13
31	38	14	16	15
34	39	18	15	13
38	41	18	15	10
34	27	16	13	11
39	30	17	12	19
37	37	16	17	13
34	31	16	13	17
28	31	13	15	13
37	27	16	13	9
33	36	16	15	11
37	38	20	16	10
35	37	16	15	9
37	33	15	16	12
32	34	15	15	12
33	31	16	14	13
38	39	14	15	13
33	34	16	14	12
29	32	16	13	15
33	33	15	7	22
31	36	12	17	13
36	32	17	13	15
35	41	16	15	13
32	28	15	14	15
29	30	13	13	10
39	36	16	16	11
37	35	16	12	16
35	31	16	14	11
37	34	16	17	11
32	36	14	15	10
38	36	16	17	10
37	35	16	12	16
36	37	20	16	12
32	28	15	11	11
33	39	16	15	16
40	32	13	9	19
38	35	17	16	11
41	39	16	15	16
36	35	16	10	15
43	42	12	10	24
30	34	16	15	14
31	33	16	11	15
32	41	17	13	11
32	33	13	14	15
37	34	12	18	12
37	32	18	16	10
33	40	14	14	14
34	40	14	14	13
33	35	13	14	9
38	36	16	14	15
33	37	13	12	15
31	27	16	14	14
38	39	13	15	11
37	38	16	15	8
33	31	15	15	11
31	33	16	13	11
39	32	15	17	8
44	39	17	17	10
33	36	15	19	11
35	33	12	15	13
32	33	16	13	11
28	32	10	9	20
40	37	16	15	10
27	30	12	15	15
37	38	14	15	12
32	29	15	16	14
28	22	13	11	23
34	35	15	14	14
30	35	11	11	16
35	34	12	15	11
31	35	8	13	12
32	34	16	15	10
30	34	15	16	14
30	35	17	14	12
31	23	16	15	12
40	31	10	16	11
32	27	18	16	12
36	36	13	11	13
32	31	16	12	11
35	32	13	9	19
38	39	10	16	12
42	37	15	13	17
34	38	16	16	9
35	39	16	12	12
35	34	14	9	19
33	31	10	13	18
36	32	17	13	15
32	37	13	14	14
33	36	15	19	11
34	32	16	13	9
32	35	12	12	18
34	36	13	13	16

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160449&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160449&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160449&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 11.6793082170166 + 0.125931034495547Connected[t] -0.00830024796043488Seperate[t] + 0.0591546588127821Happiness[t] -0.126646215144289Depression[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  11.6793082170166 +  0.125931034495547Connected[t] -0.00830024796043488Seperate[t] +  0.0591546588127821Happiness[t] -0.126646215144289Depression[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160449&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  11.6793082170166 +  0.125931034495547Connected[t] -0.00830024796043488Seperate[t] +  0.0591546588127821Happiness[t] -0.126646215144289Depression[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160449&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160449&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 11.6793082170166 + 0.125931034495547Connected[t] -0.00830024796043488Seperate[t] + 0.0591546588127821Happiness[t] -0.126646215144289Depression[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)11.67930821701662.6795114.35872.4e-051.2e-05
Connected0.1259310344955470.0550182.28890.0234170.011709
Seperate-0.008300247960434880.052293-0.15870.8740890.437045
Happiness0.05915465881278210.0883910.66920.5043240.252162
Depression-0.1266462151442890.064714-1.9570.052120.02606

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 11.6793082170166 & 2.679511 & 4.3587 & 2.4e-05 & 1.2e-05 \tabularnewline
Connected & 0.125931034495547 & 0.055018 & 2.2889 & 0.023417 & 0.011709 \tabularnewline
Seperate & -0.00830024796043488 & 0.052293 & -0.1587 & 0.874089 & 0.437045 \tabularnewline
Happiness & 0.0591546588127821 & 0.088391 & 0.6692 & 0.504324 & 0.252162 \tabularnewline
Depression & -0.126646215144289 & 0.064714 & -1.957 & 0.05212 & 0.02606 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160449&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]11.6793082170166[/C][C]2.679511[/C][C]4.3587[/C][C]2.4e-05[/C][C]1.2e-05[/C][/ROW]
[ROW][C]Connected[/C][C]0.125931034495547[/C][C]0.055018[/C][C]2.2889[/C][C]0.023417[/C][C]0.011709[/C][/ROW]
[ROW][C]Seperate[/C][C]-0.00830024796043488[/C][C]0.052293[/C][C]-0.1587[/C][C]0.874089[/C][C]0.437045[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0591546588127821[/C][C]0.088391[/C][C]0.6692[/C][C]0.504324[/C][C]0.252162[/C][/ROW]
[ROW][C]Depression[/C][C]-0.126646215144289[/C][C]0.064714[/C][C]-1.957[/C][C]0.05212[/C][C]0.02606[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160449&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160449&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)11.67930821701662.6795114.35872.4e-051.2e-05
Connected0.1259310344955470.0550182.28890.0234170.011709
Seperate-0.008300247960434880.052293-0.15870.8740890.437045
Happiness0.05915465881278210.0883910.66920.5043240.252162
Depression-0.1266462151442890.064714-1.9570.052120.02606






Multiple Linear Regression - Regression Statistics
Multiple R0.302949330488019
R-squared0.0917782968431387
Adjusted R-squared0.0686388903932824
F-TEST (value)3.96632027022925
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value0.00428666417746926
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.17745102097476
Sum Squared Residuals744.382992952811

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.302949330488019 \tabularnewline
R-squared & 0.0917782968431387 \tabularnewline
Adjusted R-squared & 0.0686388903932824 \tabularnewline
F-TEST (value) & 3.96632027022925 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value & 0.00428666417746926 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.17745102097476 \tabularnewline
Sum Squared Residuals & 744.382992952811 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160449&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.302949330488019[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0917782968431387[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0686388903932824[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.96632027022925[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]157[/C][/ROW]
[ROW][C]p-value[/C][C]0.00428666417746926[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.17745102097476[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]744.382992952811[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160449&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160449&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.302949330488019
R-squared0.0917782968431387
Adjusted R-squared0.0686388903932824
F-TEST (value)3.96632027022925
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value0.00428666417746926
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.17745102097476
Sum Squared Residuals744.382992952811






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11315.835481850485-2.83548185048501
21615.99668611965190.0033138803480847
31914.04438480818834.95561519181165
41514.49936342770610.500636572293877
51413.94075823830350.0592417616964509
61315.3912165104067-2.39121651040674
71914.3752593657744.62474063422596
81515.0972113200813-0.0972113200813124
91415.5431745109899-1.54317451098991
101515.2642661561713-0.264266156171287
111615.94654688944830.0534531105516905
121616.0413858244901-0.0413858244900537
131614.86603221019561.13396778980435
141615.44863666883090.551363331169127
151715.32624488802071.67375511197925
161514.49031134953870.509688650461344
171515.0023357354813-0.00233573548127787
182015.7617989385814.23820106141902
191815.83691221178252.16308778821753
201614.74356713026891.25643286973106
211615.61345349079980.386546509200192
221614.20351683452561.79648316547437
231915.58361978149393.41638021850611
241614.95185571662821.04814428337178
251715.93753146083911.06246853916087
261715.96944868961651.03055131038345
271615.47568295465840.524317045341595
281514.45639517704820.543604822951826
291614.6753970428471.32460295715302
301414.6913556572357-0.691355657235692
311515.1574219481923-0.157421948192293
321213.6831555510643-1.68315555106428
331415.8206158549528-1.82061585495276
341615.14806573093370.851934269066341
351414.8937203775553-0.893720377555343
36715.36849795803-8.36849795802995
371013.0734681570432-3.07346815704324
381415.3064825766222-1.3064825766222
391614.81316757898841.18683242101161
401615.31553465478970.684465345210329
411615.82065250451110.179347495488947
421414.8604827361553-0.860482736155313
432016.13238441296373.86761558703633
441414.7497951353997-0.749795135399732
451415.5490281241214-1.54902812412141
461115.0525482648015-4.05254826480146
471415.8884818032836-1.88848180328356
481515.0470354403194-0.0470354403194174
491615.00335200901270.996647990987274
501415.5749817910925-1.57498179109246
511615.07370428793920.926295712060798
521414.51521209342-0.51521209341996
531213.8062625125232-1.80626251252315
541614.5823628611021.41763713889799
55914.7602409253064-5.76024092530639
561415.4407138590782-1.44071385907819
571615.60848387581690.391516124183098
581614.79660373262581.20339626737419
591515.4833413210867-0.483341321086678
601614.6148486722951.38515132770499
611213.1651452766073-1.16514527660731
621615.43173508002730.568264919972702
631614.80140137645911.19859862354089
641414.3477798191222-0.347779819122239
651614.94457478840771.05542521159231
661714.97822347136532.0217765286347
671814.42003236972883.57996763027124
681814.42323388097323.57676611902681
691215.4995277292415-3.49952772924151
701614.89405811999631.10594188000366
711015.3476430277749-5.34764302777492
721414.3145421777222-0.314542177722209
731814.87817280472423.12182719527579
741815.74523509221842.2547649077816
751615.11275889291240.887241107087556
761714.64518894154182.35481105845822
771615.39087572175730.609124278242714
781614.3196806102051.68031938979503
791314.1889885814344-1.18898858143441
801615.74384442668770.256155573312337
811615.03043494439850.969565055601453
822015.70335946041694.29664053958306
831615.52728919571780.472710804282215
841515.4915682699305-0.491568269930532
851514.79445819067960.205541809320419
861614.75948909509941.24051090490064
871415.3818969427064-1.3818969427064
881614.86123456636231.13876543363765
891613.93501762005542.06498237994462
901513.18899005119041.81100994880959
911214.6435897627444-2.64358976274444
921714.81653486152422.18346513847579
931614.98750334329891.01249665670111
941514.40516637419650.594833625803459
951314.5848491916977-1.58484919169769
961615.84517581018460.154824189815389
971614.73176427818141.26823572181862
981615.2646435943790.735356405620966
991615.66906889592720.330931104072831
1001415.0311501250473-1.03115012504729
1011615.90504564964610.0949543503538648
1021614.73176427818141.26823572181862
1032015.33243624359324.66756375640675
1041514.73428725833540.265712741664649
1051614.37230312479581.6276968752042
1061314.5770555036781-1.57705550367811
1071715.72754502364951.2724549763505
1081615.37975140076020.620248599239828
1091614.61417014120461.38582985879544
1101214.2977697106517-2.29776971065174
1111614.28930369139991.71069630860009
1121614.06027012346051.93972987653953
1131714.74469335247532.25530664752474
1141314.3636651343944-1.36366513439437
1151215.6015773395957-3.60157733959566
1161815.75316094817952.24683905182045
1171414.5581406483112-0.558140648311159
1181414.810717897951-0.810717897950994
1191315.2328729638348-2.23287296383478
1201615.09435059748630.905649402513657
1211314.3380858594226-1.33808585942261
1221614.41418180280571.58581819719428
1231315.635189372995-2.63518937299498
1241615.89749723189270.102502768107267
1251515.0719361842007-0.0719361842007219
1261614.68516430166321.31483569833681
1271516.317470106272-1.317470106272
1281716.63573111273810.364268887261888
1291515.2670535796497-0.267053579649676
1301215.0539053269824-3.05390532698237
1311614.81109533615871.18890466384126
1321012.9392368745873-2.93923687458726
1331616.0302981530512-0.03029815305123
1341213.8180653646107-1.81806536461072
1351415.3909123713156-1.39091237131558
1361514.6418216590060.358178340994041
1371312.76061002638430.239389973615697
1381514.72557292260890.27442707739112
1391113.7910923778998-2.79109237789977
1401215.2988975093105-3.29889750931051
141814.541917590598-6.54191759059804
1421615.04775062096820.95224937903184
1431514.34845835021270.651541649787309
1441714.47514121491532.52485878508473
1451614.75982988374881.24017011625118
1461016.0126080844823-6.01260808448233
1471814.91171458521543.08828541478459
1481314.9183169823455-1.91831698234548
1491614.76854117326681.23145882673317
1501313.9474003312004-0.947400331200374
1511015.5676978166635-5.56769781666347
1521515.2773273984067-0.277327398406735
1531615.45221257207460.547787427925415
1541614.95328607792571.0467139220743
1551413.93079983527950.0692001647204952
1561014.0671033605651-4.06710336056513
1571714.81653486152422.18346513847579
1581314.4571103576969-1.45711035769692
1591515.2670535796497-0.267053579649676
1601615.32455008339880.675449916601152
1611213.8488166754151-1.84881667541507
1621314.4048255855471-1.40482558554708

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 15.835481850485 & -2.83548185048501 \tabularnewline
2 & 16 & 15.9966861196519 & 0.0033138803480847 \tabularnewline
3 & 19 & 14.0443848081883 & 4.95561519181165 \tabularnewline
4 & 15 & 14.4993634277061 & 0.500636572293877 \tabularnewline
5 & 14 & 13.9407582383035 & 0.0592417616964509 \tabularnewline
6 & 13 & 15.3912165104067 & -2.39121651040674 \tabularnewline
7 & 19 & 14.375259365774 & 4.62474063422596 \tabularnewline
8 & 15 & 15.0972113200813 & -0.0972113200813124 \tabularnewline
9 & 14 & 15.5431745109899 & -1.54317451098991 \tabularnewline
10 & 15 & 15.2642661561713 & -0.264266156171287 \tabularnewline
11 & 16 & 15.9465468894483 & 0.0534531105516905 \tabularnewline
12 & 16 & 16.0413858244901 & -0.0413858244900537 \tabularnewline
13 & 16 & 14.8660322101956 & 1.13396778980435 \tabularnewline
14 & 16 & 15.4486366688309 & 0.551363331169127 \tabularnewline
15 & 17 & 15.3262448880207 & 1.67375511197925 \tabularnewline
16 & 15 & 14.4903113495387 & 0.509688650461344 \tabularnewline
17 & 15 & 15.0023357354813 & -0.00233573548127787 \tabularnewline
18 & 20 & 15.761798938581 & 4.23820106141902 \tabularnewline
19 & 18 & 15.8369122117825 & 2.16308778821753 \tabularnewline
20 & 16 & 14.7435671302689 & 1.25643286973106 \tabularnewline
21 & 16 & 15.6134534907998 & 0.386546509200192 \tabularnewline
22 & 16 & 14.2035168345256 & 1.79648316547437 \tabularnewline
23 & 19 & 15.5836197814939 & 3.41638021850611 \tabularnewline
24 & 16 & 14.9518557166282 & 1.04814428337178 \tabularnewline
25 & 17 & 15.9375314608391 & 1.06246853916087 \tabularnewline
26 & 17 & 15.9694486896165 & 1.03055131038345 \tabularnewline
27 & 16 & 15.4756829546584 & 0.524317045341595 \tabularnewline
28 & 15 & 14.4563951770482 & 0.543604822951826 \tabularnewline
29 & 16 & 14.675397042847 & 1.32460295715302 \tabularnewline
30 & 14 & 14.6913556572357 & -0.691355657235692 \tabularnewline
31 & 15 & 15.1574219481923 & -0.157421948192293 \tabularnewline
32 & 12 & 13.6831555510643 & -1.68315555106428 \tabularnewline
33 & 14 & 15.8206158549528 & -1.82061585495276 \tabularnewline
34 & 16 & 15.1480657309337 & 0.851934269066341 \tabularnewline
35 & 14 & 14.8937203775553 & -0.893720377555343 \tabularnewline
36 & 7 & 15.36849795803 & -8.36849795802995 \tabularnewline
37 & 10 & 13.0734681570432 & -3.07346815704324 \tabularnewline
38 & 14 & 15.3064825766222 & -1.3064825766222 \tabularnewline
39 & 16 & 14.8131675789884 & 1.18683242101161 \tabularnewline
40 & 16 & 15.3155346547897 & 0.684465345210329 \tabularnewline
41 & 16 & 15.8206525045111 & 0.179347495488947 \tabularnewline
42 & 14 & 14.8604827361553 & -0.860482736155313 \tabularnewline
43 & 20 & 16.1323844129637 & 3.86761558703633 \tabularnewline
44 & 14 & 14.7497951353997 & -0.749795135399732 \tabularnewline
45 & 14 & 15.5490281241214 & -1.54902812412141 \tabularnewline
46 & 11 & 15.0525482648015 & -4.05254826480146 \tabularnewline
47 & 14 & 15.8884818032836 & -1.88848180328356 \tabularnewline
48 & 15 & 15.0470354403194 & -0.0470354403194174 \tabularnewline
49 & 16 & 15.0033520090127 & 0.996647990987274 \tabularnewline
50 & 14 & 15.5749817910925 & -1.57498179109246 \tabularnewline
51 & 16 & 15.0737042879392 & 0.926295712060798 \tabularnewline
52 & 14 & 14.51521209342 & -0.51521209341996 \tabularnewline
53 & 12 & 13.8062625125232 & -1.80626251252315 \tabularnewline
54 & 16 & 14.582362861102 & 1.41763713889799 \tabularnewline
55 & 9 & 14.7602409253064 & -5.76024092530639 \tabularnewline
56 & 14 & 15.4407138590782 & -1.44071385907819 \tabularnewline
57 & 16 & 15.6084838758169 & 0.391516124183098 \tabularnewline
58 & 16 & 14.7966037326258 & 1.20339626737419 \tabularnewline
59 & 15 & 15.4833413210867 & -0.483341321086678 \tabularnewline
60 & 16 & 14.614848672295 & 1.38515132770499 \tabularnewline
61 & 12 & 13.1651452766073 & -1.16514527660731 \tabularnewline
62 & 16 & 15.4317350800273 & 0.568264919972702 \tabularnewline
63 & 16 & 14.8014013764591 & 1.19859862354089 \tabularnewline
64 & 14 & 14.3477798191222 & -0.347779819122239 \tabularnewline
65 & 16 & 14.9445747884077 & 1.05542521159231 \tabularnewline
66 & 17 & 14.9782234713653 & 2.0217765286347 \tabularnewline
67 & 18 & 14.4200323697288 & 3.57996763027124 \tabularnewline
68 & 18 & 14.4232338809732 & 3.57676611902681 \tabularnewline
69 & 12 & 15.4995277292415 & -3.49952772924151 \tabularnewline
70 & 16 & 14.8940581199963 & 1.10594188000366 \tabularnewline
71 & 10 & 15.3476430277749 & -5.34764302777492 \tabularnewline
72 & 14 & 14.3145421777222 & -0.314542177722209 \tabularnewline
73 & 18 & 14.8781728047242 & 3.12182719527579 \tabularnewline
74 & 18 & 15.7452350922184 & 2.2547649077816 \tabularnewline
75 & 16 & 15.1127588929124 & 0.887241107087556 \tabularnewline
76 & 17 & 14.6451889415418 & 2.35481105845822 \tabularnewline
77 & 16 & 15.3908757217573 & 0.609124278242714 \tabularnewline
78 & 16 & 14.319680610205 & 1.68031938979503 \tabularnewline
79 & 13 & 14.1889885814344 & -1.18898858143441 \tabularnewline
80 & 16 & 15.7438444266877 & 0.256155573312337 \tabularnewline
81 & 16 & 15.0304349443985 & 0.969565055601453 \tabularnewline
82 & 20 & 15.7033594604169 & 4.29664053958306 \tabularnewline
83 & 16 & 15.5272891957178 & 0.472710804282215 \tabularnewline
84 & 15 & 15.4915682699305 & -0.491568269930532 \tabularnewline
85 & 15 & 14.7944581906796 & 0.205541809320419 \tabularnewline
86 & 16 & 14.7594890950994 & 1.24051090490064 \tabularnewline
87 & 14 & 15.3818969427064 & -1.3818969427064 \tabularnewline
88 & 16 & 14.8612345663623 & 1.13876543363765 \tabularnewline
89 & 16 & 13.9350176200554 & 2.06498237994462 \tabularnewline
90 & 15 & 13.1889900511904 & 1.81100994880959 \tabularnewline
91 & 12 & 14.6435897627444 & -2.64358976274444 \tabularnewline
92 & 17 & 14.8165348615242 & 2.18346513847579 \tabularnewline
93 & 16 & 14.9875033432989 & 1.01249665670111 \tabularnewline
94 & 15 & 14.4051663741965 & 0.594833625803459 \tabularnewline
95 & 13 & 14.5848491916977 & -1.58484919169769 \tabularnewline
96 & 16 & 15.8451758101846 & 0.154824189815389 \tabularnewline
97 & 16 & 14.7317642781814 & 1.26823572181862 \tabularnewline
98 & 16 & 15.264643594379 & 0.735356405620966 \tabularnewline
99 & 16 & 15.6690688959272 & 0.330931104072831 \tabularnewline
100 & 14 & 15.0311501250473 & -1.03115012504729 \tabularnewline
101 & 16 & 15.9050456496461 & 0.0949543503538648 \tabularnewline
102 & 16 & 14.7317642781814 & 1.26823572181862 \tabularnewline
103 & 20 & 15.3324362435932 & 4.66756375640675 \tabularnewline
104 & 15 & 14.7342872583354 & 0.265712741664649 \tabularnewline
105 & 16 & 14.3723031247958 & 1.6276968752042 \tabularnewline
106 & 13 & 14.5770555036781 & -1.57705550367811 \tabularnewline
107 & 17 & 15.7275450236495 & 1.2724549763505 \tabularnewline
108 & 16 & 15.3797514007602 & 0.620248599239828 \tabularnewline
109 & 16 & 14.6141701412046 & 1.38582985879544 \tabularnewline
110 & 12 & 14.2977697106517 & -2.29776971065174 \tabularnewline
111 & 16 & 14.2893036913999 & 1.71069630860009 \tabularnewline
112 & 16 & 14.0602701234605 & 1.93972987653953 \tabularnewline
113 & 17 & 14.7446933524753 & 2.25530664752474 \tabularnewline
114 & 13 & 14.3636651343944 & -1.36366513439437 \tabularnewline
115 & 12 & 15.6015773395957 & -3.60157733959566 \tabularnewline
116 & 18 & 15.7531609481795 & 2.24683905182045 \tabularnewline
117 & 14 & 14.5581406483112 & -0.558140648311159 \tabularnewline
118 & 14 & 14.810717897951 & -0.810717897950994 \tabularnewline
119 & 13 & 15.2328729638348 & -2.23287296383478 \tabularnewline
120 & 16 & 15.0943505974863 & 0.905649402513657 \tabularnewline
121 & 13 & 14.3380858594226 & -1.33808585942261 \tabularnewline
122 & 16 & 14.4141818028057 & 1.58581819719428 \tabularnewline
123 & 13 & 15.635189372995 & -2.63518937299498 \tabularnewline
124 & 16 & 15.8974972318927 & 0.102502768107267 \tabularnewline
125 & 15 & 15.0719361842007 & -0.0719361842007219 \tabularnewline
126 & 16 & 14.6851643016632 & 1.31483569833681 \tabularnewline
127 & 15 & 16.317470106272 & -1.317470106272 \tabularnewline
128 & 17 & 16.6357311127381 & 0.364268887261888 \tabularnewline
129 & 15 & 15.2670535796497 & -0.267053579649676 \tabularnewline
130 & 12 & 15.0539053269824 & -3.05390532698237 \tabularnewline
131 & 16 & 14.8110953361587 & 1.18890466384126 \tabularnewline
132 & 10 & 12.9392368745873 & -2.93923687458726 \tabularnewline
133 & 16 & 16.0302981530512 & -0.03029815305123 \tabularnewline
134 & 12 & 13.8180653646107 & -1.81806536461072 \tabularnewline
135 & 14 & 15.3909123713156 & -1.39091237131558 \tabularnewline
136 & 15 & 14.641821659006 & 0.358178340994041 \tabularnewline
137 & 13 & 12.7606100263843 & 0.239389973615697 \tabularnewline
138 & 15 & 14.7255729226089 & 0.27442707739112 \tabularnewline
139 & 11 & 13.7910923778998 & -2.79109237789977 \tabularnewline
140 & 12 & 15.2988975093105 & -3.29889750931051 \tabularnewline
141 & 8 & 14.541917590598 & -6.54191759059804 \tabularnewline
142 & 16 & 15.0477506209682 & 0.95224937903184 \tabularnewline
143 & 15 & 14.3484583502127 & 0.651541649787309 \tabularnewline
144 & 17 & 14.4751412149153 & 2.52485878508473 \tabularnewline
145 & 16 & 14.7598298837488 & 1.24017011625118 \tabularnewline
146 & 10 & 16.0126080844823 & -6.01260808448233 \tabularnewline
147 & 18 & 14.9117145852154 & 3.08828541478459 \tabularnewline
148 & 13 & 14.9183169823455 & -1.91831698234548 \tabularnewline
149 & 16 & 14.7685411732668 & 1.23145882673317 \tabularnewline
150 & 13 & 13.9474003312004 & -0.947400331200374 \tabularnewline
151 & 10 & 15.5676978166635 & -5.56769781666347 \tabularnewline
152 & 15 & 15.2773273984067 & -0.277327398406735 \tabularnewline
153 & 16 & 15.4522125720746 & 0.547787427925415 \tabularnewline
154 & 16 & 14.9532860779257 & 1.0467139220743 \tabularnewline
155 & 14 & 13.9307998352795 & 0.0692001647204952 \tabularnewline
156 & 10 & 14.0671033605651 & -4.06710336056513 \tabularnewline
157 & 17 & 14.8165348615242 & 2.18346513847579 \tabularnewline
158 & 13 & 14.4571103576969 & -1.45711035769692 \tabularnewline
159 & 15 & 15.2670535796497 & -0.267053579649676 \tabularnewline
160 & 16 & 15.3245500833988 & 0.675449916601152 \tabularnewline
161 & 12 & 13.8488166754151 & -1.84881667541507 \tabularnewline
162 & 13 & 14.4048255855471 & -1.40482558554708 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160449&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]15.835481850485[/C][C]-2.83548185048501[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.9966861196519[/C][C]0.0033138803480847[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]14.0443848081883[/C][C]4.95561519181165[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]14.4993634277061[/C][C]0.500636572293877[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]13.9407582383035[/C][C]0.0592417616964509[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.3912165104067[/C][C]-2.39121651040674[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]14.375259365774[/C][C]4.62474063422596[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]15.0972113200813[/C][C]-0.0972113200813124[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.5431745109899[/C][C]-1.54317451098991[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]15.2642661561713[/C][C]-0.264266156171287[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.9465468894483[/C][C]0.0534531105516905[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.0413858244901[/C][C]-0.0413858244900537[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]14.8660322101956[/C][C]1.13396778980435[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.4486366688309[/C][C]0.551363331169127[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]15.3262448880207[/C][C]1.67375511197925[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]14.4903113495387[/C][C]0.509688650461344[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]15.0023357354813[/C][C]-0.00233573548127787[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]15.761798938581[/C][C]4.23820106141902[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.8369122117825[/C][C]2.16308778821753[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]14.7435671302689[/C][C]1.25643286973106[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.6134534907998[/C][C]0.386546509200192[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.2035168345256[/C][C]1.79648316547437[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]15.5836197814939[/C][C]3.41638021850611[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.9518557166282[/C][C]1.04814428337178[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.9375314608391[/C][C]1.06246853916087[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]15.9694486896165[/C][C]1.03055131038345[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.4756829546584[/C][C]0.524317045341595[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]14.4563951770482[/C][C]0.543604822951826[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]14.675397042847[/C][C]1.32460295715302[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.6913556572357[/C][C]-0.691355657235692[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.1574219481923[/C][C]-0.157421948192293[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.6831555510643[/C][C]-1.68315555106428[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.8206158549528[/C][C]-1.82061585495276[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.1480657309337[/C][C]0.851934269066341[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]14.8937203775553[/C][C]-0.893720377555343[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]15.36849795803[/C][C]-8.36849795802995[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]13.0734681570432[/C][C]-3.07346815704324[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.3064825766222[/C][C]-1.3064825766222[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.8131675789884[/C][C]1.18683242101161[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.3155346547897[/C][C]0.684465345210329[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.8206525045111[/C][C]0.179347495488947[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]14.8604827361553[/C][C]-0.860482736155313[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]16.1323844129637[/C][C]3.86761558703633[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.7497951353997[/C][C]-0.749795135399732[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]15.5490281241214[/C][C]-1.54902812412141[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.0525482648015[/C][C]-4.05254826480146[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]15.8884818032836[/C][C]-1.88848180328356[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.0470354403194[/C][C]-0.0470354403194174[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.0033520090127[/C][C]0.996647990987274[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.5749817910925[/C][C]-1.57498179109246[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]15.0737042879392[/C][C]0.926295712060798[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.51521209342[/C][C]-0.51521209341996[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]13.8062625125232[/C][C]-1.80626251252315[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]14.582362861102[/C][C]1.41763713889799[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]14.7602409253064[/C][C]-5.76024092530639[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]15.4407138590782[/C][C]-1.44071385907819[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.6084838758169[/C][C]0.391516124183098[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.7966037326258[/C][C]1.20339626737419[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.4833413210867[/C][C]-0.483341321086678[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.614848672295[/C][C]1.38515132770499[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]13.1651452766073[/C][C]-1.16514527660731[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.4317350800273[/C][C]0.568264919972702[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]14.8014013764591[/C][C]1.19859862354089[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.3477798191222[/C][C]-0.347779819122239[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]14.9445747884077[/C][C]1.05542521159231[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]14.9782234713653[/C][C]2.0217765286347[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]14.4200323697288[/C][C]3.57996763027124[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.4232338809732[/C][C]3.57676611902681[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.4995277292415[/C][C]-3.49952772924151[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]14.8940581199963[/C][C]1.10594188000366[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]15.3476430277749[/C][C]-5.34764302777492[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.3145421777222[/C][C]-0.314542177722209[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]14.8781728047242[/C][C]3.12182719527579[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]15.7452350922184[/C][C]2.2547649077816[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.1127588929124[/C][C]0.887241107087556[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]14.6451889415418[/C][C]2.35481105845822[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]15.3908757217573[/C][C]0.609124278242714[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.319680610205[/C][C]1.68031938979503[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.1889885814344[/C][C]-1.18898858143441[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.7438444266877[/C][C]0.256155573312337[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.0304349443985[/C][C]0.969565055601453[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]15.7033594604169[/C][C]4.29664053958306[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.5272891957178[/C][C]0.472710804282215[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.4915682699305[/C][C]-0.491568269930532[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.7944581906796[/C][C]0.205541809320419[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.7594890950994[/C][C]1.24051090490064[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]15.3818969427064[/C][C]-1.3818969427064[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]14.8612345663623[/C][C]1.13876543363765[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]13.9350176200554[/C][C]2.06498237994462[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]13.1889900511904[/C][C]1.81100994880959[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]14.6435897627444[/C][C]-2.64358976274444[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]14.8165348615242[/C][C]2.18346513847579[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]14.9875033432989[/C][C]1.01249665670111[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]14.4051663741965[/C][C]0.594833625803459[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]14.5848491916977[/C][C]-1.58484919169769[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.8451758101846[/C][C]0.154824189815389[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]14.7317642781814[/C][C]1.26823572181862[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]15.264643594379[/C][C]0.735356405620966[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]15.6690688959272[/C][C]0.330931104072831[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]15.0311501250473[/C][C]-1.03115012504729[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.9050456496461[/C][C]0.0949543503538648[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.7317642781814[/C][C]1.26823572181862[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]15.3324362435932[/C][C]4.66756375640675[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.7342872583354[/C][C]0.265712741664649[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.3723031247958[/C][C]1.6276968752042[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]14.5770555036781[/C][C]-1.57705550367811[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]15.7275450236495[/C][C]1.2724549763505[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.3797514007602[/C][C]0.620248599239828[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.6141701412046[/C][C]1.38582985879544[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]14.2977697106517[/C][C]-2.29776971065174[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.2893036913999[/C][C]1.71069630860009[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]14.0602701234605[/C][C]1.93972987653953[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.7446933524753[/C][C]2.25530664752474[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.3636651343944[/C][C]-1.36366513439437[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]15.6015773395957[/C][C]-3.60157733959566[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]15.7531609481795[/C][C]2.24683905182045[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]14.5581406483112[/C][C]-0.558140648311159[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]14.810717897951[/C][C]-0.810717897950994[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]15.2328729638348[/C][C]-2.23287296383478[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.0943505974863[/C][C]0.905649402513657[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.3380858594226[/C][C]-1.33808585942261[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]14.4141818028057[/C][C]1.58581819719428[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.635189372995[/C][C]-2.63518937299498[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]15.8974972318927[/C][C]0.102502768107267[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.0719361842007[/C][C]-0.0719361842007219[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]14.6851643016632[/C][C]1.31483569833681[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]16.317470106272[/C][C]-1.317470106272[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.6357311127381[/C][C]0.364268887261888[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]15.2670535796497[/C][C]-0.267053579649676[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]15.0539053269824[/C][C]-3.05390532698237[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.8110953361587[/C][C]1.18890466384126[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]12.9392368745873[/C][C]-2.93923687458726[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]16.0302981530512[/C][C]-0.03029815305123[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.8180653646107[/C][C]-1.81806536461072[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.3909123713156[/C][C]-1.39091237131558[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]14.641821659006[/C][C]0.358178340994041[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.7606100263843[/C][C]0.239389973615697[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.7255729226089[/C][C]0.27442707739112[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.7910923778998[/C][C]-2.79109237789977[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]15.2988975093105[/C][C]-3.29889750931051[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]14.541917590598[/C][C]-6.54191759059804[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]15.0477506209682[/C][C]0.95224937903184[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]14.3484583502127[/C][C]0.651541649787309[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]14.4751412149153[/C][C]2.52485878508473[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.7598298837488[/C][C]1.24017011625118[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]16.0126080844823[/C][C]-6.01260808448233[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]14.9117145852154[/C][C]3.08828541478459[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]14.9183169823455[/C][C]-1.91831698234548[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]14.7685411732668[/C][C]1.23145882673317[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]13.9474003312004[/C][C]-0.947400331200374[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]15.5676978166635[/C][C]-5.56769781666347[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]15.2773273984067[/C][C]-0.277327398406735[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]15.4522125720746[/C][C]0.547787427925415[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]14.9532860779257[/C][C]1.0467139220743[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]13.9307998352795[/C][C]0.0692001647204952[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]14.0671033605651[/C][C]-4.06710336056513[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]14.8165348615242[/C][C]2.18346513847579[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]14.4571103576969[/C][C]-1.45711035769692[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]15.2670535796497[/C][C]-0.267053579649676[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]15.3245500833988[/C][C]0.675449916601152[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]13.8488166754151[/C][C]-1.84881667541507[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]14.4048255855471[/C][C]-1.40482558554708[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160449&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160449&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11315.835481850485-2.83548185048501
21615.99668611965190.0033138803480847
31914.04438480818834.95561519181165
41514.49936342770610.500636572293877
51413.94075823830350.0592417616964509
61315.3912165104067-2.39121651040674
71914.3752593657744.62474063422596
81515.0972113200813-0.0972113200813124
91415.5431745109899-1.54317451098991
101515.2642661561713-0.264266156171287
111615.94654688944830.0534531105516905
121616.0413858244901-0.0413858244900537
131614.86603221019561.13396778980435
141615.44863666883090.551363331169127
151715.32624488802071.67375511197925
161514.49031134953870.509688650461344
171515.0023357354813-0.00233573548127787
182015.7617989385814.23820106141902
191815.83691221178252.16308778821753
201614.74356713026891.25643286973106
211615.61345349079980.386546509200192
221614.20351683452561.79648316547437
231915.58361978149393.41638021850611
241614.95185571662821.04814428337178
251715.93753146083911.06246853916087
261715.96944868961651.03055131038345
271615.47568295465840.524317045341595
281514.45639517704820.543604822951826
291614.6753970428471.32460295715302
301414.6913556572357-0.691355657235692
311515.1574219481923-0.157421948192293
321213.6831555510643-1.68315555106428
331415.8206158549528-1.82061585495276
341615.14806573093370.851934269066341
351414.8937203775553-0.893720377555343
36715.36849795803-8.36849795802995
371013.0734681570432-3.07346815704324
381415.3064825766222-1.3064825766222
391614.81316757898841.18683242101161
401615.31553465478970.684465345210329
411615.82065250451110.179347495488947
421414.8604827361553-0.860482736155313
432016.13238441296373.86761558703633
441414.7497951353997-0.749795135399732
451415.5490281241214-1.54902812412141
461115.0525482648015-4.05254826480146
471415.8884818032836-1.88848180328356
481515.0470354403194-0.0470354403194174
491615.00335200901270.996647990987274
501415.5749817910925-1.57498179109246
511615.07370428793920.926295712060798
521414.51521209342-0.51521209341996
531213.8062625125232-1.80626251252315
541614.5823628611021.41763713889799
55914.7602409253064-5.76024092530639
561415.4407138590782-1.44071385907819
571615.60848387581690.391516124183098
581614.79660373262581.20339626737419
591515.4833413210867-0.483341321086678
601614.6148486722951.38515132770499
611213.1651452766073-1.16514527660731
621615.43173508002730.568264919972702
631614.80140137645911.19859862354089
641414.3477798191222-0.347779819122239
651614.94457478840771.05542521159231
661714.97822347136532.0217765286347
671814.42003236972883.57996763027124
681814.42323388097323.57676611902681
691215.4995277292415-3.49952772924151
701614.89405811999631.10594188000366
711015.3476430277749-5.34764302777492
721414.3145421777222-0.314542177722209
731814.87817280472423.12182719527579
741815.74523509221842.2547649077816
751615.11275889291240.887241107087556
761714.64518894154182.35481105845822
771615.39087572175730.609124278242714
781614.3196806102051.68031938979503
791314.1889885814344-1.18898858143441
801615.74384442668770.256155573312337
811615.03043494439850.969565055601453
822015.70335946041694.29664053958306
831615.52728919571780.472710804282215
841515.4915682699305-0.491568269930532
851514.79445819067960.205541809320419
861614.75948909509941.24051090490064
871415.3818969427064-1.3818969427064
881614.86123456636231.13876543363765
891613.93501762005542.06498237994462
901513.18899005119041.81100994880959
911214.6435897627444-2.64358976274444
921714.81653486152422.18346513847579
931614.98750334329891.01249665670111
941514.40516637419650.594833625803459
951314.5848491916977-1.58484919169769
961615.84517581018460.154824189815389
971614.73176427818141.26823572181862
981615.2646435943790.735356405620966
991615.66906889592720.330931104072831
1001415.0311501250473-1.03115012504729
1011615.90504564964610.0949543503538648
1021614.73176427818141.26823572181862
1032015.33243624359324.66756375640675
1041514.73428725833540.265712741664649
1051614.37230312479581.6276968752042
1061314.5770555036781-1.57705550367811
1071715.72754502364951.2724549763505
1081615.37975140076020.620248599239828
1091614.61417014120461.38582985879544
1101214.2977697106517-2.29776971065174
1111614.28930369139991.71069630860009
1121614.06027012346051.93972987653953
1131714.74469335247532.25530664752474
1141314.3636651343944-1.36366513439437
1151215.6015773395957-3.60157733959566
1161815.75316094817952.24683905182045
1171414.5581406483112-0.558140648311159
1181414.810717897951-0.810717897950994
1191315.2328729638348-2.23287296383478
1201615.09435059748630.905649402513657
1211314.3380858594226-1.33808585942261
1221614.41418180280571.58581819719428
1231315.635189372995-2.63518937299498
1241615.89749723189270.102502768107267
1251515.0719361842007-0.0719361842007219
1261614.68516430166321.31483569833681
1271516.317470106272-1.317470106272
1281716.63573111273810.364268887261888
1291515.2670535796497-0.267053579649676
1301215.0539053269824-3.05390532698237
1311614.81109533615871.18890466384126
1321012.9392368745873-2.93923687458726
1331616.0302981530512-0.03029815305123
1341213.8180653646107-1.81806536461072
1351415.3909123713156-1.39091237131558
1361514.6418216590060.358178340994041
1371312.76061002638430.239389973615697
1381514.72557292260890.27442707739112
1391113.7910923778998-2.79109237789977
1401215.2988975093105-3.29889750931051
141814.541917590598-6.54191759059804
1421615.04775062096820.95224937903184
1431514.34845835021270.651541649787309
1441714.47514121491532.52485878508473
1451614.75982988374881.24017011625118
1461016.0126080844823-6.01260808448233
1471814.91171458521543.08828541478459
1481314.9183169823455-1.91831698234548
1491614.76854117326681.23145882673317
1501313.9474003312004-0.947400331200374
1511015.5676978166635-5.56769781666347
1521515.2773273984067-0.277327398406735
1531615.45221257207460.547787427925415
1541614.95328607792571.0467139220743
1551413.93079983527950.0692001647204952
1561014.0671033605651-4.06710336056513
1571714.81653486152422.18346513847579
1581314.4571103576969-1.45711035769692
1591515.2670535796497-0.267053579649676
1601615.32455008339880.675449916601152
1611213.8488166754151-1.84881667541507
1621314.4048255855471-1.40482558554708






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.7297123389635010.5405753220729990.270287661036499
90.5834388585788410.8331222828423170.416561141421159
100.481669633249460.963339266498920.51833036675054
110.4032292040493520.8064584080987050.596770795950648
120.5126803212821620.9746393574356760.487319678717838
130.4179666073525350.8359332147050690.582033392647465
140.3681179759895460.7362359519790920.631882024010454
150.3942978840406590.7885957680813180.605702115959341
160.3334164659893320.6668329319786640.666583534010668
170.2669143613488190.5338287226976380.733085638651181
180.5742533629304040.8514932741391910.425746637069596
190.5620219714733030.8759560570533930.437978028526697
200.4885838612673130.9771677225346270.511416138732687
210.4160750275975470.8321500551950950.583924972402453
220.3492761560416410.6985523120832820.650723843958359
230.3907611593980140.7815223187960280.609238840601986
240.3342095269994670.6684190539989330.665790473000533
250.2916053137163520.5832106274327040.708394686283648
260.2382647761075950.476529552215190.761735223892405
270.1887101155675310.3774202311350610.811289884432469
280.1568402854703630.3136805709407260.843159714529637
290.1225444550443540.2450889100887070.877455544955646
300.1184459715999920.2368919431999850.881554028400008
310.08927464008604810.1785492801720960.910725359913952
320.1090381563722020.2180763127444040.890961843627798
330.1017962699140880.2035925398281750.898203730085912
340.07759020911383620.1551804182276720.922409790886164
350.06327997803346110.1265599560669220.936720021966539
360.6515914989250850.696817002149830.348408501074915
370.7508420551719140.4983158896561720.249157944828086
380.7232614399380240.5534771201239510.276738560061976
390.7200959384904270.5598081230191470.279904061509573
400.6787803069006950.6424393861986090.321219693099305
410.6304736831077860.7390526337844290.369526316892214
420.5898973679265890.8202052641468210.410102632073411
430.7004668127408620.5990663745182750.299533187259138
440.6646152973862230.6707694052275540.335384702613777
450.6348623197345550.730275360530890.365137680265445
460.7673683215494250.465263356901150.232631678450575
470.7706646580111740.4586706839776510.229335341988826
480.7304288963590280.5391422072819440.269571103640972
490.6930956650864920.6138086698270160.306904334913508
500.6698477651276970.6603044697446050.330152234872303
510.6297518463622070.7404963072755860.370248153637793
520.5829813438647740.8340373122704530.417018656135226
530.5700514176083720.8598971647832560.429948582391628
540.5345956278101940.9308087443796110.465404372189806
550.7597423284959050.480515343008190.240257671504095
560.7372816689731210.5254366620537580.262718331026879
570.6981781105330390.6036437789339220.301821889466961
580.6795838597915530.6408322804168950.320416140208447
590.6390267999923160.7219464000153680.360973200007684
600.6106170173060940.7787659653878130.389382982693906
610.5735717911242040.8528564177515910.426428208875796
620.5288841853612880.9422316292774240.471115814638712
630.4973040211664060.9946080423328110.502695978833594
640.4518228563635950.9036457127271910.548177143636405
650.4204581690625230.8409163381250460.579541830937477
660.4085684252198490.8171368504396980.591431574780151
670.4698864575763210.9397729151526410.530113542423679
680.5948007349762810.8103985300474390.405199265023719
690.6898640983054880.6202718033890240.310135901694512
700.6569157076449940.6861685847100110.343084292355006
710.8414952829095190.3170094341809620.158504717090481
720.8140689001734150.3718621996531690.185931099826585
730.8409453659459680.3181092681080640.159054634054032
740.8392956621879730.3214086756240540.160704337812027
750.8176205760661460.3647588478677090.182379423933854
760.8209918318547840.3580163362904320.179008168145216
770.7935299885663650.4129400228672690.206470011433635
780.780423016841490.4391539663170210.21957698315851
790.7545570288670780.4908859422658440.245442971132922
800.7198911248614660.5602177502770690.280108875138534
810.6877195587961420.6245608824077160.312280441203858
820.7945685224257130.4108629551485740.205431477574287
830.7618723216541070.4762553566917850.238127678345893
840.7268042373831280.5463915252337430.273195762616872
850.6876718954903120.6246562090193760.312328104509688
860.6601289941770420.6797420116459170.339871005822958
870.6381493575865910.7237012848268180.361850642413409
880.6064174076210090.7871651847579820.393582592378991
890.602741226543220.794517546913560.39725877345678
900.591185489870060.8176290202598810.40881451012994
910.608659007437030.7826819851259390.39134099256297
920.6131689704010110.7736620591979790.386831029598989
930.5834014898845830.8331970202308330.416598510115417
940.54184453574380.91631092851240.4581554642562
950.5231397435179140.9537205129641720.476860256482086
960.478480602491790.956961204983580.52151939750821
970.4560212017554050.912042403510810.543978798244595
980.4153267617309570.8306535234619130.584673238269043
990.3727262323809190.7454524647618380.627273767619081
1000.3382478838651880.6764957677303760.661752116134812
1010.2972918949384460.5945837898768930.702708105061553
1020.2799489793524950.559897958704990.720051020647505
1030.4689422917204940.9378845834409880.531057708279506
1040.4232185865151480.8464371730302960.576781413484852
1050.4322642919699420.8645285839398830.567735708030058
1060.4107175206777190.8214350413554380.589282479322281
1070.3925685070850080.7851370141700160.607431492914992
1080.3934379335796550.7868758671593090.606562066420345
1090.3737248938561450.747449787712290.626275106143855
1100.3892114305414050.778422861082810.610788569458595
1110.3852345776818030.7704691553636060.614765422318197
1120.3815013855999250.7630027711998510.618498614400075
1130.4139264491466910.8278528982933830.586073550853309
1140.3763001611791410.7526003223582820.623699838820859
1150.4263043581062340.8526087162124680.573695641893766
1160.4385654611709290.8771309223418580.561434538829071
1170.4012353727504810.8024707455009620.598764627249519
1180.3619772623312530.7239545246625070.638022737668747
1190.3624712487554660.7249424975109310.637528751244534
1200.3656576025620560.7313152051241120.634342397437944
1210.3250565090717340.6501130181434670.674943490928266
1220.2985973236048510.5971946472097030.701402676395149
1230.2852896530375060.5705793060750110.714710346962494
1240.2430098165642490.4860196331284980.756990183435751
1250.201840755460710.4036815109214210.79815924453929
1260.1772220133372180.3544440266744360.822777986662782
1270.1536542367979580.3073084735959170.846345763202042
1280.1440065565108710.2880131130217420.855993443489129
1290.1183181381541760.2366362763083520.881681861845824
1300.1234730616598910.2469461233197820.876526938340109
1310.1032004342266930.2064008684533850.896799565773307
1320.1080531443933080.2161062887866160.891946855606692
1330.09134309624267950.1826861924853590.908656903757321
1340.08629419481058080.1725883896211620.913705805189419
1350.0672724331843060.1345448663686120.932727566815694
1360.05001120682634120.1000224136526820.949988793173659
1370.03606652104126720.07213304208253430.963933478958733
1380.02807768410815660.05615536821631330.971922315891843
1390.0316472164678770.0632944329357540.968352783532123
1400.03355689209441490.06711378418882980.966443107905585
1410.371447164429210.7428943288584190.62855283557079
1420.3046413013516610.6092826027033220.695358698648339
1430.244133878103990.488267756207980.75586612189601
1440.2191307312165980.4382614624331960.780869268783402
1450.1655947999919950.331189599983990.834405200008005
1460.5181640891850410.9636718216299180.481835910814959
1470.5072192160665830.9855615678668330.492780783933416
1480.5125244834789480.9749510330421050.487475516521052
1490.4125739646710790.8251479293421580.587426035328921
1500.321264489413830.642528978827660.67873551058617
1510.8495616766588940.3008766466822120.150438323341106
1520.9247978628549740.1504042742900530.0752021371450265
1530.8553985109043110.2892029781913780.144601489095689
1540.8006442147029740.3987115705940520.199355785297026

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.729712338963501 & 0.540575322072999 & 0.270287661036499 \tabularnewline
9 & 0.583438858578841 & 0.833122282842317 & 0.416561141421159 \tabularnewline
10 & 0.48166963324946 & 0.96333926649892 & 0.51833036675054 \tabularnewline
11 & 0.403229204049352 & 0.806458408098705 & 0.596770795950648 \tabularnewline
12 & 0.512680321282162 & 0.974639357435676 & 0.487319678717838 \tabularnewline
13 & 0.417966607352535 & 0.835933214705069 & 0.582033392647465 \tabularnewline
14 & 0.368117975989546 & 0.736235951979092 & 0.631882024010454 \tabularnewline
15 & 0.394297884040659 & 0.788595768081318 & 0.605702115959341 \tabularnewline
16 & 0.333416465989332 & 0.666832931978664 & 0.666583534010668 \tabularnewline
17 & 0.266914361348819 & 0.533828722697638 & 0.733085638651181 \tabularnewline
18 & 0.574253362930404 & 0.851493274139191 & 0.425746637069596 \tabularnewline
19 & 0.562021971473303 & 0.875956057053393 & 0.437978028526697 \tabularnewline
20 & 0.488583861267313 & 0.977167722534627 & 0.511416138732687 \tabularnewline
21 & 0.416075027597547 & 0.832150055195095 & 0.583924972402453 \tabularnewline
22 & 0.349276156041641 & 0.698552312083282 & 0.650723843958359 \tabularnewline
23 & 0.390761159398014 & 0.781522318796028 & 0.609238840601986 \tabularnewline
24 & 0.334209526999467 & 0.668419053998933 & 0.665790473000533 \tabularnewline
25 & 0.291605313716352 & 0.583210627432704 & 0.708394686283648 \tabularnewline
26 & 0.238264776107595 & 0.47652955221519 & 0.761735223892405 \tabularnewline
27 & 0.188710115567531 & 0.377420231135061 & 0.811289884432469 \tabularnewline
28 & 0.156840285470363 & 0.313680570940726 & 0.843159714529637 \tabularnewline
29 & 0.122544455044354 & 0.245088910088707 & 0.877455544955646 \tabularnewline
30 & 0.118445971599992 & 0.236891943199985 & 0.881554028400008 \tabularnewline
31 & 0.0892746400860481 & 0.178549280172096 & 0.910725359913952 \tabularnewline
32 & 0.109038156372202 & 0.218076312744404 & 0.890961843627798 \tabularnewline
33 & 0.101796269914088 & 0.203592539828175 & 0.898203730085912 \tabularnewline
34 & 0.0775902091138362 & 0.155180418227672 & 0.922409790886164 \tabularnewline
35 & 0.0632799780334611 & 0.126559956066922 & 0.936720021966539 \tabularnewline
36 & 0.651591498925085 & 0.69681700214983 & 0.348408501074915 \tabularnewline
37 & 0.750842055171914 & 0.498315889656172 & 0.249157944828086 \tabularnewline
38 & 0.723261439938024 & 0.553477120123951 & 0.276738560061976 \tabularnewline
39 & 0.720095938490427 & 0.559808123019147 & 0.279904061509573 \tabularnewline
40 & 0.678780306900695 & 0.642439386198609 & 0.321219693099305 \tabularnewline
41 & 0.630473683107786 & 0.739052633784429 & 0.369526316892214 \tabularnewline
42 & 0.589897367926589 & 0.820205264146821 & 0.410102632073411 \tabularnewline
43 & 0.700466812740862 & 0.599066374518275 & 0.299533187259138 \tabularnewline
44 & 0.664615297386223 & 0.670769405227554 & 0.335384702613777 \tabularnewline
45 & 0.634862319734555 & 0.73027536053089 & 0.365137680265445 \tabularnewline
46 & 0.767368321549425 & 0.46526335690115 & 0.232631678450575 \tabularnewline
47 & 0.770664658011174 & 0.458670683977651 & 0.229335341988826 \tabularnewline
48 & 0.730428896359028 & 0.539142207281944 & 0.269571103640972 \tabularnewline
49 & 0.693095665086492 & 0.613808669827016 & 0.306904334913508 \tabularnewline
50 & 0.669847765127697 & 0.660304469744605 & 0.330152234872303 \tabularnewline
51 & 0.629751846362207 & 0.740496307275586 & 0.370248153637793 \tabularnewline
52 & 0.582981343864774 & 0.834037312270453 & 0.417018656135226 \tabularnewline
53 & 0.570051417608372 & 0.859897164783256 & 0.429948582391628 \tabularnewline
54 & 0.534595627810194 & 0.930808744379611 & 0.465404372189806 \tabularnewline
55 & 0.759742328495905 & 0.48051534300819 & 0.240257671504095 \tabularnewline
56 & 0.737281668973121 & 0.525436662053758 & 0.262718331026879 \tabularnewline
57 & 0.698178110533039 & 0.603643778933922 & 0.301821889466961 \tabularnewline
58 & 0.679583859791553 & 0.640832280416895 & 0.320416140208447 \tabularnewline
59 & 0.639026799992316 & 0.721946400015368 & 0.360973200007684 \tabularnewline
60 & 0.610617017306094 & 0.778765965387813 & 0.389382982693906 \tabularnewline
61 & 0.573571791124204 & 0.852856417751591 & 0.426428208875796 \tabularnewline
62 & 0.528884185361288 & 0.942231629277424 & 0.471115814638712 \tabularnewline
63 & 0.497304021166406 & 0.994608042332811 & 0.502695978833594 \tabularnewline
64 & 0.451822856363595 & 0.903645712727191 & 0.548177143636405 \tabularnewline
65 & 0.420458169062523 & 0.840916338125046 & 0.579541830937477 \tabularnewline
66 & 0.408568425219849 & 0.817136850439698 & 0.591431574780151 \tabularnewline
67 & 0.469886457576321 & 0.939772915152641 & 0.530113542423679 \tabularnewline
68 & 0.594800734976281 & 0.810398530047439 & 0.405199265023719 \tabularnewline
69 & 0.689864098305488 & 0.620271803389024 & 0.310135901694512 \tabularnewline
70 & 0.656915707644994 & 0.686168584710011 & 0.343084292355006 \tabularnewline
71 & 0.841495282909519 & 0.317009434180962 & 0.158504717090481 \tabularnewline
72 & 0.814068900173415 & 0.371862199653169 & 0.185931099826585 \tabularnewline
73 & 0.840945365945968 & 0.318109268108064 & 0.159054634054032 \tabularnewline
74 & 0.839295662187973 & 0.321408675624054 & 0.160704337812027 \tabularnewline
75 & 0.817620576066146 & 0.364758847867709 & 0.182379423933854 \tabularnewline
76 & 0.820991831854784 & 0.358016336290432 & 0.179008168145216 \tabularnewline
77 & 0.793529988566365 & 0.412940022867269 & 0.206470011433635 \tabularnewline
78 & 0.78042301684149 & 0.439153966317021 & 0.21957698315851 \tabularnewline
79 & 0.754557028867078 & 0.490885942265844 & 0.245442971132922 \tabularnewline
80 & 0.719891124861466 & 0.560217750277069 & 0.280108875138534 \tabularnewline
81 & 0.687719558796142 & 0.624560882407716 & 0.312280441203858 \tabularnewline
82 & 0.794568522425713 & 0.410862955148574 & 0.205431477574287 \tabularnewline
83 & 0.761872321654107 & 0.476255356691785 & 0.238127678345893 \tabularnewline
84 & 0.726804237383128 & 0.546391525233743 & 0.273195762616872 \tabularnewline
85 & 0.687671895490312 & 0.624656209019376 & 0.312328104509688 \tabularnewline
86 & 0.660128994177042 & 0.679742011645917 & 0.339871005822958 \tabularnewline
87 & 0.638149357586591 & 0.723701284826818 & 0.361850642413409 \tabularnewline
88 & 0.606417407621009 & 0.787165184757982 & 0.393582592378991 \tabularnewline
89 & 0.60274122654322 & 0.79451754691356 & 0.39725877345678 \tabularnewline
90 & 0.59118548987006 & 0.817629020259881 & 0.40881451012994 \tabularnewline
91 & 0.60865900743703 & 0.782681985125939 & 0.39134099256297 \tabularnewline
92 & 0.613168970401011 & 0.773662059197979 & 0.386831029598989 \tabularnewline
93 & 0.583401489884583 & 0.833197020230833 & 0.416598510115417 \tabularnewline
94 & 0.5418445357438 & 0.9163109285124 & 0.4581554642562 \tabularnewline
95 & 0.523139743517914 & 0.953720512964172 & 0.476860256482086 \tabularnewline
96 & 0.47848060249179 & 0.95696120498358 & 0.52151939750821 \tabularnewline
97 & 0.456021201755405 & 0.91204240351081 & 0.543978798244595 \tabularnewline
98 & 0.415326761730957 & 0.830653523461913 & 0.584673238269043 \tabularnewline
99 & 0.372726232380919 & 0.745452464761838 & 0.627273767619081 \tabularnewline
100 & 0.338247883865188 & 0.676495767730376 & 0.661752116134812 \tabularnewline
101 & 0.297291894938446 & 0.594583789876893 & 0.702708105061553 \tabularnewline
102 & 0.279948979352495 & 0.55989795870499 & 0.720051020647505 \tabularnewline
103 & 0.468942291720494 & 0.937884583440988 & 0.531057708279506 \tabularnewline
104 & 0.423218586515148 & 0.846437173030296 & 0.576781413484852 \tabularnewline
105 & 0.432264291969942 & 0.864528583939883 & 0.567735708030058 \tabularnewline
106 & 0.410717520677719 & 0.821435041355438 & 0.589282479322281 \tabularnewline
107 & 0.392568507085008 & 0.785137014170016 & 0.607431492914992 \tabularnewline
108 & 0.393437933579655 & 0.786875867159309 & 0.606562066420345 \tabularnewline
109 & 0.373724893856145 & 0.74744978771229 & 0.626275106143855 \tabularnewline
110 & 0.389211430541405 & 0.77842286108281 & 0.610788569458595 \tabularnewline
111 & 0.385234577681803 & 0.770469155363606 & 0.614765422318197 \tabularnewline
112 & 0.381501385599925 & 0.763002771199851 & 0.618498614400075 \tabularnewline
113 & 0.413926449146691 & 0.827852898293383 & 0.586073550853309 \tabularnewline
114 & 0.376300161179141 & 0.752600322358282 & 0.623699838820859 \tabularnewline
115 & 0.426304358106234 & 0.852608716212468 & 0.573695641893766 \tabularnewline
116 & 0.438565461170929 & 0.877130922341858 & 0.561434538829071 \tabularnewline
117 & 0.401235372750481 & 0.802470745500962 & 0.598764627249519 \tabularnewline
118 & 0.361977262331253 & 0.723954524662507 & 0.638022737668747 \tabularnewline
119 & 0.362471248755466 & 0.724942497510931 & 0.637528751244534 \tabularnewline
120 & 0.365657602562056 & 0.731315205124112 & 0.634342397437944 \tabularnewline
121 & 0.325056509071734 & 0.650113018143467 & 0.674943490928266 \tabularnewline
122 & 0.298597323604851 & 0.597194647209703 & 0.701402676395149 \tabularnewline
123 & 0.285289653037506 & 0.570579306075011 & 0.714710346962494 \tabularnewline
124 & 0.243009816564249 & 0.486019633128498 & 0.756990183435751 \tabularnewline
125 & 0.20184075546071 & 0.403681510921421 & 0.79815924453929 \tabularnewline
126 & 0.177222013337218 & 0.354444026674436 & 0.822777986662782 \tabularnewline
127 & 0.153654236797958 & 0.307308473595917 & 0.846345763202042 \tabularnewline
128 & 0.144006556510871 & 0.288013113021742 & 0.855993443489129 \tabularnewline
129 & 0.118318138154176 & 0.236636276308352 & 0.881681861845824 \tabularnewline
130 & 0.123473061659891 & 0.246946123319782 & 0.876526938340109 \tabularnewline
131 & 0.103200434226693 & 0.206400868453385 & 0.896799565773307 \tabularnewline
132 & 0.108053144393308 & 0.216106288786616 & 0.891946855606692 \tabularnewline
133 & 0.0913430962426795 & 0.182686192485359 & 0.908656903757321 \tabularnewline
134 & 0.0862941948105808 & 0.172588389621162 & 0.913705805189419 \tabularnewline
135 & 0.067272433184306 & 0.134544866368612 & 0.932727566815694 \tabularnewline
136 & 0.0500112068263412 & 0.100022413652682 & 0.949988793173659 \tabularnewline
137 & 0.0360665210412672 & 0.0721330420825343 & 0.963933478958733 \tabularnewline
138 & 0.0280776841081566 & 0.0561553682163133 & 0.971922315891843 \tabularnewline
139 & 0.031647216467877 & 0.063294432935754 & 0.968352783532123 \tabularnewline
140 & 0.0335568920944149 & 0.0671137841888298 & 0.966443107905585 \tabularnewline
141 & 0.37144716442921 & 0.742894328858419 & 0.62855283557079 \tabularnewline
142 & 0.304641301351661 & 0.609282602703322 & 0.695358698648339 \tabularnewline
143 & 0.24413387810399 & 0.48826775620798 & 0.75586612189601 \tabularnewline
144 & 0.219130731216598 & 0.438261462433196 & 0.780869268783402 \tabularnewline
145 & 0.165594799991995 & 0.33118959998399 & 0.834405200008005 \tabularnewline
146 & 0.518164089185041 & 0.963671821629918 & 0.481835910814959 \tabularnewline
147 & 0.507219216066583 & 0.985561567866833 & 0.492780783933416 \tabularnewline
148 & 0.512524483478948 & 0.974951033042105 & 0.487475516521052 \tabularnewline
149 & 0.412573964671079 & 0.825147929342158 & 0.587426035328921 \tabularnewline
150 & 0.32126448941383 & 0.64252897882766 & 0.67873551058617 \tabularnewline
151 & 0.849561676658894 & 0.300876646682212 & 0.150438323341106 \tabularnewline
152 & 0.924797862854974 & 0.150404274290053 & 0.0752021371450265 \tabularnewline
153 & 0.855398510904311 & 0.289202978191378 & 0.144601489095689 \tabularnewline
154 & 0.800644214702974 & 0.398711570594052 & 0.199355785297026 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160449&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.729712338963501[/C][C]0.540575322072999[/C][C]0.270287661036499[/C][/ROW]
[ROW][C]9[/C][C]0.583438858578841[/C][C]0.833122282842317[/C][C]0.416561141421159[/C][/ROW]
[ROW][C]10[/C][C]0.48166963324946[/C][C]0.96333926649892[/C][C]0.51833036675054[/C][/ROW]
[ROW][C]11[/C][C]0.403229204049352[/C][C]0.806458408098705[/C][C]0.596770795950648[/C][/ROW]
[ROW][C]12[/C][C]0.512680321282162[/C][C]0.974639357435676[/C][C]0.487319678717838[/C][/ROW]
[ROW][C]13[/C][C]0.417966607352535[/C][C]0.835933214705069[/C][C]0.582033392647465[/C][/ROW]
[ROW][C]14[/C][C]0.368117975989546[/C][C]0.736235951979092[/C][C]0.631882024010454[/C][/ROW]
[ROW][C]15[/C][C]0.394297884040659[/C][C]0.788595768081318[/C][C]0.605702115959341[/C][/ROW]
[ROW][C]16[/C][C]0.333416465989332[/C][C]0.666832931978664[/C][C]0.666583534010668[/C][/ROW]
[ROW][C]17[/C][C]0.266914361348819[/C][C]0.533828722697638[/C][C]0.733085638651181[/C][/ROW]
[ROW][C]18[/C][C]0.574253362930404[/C][C]0.851493274139191[/C][C]0.425746637069596[/C][/ROW]
[ROW][C]19[/C][C]0.562021971473303[/C][C]0.875956057053393[/C][C]0.437978028526697[/C][/ROW]
[ROW][C]20[/C][C]0.488583861267313[/C][C]0.977167722534627[/C][C]0.511416138732687[/C][/ROW]
[ROW][C]21[/C][C]0.416075027597547[/C][C]0.832150055195095[/C][C]0.583924972402453[/C][/ROW]
[ROW][C]22[/C][C]0.349276156041641[/C][C]0.698552312083282[/C][C]0.650723843958359[/C][/ROW]
[ROW][C]23[/C][C]0.390761159398014[/C][C]0.781522318796028[/C][C]0.609238840601986[/C][/ROW]
[ROW][C]24[/C][C]0.334209526999467[/C][C]0.668419053998933[/C][C]0.665790473000533[/C][/ROW]
[ROW][C]25[/C][C]0.291605313716352[/C][C]0.583210627432704[/C][C]0.708394686283648[/C][/ROW]
[ROW][C]26[/C][C]0.238264776107595[/C][C]0.47652955221519[/C][C]0.761735223892405[/C][/ROW]
[ROW][C]27[/C][C]0.188710115567531[/C][C]0.377420231135061[/C][C]0.811289884432469[/C][/ROW]
[ROW][C]28[/C][C]0.156840285470363[/C][C]0.313680570940726[/C][C]0.843159714529637[/C][/ROW]
[ROW][C]29[/C][C]0.122544455044354[/C][C]0.245088910088707[/C][C]0.877455544955646[/C][/ROW]
[ROW][C]30[/C][C]0.118445971599992[/C][C]0.236891943199985[/C][C]0.881554028400008[/C][/ROW]
[ROW][C]31[/C][C]0.0892746400860481[/C][C]0.178549280172096[/C][C]0.910725359913952[/C][/ROW]
[ROW][C]32[/C][C]0.109038156372202[/C][C]0.218076312744404[/C][C]0.890961843627798[/C][/ROW]
[ROW][C]33[/C][C]0.101796269914088[/C][C]0.203592539828175[/C][C]0.898203730085912[/C][/ROW]
[ROW][C]34[/C][C]0.0775902091138362[/C][C]0.155180418227672[/C][C]0.922409790886164[/C][/ROW]
[ROW][C]35[/C][C]0.0632799780334611[/C][C]0.126559956066922[/C][C]0.936720021966539[/C][/ROW]
[ROW][C]36[/C][C]0.651591498925085[/C][C]0.69681700214983[/C][C]0.348408501074915[/C][/ROW]
[ROW][C]37[/C][C]0.750842055171914[/C][C]0.498315889656172[/C][C]0.249157944828086[/C][/ROW]
[ROW][C]38[/C][C]0.723261439938024[/C][C]0.553477120123951[/C][C]0.276738560061976[/C][/ROW]
[ROW][C]39[/C][C]0.720095938490427[/C][C]0.559808123019147[/C][C]0.279904061509573[/C][/ROW]
[ROW][C]40[/C][C]0.678780306900695[/C][C]0.642439386198609[/C][C]0.321219693099305[/C][/ROW]
[ROW][C]41[/C][C]0.630473683107786[/C][C]0.739052633784429[/C][C]0.369526316892214[/C][/ROW]
[ROW][C]42[/C][C]0.589897367926589[/C][C]0.820205264146821[/C][C]0.410102632073411[/C][/ROW]
[ROW][C]43[/C][C]0.700466812740862[/C][C]0.599066374518275[/C][C]0.299533187259138[/C][/ROW]
[ROW][C]44[/C][C]0.664615297386223[/C][C]0.670769405227554[/C][C]0.335384702613777[/C][/ROW]
[ROW][C]45[/C][C]0.634862319734555[/C][C]0.73027536053089[/C][C]0.365137680265445[/C][/ROW]
[ROW][C]46[/C][C]0.767368321549425[/C][C]0.46526335690115[/C][C]0.232631678450575[/C][/ROW]
[ROW][C]47[/C][C]0.770664658011174[/C][C]0.458670683977651[/C][C]0.229335341988826[/C][/ROW]
[ROW][C]48[/C][C]0.730428896359028[/C][C]0.539142207281944[/C][C]0.269571103640972[/C][/ROW]
[ROW][C]49[/C][C]0.693095665086492[/C][C]0.613808669827016[/C][C]0.306904334913508[/C][/ROW]
[ROW][C]50[/C][C]0.669847765127697[/C][C]0.660304469744605[/C][C]0.330152234872303[/C][/ROW]
[ROW][C]51[/C][C]0.629751846362207[/C][C]0.740496307275586[/C][C]0.370248153637793[/C][/ROW]
[ROW][C]52[/C][C]0.582981343864774[/C][C]0.834037312270453[/C][C]0.417018656135226[/C][/ROW]
[ROW][C]53[/C][C]0.570051417608372[/C][C]0.859897164783256[/C][C]0.429948582391628[/C][/ROW]
[ROW][C]54[/C][C]0.534595627810194[/C][C]0.930808744379611[/C][C]0.465404372189806[/C][/ROW]
[ROW][C]55[/C][C]0.759742328495905[/C][C]0.48051534300819[/C][C]0.240257671504095[/C][/ROW]
[ROW][C]56[/C][C]0.737281668973121[/C][C]0.525436662053758[/C][C]0.262718331026879[/C][/ROW]
[ROW][C]57[/C][C]0.698178110533039[/C][C]0.603643778933922[/C][C]0.301821889466961[/C][/ROW]
[ROW][C]58[/C][C]0.679583859791553[/C][C]0.640832280416895[/C][C]0.320416140208447[/C][/ROW]
[ROW][C]59[/C][C]0.639026799992316[/C][C]0.721946400015368[/C][C]0.360973200007684[/C][/ROW]
[ROW][C]60[/C][C]0.610617017306094[/C][C]0.778765965387813[/C][C]0.389382982693906[/C][/ROW]
[ROW][C]61[/C][C]0.573571791124204[/C][C]0.852856417751591[/C][C]0.426428208875796[/C][/ROW]
[ROW][C]62[/C][C]0.528884185361288[/C][C]0.942231629277424[/C][C]0.471115814638712[/C][/ROW]
[ROW][C]63[/C][C]0.497304021166406[/C][C]0.994608042332811[/C][C]0.502695978833594[/C][/ROW]
[ROW][C]64[/C][C]0.451822856363595[/C][C]0.903645712727191[/C][C]0.548177143636405[/C][/ROW]
[ROW][C]65[/C][C]0.420458169062523[/C][C]0.840916338125046[/C][C]0.579541830937477[/C][/ROW]
[ROW][C]66[/C][C]0.408568425219849[/C][C]0.817136850439698[/C][C]0.591431574780151[/C][/ROW]
[ROW][C]67[/C][C]0.469886457576321[/C][C]0.939772915152641[/C][C]0.530113542423679[/C][/ROW]
[ROW][C]68[/C][C]0.594800734976281[/C][C]0.810398530047439[/C][C]0.405199265023719[/C][/ROW]
[ROW][C]69[/C][C]0.689864098305488[/C][C]0.620271803389024[/C][C]0.310135901694512[/C][/ROW]
[ROW][C]70[/C][C]0.656915707644994[/C][C]0.686168584710011[/C][C]0.343084292355006[/C][/ROW]
[ROW][C]71[/C][C]0.841495282909519[/C][C]0.317009434180962[/C][C]0.158504717090481[/C][/ROW]
[ROW][C]72[/C][C]0.814068900173415[/C][C]0.371862199653169[/C][C]0.185931099826585[/C][/ROW]
[ROW][C]73[/C][C]0.840945365945968[/C][C]0.318109268108064[/C][C]0.159054634054032[/C][/ROW]
[ROW][C]74[/C][C]0.839295662187973[/C][C]0.321408675624054[/C][C]0.160704337812027[/C][/ROW]
[ROW][C]75[/C][C]0.817620576066146[/C][C]0.364758847867709[/C][C]0.182379423933854[/C][/ROW]
[ROW][C]76[/C][C]0.820991831854784[/C][C]0.358016336290432[/C][C]0.179008168145216[/C][/ROW]
[ROW][C]77[/C][C]0.793529988566365[/C][C]0.412940022867269[/C][C]0.206470011433635[/C][/ROW]
[ROW][C]78[/C][C]0.78042301684149[/C][C]0.439153966317021[/C][C]0.21957698315851[/C][/ROW]
[ROW][C]79[/C][C]0.754557028867078[/C][C]0.490885942265844[/C][C]0.245442971132922[/C][/ROW]
[ROW][C]80[/C][C]0.719891124861466[/C][C]0.560217750277069[/C][C]0.280108875138534[/C][/ROW]
[ROW][C]81[/C][C]0.687719558796142[/C][C]0.624560882407716[/C][C]0.312280441203858[/C][/ROW]
[ROW][C]82[/C][C]0.794568522425713[/C][C]0.410862955148574[/C][C]0.205431477574287[/C][/ROW]
[ROW][C]83[/C][C]0.761872321654107[/C][C]0.476255356691785[/C][C]0.238127678345893[/C][/ROW]
[ROW][C]84[/C][C]0.726804237383128[/C][C]0.546391525233743[/C][C]0.273195762616872[/C][/ROW]
[ROW][C]85[/C][C]0.687671895490312[/C][C]0.624656209019376[/C][C]0.312328104509688[/C][/ROW]
[ROW][C]86[/C][C]0.660128994177042[/C][C]0.679742011645917[/C][C]0.339871005822958[/C][/ROW]
[ROW][C]87[/C][C]0.638149357586591[/C][C]0.723701284826818[/C][C]0.361850642413409[/C][/ROW]
[ROW][C]88[/C][C]0.606417407621009[/C][C]0.787165184757982[/C][C]0.393582592378991[/C][/ROW]
[ROW][C]89[/C][C]0.60274122654322[/C][C]0.79451754691356[/C][C]0.39725877345678[/C][/ROW]
[ROW][C]90[/C][C]0.59118548987006[/C][C]0.817629020259881[/C][C]0.40881451012994[/C][/ROW]
[ROW][C]91[/C][C]0.60865900743703[/C][C]0.782681985125939[/C][C]0.39134099256297[/C][/ROW]
[ROW][C]92[/C][C]0.613168970401011[/C][C]0.773662059197979[/C][C]0.386831029598989[/C][/ROW]
[ROW][C]93[/C][C]0.583401489884583[/C][C]0.833197020230833[/C][C]0.416598510115417[/C][/ROW]
[ROW][C]94[/C][C]0.5418445357438[/C][C]0.9163109285124[/C][C]0.4581554642562[/C][/ROW]
[ROW][C]95[/C][C]0.523139743517914[/C][C]0.953720512964172[/C][C]0.476860256482086[/C][/ROW]
[ROW][C]96[/C][C]0.47848060249179[/C][C]0.95696120498358[/C][C]0.52151939750821[/C][/ROW]
[ROW][C]97[/C][C]0.456021201755405[/C][C]0.91204240351081[/C][C]0.543978798244595[/C][/ROW]
[ROW][C]98[/C][C]0.415326761730957[/C][C]0.830653523461913[/C][C]0.584673238269043[/C][/ROW]
[ROW][C]99[/C][C]0.372726232380919[/C][C]0.745452464761838[/C][C]0.627273767619081[/C][/ROW]
[ROW][C]100[/C][C]0.338247883865188[/C][C]0.676495767730376[/C][C]0.661752116134812[/C][/ROW]
[ROW][C]101[/C][C]0.297291894938446[/C][C]0.594583789876893[/C][C]0.702708105061553[/C][/ROW]
[ROW][C]102[/C][C]0.279948979352495[/C][C]0.55989795870499[/C][C]0.720051020647505[/C][/ROW]
[ROW][C]103[/C][C]0.468942291720494[/C][C]0.937884583440988[/C][C]0.531057708279506[/C][/ROW]
[ROW][C]104[/C][C]0.423218586515148[/C][C]0.846437173030296[/C][C]0.576781413484852[/C][/ROW]
[ROW][C]105[/C][C]0.432264291969942[/C][C]0.864528583939883[/C][C]0.567735708030058[/C][/ROW]
[ROW][C]106[/C][C]0.410717520677719[/C][C]0.821435041355438[/C][C]0.589282479322281[/C][/ROW]
[ROW][C]107[/C][C]0.392568507085008[/C][C]0.785137014170016[/C][C]0.607431492914992[/C][/ROW]
[ROW][C]108[/C][C]0.393437933579655[/C][C]0.786875867159309[/C][C]0.606562066420345[/C][/ROW]
[ROW][C]109[/C][C]0.373724893856145[/C][C]0.74744978771229[/C][C]0.626275106143855[/C][/ROW]
[ROW][C]110[/C][C]0.389211430541405[/C][C]0.77842286108281[/C][C]0.610788569458595[/C][/ROW]
[ROW][C]111[/C][C]0.385234577681803[/C][C]0.770469155363606[/C][C]0.614765422318197[/C][/ROW]
[ROW][C]112[/C][C]0.381501385599925[/C][C]0.763002771199851[/C][C]0.618498614400075[/C][/ROW]
[ROW][C]113[/C][C]0.413926449146691[/C][C]0.827852898293383[/C][C]0.586073550853309[/C][/ROW]
[ROW][C]114[/C][C]0.376300161179141[/C][C]0.752600322358282[/C][C]0.623699838820859[/C][/ROW]
[ROW][C]115[/C][C]0.426304358106234[/C][C]0.852608716212468[/C][C]0.573695641893766[/C][/ROW]
[ROW][C]116[/C][C]0.438565461170929[/C][C]0.877130922341858[/C][C]0.561434538829071[/C][/ROW]
[ROW][C]117[/C][C]0.401235372750481[/C][C]0.802470745500962[/C][C]0.598764627249519[/C][/ROW]
[ROW][C]118[/C][C]0.361977262331253[/C][C]0.723954524662507[/C][C]0.638022737668747[/C][/ROW]
[ROW][C]119[/C][C]0.362471248755466[/C][C]0.724942497510931[/C][C]0.637528751244534[/C][/ROW]
[ROW][C]120[/C][C]0.365657602562056[/C][C]0.731315205124112[/C][C]0.634342397437944[/C][/ROW]
[ROW][C]121[/C][C]0.325056509071734[/C][C]0.650113018143467[/C][C]0.674943490928266[/C][/ROW]
[ROW][C]122[/C][C]0.298597323604851[/C][C]0.597194647209703[/C][C]0.701402676395149[/C][/ROW]
[ROW][C]123[/C][C]0.285289653037506[/C][C]0.570579306075011[/C][C]0.714710346962494[/C][/ROW]
[ROW][C]124[/C][C]0.243009816564249[/C][C]0.486019633128498[/C][C]0.756990183435751[/C][/ROW]
[ROW][C]125[/C][C]0.20184075546071[/C][C]0.403681510921421[/C][C]0.79815924453929[/C][/ROW]
[ROW][C]126[/C][C]0.177222013337218[/C][C]0.354444026674436[/C][C]0.822777986662782[/C][/ROW]
[ROW][C]127[/C][C]0.153654236797958[/C][C]0.307308473595917[/C][C]0.846345763202042[/C][/ROW]
[ROW][C]128[/C][C]0.144006556510871[/C][C]0.288013113021742[/C][C]0.855993443489129[/C][/ROW]
[ROW][C]129[/C][C]0.118318138154176[/C][C]0.236636276308352[/C][C]0.881681861845824[/C][/ROW]
[ROW][C]130[/C][C]0.123473061659891[/C][C]0.246946123319782[/C][C]0.876526938340109[/C][/ROW]
[ROW][C]131[/C][C]0.103200434226693[/C][C]0.206400868453385[/C][C]0.896799565773307[/C][/ROW]
[ROW][C]132[/C][C]0.108053144393308[/C][C]0.216106288786616[/C][C]0.891946855606692[/C][/ROW]
[ROW][C]133[/C][C]0.0913430962426795[/C][C]0.182686192485359[/C][C]0.908656903757321[/C][/ROW]
[ROW][C]134[/C][C]0.0862941948105808[/C][C]0.172588389621162[/C][C]0.913705805189419[/C][/ROW]
[ROW][C]135[/C][C]0.067272433184306[/C][C]0.134544866368612[/C][C]0.932727566815694[/C][/ROW]
[ROW][C]136[/C][C]0.0500112068263412[/C][C]0.100022413652682[/C][C]0.949988793173659[/C][/ROW]
[ROW][C]137[/C][C]0.0360665210412672[/C][C]0.0721330420825343[/C][C]0.963933478958733[/C][/ROW]
[ROW][C]138[/C][C]0.0280776841081566[/C][C]0.0561553682163133[/C][C]0.971922315891843[/C][/ROW]
[ROW][C]139[/C][C]0.031647216467877[/C][C]0.063294432935754[/C][C]0.968352783532123[/C][/ROW]
[ROW][C]140[/C][C]0.0335568920944149[/C][C]0.0671137841888298[/C][C]0.966443107905585[/C][/ROW]
[ROW][C]141[/C][C]0.37144716442921[/C][C]0.742894328858419[/C][C]0.62855283557079[/C][/ROW]
[ROW][C]142[/C][C]0.304641301351661[/C][C]0.609282602703322[/C][C]0.695358698648339[/C][/ROW]
[ROW][C]143[/C][C]0.24413387810399[/C][C]0.48826775620798[/C][C]0.75586612189601[/C][/ROW]
[ROW][C]144[/C][C]0.219130731216598[/C][C]0.438261462433196[/C][C]0.780869268783402[/C][/ROW]
[ROW][C]145[/C][C]0.165594799991995[/C][C]0.33118959998399[/C][C]0.834405200008005[/C][/ROW]
[ROW][C]146[/C][C]0.518164089185041[/C][C]0.963671821629918[/C][C]0.481835910814959[/C][/ROW]
[ROW][C]147[/C][C]0.507219216066583[/C][C]0.985561567866833[/C][C]0.492780783933416[/C][/ROW]
[ROW][C]148[/C][C]0.512524483478948[/C][C]0.974951033042105[/C][C]0.487475516521052[/C][/ROW]
[ROW][C]149[/C][C]0.412573964671079[/C][C]0.825147929342158[/C][C]0.587426035328921[/C][/ROW]
[ROW][C]150[/C][C]0.32126448941383[/C][C]0.64252897882766[/C][C]0.67873551058617[/C][/ROW]
[ROW][C]151[/C][C]0.849561676658894[/C][C]0.300876646682212[/C][C]0.150438323341106[/C][/ROW]
[ROW][C]152[/C][C]0.924797862854974[/C][C]0.150404274290053[/C][C]0.0752021371450265[/C][/ROW]
[ROW][C]153[/C][C]0.855398510904311[/C][C]0.289202978191378[/C][C]0.144601489095689[/C][/ROW]
[ROW][C]154[/C][C]0.800644214702974[/C][C]0.398711570594052[/C][C]0.199355785297026[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160449&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160449&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.7297123389635010.5405753220729990.270287661036499
90.5834388585788410.8331222828423170.416561141421159
100.481669633249460.963339266498920.51833036675054
110.4032292040493520.8064584080987050.596770795950648
120.5126803212821620.9746393574356760.487319678717838
130.4179666073525350.8359332147050690.582033392647465
140.3681179759895460.7362359519790920.631882024010454
150.3942978840406590.7885957680813180.605702115959341
160.3334164659893320.6668329319786640.666583534010668
170.2669143613488190.5338287226976380.733085638651181
180.5742533629304040.8514932741391910.425746637069596
190.5620219714733030.8759560570533930.437978028526697
200.4885838612673130.9771677225346270.511416138732687
210.4160750275975470.8321500551950950.583924972402453
220.3492761560416410.6985523120832820.650723843958359
230.3907611593980140.7815223187960280.609238840601986
240.3342095269994670.6684190539989330.665790473000533
250.2916053137163520.5832106274327040.708394686283648
260.2382647761075950.476529552215190.761735223892405
270.1887101155675310.3774202311350610.811289884432469
280.1568402854703630.3136805709407260.843159714529637
290.1225444550443540.2450889100887070.877455544955646
300.1184459715999920.2368919431999850.881554028400008
310.08927464008604810.1785492801720960.910725359913952
320.1090381563722020.2180763127444040.890961843627798
330.1017962699140880.2035925398281750.898203730085912
340.07759020911383620.1551804182276720.922409790886164
350.06327997803346110.1265599560669220.936720021966539
360.6515914989250850.696817002149830.348408501074915
370.7508420551719140.4983158896561720.249157944828086
380.7232614399380240.5534771201239510.276738560061976
390.7200959384904270.5598081230191470.279904061509573
400.6787803069006950.6424393861986090.321219693099305
410.6304736831077860.7390526337844290.369526316892214
420.5898973679265890.8202052641468210.410102632073411
430.7004668127408620.5990663745182750.299533187259138
440.6646152973862230.6707694052275540.335384702613777
450.6348623197345550.730275360530890.365137680265445
460.7673683215494250.465263356901150.232631678450575
470.7706646580111740.4586706839776510.229335341988826
480.7304288963590280.5391422072819440.269571103640972
490.6930956650864920.6138086698270160.306904334913508
500.6698477651276970.6603044697446050.330152234872303
510.6297518463622070.7404963072755860.370248153637793
520.5829813438647740.8340373122704530.417018656135226
530.5700514176083720.8598971647832560.429948582391628
540.5345956278101940.9308087443796110.465404372189806
550.7597423284959050.480515343008190.240257671504095
560.7372816689731210.5254366620537580.262718331026879
570.6981781105330390.6036437789339220.301821889466961
580.6795838597915530.6408322804168950.320416140208447
590.6390267999923160.7219464000153680.360973200007684
600.6106170173060940.7787659653878130.389382982693906
610.5735717911242040.8528564177515910.426428208875796
620.5288841853612880.9422316292774240.471115814638712
630.4973040211664060.9946080423328110.502695978833594
640.4518228563635950.9036457127271910.548177143636405
650.4204581690625230.8409163381250460.579541830937477
660.4085684252198490.8171368504396980.591431574780151
670.4698864575763210.9397729151526410.530113542423679
680.5948007349762810.8103985300474390.405199265023719
690.6898640983054880.6202718033890240.310135901694512
700.6569157076449940.6861685847100110.343084292355006
710.8414952829095190.3170094341809620.158504717090481
720.8140689001734150.3718621996531690.185931099826585
730.8409453659459680.3181092681080640.159054634054032
740.8392956621879730.3214086756240540.160704337812027
750.8176205760661460.3647588478677090.182379423933854
760.8209918318547840.3580163362904320.179008168145216
770.7935299885663650.4129400228672690.206470011433635
780.780423016841490.4391539663170210.21957698315851
790.7545570288670780.4908859422658440.245442971132922
800.7198911248614660.5602177502770690.280108875138534
810.6877195587961420.6245608824077160.312280441203858
820.7945685224257130.4108629551485740.205431477574287
830.7618723216541070.4762553566917850.238127678345893
840.7268042373831280.5463915252337430.273195762616872
850.6876718954903120.6246562090193760.312328104509688
860.6601289941770420.6797420116459170.339871005822958
870.6381493575865910.7237012848268180.361850642413409
880.6064174076210090.7871651847579820.393582592378991
890.602741226543220.794517546913560.39725877345678
900.591185489870060.8176290202598810.40881451012994
910.608659007437030.7826819851259390.39134099256297
920.6131689704010110.7736620591979790.386831029598989
930.5834014898845830.8331970202308330.416598510115417
940.54184453574380.91631092851240.4581554642562
950.5231397435179140.9537205129641720.476860256482086
960.478480602491790.956961204983580.52151939750821
970.4560212017554050.912042403510810.543978798244595
980.4153267617309570.8306535234619130.584673238269043
990.3727262323809190.7454524647618380.627273767619081
1000.3382478838651880.6764957677303760.661752116134812
1010.2972918949384460.5945837898768930.702708105061553
1020.2799489793524950.559897958704990.720051020647505
1030.4689422917204940.9378845834409880.531057708279506
1040.4232185865151480.8464371730302960.576781413484852
1050.4322642919699420.8645285839398830.567735708030058
1060.4107175206777190.8214350413554380.589282479322281
1070.3925685070850080.7851370141700160.607431492914992
1080.3934379335796550.7868758671593090.606562066420345
1090.3737248938561450.747449787712290.626275106143855
1100.3892114305414050.778422861082810.610788569458595
1110.3852345776818030.7704691553636060.614765422318197
1120.3815013855999250.7630027711998510.618498614400075
1130.4139264491466910.8278528982933830.586073550853309
1140.3763001611791410.7526003223582820.623699838820859
1150.4263043581062340.8526087162124680.573695641893766
1160.4385654611709290.8771309223418580.561434538829071
1170.4012353727504810.8024707455009620.598764627249519
1180.3619772623312530.7239545246625070.638022737668747
1190.3624712487554660.7249424975109310.637528751244534
1200.3656576025620560.7313152051241120.634342397437944
1210.3250565090717340.6501130181434670.674943490928266
1220.2985973236048510.5971946472097030.701402676395149
1230.2852896530375060.5705793060750110.714710346962494
1240.2430098165642490.4860196331284980.756990183435751
1250.201840755460710.4036815109214210.79815924453929
1260.1772220133372180.3544440266744360.822777986662782
1270.1536542367979580.3073084735959170.846345763202042
1280.1440065565108710.2880131130217420.855993443489129
1290.1183181381541760.2366362763083520.881681861845824
1300.1234730616598910.2469461233197820.876526938340109
1310.1032004342266930.2064008684533850.896799565773307
1320.1080531443933080.2161062887866160.891946855606692
1330.09134309624267950.1826861924853590.908656903757321
1340.08629419481058080.1725883896211620.913705805189419
1350.0672724331843060.1345448663686120.932727566815694
1360.05001120682634120.1000224136526820.949988793173659
1370.03606652104126720.07213304208253430.963933478958733
1380.02807768410815660.05615536821631330.971922315891843
1390.0316472164678770.0632944329357540.968352783532123
1400.03355689209441490.06711378418882980.966443107905585
1410.371447164429210.7428943288584190.62855283557079
1420.3046413013516610.6092826027033220.695358698648339
1430.244133878103990.488267756207980.75586612189601
1440.2191307312165980.4382614624331960.780869268783402
1450.1655947999919950.331189599983990.834405200008005
1460.5181640891850410.9636718216299180.481835910814959
1470.5072192160665830.9855615678668330.492780783933416
1480.5125244834789480.9749510330421050.487475516521052
1490.4125739646710790.8251479293421580.587426035328921
1500.321264489413830.642528978827660.67873551058617
1510.8495616766588940.3008766466822120.150438323341106
1520.9247978628549740.1504042742900530.0752021371450265
1530.8553985109043110.2892029781913780.144601489095689
1540.8006442147029740.3987115705940520.199355785297026






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 level40.0272108843537415OK

\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 & 4 & 0.0272108843537415 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160449&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]4[/C][C]0.0272108843537415[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160449&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160449&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level40.0272108843537415OK


Figures (Output of Computation)

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

Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; 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')
}