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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 20 Dec 2011 06:12:08 -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/20/t1324379612j1riektnfwqlksd.htm/, Retrieved Mon, 06 May 2024 02:59:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=157922, Retrieved Mon, 06 May 2024 02:59:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact93

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [Paper Multiple Re...] [2011-12-20 11:12:08] [c18e83883fa784c15a15b4fbc0636edd] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
14	1	38	13	53
18	1	32	16	86
11	1	35	19	66
12	0	33	15	67
16	1	37	14	76
18	1	29	13	78
14	1	31	19	53
14	1	36	15	80
15	1	35	14	74
15	1	38	15	76
17	0	31	16	79
19	1	34	16	54
10	0	35	16	67
16	1	38	16	54
18	1	37	17	87
14	0	33	15	58
14	0	32	15	75
17	1	38	20	88
14	0	38	18	64
16	1	32	16	57
18	0	33	16	66
11	1	31	16	68
14	1	38	19	54
12	1	39	16	56
17	0	32	17	86
9	1	32	17	80
16	0	35	16	76
14	1	37	15	69
15	1	33	16	78
11	0	33	14	67
16	1	28	15	80
13	0	32	12	54
17	1	31	14	71
15	1	37	16	84
14	0	30	14	74
16	0	33	7	71
9	0	31	10	63
15	0	33	14	71
17	1	31	16	76
13	0	33	16	69
15	0	32	16	74
16	1	33	14	75
16	0	32	20	54
12	0	33	14	52
12	1	28	14	69
11	1	35	11	68
15	1	39	14	65
15	1	34	15	75
17	1	38	16	74
13	0	32	14	75
16	1	38	16	72
14	0	30	14	67
11	0	33	12	63
12	1	38	16	62
12	0	32	9	63
15	1	32	14	76
16	1	34	16	74
15	1	34	16	67
12	0	36	15	73
12	1	34	16	70
8	0	28	12	53
13	0	34	16	77
11	1	35	16	77
14	1	35	14	52
15	1	31	16	54
10	0	37	17	80
11	1	35	18	66
12	0	27	18	73
15	1	40	12	63
15	0	37	16	69
14	0	36	10	67
16	1	38	14	54
15	1	39	18	81
15	0	41	18	69
13	0	27	16	84
12	1	30	17	80
17	1	37	16	70
13	1	31	16	69
15	0	31	13	77
13	0	27	16	54
15	0	36	16	79
16	0	38	20	30
15	1	37	16	71
16	0	33	15	73
15	1	34	15	72
14	1	31	16	77
15	0	39	14	75
14	1	34	16	69
13	1	32	16	54
7	1	33	15	70
17	1	36	12	73
13	1	32	17	54
15	1	41	16	77
14	1	28	15	82
13	1	30	13	80
16	1	36	16	80
12	1	35	16	69
14	1	31	16	78
17	0	34	16	81
15	0	36	14	76
17	1	36	16	76
12	0	35	16	73
16	1	37	20	85
11	0	28	15	66
15	1	39	16	79
9	0	32	13	68
16	1	35	17	76
15	0	39	16	71
10	0	35	16	54
10	1	42	12	46
15	1	34	16	82
11	1	33	16	74
13	1	41	17	88
14	0	33	13	38
18	1	34	12	76
16	0	32	18	86
14	1	40	14	54
14	1	40	14	70
14	1	35	13	69
14	1	36	16	90
12	1	37	13	54
14	1	27	16	76
15	1	39	13	89
15	1	38	16	76
15	1	31	15	73
13	1	33	16	79
17	0	32	15	90
17	1	39	17	74
19	1	36	15	81
15	1	33	12	72
13	0	33	16	71
9	0	32	10	66
15	1	37	16	77
15	0	30	12	65
15	0	38	14	74
16	1	29	15	82
11	0	22	13	54
14	0	35	15	63
11	1	35	11	54
15	1	34	12	64
13	0	35	8	69
15	1	34	16	54
16	0	34	15	84
14	1	35	17	86
15	0	23	16	77
16	1	31	10	89
16	1	27	18	76
11	0	36	13	60
12	0	31	16	75
9	0	32	13	73
16	1	39	10	85
13	1	37	15	79
16	0	38	16	71
12	1	39	16	72
9	1	34	14	69
13	1	31	10	78
13	1	32	17	54
14	1	37	13	69
19	1	36	15	81
13	1	32	16	84
12	1	35	12	84
13	1	36	13	69

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157922&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157922&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157922&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 4.75800142985574 + 0.47054797058559Gender[t] + 0.0823592218225284Separate[t] + 0.142931011646596Learning[t] + 0.0571479293758487Belonging[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Happiness[t] =  +  4.75800142985574 +  0.47054797058559Gender[t] +  0.0823592218225284Separate[t] +  0.142931011646596Learning[t] +  0.0571479293758487Belonging[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157922&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Happiness[t] =  +  4.75800142985574 +  0.47054797058559Gender[t] +  0.0823592218225284Separate[t] +  0.142931011646596Learning[t] +  0.0571479293758487Belonging[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157922&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157922&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
Happiness[t] = + 4.75800142985574 + 0.47054797058559Gender[t] + 0.0823592218225284Separate[t] + 0.142931011646596Learning[t] + 0.0571479293758487Belonging[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.758001429855742.2873982.08010.0391420.019571
Gender0.470547970585590.3732091.26080.2092450.104623
Separate0.08235922182252840.0504241.63330.10440.0522
Learning0.1429310116465960.0776941.83970.0677060.033853
Belonging0.05714792937584870.0164073.48320.0006420.000321

\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) & 4.75800142985574 & 2.287398 & 2.0801 & 0.039142 & 0.019571 \tabularnewline
Gender & 0.47054797058559 & 0.373209 & 1.2608 & 0.209245 & 0.104623 \tabularnewline
Separate & 0.0823592218225284 & 0.050424 & 1.6333 & 0.1044 & 0.0522 \tabularnewline
Learning & 0.142931011646596 & 0.077694 & 1.8397 & 0.067706 & 0.033853 \tabularnewline
Belonging & 0.0571479293758487 & 0.016407 & 3.4832 & 0.000642 & 0.000321 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157922&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]4.75800142985574[/C][C]2.287398[/C][C]2.0801[/C][C]0.039142[/C][C]0.019571[/C][/ROW]
[ROW][C]Gender[/C][C]0.47054797058559[/C][C]0.373209[/C][C]1.2608[/C][C]0.209245[/C][C]0.104623[/C][/ROW]
[ROW][C]Separate[/C][C]0.0823592218225284[/C][C]0.050424[/C][C]1.6333[/C][C]0.1044[/C][C]0.0522[/C][/ROW]
[ROW][C]Learning[/C][C]0.142931011646596[/C][C]0.077694[/C][C]1.8397[/C][C]0.067706[/C][C]0.033853[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0571479293758487[/C][C]0.016407[/C][C]3.4832[/C][C]0.000642[/C][C]0.000321[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157922&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157922&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)4.758001429855742.2873982.08010.0391420.019571
Gender0.470547970585590.3732091.26080.2092450.104623
Separate0.08235922182252840.0504241.63330.10440.0522
Learning0.1429310116465960.0776941.83970.0677060.033853
Belonging0.05714792937584870.0164073.48320.0006420.000321






Multiple Linear Regression - Regression Statistics
Multiple R0.370310370403946
R-squared0.137129770428707
Adjusted R-squared0.115145815535171
F-TEST (value)6.23772069642607
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value0.000110230297319935
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.19891990435089
Sum Squared Residuals759.134053082833

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.370310370403946 \tabularnewline
R-squared & 0.137129770428707 \tabularnewline
Adjusted R-squared & 0.115145815535171 \tabularnewline
F-TEST (value) & 6.23772069642607 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value & 0.000110230297319935 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.19891990435089 \tabularnewline
Sum Squared Residuals & 759.134053082833 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157922&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.370310370403946[/C][/ROW]
[ROW][C]R-squared[/C][C]0.137129770428707[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.115145815535171[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.23772069642607[/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]0.000110230297319935[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.19891990435089[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]759.134053082833[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157922&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157922&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 R0.370310370403946
R-squared0.137129770428707
Adjusted R-squared0.115145815535171
F-TEST (value)6.23772069642607
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value0.000110230297319935
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.19891990435089
Sum Squared Residuals759.134053082833






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11413.24514323802310.754856761976854
21815.06566261143082.93433738856924
31114.5985747243212-3.59857472432115
41213.44873219288-1.44873219287997
51614.62011740349171.37988259650828
61813.93260847601664.06739152398341
71413.5262147551450.473785244854992
81414.9092809108192-0.909280910819181
91514.3411031010950.658896898905036
101514.84540763696080.154592363039157
111714.11271991339172.8872800866083
121913.40164731504875.59835268495135
131013.7563816481716-3.75638164817163
141613.73108420233882.26891579766123
151815.67753766156582.32246233843416
161412.93440082849731.06559917150267
171413.82355640606420.176443593935766
181716.2458378477040.754162152295994
191414.1178775488049-0.117877548804856
201613.40837265953112.59162734046886
211813.53451527515074.46548472484928
221113.9546406608429-2.95464066084295
231414.1598772372786-0.159877237278556
241213.927739282913-1.92773928291299
251714.73804565249182.26195434750824
26914.8657060468223-5.86570604682226
271614.27071301255431.72928698744574
281414.3630129095074-0.363012909507374
291514.69083839824650.309161601753506
301113.3058011812334-2.30580118123338
311614.2504071362391.74959286376105
321312.19465685423160.805343145768376
331713.84022242567733.1597775743227
341515.3631628617917-0.363162861791699
351413.45875902139670.541240978603267
361612.53387581721063.4661241827894
37912.3407669734985-3.34076697349854
381513.53439289873681.46560710126323
391714.41182409584972.58817590415026
401313.7059590632783-0.705959063278266
411513.9093394883351.09066051166502
421614.23353258682581.76646741317424
431613.33810494740442.66189505259561
441212.4485822405956-0.448582240595647
451213.478848901458-1.47884890145802
461113.5694224899001-2.56942248990008
471514.15620862400240.84379137599756
481514.45882282029490.541177179705119
491714.87404278985572.12595721014426
501313.6806253944176-0.680625394417638
511614.7597469311041.24025306889596
521413.05872351576580.941276484234208
531112.7913474404368-1.79134744043679
541214.1882676373456-2.18826763734556
551212.2801951836745-0.280195183674474
561514.20832129437910.791678705620923
571614.54460590256561.45539409743437
581514.14457039693470.855429603065313
591214.0386974346026-2.03869743460265
601214.3160141850622-2.31601418506223
61811.8080720375657-3.80807203756566
621314.2455017201076-1.24550172010758
631114.7984089125157-3.7984089125157
641413.08384865482630.916151345173706
651513.15456964958111.84543035041893
661014.8069541853493-4.80695418534931
671114.4556437126746-3.45564371267456
681213.7262574731397-1.72625747313968
691513.83840996378011.16159003621992
701514.03539595056840.96460404943162
711412.98115480011461.01884519988542
721613.44522217904562.55477782095442
731515.6422995406024-0.642299540602402
741514.65069486115170.349305138848315
751314.0690226729808-1.06902267298083
761214.7009876031772-2.7009876031772
771714.56309185052982.43690814947018
781314.0117885902188-1.0117885902188
791513.56963101970021.43036898029979
801312.35458479170540.645415208294634
811514.52451602250430.475483977495662
821612.46070997331923.5392900266808
831514.62023977990570.379760220094333
841613.79161976913512.20838023086494
851514.28737903216730.712620967832665
861414.4689720252256-0.468972025225589
871514.25713994717530.742860052824663
881414.2588662556864-0.258866255686384
891313.2369288714036-0.236928871403598
90714.0907239515931-7.09072395159311
911714.08045237024852.91954762975155
921313.3798598830502-0.379859883050194
931515.2925642434509-0.292564243450872
941414.3647029949907-0.364702994990651
951314.1292635565908-1.12926355659082
961615.05221192246580.947788077534224
971214.3412254775089-2.34122547750891
981414.5261199546014-0.526119954601437
991714.4740934376112.52590656238902
1001514.06721021108360.9327897889164
1011714.82362020496242.17637979503762
1021214.0992692244267-2.09926922442672
1031615.99203483775390.00796516224606822
1041112.9797881543915-1.97978815439148
1051515.2421416585575-0.242141658557513
106913.1376588771401-4.1376588771401
1071614.88419199478641.11580800521355
1081514.31441025296510.685589747034866
1091013.0134585662856-3.01345856628559
1101013.0316136080357-3.03161360803571
1111515.0017893375724-0.00178933757241703
1121114.4622466807431-3.4622466807431
1131316.0641224782318-3.0641224782318
1141411.50558021768722.49441978231283
1151814.08717771473093.91282228526906
1161614.88097666413841.11902333586164
1171413.60994062269060.390059377309367
1181414.5243074927042-0.524307492704212
1191413.91243244256910.0875675574308749
1201415.6236912162243-1.62369121622426
1211213.2199319455765-1.21993194557645
1221414.0823872085596-0.0823872085596264
1231515.3848279173762-0.384827917376212
1241514.98833864860740.0116613513925614
1251514.09744929607560.902550703924402
1261314.7479863276223-1.74798632762234
1271714.6807753467022.31922465329804
1281715.09933302332491.90066697667513
1291914.9664288401954.03357115980497
1301513.7762267754051.22377322459498
1311313.82025492203-0.820254922029963
132912.5945699834486-3.59456998344862
1331514.96312735616080.0368726438392411
1341512.65856563372092.3414343662791
1351514.1176327959770.88236720402304
1361614.44706221681321.55293778318682
1371111.5139956476529-0.513995647652936
1381413.38485891902160.615141080978365
1391112.7693514786382-1.7693514786382
1401513.40140256222081.59859743777924
1411312.72722941375060.272770586249445
1421513.40164731504871.59835268495135
1431614.50260621409191.49739378590807
1441415.4556712885449-1.45567128854494
1451513.33955028005981.66044971994023
1461614.29716110785621.7028388921438
1471614.36824923185281.63175076814718
1481113.0099123294234-2.00991232942343
1491213.8841281958883-1.8841281958883
150913.4233985240193-4.42339852401934
1511614.7274431649331.27255683506697
1521314.9344922032659-1.93449220326586
1531614.23205103114261.76794896885739
1541214.8421061529266-2.84210615292657
155913.9730042323932-4.97300423239319
1561313.6685338847219-0.668533884721862
1571313.3798598830502-0.379859883050194
1581414.0771508862142-0.0771508862141818
1591914.9664288401954.03357115980497
1601314.9513667526791-1.95136675267906
1611214.6267203715603-2.62672037156026
1621313.9947916643917-0.994791664391653

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 13.2451432380231 & 0.754856761976854 \tabularnewline
2 & 18 & 15.0656626114308 & 2.93433738856924 \tabularnewline
3 & 11 & 14.5985747243212 & -3.59857472432115 \tabularnewline
4 & 12 & 13.44873219288 & -1.44873219287997 \tabularnewline
5 & 16 & 14.6201174034917 & 1.37988259650828 \tabularnewline
6 & 18 & 13.9326084760166 & 4.06739152398341 \tabularnewline
7 & 14 & 13.526214755145 & 0.473785244854992 \tabularnewline
8 & 14 & 14.9092809108192 & -0.909280910819181 \tabularnewline
9 & 15 & 14.341103101095 & 0.658896898905036 \tabularnewline
10 & 15 & 14.8454076369608 & 0.154592363039157 \tabularnewline
11 & 17 & 14.1127199133917 & 2.8872800866083 \tabularnewline
12 & 19 & 13.4016473150487 & 5.59835268495135 \tabularnewline
13 & 10 & 13.7563816481716 & -3.75638164817163 \tabularnewline
14 & 16 & 13.7310842023388 & 2.26891579766123 \tabularnewline
15 & 18 & 15.6775376615658 & 2.32246233843416 \tabularnewline
16 & 14 & 12.9344008284973 & 1.06559917150267 \tabularnewline
17 & 14 & 13.8235564060642 & 0.176443593935766 \tabularnewline
18 & 17 & 16.245837847704 & 0.754162152295994 \tabularnewline
19 & 14 & 14.1178775488049 & -0.117877548804856 \tabularnewline
20 & 16 & 13.4083726595311 & 2.59162734046886 \tabularnewline
21 & 18 & 13.5345152751507 & 4.46548472484928 \tabularnewline
22 & 11 & 13.9546406608429 & -2.95464066084295 \tabularnewline
23 & 14 & 14.1598772372786 & -0.159877237278556 \tabularnewline
24 & 12 & 13.927739282913 & -1.92773928291299 \tabularnewline
25 & 17 & 14.7380456524918 & 2.26195434750824 \tabularnewline
26 & 9 & 14.8657060468223 & -5.86570604682226 \tabularnewline
27 & 16 & 14.2707130125543 & 1.72928698744574 \tabularnewline
28 & 14 & 14.3630129095074 & -0.363012909507374 \tabularnewline
29 & 15 & 14.6908383982465 & 0.309161601753506 \tabularnewline
30 & 11 & 13.3058011812334 & -2.30580118123338 \tabularnewline
31 & 16 & 14.250407136239 & 1.74959286376105 \tabularnewline
32 & 13 & 12.1946568542316 & 0.805343145768376 \tabularnewline
33 & 17 & 13.8402224256773 & 3.1597775743227 \tabularnewline
34 & 15 & 15.3631628617917 & -0.363162861791699 \tabularnewline
35 & 14 & 13.4587590213967 & 0.541240978603267 \tabularnewline
36 & 16 & 12.5338758172106 & 3.4661241827894 \tabularnewline
37 & 9 & 12.3407669734985 & -3.34076697349854 \tabularnewline
38 & 15 & 13.5343928987368 & 1.46560710126323 \tabularnewline
39 & 17 & 14.4118240958497 & 2.58817590415026 \tabularnewline
40 & 13 & 13.7059590632783 & -0.705959063278266 \tabularnewline
41 & 15 & 13.909339488335 & 1.09066051166502 \tabularnewline
42 & 16 & 14.2335325868258 & 1.76646741317424 \tabularnewline
43 & 16 & 13.3381049474044 & 2.66189505259561 \tabularnewline
44 & 12 & 12.4485822405956 & -0.448582240595647 \tabularnewline
45 & 12 & 13.478848901458 & -1.47884890145802 \tabularnewline
46 & 11 & 13.5694224899001 & -2.56942248990008 \tabularnewline
47 & 15 & 14.1562086240024 & 0.84379137599756 \tabularnewline
48 & 15 & 14.4588228202949 & 0.541177179705119 \tabularnewline
49 & 17 & 14.8740427898557 & 2.12595721014426 \tabularnewline
50 & 13 & 13.6806253944176 & -0.680625394417638 \tabularnewline
51 & 16 & 14.759746931104 & 1.24025306889596 \tabularnewline
52 & 14 & 13.0587235157658 & 0.941276484234208 \tabularnewline
53 & 11 & 12.7913474404368 & -1.79134744043679 \tabularnewline
54 & 12 & 14.1882676373456 & -2.18826763734556 \tabularnewline
55 & 12 & 12.2801951836745 & -0.280195183674474 \tabularnewline
56 & 15 & 14.2083212943791 & 0.791678705620923 \tabularnewline
57 & 16 & 14.5446059025656 & 1.45539409743437 \tabularnewline
58 & 15 & 14.1445703969347 & 0.855429603065313 \tabularnewline
59 & 12 & 14.0386974346026 & -2.03869743460265 \tabularnewline
60 & 12 & 14.3160141850622 & -2.31601418506223 \tabularnewline
61 & 8 & 11.8080720375657 & -3.80807203756566 \tabularnewline
62 & 13 & 14.2455017201076 & -1.24550172010758 \tabularnewline
63 & 11 & 14.7984089125157 & -3.7984089125157 \tabularnewline
64 & 14 & 13.0838486548263 & 0.916151345173706 \tabularnewline
65 & 15 & 13.1545696495811 & 1.84543035041893 \tabularnewline
66 & 10 & 14.8069541853493 & -4.80695418534931 \tabularnewline
67 & 11 & 14.4556437126746 & -3.45564371267456 \tabularnewline
68 & 12 & 13.7262574731397 & -1.72625747313968 \tabularnewline
69 & 15 & 13.8384099637801 & 1.16159003621992 \tabularnewline
70 & 15 & 14.0353959505684 & 0.96460404943162 \tabularnewline
71 & 14 & 12.9811548001146 & 1.01884519988542 \tabularnewline
72 & 16 & 13.4452221790456 & 2.55477782095442 \tabularnewline
73 & 15 & 15.6422995406024 & -0.642299540602402 \tabularnewline
74 & 15 & 14.6506948611517 & 0.349305138848315 \tabularnewline
75 & 13 & 14.0690226729808 & -1.06902267298083 \tabularnewline
76 & 12 & 14.7009876031772 & -2.7009876031772 \tabularnewline
77 & 17 & 14.5630918505298 & 2.43690814947018 \tabularnewline
78 & 13 & 14.0117885902188 & -1.0117885902188 \tabularnewline
79 & 15 & 13.5696310197002 & 1.43036898029979 \tabularnewline
80 & 13 & 12.3545847917054 & 0.645415208294634 \tabularnewline
81 & 15 & 14.5245160225043 & 0.475483977495662 \tabularnewline
82 & 16 & 12.4607099733192 & 3.5392900266808 \tabularnewline
83 & 15 & 14.6202397799057 & 0.379760220094333 \tabularnewline
84 & 16 & 13.7916197691351 & 2.20838023086494 \tabularnewline
85 & 15 & 14.2873790321673 & 0.712620967832665 \tabularnewline
86 & 14 & 14.4689720252256 & -0.468972025225589 \tabularnewline
87 & 15 & 14.2571399471753 & 0.742860052824663 \tabularnewline
88 & 14 & 14.2588662556864 & -0.258866255686384 \tabularnewline
89 & 13 & 13.2369288714036 & -0.236928871403598 \tabularnewline
90 & 7 & 14.0907239515931 & -7.09072395159311 \tabularnewline
91 & 17 & 14.0804523702485 & 2.91954762975155 \tabularnewline
92 & 13 & 13.3798598830502 & -0.379859883050194 \tabularnewline
93 & 15 & 15.2925642434509 & -0.292564243450872 \tabularnewline
94 & 14 & 14.3647029949907 & -0.364702994990651 \tabularnewline
95 & 13 & 14.1292635565908 & -1.12926355659082 \tabularnewline
96 & 16 & 15.0522119224658 & 0.947788077534224 \tabularnewline
97 & 12 & 14.3412254775089 & -2.34122547750891 \tabularnewline
98 & 14 & 14.5261199546014 & -0.526119954601437 \tabularnewline
99 & 17 & 14.474093437611 & 2.52590656238902 \tabularnewline
100 & 15 & 14.0672102110836 & 0.9327897889164 \tabularnewline
101 & 17 & 14.8236202049624 & 2.17637979503762 \tabularnewline
102 & 12 & 14.0992692244267 & -2.09926922442672 \tabularnewline
103 & 16 & 15.9920348377539 & 0.00796516224606822 \tabularnewline
104 & 11 & 12.9797881543915 & -1.97978815439148 \tabularnewline
105 & 15 & 15.2421416585575 & -0.242141658557513 \tabularnewline
106 & 9 & 13.1376588771401 & -4.1376588771401 \tabularnewline
107 & 16 & 14.8841919947864 & 1.11580800521355 \tabularnewline
108 & 15 & 14.3144102529651 & 0.685589747034866 \tabularnewline
109 & 10 & 13.0134585662856 & -3.01345856628559 \tabularnewline
110 & 10 & 13.0316136080357 & -3.03161360803571 \tabularnewline
111 & 15 & 15.0017893375724 & -0.00178933757241703 \tabularnewline
112 & 11 & 14.4622466807431 & -3.4622466807431 \tabularnewline
113 & 13 & 16.0641224782318 & -3.0641224782318 \tabularnewline
114 & 14 & 11.5055802176872 & 2.49441978231283 \tabularnewline
115 & 18 & 14.0871777147309 & 3.91282228526906 \tabularnewline
116 & 16 & 14.8809766641384 & 1.11902333586164 \tabularnewline
117 & 14 & 13.6099406226906 & 0.390059377309367 \tabularnewline
118 & 14 & 14.5243074927042 & -0.524307492704212 \tabularnewline
119 & 14 & 13.9124324425691 & 0.0875675574308749 \tabularnewline
120 & 14 & 15.6236912162243 & -1.62369121622426 \tabularnewline
121 & 12 & 13.2199319455765 & -1.21993194557645 \tabularnewline
122 & 14 & 14.0823872085596 & -0.0823872085596264 \tabularnewline
123 & 15 & 15.3848279173762 & -0.384827917376212 \tabularnewline
124 & 15 & 14.9883386486074 & 0.0116613513925614 \tabularnewline
125 & 15 & 14.0974492960756 & 0.902550703924402 \tabularnewline
126 & 13 & 14.7479863276223 & -1.74798632762234 \tabularnewline
127 & 17 & 14.680775346702 & 2.31922465329804 \tabularnewline
128 & 17 & 15.0993330233249 & 1.90066697667513 \tabularnewline
129 & 19 & 14.966428840195 & 4.03357115980497 \tabularnewline
130 & 15 & 13.776226775405 & 1.22377322459498 \tabularnewline
131 & 13 & 13.82025492203 & -0.820254922029963 \tabularnewline
132 & 9 & 12.5945699834486 & -3.59456998344862 \tabularnewline
133 & 15 & 14.9631273561608 & 0.0368726438392411 \tabularnewline
134 & 15 & 12.6585656337209 & 2.3414343662791 \tabularnewline
135 & 15 & 14.117632795977 & 0.88236720402304 \tabularnewline
136 & 16 & 14.4470622168132 & 1.55293778318682 \tabularnewline
137 & 11 & 11.5139956476529 & -0.513995647652936 \tabularnewline
138 & 14 & 13.3848589190216 & 0.615141080978365 \tabularnewline
139 & 11 & 12.7693514786382 & -1.7693514786382 \tabularnewline
140 & 15 & 13.4014025622208 & 1.59859743777924 \tabularnewline
141 & 13 & 12.7272294137506 & 0.272770586249445 \tabularnewline
142 & 15 & 13.4016473150487 & 1.59835268495135 \tabularnewline
143 & 16 & 14.5026062140919 & 1.49739378590807 \tabularnewline
144 & 14 & 15.4556712885449 & -1.45567128854494 \tabularnewline
145 & 15 & 13.3395502800598 & 1.66044971994023 \tabularnewline
146 & 16 & 14.2971611078562 & 1.7028388921438 \tabularnewline
147 & 16 & 14.3682492318528 & 1.63175076814718 \tabularnewline
148 & 11 & 13.0099123294234 & -2.00991232942343 \tabularnewline
149 & 12 & 13.8841281958883 & -1.8841281958883 \tabularnewline
150 & 9 & 13.4233985240193 & -4.42339852401934 \tabularnewline
151 & 16 & 14.727443164933 & 1.27255683506697 \tabularnewline
152 & 13 & 14.9344922032659 & -1.93449220326586 \tabularnewline
153 & 16 & 14.2320510311426 & 1.76794896885739 \tabularnewline
154 & 12 & 14.8421061529266 & -2.84210615292657 \tabularnewline
155 & 9 & 13.9730042323932 & -4.97300423239319 \tabularnewline
156 & 13 & 13.6685338847219 & -0.668533884721862 \tabularnewline
157 & 13 & 13.3798598830502 & -0.379859883050194 \tabularnewline
158 & 14 & 14.0771508862142 & -0.0771508862141818 \tabularnewline
159 & 19 & 14.966428840195 & 4.03357115980497 \tabularnewline
160 & 13 & 14.9513667526791 & -1.95136675267906 \tabularnewline
161 & 12 & 14.6267203715603 & -2.62672037156026 \tabularnewline
162 & 13 & 13.9947916643917 & -0.994791664391653 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157922&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]14[/C][C]13.2451432380231[/C][C]0.754856761976854[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]15.0656626114308[/C][C]2.93433738856924[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]14.5985747243212[/C][C]-3.59857472432115[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]13.44873219288[/C][C]-1.44873219287997[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]14.6201174034917[/C][C]1.37988259650828[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]13.9326084760166[/C][C]4.06739152398341[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]13.526214755145[/C][C]0.473785244854992[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]14.9092809108192[/C][C]-0.909280910819181[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]14.341103101095[/C][C]0.658896898905036[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.8454076369608[/C][C]0.154592363039157[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]14.1127199133917[/C][C]2.8872800866083[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]13.4016473150487[/C][C]5.59835268495135[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]13.7563816481716[/C][C]-3.75638164817163[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]13.7310842023388[/C][C]2.26891579766123[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]15.6775376615658[/C][C]2.32246233843416[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]12.9344008284973[/C][C]1.06559917150267[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]13.8235564060642[/C][C]0.176443593935766[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]16.245837847704[/C][C]0.754162152295994[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]14.1178775488049[/C][C]-0.117877548804856[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]13.4083726595311[/C][C]2.59162734046886[/C][/ROW]
[ROW][C]21[/C][C]18[/C][C]13.5345152751507[/C][C]4.46548472484928[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]13.9546406608429[/C][C]-2.95464066084295[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]14.1598772372786[/C][C]-0.159877237278556[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]13.927739282913[/C][C]-1.92773928291299[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.7380456524918[/C][C]2.26195434750824[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]14.8657060468223[/C][C]-5.86570604682226[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.2707130125543[/C][C]1.72928698744574[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]14.3630129095074[/C][C]-0.363012909507374[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]14.6908383982465[/C][C]0.309161601753506[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]13.3058011812334[/C][C]-2.30580118123338[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]14.250407136239[/C][C]1.74959286376105[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]12.1946568542316[/C][C]0.805343145768376[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]13.8402224256773[/C][C]3.1597775743227[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]15.3631628617917[/C][C]-0.363162861791699[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]13.4587590213967[/C][C]0.541240978603267[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]12.5338758172106[/C][C]3.4661241827894[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]12.3407669734985[/C][C]-3.34076697349854[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]13.5343928987368[/C][C]1.46560710126323[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]14.4118240958497[/C][C]2.58817590415026[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]13.7059590632783[/C][C]-0.705959063278266[/C][/ROW]
[ROW][C]41[/C][C]15[/C][C]13.909339488335[/C][C]1.09066051166502[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]14.2335325868258[/C][C]1.76646741317424[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]13.3381049474044[/C][C]2.66189505259561[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]12.4485822405956[/C][C]-0.448582240595647[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]13.478848901458[/C][C]-1.47884890145802[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]13.5694224899001[/C][C]-2.56942248990008[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]14.1562086240024[/C][C]0.84379137599756[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.4588228202949[/C][C]0.541177179705119[/C][/ROW]
[ROW][C]49[/C][C]17[/C][C]14.8740427898557[/C][C]2.12595721014426[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]13.6806253944176[/C][C]-0.680625394417638[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]14.759746931104[/C][C]1.24025306889596[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.0587235157658[/C][C]0.941276484234208[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]12.7913474404368[/C][C]-1.79134744043679[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]14.1882676373456[/C][C]-2.18826763734556[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]12.2801951836745[/C][C]-0.280195183674474[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]14.2083212943791[/C][C]0.791678705620923[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]14.5446059025656[/C][C]1.45539409743437[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]14.1445703969347[/C][C]0.855429603065313[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]14.0386974346026[/C][C]-2.03869743460265[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]14.3160141850622[/C][C]-2.31601418506223[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]11.8080720375657[/C][C]-3.80807203756566[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]14.2455017201076[/C][C]-1.24550172010758[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]14.7984089125157[/C][C]-3.7984089125157[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.0838486548263[/C][C]0.916151345173706[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]13.1545696495811[/C][C]1.84543035041893[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]14.8069541853493[/C][C]-4.80695418534931[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]14.4556437126746[/C][C]-3.45564371267456[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]13.7262574731397[/C][C]-1.72625747313968[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]13.8384099637801[/C][C]1.16159003621992[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]14.0353959505684[/C][C]0.96460404943162[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]12.9811548001146[/C][C]1.01884519988542[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]13.4452221790456[/C][C]2.55477782095442[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]15.6422995406024[/C][C]-0.642299540602402[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]14.6506948611517[/C][C]0.349305138848315[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]14.0690226729808[/C][C]-1.06902267298083[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]14.7009876031772[/C][C]-2.7009876031772[/C][/ROW]
[ROW][C]77[/C][C]17[/C][C]14.5630918505298[/C][C]2.43690814947018[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]14.0117885902188[/C][C]-1.0117885902188[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]13.5696310197002[/C][C]1.43036898029979[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]12.3545847917054[/C][C]0.645415208294634[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]14.5245160225043[/C][C]0.475483977495662[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]12.4607099733192[/C][C]3.5392900266808[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]14.6202397799057[/C][C]0.379760220094333[/C][/ROW]
[ROW][C]84[/C][C]16[/C][C]13.7916197691351[/C][C]2.20838023086494[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.2873790321673[/C][C]0.712620967832665[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.4689720252256[/C][C]-0.468972025225589[/C][/ROW]
[ROW][C]87[/C][C]15[/C][C]14.2571399471753[/C][C]0.742860052824663[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]14.2588662556864[/C][C]-0.258866255686384[/C][/ROW]
[ROW][C]89[/C][C]13[/C][C]13.2369288714036[/C][C]-0.236928871403598[/C][/ROW]
[ROW][C]90[/C][C]7[/C][C]14.0907239515931[/C][C]-7.09072395159311[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]14.0804523702485[/C][C]2.91954762975155[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]13.3798598830502[/C][C]-0.379859883050194[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]15.2925642434509[/C][C]-0.292564243450872[/C][/ROW]
[ROW][C]94[/C][C]14[/C][C]14.3647029949907[/C][C]-0.364702994990651[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]14.1292635565908[/C][C]-1.12926355659082[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.0522119224658[/C][C]0.947788077534224[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]14.3412254775089[/C][C]-2.34122547750891[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]14.5261199546014[/C][C]-0.526119954601437[/C][/ROW]
[ROW][C]99[/C][C]17[/C][C]14.474093437611[/C][C]2.52590656238902[/C][/ROW]
[ROW][C]100[/C][C]15[/C][C]14.0672102110836[/C][C]0.9327897889164[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]14.8236202049624[/C][C]2.17637979503762[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]14.0992692244267[/C][C]-2.09926922442672[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]15.9920348377539[/C][C]0.00796516224606822[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]12.9797881543915[/C][C]-1.97978815439148[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]15.2421416585575[/C][C]-0.242141658557513[/C][/ROW]
[ROW][C]106[/C][C]9[/C][C]13.1376588771401[/C][C]-4.1376588771401[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]14.8841919947864[/C][C]1.11580800521355[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]14.3144102529651[/C][C]0.685589747034866[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]13.0134585662856[/C][C]-3.01345856628559[/C][/ROW]
[ROW][C]110[/C][C]10[/C][C]13.0316136080357[/C][C]-3.03161360803571[/C][/ROW]
[ROW][C]111[/C][C]15[/C][C]15.0017893375724[/C][C]-0.00178933757241703[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]14.4622466807431[/C][C]-3.4622466807431[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]16.0641224782318[/C][C]-3.0641224782318[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]11.5055802176872[/C][C]2.49441978231283[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]14.0871777147309[/C][C]3.91282228526906[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]14.8809766641384[/C][C]1.11902333586164[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.6099406226906[/C][C]0.390059377309367[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]14.5243074927042[/C][C]-0.524307492704212[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]13.9124324425691[/C][C]0.0875675574308749[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]15.6236912162243[/C][C]-1.62369121622426[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]13.2199319455765[/C][C]-1.21993194557645[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]14.0823872085596[/C][C]-0.0823872085596264[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]15.3848279173762[/C][C]-0.384827917376212[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]14.9883386486074[/C][C]0.0116613513925614[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]14.0974492960756[/C][C]0.902550703924402[/C][/ROW]
[ROW][C]126[/C][C]13[/C][C]14.7479863276223[/C][C]-1.74798632762234[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]14.680775346702[/C][C]2.31922465329804[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]15.0993330233249[/C][C]1.90066697667513[/C][/ROW]
[ROW][C]129[/C][C]19[/C][C]14.966428840195[/C][C]4.03357115980497[/C][/ROW]
[ROW][C]130[/C][C]15[/C][C]13.776226775405[/C][C]1.22377322459498[/C][/ROW]
[ROW][C]131[/C][C]13[/C][C]13.82025492203[/C][C]-0.820254922029963[/C][/ROW]
[ROW][C]132[/C][C]9[/C][C]12.5945699834486[/C][C]-3.59456998344862[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]14.9631273561608[/C][C]0.0368726438392411[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]12.6585656337209[/C][C]2.3414343662791[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]14.117632795977[/C][C]0.88236720402304[/C][/ROW]
[ROW][C]136[/C][C]16[/C][C]14.4470622168132[/C][C]1.55293778318682[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]11.5139956476529[/C][C]-0.513995647652936[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]13.3848589190216[/C][C]0.615141080978365[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]12.7693514786382[/C][C]-1.7693514786382[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]13.4014025622208[/C][C]1.59859743777924[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]12.7272294137506[/C][C]0.272770586249445[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]13.4016473150487[/C][C]1.59835268495135[/C][/ROW]
[ROW][C]143[/C][C]16[/C][C]14.5026062140919[/C][C]1.49739378590807[/C][/ROW]
[ROW][C]144[/C][C]14[/C][C]15.4556712885449[/C][C]-1.45567128854494[/C][/ROW]
[ROW][C]145[/C][C]15[/C][C]13.3395502800598[/C][C]1.66044971994023[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]14.2971611078562[/C][C]1.7028388921438[/C][/ROW]
[ROW][C]147[/C][C]16[/C][C]14.3682492318528[/C][C]1.63175076814718[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]13.0099123294234[/C][C]-2.00991232942343[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]13.8841281958883[/C][C]-1.8841281958883[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]13.4233985240193[/C][C]-4.42339852401934[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]14.727443164933[/C][C]1.27255683506697[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]14.9344922032659[/C][C]-1.93449220326586[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]14.2320510311426[/C][C]1.76794896885739[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]14.8421061529266[/C][C]-2.84210615292657[/C][/ROW]
[ROW][C]155[/C][C]9[/C][C]13.9730042323932[/C][C]-4.97300423239319[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]13.6685338847219[/C][C]-0.668533884721862[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]13.3798598830502[/C][C]-0.379859883050194[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]14.0771508862142[/C][C]-0.0771508862141818[/C][/ROW]
[ROW][C]159[/C][C]19[/C][C]14.966428840195[/C][C]4.03357115980497[/C][/ROW]
[ROW][C]160[/C][C]13[/C][C]14.9513667526791[/C][C]-1.95136675267906[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]14.6267203715603[/C][C]-2.62672037156026[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]13.9947916643917[/C][C]-0.994791664391653[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157922&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157922&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
11413.24514323802310.754856761976854
21815.06566261143082.93433738856924
31114.5985747243212-3.59857472432115
41213.44873219288-1.44873219287997
51614.62011740349171.37988259650828
61813.93260847601664.06739152398341
71413.5262147551450.473785244854992
81414.9092809108192-0.909280910819181
91514.3411031010950.658896898905036
101514.84540763696080.154592363039157
111714.11271991339172.8872800866083
121913.40164731504875.59835268495135
131013.7563816481716-3.75638164817163
141613.73108420233882.26891579766123
151815.67753766156582.32246233843416
161412.93440082849731.06559917150267
171413.82355640606420.176443593935766
181716.2458378477040.754162152295994
191414.1178775488049-0.117877548804856
201613.40837265953112.59162734046886
211813.53451527515074.46548472484928
221113.9546406608429-2.95464066084295
231414.1598772372786-0.159877237278556
241213.927739282913-1.92773928291299
251714.73804565249182.26195434750824
26914.8657060468223-5.86570604682226
271614.27071301255431.72928698744574
281414.3630129095074-0.363012909507374
291514.69083839824650.309161601753506
301113.3058011812334-2.30580118123338
311614.2504071362391.74959286376105
321312.19465685423160.805343145768376
331713.84022242567733.1597775743227
341515.3631628617917-0.363162861791699
351413.45875902139670.541240978603267
361612.53387581721063.4661241827894
37912.3407669734985-3.34076697349854
381513.53439289873681.46560710126323
391714.41182409584972.58817590415026
401313.7059590632783-0.705959063278266
411513.9093394883351.09066051166502
421614.23353258682581.76646741317424
431613.33810494740442.66189505259561
441212.4485822405956-0.448582240595647
451213.478848901458-1.47884890145802
461113.5694224899001-2.56942248990008
471514.15620862400240.84379137599756
481514.45882282029490.541177179705119
491714.87404278985572.12595721014426
501313.6806253944176-0.680625394417638
511614.7597469311041.24025306889596
521413.05872351576580.941276484234208
531112.7913474404368-1.79134744043679
541214.1882676373456-2.18826763734556
551212.2801951836745-0.280195183674474
561514.20832129437910.791678705620923
571614.54460590256561.45539409743437
581514.14457039693470.855429603065313
591214.0386974346026-2.03869743460265
601214.3160141850622-2.31601418506223
61811.8080720375657-3.80807203756566
621314.2455017201076-1.24550172010758
631114.7984089125157-3.7984089125157
641413.08384865482630.916151345173706
651513.15456964958111.84543035041893
661014.8069541853493-4.80695418534931
671114.4556437126746-3.45564371267456
681213.7262574731397-1.72625747313968
691513.83840996378011.16159003621992
701514.03539595056840.96460404943162
711412.98115480011461.01884519988542
721613.44522217904562.55477782095442
731515.6422995406024-0.642299540602402
741514.65069486115170.349305138848315
751314.0690226729808-1.06902267298083
761214.7009876031772-2.7009876031772
771714.56309185052982.43690814947018
781314.0117885902188-1.0117885902188
791513.56963101970021.43036898029979
801312.35458479170540.645415208294634
811514.52451602250430.475483977495662
821612.46070997331923.5392900266808
831514.62023977990570.379760220094333
841613.79161976913512.20838023086494
851514.28737903216730.712620967832665
861414.4689720252256-0.468972025225589
871514.25713994717530.742860052824663
881414.2588662556864-0.258866255686384
891313.2369288714036-0.236928871403598
90714.0907239515931-7.09072395159311
911714.08045237024852.91954762975155
921313.3798598830502-0.379859883050194
931515.2925642434509-0.292564243450872
941414.3647029949907-0.364702994990651
951314.1292635565908-1.12926355659082
961615.05221192246580.947788077534224
971214.3412254775089-2.34122547750891
981414.5261199546014-0.526119954601437
991714.4740934376112.52590656238902
1001514.06721021108360.9327897889164
1011714.82362020496242.17637979503762
1021214.0992692244267-2.09926922442672
1031615.99203483775390.00796516224606822
1041112.9797881543915-1.97978815439148
1051515.2421416585575-0.242141658557513
106913.1376588771401-4.1376588771401
1071614.88419199478641.11580800521355
1081514.31441025296510.685589747034866
1091013.0134585662856-3.01345856628559
1101013.0316136080357-3.03161360803571
1111515.0017893375724-0.00178933757241703
1121114.4622466807431-3.4622466807431
1131316.0641224782318-3.0641224782318
1141411.50558021768722.49441978231283
1151814.08717771473093.91282228526906
1161614.88097666413841.11902333586164
1171413.60994062269060.390059377309367
1181414.5243074927042-0.524307492704212
1191413.91243244256910.0875675574308749
1201415.6236912162243-1.62369121622426
1211213.2199319455765-1.21993194557645
1221414.0823872085596-0.0823872085596264
1231515.3848279173762-0.384827917376212
1241514.98833864860740.0116613513925614
1251514.09744929607560.902550703924402
1261314.7479863276223-1.74798632762234
1271714.6807753467022.31922465329804
1281715.09933302332491.90066697667513
1291914.9664288401954.03357115980497
1301513.7762267754051.22377322459498
1311313.82025492203-0.820254922029963
132912.5945699834486-3.59456998344862
1331514.96312735616080.0368726438392411
1341512.65856563372092.3414343662791
1351514.1176327959770.88236720402304
1361614.44706221681321.55293778318682
1371111.5139956476529-0.513995647652936
1381413.38485891902160.615141080978365
1391112.7693514786382-1.7693514786382
1401513.40140256222081.59859743777924
1411312.72722941375060.272770586249445
1421513.40164731504871.59835268495135
1431614.50260621409191.49739378590807
1441415.4556712885449-1.45567128854494
1451513.33955028005981.66044971994023
1461614.29716110785621.7028388921438
1471614.36824923185281.63175076814718
1481113.0099123294234-2.00991232942343
1491213.8841281958883-1.8841281958883
150913.4233985240193-4.42339852401934
1511614.7274431649331.27255683506697
1521314.9344922032659-1.93449220326586
1531614.23205103114261.76794896885739
1541214.8421061529266-2.84210615292657
155913.9730042323932-4.97300423239319
1561313.6685338847219-0.668533884721862
1571313.3798598830502-0.379859883050194
1581414.0771508862142-0.0771508862141818
1591914.9664288401954.03357115980497
1601314.9513667526791-1.95136675267906
1611214.6267203715603-2.62672037156026
1621313.9947916643917-0.994791664391653






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.3480217320634830.6960434641269650.651978267936517
90.2124205512010940.4248411024021880.787579448798906
100.1360242120064530.2720484240129060.863975787993547
110.24461077135690.48922154271380.7553892286431
120.6977277210558840.6045445578882320.302272278944116
130.6945077524108950.610984495178210.305492247589105
140.7275142190513850.544971561897230.272485780948615
150.7947431412318130.4105137175363740.205256858768187
160.744878075019160.5102438499616810.25512192498084
170.6707902432347530.6584195135304940.329209756765247
180.6601359496868140.6797281006263710.339864050313186
190.647839380007910.704321239984180.35216061999209
200.5882666451347910.8234667097304180.411733354865209
210.7590370998675350.4819258002649290.240962900132465
220.9002859598444950.1994280803110110.0997140401555055
230.8663501855331130.2672996289337740.133649814466887
240.8580343365402080.2839313269195830.141965663459792
250.8375731042643960.3248537914712090.162426895735604
260.9811586237934210.03768275241315820.0188413762065791
270.9755724843391230.04885503132175380.0244275156608769
280.9667991244618220.06640175107635560.0332008755381778
290.9542577353525380.09148452929492470.0457422646474624
300.9651356535518870.06972869289622630.0348643464481131
310.9548150352912230.09036992941755350.0451849647087768
320.9406546549334720.1186906901330560.0593453450665281
330.9397976092915870.1204047814168260.0602023907084132
340.922082515374330.155834969251340.0779174846256701
350.9018216971709510.1963566056580990.0981783028290495
360.8980677084927440.2038645830145130.101932291507256
370.9531133622744690.09377327545106270.0468866377255313
380.94236756163990.1152648767201990.0576324383600996
390.9387013810426510.1225972379146980.0612986189573491
400.9234391650280190.1531216699439620.0765608349719808
410.9060539643056590.1878920713886820.093946035694341
420.8896625888967680.2206748222064650.110337411103232
430.9001771679665270.1996456640669460.0998228320334732
440.8792718453550050.241456309289990.120728154644995
450.8856065022558170.2287869954883660.114393497744183
460.9011693808428610.1976612383142770.0988306191571385
470.8810020393618260.2379959212763480.118997960638174
480.8551265502051790.2897468995896410.144873449794821
490.8492306265508150.3015387468983690.150769373449185
500.8259819216350950.348036156729810.174018078364905
510.8007823985682050.3984352028635890.199217601431795
520.7689263798272770.4621472403454460.231073620172723
530.7607060241947990.4785879516104020.239293975805201
540.7609150236812940.4781699526374110.239084976318706
550.7230094875589180.5539810248821630.276990512441082
560.684733064746130.630533870507740.31526693525387
570.6546311710293790.6907376579412410.345368828970621
580.6143295487068450.7713409025863110.385670451293155
590.6078582903799220.7842834192401560.392141709620078
600.6244525579342180.7510948841315640.375547442065782
610.7137184369483470.5725631261033060.286281563051653
620.6883761261738730.6232477476522550.311623873826127
630.7739569726094670.4520860547810670.226043027390533
640.7440147085934510.5119705828130990.255985291406549
650.7295085683900670.5409828632198650.270491431609933
660.8442988253073090.3114023493853810.155701174692691
670.8814464359648550.2371071280702890.118553564035145
680.8746294670461070.2507410659077860.125370532953893
690.8568822304178170.2862355391643660.143117769582183
700.8371056944243510.3257886111512980.162894305575649
710.8135206787643880.3729586424712250.186479321235612
720.8238873118700420.3522253762599150.176112688129958
730.7950324015250360.4099351969499270.204967598474964
740.7647798145027170.4704403709945660.235220185497283
750.7376887468711370.5246225062577250.262311253128863
760.7578116591494620.4843766817010770.242188340850538
770.7644367220249580.4711265559500850.235563277975042
780.7360810520617490.5278378958765010.263918947938251
790.712552933572850.5748941328543010.28744706642715
800.676276457639360.647447084721280.32372354236064
810.6361370286860810.7277259426278390.363862971313919
820.7154453976277090.5691092047445830.284554602372291
830.6776923538291830.6446152923416340.322307646170817
840.6794140951748480.6411718096503050.320585904825152
850.6429570277279330.7140859445441350.357042972272067
860.6007776720853610.7984446558292780.399222327914639
870.5610946709888270.8778106580223450.438905329011173
880.5165268687046180.9669462625907640.483473131295382
890.4747896389556720.9495792779113440.525210361044328
900.8391575214481580.3216849571036840.160842478551842
910.8615277495663510.2769445008672980.138472250433649
920.835092665045260.329814669909480.16490733495474
930.8051114087469040.3897771825061910.194888591253095
940.7727097829765840.4545804340468330.227290217023416
950.7470603876720410.5058792246559180.252939612327959
960.7158750542127740.5682498915744520.284124945787226
970.7163349917045660.5673300165908670.283665008295434
980.6777074827539660.6445850344920680.322292517246034
990.6933264057519090.6133471884961810.306673594248091
1000.6620416560678390.6759166878643220.337958343932161
1010.6643798248082280.6712403503835440.335620175191772
1020.6532020297119610.6935959405760780.346797970288039
1030.6077662707401120.7844674585197750.392233729259888
1040.5950794038503120.8098411922993760.404920596149688
1050.5482189748247630.9035620503504740.451781025175237
1060.6627318938603420.6745362122793160.337268106139658
1070.6315981143254690.7368037713490610.368401885674531
1080.5942478865853310.8115042268293380.405752113414669
1090.6211301272265750.757739745546850.378869872773425
1100.6434237619513830.7131524760972340.356576238048617
1110.5955172767478780.8089654465042430.404482723252122
1120.6639082779368230.6721834441263540.336091722063177
1130.7036113279053450.592777344189310.296388672094655
1140.7388728343542350.5222543312915310.261127165645765
1150.8275136768337170.3449726463325660.172486323166283
1160.7969845637359220.4060308725281570.203015436264078
1170.7678663388857720.4642673222284550.232133661114228
1180.7262729449917750.547454110016450.273727055008225
1190.681603154888150.6367936902237010.31839684511185
1200.674993080528510.650013838942980.32500691947149
1210.6301305185421120.7397389629157750.369869481457888
1220.5793959124676820.8412081750646360.420604087532318
1230.5288711771136650.942257645772670.471128822886335
1240.4732321563113340.9464643126226680.526767843688666
1250.4252700195767640.8505400391535280.574729980423236
1260.4167631300259390.8335262600518790.583236869974061
1270.3978368514209490.7956737028418990.602163148579051
1280.3802220985784190.7604441971568380.619777901421581
1290.5076851018348330.9846297963303340.492314898165167
1300.4750342009596640.9500684019193270.524965799040336
1310.4207694685245370.8415389370490740.579230531475463
1320.495810399273840.9916207985476810.50418960072616
1330.4356007458935860.8712014917871720.564399254106414
1340.4455387234722990.8910774469445990.554461276527701
1350.4037388490198650.8074776980397310.596261150980135
1360.3658759931151550.7317519862303090.634124006884845
1370.307420163974320.614840327948640.69257983602568
1380.2661471684482160.5322943368964320.733852831551784
1390.2254633177017750.450926635403550.774536682298225
1400.2165276909387770.4330553818775540.783472309061223
1410.1817355857126510.3634711714253010.818264414287349
1420.2141419009555010.4282838019110020.785858099044499
1430.1838982419820670.3677964839641340.816101758017933
1440.160664008445090.321328016890180.83933599155491
1450.1492286210868430.2984572421736860.850771378913157
1460.1488076592291580.2976153184583160.851192340770842
1470.1807458833801750.361491766760350.819254116619825
1480.1315610767309640.2631221534619280.868438923269036
1490.08994787335016510.179895746700330.910052126649835
1500.1437804676416040.2875609352832090.856219532358396
1510.1150152891097240.2300305782194490.884984710890276
1520.0747800182063870.1495600364127740.925219981793613
1530.04037137457521530.08074274915043060.959628625424785
1540.03948978390471150.07897956780942310.960510216095289

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.348021732063483 & 0.696043464126965 & 0.651978267936517 \tabularnewline
9 & 0.212420551201094 & 0.424841102402188 & 0.787579448798906 \tabularnewline
10 & 0.136024212006453 & 0.272048424012906 & 0.863975787993547 \tabularnewline
11 & 0.2446107713569 & 0.4892215427138 & 0.7553892286431 \tabularnewline
12 & 0.697727721055884 & 0.604544557888232 & 0.302272278944116 \tabularnewline
13 & 0.694507752410895 & 0.61098449517821 & 0.305492247589105 \tabularnewline
14 & 0.727514219051385 & 0.54497156189723 & 0.272485780948615 \tabularnewline
15 & 0.794743141231813 & 0.410513717536374 & 0.205256858768187 \tabularnewline
16 & 0.74487807501916 & 0.510243849961681 & 0.25512192498084 \tabularnewline
17 & 0.670790243234753 & 0.658419513530494 & 0.329209756765247 \tabularnewline
18 & 0.660135949686814 & 0.679728100626371 & 0.339864050313186 \tabularnewline
19 & 0.64783938000791 & 0.70432123998418 & 0.35216061999209 \tabularnewline
20 & 0.588266645134791 & 0.823466709730418 & 0.411733354865209 \tabularnewline
21 & 0.759037099867535 & 0.481925800264929 & 0.240962900132465 \tabularnewline
22 & 0.900285959844495 & 0.199428080311011 & 0.0997140401555055 \tabularnewline
23 & 0.866350185533113 & 0.267299628933774 & 0.133649814466887 \tabularnewline
24 & 0.858034336540208 & 0.283931326919583 & 0.141965663459792 \tabularnewline
25 & 0.837573104264396 & 0.324853791471209 & 0.162426895735604 \tabularnewline
26 & 0.981158623793421 & 0.0376827524131582 & 0.0188413762065791 \tabularnewline
27 & 0.975572484339123 & 0.0488550313217538 & 0.0244275156608769 \tabularnewline
28 & 0.966799124461822 & 0.0664017510763556 & 0.0332008755381778 \tabularnewline
29 & 0.954257735352538 & 0.0914845292949247 & 0.0457422646474624 \tabularnewline
30 & 0.965135653551887 & 0.0697286928962263 & 0.0348643464481131 \tabularnewline
31 & 0.954815035291223 & 0.0903699294175535 & 0.0451849647087768 \tabularnewline
32 & 0.940654654933472 & 0.118690690133056 & 0.0593453450665281 \tabularnewline
33 & 0.939797609291587 & 0.120404781416826 & 0.0602023907084132 \tabularnewline
34 & 0.92208251537433 & 0.15583496925134 & 0.0779174846256701 \tabularnewline
35 & 0.901821697170951 & 0.196356605658099 & 0.0981783028290495 \tabularnewline
36 & 0.898067708492744 & 0.203864583014513 & 0.101932291507256 \tabularnewline
37 & 0.953113362274469 & 0.0937732754510627 & 0.0468866377255313 \tabularnewline
38 & 0.9423675616399 & 0.115264876720199 & 0.0576324383600996 \tabularnewline
39 & 0.938701381042651 & 0.122597237914698 & 0.0612986189573491 \tabularnewline
40 & 0.923439165028019 & 0.153121669943962 & 0.0765608349719808 \tabularnewline
41 & 0.906053964305659 & 0.187892071388682 & 0.093946035694341 \tabularnewline
42 & 0.889662588896768 & 0.220674822206465 & 0.110337411103232 \tabularnewline
43 & 0.900177167966527 & 0.199645664066946 & 0.0998228320334732 \tabularnewline
44 & 0.879271845355005 & 0.24145630928999 & 0.120728154644995 \tabularnewline
45 & 0.885606502255817 & 0.228786995488366 & 0.114393497744183 \tabularnewline
46 & 0.901169380842861 & 0.197661238314277 & 0.0988306191571385 \tabularnewline
47 & 0.881002039361826 & 0.237995921276348 & 0.118997960638174 \tabularnewline
48 & 0.855126550205179 & 0.289746899589641 & 0.144873449794821 \tabularnewline
49 & 0.849230626550815 & 0.301538746898369 & 0.150769373449185 \tabularnewline
50 & 0.825981921635095 & 0.34803615672981 & 0.174018078364905 \tabularnewline
51 & 0.800782398568205 & 0.398435202863589 & 0.199217601431795 \tabularnewline
52 & 0.768926379827277 & 0.462147240345446 & 0.231073620172723 \tabularnewline
53 & 0.760706024194799 & 0.478587951610402 & 0.239293975805201 \tabularnewline
54 & 0.760915023681294 & 0.478169952637411 & 0.239084976318706 \tabularnewline
55 & 0.723009487558918 & 0.553981024882163 & 0.276990512441082 \tabularnewline
56 & 0.68473306474613 & 0.63053387050774 & 0.31526693525387 \tabularnewline
57 & 0.654631171029379 & 0.690737657941241 & 0.345368828970621 \tabularnewline
58 & 0.614329548706845 & 0.771340902586311 & 0.385670451293155 \tabularnewline
59 & 0.607858290379922 & 0.784283419240156 & 0.392141709620078 \tabularnewline
60 & 0.624452557934218 & 0.751094884131564 & 0.375547442065782 \tabularnewline
61 & 0.713718436948347 & 0.572563126103306 & 0.286281563051653 \tabularnewline
62 & 0.688376126173873 & 0.623247747652255 & 0.311623873826127 \tabularnewline
63 & 0.773956972609467 & 0.452086054781067 & 0.226043027390533 \tabularnewline
64 & 0.744014708593451 & 0.511970582813099 & 0.255985291406549 \tabularnewline
65 & 0.729508568390067 & 0.540982863219865 & 0.270491431609933 \tabularnewline
66 & 0.844298825307309 & 0.311402349385381 & 0.155701174692691 \tabularnewline
67 & 0.881446435964855 & 0.237107128070289 & 0.118553564035145 \tabularnewline
68 & 0.874629467046107 & 0.250741065907786 & 0.125370532953893 \tabularnewline
69 & 0.856882230417817 & 0.286235539164366 & 0.143117769582183 \tabularnewline
70 & 0.837105694424351 & 0.325788611151298 & 0.162894305575649 \tabularnewline
71 & 0.813520678764388 & 0.372958642471225 & 0.186479321235612 \tabularnewline
72 & 0.823887311870042 & 0.352225376259915 & 0.176112688129958 \tabularnewline
73 & 0.795032401525036 & 0.409935196949927 & 0.204967598474964 \tabularnewline
74 & 0.764779814502717 & 0.470440370994566 & 0.235220185497283 \tabularnewline
75 & 0.737688746871137 & 0.524622506257725 & 0.262311253128863 \tabularnewline
76 & 0.757811659149462 & 0.484376681701077 & 0.242188340850538 \tabularnewline
77 & 0.764436722024958 & 0.471126555950085 & 0.235563277975042 \tabularnewline
78 & 0.736081052061749 & 0.527837895876501 & 0.263918947938251 \tabularnewline
79 & 0.71255293357285 & 0.574894132854301 & 0.28744706642715 \tabularnewline
80 & 0.67627645763936 & 0.64744708472128 & 0.32372354236064 \tabularnewline
81 & 0.636137028686081 & 0.727725942627839 & 0.363862971313919 \tabularnewline
82 & 0.715445397627709 & 0.569109204744583 & 0.284554602372291 \tabularnewline
83 & 0.677692353829183 & 0.644615292341634 & 0.322307646170817 \tabularnewline
84 & 0.679414095174848 & 0.641171809650305 & 0.320585904825152 \tabularnewline
85 & 0.642957027727933 & 0.714085944544135 & 0.357042972272067 \tabularnewline
86 & 0.600777672085361 & 0.798444655829278 & 0.399222327914639 \tabularnewline
87 & 0.561094670988827 & 0.877810658022345 & 0.438905329011173 \tabularnewline
88 & 0.516526868704618 & 0.966946262590764 & 0.483473131295382 \tabularnewline
89 & 0.474789638955672 & 0.949579277911344 & 0.525210361044328 \tabularnewline
90 & 0.839157521448158 & 0.321684957103684 & 0.160842478551842 \tabularnewline
91 & 0.861527749566351 & 0.276944500867298 & 0.138472250433649 \tabularnewline
92 & 0.83509266504526 & 0.32981466990948 & 0.16490733495474 \tabularnewline
93 & 0.805111408746904 & 0.389777182506191 & 0.194888591253095 \tabularnewline
94 & 0.772709782976584 & 0.454580434046833 & 0.227290217023416 \tabularnewline
95 & 0.747060387672041 & 0.505879224655918 & 0.252939612327959 \tabularnewline
96 & 0.715875054212774 & 0.568249891574452 & 0.284124945787226 \tabularnewline
97 & 0.716334991704566 & 0.567330016590867 & 0.283665008295434 \tabularnewline
98 & 0.677707482753966 & 0.644585034492068 & 0.322292517246034 \tabularnewline
99 & 0.693326405751909 & 0.613347188496181 & 0.306673594248091 \tabularnewline
100 & 0.662041656067839 & 0.675916687864322 & 0.337958343932161 \tabularnewline
101 & 0.664379824808228 & 0.671240350383544 & 0.335620175191772 \tabularnewline
102 & 0.653202029711961 & 0.693595940576078 & 0.346797970288039 \tabularnewline
103 & 0.607766270740112 & 0.784467458519775 & 0.392233729259888 \tabularnewline
104 & 0.595079403850312 & 0.809841192299376 & 0.404920596149688 \tabularnewline
105 & 0.548218974824763 & 0.903562050350474 & 0.451781025175237 \tabularnewline
106 & 0.662731893860342 & 0.674536212279316 & 0.337268106139658 \tabularnewline
107 & 0.631598114325469 & 0.736803771349061 & 0.368401885674531 \tabularnewline
108 & 0.594247886585331 & 0.811504226829338 & 0.405752113414669 \tabularnewline
109 & 0.621130127226575 & 0.75773974554685 & 0.378869872773425 \tabularnewline
110 & 0.643423761951383 & 0.713152476097234 & 0.356576238048617 \tabularnewline
111 & 0.595517276747878 & 0.808965446504243 & 0.404482723252122 \tabularnewline
112 & 0.663908277936823 & 0.672183444126354 & 0.336091722063177 \tabularnewline
113 & 0.703611327905345 & 0.59277734418931 & 0.296388672094655 \tabularnewline
114 & 0.738872834354235 & 0.522254331291531 & 0.261127165645765 \tabularnewline
115 & 0.827513676833717 & 0.344972646332566 & 0.172486323166283 \tabularnewline
116 & 0.796984563735922 & 0.406030872528157 & 0.203015436264078 \tabularnewline
117 & 0.767866338885772 & 0.464267322228455 & 0.232133661114228 \tabularnewline
118 & 0.726272944991775 & 0.54745411001645 & 0.273727055008225 \tabularnewline
119 & 0.68160315488815 & 0.636793690223701 & 0.31839684511185 \tabularnewline
120 & 0.67499308052851 & 0.65001383894298 & 0.32500691947149 \tabularnewline
121 & 0.630130518542112 & 0.739738962915775 & 0.369869481457888 \tabularnewline
122 & 0.579395912467682 & 0.841208175064636 & 0.420604087532318 \tabularnewline
123 & 0.528871177113665 & 0.94225764577267 & 0.471128822886335 \tabularnewline
124 & 0.473232156311334 & 0.946464312622668 & 0.526767843688666 \tabularnewline
125 & 0.425270019576764 & 0.850540039153528 & 0.574729980423236 \tabularnewline
126 & 0.416763130025939 & 0.833526260051879 & 0.583236869974061 \tabularnewline
127 & 0.397836851420949 & 0.795673702841899 & 0.602163148579051 \tabularnewline
128 & 0.380222098578419 & 0.760444197156838 & 0.619777901421581 \tabularnewline
129 & 0.507685101834833 & 0.984629796330334 & 0.492314898165167 \tabularnewline
130 & 0.475034200959664 & 0.950068401919327 & 0.524965799040336 \tabularnewline
131 & 0.420769468524537 & 0.841538937049074 & 0.579230531475463 \tabularnewline
132 & 0.49581039927384 & 0.991620798547681 & 0.50418960072616 \tabularnewline
133 & 0.435600745893586 & 0.871201491787172 & 0.564399254106414 \tabularnewline
134 & 0.445538723472299 & 0.891077446944599 & 0.554461276527701 \tabularnewline
135 & 0.403738849019865 & 0.807477698039731 & 0.596261150980135 \tabularnewline
136 & 0.365875993115155 & 0.731751986230309 & 0.634124006884845 \tabularnewline
137 & 0.30742016397432 & 0.61484032794864 & 0.69257983602568 \tabularnewline
138 & 0.266147168448216 & 0.532294336896432 & 0.733852831551784 \tabularnewline
139 & 0.225463317701775 & 0.45092663540355 & 0.774536682298225 \tabularnewline
140 & 0.216527690938777 & 0.433055381877554 & 0.783472309061223 \tabularnewline
141 & 0.181735585712651 & 0.363471171425301 & 0.818264414287349 \tabularnewline
142 & 0.214141900955501 & 0.428283801911002 & 0.785858099044499 \tabularnewline
143 & 0.183898241982067 & 0.367796483964134 & 0.816101758017933 \tabularnewline
144 & 0.16066400844509 & 0.32132801689018 & 0.83933599155491 \tabularnewline
145 & 0.149228621086843 & 0.298457242173686 & 0.850771378913157 \tabularnewline
146 & 0.148807659229158 & 0.297615318458316 & 0.851192340770842 \tabularnewline
147 & 0.180745883380175 & 0.36149176676035 & 0.819254116619825 \tabularnewline
148 & 0.131561076730964 & 0.263122153461928 & 0.868438923269036 \tabularnewline
149 & 0.0899478733501651 & 0.17989574670033 & 0.910052126649835 \tabularnewline
150 & 0.143780467641604 & 0.287560935283209 & 0.856219532358396 \tabularnewline
151 & 0.115015289109724 & 0.230030578219449 & 0.884984710890276 \tabularnewline
152 & 0.074780018206387 & 0.149560036412774 & 0.925219981793613 \tabularnewline
153 & 0.0403713745752153 & 0.0807427491504306 & 0.959628625424785 \tabularnewline
154 & 0.0394897839047115 & 0.0789795678094231 & 0.960510216095289 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157922&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]0.348021732063483[/C][C]0.696043464126965[/C][C]0.651978267936517[/C][/ROW]
[ROW][C]9[/C][C]0.212420551201094[/C][C]0.424841102402188[/C][C]0.787579448798906[/C][/ROW]
[ROW][C]10[/C][C]0.136024212006453[/C][C]0.272048424012906[/C][C]0.863975787993547[/C][/ROW]
[ROW][C]11[/C][C]0.2446107713569[/C][C]0.4892215427138[/C][C]0.7553892286431[/C][/ROW]
[ROW][C]12[/C][C]0.697727721055884[/C][C]0.604544557888232[/C][C]0.302272278944116[/C][/ROW]
[ROW][C]13[/C][C]0.694507752410895[/C][C]0.61098449517821[/C][C]0.305492247589105[/C][/ROW]
[ROW][C]14[/C][C]0.727514219051385[/C][C]0.54497156189723[/C][C]0.272485780948615[/C][/ROW]
[ROW][C]15[/C][C]0.794743141231813[/C][C]0.410513717536374[/C][C]0.205256858768187[/C][/ROW]
[ROW][C]16[/C][C]0.74487807501916[/C][C]0.510243849961681[/C][C]0.25512192498084[/C][/ROW]
[ROW][C]17[/C][C]0.670790243234753[/C][C]0.658419513530494[/C][C]0.329209756765247[/C][/ROW]
[ROW][C]18[/C][C]0.660135949686814[/C][C]0.679728100626371[/C][C]0.339864050313186[/C][/ROW]
[ROW][C]19[/C][C]0.64783938000791[/C][C]0.70432123998418[/C][C]0.35216061999209[/C][/ROW]
[ROW][C]20[/C][C]0.588266645134791[/C][C]0.823466709730418[/C][C]0.411733354865209[/C][/ROW]
[ROW][C]21[/C][C]0.759037099867535[/C][C]0.481925800264929[/C][C]0.240962900132465[/C][/ROW]
[ROW][C]22[/C][C]0.900285959844495[/C][C]0.199428080311011[/C][C]0.0997140401555055[/C][/ROW]
[ROW][C]23[/C][C]0.866350185533113[/C][C]0.267299628933774[/C][C]0.133649814466887[/C][/ROW]
[ROW][C]24[/C][C]0.858034336540208[/C][C]0.283931326919583[/C][C]0.141965663459792[/C][/ROW]
[ROW][C]25[/C][C]0.837573104264396[/C][C]0.324853791471209[/C][C]0.162426895735604[/C][/ROW]
[ROW][C]26[/C][C]0.981158623793421[/C][C]0.0376827524131582[/C][C]0.0188413762065791[/C][/ROW]
[ROW][C]27[/C][C]0.975572484339123[/C][C]0.0488550313217538[/C][C]0.0244275156608769[/C][/ROW]
[ROW][C]28[/C][C]0.966799124461822[/C][C]0.0664017510763556[/C][C]0.0332008755381778[/C][/ROW]
[ROW][C]29[/C][C]0.954257735352538[/C][C]0.0914845292949247[/C][C]0.0457422646474624[/C][/ROW]
[ROW][C]30[/C][C]0.965135653551887[/C][C]0.0697286928962263[/C][C]0.0348643464481131[/C][/ROW]
[ROW][C]31[/C][C]0.954815035291223[/C][C]0.0903699294175535[/C][C]0.0451849647087768[/C][/ROW]
[ROW][C]32[/C][C]0.940654654933472[/C][C]0.118690690133056[/C][C]0.0593453450665281[/C][/ROW]
[ROW][C]33[/C][C]0.939797609291587[/C][C]0.120404781416826[/C][C]0.0602023907084132[/C][/ROW]
[ROW][C]34[/C][C]0.92208251537433[/C][C]0.15583496925134[/C][C]0.0779174846256701[/C][/ROW]
[ROW][C]35[/C][C]0.901821697170951[/C][C]0.196356605658099[/C][C]0.0981783028290495[/C][/ROW]
[ROW][C]36[/C][C]0.898067708492744[/C][C]0.203864583014513[/C][C]0.101932291507256[/C][/ROW]
[ROW][C]37[/C][C]0.953113362274469[/C][C]0.0937732754510627[/C][C]0.0468866377255313[/C][/ROW]
[ROW][C]38[/C][C]0.9423675616399[/C][C]0.115264876720199[/C][C]0.0576324383600996[/C][/ROW]
[ROW][C]39[/C][C]0.938701381042651[/C][C]0.122597237914698[/C][C]0.0612986189573491[/C][/ROW]
[ROW][C]40[/C][C]0.923439165028019[/C][C]0.153121669943962[/C][C]0.0765608349719808[/C][/ROW]
[ROW][C]41[/C][C]0.906053964305659[/C][C]0.187892071388682[/C][C]0.093946035694341[/C][/ROW]
[ROW][C]42[/C][C]0.889662588896768[/C][C]0.220674822206465[/C][C]0.110337411103232[/C][/ROW]
[ROW][C]43[/C][C]0.900177167966527[/C][C]0.199645664066946[/C][C]0.0998228320334732[/C][/ROW]
[ROW][C]44[/C][C]0.879271845355005[/C][C]0.24145630928999[/C][C]0.120728154644995[/C][/ROW]
[ROW][C]45[/C][C]0.885606502255817[/C][C]0.228786995488366[/C][C]0.114393497744183[/C][/ROW]
[ROW][C]46[/C][C]0.901169380842861[/C][C]0.197661238314277[/C][C]0.0988306191571385[/C][/ROW]
[ROW][C]47[/C][C]0.881002039361826[/C][C]0.237995921276348[/C][C]0.118997960638174[/C][/ROW]
[ROW][C]48[/C][C]0.855126550205179[/C][C]0.289746899589641[/C][C]0.144873449794821[/C][/ROW]
[ROW][C]49[/C][C]0.849230626550815[/C][C]0.301538746898369[/C][C]0.150769373449185[/C][/ROW]
[ROW][C]50[/C][C]0.825981921635095[/C][C]0.34803615672981[/C][C]0.174018078364905[/C][/ROW]
[ROW][C]51[/C][C]0.800782398568205[/C][C]0.398435202863589[/C][C]0.199217601431795[/C][/ROW]
[ROW][C]52[/C][C]0.768926379827277[/C][C]0.462147240345446[/C][C]0.231073620172723[/C][/ROW]
[ROW][C]53[/C][C]0.760706024194799[/C][C]0.478587951610402[/C][C]0.239293975805201[/C][/ROW]
[ROW][C]54[/C][C]0.760915023681294[/C][C]0.478169952637411[/C][C]0.239084976318706[/C][/ROW]
[ROW][C]55[/C][C]0.723009487558918[/C][C]0.553981024882163[/C][C]0.276990512441082[/C][/ROW]
[ROW][C]56[/C][C]0.68473306474613[/C][C]0.63053387050774[/C][C]0.31526693525387[/C][/ROW]
[ROW][C]57[/C][C]0.654631171029379[/C][C]0.690737657941241[/C][C]0.345368828970621[/C][/ROW]
[ROW][C]58[/C][C]0.614329548706845[/C][C]0.771340902586311[/C][C]0.385670451293155[/C][/ROW]
[ROW][C]59[/C][C]0.607858290379922[/C][C]0.784283419240156[/C][C]0.392141709620078[/C][/ROW]
[ROW][C]60[/C][C]0.624452557934218[/C][C]0.751094884131564[/C][C]0.375547442065782[/C][/ROW]
[ROW][C]61[/C][C]0.713718436948347[/C][C]0.572563126103306[/C][C]0.286281563051653[/C][/ROW]
[ROW][C]62[/C][C]0.688376126173873[/C][C]0.623247747652255[/C][C]0.311623873826127[/C][/ROW]
[ROW][C]63[/C][C]0.773956972609467[/C][C]0.452086054781067[/C][C]0.226043027390533[/C][/ROW]
[ROW][C]64[/C][C]0.744014708593451[/C][C]0.511970582813099[/C][C]0.255985291406549[/C][/ROW]
[ROW][C]65[/C][C]0.729508568390067[/C][C]0.540982863219865[/C][C]0.270491431609933[/C][/ROW]
[ROW][C]66[/C][C]0.844298825307309[/C][C]0.311402349385381[/C][C]0.155701174692691[/C][/ROW]
[ROW][C]67[/C][C]0.881446435964855[/C][C]0.237107128070289[/C][C]0.118553564035145[/C][/ROW]
[ROW][C]68[/C][C]0.874629467046107[/C][C]0.250741065907786[/C][C]0.125370532953893[/C][/ROW]
[ROW][C]69[/C][C]0.856882230417817[/C][C]0.286235539164366[/C][C]0.143117769582183[/C][/ROW]
[ROW][C]70[/C][C]0.837105694424351[/C][C]0.325788611151298[/C][C]0.162894305575649[/C][/ROW]
[ROW][C]71[/C][C]0.813520678764388[/C][C]0.372958642471225[/C][C]0.186479321235612[/C][/ROW]
[ROW][C]72[/C][C]0.823887311870042[/C][C]0.352225376259915[/C][C]0.176112688129958[/C][/ROW]
[ROW][C]73[/C][C]0.795032401525036[/C][C]0.409935196949927[/C][C]0.204967598474964[/C][/ROW]
[ROW][C]74[/C][C]0.764779814502717[/C][C]0.470440370994566[/C][C]0.235220185497283[/C][/ROW]
[ROW][C]75[/C][C]0.737688746871137[/C][C]0.524622506257725[/C][C]0.262311253128863[/C][/ROW]
[ROW][C]76[/C][C]0.757811659149462[/C][C]0.484376681701077[/C][C]0.242188340850538[/C][/ROW]
[ROW][C]77[/C][C]0.764436722024958[/C][C]0.471126555950085[/C][C]0.235563277975042[/C][/ROW]
[ROW][C]78[/C][C]0.736081052061749[/C][C]0.527837895876501[/C][C]0.263918947938251[/C][/ROW]
[ROW][C]79[/C][C]0.71255293357285[/C][C]0.574894132854301[/C][C]0.28744706642715[/C][/ROW]
[ROW][C]80[/C][C]0.67627645763936[/C][C]0.64744708472128[/C][C]0.32372354236064[/C][/ROW]
[ROW][C]81[/C][C]0.636137028686081[/C][C]0.727725942627839[/C][C]0.363862971313919[/C][/ROW]
[ROW][C]82[/C][C]0.715445397627709[/C][C]0.569109204744583[/C][C]0.284554602372291[/C][/ROW]
[ROW][C]83[/C][C]0.677692353829183[/C][C]0.644615292341634[/C][C]0.322307646170817[/C][/ROW]
[ROW][C]84[/C][C]0.679414095174848[/C][C]0.641171809650305[/C][C]0.320585904825152[/C][/ROW]
[ROW][C]85[/C][C]0.642957027727933[/C][C]0.714085944544135[/C][C]0.357042972272067[/C][/ROW]
[ROW][C]86[/C][C]0.600777672085361[/C][C]0.798444655829278[/C][C]0.399222327914639[/C][/ROW]
[ROW][C]87[/C][C]0.561094670988827[/C][C]0.877810658022345[/C][C]0.438905329011173[/C][/ROW]
[ROW][C]88[/C][C]0.516526868704618[/C][C]0.966946262590764[/C][C]0.483473131295382[/C][/ROW]
[ROW][C]89[/C][C]0.474789638955672[/C][C]0.949579277911344[/C][C]0.525210361044328[/C][/ROW]
[ROW][C]90[/C][C]0.839157521448158[/C][C]0.321684957103684[/C][C]0.160842478551842[/C][/ROW]
[ROW][C]91[/C][C]0.861527749566351[/C][C]0.276944500867298[/C][C]0.138472250433649[/C][/ROW]
[ROW][C]92[/C][C]0.83509266504526[/C][C]0.32981466990948[/C][C]0.16490733495474[/C][/ROW]
[ROW][C]93[/C][C]0.805111408746904[/C][C]0.389777182506191[/C][C]0.194888591253095[/C][/ROW]
[ROW][C]94[/C][C]0.772709782976584[/C][C]0.454580434046833[/C][C]0.227290217023416[/C][/ROW]
[ROW][C]95[/C][C]0.747060387672041[/C][C]0.505879224655918[/C][C]0.252939612327959[/C][/ROW]
[ROW][C]96[/C][C]0.715875054212774[/C][C]0.568249891574452[/C][C]0.284124945787226[/C][/ROW]
[ROW][C]97[/C][C]0.716334991704566[/C][C]0.567330016590867[/C][C]0.283665008295434[/C][/ROW]
[ROW][C]98[/C][C]0.677707482753966[/C][C]0.644585034492068[/C][C]0.322292517246034[/C][/ROW]
[ROW][C]99[/C][C]0.693326405751909[/C][C]0.613347188496181[/C][C]0.306673594248091[/C][/ROW]
[ROW][C]100[/C][C]0.662041656067839[/C][C]0.675916687864322[/C][C]0.337958343932161[/C][/ROW]
[ROW][C]101[/C][C]0.664379824808228[/C][C]0.671240350383544[/C][C]0.335620175191772[/C][/ROW]
[ROW][C]102[/C][C]0.653202029711961[/C][C]0.693595940576078[/C][C]0.346797970288039[/C][/ROW]
[ROW][C]103[/C][C]0.607766270740112[/C][C]0.784467458519775[/C][C]0.392233729259888[/C][/ROW]
[ROW][C]104[/C][C]0.595079403850312[/C][C]0.809841192299376[/C][C]0.404920596149688[/C][/ROW]
[ROW][C]105[/C][C]0.548218974824763[/C][C]0.903562050350474[/C][C]0.451781025175237[/C][/ROW]
[ROW][C]106[/C][C]0.662731893860342[/C][C]0.674536212279316[/C][C]0.337268106139658[/C][/ROW]
[ROW][C]107[/C][C]0.631598114325469[/C][C]0.736803771349061[/C][C]0.368401885674531[/C][/ROW]
[ROW][C]108[/C][C]0.594247886585331[/C][C]0.811504226829338[/C][C]0.405752113414669[/C][/ROW]
[ROW][C]109[/C][C]0.621130127226575[/C][C]0.75773974554685[/C][C]0.378869872773425[/C][/ROW]
[ROW][C]110[/C][C]0.643423761951383[/C][C]0.713152476097234[/C][C]0.356576238048617[/C][/ROW]
[ROW][C]111[/C][C]0.595517276747878[/C][C]0.808965446504243[/C][C]0.404482723252122[/C][/ROW]
[ROW][C]112[/C][C]0.663908277936823[/C][C]0.672183444126354[/C][C]0.336091722063177[/C][/ROW]
[ROW][C]113[/C][C]0.703611327905345[/C][C]0.59277734418931[/C][C]0.296388672094655[/C][/ROW]
[ROW][C]114[/C][C]0.738872834354235[/C][C]0.522254331291531[/C][C]0.261127165645765[/C][/ROW]
[ROW][C]115[/C][C]0.827513676833717[/C][C]0.344972646332566[/C][C]0.172486323166283[/C][/ROW]
[ROW][C]116[/C][C]0.796984563735922[/C][C]0.406030872528157[/C][C]0.203015436264078[/C][/ROW]
[ROW][C]117[/C][C]0.767866338885772[/C][C]0.464267322228455[/C][C]0.232133661114228[/C][/ROW]
[ROW][C]118[/C][C]0.726272944991775[/C][C]0.54745411001645[/C][C]0.273727055008225[/C][/ROW]
[ROW][C]119[/C][C]0.68160315488815[/C][C]0.636793690223701[/C][C]0.31839684511185[/C][/ROW]
[ROW][C]120[/C][C]0.67499308052851[/C][C]0.65001383894298[/C][C]0.32500691947149[/C][/ROW]
[ROW][C]121[/C][C]0.630130518542112[/C][C]0.739738962915775[/C][C]0.369869481457888[/C][/ROW]
[ROW][C]122[/C][C]0.579395912467682[/C][C]0.841208175064636[/C][C]0.420604087532318[/C][/ROW]
[ROW][C]123[/C][C]0.528871177113665[/C][C]0.94225764577267[/C][C]0.471128822886335[/C][/ROW]
[ROW][C]124[/C][C]0.473232156311334[/C][C]0.946464312622668[/C][C]0.526767843688666[/C][/ROW]
[ROW][C]125[/C][C]0.425270019576764[/C][C]0.850540039153528[/C][C]0.574729980423236[/C][/ROW]
[ROW][C]126[/C][C]0.416763130025939[/C][C]0.833526260051879[/C][C]0.583236869974061[/C][/ROW]
[ROW][C]127[/C][C]0.397836851420949[/C][C]0.795673702841899[/C][C]0.602163148579051[/C][/ROW]
[ROW][C]128[/C][C]0.380222098578419[/C][C]0.760444197156838[/C][C]0.619777901421581[/C][/ROW]
[ROW][C]129[/C][C]0.507685101834833[/C][C]0.984629796330334[/C][C]0.492314898165167[/C][/ROW]
[ROW][C]130[/C][C]0.475034200959664[/C][C]0.950068401919327[/C][C]0.524965799040336[/C][/ROW]
[ROW][C]131[/C][C]0.420769468524537[/C][C]0.841538937049074[/C][C]0.579230531475463[/C][/ROW]
[ROW][C]132[/C][C]0.49581039927384[/C][C]0.991620798547681[/C][C]0.50418960072616[/C][/ROW]
[ROW][C]133[/C][C]0.435600745893586[/C][C]0.871201491787172[/C][C]0.564399254106414[/C][/ROW]
[ROW][C]134[/C][C]0.445538723472299[/C][C]0.891077446944599[/C][C]0.554461276527701[/C][/ROW]
[ROW][C]135[/C][C]0.403738849019865[/C][C]0.807477698039731[/C][C]0.596261150980135[/C][/ROW]
[ROW][C]136[/C][C]0.365875993115155[/C][C]0.731751986230309[/C][C]0.634124006884845[/C][/ROW]
[ROW][C]137[/C][C]0.30742016397432[/C][C]0.61484032794864[/C][C]0.69257983602568[/C][/ROW]
[ROW][C]138[/C][C]0.266147168448216[/C][C]0.532294336896432[/C][C]0.733852831551784[/C][/ROW]
[ROW][C]139[/C][C]0.225463317701775[/C][C]0.45092663540355[/C][C]0.774536682298225[/C][/ROW]
[ROW][C]140[/C][C]0.216527690938777[/C][C]0.433055381877554[/C][C]0.783472309061223[/C][/ROW]
[ROW][C]141[/C][C]0.181735585712651[/C][C]0.363471171425301[/C][C]0.818264414287349[/C][/ROW]
[ROW][C]142[/C][C]0.214141900955501[/C][C]0.428283801911002[/C][C]0.785858099044499[/C][/ROW]
[ROW][C]143[/C][C]0.183898241982067[/C][C]0.367796483964134[/C][C]0.816101758017933[/C][/ROW]
[ROW][C]144[/C][C]0.16066400844509[/C][C]0.32132801689018[/C][C]0.83933599155491[/C][/ROW]
[ROW][C]145[/C][C]0.149228621086843[/C][C]0.298457242173686[/C][C]0.850771378913157[/C][/ROW]
[ROW][C]146[/C][C]0.148807659229158[/C][C]0.297615318458316[/C][C]0.851192340770842[/C][/ROW]
[ROW][C]147[/C][C]0.180745883380175[/C][C]0.36149176676035[/C][C]0.819254116619825[/C][/ROW]
[ROW][C]148[/C][C]0.131561076730964[/C][C]0.263122153461928[/C][C]0.868438923269036[/C][/ROW]
[ROW][C]149[/C][C]0.0899478733501651[/C][C]0.17989574670033[/C][C]0.910052126649835[/C][/ROW]
[ROW][C]150[/C][C]0.143780467641604[/C][C]0.287560935283209[/C][C]0.856219532358396[/C][/ROW]
[ROW][C]151[/C][C]0.115015289109724[/C][C]0.230030578219449[/C][C]0.884984710890276[/C][/ROW]
[ROW][C]152[/C][C]0.074780018206387[/C][C]0.149560036412774[/C][C]0.925219981793613[/C][/ROW]
[ROW][C]153[/C][C]0.0403713745752153[/C][C]0.0807427491504306[/C][C]0.959628625424785[/C][/ROW]
[ROW][C]154[/C][C]0.0394897839047115[/C][C]0.0789795678094231[/C][C]0.960510216095289[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157922&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157922&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
80.3480217320634830.6960434641269650.651978267936517
90.2124205512010940.4248411024021880.787579448798906
100.1360242120064530.2720484240129060.863975787993547
110.24461077135690.48922154271380.7553892286431
120.6977277210558840.6045445578882320.302272278944116
130.6945077524108950.610984495178210.305492247589105
140.7275142190513850.544971561897230.272485780948615
150.7947431412318130.4105137175363740.205256858768187
160.744878075019160.5102438499616810.25512192498084
170.6707902432347530.6584195135304940.329209756765247
180.6601359496868140.6797281006263710.339864050313186
190.647839380007910.704321239984180.35216061999209
200.5882666451347910.8234667097304180.411733354865209
210.7590370998675350.4819258002649290.240962900132465
220.9002859598444950.1994280803110110.0997140401555055
230.8663501855331130.2672996289337740.133649814466887
240.8580343365402080.2839313269195830.141965663459792
250.8375731042643960.3248537914712090.162426895735604
260.9811586237934210.03768275241315820.0188413762065791
270.9755724843391230.04885503132175380.0244275156608769
280.9667991244618220.06640175107635560.0332008755381778
290.9542577353525380.09148452929492470.0457422646474624
300.9651356535518870.06972869289622630.0348643464481131
310.9548150352912230.09036992941755350.0451849647087768
320.9406546549334720.1186906901330560.0593453450665281
330.9397976092915870.1204047814168260.0602023907084132
340.922082515374330.155834969251340.0779174846256701
350.9018216971709510.1963566056580990.0981783028290495
360.8980677084927440.2038645830145130.101932291507256
370.9531133622744690.09377327545106270.0468866377255313
380.94236756163990.1152648767201990.0576324383600996
390.9387013810426510.1225972379146980.0612986189573491
400.9234391650280190.1531216699439620.0765608349719808
410.9060539643056590.1878920713886820.093946035694341
420.8896625888967680.2206748222064650.110337411103232
430.9001771679665270.1996456640669460.0998228320334732
440.8792718453550050.241456309289990.120728154644995
450.8856065022558170.2287869954883660.114393497744183
460.9011693808428610.1976612383142770.0988306191571385
470.8810020393618260.2379959212763480.118997960638174
480.8551265502051790.2897468995896410.144873449794821
490.8492306265508150.3015387468983690.150769373449185
500.8259819216350950.348036156729810.174018078364905
510.8007823985682050.3984352028635890.199217601431795
520.7689263798272770.4621472403454460.231073620172723
530.7607060241947990.4785879516104020.239293975805201
540.7609150236812940.4781699526374110.239084976318706
550.7230094875589180.5539810248821630.276990512441082
560.684733064746130.630533870507740.31526693525387
570.6546311710293790.6907376579412410.345368828970621
580.6143295487068450.7713409025863110.385670451293155
590.6078582903799220.7842834192401560.392141709620078
600.6244525579342180.7510948841315640.375547442065782
610.7137184369483470.5725631261033060.286281563051653
620.6883761261738730.6232477476522550.311623873826127
630.7739569726094670.4520860547810670.226043027390533
640.7440147085934510.5119705828130990.255985291406549
650.7295085683900670.5409828632198650.270491431609933
660.8442988253073090.3114023493853810.155701174692691
670.8814464359648550.2371071280702890.118553564035145
680.8746294670461070.2507410659077860.125370532953893
690.8568822304178170.2862355391643660.143117769582183
700.8371056944243510.3257886111512980.162894305575649
710.8135206787643880.3729586424712250.186479321235612
720.8238873118700420.3522253762599150.176112688129958
730.7950324015250360.4099351969499270.204967598474964
740.7647798145027170.4704403709945660.235220185497283
750.7376887468711370.5246225062577250.262311253128863
760.7578116591494620.4843766817010770.242188340850538
770.7644367220249580.4711265559500850.235563277975042
780.7360810520617490.5278378958765010.263918947938251
790.712552933572850.5748941328543010.28744706642715
800.676276457639360.647447084721280.32372354236064
810.6361370286860810.7277259426278390.363862971313919
820.7154453976277090.5691092047445830.284554602372291
830.6776923538291830.6446152923416340.322307646170817
840.6794140951748480.6411718096503050.320585904825152
850.6429570277279330.7140859445441350.357042972272067
860.6007776720853610.7984446558292780.399222327914639
870.5610946709888270.8778106580223450.438905329011173
880.5165268687046180.9669462625907640.483473131295382
890.4747896389556720.9495792779113440.525210361044328
900.8391575214481580.3216849571036840.160842478551842
910.8615277495663510.2769445008672980.138472250433649
920.835092665045260.329814669909480.16490733495474
930.8051114087469040.3897771825061910.194888591253095
940.7727097829765840.4545804340468330.227290217023416
950.7470603876720410.5058792246559180.252939612327959
960.7158750542127740.5682498915744520.284124945787226
970.7163349917045660.5673300165908670.283665008295434
980.6777074827539660.6445850344920680.322292517246034
990.6933264057519090.6133471884961810.306673594248091
1000.6620416560678390.6759166878643220.337958343932161
1010.6643798248082280.6712403503835440.335620175191772
1020.6532020297119610.6935959405760780.346797970288039
1030.6077662707401120.7844674585197750.392233729259888
1040.5950794038503120.8098411922993760.404920596149688
1050.5482189748247630.9035620503504740.451781025175237
1060.6627318938603420.6745362122793160.337268106139658
1070.6315981143254690.7368037713490610.368401885674531
1080.5942478865853310.8115042268293380.405752113414669
1090.6211301272265750.757739745546850.378869872773425
1100.6434237619513830.7131524760972340.356576238048617
1110.5955172767478780.8089654465042430.404482723252122
1120.6639082779368230.6721834441263540.336091722063177
1130.7036113279053450.592777344189310.296388672094655
1140.7388728343542350.5222543312915310.261127165645765
1150.8275136768337170.3449726463325660.172486323166283
1160.7969845637359220.4060308725281570.203015436264078
1170.7678663388857720.4642673222284550.232133661114228
1180.7262729449917750.547454110016450.273727055008225
1190.681603154888150.6367936902237010.31839684511185
1200.674993080528510.650013838942980.32500691947149
1210.6301305185421120.7397389629157750.369869481457888
1220.5793959124676820.8412081750646360.420604087532318
1230.5288711771136650.942257645772670.471128822886335
1240.4732321563113340.9464643126226680.526767843688666
1250.4252700195767640.8505400391535280.574729980423236
1260.4167631300259390.8335262600518790.583236869974061
1270.3978368514209490.7956737028418990.602163148579051
1280.3802220985784190.7604441971568380.619777901421581
1290.5076851018348330.9846297963303340.492314898165167
1300.4750342009596640.9500684019193270.524965799040336
1310.4207694685245370.8415389370490740.579230531475463
1320.495810399273840.9916207985476810.50418960072616
1330.4356007458935860.8712014917871720.564399254106414
1340.4455387234722990.8910774469445990.554461276527701
1350.4037388490198650.8074776980397310.596261150980135
1360.3658759931151550.7317519862303090.634124006884845
1370.307420163974320.614840327948640.69257983602568
1380.2661471684482160.5322943368964320.733852831551784
1390.2254633177017750.450926635403550.774536682298225
1400.2165276909387770.4330553818775540.783472309061223
1410.1817355857126510.3634711714253010.818264414287349
1420.2141419009555010.4282838019110020.785858099044499
1430.1838982419820670.3677964839641340.816101758017933
1440.160664008445090.321328016890180.83933599155491
1450.1492286210868430.2984572421736860.850771378913157
1460.1488076592291580.2976153184583160.851192340770842
1470.1807458833801750.361491766760350.819254116619825
1480.1315610767309640.2631221534619280.868438923269036
1490.08994787335016510.179895746700330.910052126649835
1500.1437804676416040.2875609352832090.856219532358396
1510.1150152891097240.2300305782194490.884984710890276
1520.0747800182063870.1495600364127740.925219981793613
1530.04037137457521530.08074274915043060.959628625424785
1540.03948978390471150.07897956780942310.960510216095289






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0136054421768707OK
10% type I error level90.0612244897959184OK

\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 & 2 & 0.0136054421768707 & OK \tabularnewline
10% type I error level & 9 & 0.0612244897959184 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157922&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]2[/C][C]0.0136054421768707[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]9[/C][C]0.0612244897959184[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157922&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157922&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 level20.0136054421768707OK
10% type I error level90.0612244897959184OK


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